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REVIEW OF THE WATER QUALITY MODELING

NorthMet Mine and Plant Site, Minnesota

March 2, 2014

Prepared for: Minnesota Center for Environmental Advocacy

Prepared by: Tom Myers, PhD, Hydrologic Consultant, Reno NV

Polymet presents an immense modeling project in support of their draft supplemental

environmental impact statement for their proposed Northmet Mining Project. This technical

memorandum reviews this modeling, including the conceptual model for flow and transport,

parameterization of the Goldsim simulation model, and the results.

Polymet presents its model in a report titled: Northmet Project, Water Modeling Data Package,

Volume 1 Mine Site. All references to Polymet or simply a page number in this memorandum

are to their model report. Also, a separate modeling report has been prepared to test the

assumptions presented by Polymet and is referenced in this review (Myers 2014).

Goldsim Modeling and Simulations

This section considers aspects of the Goldsim modeling the basic modeling of the conceptual

flow model including the geologic conceptualization and the hydrology. The next section

considers the conceptual flow and transport model. It is important to consider whether the

simplifications required in Goldsim are accurate and true to the hydrology of the site. Polymet

describes the objectives of the modeling and the selection of Goldsim in Section 3.

Polymet indicates that uncertainty in parameter values is due to both inherent variability and a

lack of complete knowledge, which they call aleatory and epistemic uncertainty, respectively.

They ignore the uncertainty that arises due to scale. For example, system parameters may be

known at a few points, but the basic value can be quite different is considered at scales larger

than a point (Schulz-Mackuch et al. 1999). Scale, for example, commonly causes parameters

such as conductivity (K) to increase as a larger volume is considered as more high permeability

zones are included. The modeling therefore uses K values that are too small.

The modeling uses Monte Carlo methods to randomly sample both input drivers and system

parameters to create multiple simulations with each representing equally-likely possible

performance of the system (p 11). If true, then the output is a probability distribution that

describes the likely performance of a given model result (Id.) meaning the output for any given

result, such as the concentration of antimony at a given monitoring point in the Partridge River.

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This is true if the probability distribution does accurately and honestly capture the likelihood

of potential value and event (Id.) and also if the model equations and basic conceptual model

accurately describes flow and transport through the system. This review points out many

inaccuracies with the parameters, the assumptions, the conceptual model, and even the

equations used in the model to solve the transport. It indicates the overall results are

inaccurate and probably give a false sense of security.

There were four desired capabilities established for selecting the model platform (p 12). The

first is the model should be probabilistic, which if everything is parameterized correctly is an

excellent goal. The second is the ability to handle large quantities of data. It is possible that

the model structure could be too simplified just because of the desire to somehow

accommodate every potential input and generate immense loads of output. It may not be

necessary to simulate more than a dozen contaminant sources to a given facility if just a couple

of those sources provide overwhelmingly more load than others (Myers 2014). Similarly, the

third capability is the flexibility to simulate a variety of different purposes meaning the ability

to assign a probability distribution to every engineered source of contaminants, and the same

comment as to the second capability applies. The fourth capability is transparency, which

Goldsim does not seem to meet.

Polymet chose the Goldsim model as meeting the above capabilities. They apparently did not

consider the stochastic modeling capabilities of MODFLOW and MT3DMS, which could have

considered many of the parameters in a probabilistic manner without oversimplifying the

analysis (discussed more below). Modelers using MODFLOW/MT3DMS would have had

difficulty considering the randomness of the engineering inputs used in Goldsim, but for various

reasons that may not have been necessary or could have been accomplished in different ways.

For example, it would have been appropriate to run a model stochastically for each of several

sets of deterministic engineering inputs to show the effects of there being more seepage from a

given facility than as designed. This would have eliminated the problems caused by the

conceptual model used with Goldsim and discussed below.

Polymet also claims that Goldsim is widely used in the industry for conducting water and

chemical balances for mining applications (p 14). This reviewer has reviewed many

environmental documents for mining projects all over the country and somewhat globally and

has never seen the model used. To justify the claim of wide use, Polymet should provide an

extensive list of mines on which Goldsim has been used to simulate contaminant transport.

Goldsim uses stochastic modeling, implemented with Monte Carlo like random sampling of

input values and parameter, of deterministic equations to estimate the various outputs from

the model. The model is solved for each set of random selections to yield a probability

distribution for the output, rather than a single value, so that the user can assess risk.

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The model output can be useful, a good representation of the potential outcomes of imposing a

development, here the proposed Northmet Mine, on a given natural system, if the natural and

proposed system is accurately conceptualized and if the equations used within Goldsim are

accurate for the conceptual model. Especially for water quality modeling, the flow and

transport equations must be appropriate to implement the conceptual model. The results are

useful only if the random selection of input and model parameters is from an accurate

probability distribution. Probability based modeling is a substitute for a good understanding of

the processes and the parameters that describe the process.

The model fails the transparency requirement because it is proprietary and it is not possible to

review or run the model without purchasing the model platform. Without this capability, it is

not possible for the public to independently test Polymets assumptions.

Conceptual Model

The conceptual model as implemented by Polymets modeling includes a standard definition of

a conceptual model as normally considered flows through the project domain as well as

how it is implemented into Goldsim; most of Polymets discussion does not distinguish among

the hydrology and its Goldsim implementation.

Hydrogeology

Polymet treats the bedrock as far more impermeable that it actually is, and that is based on

their pump test data. Polymet notes that K resulting from their pump tests ranges from 0.0024

to 1.0 feet/day, with a geometric mean of 0.17 feet/day (p 22). The statement is correct but

highly misleading. The original observations are shown in the following table, clipped from Barr

(2006b). Only three of the twelve observed Ks are less than the geometric mean, with one

being just 0.01 ft/d less than it. Well P-1 was essentially dry, but the data resulted in a 0.0024

ft/d value which controlled the geometric mean and essentially drives discussion of bedrock

being non-conductive. The Duluth complex is a large mafic intrusion and the Virginia

formation is a sedimentary unit (p 20), which according to Freeze and Cherry (1979) would have

K ranging from 10-8 to 10-3 and 10-10 to 10-5 m/s (0.00026 to 2.63 or 2.63x10-6 to 0.263 ft/d,

respectively). The pump test values are realistic and it would be better to consider that 70% of

the site had bedrock K greater than 0.1 ft/d. Scale issues matter more in bedrock because as

the rock volume being considered becomes larger, to a certain degree, the proportion of

fractures becomes larger as does the effective K. Polymet acknowledges that fractures in the

upper several hundred feet lead to a higher K (p 22).

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Vertical Connection among Aquifers: There is a need for better information regarding vertical

gradients and the connection between the surficial and bedrock aquifers. The three nested

wells are nested only in the surficial aquifer and generally show a downward gradient, but they

are all located downhill from the pits. The vertical gradient may differ near the groundwater

divide. There is also no indication of gradient between the bedrock and surficial aquifer. The

discussion regarding bedrock groundwater levels (p 22) notes that the levels measured in five

bedrock observation wells were generally below the top of the bedrock contact. This would

indicate a lack of connection if the surficial aquifer were saturated but there is no information

at those points. There is simply not enough data to assess gradients among aquifers. There is

also only one pump test which suggests there is not connection. Because each significant

bedrock fracture may have a different hydraulic connection, this is insufficient information to

conclude there is no connection. This is perhaps the most important data that Polymet could

have because dewatering the bedrock will draw water from the surficial aquifer and could

dewater that aquifer if there is a connection.

Polymet reports a thirty-day pump test based on pumping at well P-2. As may be seen in the

table above, this well had the second lowest K. Its fractures may have poor connection with the

surface or pumping effects may take longer to propagate to the surface. This is far too little

data with which to assume little connection between the bedrock and surficial aquifers. For

modeling, Polymet has not justified their assumptions of no connection among aquifers.

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Water Quality

The background water quality data is primarily important as the base on to which Polymets

mine discharges would add. They state that background SO4 is 18.3 and 9.1 mg/l in the

bedrock and surficial aquifer, respectively. Further, the modeling results for the Partridge River

are that the median SO4 concentration at SW004 is about 9.1 mg/l. This reflects the lack of

simulated transport through bedrock pathways. Considering copper, there is a significant

variety of values shown on Polymet Large Figure 17, but they do not exceed the drinking water

standard so Polymet is not concerned. Many of the observed groundwater values exceed the

simulated model results, which may reflect the simulation of sorption, which is discussed

below.

Additionally, the SO4 readings in the Partridge River have exceedences during the critical wild

rice period of April 1 through August 30 (p61, 62). They also note that all of the discharge that

enters from the Peter Mitchell Pit exceeds the standard. Importantly, this discharge is part of

the existing conditions, but not the natural background. Most of the modeling assumes this

discharge continues. Polymet should model a scenario w/o continued discharge from PMP

because it dilutes some constituents and causes some to have exceedences even without this

project.

Baseflow

Baseflow for the Partridge River may be incorrectly estimated. It is not uncommon to use the

annual 30-day low flow as a baseflow estimate, but there are various reasons why that may be

inaccurate here. Myers (2014, Part A) explains in detail the problems with this and presents an

independent estimate of baseflow for the area. This brief review mentions a few items from

Polymets modeling study.

1. Figure 4-8 demonstrates the immense variability in annual 30-day low flows. This reflects the

many factors which control flow, as outlined in Myers (2014), such frozen precipitation.

2. Dewatering discharge from the Peter Mitchell Pit cannot be simply subtracted from the flow

because the data presented in the modeling studies demonstrates that it is not simply an

additive process. In some months, there is more discharge than flow recorded on the river.

Water apparently gets stored along the river above the gage.

3. Mine dewatering may affect river flows in a way not considered drawdown from the

dewatering may draw water from the river into the aquifer. Polymet does not consider this

probably because they believe there is no connection. But there is no justification for not

considering it.

Polymet estimates river flow based on a distribution of monthly flows at SW006 as shown in

Table 5-13 (p 127). A problem with this scheme is that it is a random selection by month

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without regard to the previous month. A dry month could follow a wet month, which is

unrealistic. The model then distributes the SW006 flow to other evaluation points according to

area draining to them and the year of mining.

Baseflow matters for several reasons. Baseflow is groundwater discharge and the groundwater

flow moves contaminants from the minesite to the river. For water quality modeling, it can

dilute the load discharging from the minesite but it also carries a part of the load. Dilution

would occur if most of the load is surface flow but if most is seepage, higher groundwater flow

may cause predictions to be higher.

Additionally, baseflow matters in the calculation of recharge, as discussed in the following

section.

Recharge

Recharge rates for the watershed have been underestimated. Recharge is the water that

enters the aquifers and controls the flux through an aquifer to a downgradient river. The

recharge estimate therefore drives modeling and controls the results. It is common to set

recharge equal to river baseflow so the error comes in how baseflow is estimated. Polymet

estimated an average recharge of 0.74 in/y for the drainage above Colby Lake (p 130), based on

a simulated groundwater inflow to the river of 5.3 cfs and 97 square miles. The watershed yield

(at gage 04015475 Partridge River above Colby Lake) is 0.84 cfs/mi2 (p 39) which is 11.4 in/y, a

value consistent with the precipitation and ET estimates. Recharge is the amount of 11.4 in/y

that is not storm runoff (assuming that none of the recharge reaches bedrock and flows from

the watershed). Based on comparisons with other methods, 0.74 in/y is an extremely low

(Myers 2014). It is critical to estimate the baseflow that groundwater contributed to correctly

set the recharge equal to it.

Polymet simply set baseflow equal to the lowest 30-day flow in this watershed. There are many

reasons that this is not correct or not representative in this basin (Myers 2014). Most 30-day

low flow observations occur during late winter or early spring. Much of the watershed and

most precipitation is frozen so there would be no recharge during that time. After a long frozen

period, the groundwater level in the water would have lowered substantially as the

groundwater supply is depleted. The gradient controlling discharge to the river is at its smallest

at this point. The baseflow at this time is groundwater but is very low compared to the flow

that occurred earlier in the winter when the water table, and gradient, was higher.

Additionally, some tributaries which carry groundwater to the river may freeze to the point that

discharge from parts of the watershed simply do not reach the river until the system thaws.

This is especially true if the tributary flows through wetlands or small ponds.

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Evidence that the recharge rate is underestimated is the amount of dewatering that was

necessary from the Peter Mitchell Pit in the headwaters of the Partridge River. The average

dewatering rate for that pit was 11.8 cfs for about 11 years which was 13% of the discharge

recorded at the Partridge River gage near Colby Lake for that time period. Considering the

small area that would provide groundwater flow to the Peter Mitchell Pit, the dewatering rate

is very high. Because dewatering is water that had recharged within the capture zone of the

pit, it is apparent that the recharge rate within that capture zone is much higher than it is

within the watershed as a whole as estimated in the data report.

Myers (2014, Part 1) reestimated recharge for both Partridge and Embarrass River watershed

and found that Polymet underestimated it by three to five times. Myers (2014, Part 1) provides

substantial justification for using a higher recharge value, and is cited here for completeness.

The recharge rate matters because it controls the flux rate and mass loading along the model

pathways. It also controls the dewatering rate and how fast pits will refill. It will affect the

water balance in the pits and the amount of groundwater discharge into the streams. It will

also affect the amount of water that must be pumped, and discharged, to avoid having the

West Pit overflow. The higher rates will enter the shallow, surficial groundwater aquifer and

drive the flows and the mass loading to the river. Calibration with higher recharge would

require higher K in the bedrock and likely more bedrock flow to the river. It therefore appears

that the very driving force of the groundwater model, and the Goldsim contaminant transport

model, has been vastly underestimated.

The recharge rates should be reestimated considering the above and Myers (2014).

Everything in the model that depends on the recharge rate should be reestimated, including

the basic groundwater model used to estimate Goldsim parameters.

Flow Pathways

GoldSim uses 1-dimensional (1-d) flowpaths through the site to discharge points at the river.

The method is similar to the analytical element method sometimes used with MODFLOW in

which a section of a model domain is simulated with the analytic equation rather than with the

finite difference solution. Flow along the path would accumulate natural and minesite recharge

and interflow along the pathway. The modelers established eight flow paths based on

groundwater contours determined with MODFLOW modeling, including five surficial aquifer

flowpaths emanating from waste piles or pits and three bedrock flowpaths emanating from the

pits (Figures 1 and 2). They reach the river between SW003 and SW004. Only the flow paths

emanating from pit lakes have discharge entering at the upstream end during closure.

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Figure 1: Snapshop of large figure 28, Polymet (2013c) showing surficial flowpaths.

Figure 2: Snapshot from Large Figure 29 showing bedrock pathways.

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Polymet should address whether there should be pathways north to the river. None of the

model pathways discharge north even though there is a groundwater divide under the Cat 1

stockpile in pre-mining conditions. There is no pathway from the Cat 1 Stockpile to any

discharge point because Polymet assumes that any escaping seepage will discharge to the West

Pit (Figures 1 and 2 show the waste area in green and the liner around it). Pre-mine contours

suggest there was a groundwater divide through this area (and through the East Pit) such that

some seepage could discharge to the river on the north side.

Simulation of Flow and Transport along a Groundwater Pathway: The Goldsim model

estimates groundwater discharge to a given river reach at the downgradient end of the

flowpaths as equal to the recharge in the area draining to that reach. That includes flow into

the upstream end of the flowpath, facility seepage along the flowpath, and natural recharge

(with concentration set equal to the background concentration) (Equ 5-13). (Flow into the

upstream end is zero if the upstream end is a divide, no flow boundary, or anything other than

the pit lake.) Goldsim assumes a constant aquifer thickness (transmissivity) and a constant river

stage, so the downstream end of any gradient calculation is constant (p 104). The model does

apparently account for changes in the water level at the upstream end, which change the flow

rate or reverse its directions so that flow and contaminants could flow toward the pit rather

than toward the river discharge (p 105).

The flow calculations rely on an assumption that the surficial aquifer is 5-m thick rather than on

reality that the piezometric surface along an aquifer receiving recharge is parabolic in shape

(Id.). In other words, they have assumed constant thickness where in reality the thickness is

not constant. Polymet also ignores the actual variability in thickness caused by the actual depth

to bedrock ranging to almost 20 m, as seen in Large Figures 4 through 10. The actual

transmissivity varies spatially around the model domain and longitudinally along the modeled

flowpath. Polymet justifies using a singular thin aquifer by suggesting that a uniform aquifer

thickness results in a smaller aquifer area to accommodate flow and faster groundwater

velocities (Id.). Continuing, Polymet claims that [f]aster groundwater velocities result in less

dilution from recharge and higher concentrations observed at the evaluation points. This

justification is incorrect because the flow equations are steady state based on conservation of

mass the flow entering the cell during a time step equals the flow leaving. The concentration

of the flow leaving depends on the load that entered the cell being dispersed immediately

through the volume of the cell.

The GoldSim modeling also uses K values randomly chosen for each 1-d flow path; it is unclear

whether there is a separate value for each flowpath during each simulation. The

documentation does not discuss conditioning the value based on the chosen value for an

adjacent flowpath, which means accounting for correlation in the heterogeneity. In other

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words, if it is strictly random then adjacent flowpaths could have widely different K which

would be inappropriate. On the other hand, if one K value is chosen for all the flowpaths within

the same aquifer type, the modeling would be ignoring spatial heterogeneity. It is impossible to

independently verify these values due to transparency issues discussed above.

The modeling uses bedrock K values that are much lower than their pump tests indicate and

lower than calibrated by Myers (2014). Attachment B, Table 1-15 shows that Polymet used

bedrock K ranging from 0.000047 to 0.00047 m/d with a mode of 0.00015 m/d. These values

are at least two to three orders of magnitude less than reported in the model report and

discussed above herein. It is another example of how Polymet has downplayed the potential

for bedrock to pass any flow to the rivers. Transport through the bedrock would be faster

because of the preferential flow but Polymets assumptions prevent such transport. The same

table shows reasonable ranges for surficial aquifer pathways that are basically confirmed by

Myers (2014, Part 2). However, Polymet used a thickness of only 5 m for the surficial aquifer,

which may be too thin and result in too low a transmissivity in places. It is unclear how this

would affect transport because it is unclear, due to the lack of transparency in the model

documentation, where velocity would be based on all of the simulated flow occurring through

the 5-m aquifer or whether it would be based on gradient. In other words, it is unclear whether

it is based on conservation of mass or Darcys law. The former would cause higher velocities

and faster transport and the latter would cause slower velocities and transport because the

gradient is established separately.

GoldSim constructs flowpaths using adjacent cells and models the water balance

simultaneously among each cell. It apparently utilizes one K for the entire flowpath. This

assumption violates any realistic conceptual model of flow in the aquifers by ignoring

heterogeneities. The K could vary along the flow path but the modeling as proposed here fails

to account for that. If possible, the GoldSim modeling should use different K estimates for each

cell but they should be selected with spatial correlation considered.

Polymet treats the surface and bedrock pathways separately. The bottom of the surficial

aquifer is treated as an impermeable boundary, effectively separating the surficial aquifer flow

paths from the bedrock flow paths in the model (p 105). This is based on Polymets

underestimate of bedrock conductivity. Polymet claims that by preventing mass from entering

bedrock flow paths causes the mass to reach the evaluation points more quickly because

allowing mass to enter the bedrock would slow the transport. This is most likely incorrect

because the porosity in the bedrock is lower and the actual groundwater particle velocity is

likely higher. If the model accurately accounted for the preferential flowpaths in the bedrock

(Siegel and Ericson 1981), modeling flow through the bedrock would cause it to reach

evaluation points more quickly.

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Contaminants move along the flow paths by advection and dispersion. Dispersion is based on

cell length. Goldsim models dispersion numerically, with flow containing a constituent load

instantaneously dispersing through the entire cell. This means the load would be dispersed

evenly through the volume of a downstream cell. The Goldsim manual, cited in Polymet,

specifies that it is common to set the dispersion length equal to one-half of the cell length. This

led to Polymet using 42 cells approximately 23.6 m in length (p 114). The time step for the

mine site was one month (Polymet 2013c, p 15) which bears no relation to the length of model

cells and dispersion. The process of dispersion in the Goldsim model, by instantaneously

spreading contaminant through a cell, differs from actual dispersion by which contaminants

disperse continuously through the entire domain. If the time step used in Goldsim is

significantly shorter than the time normally required for contaminant to disperse over that

distance, it may be moving contaminant too quickly which would dilute the contaminant.

Alternatively, if the time step is longer than the actual dispersion time, it might slow transport.

While this simplification within Goldsim is an inaccuracy, whether it biases the results toward

faster or slower transport or higher or lower concentrations depends on how the cell length

and time step compares to actual dispersion time as described above. As described, with 42

cells it will require 42 time steps for breakthrough of contaminants from the upstream end of

the pathway to reach the discharge point at the river, although the breakthrough concentration

may be extremely low.

The concentration at the downstream end depends on the routing of contaminants through 42

sets of steady state flow and balance equations (one set for each cell). Dividing the flow paths

in the Polymet model (Figures 1 and 2) into 42 cells makes apparent that the width is much

larger than the length. The load entering the cell disperses uniformly through the cell,

therefore the lateral dispersion in each cell is much greater than longitudinal dispersion. This is

not in keeping with reality because the lateral and vertical dispersion is usually 0.2 and 0.1

times the longitudinal dispersion. This excessive horizontal dispersion dilutes the contaminant

along its pathway more than would be estimated with standard transport equations (Fetter

1999). This would inappropriately dilute the estimated concentration for contaminants arriving

at the monitoring points or the river.

Goldsim Simulation of Mine Facilities

The efficacy of the proposed mine depends on the engineered solutions working as designed

and on the assumptions used in the Goldsim modeling manifesting as assumed. The

engineering designs are discussed elsewhere. The following section discusses aspects of the

Goldsim modeling and Polymets application of it. Following that are two sections concerning

specifics of modeling sulfate and copper transport through the mine site. A necessary

conclusion is that the assumptions about the engineering, geochemistry, the hydrology, and the

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geological conceptualization combine to provide predictions suggesting that the project will not

degrade waters.

Seepage through a waste stockpile is a function of the stockpiles water balance, which is

considered steady state by time step. Seepage is the difference between meteoric water at the

top of the pile, evaporation, runoff and infiltration. The water balance of waste rock stockpiles

(p 81) does not include storage. Rather the model assumes that all water entering the stockpile

during a year also exits it. Monthly climate and soil properties are chosen randomly. By not

considering storage, the model does not allow water to accumulate so that large amounts could

seep all at once. By ignoring the autocorrelation of climate among months and antecedent

conditions, the model also ignores the potential additional seepage that would occur during

subsequent wet months. Both assumptions decrease the amount of water available to seep

through the waste stockpiles. The assumption could limit the effect of high precipitation

years, when leftover storage from the preceding year combines with infiltration during the

current year to cause a larger of water reaching the liner or containment system.

The method for modeling evapotranspiration (ET) from bare and reclaimed stockpiles does not

appear to be correct). The document says they need to represent the mean fractional

evapotranspiration for the stockpiles throughout time and across the entire stockpile footprint

(p 81), therefore they need a distribution for the uncertainty in the mean of the entire

population (as opposed to the uncertainty in the range of possible observed values) (p 82).

They select a single fractional value of ET as a proportion of annual precipitation for use

through the entire simulation, whether wet or dry year. There is no consideration to ET being a

higher proportion of precipitation during dry years and also may evaporate more water during

wet years. This could limit the amount of infiltration by losing more precipitation to ET during

wet years. The data used to analyze the variability shows that the amount of precipitation that

becomes ET varied substantially from year to year (Table 5-4), with the highest precipitation

year (1982) not having the lowest proportion lost to ET. This means that ET is not simply a

function of water availability and assuming that a set fraction of annual precipitation becomes

ET introduces an error into the simulation. The better way to model this would be to select an

ET proportion from the observations using the sample rather than population standard

deviation.

Flow to/from Pits

Polymets model accounts for mine dewatering as part of the water balance for the two pits.

Their MODFLOW modeling was used to determine groundwater inflow to the pits. Figure 6-12

below is an example of the water balance simulated in the West Pit during year 14. The

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groundwater inflow, 55 gpm, is a small fraction of the overall amount of water removed from

the plant (313 gpm). Table I-22 lists the groundwater inflows, which do not exceed 100 gpm at

any time. Figure 5-10 shows the log-normal distribution that Polymet applies to the estimate of

pit flow, during year 14 the range would be from about 75% to 200% of 55 gpm. Myers (2014)

estimates are substantially higher because of the higher K in the bedrock.

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During reclamation, the groundwater inflows to the West Pit are similar. The largest natural

source is pit wall runoff, with rates of about 270 gpm for about 15 years. The largest inflow

simulated by Polymet was pumping into the pit from either the WWTF or untreated from the

Plant Site at rates up to 900 gpm. Myers (2014) found this pumping may increase loads in the

groundwater, but Polymets method of modeling flowpaths precludes including this if the

modeler does not specifically include it. Polymet (2013) assumes that discharge into

groundwater begins when the water level rises above the bedrock into the surficial aquifer.

The discharge into the surficial aquifer ranges from 3.1 to 6.4 gpm (Polymet 2013, p 160). Into

bedrock, Polymet considers two flow paths and the total range is from .03 to .08 gpm. This low

flow rate into bedrock reflects Polymets extremely low conductivity estimate, but it is also

incorrect as reviewed above and simulated by Myers (2014).

This flow is the discharge into the upper end of the flowpaths and therefore controls the load.

It demonstrates one way that Polymets modeling limits the load reaching the groundwater

flow paths. If the discharge into the aquifers is significantly wrong, the load is wrong and the

transport calculations yield a value that is too small. Simulating a probability distribution for the

discharge is not a substitute for calculating the load. Polymet should complete a more realistic

analysis of flow from the West Pit into the groundwater.

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Inflow to and from the pits should be determined as a function of depth using MODFLOW and

then adjusted for uncertainty based on a probability distribution. The basis for this distribution

should be described. If it is to be legitimate, it should be determined using a stochastic

simulation within MODFLOW.

Results for Sulfate

Polymets simulations estimate that sulfate concentrations entering the river are mostly

constant through the entire simulation, as shown on their figure K-03-24.2 and K-04-24.2 for

the no action and the project alternatives for stations SW004 and SW004a. The primary change

in concentration at points downstream from SW004a is due to the discharge from the WWTF

which has a specified concentration. At SW004a (Figure K-04-24.2) for example, the

concentration with the project at P90 is slightly less than the no action alternative presumably

due to the dilution by the discharge from the WWTF. A similar effect occurs at all of the

stations from SW004a to Colby Lake.

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Figure M-03-24 for SW004 shows that natural groundwater flow, runoff, and dewatering

discharge from Peter Mitchell is 99% of the total SO4 load, between 50 and 55 tonnes/yr for the

entire 200-yr simulation period. Further downstream, the WWTF contributes a small but

consistent load of less than 10 tonnes/yr. The total load increases as background RO and

groundwater increases. In other words, most of the load simulated by Polymet to be in the

lower river is already there, with the WWTF load representing only a few percent of the total

load at SW006. The load simulated as contributed by the mine is on the order of tonnes per

year or only a few percent of the natural load. River concentrations are very near the

background groundwater concentrations which are shown below. Again this reflects the fact

that natural conditions control the simulated concentrations in Polymets modeling.

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The lack of mine-induced SO4 in the river occurs even though the mine adds very significant

SO4 loads to the system. Considering the East Pit only, over 30,000 tones are added during

year 11 from backfilling category 4 waste rock. By year 22, over 50,000 tones have been added

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just to the East Pit from various sources as shown in Figure 1-01-24.1. This load obviously does

not reach the river, so the question is where does it go?

Polymets Figure 6-41 shows the median East Pit SO4 concentration reaches 2600 mg/l for a

couple years after the Cat 4 waste rock loading but falls afterward even with the continued

loading. This upper limit is due to the concentration caps that Polymet assumes it can attain by

controlling the pH to between 6.0 and 7.5 (Polymet 2013). They would do this by adjusting the

pH of the WWTF effluent, lime/limestone addition or other adaptive water quality management

strategies (p 185). Their modeling assumes the caps will work perfectly, but it is important to

question what must be done to make the assumption realistic. The addition of lime/limestone

and proper mixing would have to occur during the initial backfill. Inadequate mixing could

result in volumes of waste with much higher SO4 concentrations. Theoretically, lime could be

injected through injection wells after backfilling, but this has not been proposed nor shown to

be possible. The same applies to the adding of WWTF effluent to keep the waste saturated it

would have to be mixed thoroughly into the waste which would require multiple injection wells

into the backfilled waste rock to thoroughly mix the amending water. The modeling assumes

they would get perfect mixing of both WWTF effluent or lime/limestone, but in practice this is

much more difficult than assumed in Polymets simplified modeling.

19

Polymets reliance on optimistic engineering and modeling assumptions continues during the

reclamation period. Concentrations of many constituents in the East Pit porewater change

significantly due to the extraction and treatment of the porewater in the WWTF (p 187).

They consider the reclamation period as beginning when the East Pit wetland is completely

flooded and [ending] when the sulfate concentration in the backfill porewater is brought to 250

mg/l (Id.). This is expected to occur between years 25 and 60 with an average of year 36. It is

not totally clear, but it appears that Polymet will pump water from the pit, treat it, and

discharge it elsewhere. The SO4 load to the WWTF during this period as shown in Figure H-15-

24.2 reflects this probable source. Their estimated SO4 loading to the East Pit beyond year 20

does not include any WWTF return flow or a load from the backfilled waste rock (Figure I-

01.24.2 below). This means they are accounting for no load from any water added to the pit to

keep it saturated but they are assuming the pit remains saturated so there is no continued

oxidation. It appears therefore that Polymet is missing two large components of load to the

East Pit.

20

The plan appears to be to pump water from the pit to treat it but to keep the backfill in the pit

saturated at the same time, presumably by pumping water back into the pit. The Adaptive

Water Management Plan (Table 2-1) calls for pumping c1750 gpm from the East Pit to the

WWTF during reclamation for treatment. This pumping rate is approximately four times the

21

pumping rate used to dewater the pit, therefore the pumping alone would obviously dry the pit

and desaturate the backfill. However, they assume that the submerged waste rock backfill

contributes no additional load to the porewater during [years 22 through the end of East Pit

reclamation] because it is completely subaqueous (p 187) and its load is being removed via

pumping for treatment in the WWTF (Id.). The Adaptive Water Management Plan claims that

during reclamation WWTF effluent will be pumped to the East Pit to maintain water levels (p

16). Essentially, they assume and model that the combination of pumping from the WWTF and

the percolation of meteoric water would counter the pumping of 1750 gpm and allow the

backfill to remain saturated. Polymet presents no plan as to how this would work in practice

even though it underpins their modeling.

It may not be possible to pump at this rate from the backfilled waste because of its conductivity

and because relatively impermeable bedrock surrounds the pit. This has to be considered

seriously along with all of the other things Polymet promises to capture and treat. While

theoretically possible to simultaneously remove and return 1750 gpm to the pit for many years,

there is no guarantee that the backfill would remain saturated. Flow through it would choose

preferential flow paths and some areas may actually be unsaturated. Once the removed water

reaches its treatment goal and all pumping ceases, groundwater flow through the pit may flush

oxidation products from areas that had not accessed by the pumping. Additionally, because

these potential loads to the East Pit are not considered in the modeling, Polymets predictions

of time for the porewater to be treated to a given level could be gross underestimates. Myers

(2014) simulated a pump and treat scenario for the East Pit and found that concentrations at

various points in the backfill would be highly variable and in some areas would exceed the

target even 100 years after the end of mining.

Polymet appears to rely on the pit being a sink that prevents constituents from flowing into

surrounding groundwater. Because they are keeping the pit full of water even while pumping

from it, there may be gradients allowing flow from the pit to the surrounding groundwater.

Polymet should describe at some place in the SDEIS how this would work in practice and

consider what could happen if it does not work. Myers (2014) modeling does consider flow

through the pit and shows that loads begin to reach the river after 50 to 80 years. A similar

condition occurs during operations from year 11 to year 22, although water escaping from the

East Pit may reach the West Pit due to continued dewatering there. Concentration graphs of

simulated monitoring wells in the Central Pit and Cat 4 stockpile (after year 11) in Myers (2014)

confirms this movement. There are no similar graphs in Polymets work, so it is difficult to

know where the massive SO4 load actually goes.

22

Results for Copper

The background groundwater concentrations for copper range to about 10 ug/l, as discussed

above and shown on Polymets Large Figure 17. Figure K-04-13.2 shows that modeled Cu

concentrations rarely exceed 3 ug/l, which is significantly less than the standard. The slight

increase occurring at year 40 for the Project is probably due to WWTF discharge to the river.

The river concentration is less than observed in the groundwater but about what is observed at

SW004, with substantial variability.

The copper load to the East and West Pits, shown in Figure I-01-13.1 and I-02.13.1, are two

examples of substantial loads added to the system that do not apparently make it to the river at

all. Cu is pumped from the East Pit, as described above for SO4.

23

24

Polymet may assume that Cu is pumped from the East Pit during reclamation, but the pumping

into the West Pit to fill it causes a gradient away from the pit and drives a load from the pit into

the surrounding groundwater. Sorption as modeled by Polymet is the primary factor

preventing Cu from reaching the river (Myers 2014).

Polymets model treats Cu sorption as deterministic, meaning they do not examine any scenario

except for full sorption. They do not model any of the factors which could affect sorption, such

as pH, contact time, or the relative particle surface areas. As found by Myers (2014), if

sorption does not occur or occurs in a much smaller amount, Cu will reach the river at rates

which could violate the standard.

In addition to the engineering features, Polymets assumed sorption essentially prevents any

Cu from the mine from reaching the river. They have not justified that this will occur nor test

what could occur if their assumptions are wrong.

Summary

Polymets modeling is the basis for the water quality predictions made in the SDEIS. The

modeling is simplified to a one-dimensional system, either along flowpaths or along the river

channel. Uncertainty is considered by treating all non-deterministic inputs or parameters as a

probability distribution. The model, completed using the Goldsim platform, has many

conceptualization issues that indicate that its simulations may oversimplify the system. Several

assumptions implemented in the Polymet model, both about the modeling and the engineering

25

of the site, combine to yield predictions that there will be effectively no water quality impact

caused by this proposed mine. Predicted concentrations at the various surface water locations

show that the project does not increase concentrations much compared with the no action

alternative. The load at these locations tend to be from natural runoff, natural groundwater,

and from dewatering discharge at the Peter Mitchell Pit much more than from the proposed

Northmet. The following are specific comments on the modeling or the results of the

modeling:

1. The model assumes that all waste rock covers, liners, and containment systems will work

perfectly. The probability distributions do not account for substantial failures. If the seepage

through one or more stockpiles is double that planned, there would be substantially higher load

reaching the rivers.

2. The model assumes sorption for four contaminants, including copper, which causes the model

to predict that effectively none of the contaminant reaches the river. This sorption occurs even

though the high background load continues to flow to and discharge into the river.

3. The model assumes that plans to keep backfilled waste in the East Pit saturated during

operations and to pump and treat it during reclamation will prevent all contaminants from

escaping the pit and reaching the river. This is a very optimistic. Polymet should analyze a

scenario which does not rely on this assumption.

4. The modeling does not consider two loads to the East Pit the load from pumping water from

the WWTF to the pit to keep it saturated during reclamation and the load caused by oxidation of

backfill if perfectly saturated conditions are not maintained.

5. Pumping the West Pit full of water causes a mound which allows water and contaminant load to

escape and flow from the pit to downgradient groundwater.

Myers (2014) presented an alternative model that was more physically realistic than Polymets.

The agencies should consider how the realistic modeling completed by Myers (2014) differed

from Polymets and give particular consideration as to whether Polymets modeling was

physically realistic. The agencies should further consider that Polymets modeling includes

assumptions that prevent much of the transport that may actually occur. The agencies should

ultimately reject the results of Polymets modeling and require them to complete more

physically and conceptually realistic models

References

Anderson MP, Woessner WW (1992) Applied Groundwater Modeling: Simulation of Flow and

Advective Transport. Academic Press

Barr Engineering (Barr) (2006a) RS 02 Hydrogeological Drill Hole Monitoring and Data

Collection Phase 1, Hydrogeologic Investigation Phase I, PolyMet NorthMet Mine Site, RS -

02. November 16, 2006

26

Barr Engineering (Barr) (2006b) RS 10 Hydrogeological Drill Hole Monitoring nd Data

Collection Phase 2, Hydrogeologic Investigation Phase II, PolyMet NorthMet Mine Site, RS -

10. November 16, 2006

Fetter CW (1999) Contaminant Hydrogeology, 2nd Edition. Prentice-Hall.

Freeze RA, Cherry JA (1979) Groundwater. Prentice Hall, Englewood Cliffs, NF.

Myers T (2014) Groundwater Flow and Transport Modeling, NorthMet Mine and Plant Site.

Prepared for the Minnesota Center for Environmental Advocacy.

Polymet Mining (2013c) NorthMet Project, Water Modeling Data Package, Volume 1 Mine

Site, Version 11. March 8, 2013.

Schulze-Makuch, D., D.A. Carlson, D.S. Cherkauer, and P. Malik. 1999. Scale dependence of

hydraulic conductivity in heterogeneous media. Ground Water 37, no. 6: 904-919.

Siegel DI, DW Ericson (1981) Hydrology and Water Quality of the Copper-Nickel Study Region,

Northeastern Minnesota. US Geological Survey Water-Resources Investigations 80-739 Open

File Report. St Paul MN