IR# JRP.166 Downstream Effects below Muskrat Falls INFORMATION REQUESTS RESPONSES| LOWER CHURCHILL HYDROELECTRIC GENERATION PROJECT
Requesting Organization – Joint Review Panel Information Request No.: JRP.166
Subject – Downstream Effects below Muskrat Falls
References:
EIS Guidelines, Section 4.5.1 (Environmental Effects General)
Related Comments / Information Requests:
IR # JRP.43, IR # JRP.149, IR # JRP.153
Information Requested:
a. Nalcor hydrology studies indicate the Churchill River exerts a strong influence on the estuarine regime in Goose Bay and concerns have been expressed by a number of participants regarding the downstream effects of the Project. Explain the specific criteria used in Nalcor's response to Information Request (IR) JRP.43 to predict no measurable effect on downstream total phosphorus and total suspended solids, transport distances, fish productivity, salinity, velocity and thermal profiles from Goose Bay to Lake Melville, ice dynamics, ringed and harbour seal use of Lake Melville, bank stability, fish habitat utilization and fish migration. Identify whether and how these predictions apply to the period of reservoir impoundment, and the post‐impoundment transitional period before water quality stabilizes.
Response:
The influence of the lower Churchill River freshwater hydrology on Goose Bay and Lake Melville is recognized by Nalcor Energy (Nalcor) and has been central to limiting downstream effects to the extent possible. The minimal change in flow regime below Muskrat Falls as a result of the Project as described in IR# JRP.43, IR# JRP.149 and IR# JRP.153 mitigates most potential effects in terms of changes in salinity, circulation/current influenced by freshwater flows of the Churchill River, shoreline erosion (due to changes in water levels), tributary access, fish movements, habitat utilization and transportation distances.
Discussion of the downstream effects below Muskrat Falls is assisted by delineating various features as the Churchill River meets Goose Bay and beyond. Figure 1 illustrates the area of consideration and offers labels for distinct features (sub areas and/or boundaries). These features will be referred to when describing the extent of downstream effects below Muskrat Falls.
Nalcor’s response to IR# JRP.166 (a) is based upon the documentation contained in the EIS, Component Studies, previous responses to IRs, and other supporting material that has been gathered over a number of years. In order to provide additional analytical support and clarity to EIS predictions and responses (e.g., IR# JRP.43 and IR# JRP.152), additional dispersion modelling was conducted to further describe the extent of potential downstream effects related to variables of interest such as mercury, phosphorus, and temperature (Oceans 2010) (Attachment A). IR# JRP.166 (a) is organized to provide some background on the body of work supporting the conclusions, and then comments on each of the parameters of interest.
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Background
Nalcor has considered downstream effects on the aquatic environment in a number of component and supporting studies. Some of these studies have used the mouth of the river at Goose Bay (e.g., JWEL 2000; AMEC and Sikumiut 2007) or Goose Bay Estuary as the downstream boundary (e.g., AMEC – BAE 2001; JWEL 2001) and some have included Lake Melville (e.g., JWEL 2001; Sikumiut 2007). Figures 2 and 3 illustrate the study limits for various attributes studied in detail as part of this Project. They clearly extend well beyond the mouth of the river, with some overlapping extensively with Goose Bay and Lake Melville. The rationale for defining the downstream boundaries for the EIS was based on two main premises:
1. The mouth of the river is the end of the riverine habitat; and 2. With a few potential exceptions (discussed later), Goose Bay dilutes any effects originating from upstream to “no measureable effects” level on the key indicators (KI) The term “no measureable effects” as used in the EIS means that any effect or changes to the KIs, if they occur, are within the range of natural variability. Dilution in the area of Goose Bay is caused by freshwater inputs from a number of sources and by mixing with the salt water that enters Goose Bay from Lake Melville. The Churchill River accounts for between 38 to 81 percent of the freshwater input to Lake Melville, Northwest River 13 to 61 percent, and the Goose and Kenamu Rivers 2 to 28 percent (Coachman 1953 in AMEC ‐ BAE 2001). The percentages vary seasonally with precipitation and the operating regime of the Upper Churchill Facility. The Goose and Traverspine rivers enter Goose Bay proper and the Northwest and Kenamu rivers enter at the entrance to Goose Bay. In addition, there are several tributaries below Muskrat Falls that provide freshwater inputs to the Churchill River. Erring on the conservative side, these rivers were not typically included in the modelling exercises involving the Churchill River below Muskrat Falls. Other biological (e.g., uptake), physical (e.g., settling) and chemical (e.g., photochemical) processes not accounted for in the modelling will also tend to dampen any effects going downstream.
The dilution predictions in the EIS are further refined by a modelling exercise conducted using the MIKE3 dispersion model (Oceans 2010). As stated in the EIS, the shallows at Goose Bay Narrows act as a hydraulic control that slow exchange with Lake Melville (Hatch 2008a) and likely provide at least a partial barrier to plankton and fish because of the abrupt vertical mixing of fresh and saline water at this location. In the case of increased mercury in fish (a potential effect of the project as predicted in the EIS), the main pathways are water, total suspended solids (TSS), plankton and fish. Water, TSS and plankton are progressively “diluted“ going downstream from Muskrat Falls and most sediment will settle out along the way; the Narrows will further “block” sediment, plankton, and fish to some degree. Many freshwater species cannot tolerate abrupt changes in salinity thus limiting their movement past the Narrows.
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Effects Assessment Criteria
Environmental effects assessments under the Canadian Environmental Assessment Act (CEAA) must meet certain prescribed criteria. In Canada, the Valued Environmental Component (VEC) and the KI are common approaches and were used in the EIS. The criteria used to characterize potential environmental effects for VECs and KIs are described below. The criteria that are listed below are consistent with those outlined in CEAA guidance documents and the EIS Guidelines.
• nature: the ultimate long term trend of the environmental effect (e.g., positive, neutral or adverse); • magnitude: the amount or degree of change in a measurable parameter or variable relative to existing conditions; • geographical extent: the area over which the environmental effect will occur; • timing: the Project phase within which the environmental effect will occur; • frequency: the number of times during the Project or a specific Project phase that an environmental effect might occur (e.g., one time or multiple times); • duration: the period of time over which the environmental effect will occur; • reversibility: the likelihood that a VEC or KI will recover from an environmental effect, including consideration of active management techniques (e.g., habitat restoration works). This may be due to the removal of a Project component/activity or due to the ability of a VEC or KI to recover or habituate. As well, reversibility is considered on a population level for biophysical VECs. Therefore, although an environmental effect like mortality is irreversible to an individual animal, the environmental effect on the population may be reversible; • ecological or social context: the general characteristics of the area in which the Project is located, as indicated by existing levels of human activity and associated disturbance; and • level and degree of certainty of knowledge: level of confidence in the knowledge that supports the prediction.
In order to support the CEAA‐compliant effects assessment as described above, a variety of baseline, analytical and modelling studies were conducted. Many of these used criteria specific to the particular exercise. For example, the STELLA® Version 8.0 modelling software was used to develop a water quality model for TP and TSS for the proposed Project (Minaskuat 2008) and MIKE3 software was used to model sediment plumes during construction (Hatch 2008b). Baseline data on water and sediments included JWEL (2000), Minaskuat (2007), and AMEC ‐ BAE (2001).
Specific Criteria for effects prediction:
The relevant VEC in IR# JRP.43 and as described in Volume II A, Section 9.2 of the EIS, is the Aquatic Environment and the KI is Fish and Fish Habitat. The measurable parameters or specific criteria that were used to assess effects of the Project on Fish and Fish Habitat included:
• Habitat quantity in units / hectares for river and standing water; • Change in fish habitat quality, including TSS, TP, and temperature; • Change in fish distribution and abundance, including habitat utilization and mortality; and • Change in fish health
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IR# JRP.43 referred to Nalcor’s predictions concerning these measurable parameters and how they related to Fish and Fish Habitat. The sections below provide additional explanation on the specific criteria used to conclude no measurable effects on Fish and Fish Habitat.
Total Phosphorous and Total Suspended Solids
Total phosphorus dynamics in aquatic systems are important determinants of productivity and trophic state. In addition to being rapidly taken up and cycled by phytoplankton and other aquatic biota, phosphorus may be adsorbed to suspended sediment particles. It is estimated that only approximately 5 to 10 percent of the phosphorus eroded from continental rock is carried to the oceans in dissolved form. The rest is likely carried as inert phosphorus in fluvial detritus (Froelich 1988).
Average concentrations of TP and TSS were modeled in surface water, deep water and bottom sediment cells. The primary sediment and phosphorus inputs were shoreline erosion and vegetation decomposition, with minor input from unregulated surface runoff. The Muskrat Falls reservoir and Happy Valley (also called mainstem and/or Section 1) reaches have the greatest shoreline erosion potential. Therefore, these same reaches are predicted to have the greatest increases in TP and TSS concentrations. Concentrations of both variables peaked in the first two years post impoundment and decreased thereafter. The models were most sensitive to variation in the rates of river flow (affecting both TP and TSS), vegetation decomposition (TP only), reservoir clearing (TP only), and sediment settling (TSS only). The reaches modeled for water quality by Minaskuat (2008) are shown in Figure 4 (from Figure 4‐1 in Minaskuat 2007) below.
Figure 4 Churchill River Power Project 2006/07 Water and Sediment Quality Study Area.
In the model reaches of the Muskrat Falls reservoir (Section 2 in Figure 4) and in the mainstem (Section 1 in Figure 4) used in predicting post‐Project concentrations, TP concentrations of between 0.054 and 0.115 mg/L were predicted. It should be noted that only 5 to 10 percent of the TP is actually bioavailable. While predicted peak concentrations represent increases above mean annual concentrations, they remain within the range that could result in increased fish production but below concentrations where summer kill due to anoxia from algal blooms would likely occur (Stockner et al. 2000; Jeppesen et al. 2005). Furthermore, predicted peak TP
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concentrations are within the range of current (background variability) TP concentrations observed during 2006 and 2007 (Figure 1.2 in Minaskuat 2007). Kimmel et al. (1990) indicated that generally productivity of reservoirs may increase for a period of 5 to 20 years post‐impoundment. The length of this period depends on the amount of flooded biomass, the magnitude of water level changes, and other factors including water retention time (flushing rate). This period is typically followed by a 3 to 30 year period of reduced production, then a possible gradual increase in fish production (Lindström 1973 in Hall et al. 1999).
Baseline concentrations of TSS are typically low (<5 mg/L) in the upper river (above Gull Island) but increase in the lower reaches of the river due to erosion of the predominantly sandy shorelines. Baseline concentrations of TSS in the lower river also display higher concentrations during spring (11 to 127 mg/L) when runoff and erosion rates typically increase. The TSS in the lower river has been linked to an increase in mineral particles (clay and silt) in suspension in the river water (Minaskuat 2007). Currently, the baseline concentration of TSS in the lower Churchill River are very low and typically below detection limits (5 mg/L) (Minaskuat 2007); baseline was assigned a value of 1 mg/L for modelling purposes (Minaskuat 2008). However, during the spring freshet, baseline concentrations may rise to between 11 and 127 mg/L, depending on the river reach, due to high turbulence and scouring of sediment in the streambed and river banks (Minaskuat 2007). This is a typical cycle of TSS concentrations for rivers. After impoundment (with the exception of the mainstem reach below Muskrat Falls generating facility), unregulated runoff (i.e., from tributaries) will continue to occur in the spring, but river levels will not rise as much as they did pre‐impoundment, and water velocities within the impoundments will be lower. Therefore, the spring freshet spike in TSS concentrations will be weaker.
Greater erosion potential of the banks in the lower river (AMEC 2007) was the main factor influencing model results for the lower sections. Beyond Year 15, concentrations in all river reaches returned to levels approximating those of current baseline concentrations (Minaskuat 2007) and will continue to fluctuate seasonally beyond the 20‐year model (i.e., years 20 to 50).
Projected TSS concentrations from the lower Churchill River model are consistent with background TSS concentrations from several large Canadian river systems. For example, in the MacKenzie River, Northwest Territories, TSS concentrations during average flow range from approximately 40 to 60 mg/L in the river delta (Emmerton 2006). These concentrations do not represent spring runoff, when TSS concentrations rise substantially. Similarly, in the Fraser River, British Columbia, TSS concentrations average 48 mg/L (ranging from 1 to 380 mg/L) near Hope, British Columbia (Swain et al. 1998).
Specific criteria used to predict no measureable downstream effects of TP and TSS past the river mouth were the model results from a mass balance model developed by Minaskuat (2008) using STELLA and the MIKE3 model (Hatch 2008a). These models predicted TP and TSS concentration in the mainstem to fall within baseline sampling results.
As post‐Project TP and TSS was predicted to fall within natural variability in the mainstem, then the conclusion was no measurable effects to Fish and Fish Habitat in the mainstem. This logic was extrapolated to Goose Bay and Lake Melville.
There will be a short term (days) pulse of increased TSS during dam construction as modeled by Hatch (2008b) but little difference in TSS between impoundment and post‐impoundment because much of the TSS will settle out in the reservoirs. The Hatch (2008b) plume modelling for construction did not consider the finest range of clay particles as this size fraction was not found in the site specific samples and thus the degree of transport of clay size particles was not predicted by that modelling exercise. As noted above, model results contained in Minaskuat (2008) did contain all particle sizes.
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Uncertainties in the TP, TSS or transport distances as predicted by the models do not affect the predictions related to the VEC or KI. Any TP above baseline not rapidly assimilated by organisms will be dispersed by inflow from other rivers below Muskrat Falls and entering Goose Bay (Oceans 2010) and/or utilized by phytoplankton, algae and other aquatic vegetation prior to entering Lake Melville. TSS will likely be dispersed by inflows or settled out in Goose Bay due to reduced water velocities compared to the river and to the hydraulic control caused by the Goose Bay Narrows.
Transport Distances
For construction, transport distances for TSS related to various construction components are predicted by the plume modelling studies conducted by Hatch (2008b). Most of the modeled TSS is predicted to settle out within several kilometers of the construction sites, and therefore not anticipated to be detected at the mouth of the Churchill River nor Goose Bay and beyond.
For operations, Minaskuat (2008) modeled water quality (including TSS and TP) as described previously and predicted that concentrations of TSS below Muskrat Falls would fall within the range of baseline variability. TP below Muskrat Falls was predicted to increase above baseline but fall to baseline by Year 11. Dispersion modelling (barotropic, assuming no stratification) (Oceans 2010) indicate no measurable increase in TP is anticipated beyond Goose Bay harbour limits (Figure 5).
Fish Productivity
Fish productivity is related to a number of environmental factors such as water quality (including plant nutrients such as phosphorus), physical habitat (e.g., cover from predators, substrate, depth, velocity), predation, and food availability (see also IR# JRP.152). The reference in IR# JRP.43 to “no measureable effects” was in the context of TP which is a key nutrient at the base of the freshwater food chain. Thus, if there is no measureable effect on TP it is reasonable to predict no measureable effect on algae and other plant life that support benthic invertebrates and fish; hence, “no measureable changes in terms of fish productivity as a result of TP changes in the main stem below Muskrat Falls” (IR# JRP.43).
To account for inherent uncertainty in the model a conservative approach was taken by ignoring potential dispersion effects on TP from the other freshwater sources entering the river and Goose Bay and the actual quantity of phosphorus available for biological uptake.
Salinity
For the construction phase, the interaction of Project activity with the aquatic environment downstream of Muskrat falls is impoundment of the reservoirs. The Project intends to provide 30 percent Mean Annual Flow (MAF) compensation flows and thus there should be no changes to salinity downstream of Muskrat Falls due to salt intrusion (A. Pryse‐Phillips, Hatch, pers. comm.; see also IR# JRP.165 and Hatch 2008a).
During the operations phase as described in IR# JRP.43, it is predicted that there will be no measureable changes to the salinity regime.
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Figure 5 TP Initial Barotropic Model Run (Oceans 2010).
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Velocity and Temperature
Hatch (2007) using the HEATSIM Model and AMEC and Sikumiut (2007) predicted that the cool‐down period would be delayed by approximately two weeks and the warm‐up period would be delayed by a week for a “net effect” of a one week change in thermal regime downstream of Muskrat Falls. These studies did not extend beyond the mouth of the river because it was predicted that these effects would be dissipated by inflow from other rivers and further dilution in Goose Bay (Hatch 2008a).
It is predicted that the range of temperatures and velocities during project operations will be within the range presently encountered by fish populations in the area and changes in fish productivity from these potential sources of effects are unlikely.
Velocity and thermal profiles were developed using the MEFL3D model (AMEC ‐ BAE 2001). This oceanographic study found a barrier effect to flow and mixing at the Goose Bay Narrows. The modelling study (Oceans 2010) has also provided temperature profiles that demonstrate the “barrier” effect of the Goose Bay Narrows (see Figure 6). Original data were provided by CTD transects, ADCP and moored current meters deployed over a one‐ year time period (8 field surveys). The 2001 model domain covered the entire Goose Bay estuary, the Goose Bay Narrows, and the southwest part of Lake Melville up to the northern open boundary limit known as the "Goose Bay Harbour Limit” (Figure 1). It also incorporated the Goose River / Terrington Basin channel mouth as an open boundary at the western end of the model domain, and Kenamu River discharge north of Goose Bay Narrows. The discharges from these rivers were estimated from historical data and used as input to the model simulations.
Ice Dynamics
Hatch (2007) used three models to simulate ice conditions between Goose Bay and upstream: HEC‐RAS for open water flows and velocities, HEATSIM for water temperatures post‐Project, and ICESIM for ice formation. Additional information on ice dynamics is contained in Hatch (2008a). Goose Bay was not included in the HEATSIM modelling since any changes to the thermal and ice regimes of Goose Bay as a result of the project are expected to be localized and small. Since Goose Bay is tidal, there is a twice daily mixing of its waters that will help disperse the Churchill River inflows (Hatch 2008b). Thus, any temperature shifts are expected to be noticeable only in the freshwater surface flow as it first enters Goose Bay. Given the anticipated foregoing condition for Goose Bay, it is anticipated that changes in Lake Melville would be negligible. Similarly, Goose Bay was not included in the ICESIM model since, as mentioned previously, any changes to the thermal and ice regimes of the Bay as a result of the Project are expected to be localized and small (Hatch 2007).
Baseline and post‐Project ice thicknesses and progression of ice from Goose Bay to Muskrat Falls were analysed and compared for five different climate years using the ICESIM model. The start of progression in the post‐Project case is delayed by approximately two weeks, and the rate of progression is less than the pre‐Project case. In the pre‐Project case, for the years simulated, progression of the ice cover from Goose Bay to Blackrock Bridge takes just over one week on average; in the post‐Project case, the same extent of progression takes an average of four weeks. In terms of ice thickness, the results indicate that in the post‐Project conditions, there is a large decrease in ice volume just downstream of Muskrat Falls (i.e., the large hanging dam that currently forms downstream of Muskrat Falls will not form during post‐Project conditions). The pre‐ and post‐Project thicknesses in the reach downstream of Muskrat Falls are expected to be comparable.
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Figure 6 Temperature Along the Transect Line on September 1, 2008 and October 1, 2008.
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Figure 6 Temperature Along the Transect Line on September 1, 2008 and October 1, 2008 (continued)
Based on the results presented above, it is expected that there will be a delay in ice formation of approximately two weeks near Mud Lake. The warm up period is expected to be delayed by approximately one week in the same location; hence the overall duration of ice coverage within the Churchill River below Muskrat Falls will be reduced by approximately one week.
Seal Distribution
Nalcor conducted a study of seal abundance and distribution in Lake Melville in early May 2006 (Sikumiut 2007). Aerial surveys were conducted along transects covering all Goose Bay and Lake Melville. Ice conditions were approximately two weeks early that year and the surveys were conducted over mostly rotten ice. Calculations of seal density were made after Lunn et al. (1997). Analysis of all results was completed and the western most transects showed very low seal density. As this area is heavily traveled by residents of Happy Valley‐Goose Bay, North West River, Sheshatshiu and Mud Lake via snowmobile, it is possible that the disturbance and hunting pressure (seals are actively hunted from snowmobile during spring) might affect density and distribution in the area. Other parts of Lake Melville are used for similar purposes, but by much lower concentrations of people.
The overall ringed seal density calculated for Lake Melville was 1.66 seals/km2 (SE = 0.221, error variance = 2.08 x 10‐4). When the area was split to segregate the heavily disturbed section in the western end of Lake Melville, density calculations rose to 2.25 seals/km2 (SE = 0.246; error variance = 6.58 x 10‐4). Density estimates were high relative to some of those recorded for the Arctic (Sikumiut 2007). No harbour seals were observed during the surveys although they are known to occur in the area and no ringed seals (the primary species of interest) were observed in Goose Bay (see Figure 4.2.2 in Sikumiut 2007). Sixteen harp seals were observed on transects during the study but sighting locations were not provided.
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No measureable effects were predicted for seal use of the area because any changes in ice dynamics will be localized within Goose Bay and within the range of natural variability (see Ice Dynamics above). In addition, no seals were observed near the mouth of the river or in Goose Bay during the baseline surveys. However, there have been anecdotal reports (based on local knowledge) of a few seals utilizing the lower Churchill River upstream as far as Muskrat Falls during open water.
Bank Stability
IR# JRP.43 does in fact predict measureable effects on bank stability at individual locations downstream of Muskrat Falls. The reviewer is referred to the Component Study on bank stability prepared by Northwest Hydraulic Consultants (NHC) (2008) which states:
• The majority of the existing erosion in this area is due to the undermining and subsequent failure of high, steep embankments. Some of this material along with sediment from further up the river is deposited near the mouth of the river in the form of islands and sand bars; • Failures are typically localized and small in nature. Based on a review of the LiDAR imagery, five recent slope failures were identified; • The water elevation of this section of the river is not expected to change substantially from its existing regime. The shoreline development in this area is for the most part undergoing Stage 3 erosion, i.e., existing erosion and depositional processes are expected to continue; • Scouring may occur in the area of the tailrace of the Muskrat Fall Generating Station pending final design and location; • Movement of the channels and islands within this section of the river may result in accelerated erosion of the existing shorelines; • The majority (78 percent) of this area has a Terrain Stability rating of low; • The majority (72 percent) of this area has a Soil Erosion Potential rating of high; • The majority (75 percent) of this area has a Wave Energy Potential rating of low; and • The majority (65 percent) of the Erosion Potential within this area is rated as low (0.5 to 1.5 m/yr/m). Due to the nature of the river and the river sediments, it is difficult to accurately predict the future actions of the river.
Fish Habitat Utilization and Migration
Over the past decade, a model has been developed for measuring and predicting productive fish habitat within the lower Churchill River (McCarthy et al. 2006) and just beyond the mouth of the river in Goose Bay. The model is based on extensive field surveys that characterized the physical features of the habitat (substrate, water velocity) and habitual use patterns by fish species (and life cycle stage). A large inventory has been accumulated to describe habitat use for every aquatic habitat type in the affected watershed extending to the mouth of the Churchill River and in to Goose Bay. The database was cross‐checked and validated through additional field sampling. The data were then run through models which produced a measurable parameter (Habitat Utilization Index (HUI) expressed in hectares) that could be used in effects prediction.
Fish habitat utilization and migration depends to a large degree on the factors or criteria such as TSS, TP, velocity, temperature and salinity, as discussed above. With the exception of some localized effects (e.g., bank stability), no measureable effects on the physical and chemical criteria should translate to no measureable effects on fish habitat utilization and migration. Fish habitat utilization and migration will be a component of any effects monitoring programs.
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A summary of changes in fish habitat utilization was presented in IR# JRP.153.
Impoundment vs Post‐impoundment
During construction, TSS will increase for a short time in the vicinity of the dams because of disturbance to river bed and banks. These effects are detailed in the EIS (Sections 4.12 and 4.15) and supporting studies.
During impoundment, the TSS will likely decrease downstream as water is being held behind the dam. These effects are detailed in the EIS (Sections 4.12 and 4.15) and supporting studies (NHC 2008). TSS that originates from upstream will decrease further during post‐impoundment as most sediment from upstream is trapped in the reservoir. Therefore, TSS and bank stability immediately downstream of Muskrat Falls could be affected during post‐impoundment due to increased scour potential from “sediment‐poor” water. These effects are described in the EIS (Sections 4.12 and 4.15) and supporting studies (NHC 2008).
Post‐impoundment TP should peak and then decline within several years but could be elevated above baseline for 20 years until water quality stabilizes as described in the EIS (Section 4.7.7) and supporting studies (Minaskuat 2008). The downstream extent of any increases in TP is not anticipated beyond Goose Bay harbour limits (Oceans 2010).
There should be no noticeable difference between impoundment and post‐impoundment in the other variables listed in the IR especially since some flow will be released during impoundment to maintain fish habitat and impoundment is not planned to occur during the ice‐covered period. Any uncertainty in key predictions will be addressed during the monitoring program.
References
AMEC ‐ BAE (AMEC Earth & Environmental Ltd. and BAE‐NewPlan). 2001. 1998 Environmental Studies: The Churchill River Power Project, Final Report, Aquatic Environment in the Goose Bay Estuary. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. AMEC Earth and Environmental Ltd. and Sikumiut Environmental Management Ltd. 2007. Lower Churchill Hydroelectric Generation Project Habitat Quantification. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Emmerton, C.A. 2006. Downstream Nutrient Changes through the Mackenzie River Delta and Estuary, Western Canadian Arctic. M.Sc. Thesis. Simon Fraser University, Burnaby, BC. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Froelich, P.N. 1988. Kinetic Control of Dissolved Phosphate in Natural Rivers and Estuaries: A Primer on the Phosphate Buffer Mechanism. Limnology and Oceanography. 33: 649‐668. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Hall, R.I., P.R. Leavitt, A.S. Dixit, R. Quinlan and J.P. Smol. 1999. Limnological Succession in Reservoirs: A Paleolimnological Comparison of Two Methods of Reservoir Formation. Canadian Journal of Fisheries and Aquatic Sciences. 56: 1109‐1121. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Hatch Ltd. 2007. Ice Dynamics of the Lower Churchill River. Draft report prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the
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Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Hatch Ltd. 2008a. Salt Water Intrusion 3D Model Study. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Hatch Ltd. 2008b. Sediment Plume Analysis. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Hatch Ltd. 2008c. Further Clarification and Updating of the 2007 Ice Dynamics Report. Prepared for Newfoundland and Labrador Hydro, St. John's, NL. Jeppesen, E., M. Søndergaard, J.P. Jensen, K.E. Havens, O. Anneville, L. Carvalho, M.F. Coveney, R. Deneke, M.T. Dokulil, B. Foy, D. Gerdeaux, S.E. Hampton, S. Hilt, K, Kangur, J. Köhler, E.H.H.R. Lammens, T. L. Lauridsen, M. Manca, M.R. Miracle, B. Moss, P. Nõges, G. Persson, G. Phillips, R. Porteilje, S. Romo, C.L. Schelske, D. Straile, I. Tatrai, E. Willén and M. Winder. 2005. Lake Responses to Reduced Nutrient Loading – An Analysis of Contemporary Long‐term Data from 35 Case Studies. Freshwater Biology. 50: 1747‐1771. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. JWEL (Jacques Whitford. Environmental Limited) 1998. 1998 Water and Sediment Quality of the Churchill River (LHP98‐08). Jacques Whitford Limited report prepared for Labrador Hydro Project, St. John’s, NL. JWEL (Jacques Whitford Environmental Limited). 2000. Water Quality and Chlorophyll Study (LHP 99‐08). Report prepared by Jacques Whitford Environmental Limited and Submitted to Newfoundland and Labrador Hydro. 32 p + appendices. JWEL. 2001. Biological Study of the Goose Bay Estuary (JWEL Project No.1201). Report prepared by Jacques Whitford Environmental Limited and Submitted to Newfoundland and Labrador Hydro. 91 p + appendices. Kimmel, B.L., O.T. Lind and L.J. Paulson. 1990. Reservoir Primary Production. pp 133‐194. In Thornton, K.W., B.L. Kimmel and F.E. Payne (ed.). Reservoir Limnology: Ecological Perspectives. John Wiley & Sons. New York, NY. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Lunn, N. Stirling, I. And S. Nowicki. 1997. Distribution and Abundance of Ringed (Phoca hispida) and Bearded Seals (Erignathus barbatus) in Western Hudson Bay. Canadian Journal of Fisheries and Aquatic Sciences. 54: 914‐921. Cited In Sikumiut Environmental Management Ltd. 2007. Lower Churchill Hydroelectric Generation Project Environmental Baseline Report: Seal Abundance and Distribution. Report prepared for Newfoundland and Labrador Hydro, St. John’s, NL. McCarthy, J.H., L.J. LeDrew and B.R. LeDrew. 2006. A Framework for Aquatic Habitat Classification and Quantification for Large Northern Ecosystems: Applications to the Proposed Churchill River Power Project, Churchill River, Labrador, Canada. pp 587‐603. In: American Fisheries Society Symposium. 2006. Proceedings of the 4th World Fisheries Congress, May 2‐6, 2004. Vancouver, BC. Minaskuat Inc. 2007. Water and Sediment Quality in the Churchill River Environmental Baseline Report LHP 06‐ 04. Report prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL)
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NHC (Northwest Hydraulic Consultants). 2008. Lower Churchill Hydroelectric Generation Project Sedimentation and Morphodynamics Study. Final Report. Prepared for AMEC Earth & Environmental Ltd, Edmonton, AB. 60 p. Oceans Ltd. 2010. Modeling the Dispersion of Mercury and Phosphorus in Lake Melville. Technical Memorandum prepared for Nalcor Energy. Sikumiut Environmental Management Ltd. 2007. Seal Abundance and Distribution Environmental Baseline Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Stockner, J.G., R. Rydin and P. Hyenstrand. 2000. Cultural Oligotrophication: Causes and Consequences for Fisheries Resources. Fisheries. 25: 7‐14. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Swain, L.G., D.G. Walton, B. Phippen, H. Lewis, S. Brown, G. Bamford, D. Newsom and I. Lundman. 1998. Water Quality Assessment and Objectives for the Fraser River from Hope to Sturgeon and Robertson Banks. Ministry of Environment, Lands and Parks, BC. First Update, Technical Appendix. Cited In Minaskuat Inc. 2008. Water and Sediment Modelling in the Lower Churchill River Environmental Baseline Report LCP #535725. Final Report. Prepared for Newfoundland and Labrador Hydro, St. John’s, NL. Personal Communications
A. Pryse‐Phillps Engineer, Hatch Ltd.
JOINT REVIEW PANEL – IR# JRP.166 PAGE 17
INFORMATION REQUESTS RESPONSES| LOWER CHURCHILL HYDROELECTRIC GENERATION PROJECT
Requesting Organization – Joint Review Panel Information Request No.: JRP.166
Information Requested:
b. Nalcor’s fish studies indicate that sea run brook trout, lake whitefish, longnose and white suckers are found in the Goose Bay estuary and upstream to Muskrat Falls (EIS Component Studies Aquatic (1) Fish Migration and Habitat Use, Biological Study of the Goose Bay Estuary). In its response to IR JRP.43 (d), Nalcor has acknowledged the need to make conservative assumptions regarding mercury concentrations downstream of Muskrat Falls. In the AMEC 2000 Freshwater Fish Mercury Sampling study, Goose Bay estuary and main stem river fish samples were collected for mercury analysis. However, to date, raw data was provided but no analysis of mercury results for fish downstream of Muskrat Falls have been presented.
Provide an analysis of the results of the AMEC 2000 and any other studies that have been conducted on fish mercury concentrations for the species listed above in both the Goose Bay estuary and the main stem below Muskrat Falls. If no information is available, indicate when and how this baseline sampling will be done prior to the start of construction. Provide a full assessment of the movement of methylmercury, engendered by project activities, into Goose Bay and beyond in all forms (e.g. as dissolved fraction, in planktonic and benthic organisms, and in fish body burdens) and indicate its ultimate fate, and the significance of these methylmercury impacts. Indicate if fish consumption advisories would likely be applied to areas beyond the mouth of the Lower Churchill River.
Response:
Analysis of Fish Mercury Concentrations
Nalcor has undertaken an analysis of the studies that have been conducted on fish mercury concentrations in both the Goose Bay estuary and the main stem below Muskrat Falls. The results of this analysis are presented in the Statistical Analysis of Mercury Concentrations in Fish from the lower Churchill River and estuary (Stantec 2010) (Attachment B).
Data sets used to assess historical, existing and predicted fish mercury concentrations in the lower Churchill River system have been enhanced with data collected in 2010 as well as incorporating the raw data obtained for previous years (including 1999 referred to above as AMEC 2000). The complete data set used in mercury related analysis now includes the following components:
• Results of 2010 sampling in Winokapau Lake, Gull Lake, and the portion of the Churchill River between Muskrat Falls and Happy Valley – Goose Bay (Stantec 2010); • Data collected in 1999 and 1977‐78 downstream of Muskrat Falls (Stantec 2010), including data obtained by AMEC; • Data collected in 1987 at two stations in the Churchill River System: 3 km downstream of Churchill Falls, and near Happy Valley ‐ Goose Bay (LGL 1987); and • Datasets previously used in the analysis associated with the response to IR# JRP.156 (Jacques Whitford 2006).
The Stantec 2010 report has compiled and analyzed all the above data that was not addressed in JWEL 2006. The ‘new’ data are from locations sampled in 1977‐78 that were not included in the scope of the 2006 study, which focussed on lakes and reservoirs both above and below Churchill Falls. Other ‘new’ data included sampling conducted in 1999 (AMEC 2000) and in 2010 (also by AMEC) at locations on the Churchill River, both above and below Muskrat Falls.
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In the 2010 field campaign, efforts were made to catch all fish species present in the lower Churchill River and near the mouth of the river. The focus of the 2010 program was to augment the existing habitat utilization dataset and to collect additional baseline sampling. In addition to sampling species for habitat utilization, selected fish were also analyzed for mercury body burden. While samples were obtained for 10 species within the Lower Churchill main stem, some datasets did not include fish individuals within the length ranges needed to estimate fish mercury levels for 300 mm and/or adult standard lengths.
The complete and updated dataset from Lake Winokapau, Gull Lake and the lower portion of the Churchill River were analyzed to estimate baseline fish mercury concentrations for Gull Island Reservoir, Muskrat Falls Reservoir and downstream to the entrance to Goose Bay (Figure 7). Baseline values (for standardized lengths) were estimated using the average concentrations for any years with data during the period 1999‐2010 (up to three years: 1999, 2004, 2010). While some fish mercury concentrations may still have been declining in 1999 as a result of historic influence of the Smallwood Reservoir, the inclusion of those data would be expected to conservatively tend towards higher estimates of baseline fish mercury levels. Revised baseline fish mercury levels are shown in Table 1 for standard lengths representing adult fish and for 300 mm fish of all species. These baseline concentrations are similar to those used in previous analyses and presented in the response to IR# JRP.156.
To estimate baseline values, the data was analyzed using linear and polynomial regressions to obtain the best fit to the available data. Methods adopted are the same as those described in IR# JRP.156. Mercury levels were estimated (as part of this analysis) for standard length fish and these levels were provided for use in modelling mercury levels in fish at different times and locations as well as at different times at the same locations.
Figure 7 Mercury concentrations in selected fish species in the Churchill River System from 1977‐2010. Results are presented for standardized lengths for each species. Estimated baseline values for the Lower Churchill Development are shown with green dashed line. Data from Jacques Whitford (2006), Stantec (2010a,b), and LGL (1987)
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Table 1 Fish mercury concentrations in the lower Churchill River 1999‐2010. (Derived using data from Jacques Whitford 2006, Stantec 2010a, b).
Location
Fish Species Length Baseline estimate (average of Winokapau Gull Lake Section 1 Gull Lake, Winokapau, Section 1) 1999 2004 2010 1999 2004 2010 1999 2004 2010 Brook Trout 300 mm 0.09 0.09 0.08 0.08
Lake Trout 600 mm 0.90 0.90 300 mm 0.33 0.33
Lake Whitefish 400 mm 0.21 0.19 0.15 0.18 0.14 0.13 0.17 300 mm 0.13 0.11 0.08 0.09 0.06 0.10 0.09
Longnose Sucker 400 mm 0.18 0.11 0.27 0.20 0.12 0.17 300 mm 0.11 0.26 0.14 0.14 0.10 0.15
Northern Pike 700 mm 0.81 0.81 300 mm 0.11 0.11
Ounaniche 300 mm 0.09 0.09
White Sucker 400 mm 0.22 0.32 0.27 300 mm 0.14 0.14 0.14
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Fish mercury concentrations in the lower Churchill River were not assumed to represent baseline conditions in Goose Bay or Lake Melville, which are transitional environments from freshwater to marine environments. Fish mercury data from 1999 were available for Goose Bay (Table 2). Additional studies are planned to better define baseline fish Hg concentrations in the Lower Churchill system, including Goose Bay and Lake Melville.
Table 2 Observed fish mercury concentrations in Goose Bay in 1999.
Fish Species Length Concentration Brook Trout 300 mm 0.16 Lake Whitefish 400 mm 0.16 300 mm 0.14 Longnose Sucker 300 mm 0.10 Rainbow Smelt 200 mm 0.18 Tom Cod 225 mm 0.16
Revised Predictions for Peak Fish Mercury Concentrations
Using the updated baseline mercury concentrations in fish (Table 1) and the methodology and regression model presented in the response to IR# JRP.156, peak fish mercury levels in fish due to the creation of Muskrat Falls and Gull Island Reservoirs were re‐estimated (Table 3 for standard length fish, and Table 4 for 300 mm fish). It is estimated that fish mercury concentrations in adult sized fish will increase between 2.3X and 4.8X fold above baseline values. The regression modelling described in the response to IR# JRP.156 (Figure 2 of that response) predicted relative increases for adult northern pike ranging from 1.9X for Muskrat Falls without Gull Island increasing slightly to 2.3X in Muskrat Falls reservoir if it was downstream of Gull Island Reservoir, and both reservoirs were constructed at similar times (e.g., within 5 years of each other). Given the level of accuracy of the method, it was conservatively decided to use the higher value (2.3X) for all scenarios, including Gull Island Reservoir, and that approach is carried forward. The value of 2.3X remains the minimum relative increase for all fish species following the creation of Muskrat Falls and Gull Island Reservoirs. In the case of 300 mm fish, peak mercury concentrations are expected to be 3.4X to 4.8X above baseline values. For species where 300 mm fish are smaller than adults, the relative increases predicted tend to be greater than for adults, reflecting an observed trend in reservoirs, as presented in the response to IR# JRP.156.
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Table 3 Re‐estimated peak fish mercury concentrations (standard lengths related to sample means) associated with the proposed Lower Churchill River Project.
Peak Maximum Concentration Baseline Predicted Peak Site Species and Standard Length Increase Observed from Smallwood Notes Concentration Concentration Factor Used Reservoir ug/g wet ug/g wet Gull Island ug/g wet muscle muscle muscle Lake Trout (600 mm) 0.90 2.3 2.04 1.40 4 Northern Pike (700 mm) 0.81 2.3 1.83 1.16 1 White Sucker (400 mm) 0.27 2.3 0.61 0.32 6 Longnose sucker (400 mm) 0.17 2.3 0.39 0.44 3 Lake Whitefish (400 mm) 0.17 2.3 0.38 0.34 2 Brook Trout (300 mm) 0.08 4.3 0.36 No data 5 Ouananiche (300 mm) 0.09 4.3 0.37 No data 7
Maximum Concentration Muskrat Falls and Observed for Winokapau Lake or downstream in river* Gull Lake Lake Whitefish (400mm) 0.17 4.8 0.82 0.75 8 Longnose sucker (400 mm) 0.17 3.2 0.56 0.80 9 White Sucker (400mm) 0.26 3.2 0.59 0.34 10
Notes: 1‐ Baseline from Winokapau (2004). Max observed from Sandgirt (1987) 2‐ Baseline from Gull Lake (1999, 2004, 2010), Winokapau (1999, 2004) and Section 1 (1999). Max observed from Lobstick (1977) 3‐ Baseline from Gull Lake (1999, 2010), Winokapau (1999, 2010) and Section 1 (1999). Max observed from Lobstick (1977). 4‐ Baseline from Winokapau (1999). Max observed from Sandgirt (1977). 5‐ Baseline from Gull Lake (1999 and 2004) and Winokapau (2010). No observations during peak years. Peak increase factor assumed to be same as estimated for 300 mm pike. 6‐ Baseline from Gull Lake and Winokapau (2004). Max observed from Sandgirt (1977). 7‐ Baseline from Winokapau (1999). No observations during peak years. Peak increase factor assumed to be same as estimated for 300 mm pike. 8‐ Baseline from Gull Lake (1999, 2004, 2010), Winokapau (1999, 2004) and Section 1 (1999). Maximum observed from Winokapau (1977). 9‐ Baseline from Gull Lake (1999, 2010), Winokapau (1999, 2010) and Section 1 (1999). Max obs. from Winokapau (1977). 10‐ Baseline from Gull Lake and Winokapau (2004). Limited observations in peak years. Peak ratio assumed same as for 300 mm longnose sucker. * Section 1.
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Table 4 Re‐estimated peak fish mercury concentrations (300 mm length standard) associated with the proposed Lower Churchill River Project.
Baseline Peak Increase Ratio of PIF for Peak Increase Peak Observed Predicted Peak Site Species Concentration (ug/g Factor for adult 300mm/adult factor for 300 Concentration from Notes Concentration wet muscle) fish fish* mm fish Smallwood Reservoir Gull Island ug/g wet muscle ug/g wet muscle Lake Trout 0.33 2.3 1.9 4.3 1.42 1.30 4 Longnose sucker 0.15 2.3 1.7 3.9 0.59 0.46 3 White Sucker 0.14 2.3 1.9 4.3 0.60 0.22 6 Lake Whitefish 0.09 2.3 1.5 3.4 0.32 0.35 2 Northern Pike 0.11 2.3 1.9 4.3 0.48 0.61 1 Brook Trout 0.08 4.3 1.0 4.3 0.36 No data 5 Ouananiche 0.09 4.3 1.0 4.3 0.37 No data 7
Muskrat Falls Peak Observed and Concentration for downstream in Winokapau Lake or Gull river Lake
Lake Whitefish 0.09 4.8 1.0 4.8 0.46 0.38 8 Longnose sucker 0.15 3.2 1.2 3.9 0.58 0.68 9 White Sucker 0.14 4.3 0.60 0.24 10 Notes: 1 ‐ Baseline from Section 1 (2010). Max observed from Sandgirt 1977 2‐ Baseline from Gull Lake (1999, 2004, 2010), Winokapau (1999, 2004) and Section 1 (1999). Max observed from Sandgirt 1977 3‐ Baseline from Gull Lake (1999, 2010), Winokapau (1999, 2010) and Section 1 (1999). Max observed from Lobstick 1977 4‐ Baseline from Winokapau 1999. Max observed from Sandgirt 1977. 5‐ Baseline from Gull Lake (1999 and 2004) and Winokapau 2010. No observations during peak years. Peak ratio assumed to be same as estimated for 300 mm pike. 6‐ Baseline from Gull Lake and Winokapau (2004). No observations during peak years. Peak ratio assumed to be same as estimated for 300 mm pike. 7‐ Baseline from Winokapau 1999. No observations during peak years. Peak ratio assumed to be same as estimated for 300 mm pike. 8‐ Baseline from Gull Lake (1999, 2004, 2010), Winokapau (1999, 2004) and Section 1 (1999). Max obs. from Winokapau 1977. Used PIF for 400 mm fish as PIF for 300 mm fish was less. 9‐ Baseline from Gull Lake (1999, 2010), Winokapau (1999, 2010) and Section 1 (1999). Max obs. from Winokapau 1977. 10‐ Baseline from Gull Lake and Winokapau (2004). Limited observations. Used estimated PIF for Muskrat Falls. * 1.9 is average from 9 sites in Quebec and Manitoba. Used for predators and species with no data from Smallwood. A value of 1.0 was used for brook trout and ouananiche because adult size was also 300 mm. 1.0 used conservatively for lake whitefish downstream because observed value was less than 1.
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Given the accuracy associated with efforts to predict peak mercury concentrations in fish due to reservoir creation, these results and their interpretation are similar to information presented in the response to IR# JRP.156. Recognizing that increased fish mercury concentrations are always expected to occur in connection with flooding, the predicted increases in adult fish mercury levels for Muskrat Falls and Gull Island Reservoirs are in the low to moderate range of observations from reservoirs in Quebec and Manitoba (Bodaly et al. 2007; Schetagne et al. 2003). For example, the low category would apply to lake whitefish (400 mm), currently in the 0.15 to 0.20 ug/g range, predicted to reach peak values in the range of 0.4 ug/g in Gull Island Reservoir. The moderate category is more applicable to species where baseline levels on record are relatively high already (0.8 ug/g baseline, 1.8 ug/g peak for 700 mm northern pike), or where a switch to piscivory (fish‐eating) may occur for some species downstream of tailraces (discussed below).
Downstream of new reservoirs, some fish species such as lake whitefish and longnose suckers that are normally not piscivorous (fish‐eating) have been observed to introduce some degree of piscivory into their diet (Schetagne et al. 2003). As a result, mercury concentrations in these species can increase more downstream than in the reservoir. This was also observed in Winokapau Lake downstream of Smallwood Reservoir (Figure 7). The potential for this phenomenon to occur was recognized in the response to IR# JRP.156 and in the current analysis, with equal or higher peak concentrations predicted downstream of Gull Island Reservoir than in the reservoir itself, for lake whitefish, longnose sucker and white sucker (Table 3 and Table 4).
Movement of Methylmercury into Goose Bay and Beyond
Fish
In Goose Bay, it is expected that the increases in methylmercury levels in fish will be moderated compared to the river, as the overall fish exposure to methylmercury incorporates dietary items progressively less impacted by the reservoir in habitats farther downstream. It is also expected that the fresh water fish species that may become piscivorous will stay in the mainstem or where there is reliably more freshwater and not migrate in to Goose Bay.
Methylmercury in Water and Plankton
Methylmercury concentrations in the water column and lower food web organisms will increase due to the creation of Gull Island and Muskrat Falls Reservoirs. The regression model that was used to estimate peak levels in fish does not predict methylmercury concentrations in other environmental compartments. To predict the potential increases in methylmercury in water and zooplankton (which are both exported downstream of the reservoir), an existing mechanistic model was used (Harris et al. 2010) (Attachment C). The model, known as RESMERC, uses a mass‐balance, process‐based approach to predict the transport, fate, and bioaccumulation of three mercury forms, including methylmercury, in lakes and reservoirs. A conceptual diagram of the model is shown in Figure 8. A key process in the model is the surge in decomposition of readily degradable organic matter after flooding, and an associated surge in methylmercury production until the supply of labile carbon is consumed. The model also considers the effects of flow as a potential source of dilution of methylmercury increases in the water column.
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Figure 8 Conceptual Diagram of Mechanistic Model for Mercury in Reservoirs (RESMERC, Harris et al. 2009). The RESMERC model has been calibrated using results of experimental upland and wetland reservoirs in the Canadian Shield at the Experimental Lakes Area (ELA), Ontario (Harris et al., 2009). Additional testing is planned for full‐scale reservoirs with existing long term datasets of fish mercury levels. In principle, a mass balance model is more able to accommodate variations in site conditions that affect the increase in fish mercury levels in reservoirs, but additional model calibration is needed to reach that stage. For this reason, the regression model is viewed as currently having at least as good predictive ability as the mechanistic model regarding estimates of peak fish mercury levels. The mechanistic model is sufficiently developed to provide insights into the potential increases in methylmercury levels in the reservoir water column and zooplankton following the creation of Muskrat Falls Reservoir, and to consider the potential for flow dilution, and has been applied in this manner. Additional information is provided in supporting documentation (Harris et al. 2010).
Data characterizing mercury levels in the water column and zooplankton in the Churchill River are limited to samples from 1999 (Jacques Whitford 2001; Nalcor 2009). Observed levels of methylmercury in water were below the detection limit of 0.04 ng/L. Total mercury concentrations in water were in the range of 1 ng/L. Methylmercury levels in zooplankton in 1999 were in the range of 70 ng/g dry weight or less. These concentrations are low to moderate for both total mercury and methylmercury in water and plankton, in the context of observations in freshwaters in the Canadian Shield. For the Project, the RESMERC model was first calibrated to reproduce concentrations of total and methylmercury in water and zooplankton similar to the values reported above, for the stretch of the Churchill River that will become reservoirs (Harris et al. 2010). Simulated mercury concentrations (total and methyl) in the water column were largely controlled by the inflowing concentrations assigned in model simulations, due to rapid water throughput. Once calibrated to existing conditions, the effects of the creation of Muskrat Falls and Gull Island Reservoirs were simulated. Methylmercury concentrations in the water column were predicted to roughly double from 0.05 to approximately 0.08 ng/L at the Muskrat Falls tailrace. The predicted increase is significantly moderated by the rapid throughput of water. Estimated mean annual hydraulic residence times are approximately 10 days at Muskrat Falls Reservoir 28 days and at Gull Island Reservoir. These predicted methylmercury increases in water
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and plankton are relatively low, remaining in the natural ranges observed for lakes and rivers (e.g., Scudder et al. 2009).
Concentrations of methylmercury predicted for water and zooplankton in the reservoir are assumed to persist downstream of Muskrat Falls Reservoir until dilution occurs at the inflow to Goose Bay. Hydrodynamic simulations (barotropic) of the mixing of freshwater and seawater in Goose Bay and Lake Melville (Oceans 2010) predict that dilution with seawater will then reduce methylmercury levels in Goose Bay from river levels, transitioning to background seawater levels relatively quickly in Lake Melville (Figure 9). Further reductions in methylmercury concentrations in Goose Bay waters are likely due to the effects of sedimentation and photochemical reactions, not included in the simulations. While these removal rates are difficult to predict, these processes will reduce levels beyond those used in the model.
Overall the combined efforts to predict methylmercury levels in the water column of the reservoirs and downstream should be interpreted as predicting that the increases in methylmercury levels in water in the reservoirs and downstream river (Section 1 or mainstem) will be modest. Methylmercury concentrations in the water column of Goose Bay will transition from levels in the river towards background levels in the vicinity of the entrance to Lake Melville. Predicted peak concentrations of total mercury and methylmercury remain orders of magnitude below the Health Canada drinking water criterion of 1,000 ng/L (as total mercury, Health Canada 2010), indicating that the primary pathway for methylmercury exposure remains dietary uptake, not drinking water.
Zooplankton methylmercury concentrations are predicted to mimic the relative increases estimated for methylmercury in the water column in the reservoir, downstream river, and Goose Bay, rising perhaps by a factor of two in the reservoir a few years after flooding, declining thereafter towards background levels. Existing data suggest low baseline methylmercury concentrations in zooplankton in the lower Churchill River, such that peak levels in zooplankton would likely remain within the range observed for natural waters.
Figure 9 Predicted methylmercury concentrations in Goose Bay and Lake Melville in response to sustained inflowing methylmercury concentration of 0.10 ng/L (peak value predicted for river). Results are presented after five months of simulation, by which time stable concentrations are predicted. Preliminary results from Oceans (2010).
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No data are currently available for methylmercury levels in sediments or benthic organisms in the study area. The RESMERC model predicts an increase of approximately 3 to 4X for methylmercury levels in sediments and benthos in the reservoir flood zones. This increase is greater than predicted for the water column, because the sediments are a source of methylmercury production, and the effects of dilution have a greater effect in the water column than in sediments. Fish methylmercury levels are predicted to increase to an intermediate degree (in relative terms) between estimates for water and sediments, partly due to a diet that reflects methylmercury exposure associated with both water and sediments through plankton and benthos. Additional supporting information is provided by Harris et al. (2010).
Significance of Effects
As stated in the EIS (Volume II Part A, Table 4‐22) the residual environmental effect of the Project during construction and operation on Fish and Fish Habitat is predicted to be not significant.
Consumption Advisories
Based on the EIS, results of the mechanistic and dispersion modelling, and effects predictions, consumption advisories are unlikely to be required beyond the mouth of the lower Churchill River.
References
Bodaly, R.A., W. A. Jansen, A.R. Majewski, R.J.P. Fudge, N.E. Strange, A.J. Derksen and D.J. Green (2007) Postimpoundment Time Course of Increased Mercury Concentrations in Fish in Hydroelectric Reservoirs of Northern Manitoba, Canada. Arch. Environ. Contam. Toxicol. 53: 379–389. Harris, R., D. Hutchinson and D. Beals. (2010) Application of a mechanistic model to predict the response of mercury levels in water, sediments and biota due to the Lower Churchill River Development. Technical memorandum, December 2010. Harris, R., D. Hutchinson and D. Beals. 2009. Predicting Mercury Cycling and Bioaccumulation in Reservoirs: Development and Application of the RESMERC Simulation Model. Final Report, April 2009. Prepared for Manitoba Hydro Health Canada. 2010. Guidelines for chemical and physical parameters. Table 4. Health‐based and aesthetic guidelines. Available at: Http://Www.Hc‐Sc.Gc.Ca/Ewh‐Semt/Pubs/Water‐Eau/Sum_Guide‐ Res_Recom/Chemical‐Chimiques‐Eng.Php. Jacques Whitford. 2001. Water Quality and Chlorophyll Study LHP 99‐08. JWEL project 1218, February 2001. Jacques Whitford. 2006. Statistical Analysis of Mercury Data from Churchill Falls (Labrador) Corporation Reservoirs. Report No. 1005158. LGL Limited. 1987. Mercury Concentrations In Fishes of the Smallwood Reservoirs and Churchill River System, Labrador. Contract No. ENVC‐87‐ENV‐038. Prepared for Hydro Quebec. Nalcor. 2009. Component Studies ‐ Aquatic Environment (2) – Mercury – Report 1 of 5 ‐ Assessment of the Potential for Increased Mercury Concentrations – January 2009. Schetagne, R., J. Therrien, J. and R. Lalumière. 2003. Environmental Monitoring at the La Grande Complex. Evolution of Fish Mercury Levels. Summary Report 1978–2000. Direction Barrages et Environnement, Hydro‐Québec Production and Groupe Conseil GENIVAR Inc. 185 p. and appendix. Scudder, B.C., L.C. Chasar, D.A. Wentz, N.J. Bauch, M.E. Brigham, P.W. Moran, and D.P. Krabbenhoft. 2009. Mercury in Fish, Bed Sediment, and Water from Streams Across the United States, 1998–2005 National
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Water‐Quality Assessment Program. Toxic Substances Hydrology Program. Scientific Investigations Report 2009‐5109. U.S. Department of the Interior. U.S. Geological Survey. Stantec Consulting Ltd. 2010. Statistical Analysis of Mercury Concentrations in Fish from the Lower Churchill River and Estuary Prepared for Nalcor Energy Draft Report File No. 121510170, December 24, 2010.
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Requesting Organization – Joint Review Panel Information Request No.: JRP.166
Information Requested:
c. When responding to both (a) and (b), identify and explain the level of scientific certainty applicable to these predictions. In the event that full scientific certainty has not been achieved, explain how Nalcor would apply the precautionary approach to project design operation and decommissioning as well as monitoring of downstream effects, establishing levels of impact that would trigger adaptive measures, the types of mitigation that could and would be applied and the involvement of Aboriginal rights‐holders and of other resource users.
Response:
The EIS is founded upon a large number of studies conducted by professional and expert scientists and engineers over a number of years. Nalcor has based its effects predictions on the best available information using proven methods and models, and is highly confident in those predictions. Limitations, uncertainties and assumptions underlying all predictions have previously been summarized in detail in IR# JRP.19. The limitations of each model were also described in each respective study report. Scientific certainty specifically related to the variables listed in this IR is addressed within the responses to (a) and (b) above.
While any model has inherent limitations, the methods and models used in the analysis underlying the response to (a) and (b) are accepted by the scientific community and have also been used for other environmental assessments. Therefore, the models are both valid and accepted for the environmental assessment. In addition, in the case of methylmercury modelling, two independent models have been used, and both models provide consistent results.
In its response to (a) and (b) Nalcor provided the conservative assumptions used as input for the models. As a further conservative measure factors and processes which would tend to mitigate the effects under study have not been included. The mitigating effects of sedimentation and photochemical processes on methylmercury are an example of this approach.
Notwithstanding the lack of predicted effects by the models, Nalcor has consistently applied a precautionary approach with respect to potential effects. The ‘precautionary principle’ holds that “where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost effective measures to prevent environmental degradation.” Nalcor has indicated its commitment to the precautionary principle in Volume I, Section 9.12 of the EIS and IR# JRP.19, which can be summarized as follows:
• Environmental effects predictions are based on conservative values or modelling inputs and assumptions (at the high end of the scale when predictions result in a range of possibilities) and are used to address uncertainty and propose mitigation that will prevent and reduce adverse effects; • The lack of scientific certainty regarding the probability of an environmental effect occurring has not been used as a reason to postpone mitigation; • Mitigation has been proposed and incorporated into design and operating plans for project effects, including those that are not likely to be significant and adverse; and • Follow‐up and monitoring have been proposed.
JOINT REVIEW PANEL – IR# JRP.166 PAGE 29
INFORMATION REQUESTS RESPONSES| LOWER CHURCHILL HYDROELECTRIC GENERATION PROJECT
With respect to Nalcor’s approach to design, construction, and operation, the design, construction, and operation of the facilities is not predicted to create significant residual adverse environmental effects. This has been documented throughout the EIS.
Nalcor has proposed a series of environmental effects monitoring initiatives throughout the EIS. Should these environmental effects monitoring initiatives or other scientific data indicate an unpredicted or different Project effect, Nalcor will extend its effects monitoring efforts to determine the extent to which the effect in question results from the Project, and also its physical extent.
Specifically in consideration of (a) and (b), Nalcor notes that adaptive management and mitigation approaches are available. Nalcor has previously indicated its intent to maintain river flows within the range of those currently experienced on the river. As indicated in (b), fish consumption advisories are a common mitigation approach for dealing with increased fish methylmercury levels, and Nalcor will cooperate with appropriate authorities (such as Health Canada) in order to mitigate this issue.
To summarize, Nalcor is confident that the potential effects of the Project have been appropriately considered, their extent conservatively modelled, and the necessary mitigation steps have already been proposed. In the event of unforeseen consequences, further action will be considered in light of the nature and extent of the facts at the time, and appropriate consultation will take place with Aboriginal rights‐holders and stakeholders.
PAGE 30 JOINT REVIEW PANEL – IR# JRP.166
INFORMATION RESPONSES LOWER CHURCHILL PROJECT CEAA REFERENCE NO.07‐05‐26178
JOINT REVIEW PANEL
Attachment A
Modeling the Dispersion of Mercury and Phosphorous in Lake Melville IR# JRP.166
Modeling the Dispersion of Mercury and Phosphorous in Lake Melville
Technical Memorandum in support of the Nalcor response to IR# JRP.166
Prepared for
Nalcor
Prepared by
Oceans Limited 85 LeMarchant Road St. John’s, Newfoundland, Canada A1C 2H1
December 2010
1. Study Objective
The objective of the dispersion model study is to develop a three-dimensional numerical model of the Lake Melville and simulate the transport and dispersion of mercury and phosphorous in Goose Bay and Lake Melville.
Specific components of this study include:
Collect data on inflow rates of the Churchill River and initial concentrations of mercury and phosphorous; Collect bathymetry and review existing hydrographic data of the lake Melville; Setup a three-dimensional numerical model to simulate the transport and dispersion process.
2. Study Area
The study area covers entire Goose Bay and Lake Melville with the most Southern point at the mouth of Churchill River and the most Northern point at Rigolet. The most western point is at the mouth of Goose River (Figure 1).
Figure 1 Study Area
3. Model Setup and Results
This section describes the setup and results of the dispersion modeling of Lake Melville and Goose Bay estuary. The model was used to predict the concentration level of Mercury (total mercury,THg and Methyl mercury, MeHg) and total phosphorous (TP).
1
3.1 MIKE 3 Model Overview
MIKE 3 Flow Model FM is composed of several modules and the hydrodynamic module and transport module were used in this study. The Hydrodynamic Module is the basic computational component of the entire MIKE 3 Flow Model FM modelling system providing the hydrodynamic basis for other modules such as the transport module.
The Hydrodynamic Module is based on the numerical solution of the three-dimensional incompressible Reynolds averaged Navier-Stokes equations invoking the assumptions of Boussinesq and of hydrostatic pressure. Thus, the model consists of continuity, momentum, temperature, salinity and density equations and is closed by a turbulent closure scheme. In the horizontal domain both Cartesian and spherical coordinates can be used. The free surface is taken into account using a sigma-coordinate transformation approach.
The spatial discretization of the primitive equations is performed using a cell-centered finite volume method. The spatial domain is discretized by subdivision of the continuum into non- overlapping element/cells. In the horizontal plane an unstructured grid is used while in the vertical domain a structured discretization is used. The elements can be prisms or bricks whose horizontal faces are triangles and quadrilateral elements, respectively. An approximative Riemann solver is used for computation of the convective fluxes, which makes it possible to handle discontinuous solutions.
For the time integration a semi-implicit approach is used where the horizontal terms are treated explicitly and the vertical terms are treated implicitly
The application areas are generally problems where flow and transport phenomena are important with emphasis on oceanographic, coastal and marine applications, where the flexibility inherited in the unstructured meshes can be utilized.
The model can be run in two different modes.
Barotropic mode. Salinity and temperature are assumed to be constant and uniform. The model provides water levels and flow velocity without consideration of stratification.
Baroclinic mode. Density is a function of varying salinity and temperature, and the model takes into account the density structure of the water column. This consumes more computing time than barotropic mode. The density is calculated using UNESCO’s standard equation of state for sea water.
3.2 Scenarios
Two scenarios were simulated using the model. The first simulation was run in barotropic model. Although this scenario neglects the effects of stratification, it enables a better understanding of the tidal driven mixing process. The barotropic simulation covers a period of six month.
As the stratification is very important in Lake Melville, a baroclinic scenario was also simulated. Due to the limitation of sigma-layer model in areas with sharp topographic changes (the case for Lake Melville), the six month simulation period was divided into a series of five day short simulation periods. Initial concentration for each of these short period simulations was calibrated
2
with measured data to correct the drifts. This enables an improved simulation of density driven flow and therefore better simulation of the parameter transport process.
3.3 Model Domain/Mesh
All coordinates in the model were specified as northings and eastings in the Universal Transverse Mercator coordinate system, Longitude Zone 20 (UTM-20). The model boundary (shoreline) was obtained from National Oceanic and Atmospheric Administration’s (NOAA’s) shoreline/coastline databases (http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html).
The model uses a flexible mesh of triangular grids. The mesh density was varied throughout the model domain. The recommended approach for the best combination of accuracy, computational stability and efficient use of computational time is to use a denser (high resolution) mesh in areas that have shallow depth or higher current velocity, or are otherwise of particular hydraulic interest. A coarser mesh may be used in deeper areas with low velocity. In this case, the highest mesh density was used in the Churchill River, at the river mouth. A relatively high density was also used in Goose Bay Narrows (Figure 2).
The vertical mesh is based on sigma coordinates. Three layers were used for barotropic mode and this is based on previous modeling study which has demonstrated that three layers to be adequate for providing a reasonable representation of the study area while maintaining a manageable computer simulation time (Amec/BAE-NewPlan 2001). Fourteen layers were used for baroclinic model to better resolve the vertical density difference (Figure 3).
The water depth (Figure 4) in the computational mesh was interpolated from bathymetric data. Highly detailed depth contours and soundings for Lake Melville, Goose Bay and the Churchill River mouth were digitized to x-y-z coordinates from the nautical chart (CHS 2010a).
3.4 Model Parameters
Same key model parameters as used in Hatch (2009) were used in this study. Solution technique: low-order (fast algorithm) Computational time step: 2 s Horizontal eddy viscosity: Smagorinsky formulation . coefficient 0.28 m2/s . minimum viscosity 1.8x10-6 m2/s . maximum viscosity 1.0x1010 m2/s Vertical eddy viscosity: k-epsilon formulation . minimum viscosity 1.8x10-6 m2/s . maximum viscosity 180 m2/s Turbulence equation: k-epsilon formulation, default parameters Bed resistance: 0.3 m roughness height
3
Coriolis force: constant in domain, 53.75°N latitude Horizontal temperature/salinity dispersion: scaled eddy viscosity formulation, factor 1.0 Vertical temperature/salinity dispersion: scaled eddy viscosity formulation, factor 0.1
Viscosity and turbulence parameters were based on recommended values in the model reference literature to support a stratified regime (DHI 2009).
In baroclinic mode, the model simulated 6 months days with about 220 hours of computing time, on a desktop platform with four 2.66 GHz processor and 2 GB of memory.
Figure 2 Model horizontal Computational Mesh.
4
Figure 3 Model Vertical Computational Mesh: 3 Layers for Barotropic Mode (Top), and 14 Layers for Baroclinic Mode (Bottom).
5
Figure 4 Model Bathymetry
3.5 Boundary and Initial Conditions
The model used different boundary conditions for the barotropic and baroclinic scenarios.
For the barotropic mode, the boundary conditions are specific in a way similar to Hatch (2009). At the downstream open boundary (Rigolet), the water level was specified as a stage hydrograph with hourly tide levels. The values were obtained from hourly tide predictions for May 2010 to November 2010 for the Canadian Hydrographic Service tide prediction at Rigolet, which lies just outside the model boundary (CHS 2010b). The hourly tide levels are shown in Figure 5. The upstream and lateral inflow boundaries (Goose, Kenamu, Northwest and Churchill) were specified as constant discharges. The land boundary was specified as zero normal velocity (full slip boundary condition).
For baroclinic model, the inflow boundaries are closed (specified as land). The inflows were specific as sources at top layer. The purpose for this change is to minimize the artificial vertical mixing introduced by sigma-layer model.
6
Figure 5 Hourly tide level in meters at Rigolet from May 30 to December 1, 2010.
At the downstream open boundary, the salinity and temperature were specified as varying with both time and depth (Figure 6). Both salinity and temperature data from July to October were derived from Bobbitt & Akenhead (1982). The temperature for May, June and November were estimated based on data from Cardoso and deYoung (2002) (Figure 7). There is no salinity data available for May, June, and November and it is assumed that May and June data are the same as July, and November is the same as October.
The initial temperature and salinity for Lake Melville were from Cardoso and deYoung (2002) (Figure 8).
3.6 River Inflow
The model used mean monthly inflow rate for Churchill River (Figure 9). For the period studied, the maximum rate was 2655 and minimum rate was 1492 m3/s. River flow rate for Goose River, Kenamu River and Northwest River were from Hatch (2009) and Bobbitt and Akenhead (1982).
The upstream Churchill River inflow boundary was assigned constant values of salinity 3 PSU and other rivers are assigned value of 0 to be consistent with Hatch (2009). Unlike previous studies in which constant temperature of 0oC for Churchill River was used, this study used time varying water temperature (Figure 10).
The maximum total mercury concentration is 1.3 ng/L and the maximum methyl mercury is 0.08 ng/L based on information from Harris et al. (2010) (Figure 11). Although the concentration decreases with time, the maximum values were used to simulate the worst case scenario. Similarly, the maximum concentration of total phosphorus is 0.11 mg/L based on Minaskuat, (2008). This concentration occurs in September in the second year post-impoundment. The time series TP concentration (Figure 12) from May to November can be representative for worst case scenario.
7
0
20
40 Depth (m) Depth
60 Jul (B&A, 1982) Aug (B&A, 1982) Sep (B&A, 1982) Oct (B&A, 1982) 80
20 22 24 26 28 30 32 34 Salinity (‰)
0
20
40
Depth (m) Jul (B&A, 1982) Aug (B&A, 1982) Sep (B&A, 1982) 60 Oct (B&A, 1982) May (C&D, 2002) Jun (C&D, 2002) Nov (C&D, 2002) 80
-2 0 2 4 6 8 10
o Temperature ( C) Figure 6 The Narrows Salinity and Temperature for Model Input. Bobbitt & Akenhead (1982) and Cardoso and deYoung (2002).
8
Figure 7 The Narrows Temperature (Cardoso and deYoung, 2002)
9
Figure 8 lake Melville Temperature and Salinity (Cardoso and deYoung, 2002).
10
3000
2500 /s) 3 2000 (m
Rate 1500 Flow 1000 Mean 500
0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Figure 9 Churchill River Mean Flow Rate in m3/s
16
14
12 C) o 10
8
6
4
Temperature ( Temperature 2
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date
Figure 10 Churchill River Monthly Temperature
11
Predicted Total Mercury Concentration in River Water at Entrance to Goose Bay 1.4 1.2
(ng/L) 1.0 0.8 0.6 0.4 concentration 0.2 Hg 0.0 0 5 10 15 20 25 30 35 Years after flooding
Predicted Methylmercury Concentration in River Water at Entrance to Goose Bay 0.09 0.08
(ng/L) 0.07 0.06 0.05 0.04 0.03
concentration 0.02
0.01 0.00 MeHg 0 5 10 15 20 25 30 35 Years after flooding
Figure 11 Concentration of Mercury in River Inflow (based on Harris et al 2010).
12
Figure 12 TP in Churchill River (based on Minaskuat, 2008)
3.7 Other Factors
The model considered the effects of wind induced currents. Time varying wind data for Happy- Valley Goose Bay was obtained from Environment Canada’s historical weather database (Figure 13). Heat exchange between the lake surface and atmospheric was also simulated. The air temperature data (Figure 14) was from the same Environment Canada database.
Figure 13 Wind Rose (May 2010-Nov 2010)
13
Figure 14 Air Temperature (oC) at Happy-Valley Goose Bay (May 2010-Nov 2010)
4. Validation
As there is no measured current for model validation, water level comparison was conducted. Canadian Hydrographic Service has tidal data for both Rigolet and Northwest River. While the Rigolet data was used by model to generate tide induced flow, the Northwest River data was used to compare with model simulation. As it is shown in Figure 15, despite the slightly under prediction of water level, overall good agreement has been found.
2.0
1.5
1.0
Tide level (m) level Tide 0.5 Rigolet (Model/CHS) Northwest River (model) Northwest River (CHS) 0.0 14/06/2010 16/06/2010 18/06/2010 20/06/2010 22/06/2010 24/06/2010 26/06/2010 28/06/2010 Date Figure 15 Model Validation.
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5. Results
5.1 Barotropic
The concentration of THg, MeHg, and TP in surface layer after 5 months have been plotted in Figure 16 to Figure 18 together with time series plot at five points in the lake (locations of t1 to t5 is shown in Figure 19). The vertical profile for THg in Figure 20 shows that the THg has been fully mixed vertically due to the neglecting of density stratification.
The result indicated the concentration in Goose are at similar level to the river input and the level at the entrance of the lake (t2) is about 2 times the background in sea water. At the center of the lake (t5), the concentration is not significantly different from background.
5.2 Baroclinic
The THg concentrations after 2.5 month are plotted in Figure 21. The main difference from barotropic results is that surface layer has slightly higher concentration and bottom layer has slightly lower concentration. The simulated temperature and salinity profiles are shown in Figure 22.
15
1.40 t1 t2 1.20 t3 t4 1.00 t5
0.80 (ng/L)
THg 0.60
0.40
0.20
0.00 30‐May‐10 29‐Jun‐10 29‐Jul‐10 28‐Aug‐10 27‐Sep‐10 27‐Oct‐10 26‐Nov‐10 Date
Figure 16 Concentration of THg after 5months.
16
0.08 t1
0.07 t2 t3 0.06 t4 t5 0.05 (ng/L) 0.04 MeHg 0.03
0.02
0.01
0.00 30‐May‐10 29‐Jun‐10 29‐Jul‐10 28‐Aug‐10 27‐Sep‐10 27‐Oct‐10 26‐Nov‐10
Date
Figure 17 Concentration of MeHg after 5months.
17
t1 0.10 t2 t3
0.08 t4 t5
0.06 (mg/L)
TP
0.04
0.02
0.00 30‐May‐10 29‐Jun‐10 29‐Jul‐10 28‐Aug‐10 27‐Sep‐10 27‐Oct‐10 26‐Nov‐10
Date
Figure 18 Concentration of TP after 5months.
18
Figure 19 Location for Time Series Plot
Figure 20 Vertical Profile of THg.
19
Figure 21 THg Concentration after 2.5 Month.
20
Figure 22 Simulated Temperature and Salinity Profiles.
6. Summary and Limitations
The model used tide, wind, and heat exchange to simulate the hydrodynamics and dispersion of THg, MeHg, and TP in Lake Melville. The comparison of simulated water levels at Northwest River with CHS data is found to have good agreement. One of the limitations of this model work is the lack of long term Temperature and salinity data. For example, the lack of salinity of the Lake for the month of May, June, and November. Another limitation is that the using of sigma- layer model for this site may introduced some artificial vertical mixing. As a result, the concentration in surface layer may be underestimated and the concentration in bottom layer may be overestimated.
21
Reference
Amec Earth & Environmental Limited and BAE-NewPlan Group Limited. (2001). Aquatic Environment in the Goose Bay Estuary. Prepared for Labrador Hydro Project, St. John’s, NL.
Bobbitt, J. and Akenhead, S. (1982). Influence of controlled discharge from the Churchill River on the oceanography of Groswater Bay, Labrador. Can. Tech. Rep. Aquat. Sci., 1097: 43 p.
CHS (Canadian Hydrographic Service). (2010a). Digital Charts: V-ENC-4724, 4725, 4728, 5140, 5143.
CHS (Canadian Hydrographic Service). (2010b). CHS-Tides, Currents, and Water Levels. On-line at: http://www.waterlevels.gc.ca/english/Canada.shtml
Danish Hydraulic Institute (2009). MIKE 3 Flow Model FM Hydrodynamic Module, Hørsholm, Denmark.
Cardoso, D., and deYoung, B. (2002). Historical Hydrographic Data from Goose Bay, Lake Melville and Groswater Bay, Labrador:1950-1997. Physics and Physical Oceanography Data Report 2002-2. Memorial University of Newfoundland.
Harris, R., Hutchinson, D., Beal, D. (2010). Application of a Mechanistic Mercury Model to the Proposed Lower Churchill Reservoirs: Preliminary Findings
Hatch (2009). Salt Water Intrusion 3D Model Study. Report Prepared for Nalcor.
Minaskuat Inc. (2008) Water and Sediment Modeling in the Lower Churchill River Environment baseline Report, LCP #535725, August 20, 2008.
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INFORMATION RESPONSES LOWER CHURCHILL PROJECT CEAA REFERENCE NO.07‐05‐26178
JOINT REVIEW PANEL
Attachment B
Statistical Analysis of Mercury Concentrations in Fish from the Lower Churchill River and Estuary
IR# JRP.166 Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Stantec Consulting Ltd. 607 Torbay Road St. John’s, NL A1A 4Y6 Tel: (709) 576-1458 Prepared for Fax: (709) 576-2126 Nalcor Energy Hydro Place, 500 Columbus Drive P.O. Box 12800 St. John’s, Newfoundland and Labrador Canada A1B 0C9
Report
File No. 121510170
Date: December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Table of Contents
1.0 INTRODUCTION ...... 1
1.1 Scope of This Study ...... 1 1.2 Related Previous Studies ...... 2
2.0 STATISTICAL METHODS ...... 4
3.0 RESULTS ...... 6
4.0 DISCUSSION ...... 25
4.1 Temporal Trends in Mercury Concentrations ...... 25 4.2 Upriver-Downriver Trends in Mercury Concentrations ...... 26 4.3 Trophic Trends in Mercury Concentrations ...... 26
5.0 REFERENCES ...... 35
List of Figures
Figure 1.1 Site Location Plan ...... 3 Figure 3.1 Comparison of Predicted Mercury Concentration in Fish of Standard Length Using the Linear and Polynomial Regression Methods ...... 12 Figure 3.2 Multi-year Comparisons for Lake Whitefish, Gull Lake ...... 13 Figure 3.3 Multi-year Comparisons for Longnose Sucker, Gull Lake ...... 14 Figure 3.4 Multi-year Comparisons for Brook Trout, Gull Lake ...... 15 Figure 3.5 Multi-year Comparisons for Brook Trout, Winokapau Lake ...... 16 Figure 3.6 Multi-year Comparisons for Longnose Sucker, Winokapau Lake ...... 17 Figure 3.7 Multi-year Comparisons for Lake Trout, Winokapau Lake ...... 18 Figure 3.8 Multi-year Comparisons for Lake Whitefish, Winokapau Lake ...... 19 Figure 3.9 Multi-year Comparisons for Ouananiche, Winokapau Lake ...... 20 Figure 3.10 Multi-year Comparisons for White Sucker, Winokapau Lake ...... 21 Figure 3.11 Multi-year Comparisons for Lake Whitefish, Churchill River / Section 1...... 22 Figure 3.12 Multi-year Comparisons for Longnose Sucker, Churchill River / Section 1 ... 23 Figure 3.13 Multi-year Comparisons for Northern Pike, Churchill River / Section 1 ...... 24 Figure 4.1 Temporal Variation of Mercury Concentrations in Standard Length Brook Trout by Location ...... 28 Figure 4.2 Temporal Variation of Mercury Concentrations in Standard Length Lake Chub by Location ...... 29 Figure 4.3 Temporal Variation of Mercury Co ncentrations in Standar d Length L ake Whitefish by Location ...... 30
121510170 REPORT i December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 4.4 Temporal Variation of Mercury Concentrations in Standard Length Longnose Sucker by Location ...... 31 Figure 4.5 Temporal Variation of Mercury Concentrations in Standard Length Northern Pike by Location ...... 32 Figure 4.6 Temporal Variation of Mercury Co ncentrations in Standar d Length Round Whitefish by Location ...... 33 Figure 4.7 Temporal Variation of Mercury Co ncentrations in Standar d Length White Sucker by Location ...... 34
List of Tables
Table 3.1 Standard Lengths (mm) for Fish in the Churchill River Drainage ...... 7 Table 3.2 Predicted Mercury Concentrations in Standard Sized Fish (mg/kg) ...... 8
List of Appendices
Appendix A Figures A1 to A60 Appendix B Raw Data
121510170 REPORT ii December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
1.0 INTRODUCTION
Nalcor Energy require d consultin g services to conduct statist ical analysis of mercury concentrations in the t issues of fish that were previously collected (be tween 1977 and 2010) from the lower portion of the Churchill River a nd the estu ary where the Churchill River enters Lake Melville. Stantec Consulting Ltd. (Stantec) was retained to conduct this analysis using data provided by Nalcor Energy.
1.1 Scope of This Study
The data pr esently und er consider ation were collected by various ent ities, including but not limited to co nsultants (AMEC, LGL) and DFO, between 1977 and 2010. The variou s sampling programs collected fish to determine muscle tissue total mercury concentrations (mg/kg) from a variety of species. Sa mpling was conducted at location s on the C hurchill Riv er including Winokapau Lake, Gull Lake, and Churchill River Section 1 (from Muskrat Falls to th e mouth of the river at Goose Bay). Samples were also collected at Mud Lake and in Goose Bay estuary and Sandy Point, which is part of the estuary. These locations are illustrated on Figure 1.1.
As was done in past a nalytical studies, only samples of 10 or more fish of the sa me species at the same location were Statistically analyzed for this stu dy (Jacques Whitford and Geniva r 2002; Jacques Whitford 2006) as further detailed below. In some cases, only single collections of fish are available (i.e., fish of a particular species were only collected at a given lo cation on a single occa sion between 1977 and 2010). In other cases, multiple collect ions were, made representing a particular species of fish at the same location in two or more years.
The fish species collections representing species that were only collected once at a given location are listed below, with the number of fish collected presented in parentheses:
Brook Trout (Salvelinus fontinalis) – 1977 Mud Lake (14); Brook Trout – 1999 Estuary / Sandy Point (12); Burbot (Lota lota) – 2010 Gull Lake (27); Lake Chub (Couesius plumbeus) – 2010 Gull Lake (11); Lake Chub – 2010 Churchill River / Section 1 (31); Lake Whitefish – 1999 Estuary / Sandy Point (10); Longnose Sucker (Catostomus catostomus) – 1977 Mud Lake (15); Longnose Sucker– 1999 Estuary / Sandy Point (15); Northern Pike (Esox lucius) – 1977-78 Gull Lake (22); Northern Pike – 2004 Winokapau Lake (14); Rainbow Smelt (Osmerus mordax) – 1999 Estuary / Sandy Point (30); Round Whitefish (Prospium cylindraceum) – 1992 Winokapau Lake (27); Round Whitefish– 2010 Section 1 / Churchill River / Section 1 (23); Tom Cod (Microgadus tomcod) – 1999 Estuary / Sandy Point (30); White Sucker (Catostomus commersoni) – 1977 Estuary / Sandy Point (27); and White Sucker – 2004 Gull Lake (26).
121510170 REPORT 1 December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
The following data set s represent multi-year evaluations of total mercury concentrations in muscle tissue of fish from the same general area of the river. These include:
Brook Trout – 1992, 1996, 1999 and 2004 Gull Lake (34, 25, 26, 15); Brook Trout – 1987 and 2010 – Winokapau Lake (16, 12); Lake Trout (Salvelinus namaycush) – 1992 and 1999 – Winokapau Lake (21, 10); Lake Whitefish (Coregonus clupeaformis) – 1978, 1999 Churchill River / Section 1 (12, 20); Lake Whitefish – 1977, 1992, 1996, 1999, 2004 and 2010 – Gull Lake (40, 40, 12, 29, 20, 11); Lake Whitefish – 1977-78, 1987, 1992, 1996, 1999 and 2004 – Winokapau Lake (27, 32, 13, 18, 26, 16); Longnose Sucker – 1992, 1996, 1999 and 2010 – Gull Lake (13, 51, 30, 30); Longnose Sucker – 1978, 1999 and 2010 – Churchill River / Section 1 (11, 30, 21); Longnose Sucker – 197 7, 1987, 19 92, 1996, 1 999 and 20 10 – Winokapau Lake ( 26, 29, 25, 15, 29, 10); Northern Pike – 1978 and 2010 – Churchill River / Section 1 (18, 11); Ouananiche (Salmo salar – landlocked) – 1987 and 1999 – Winokapau Lake (18, 19); and White Sucker – 1977-78, 1987, 1992, 1996 and 2004 – Winokapau Lake (14, 14, 36, 43, 43).
A number of additional datasets are available for mercury concentrations in fish from the lower Churchill Ri ver and est uary, however, where the number of fi sh is less than 10 , it was not considered reliable to conduct regression analysis. These minor data sets (i.e., the non-target fish species or bycatch) representing between 1 and 9 fi sh each are not discu ssed in this analysis.
This report was prepared by Paul Mazzocco , B.Sc., and Malcolm Stephenson, Ph.D., and was reviewed by Bruce Bennett, B.Sc.
1.2 Related Previous Studies
Jacques Whitford (2006) previously completed st atistical analysis of m ercury concentrations in fish from the Churchill Falls reservoirs. This work was carried out o n behalf of Newfoundland and Labrador Hydro, and was presented in the following report:
Jacques Whitford. 2006. Statistical Analysis of Mercury Data from Churchill Fal ls (Labrador) Corporation Reservoirs. Report prepared for Newfoundland and Lab rador Hydro . October 30, 2006.
In the above noted report, regression curv es were fit to fish muscle tissu e mercury concentrations and f ish length for a pproximately 90 datase ts, each comprising 10 to 113 f ish. Fork length was used for all species. Fish species included brook trout, lake trout, ouananiche (landlocked Atlantic salmon), lake whitefish, round whitefish, longnose sucker, white sucker and northern pike. Fish were collected between 1977 and 2004.
121510170 REPORT 2 December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 1.1 Site Location Plan
121510170 REPORT 3 December 24, 2010
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Statistical analysis included the fitting of polynomial regressions to th e mercury c oncentrations in fish tissue, as a function of length, following the approach outlined by Tremblay et al. (1998).
A previous study (Jacq ues Whitford and Genivar 2002) an alyzed data collected between 1997 and 1999.
2.0 STATISTICAL METHODS
Since many of the data sets repre sent relatively few fish (i.e., 27 of th e 60 retaine d datasets include bet ween 25 an d 51 fish, the remaind er comprising 10 to 24 fish), confidence in the statistical analysis may in some cases be limited due to the small sample sizes. The statistical uncertainty arising from small sample size s may limit conclusions that can be drawn about the statistical distributions of the dependent variabl e (mercury concentration), which can affect choices that are made regarding the appropriate statistical methods to use. The use of the data is also potentially limited by the size range of fish collected . As much as possible, Stantec has defined a standard length for each fish species, and calculated a best estimate and confidence interval for the mercury concentration of fish tissue at this st andard length. Howe ver, in some cases the size range of captured fish does not encompass the standard length, an d statistical extrapolation must be used to estimate the mercury concentration at standard length. Th is statistical e xtrapolation can result in high u ncertainty, reflected in the confide nce interval surrounding the predicted value.
Stantec has therefore approached the data cautiously. The analysis sta rts with an examination
of the scatterplot of log 10-transformed mercury concentrations as a function of fish length, t o identify potential outlier data points. Again, forklength was used for all species except for burbot where total length was recorded. The confide nce ellip se procedure i n the Systat statisti cal software program was used as one tool in thi s regard, such that points falling outside the 95% confidence ellipse were considered potential outliers. In addition, poin ts identified as potential
outliers or as having high leverage in the linear regression of log 10-transformed mercury concentrations vs. fish length were also given consideration for designation as outliers. All sample points that were designat ed as outlie rs are displayed in figures incorpor ated in the report using a distinct symbol, so that the potential effects of removing outliers on the regression equations can readily be evaluated by the reader.
After identif ying and coding outlie rs so that t hey would not be considered in t he statistica l analysis, simple linear regression of the raw mercury co ncentrations in fish was conducted ,
followed by the application of log10 transformation, and square root transformation, as these are commonly applied to enhance th e degree t o which th e data con form to the underlying assumptions of linear regression. These are:
There is a linear relatio nship betwe en the true response (t he Y variable, mercury) and the independent variable (the X variable, length); The observed values of X are measured without error; and
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The errors for Y are independent random variables, normally distributed.
The most common violation of t hese assu mptions is that the errors for Y increase wit h increasing values of X (a condit ion known as h eteroscedasticity). This vi olation is classica lly
managed by applying the log10 transformation to the mercury data. As noted, with small sample sizes, it is often problematic to diagnose problems in the underlying data. However, inspectio n of residuals for the regression (i.e., the distribu tion of point s above an d below the regression line) is one simple way to identify non-linearity, and problems with the normality of the data.
After completing the lin ear regression analysis, the same data were subjected to non-linear regression using the following polynomial equation:
Y = (a x X2) + (b x X) + c ;
where the variables a, b and c are constants, to be estimated in the re gression fitting process. This polynomial is able to assu me a variety of shapes, and is of particular use whe n relationships between mercury and fish length do not confor m to a linear model. Transformations may also be applied to the p olynomial model. Some examples of situat ions where the polynomial model may be of great benefit include the following.
Fish tissue mercury concentrations change ma rkedly at a particular life stage. Fo r example, at a certain length, fish may switc h from being insectivor ous or plan ktivorous, t o become piscivorous. When this happens, mercury conc entrations may increase rapidly, no longer conforming to a linear model. When the e nvironment changes rapidly (as ma y occur whe n a reservoi r is flooded), mercury concentrations in small f ish may change more rapidly than concentration s in large fish. As a result, for a number of years following flooding, the largest fish in the population may ha ve lower mercury concentrations than mid-sized fish. At some point in time af ter flooding, mercury mobilization is diminished, and smaller fish may have substantially lower mercury co ncentrations than older f ish, which were actively growing during the period when mercury was mobilized.
Direct comparisons of model fit using the corre lation coefficient or “r 2” parameter for the linear and polynomial models are not recommended, particularly for small dataset s, since the polynomial model can conform to the random scatter of points. By fitting a cur ve through a small number of points, the polynomial model will usual ly achieve a h igher r 2 value than the linear model, even for data that truly conform to a linear model.
Since test s to determine normality are of low power when applied to small datasets, Stantec applied the following process to the statistical analysis.
Check the datasets for potential “ outlier” poin ts that will exert undue influence on the regression, or may in fact represent errors. This check was performed, after some preliminary data investigation showing that the log 10 transformation was generally appropriate, by plotting the log 10-transformed mercury and raw length data for each dataset , along with a 95 % confidence ellipse which should enclose most of the poin ts. As note d above, co nsideration was also given to removing points that were identified by the Systat software program as potential ou tliers or hav ing strong leverage on the regression for log 10-transformed mercury
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and length data. Points that were considered outliers are identified in the graphs representing the analysis, but were not included in the statistical calculations or in the appended raw data. Calculate re gression models u sing linear regr ession usin g raw data, log 10-transformed and square root transformed data. Calculate regression models using polynomial regression using raw data, log 10-transformed and square root transformed data. Examine the residuals for the linear regression using ra w data. If there is e vidence of heteroscedasticity, consider mo ving to the log 10-transformed data. If th ere is no evidence of heteroscedasticity, and if the residu als otherwise show a balanced dist ribution the process may stop here. Examine the residuals for the linear regression using the log10-transformed data. If there is no evidence of heteroscedasticity, and if the residuals otherwise show a balanced distribution the process may stop here. Continue th is proce ss looking at t he linear and polynomial regressions until th e simplest acceptable representation of the data has been identified.
3.0 RESULTS
Appendix A (Figures A1 to A60) pre sents the regression eq uations, fit to the data, f or mercury concentrations in fish tissues. The r aw data up on which th e Figures a nd regression equations are based can be found in Appendix B.
Inspection of the Figures in Appendix A shows that the logarithmic transformation is generally required in order to r educe hete roscedasticity in the m ercury data. However, regression
equations for all six regression combinations (linear and polynomial regression using raw, log10- transformed and square root transformed data) are provided on the various graph panels presented in Appendix A.
Standard lengths for some of the fi sh species involved in the present study were proposed by Jacques Whitford (2006) based on the available Churchill system data a nd other populations on the Island of Newfoundland and in Quebec (see Table 3.1). The stand ard length of fish should ideally be close to the mid-range of the length data, in or der to minimize uncertainty in the mercury est imated. Ho wever, since the purpos e of predicting mercury concentrations is to understand potential human e xposure to mercury, the predi ction length should also represent “catchable” sized fish. For this reason, the proposed stand ard lengths either lie in the middle of the range ( as for example smelt, where it can be assumed that all fish will p otentially be consumed), or somewhat biased towards the upper end of t he range, if it is likely that potential human consumers will select larger fish for consumption.
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Table 3.1 Standard Lengths (mm) for Fish in the Churchill River Drainage
Species Jacques Whitford 2006 This Study
Brook Trout 250 250
Burbot n/a 500
Lake Chub n/a 125
Lake Trout 600 500
Lake Whitefish 350 350
Longnose Sucker 400 300
Northern Pike 700 700
Ouananiche 250 400
Rainbow Smelt n/a 200
Round Whitefish 300 150 / 300
Tom Cod n/a 250
White Sucker 400 330 n/a = not applicable
As can be seen from Table 3.1, for the present study, the proposed standard lengths for lake trout, longnose sucker and white sucker captu red from the lower river and estuary are smalle r than was used by Jacques Whitfor d (2006) for fish colle cted elsewhere in Newfou ndland and Labrador or Quebec. Conversely, the propo sed standard lengths for ouananich e are longe r. For round whitefish, two standard lengths (i.e., 150 mm and 300 mm) are used for fish collected from Churchill River / Section 1 and Winokapau Lake, respe ctively, since the size ranges of the collected fish were not overlapping. Burbot, la ke chub, rainbow smelt and tomco d were not previously reported.
Table 3.2 p rovides a summary of the predicte d baseline mercury concentrations in fish of standard length for a total of 60 data sets, representing 12 species of fish, 5 areas of the lower Churchill River and estuary, and seven time periods of da ta collection. The linear regressions using the log10-transformed mercury data are generally consistent with the assumptions of linear regression. There is no need to invoke a more complex model than the linear regression model, using log10-transformed data, for an y of the seven data set s presently under consideration. I n Table 3.2, the linear regression model using log10-transformed data is identified as the preferred
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Table 3.2 Predicted Mercury Concentrations in Standard Sized Fish (mg/kg)
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary model representing mercury concentrations in fish tissue. Out of 60 c omparisons, the mercury concentration predicted using the polynomial model based on log10-transformed data lay outside the 95% confidence interval for the linear model in only 3 cases; about the frequency that would be expected by chance.
When the predicted mercury concentrations based on the linear model are directly compared to the concent rations predicted using the polyno mial model (Figure 3.1), the scatterplot of the predicted values con forms to a st raight line having a slope of unit y. Overall, there is n o significant d ifference be tween the regression predictions b ased on the linear and polynomial models. This conclusion does not detract from the conclusion of Tremblay et al (19 98) that the polynomial model has advantages over the lin ear model. The polyno mial model may pro vide more powerful explanatory and pre dictive power when fish tissue mercury concentrations are not at ste ady state (for examp le, during a period following impoundment, mercury concentrations in rapidly growing fish may be gr eater than mercury con centrations in older fish in the population that elaborated tissue before impoundmen t and are consuming a maintenance ration, a condition th at could le ad to a complex rel ationship b etween tissue mercury concentrations and body length). Rather, the b aseline data for the Churchill River suggest that fish tissue mercury concentrations are relative ly stable, a nd are well described by the linea r model, particularly for fish at standard length that are close to the mid-range of the length range for a given sample.
Where the data permit the comparison of mercury concentr ations in fish tissu e collected at the same location in differe nt years, these comparisons are pr esented in Figures 3.2 to 3.13. In each of these figures, the regression lines based on the linear model using log 10-transformed data are presented in t he upper panel, with the appropriate standard fish length in dicated. In the lower panel, the mercury concentrations a nd confiden ce intervals for standar d length fish are displayed as a function of sampling year.
Figure 3.2 shows the multi-year co mparison for lake whitefish collected in Gull La ke in 1977, 1992, 1996, 1999, 2004 and 2010. In general, mercury concentrations increase with increasing fish size, alt hough the r esults from 1996 run counter to th is trend. O ver time, however, the mercury concentration in lake white fish shows a steady decline, from 0.259 mg/kg in 1977 to 0.089 mg/kg in 2010.
Figure 3.3 shows the multi-year co mparison for longnose sucker collected in Gull Lake in 1992, 1996, 1999 and 2010. Mercury concentrations increase with increa sing fish size. Over time, the mean mercury concentration in longnose sucker decreases, from 0. 180 mg/kg in 1992 to 0.137 mg/kg in 2010, however, th e confidence inte rvals for the estimated valu es overlap, suggesting that the trend may not be significant.
Figure 3.4 shows the multi-year comparison for brook trout collected in Gull Lake in 1992, 1996, 1999 and 2004. In general, mercury concentrations increase with increasing fish size, although the results from 1996 run counter to this trend. Over time, t he mercury concentratio n in broo k trout shows no clear trend, ranging from 0.039 mg/kg in 1996 to 0.090 mg/kg in 1999.
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.5 shows the multi-year comparison for brook trout collected in Winokapau Lake in 1987 and 2010. Mercury concentrations increase with increasin g fish size. The predict ed mercury concentration at standard length in brook trout from Win okapau Lake was lowe r in 2010 (0.049 mg/kg) than in 1 987 (0.092 mg/kg), although the co nfidence intervals for the estimated values overlap, suggesting that the difference may not be significant.
Figure 3.6 shows the multi-year comparison for longnose sucker collected in Winokapau Lake in 1977, 1987, 1992, 199 6, 1999 an d 2010. In general, mercury concentrations increase with increasing f ish size, a lthough the r esults from 1977 run counter to t his trend. Over time , however, th e mercury concentration in longn ose sucker from Winokapau Lake shows a substantial decline, from 0.700 mg/kg in 1977 to 0.123 mg/kg in 1999. The estimated mercury concentration at standar d length in 2010 was 0 .219 mg/kg, but the confidence interval for this value overlaps the range of values between 1987 and 1999.
Figure 3.7 shows the multi-year co mparison for lake trout collected in Winokapau Lake in 1992 and 1999. Mercury concentrations increase with increasin g fish size. The predict ed mercury concentration at stand ard length in lake tro ut from Winokapau La ke was lo wer in 199 9 (0.593 mg/kg) than in 1 992 (1.033 mg/kg), although the co nfidence intervals for the estimated values overlap, suggesting that the difference may not be significant.
Figure 3.8 shows the multi-year comparison for lake whitefish collected in Winoka pau Lake in 1977-78, 1987, 1992, 1996, 1999 and 2004. Mercury concentrations in crease with increasin g fish size. Over time, the mercury concentration in lake whitefish from Winokapau Lake shows a substantial decline, from 0.952 mg/kg in 1977 to 0.144 mg/kg in 2004. However, between 1987 and 2004 the predicted mercury concentrations were broadly overlapping showing that the main trend was the substantial decline recorded between 1977-78 and 1987.
Figure 3.9 shows the multi-year c omparison for ouananiche collected in Winokapau Lake in 1987 and 1999. Merc ury concentrations incr ease with increasing fish size. T he predicte d mercury concentration at standard length in ou ananiche fr om Winokapau Lake w as higher in 1999 (0.173 mg/kg) than in 1987 (0.104 mg/kg), with non-overlapping confidence intervals, suggesting that the difference is significant.
Figure 3.10 shows the multi-year comparison f or white su cker collected in W inokapau Lake in 1977-78, 1987, 1992, 1996, 1999 and 2004. Mercury concentrations in crease with increasin g fish size. O ver time, the mercury concentration in white su cker from Winokapau La ke shows a decline, most notably fr om 0.221 mg/kg in 197 7 to 0.105 mg/kg in 19 87. Between 1992 and 2004, mercury concentrations at standard len gth varied between 0.140 and 0.164 mg/kg, remaining significantly lower than the value in 1977-78.
Figure 3.11 shows the multi-year comparison for lake whit efish co llected in Churchill River / Section 1 in 1978 and 1999. Merc ury concentrations incre ase with increasing fish size. The predicted mercury concentration at standard length in lake whitefish from Churchill River / Section 1 a re not signif icantly differ ent, howeve r, it is note worthy that the value at standard length for 1978 is extrapolated beyond the data range for that year, and the resulting confidence
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary interval surr ounding the predicted value is lar ge. As a r esult, it is impossible t o say with certainty whether the mercury concentration changed between 1978 and 1999.
Figure 3.12 shows the multi-year comparison for longnose sucker collected in Churchill River / Section 1 in 1978 and 2010. Merc ury concentrations incre ase with increasing fish size. The predicted mercury concentration at standard le ngth in l ongnose su cker from Churchill River / Section 1 are not significantly different, although the mean predicted values show a substantial decline from 0.128 mg/kg in 1978 to 0.030 mg/kg in 2010. However, it is noteworthy that the value at sta ndard length for 2010 i s extrapolated beyond t he data range for that year, and the resulting co nfidence int erval surrounding the predicted value is large. As a result, it is impossible to say with certainty whe ther the mercury concentration chan ged between 1978 and 2010.
Figure 3.13 shows the multi-year comparison for northern pike col lected in Churchill River / Section 1 in 1978 and 2010. Merc ury concentrations incre ase with increasing fish size. The predicted mercury concentration at standard l ength in no rthern pike from Churchill River / Section 1 a re not signif icantly differ ent, howeve r, it is note worthy that the value at standard length for 2010 is extrapolated beyond the data range for that year, and the resulting confidence interval surrounding the predicted value is extremely large. As a result, it is impossible to say with certainty whether the mercury concentration changed between 1978 and 1999.
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.1 Comparison of Predicted Mercury Concentration in Fish of Standard Length Using the Linear and Polynomial Regression Methods
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.2 Multi-year Comparisons for Lake Whitefish, Gull Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.3 Multi-year Comparisons for Longnose Sucker, Gull Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.4 Multi-year Comparisons for Brook Trout, Gull Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.5 Multi-year Comparisons for Brook Trout, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.6 Multi-year Comparisons for Longnose Sucker, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.7 Multi-year Comparisons for Lake Trout, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.8 Multi-year Comparisons for Lake Whitefish, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.9 Multi-year Comparisons for Ouananiche, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.10 Multi-year Comparisons for White Sucker, Winokapau Lake
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.11 Multi-year Comparisons for Lake Whitefish, Churchill River / Section 1
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.12 Multi-year Comparisons for Longnose Sucker, Churchill River / Section 1
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 3.13 Multi-year Comparisons for Northern Pike, Churchill River / Section 1
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
4.0 DISCUSSION
One of the advantages of applying regression models to fish mercury data is that the resulting equations can be used to predict mercury co ncentrations for fish at standard lengths, and to provide uncertainty estimates with these predictions.
Table 3.2 p rovides the estimated mercury concentrations of stand ard length f ish based upon this study. Also provided in Tab le 3.2 are the 95% lower a nd upper co nfidence limits (a slight asymmetry in these limits is d ue to back-transformation from logarithmic data to re al numbers).
Lastly, the predicted mean mercury concentration using the polynomial model, also using log 10- transformed data, are p rovided for comparison. Difference s between t he means p redicted by the linear and polynomial models are generally not significant, and all but 3 of the 60 polynomial model comparisons lie within the confidence interval for the estimate based on the linear model, a result that might be expected based on chance alone.
The three d ata sets for which the predicted t issue mercury concentra tion at standard length based on the polynomial model falls outside the confidence interval for t he linear model include the following.
Lake Whitefish from Wi nokapau Lake in 1977 (Figure A23). Evidence for a non-linear trend through the data is weak, being driven largely b y two data points, representing the largest two fish in the data set, which have r elatively low mercury concentratio ns. T he linear model provides a good fit to the data, and would tend to provide conservative (i .e., higher) estimates of tissue mercury concentration in larger fish.
Longnose Sucker from Winokapau Lake in 1992 (Figure A40). Evidence for a non-linear trend through the data is wea k, although clumping of the data points suggest s two distin ct cohorts may be pre sent. The polynomial model introdu ces an upw ard “hook” in predicted values for small fish, which is not likely to be a real feature of the data. The polynomial model is rejected for this data set.
White Sucker from Winokapau La ke in 199 6 (Figure A5 9). This d ata set pro vides good evidence for a trend in the data that is not well fit by any of the linear models, but for which the polynomial model provides a goo d fit. Spe cific equations and r 2 values for the three
polynomial models (no transformation, log 10-transformed and square root transformed) are provided in Figure A59. There is no meaningful difference in the fit provided by the three polynomial models (r2 values ranging from 0.69 to 0.71). In the range of the standard length
fish (330 mm for white sucker), the log 10-transformed linear model pro vides a slig htly higher (i.e., conservative) predicted tissue mercury concentration.
4.1 Temporal Trends in Mercury Concentrations
A majority o f the individ ual species/location plots over time (Figures 3.2 to 3.13) sh ow at least some evidence for a de cline in fish tissue mercury concentrations at sta ndard length over time. This trend is most evident for lake whitefish from Gull Lake, and lake whitefish, longnose sucker
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
and white sucker from Winokapau Lake, altho ugh potential trends ar e also evid ent for othe r species. A potential reason for the decline can be postulated. Possibly, mercury concentrations in fish in the lower Churchill River were increas ed by mercury and/or me thyl mercury exports to downstream receptors following impoundment of the Smallwood Reservoir by the Churchill Falls development. Over the decades following impoundment, the peak in mercury mobil ization and methylation in the Smallwood Reservoir would have passed, and mercury concentrations in fish living downstream have declined. Importantly, most of the decline is o bserved between 1977 and 1992, indicating that conditions in the lower Churchill River are presently relatively stable.
4.2 Upriver-Downriver Trends in Mercury Concentrations
The Smallwood Reservoir may have acted as a source of total or methyl mercury to the lowe r river in the first years or decade following its impoundment. If this was the case, then one might expect Winokapau Lake to act as a trap for mercury, due to its large size and great depth, which cause it to f unction as a sediment trap. Comp arisons of mercury concentrations in fish tissue are potentially of interest for several species and locations as follows:
Brook Trout – Winokapau Lake, Gull Lake, Mud Lake and Estuary / Sandy Point; Lake Chub – Churchill River / Section 1 and Gull Lake; Lake Whitefish – Estuary / Sandy Point, Churchill River / Section 1, Gull Lake and Winokapau Lake; Longnose Sucker – Winokapau Lake, Gull Lake , Churchill River / Section 1, Mud Lake and Estuary / Sandy Point; Northern Pike – Winokapau Lake, Gull Lake and Churchill River / Section 1; Round Whitefish – Winokapau Lake and Churchill River / Section 1; and White Sucker – Winokapau Lake, Gull Lake and Estuary / Sandy Point.
For brook trout (Figure 4.1), there is little evidence for longitudinal tren ds in the river, although mercury concentrations for some brook tro ut in Gull Lake may have been lower tha n concentrations in Mud Lake or in th e Estuary / Sandy Point reaches. Similarly, there is little to suggest lo ngitudinal t rends for lake chub (Figure 4.2) or lake whitefish ( Figure 4.3), notwithstanding the single high value for lake whitefish in Winokapau Lake in 1977. Results for longnose sucker (Figure 4.4) are likewise influenced by high values in Winokapau Lake in 1977- 78 and 1987, such that when these points are disregarded, the evidence for a longitudinal trend in the river becomes weak. Data for northern pike (Figure 4.5) and round whitefish (Figure 4.6) are equivocal. Wh ite sucker (Figur e 4.7) also show little e vidence of an overall longitudina l trend.
Taken as a whole, therefore, evidence for a longitudinal trend in mercu ry concentrations in the lower Churchill River, such as might be expected if the Smallwood Reservoir acted as a substantial source of to tal or methyl mercury to the lower river, and Winokapau Lake as a sink, is weak.
4.3 Trophic Trends in Mercury Concentrations
As might be expected, the most noteworthy (i.e., highest) mercury concentrations in the dataset are those for larger an d longer-living piscivorous fish, such as lake trout and northern pike.
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
However, both lake whitefish and longnose su cker from W inokapau Lake in 1977-78 also had relatively high mercury concentrations. For la ke whitefish and longnose sucker in Winokapau Lake, substantial declines in mercury concentration were recorded bet ween 1977-78 and later years. For the remaining observations, the time series data are not sufficien tly available to determine whether trends are present, or meaningful. The following fish samples had predicted total mercury concentrations in muscle tissue at standard length that exceeded 0.5 mg/kg:
Lake Trout f rom Winokapau Lake in 1992 and 1999 (1.033 and 0.593 mg/kg, respectively at standard length of 500 mm); Lake Whitefish from Winokapau Lake in 1977-78 (0.952 mg/kg at standard length of 350 mm); Longnose Sucker from Winokapau Lake in 1977 (0.700 mg/kg at standard length of 300 mm); Northern Pike from Gull Lake in 1977-78 (0.985 mg/kg at standard length of 700 mm); Northern Pike from Churchill River / Section 1 i n 2010 (1.279 mg/kg at standard le ngth of 700 mm, although this value is suspect, being extrapolated beyond the data range); and Northern Pike from Winokapau Lake in 2004 (0.713 mg/kg at standard length of 700 mm).
Overall, however, mercury concen trations in fi sh at standard length f rom the lower Churchill River are re latively low, the majority being less than 0.2 mg/kg. Of nin e fish spe cies/locations sampled in 2010, only northern pike from Gull Lake (1. 279 mg/kg, although this value is suspect, being extrapolated beyond the data range) and longnose su cker from Gull Lake and Winokapau Lake (0.137 and 0.219 mg/kg respectively) had mercury concentrations at standard length that exceeded 0.1 mg/kg. The remaining species (including bro ok trout, lake chub, lake whitefish, longnose sucker from Churchill River / Section 1, and round whitefish) all had mercury concentrations at standard length between 0.030 and 0.089 mg/kg.
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
Figure 4.1 Temporal Variation of Mercury Concentrations in Standard Length Brook Trout by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.2 Temporal Variation of Mercury Concentrations in Standard Length Lake Chub by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.3 Temporal Variation of Mercury Concentrations in Standard Length Lake Whitefish by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.4 Temporal Variation of Mercury Concentrations in Standard Length Longnose Sucker by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.5 Temporal Variation of Mercury Concentrations in Standard Length Northern Pike by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.6 Temporal Variation of Mercury Concentrations in Standard Length Round Whitefish by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary Figure 4.7 Temporal Variation of Mercury Concentrations in Standard Length White Sucker by Location
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Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
5.0 REFERENCES
Jacques Whitford. 200 6. Statisti cal Analysis of Mercury Data from Churchill Fa lls (Labrador) Corporation Reservoirs. Prepared for Newfoundland and L abrador Hydro, St. John’s, NL. 25 pp. + app.
Jacques Whitford and Genivar. 2002. Statistica l Analysis of Mercury Data from Newfoundland and Labrador Hydro’s Reservoirs – Labrador Monitoring. Prepared for Newfoun dland and Labrador Hydro, St. John’s, NL. 32 pp. + app.
Tremblay, G., P. Lege ndre, J.-F. Doyon, R. Verdon and R. Scheta gne. 1998. The use of polynomial regression a nalysis within indicator variables for interpretation of mercury in fish data. Biogeochemistry 40: 189-201.
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APPENDIX A
Figures
Figure A1: Brook Trout - Estuary / Sandy Point, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.20 0.16 0.14 0.15 0.12
Hg/kg) Hg/kg) 0.10
(mg 0.10 (mg 0.08
0.06 0.05 0.04
Mercury Mercury 0.02 y = ‐3.857E‐06x2 + 2.801E‐03x ‐ 3.326E‐01 y = 0.0011x ‐ 0.1462 R² = 7 302E‐01 0.00 R² = 0.7196, p‐value = 4.88E‐04 0.00 160 180 200 220 240 260 280 160 180 200 220 240 260 280 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4 y = 0.0056x ‐ 2.3123 y = ‐2.660E‐05x2 + 1.742E‐02x ‐ 3.598E+00 ‐0.6 R² = 0.6934, p‐value = 4 88E‐04 ‐0.6 R² = 7.117E‐01
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 (mg (mg ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 Log10(Mercury 160 180 200 220 240 260 280 Log10(Mercury 160 180 200 220 240 260 280 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.45 0.45 y = ‐7.653E‐06x2 + 5.249E‐03x ‐ 4.795E‐01 0.40 y = 0.0018x ‐ 0.1098 0.40 R² = 7 276E‐01 R² = 0.7133, p‐value = 4.88E‐04 0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30 (mg (mg 0.25 0.25 0.20 0.20 0.15 0.15 (Mercury (Mercury 160 180 200 220 240 260 280 160 180 200 220 240 260 280 Length (mm) Length (mm) Figure A2: Brook Trout - Gull Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.20 y = 0 0002x + 0.0212 0.20 y = 9.359E‐07x2 ‐ 2 046E‐04x + 6.584E‐02 0.18 R² = 0 2747, p‐value = 1.74E‐03 0.18 R² = 3 011E‐01 0.16 0.16 0.14 0.14 Hg/kg) 0.12 Hg/kg) 0.12
(mg 0.10 (mg 0.10 0.08 0.08 0.06 0.06 0.04 0.04 Mercury 0.02 Mercury 0.02 0.00 0.00 100 200 300 400 100 200 300 400 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.7 ‐0.7 y = 0.0014x ‐ 1.4783 y = 4.691E‐06x2 ‐ 7.528E‐04x ‐ 1.254E+00 ‐0.8 R² = 0.316, p‐value = 1.74E‐03 ‐0.8 R² = 3.354E‐01
Hg/kg)) ‐0.9 Hg/kg)) ‐0.9 ‐1.0 ‐1.0 (mg (mg
‐1.1 ‐1.1 ‐1.2 ‐1.2 ‐1.3 ‐1.3 ‐1.4 ‐1.4 Log10(Mercury 100 200 300 400 Log10(Mercury 100 200 300 400 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.40 0.40 y = 0.0004x + 0.1696 y = 1.592E‐06x2 ‐ 3.070E‐04x + 2.456E‐01 R² = 0.2958, p‐value = 1.74E‐03 R² = 3.188E‐01 0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30 (mg (mg
0.25 0.25
0.20 0.20 (Mercury (Mercury 100 200 300 400 100 200 300 400 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A3: Brook Trout - Gull Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.12 y = ‐0.0002x + 0.0976 0.12 y = ‐1.334E‐06x2 + 4.710E‐04x + 1 829E‐02 R² = 1.685E‐01 0.10 R² = 0.1453, p‐value = 6 01E‐02 0.10
Hg/kg) 0.08 Hg/kg) 0.08
(mg 0.06 (mg 0.06
0.04 0.04 0.02 0.02 Mercury Mercury 0.00 0.00 150 200 250 300 350 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐1.0 ‐1.0 ‐1.1 ‐1.2 ‐1.2
Hg/kg)) ‐1.3 Hg/kg)) ‐1.4 ‐1.4 (mg (mg ‐1.5 ‐1.6 ‐1.6 ‐1.7
‐1.8 ‐1.8 2 ‐1.9 y = ‐0 0026x ‐ 0.7498 y = ‐2.371E‐05x + 9.222E‐03x ‐ 2.160E+00 R² = 0.2157, p‐value = 6.01E‐02 ‐2.0 ‐2.0 R² = 2.761E‐01 Log10(Mercury 150 200 250 300 350 Log10(Mercury 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 Hg/kg))^0.5 Hg/kg))^0.5 0.15 0.15 (mg (mg 0.1 0.1 0.05 y = ‐0.0005x + 0.3397 0.05 y = ‐4.078E‐06x2 + 1.521E‐03x + 9.724E‐02 R² = 0.1749, p‐value = 6 01E‐02 R² = 2.123E‐01 0 0 (Mercury (Mercury 150 200 250 300 350 150 200 250 300 350 Length (mm) Length (mm) Figure A4: Brook Trout - Gull Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.175 0.175 y = 0.0005x ‐ 0.0349 y = 9.727E‐07x2 + 1.251E‐04x + 5 322E‐03 0.150 R² = 0.319, p‐value = 2.65E‐03 0.150 R² = 3.204E‐01 0.125 0.125 Hg/kg) Hg/kg) 0.100 0.100 (mg (mg 0.075 0.075 0.050 0.050
Mercury 0.025 Mercury 0.025 0.000 0.000 125 150 175 200 225 250 275 300 125 150 175 200 225 250 275 300 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.50 ‐0.50 y = 0.0031x ‐ 1.8168 y = ‐5.931E‐06x2 + 5.535E‐03x ‐ 2.062E+00 R² = 3.083E‐01 ‐0.75 R² = 0 3068, p‐value = 2.65E‐03 ‐0.75 Hg/kg)) Hg/kg)) ‐1.00 ‐1.00 (mg (mg
‐1.25 ‐1.25
‐1.50 ‐1.50
‐1.75 ‐1.75 Log10(Mercury 125 150 175 200 225 250 275 300 Log10(Mercury 125 150 175 200 225 250 275 300 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.45 0.45 y = 0 0009x + 0 0689 y = 3.273E‐08x2 + 9 322E‐04x + 7.028E‐02 0.40 R² = 0 3152, p‐value = 2.65E‐03 0.40 R² = 3.152E‐01 0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30 (mg (mg 0.25 0.25 0.20 0.20 0.15 0.15 (Mercury (Mercury 125 150 175 200 225 250 275 300 125 150 175 200 225 250 275 300 Length (mm) Length (mm) Figure A5: Brook Trout - Gull Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.25 y = 0 0004x + 0 003 0.25 y = 9 088E‐07x2 + 1 219E‐05x + 3.362E‐02 R² = 0 5029, p‐value = 3.07E‐03 R² = 5.177E‐01 0.20 0.20
Hg/kg) 0.15 Hg/kg) 0.15 (mg (mg 0.10 0.10
0.05 0.05 Mercury Mercury 0.00 0.00 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.4 ‐0.4 2 y = 0 0023x ‐ 1.6584 y = 8 253E‐06x ‐ 1.125E‐03x ‐ 1.380E+00 ‐0.6 R² = 0.5051, p‐value = 3.07E‐03 ‐0.6 R² = 5.401E‐01
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 ‐1.0 ‐1.0 (mg (mg
‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 ‐1.8 ‐1.8 Log10(Mercury 0 50 100 150 200 250 300 350 400 Log10(Mercury 0 50 100 150 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.50 0.50 0.45 0.45 0.40 0.40 0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30
(mg 0.25 (mg 0.25
0.20 0.20 0.15 y = 0.0007x + 0.1286 0.15 y = 2.114E‐06x2 ‐ 1.798E‐04x + 1.998E‐01 0.10 R² = 0.5197, p‐value = 3.07E‐03 0.10 R² = 5.454E‐01 (Mercury (Mercury 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 Length (mm) Length (mm) Figure A6: Brook Trout - Mud Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = 0.0002x + 0.1102 y = 5.585E‐06x2 ‐ 2.724E‐03x + 4.716E‐01 0.30 R² = 0.0128, p‐value = 7 01E‐01 0.30 R² = 4.747E‐02 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 175 200 225 250 275 300 325 350 175 200 225 250 275 300 325 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.2 ‐0.2 y = 0.001x ‐ 1.1091 y = 1.317E‐05x2 ‐ 5 819E‐03x ‐ 2.570E‐01 R² = 0 0652, p‐value = 7.01E‐01 ‐0.4 ‐0.4 R² = 9.213E‐02 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8
‐1.0 ‐1.0
‐1.2 ‐1.2 Log10(Mercury 175 200 225 250 275 300 325 350 Log10(Mercury 175 200 225 250 275 300 325 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.60 0.60 0.55 0.55 0.50 0.50 0.45 0.45
Hg/kg))^0.5 0.40 Hg/kg))^0.5 0.40
(mg 0.35 (mg 0.35
0.30 0.30 0.25 y = 0.0003x + 0 2979 0.25 y = 6.461E‐06x2 ‐ 3 014E‐03x + 7.160E‐01 0.20 R² = 0.0342, p‐value = 7.01E‐01 0.20 R² = 6.548E‐02 (Mercury (Mercury 175 200 225 250 275 300 325 350 175 200 225 250 275 300 325 350 Length (mm) Length (mm) Figure A7: Brook Trout - Winokapau Lake, 1987
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.40 0.40 y = 0 0003x + 0.0285 y = ‐2 510E‐06x2 + 1.678E‐03x ‐ 1 368E‐01 0.35 R² = 0.1395, p‐value = 1 54E‐01 0.35 R² = 2.058E‐01 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0011x ‐ 1.2984 y = ‐1 081E‐05x2 + 6 894E‐03x ‐ 2 010E+00 ‐0.2 R² = 0.1328, p‐value = 1 54E‐01 ‐0.2 R² = 2.422E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 100 150 200 250 300 350 400 450 Log10(Mercury 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.6 0.6 0.5 0.5 0.4 0.4
Hg/kg))^0.5 0.3 Hg/kg))^0.5 0.3 (mg (mg 0.2 0.2
0.1 y = 0.0004x + 0 2082 0.1 y = ‐3.864E‐06x2 + 2.518E‐03x ‐ 4.617E‐02 R² = 0.1365, p‐value = 1.54E‐01 R² = 2.222E‐01 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 Length (mm) Length (mm) Figure A8: Brook Trout - Winokapau Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.18 0.18 y = 0.0003x ‐ 0.0094 y = ‐3 081E‐06x2 + 2 015E‐03x ‐ 2.403E‐01 R² = 0.1709, p‐value = 1.82E‐01 R² = 2.284E‐01 0.13 0.13 Hg/kg) Hg/kg)
(mg 0.08 (mg 0.08
0.03 0.03 Mercury Mercury
‐0.02 180 200 220 240 260 280 300 320 340 360 380 ‐0.02 180 200 220 240 260 280 300 320 340 360 380 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.4 ‐0.4 2 y = 0.0021x ‐ 1.8471 y = ‐2.301E‐05x + 1.508E‐02x ‐ 3.572E+00 ‐0.6 R² = 0 2335, p‐value = 1.82E‐01 ‐0.6 R² = 3.090E‐01
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 ‐1.0 ‐1.0 (mg (mg
‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 ‐1.8 ‐1.8 Log10(Mercury 180 200 220 240 260 280 300 320 340 360 380 Log10(Mercury 180 200 220 240 260 280 300 320 340 360 380 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.40 0.40 0.35 0.35 0.30 0.30 0.25 0.25
Hg/kg))^0.5 0.20 Hg/kg))^0.5 0.20
(mg 0.15 (mg 0.15
0.10 0.10 0.05 y = 0.0006x + 0.0907 0.05 y = ‐6.155E‐06x2 + 4.034E‐03x ‐ 3.707E‐01 R² = 0.1994, p‐value = 1.82E‐01 0.00 0.00 R² = 2.642E‐01 (Mercury (Mercury 180 200 220 240 260 280 300 320 340 360 380 180 200 220 240 260 280 300 320 340 360 380 Length (mm) Length (mm) Figure A9: Burbot - Gull Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.50 0.50 y = 0.0004x ‐ 0.0803 y = 2.543E‐06x2 ‐ 1 854E‐03x + 4.137E‐01 R² = 3 904E‐01 0.40 R² = 0.2653, p‐value = 5.98E‐03 0.40
Hg/kg) 0.30 Hg/kg) 0.30 (mg (mg 0.20 0.20
0.10 0.10 Mercury Mercury 0.00 0.00 250 300 350 400 450 500 550 600 650 250 300 350 400 450 500 550 600 650 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0014x ‐ 1.6217 y = 4.325E‐06x2 ‐ 2.537E‐03x ‐ 7.816E‐01 ‐0.2 R² = 0.266, p‐value = 5 98E‐03 ‐0.2 R² = 3.044E‐01 ‐0.4 ‐0.4 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 Log10(Mercury 250 300 350 400 450 500 550 600 650 Log10(Mercury 250 300 350 400 450 500 550 600 650 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0 0006x + 0.0753 y = 2 562E‐06x2 ‐ 1.749E‐03x + 5.729E‐01 0.6 R² = 0 2719, p‐value = 5.98E‐03 0.6 R² = 3.525E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 250 300 350 400 450 500 550 600 650 250 300 350 400 450 500 550 600 650 Length (mm) Length (mm) Figure A10: Lake Chub - Churchill River / Section 1, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.35 y = 0 0008x ‐ 0 0197 0.35 y = ‐4.110E‐06x2 + 1.662E‐03x ‐ 6.356E‐02 0.30 R² = 0.1536, p‐value = 3 55E‐02 0.30 R² = 1 541E‐01 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 85 90 95 100 105 110 115 120 125 130 85 90 95 100 105 110 115 120 125 130 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0055x ‐ 1.8239 y = 6 805E‐05x2 ‐ 8.714E‐03x ‐ 1.098E+00 R² = 8.465E‐02 ‐0.5 R² = 0.0832, p‐value = 3.55E‐02 ‐0.5 Hg/kg)) Hg/kg)) ‐1.0 ‐1.0 (mg (mg
‐1.5 ‐1.5
‐2.0 ‐2.0
‐2.5 ‐2.5 Log10(Mercury 85 90 95 100 105 110 115 120 125 130 Log10(Mercury 85 90 95 100 105 110 115 120 125 130 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed
0.7 0.7 2 y = 0 0016x + 0.0823 y = 3.054E‐06x + 9.301E‐04x + 1.148E‐01 0.6 R² = 0.1194, p‐value = 3.55E‐02 0.6 R² = 1.194E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 85 90 95 100 105 110 115 120 125 130 85 90 95 100 105 110 115 120 125 130 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A11: Lake Chub - Gull Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.16 0.16 0.14 0.14 0.12 0.12
Hg/kg) 0.10 Hg/kg) 0.10
(mg 0.08 (mg 0.08
0.06 0.06 0.04 0.04 y = 3.731E‐06x2 ‐ 1.137E‐03x + 1.744E‐01 Mercury 0.02 y = ‐0.0002x + 0.1132 Mercury 0.02 R² = 1 056E‐02 0.00 R² = 0.0096, p‐value = 7.74E‐01 0.00 100 110 120 130 140 150 160 100 110 120 130 140 150 160 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.7 ‐0.7 ‐0.8 ‐0.8
Hg/kg)) ‐0.9 Hg/kg)) ‐0.9 ‐1.0 ‐1.0 (mg (mg
‐1.1 ‐1.1 ‐1.2 ‐1.2 ‐1.3 y = ‐0.001x ‐ 0 9229 ‐1.3 y = 3.393E‐05x2 ‐ 9.753E‐03x ‐ 3.668E‐01 R² = 0 0153, p‐value = 7.74E‐01 R² = 1.897E‐02 ‐1.4 ‐1.4 Log10(Mercury 100 110 120 130 140 150 160 Log10(Mercury 100 110 120 130 140 150 160 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.40 0.40
0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30 (mg (mg
0.25 0.25 y = ‐0.0003x + 0.34 y = 9.150E‐06x2 ‐ 2.675E‐03x + 4.900E‐01 R² = 0.0121, p‐value = 7.74E‐01 R² = 1.431E‐02 0.20 0.20 (Mercury (Mercury 100 110 120 130 140 150 160 100 110 120 130 140 150 160 Length (mm) Length (mm) Figure A12: Lake Trout - Winokapau Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 4.0 4.0 y = ‐4.014E‐06x2 + 8.818E‐03x ‐ 2.140E+00 3.5 y = 0.004x ‐ 0.7772 3.5 R² = 0.2447, p‐value = 2.26E‐02 R² = 2.546E‐01 3.0 3.0
Hg/kg) 2.5 Hg/kg) 2.5
(mg 2.0 (mg 2.0
1.5 1.5 1.0 1.0
Mercury 0.5 Mercury 0.5 0.0 0.0 300 400 500 600 700 800 900 300 400 500 600 700 800 900 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
0.75 0.75 2 y = 0 0012x ‐ 0.6106 y = ‐2.066E‐06x + 3.718E‐03x ‐ 1.312E+00 0.50 R² = 0.3716, p‐value = 2.26E‐02 0.50 R² = 4.128E‐01
Hg/kg)) 0.25 Hg/kg)) 0.25 (mg (mg 0.00 0.00 ‐0.25 ‐0.25 ‐0.50 ‐0.50 ‐0.75 ‐0.75 Log10(Mercury 300 400 500 600 700 800 900 Log10(Mercury 300 400 500 600 700 800 900 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 2.0 2.0
1.5 1.5
Hg/kg))^0.5 1.0 Hg/kg))^0.5 1.0 (mg (mg
0.5 0.5 y = 0.0017x + 0.2363 y = ‐2.141E‐06x2 + 4.209E‐03x ‐ 4.907E‐01 R² = 0.3049, p‐value = 2.26E‐02 0.0 0.0 R² = 3 257E‐01 (Mercury (Mercury 300 400 500 600 700 800 900 300 400 500 600 700 800 900 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A13: Lake Trout - Winokapau Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.8 0.8 0.7 0.7
Hg/kg) 0.6 Hg/kg) 0.6
(mg 0.5 (mg 0.5
0.4 0.4
0.3 y = 0.001x + 0.0507 0.3 2
Mercury Mercury y = ‐6.114E‐06x + 5.889E‐03x ‐ 8.489E‐01 R² = 0.4325, p‐value = 3.88E‐02 R² = 6.782E‐01 0.2 0.2 200 300 400 500 600 200 300 400 500 600 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 ‐0.1 ‐0.1 ‐0.2 ‐0.2 Hg/kg)) Hg/kg)) ‐0.3 ‐0.3 (mg (mg ‐0.4 ‐0.4 ‐0.5 ‐0.5 ‐0.6 ‐0.6 ‐0.7 y = 0.0013x ‐ 0.8547 ‐0.7 y = ‐7 045E‐06x2 + 6 843E‐03x ‐ 1 891E+00 ‐0.8 R² = 0.5204, p‐value = 3.88E‐02 ‐0.8 R² = 7.895E‐01 Log10(Mercury 200 300 400 500 600 Log10(Mercury 200 300 400 500 600 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.9 0.9
0.8 0.8
0.7 0.7 Hg/kg))^0.5 Hg/kg))^0.5 0.6 0.6 (mg (mg
0.5 0.5 2 y = 0.0009x + 0.3309 y = ‐4 923E‐06x + 4.765E‐03x ‐ 3 933E‐01 0.4 R² = 0.4821, p‐value = 3.88E‐02 0.4 R² = 7.412E‐01 (Mercury (Mercury 200 300 400 500 600 200 300 400 500 600 Length (mm) Length (mm) Figure A14: Lake Whitefish - Churchill River / Section 1, 1978
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.50 0.50 y = ‐3E‐05x + 0 2001 y = 1.702E‐05x2 ‐ 8.823E‐03x + 1.312E+00 0.40 R² = 9E‐05, p‐value = 9.77E‐01 0.40 R² = 3.169E‐02
Hg/kg) 0.30 Hg/kg) 0.30 (mg (mg 0.20 0.20
0.10 0.10 Mercury Mercury 0.00 0.00 200 225 250 275 300 325 200 225 250 275 300 325 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.2 ‐0.2 ‐0.4 ‐0.4
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0
‐1.2 y = 0 0008x ‐ 0 9404 ‐1.2 y = 1.229E‐05x2 ‐ 5.548E‐03x ‐ 1.379E‐01 R² = 0.0131, p‐value = 9.77E‐01 R² = 1.638E‐02 ‐1.4 ‐1.4 Log10(Mercury 200 225 250 275 300 325 Log10(Mercury 200 225 250 275 300 325 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7
0.6 0.6
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3 y = 0 0002x + 0.389 y = 1.332E‐05x2 ‐ 6.705E‐03x + 1.259E+00 R² = 0 0027, p‐value = 9.77E‐01 R² = 1 923E‐02 0.2 0.2 (Mercury (Mercury 200 225 250 275 300 325 200 225 250 275 300 325 Length (mm) Length (mm) Figure A15: Lake Whitefish - Churchill River / Section 1, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.25 0.25 y = 0.0003x + 0.0123 y = ‐3.378E‐06x2 + 1.885E‐03x ‐ 1.431E‐01 R² = 0 241, p‐value = 2.80E‐02 R² = 3 849E‐01 0.20 0.20
Hg/kg) 0.15 Hg/kg) 0.15 (mg (mg 0.10 0.10
0.05 0.05 Mercury Mercury 0.00 0.00 100 150 200 250 300 350 400 100 150 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.4 ‐0.4 y = ‐1.398E‐05x2 + 8.308E‐03x ‐ 2.224E+00 y = 0.0018x ‐ 1.5809 R² = 4.001E‐01 ‐0.6 R² = 0.3052, p‐value = 2.80E‐02 ‐0.6
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 ‐1.0 ‐1.0 (mg (mg
‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 ‐1.8 ‐1.8 Log10(Mercury 100 150 200 250 300 350 400 Log10(Mercury 100 150 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.5 0.5
0.4 0.4
0.3 0.3 Hg/kg))^0.5 Hg/kg))^0.5 0.2 0.2 (mg (mg
0.1 0.1 2 y = 0.0006x + 0.1478 y = ‐5.159E‐06x + 2.957E‐03x ‐ 8.962E‐02 0.0 R² = 0.2852, p‐value = 2.80E‐02 0.0 R² = 4.117E‐01 (Mercury (Mercury 100 150 200 250 300 350 400 100 150 200 250 300 350 400 Length (mm) Length (mm) Figure A16: Lake Whitefish - Estuary / Sandy Point, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.25 0.25
0.20 0.20
Hg/kg) 0.15 Hg/kg) 0.15 (mg (mg 0.10 0.10
0.05 0.05 y = ‐9.479E‐06x2 + 5.746E‐03x ‐ 7.186E‐01
Mercury y = 0.0002x + 0.0792 Mercury R² = 0.0271, p‐value = 6 50E‐01 R² = 2.075E‐01 0.00 0.00 200 250 300 350 400 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.6 ‐0.6
‐0.8 ‐0.8 Hg/kg)) Hg/kg)) ‐1.0 ‐1.0 (mg (mg
‐1.2 ‐1.2
‐1.4 ‐1.4 2 y = 0.002x ‐ 1.4901 y = ‐5.148E‐05x + 3 216E‐02x ‐ 5 823E+00 R² = 3.615E‐01 ‐1.6 R² = 0.1274, p‐value = 6.50E‐01 ‐1.6 Log10(Mercury 200 250 300 350 400 Log10(Mercury 200 250 300 350 400 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.50 0.50 0.45 0.45 0.40 0.40 0.35 0.35 Hg/kg))^0.5 Hg/kg))^0.5 0.30 0.30 (mg (mg 0.25 0.25 0.20 y = 0 0005x + 0 2175 0.20 y = ‐1.621E‐05x2 + 9.989E‐03x ‐ 1.147E+00 R² = 0 0701, p‐value = 6.50E‐01 R² = 2.802E‐01 0.15 0.15 (Mercury (Mercury 200 250 300 350 400 200 250 300 350 400 Length (mm) Length (mm) Figure A17: Lake Whitefish - Gull Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.9 0.9 y = 0 0023x ‐ 0 5149 y = 2.159E‐05x2 ‐ 1 238E‐02x + 1.932E+00 R² = 0.4982, p‐value = 7.42E‐07 R² = 5.712E‐01 0.7 0.7
Hg/kg) 0.5 Hg/kg) 0.5 (mg (mg
0.3 0.3
0.1 0.1 Mercury Mercury
‐0.1 250 275 300 325 350 375 400 425 ‐0.1 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0035x ‐ 1.8168 y = 1.684E‐05x2 ‐ 7.926E‐03x + 9 242E‐02 ‐0.2 R² = 0.6521, p‐value = 7.42E‐07 ‐0.2 R² = 6.767E‐01 Hg/kg)) Hg/kg)) ‐0.4 ‐0.4 (mg (mg
‐0.6 ‐0.6
‐0.8 ‐0.8
‐1.0 ‐1.0 Log10(Mercury 250 275 300 325 350 375 400 425 Log10(Mercury 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1.0 1.0 y = 0.0021x ‐ 0.2166 y = 1.489E‐05x2 ‐ 8 003E‐03x + 1.471E+00 0.8 R² = 0.5827, p‐value = 7.42E‐07 0.8 R² = 6.304E‐01
0.6 0.6 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.2 0.2
0.0 0.0 (Mercury (Mercury 250 275 300 325 350 375 400 425 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A18: Lake Whitefish - Gull Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.50 0.50 y = 0 0019x ‐ 0.4662 y = 1 352E‐05x2 ‐ 7.587E‐03x + 1.184E+00 0.40 R² = 0.4933, p‐value = 6 24E‐07 0.40 R² = 5 330E‐01
Hg/kg) 0.30 Hg/kg) 0.30 (mg (mg 0.20 0.20
0.10 0.10 Mercury Mercury 0.00 0.00 275 300 325 350 375 400 425 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.3 y = 0.004x ‐ 2.1367 ‐0.3 y = 1.780E‐05x2 ‐ 8.481E‐03x + 3.719E‐02 R² = 0.5215, p‐value = 6 24E‐07 R² = 5.377E‐01
Hg/kg)) ‐0.5 Hg/kg)) ‐0.5 (mg (mg ‐0.7 ‐0.7
‐0.9 ‐0.9
‐1.1 ‐1.1 Log10(Mercury 275 300 325 350 375 400 425 Log10(Mercury 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed
0.65 y = 0 0021x ‐ 0 2871 0.65 y = 1 205E‐05x2 ‐ 6.386E‐03x + 1.184E+00 R² = 0 5127, p‐value = 6 24E‐07 R² = 5.403E‐01 0.55 0.55 Hg/kg))^0.5 Hg/kg))^0.5 0.45 0.45 (mg (mg
0.35 0.35
0.25 0.25 (Mercury (Mercury 275 300 325 350 375 400 425 275 300 325 350 375 400 425 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A19: Lake Whitefish - Gull Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = ‐1E‐05x + 0.1542 y = ‐7.682E‐06x2 + 5.326E‐03x ‐ 7.580E‐01 0.30 R² = 0 0002, p‐value = 9.69E‐01 0.30 R² = 7.666E‐02 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 250 275 300 325 350 375 400 425 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = ‐0 0002x ‐ 0.7678 y = ‐1.622E‐05x2 + 1.104E‐02x ‐ 2.694E+00 ‐0.2 R² = 0.0058, p‐value = 9.69E‐01 ‐0.2 R² = 5.190E‐02
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 (mg (mg ‐0.6 ‐0.6 ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 Log10(Mercury 250 275 300 325 350 375 400 425 Log10(Mercury 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.60 0.60 y = ‐6E‐05x + 0.403 y = ‐8.521E‐06x2 + 5.861E‐03x ‐ 6.088E‐01 R² = 0 002, p‐value = 9.69E‐01 R² = 6.385E‐02 0.50 0.50
Hg/kg))^0.5 0.40 Hg/kg))^0.5 0.40 (mg (mg
0.30 0.30
0.20 0.20 (Mercury (Mercury 250 275 300 325 350 375 400 425 250 275 300 325 350 375 400 425 Length (mm) Length (mm) Figure A20: Lake Whitefish - Gull Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.40 0.40 y = 0.0004x ‐ 0.008 y = 1.793E‐06x2 ‐ 6.034E‐04x + 1.140E‐01 0.35 R² = 0 3132, p‐value = 1.60E‐03 0.35 R² = 3.667E‐01 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0014x ‐ 1.4289 y = 5.431E‐06x2 ‐ 1.651E‐03x ‐ 1.059E+00 ‐0.2 R² = 0.4387, p‐value = 1.60E‐03 ‐0.2 R² = 4.962E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 100 150 200 250 300 350 400 450 500 Log10(Mercury 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0.0006x + 0.1568 y = 2.348E‐06x2 ‐ 7.633E‐04x + 3.167E‐01 0.6 R² = 0.3787, p‐value = 1.60E‐03 0.6 R² = 4 373E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm) Figure A21: Lake Whitefish - Gull Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = 0 0011x ‐ 0 2245 y = 8 341E‐07x2 + 4 976E‐04x ‐ 1 321E‐01 0.30 R² = 0 3993, p‐value = 3.70E‐03 0.30 R² = 4.002E‐01 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 225 250 275 300 325 350 375 400 425 450 225 250 275 300 325 350 375 400 425 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4 y = 0.0037x ‐ 2.2154 y = 9 380E‐07x2 + 3.107E‐03x ‐ 2.112E+00 ‐0.6 R² = 0.53, p‐value = 3.70E‐03 ‐0.6 R² = 5.301E‐01 Hg/kg)) Hg/kg)) ‐0.8 ‐0.8 (mg (mg
‐1.0 ‐1.0
‐1.2 ‐1.2
‐1.4 ‐1.4 Log10(Mercury 225 250 275 300 325 350 375 400 425 450 Log10(Mercury 225 250 275 300 325 350 375 400 425 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0.0015x ‐ 0.1494 y = 8.412E‐07x2 + 9.101E‐04x ‐ 5.631E‐02 0.6 R² = 0.4679, p‐value = 3.70E‐03 0.6 R² = 4.685E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0 0 (Mercury (Mercury 225 250 275 300 325 350 375 400 425 450 225 250 275 300 325 350 375 400 425 450 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A22: Lake Whitefish - Gull Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.20 0.20 y = 0.0006x ‐ 0.1128 y = 5.594E‐06x2 ‐ 3.129E‐03x + 4.974E‐01 0.15 R² = 0.4177, p‐value = 3.17E‐02 0.15 R² = 5 045E‐01 Hg/kg) Hg/kg)
(mg 0.10 (mg 0.10
0.05 0.05 Mercury Mercury 0.00 0.00 260 280 300 320 340 360 380 400 420 260 280 300 320 340 360 380 400 420 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.7 ‐0.7 y = 0.0029x ‐ 2.0593 y = 2 960E‐05x2 ‐ 1.682E‐02x + 1.170E+00 ‐0.8 R² = 0.4108, p‐value = 3.17E‐02 ‐0.8 ‐0.9 ‐0.9 R² = 5.122E‐01 Hg/kg)) Hg/kg)) ‐1.0 ‐1.0 (mg (mg ‐1.1 ‐1.1 ‐1.2 ‐1.2 ‐1.3 ‐1.3 ‐1.4 ‐1.4 ‐1.5 ‐1.5 Log10(Mercury 260 280 300 320 340 360 380 400 420 Log10(Mercury 260 280 300 320 340 360 380 400 420 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.45 0.45 y = 0.001x ‐ 0.0408 y = 9.626E‐06x2 ‐ 5.423E‐03x + 1.009E+00 0.40 R² = 0.4236, p‐value = 3.17E‐02 0.40 R² = 5.186E‐01 0.35 0.35
Hg/kg))^0.5 0.30 Hg/kg))^0.5 0.30 (mg (mg 0.25 0.25 0.20 0.20 0.15 0.15 (Mercury (Mercury 260 280 300 320 340 360 380 400 420 260 280 300 320 340 360 380 400 420 Length (mm) Length (mm) Figure A23: Lake Whitefish - Winokapau Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation
3 y = 0.0072x ‐ 1.4307 3 y = ‐2.733E‐04x2 + 1.939E‐01x ‐ 3.309E+01 R² = 3.337E‐01 2.5 R² = 0.13, p‐value = 6.46E‐02 2.5
Hg/kg) 2 Hg/kg) 2
(mg 1.5 (mg 1.5
1 1 0.5 0.5 Mercury Mercury 0 0 275 300 325 350 375 400 275 300 325 350 375 400 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.6 0.6 0.4 0.4
Hg/kg)) 0.2 Hg/kg)) 0.2 (mg (mg 0.0 0.0 ‐0.2 ‐0.2
‐0.4 y = 0 0046x ‐ 1.6203 ‐0.4 y = ‐1.240E‐04x2 + 8.926E‐02x ‐ 1.598E+01 ‐0.6 R² = 0.2951, p‐value = 6.46E‐02 ‐0.6 R² = 5.310E‐01 Log10(Mercury 275 300 325 350 375 400 Log10(Mercury 275 300 325 350 375 400 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed
1.8 1.8 2 y = 0 0043x ‐ 0.4858 y = ‐1.358E‐04x + 9.706E‐02x ‐ 1.622E+01 1.6 R² = 0.2039, p‐value = 6.46E‐02 1.6 R² = 4.273E‐01 1.4 1.4 1.2 1.2 Hg/kg))^0.5 Hg/kg))^0.5 1 1 (mg (mg 0.8 0.8 0.6 0.6 0.4 0.4 (Mercury (Mercury 275 300 325 350 375 400 275 300 325 350 375 400 Length (mm) Length (mm) Figure A24: Lake Whitefish - Winokapau Lake, 1987
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.7 0.7 y = 0.0005x + 0 06 y = 1.167E‐05x2 ‐ 5.085E‐03x + 6.149E‐01 0.6 R² = 0.1324, p‐value = 4 06E‐02 0.6 R² = 5.942E‐01 0.5 0.5 Hg/kg) Hg/kg) 0.4 0.4 (mg (mg 0.3 0.3 0.2 0.2
Mercury 0.1 Mercury 0.1 0 0 50 150 250 350 450 50 150 250 350 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0 0 y = 0.0007x ‐ 1.0228 y = 2.448E‐05x2 ‐ 1 095E‐02x + 1.417E‐01 ‐0.2 R² = 0.0719, p‐value = 4 06E‐02 ‐0.2 ‐0.4 ‐0.4 R² = 5.487E‐01 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1 ‐1 ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 Log10(Mercury 50 150 250 350 450 Log10(Mercury 50 150 250 350 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1 1 y = 0.0004x + 0.2942 y = 1 225E‐05x2 ‐ 5.421E‐03x + 8.772E‐01 0.8 R² = 0.1028, p‐value = 4 06E‐02 0.8 R² = 6.040E‐01 0.6 0.6 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.2 0.2
0 0 (Mercury (Mercury 50 150 250 350 450 50 150 250 350 450 Length (mm) Length (mm) Figure A25: Lake Whitefish - Winokapau Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 1.2 1.2 y = 0 0047x ‐ 1.4457 y = 4 929E‐05x2 ‐ 3.388E‐02x + 5 982E+00 1 R² = 0.6119, p‐value = 1 58E‐03 1 R² = 8 307E‐01
Hg/kg) 0.8 Hg/kg) 0.8
(mg 0.6 (mg 0.6
0.4 0.4 0.2 0.2 Mercury Mercury 0 0 250 300 350 400 450 500 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.2 0.2 y = 0.0042x ‐ 2.1186 y = 3.568E‐05x2 ‐ 2.376E‐02x + 3.258E+00 R² = 0.6853, p‐value = 1.58E‐03 0 0 R² = 8.497E‐01
Hg/kg)) ‐0.2 Hg/kg)) ‐0.2 (mg (mg ‐0.4 ‐0.4 ‐0.6 ‐0.8 ‐0.6 ‐1 ‐0.8 Log10(Mercury 250 300 350 400 450 500 Log10(Mercury 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed
1 y = 0.0033x ‐ 0.6804 1 y = 3.111E‐05x2 ‐ 2.109E‐02x + 4.008E+00 R² = 0.6556, p‐value = 1.58E‐03 R² = 8.501E‐01 0.8 0.8 Hg/kg))^0.5 Hg/kg))^0.5 0.6 0.6 (mg (mg
0.4 0.4
0.2 0.2 (Mercury (Mercury 250 300 350 400 450 500 250 300 350 400 450 500 Length (mm) Length (mm) Figure A26: Lake Whitefish - Winokapau Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation 1.0 1.0 y = 0.0011x ‐ 0.0874 y = 7.474E‐06x2 ‐ 2.644E‐03x + 2.890E‐01 0.8 R² = 0.4678, p‐value = 1.75E‐03 0.8 R² = 6.684E‐01
Hg/kg) 0.6 Hg/kg) 0.6 (mg (mg 0.4 0.4
0.2 0.2 Mercury Mercury 0.0 0.0 50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 2 ‐0.2 y = 0.0023x ‐ 1.4141 ‐0.2 y = 7.274E‐06x ‐ 1.383E‐03x ‐ 1 048E+00 R² = 0.6673, p‐value = 1.75E‐03 R² = 7.316E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 50 100 150 200 250 300 350 400 450 Log10(Mercury 50 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1.0 1.0 y = 0.0011x + 0.1204 y = 5.539E‐06x2 ‐ 1.648E‐03x + 3.994E‐01 0.9 R² = 0.5988, p‐value = 1.75E‐03 0.9 R² = 7.333E‐01 0.8 0.8 0.7 0.7
Hg/kg))^0.5 0.6 Hg/kg))^0.5 0.6
(mg 0.5 (mg 0.5
0.4 0.4 0.3 0.3 0.2 0.2 (Mercury (Mercury 50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450 Length (mm) Length (mm) Figure A27: Lake Whitefish - Winokapau Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.5 0.5 y = 0.0006x ‐ 0.0162 y = 4.442E‐06x2 ‐ 2.022E‐03x + 3 251E‐01 R² = 0.4743, p‐value = 1 00E‐04 0.4 0.4 R² = 6.959E‐01
Hg/kg) 0.3 Hg/kg) 0.3 (mg (mg 0.2 0.2
0.1 0.1 Mercury Mercury 0.0 0.0 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.4 y = 0 0014x ‐ 1.2609 ‐0.4 y = 6.748E‐06x2 ‐ 2 563E‐03x ‐ 7.424E‐01 R² = 0.5396, p‐value = 1.00E‐04 R² = 6.421E‐01
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8
‐1.0 ‐1.0
‐1.2 ‐1.2 Log10(Mercury 100 200 300 400 500 Log10(Mercury 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0 0007x + 0.1879 y = 4.169E‐06x2 ‐ 1.777E‐03x + 5.082E‐01 0.6 R² = 0 5153, p‐value = 1.00E‐04 0.6 R² = 6.776E‐01
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3
0.2 0.2 (Mercury (Mercury 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm) Figure A28: Lake Whitefish - Winokapau Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.24 0.24 y = 0.0002x + 0.0626 0.22 0.22 y = 2.971E‐06x2 ‐ 1.311E‐03x + 2.389E‐01 R² = 0.2909, p‐value = 3.11E‐02 R² = 7.767E‐01 0.20 0.20
Hg/kg) 0.18 Hg/kg) 0.18
(mg 0.16 (mg 0.16
0.14 0.14 0.12 0.12
Mercury 0.10 Mercury 0.10 0.08 0.08 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.5 ‐0.5 y = 0 0007x ‐ 1 0954 y = 8.375E‐06x2 ‐ 3.671E‐03x ‐ 5.985E‐01 R² = 0.2803, p‐value = 3.11E‐02 ‐0.6 ‐0.6 R² = 7.188E‐01
Hg/kg)) ‐0.7 Hg/kg)) ‐0.7 (mg (mg ‐0.8 ‐0.8 ‐0.9 ‐0.9 ‐1 ‐1 ‐1.1 ‐1.1 Log10(Mercury 100 150 200 250 300 350 400 450 Log10(Mercury 100 150 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.5 0.5 y = 0.0003x + 0.2713 y = 3.759E‐06x2 ‐ 1.653E‐03x + 4.943E‐01 0.45 R² = 0.287, p‐value = 3.11E‐02 0.45 R² = 7.517E‐01
0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.35 0.35 (mg (mg
0.3 0.3
0.25 0.25 (Mercury (Mercury 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 Length (mm) Length (mm) Figure A29: Longnose Sucker - Churchill River / Section 1, 1978
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = 0.0009x ‐ 0.1058 y = 1.078E‐05x2 ‐ 5.375E‐03x + 7.734E‐01 0.30 R² = 0.2561, p‐value = 1.12E‐01 0.30 R² = 2.838E‐01 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 200 225 250 275 300 325 350 375 200 225 250 275 300 325 350 375 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4
‐0.6 ‐0.6 Hg/kg)) Hg/kg)) ‐0.8 ‐0.8 (mg (mg
‐1.0 ‐1.0
‐1.2 ‐1.2 y = 0 0017x ‐ 1.4167 y = 2.586E‐05x2 ‐ 1.323E‐02x + 6.915E‐01 R² = 0.1188, p‐value = 1.12E‐01 ‐1.4 ‐1.4 R² = 1.372E‐01 Log10(Mercury 200 225 250 275 300 325 350 375 Log10(Mercury 200 225 250 275 300 325 350 375 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0 001x + 0.0915 y = 1.183E‐05x2 ‐ 5.904E‐03x + 1.056E+00 0.6 R² = 0.1958, p‐value = 1.12E‐01 0.6 R² = 2.173E‐01
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3
0.2 0.2 (Mercury (Mercury 200 225 250 275 300 325 350 375 200 225 250 275 300 325 350 375 Length (mm) Length (mm) Figure A30: Longnose Sucker - Churchill River / Section 1, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation
2 0.22 y = 0.0002x + 0 0388 0.22 y = ‐1.684E‐06x + 9.145E‐04x ‐ 2 826E‐02 0.20 0.20 R² = 1.538E‐01 0.18 R² = 0.1312, p‐value = 4.92E‐02 0.18 0.16 0.16
Hg/kg) 0.14 Hg/kg) 0.14 0.12 0.12 (mg (mg 0.10 0.10 0.08 0.08 0.06 0.06 0.04 0.04 Mercury 0.02 Mercury 0.02 0.00 0.00 100 150 200 250 300 350 100 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4 y = 0.0011x ‐ 1.3861 y = ‐1.165E‐05x2 + 6.064E‐03x ‐ 1.850E+00 ‐0.6 R² = 0.1241, p‐value = 4.92E‐02 ‐0.6 R² = 1.572E‐01
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 ‐1.0 ‐1.0 (mg (mg
‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 ‐1.8 ‐1.8 Log10(Mercury 100 150 200 250 300 350 Log10(Mercury 100 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.50 0.50
0.40 0.40
0.30 0.30 Hg/kg))^0.5 Hg/kg))^0.5 0.20 0.20 (mg (mg
0.10 0.10
y = 0.0003x + 0.2024 2 0.00 0.00 y = ‐3.326E‐06x + 1.759E‐03x + 6 984E‐02 (Mercury R² = 0.1274, p‐value = 4 92E‐02 (Mercury R² = 1.560E‐01 100 150 200 250 300 350 100 150 200 250 300 350 Length (mm) Length (mm) Figure A31: Longnose Sucker - Churchill River / Section 1, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.10 y = 2E‐05x + 0.0229 0.10 R² = 0.0018, p‐value = 8.57E‐01 0.08 0.08 y = ‐1.770E‐06x2 + 4.130E‐04x + 1.662E‐03 R² = 6.115E‐03 Hg/kg) 0.06 Hg/kg) 0.06 (mg (mg 0.04 0.04
0.02 0.02 Mercury Mercury 0.00 0.00 80 90 100 110 120 130 140 150 80 90 100 110 120 130 140 150 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.75 ‐0.75 ‐1.00 ‐1.00
Hg/kg)) ‐1.25 Hg/kg)) ‐1.25 (mg (mg ‐1.50 ‐1.50 ‐1.75 ‐1.75
‐2.00 y = 0.0005x ‐ 1.6723 ‐2.00 y = ‐5.598E‐06x2 + 1.726E‐03x ‐ 1.739E+00 R² = 0.0031, p‐value = 8.57E‐01 R² = 3.231E‐03 ‐2.25 ‐2.25 Log10(Mercury 80 90 100 110 120 130 140 150 Log10(Mercury 80 90 100 110 120 130 140 150 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.35 0.35 0.30 0.30 0.25 0.25 0.20 0.20 Hg/kg))^0.5 Hg/kg))^0.5 0.15 0.15 (mg (mg 0.10 0.10 0.05 y = 7E‐05x + 0.1486 0.05 y = ‐3.408E‐06x2 + 8 306E‐04x + 1.077E‐01 R² = 0.0024, p‐value = 8.57E‐01 R² = 4 002E‐03 0.00 0.00 (Mercury (Mercury 80 90 100 110 120 130 140 150 80 90 100 110 120 130 140 150 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A32: Longnose Sucker - Estuary / Sandy Point, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.16 0.16 y = 0.0003x ‐ 0.0063 y = 2.470E‐06x2 ‐ 7.025E‐04x + 9 210E‐02 0.14 R² = 0.3355, p‐value = 2.37E‐02 0.14 R² = 3.873E‐01 0.12 0.12
Hg/kg) 0.10 Hg/kg) 0.10
(mg 0.08 (mg 0.08
0.06 0.06 0.04 0.04
Mercury 0.02 Mercury 0.02 0.00 0.00 100 150 200 250 300 350 100 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.6 ‐0.6 y = 0 0022x ‐ 1.7323 y = 2.855E‐05x2 ‐ 9.781E‐03x ‐ 5.954E‐01 ‐0.8 R² = 0.2342, p‐value = 2.37E‐02 ‐0.8 R² = 3.475E‐01
Hg/kg)) ‐1.0 Hg/kg)) ‐1.0 (mg (mg ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 ‐1.8 ‐1.8 Log10(Mercury 100 150 200 250 300 350 Log10(Mercury 100 150 200 250 300 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.40 0.40 0.35 0.35 0.30 0.30 0.25 0.25
Hg/kg))^0.5 0.20 Hg/kg))^0.5 0.20
(mg 0.15 (mg 0.15
0.10 0.10 0.05 y = 0.0006x + 0.1077 0.05 y = 6.294E‐06x2 ‐ 1 999E‐03x + 3.584E‐01 0.00 R² = 0.2926, p‐value = 2 37E‐02 0.00 R² = 3.727E‐01 (Mercury (Mercury 100 150 200 250 300 350 100 150 200 250 300 350 Length (mm) Length (mm) Figure A33: Longnose Sucker - Gull Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.6 0.6 y = 0.0015x ‐ 0.2574 y = 1.282E‐05x2 ‐ 6.471E‐03x + 9.493E‐01 0.5 R² = 0.3918, p‐value = 2.21E‐02 0.5 R² = 4.686E‐01
Hg/kg) 0.4 Hg/kg) 0.4
(mg 0.3 (mg 0.3
0.2 0.2 0.1 0.1 Mercury Mercury 0.0 0.0 200 225 250 275 300 325 350 375 400 425 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0 0027x ‐ 1 568 y = 5.689E‐06x2 ‐ 8.053E‐04x ‐ 1 033E+00 ‐0.2 R² = 0.4463, p‐value = 2.21E‐02 ‐0.2 R² = 4.516E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 (mg (mg ‐0.6 ‐0.6 ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 Log10(Mercury 200 225 250 275 300 325 350 375 400 425 Log10(Mercury 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.8 0.8 2 0.7 y = 0.0015x ‐ 0.0158 0.7 y = 8.151E‐06x ‐ 3 578E‐03x + 7.512E‐01 R² = 0.4232, p‐value = 2.21E‐02 R² = 4 578E‐01 0.6 0.6 0.5 0.5
Hg/kg))^0.5 0.4 Hg/kg))^0.5 0.4
(mg 0.3 (mg 0.3
0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 200 225 250 275 300 325 350 375 400 425 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm) Figure A34: Longnose Sucker - Gull Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = 0.0008x ‐ 0.0552 y = 3.317E‐06x2 ‐ 1.042E‐03x + 1.773E‐01 0.30 R² = 0.6372, p‐value = 2.29E‐12 0.30 R² = 6.772E‐01 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 150 175 200 225 250 275 300 325 350 375 400 425 150 175 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0021x ‐ 1.4154 y = 1 314E‐06x2 + 1.366E‐03x ‐ 1 323E+00 ‐0.2 R² = 0.6132, p‐value = 2 29E‐12 ‐0.2 R² = 6.141E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 150 175 200 225 250 275 300 325 350 375 400 425 Log10(Mercury 150 175 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0 001x + 0.1292 y = 2.279E‐06x2 ‐ 3.071E‐04x + 2.889E‐01 0.6 R² = 0.6441, p‐value = 2 29E‐12 0.6 R² = 6.576E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 150 175 200 225 250 275 300 325 350 375 400 425 150 175 200 225 250 275 300 325 350 375 400 425 Length (mm) Length (mm) Figure A35: Longnose Sucker - Gull Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.6 0.6 y = 0.0008x ‐ 0.0535 y = 3.773E‐06x2 ‐ 1 298E‐03x + 1.849E‐01 0.5 R² = 0.4905, p‐value = 1.64E‐05 0.5 R² = 5.682E‐01
Hg/kg) 0.4 Hg/kg) 0.4
(mg 0.3 (mg 0.3
0.2 0.2 0.1 0.1 Mercury Mercury 0.0 0.0 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 2 ‐0.2 y = 0 002x ‐ 1.4501 ‐0.2 y = 3.464E‐06x + 9.575E‐05x ‐ 1.231E+00 R² = 0.6122, p‐value = 1.64E‐05 R² = 6.247E‐01 ‐0.4 ‐0.4 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 Log10(Mercury 100 150 200 250 300 350 400 450 500 Log10(Mercury 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.8 0.8 2 0.7 y = 0.0009x + 0.1275 0.7 y = 3.014E‐06x ‐ 7 534E‐04x + 3.179E‐01 R² = 0.5718, p‐value = 1.64E‐05 R² = 6.148E‐01 0.6 0.6 0.5 0.5
Hg/kg))^0.5 0.4 Hg/kg))^0.5 0.4
(mg 0.3 (mg 0.3
0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm) Figure A36: Longnose Sucker - Gull Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.40 0.40 y = 0.0003x + 0.0671 y = 2.327E‐06x2 ‐ 1 061E‐03x + 2.490E‐01 0.35 R² = 0.1081, p‐value = 7.60E‐02 0.35 R² = 1.652E‐01 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0008x ‐ 1.0891 y = 6.096E‐06x2 ‐ 2.810E‐03x ‐ 6.125E‐01 ‐0.2 R² = 0 0779, p‐value = 7.60E‐02 ‐0.2 R² = 1 222E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 100 150 200 250 300 350 400 450 500 Log10(Mercury 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0 0003x + 0 2793 y = 2 810E‐06x2 ‐ 1.297E‐03x + 4 990E‐01 0.6 R² = 0 0924, p‐value = 7.60E‐02 0.6 R² = 1.453E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm) Figure A37: Longnose Sucker - Mud Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.40 y = 0.0002x + 0.0526 0.40 y = 5.004E‐06x2 ‐ 2 268E‐03x + 3.535E‐01 0.35 R² = 0.0252, p‐value = 5.72E‐01 0.35 R² = 4.779E‐02 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 175 200 225 250 275 300 325 175 200 225 250 275 300 325 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.2 y = 0.0009x ‐ 1.2328 ‐0.2 y = 3.490E‐05x2 ‐ 1.654E‐02x + 8.664E‐01 ‐0.4 R² = 0.0356, p‐value = 5.72E‐01 ‐0.4 R² = 1.423E‐01
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 175 200 225 250 275 300 325 Log10(Mercury 175 200 225 250 275 300 325 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed
0.7 y = 0.0003x + 0 2368 0.7 y = 1.067E‐05x2 ‐ 4.991E‐03x + 8.787E‐01 R² = 0.0313, p‐value = 5.72E‐01 R² = 9.102E‐02 0.6 0.6
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3
0.2 0.2 (Mercury (Mercury 175 200 225 250 275 300 325 175 200 225 250 275 300 325 Length (mm) Length (mm) Figure A38: Longnose Sucker - Winokapau Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation 1.8 1.8 y = ‐0 001x + 1 0782 y = ‐1.992E‐05x2 + 1.454E‐02x ‐ 1.832E+00 R² = 0.0719, p‐value = 1.85E‐01 R² = 2.511E‐01 1.3 1.3 Hg/kg) Hg/kg)
(mg 0.8 (mg 0.8
0.3 0.3 Mercury Mercury
‐0.2 200 250 300 350 400 450 500 550 ‐0.2 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.2 0.2
0.0 0.0 Hg/kg)) Hg/kg)) ‐0.2 ‐0.2 (mg (mg
‐0.4 ‐0.4
‐0.6 y = ‐0 0007x + 0.0413 ‐0.6 y = ‐1.258E‐05x2 + 9.183E‐03x ‐ 1.797E+00 R² = 0 0649, p‐value = 1 85E‐01 R² = 2.260E‐01 ‐0.8 ‐0.8 Log10(Mercury 200 250 300 350 400 450 500 550 Log10(Mercury 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 Hg/kg))^0.5 Hg/kg))^0.5 0.6 0.6 (mg (mg 0.4 0.4 0.2 y = ‐0.0006x + 1.042 0.2 y = ‐1.188E‐05x2 + 8.674E‐03x ‐ 6.937E‐01 R² = 0.0693, p‐value = 1.85E‐01 R² = 2.427E‐01 0.0 0.0 (Mercury (Mercury 200 250 300 350 400 450 500 550 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A39: Longnose Sucker - Winokapau Lake, 1987
Linear ‐ No Transformation Polynomial ‐ No Transformation 1.4 1.4 y = 0 0031x ‐ 0.4396 y = 4.470E‐06x2 + 8.367E‐04x ‐ 1.756E‐01 1.2 R² = 0.5621, p‐value = 2.85E‐06 1.2 R² = 5.650E‐01 1.0 1.0 Hg/kg) Hg/kg) 0.8 0.8 (mg (mg 0.6 0.6 0.4 0.4
Mercury 0.2 Mercury 0.2 0.0 0.0 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.50 0.50 2 0.25 y = 0.005x ‐ 1 9186 0.25 y = ‐9.425E‐06x + 9.737E‐03x ‐ 2.475E+00 R² = 0 5851, p‐value = 2 85E‐06 R² = 5.903E‐01 0.00 0.00 Hg/kg)) Hg/kg)) ‐0.25 ‐0.25 (mg (mg ‐0.50 ‐0.50 ‐0.75 ‐0.75 ‐1.00 ‐1.00 ‐1.25 ‐1.25 ‐1.50 ‐1.50 Log10(Mercury 100 150 200 250 300 350 400 450 500 Log10(Mercury 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1.4 1.4 y = 0 0028x ‐ 0.1809 y = ‐9.910E‐07x2 + 3.311E‐03x ‐ 2.394E‐01 1.2 R² = 0.5881, p‐value = 2.85E‐06 1.2 R² = 5.883E‐01 1.0 1.0 0.8 0.8 Hg/kg))^0.5 Hg/kg))^0.5 0.6 0.6 (mg (mg 0.4 0.4 0.2 0.2 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A40: Longnose Sucker - Winokapau Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 1.2 1.2 y = 0 0018x ‐ 0 2754 y = 7.935E‐06x2 ‐ 3 304E‐03x + 4.157E‐01 1.0 R² = 0.6993, p‐value = 1.93E‐07 1.0 R² = 9.198E‐01
Hg/kg) 0.8 Hg/kg) 0.8
(mg 0.6 (mg 0.6
0.4 0.4 0.2 0.2 Mercury Mercury 0.0 0.0 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 ‐0.2 ‐0.2
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 y = 0.0024x ‐ 1.4776 ‐1.2 y = 9.531E‐06x2 ‐ 3.665E‐03x ‐ 6.475E‐01 R² = 0.7269, p‐value = 1 93E‐07 R² = 9.021E‐01 ‐1.4 ‐1.4 Log10(Mercury 0 100 200 300 400 500 600 700 800 Log10(Mercury 0 100 200 300 400 500 600 700 800 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1.4 1.4 y = 0.0015x + 0.0061 y = 6.387E‐06x2 ‐ 2 572E‐03x + 5.624E‐01 1.2 R² = 0.7272, p‐value = 1.93E‐07 1.2 R² = 9.306E‐01 1.0 1.0 0.8 0.8 Hg/kg))^0.5 Hg/kg))^0.5 0.6 0.6 (mg (mg 0.4 0.4 0.2 0.2 0.0 0.0 (Mercury (Mercury 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A41: Longnose Sucker - Winokapau Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 y = 0 0004x + 0.0391 y = 1.446E‐06x2 ‐ 4.755E‐04x + 1 539E‐01 0.30 R² = 0.4482, p‐value = 6 34E‐03 0.30 R² = 5.074E‐01 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.001x ‐ 1.1435 y = 8.749E‐07x2 + 4.969E‐04x ‐ 1.074E+00 ‐0.2 R² = 0.4319, p‐value = 6.34E‐03 ‐0.2 R² = 4.349E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 100 150 200 250 300 350 400 450 500 Log10(Mercury 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.6 0.6 y = 0.0005x + 0.2469 y = 1.117E‐06x2 ‐ 1 988E‐04x + 3.356E‐01 0.5 R² = 0.4454, p‐value = 6.34E‐03 0.5 R² = 4.695E‐01 0.4 0.4
Hg/kg))^0.5 0.3 Hg/kg))^0.5 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 Length (mm) Length (mm) Figure A42: Longnose Sucker - Winokapau Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.5 0.5 y = 0.0004x + 0.0304 y = 2.406E‐06x2 ‐ 1 011E‐03x + 1.967E‐01 0.4 R² = 0.3017, p‐value = 2.03E‐03 0.4 R² = 4.697E‐01
Hg/kg) 0.3 Hg/kg) 0.3 (mg (mg 0.2 0.2
0.1 0.1 Mercury Mercury 0.0 0.0 100 150 200 250 300 350 400 450 500 550 100 150 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0011x ‐ 1.2397 y = 3.428E‐06x2 ‐ 8.699E‐04x ‐ 1.003E+00 R² = 0.3241, p‐value = 2 03E‐03 R² = 3.662E‐01 ‐0.5 ‐0.5 Hg/kg)) Hg/kg)) (mg (mg ‐1.0 ‐1.0
‐1.5 ‐1.5
‐2.0 ‐2.0 Log10(Mercury 100 150 200 250 300 350 400 450 500 550 Log10(Mercury 100 150 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0.0005x + 0.2233 y = 2.196E‐06x2 ‐ 7 952E‐04x + 3.751E‐01 0.6 R² = 0.3201, p‐value = 2.03E‐03 0.6 R² = 4.147E‐01 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 0.1 0.0 0.0 (Mercury (Mercury 100 150 200 250 300 350 400 450 500 550 100 150 200 250 300 350 400 450 500 550 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A43: Longnose Sucker - Winokapau Lake, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.5 y = 0.0008x + 0.0081 0.5 y = ‐1.122E‐05x2 + 6.302E‐03x ‐ 6.203E‐01 R² = 0.1507, p‐value = 2.68E‐01 R² = 1.993E‐01 0.4 0.4
Hg/kg) 0.3 Hg/kg) 0.3 (mg (mg 0.2 0.2
0.1 0.1 Mercury Mercury 0.0 0.0 160 180 200 220 240 260 280 300 320 160 180 200 220 240 260 280 300 320 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
0.0 0.0 2 y = 0.0015x ‐ 1.0954 y = ‐2.637E‐05x + 1.428E‐02x ‐ 2.573E+00 ‐0.2 R² = 0.1095, p‐value = 2.68E‐01 ‐0.2 R² = 1.754E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 (mg (mg ‐0.6 ‐0.6 ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 Log10(Mercury 160 180 200 220 240 260 280 300 320 Log10(Mercury 160 180 200 220 240 260 280 300 320 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 Hg/kg))^0.5 Hg/kg))^0.5 0.3 0.3 (mg (mg 0.2 0.2 0.1 y = 0.0008x + 0.2418 0.1 y = ‐1.298E‐05x2 + 7.144E‐03x ‐ 4.852E‐01 R² = 0.1296, p‐value = 2.68E‐01 0.0 0.0 R² = 1.878E‐01 (Mercury (Mercury 160 180 200 220 240 260 280 300 320 160 180 200 220 240 260 280 300 320 Length (mm) Length (mm) Figure A44: Northern Pike - Churchill River / Section 1, 1978
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.70 0.70 y = 0.0006x + 0.0449 y = 5.535E‐07x2 ‐ 1.132E‐04x + 2.711E‐01 0.60 R² = 0.6952, p‐value = 1.71E‐05 0.60 R² = 7.066E‐01
Hg/kg) 0.50 Hg/kg) 0.50 (mg (mg 0.40 0.40
0.30 0.30 Mercury Mercury 0.20 0.20 400 450 500 550 600 650 700 750 800 850 900 950 400 450 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0 0006x ‐ 0.7761 y = 3.305E‐07x2 + 1.919E‐04x ‐ 6.411E‐01 ‐0.1 R² = 0.5798, p‐value = 1.71E‐05 ‐0.1 R² = 5.831E‐01 ‐0.2 ‐0.2 Hg/kg)) Hg/kg)) ‐0.3 ‐0.3 (mg (mg ‐0.4 ‐0.4 ‐0.5 ‐0.5 ‐0.6 ‐0.6 ‐0.7 ‐0.7 ‐0.8 ‐0.8 Log10(Mercury 400 450 500 550 600 650 700 750 800 850 900 950 Log10(Mercury 400 450 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.90 0.90 y = 3.351E‐07x2 + 2.690E‐05x + 4 935E‐01 y = 0.0005x + 0.3566 R² = 6.482E‐01 0.80 R² = 0.6415, p‐value = 1.71E‐05 0.80 0.70 0.70 Hg/kg))^0.5 Hg/kg))^0.5 0.60 0.60 (mg (mg
0.50 0.50
0.40 0.40 (Mercury (Mercury 400 450 500 550 600 650 700 750 800 850 900 950 400 450 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm) Figure A45: Northern Pike - Churchill River / Section 1, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.10 0.10 y = 0 0003x ‐ 0 0143 y = 5 943E‐06x2 ‐ 1.957E‐03x + 1.646E‐01 0.08 R² = 0.6175, p‐value = 4.14E‐03 0.08 R² = 8.850E‐01
Hg/kg) 0.06 Hg/kg) 0.06 (mg (mg 0.04 0.04
0.02 0.02 Mercury Mercury 0.00 0.00 100 125 150 175 200 225 250 275 100 125 150 175 200 225 250 275 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 y = 0.0033x ‐ 2.1869 y = 8.465E‐05x2 ‐ 2.832E‐02x + 3.603E‐01 ‐0.5 R² = 0.5258, p‐value = 4.14E‐03 ‐0.5 R² = 8.188E‐01 Hg/kg)) Hg/kg)) ‐1.0 ‐1.0 (mg (mg
‐1.5 ‐1.5
‐2.0 ‐2.0
‐2.5 ‐2.5 Log10(Mercury 100 125 150 175 200 225 250 275 Log10(Mercury 100 125 150 175 200 225 250 275 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.30 0.30 y = 0.0007x + 0.0472 y = 1.626E‐05x2 ‐ 5.387E‐03x + 5.364E‐01 R² = 0.5879, p‐value = 4.14E‐03 0.25 0.25 R² = 8.677E‐01 0.20 0.20
Hg/kg))^0.5 0.15 Hg/kg))^0.5 0.15 (mg (mg 0.10 0.10 0.05 0.05 0.00 0.00 (Mercury (Mercury 100 125 150 175 200 225 250 275 100 125 150 175 200 225 250 275 Length (mm) Length (mm) Figure A46: Northern Pike - Gull Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation
3.5 y = 0.0018x ‐ 0.2312 3.5 y = 7.770E‐06x2 ‐ 8.636E‐03x + 3.208E+00 3.0 R² = 0.4262, p‐value = 1.34E‐03 3.0 R² = 4.628E‐01 2.5 2.5 Hg/kg) Hg/kg) 2.0 2.0 (mg (mg 1.5 1.5 1.0 1.0
Mercury 0.5 Mercury 0.5 0.0 0.0 500 550 600 650 700 750 800 850 900 950 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.6 y = 0 0008x ‐ 0 5489 y = 3.476E‐06x2 ‐ 3.881E‐03x + 9 893E‐01 0.5 R² = 0.4165, p‐value = 1.34E‐03 0.5 R² = 4.540E‐01 0.4
Hg/kg)) 0.3 Hg/kg)) 0.3 0.2 (mg (mg 0.1 0.1 0.0 ‐0.1 ‐0.1 ‐0.2 ‐0.3 ‐0.3 Log10(Mercury 500 550 600 650 700 750 800 850 900 950 Log10(Mercury 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 2.00 2.00 y = 0.0009x + 0 3788 y = 3.919E‐06x2 ‐ 4 364E‐03x + 2.113E+00 1.75 R² = 0.4241, p‐value = 1.34E‐03 1.75 R² = 4.613E‐01 1.50 1.50
Hg/kg))^0.5 1.25 Hg/kg))^0.5 1.25 (mg (mg 1.00 1.00 0.75 0.75 0.50 0.50 (Mercury (Mercury 500 550 600 650 700 750 800 850 900 950 500 550 600 650 700 750 800 850 900 950 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A47: Northern Pike - Winokapau Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation
3 3 2 y = 0 0029x ‐ 1.1006 y = ‐1.744E‐06x + 5.309E‐03x ‐ 1.920E+00 2.5 R² = 0.4484, p‐value = 8.80E‐03 2.5 R² = 4.524E‐01
Hg/kg) 2 Hg/kg) 2
(mg 1.5 (mg 1.5
1 1 0.5 0.5 Mercury Mercury 0 0 400 500 600 700 800 900 1000 400 500 600 700 800 900 1000 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.6 0.6 2 y = 0.0017x ‐ 1.3188 y = ‐1 322E‐06x + 3 531E‐03x ‐ 1 940E+00 0.4 R² = 0.7162, p‐value = 8.80E‐03 0.4 R² = 7 269E‐01
Hg/kg)) 0.2 Hg/kg)) 0.2 0 0 (mg (mg
‐0.2 ‐0.2 ‐0.4 ‐0.4 ‐0.6 ‐0.6 ‐0.8 ‐0.8 Log10(Mercury 400 500 600 700 800 900 1000 Log10(Mercury 400 500 600 700 800 900 1000 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 2 2 2 y = 0 0016x ‐ 0 2347 y = ‐1.003E‐06x + 3.026E‐03x ‐ 7.063E‐01 1.5 R² = 0.6061, p‐value = 8.80E‐03 1.5 R² = 6.117E‐01
Hg/kg))^0.5 1 Hg/kg))^0.5 1 (mg (mg
0.5 0.5
0 0 (Mercury (Mercury 400 500 600 700 800 900 1000 400 500 600 700 800 900 1000 Length (mm) Length (mm) Figure A48: Ouananiche - Winokapau Lake, 1987
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.50 0.50 0.45 0.40 0.40 2 y = 0.0012x ‐ 0 3502 0.35 y = 9.790E‐06x ‐ 6.354E‐03x + 1.078E+00 Hg/kg) Hg/kg) 0.30 R² = 0.6706, p‐value = 5 81E‐05 0.30 R² = 9 072E‐01
(mg (mg 0.25 0.20 0.20 0.15 0.10 0.10 Mercury Mercury 0.05 0.00 0.00 250 300 350 400 450 500 550 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 ‐0.2 ‐0.2 y = 1.093E‐05x2 ‐ 4.404E‐03x ‐ 1.005E+00 ‐0.4 ‐0.4 R² = 8 338E‐01 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 y = 0.004x ‐ 2.5988 ‐1.6 R² = 0.8022, p‐value = 5.81E‐05 ‐1.6 Log10(Mercury 250 300 350 400 450 500 550 Log10(Mercury 250 300 350 400 450 500 550 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5
Hg/kg))^0.5 0.4 Hg/kg))^0.5 0.4
(mg 0.3 (mg 0.3
0.2 0.2 0.1 y = 0 0016x ‐ 0 2951 0.1 y = 8.859E‐06x2 ‐ 5.252E‐03x + 9.971E‐01 0 R² = 0.7594, p‐value = 5.81E‐05 0 R² = 8.859E‐01 (Mercury (Mercury 250 300 350 400 450 500 550 250 300 350 400 450 500 550 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A49: Ouananiche - Winokapau Lake, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.5 0.5 y = 0 0013x ‐ 0 3166 y = 1.013E‐05x2 ‐ 6 227E‐03x + 1.043E+00 0.4 R² = 0.6548, p‐value = 2.72E‐05 0.4 R² = 7.489E‐01
Hg/kg) 0.3 Hg/kg) 0.3 (mg (mg 0.2 0.2
0.1 0.1 Mercury Mercury 0.0 0.0 250 300 350 400 450 500 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.4 y = 0 0032x ‐ 2 0357 ‐0.4 y = 1.703E‐05x2 ‐ 9.492E‐03x + 2.499E‐01 R² = 0.6953, p‐value = 2.72E‐05 R² = 7.432E‐01
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8
‐1.0 ‐1.0
‐1.2 ‐1.2 Log10(Mercury 250 300 350 400 450 500 Log10(Mercury 250 300 350 400 450 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0.0015x ‐ 0.1685 y = 9.920E‐06x2 ‐ 5.873E‐03x + 1.163E+00 0.6 R² = 0.6915, p‐value = 2.72E‐05 0.6 R² = 7.633E‐01
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3
0.2 0.2 (Mercury (Mercury 250 300 350 400 450 500 250 300 350 400 450 500 Length (mm) Length (mm) Figure A50: Rainbow Smelt - Estuary / Sandy Point, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 0.30 0.30 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 y = 0.0022x ‐ 0 2472 Mercury 0.05 y = ‐1 260E‐05x2 + 7.129E‐03x ‐ 7 315E‐01 0.00 R² = 0.4226, p‐value = 1 81E‐04 0.00 R² = 4.276E‐01 125 150 175 200 225 250 275 125 150 175 200 225 250 275 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐0.2 ‐0.2 2 y = 0.0057x ‐ 1.8949 y = ‐3.512E‐05x + 1.952E‐02x ‐ 3.244E+00 R² = 4.666E‐01 ‐0.4 R² = 0.4605, p‐value = 1 81E‐04 ‐0.4 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8
‐1.0 ‐1.0
‐1.2 ‐1.2 Log10(Mercury 125 150 175 200 225 250 275 Log10(Mercury 125 150 175 200 225 250 275 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.60 0.60 0.55 0.55 0.50 0.50 0.45 0.45
Hg/kg))^0.5 0.40 Hg/kg))^0.5 0.40
(mg 0.35 (mg 0.35
0.30 0.30 0.25 y = 0 0026x ‐ 0.1031 0.25 y = ‐1.567E‐05x2 + 8.810E‐03x ‐ 7.053E‐01 0.20 R² = 0.4458, p‐value = 1 81E‐04 0.20 R² = 4.512E‐01 (Mercury (Mercury 125 150 175 200 225 250 275 125 150 175 200 225 250 275 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A51: Round Whitefish - Churchill River / Section 1, 2010
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.07 0.07 y = 0.0001x + 0.0105 y = ‐2 977E‐06x2 + 9.475E‐04x ‐ 4.155E‐02 0.06 R² = 0.0955, p‐value = 1.73E‐01 0.06 R² = 1.072E‐01 0.05 0.05 Hg/kg) Hg/kg) 0.04 0.04 (mg (mg 0.03 0.03 0.02 0.02
Mercury 0.01 Mercury 0.01 0.00 0.00 100 110 120 130 140 150 160 170 180 100 110 120 130 140 150 160 170 180 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
‐1.1 ‐1.1 2 y = 0 0019x ‐ 1.7948 y = ‐2 392E‐05x + 8 291E‐03x ‐ 2 213E+00 R² = 8 816E‐02 ‐1.3 R² = 0 084, p‐value = 1.73E‐01 ‐1.3 Hg/kg)) Hg/kg)) ‐1.5 ‐1.5 (mg (mg
‐1.7 ‐1.7
‐1.9 ‐1.9
‐2.1 ‐2.1 Log10(Mercury 100 110 120 130 140 150 160 170 180 Log10(Mercury 100 110 120 130 140 150 160 170 180 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.30 0.30 y = 0 0004x + 0.1188 y = ‐6.504E‐06x2 + 2.140E‐03x + 5.039E‐03 0.25 R² = 0 0899, p‐value = 1.73E‐01 0.25 R² = 9.729E‐02 0.20 0.20
Hg/kg))^0.5 0.15 Hg/kg))^0.5 0.15 (mg (mg 0.10 0.10 0.05 0.05 0.00 0.00 (Mercury (Mercury 100 110 120 130 140 150 160 170 180 100 110 120 130 140 150 160 170 180 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A52: Round Whitefish - Winokapau Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.35 0.35 0.30 0.30 0.25 0.25 Hg/kg) Hg/kg) 0.20 0.20 (mg (mg 0.15 0.15 0.10 0.10
Mercury 0.05 y = 0 0015x ‐ 0.2326 Mercury 0.05 y = 3.781E‐06x2 ‐ 6.680E‐04x + 7.574E‐02 0.00 R² = 0.2289, p‐value = 1.16E‐02 0.00 R² = 2.298E‐01 225 250 275 300 325 350 225 250 275 300 325 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4
‐0.6 ‐0.6 Hg/kg)) Hg/kg)) ‐0.8 ‐0.8 (mg (mg
‐1.0 ‐1.0
‐1.2 ‐1.2 y = ‐9.941E‐06x2 + 9.166E‐03x ‐ 2.548E+00 y = 0.0035x ‐ 1.7372 R² = 2 055E‐01 ‐1.4 R² = 0 2045, p‐value = 1.16E‐02 ‐1.4 Log10(Mercury 225 250 275 300 325 350 Log10(Mercury 225 250 275 300 325 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.60 0.60
0.50 0.50
Hg/kg))^0.5 0.40 Hg/kg))^0.5 0.40 (mg (mg
0.30 0.30 y = 0 0017x ‐ 0 0529 y = ‐1.996E‐07x2 + 1.816E‐03x ‐ 6.920E‐02 0.20 R² = 0.218, p‐value = 1.16E‐02 0.20 R² = 2.180E‐01 (Mercury (Mercury 225 250 275 300 325 350 225 250 275 300 325 350 Length (mm) Length (mm) Figure A53: Tom Cod - Estuary / Sandy Point, 1999
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.40 0.40 y = 0 0011x ‐ 0.0806 y = ‐2.108E‐06x2 + 2.012E‐03x ‐ 1.733E‐01 0.35 R² = 0.368, p‐value = 4.85E‐04 0.35 R² = 3.694E‐01 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 125 150 175 200 225 250 275 300 125 150 175 200 225 250 275 300 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.2 ‐0.2 y = 0.0034x ‐ 1.5844 y = ‐1.200E‐05x2 + 8.546E‐03x ‐ 2.112E+00 ‐0.4 R² = 0.374, p‐value = 4 85E‐04 ‐0.4 R² = 3.787E‐01
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 125 150 175 200 225 250 275 300 Log10(Mercury 125 150 175 200 225 250 275 300 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.70 0.70 2 y = 0 0014x + 0.0766 y = ‐3.909E‐06x + 3.115E‐03x ‐ 9.525E‐02 0.60 R² = 0 3798, p‐value = 4 85E‐04 0.60 R² = 3.827E‐01 0.50 0.50 Hg/kg))^0.5 Hg/kg))^0.5 0.40 0.40 (mg (mg
0.30 0.30
0.20 0.20 (Mercury (Mercury 125 150 175 200 225 250 275 300 125 150 175 200 225 250 275 300 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A54: White Sucker - Estuary / Sandy Point, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.30 y = 0 0007x ‐ 0 0816 0.30 y = 1.392E‐04x2 ‐ 8 989E‐02x + 1.464E+01 0.25 R² = 0.0234, p‐value = 4 56E‐01 0.25 R² = 1.412E‐01
Hg/kg) 0.20 Hg/kg) 0.20
(mg 0.15 (mg 0.15
0.10 0.10 0.05 0.05 Mercury Mercury 0.00 0.00 300 310 320 330 340 350 300 310 320 330 340 350 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.4 ‐0.4 ‐0.6 ‐0.6
Hg/kg)) ‐0.8 Hg/kg)) ‐0.8 (mg (mg ‐1.0 ‐1.0 ‐1.2 ‐1.2
‐1.4 ‐1.4 2 y = 0.0018x ‐ 1.4511 y = 4.480E‐04x ‐ 2 896E‐01x + 4.592E+01 ‐1.6 R² = 0 0161, p‐value = 4.56E‐01 ‐1.6 R² = 1.401E‐01 Log10(Mercury 300 310 320 330 340 350 Log10(Mercury 300 310 320 330 340 350 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.60 0.60
0.50 0.50
0.40 0.40 Hg/kg))^0.5 Hg/kg))^0.5 0.30 0.30 (mg (mg
0.20 0.20
y = 0 0009x + 0.0971 2 0.10 0.10 y = 1 878E‐04x ‐ 1.213E‐01x + 1 995E+01
(Mercury R² = 0 0204, p‐value = 4 56E‐01 (Mercury R² = 1.437E‐01 300 310 320 330 340 350 300 310 320 330 340 350 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A55: White Sucker - Gull Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.7 0.7 y = 0 0014x ‐ 0 2247 y = 5.679E‐06x2 ‐ 2 060E‐03x + 2.578E‐01 0.6 R² = 0.6124, p‐value = 3.79E‐06 0.6 R² = 7 256E‐01 0.5 0.5 Hg/kg) Hg/kg) 0.4 0.4 (mg (mg 0.3 0.3 0.2 0.2
Mercury 0.1 Mercury 0.1 0.0 0.0 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0 2 ‐0.2 y = 0 0028x ‐ 1.6425 ‐0.2 y = 4.164E‐06x + 2 258E‐04x ‐ 1 289E+00 R² = 0.7493, p‐value = 3.79E‐06 R² = 7.686E‐01 ‐0.4 ‐0.4 Hg/kg)) Hg/kg)) ‐0.6 ‐0.6 (mg (mg ‐0.8 ‐0.8 ‐1.0 ‐1.0 ‐1.2 ‐1.2 ‐1.4 ‐1.4 ‐1.6 ‐1.6 Log10(Mercury 100 200 300 400 500 Log10(Mercury 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1 1
y = 0 0014x ‐ 0 0119 2 0.8 0.8 y = 4 232E‐06x ‐ 1.155E‐03x + 3.476E‐01 R² = 0.6985, p‐value = 3.79E‐06 R² = 7.682E‐01 0.6 0.6 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.2 0.2
0 0 (Mercury (Mercury 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A56: White Sucker - Winokapau Lake, 1977
Linear ‐ No Transformation Polynomial ‐ No Transformation 1 1
0.8 0.8 y = ‐1 509E‐06x2 + 1.731E‐03x ‐ 1 580E‐01 y = 0 0007x ‐ 0 0035
Hg/kg) Hg/kg) R² = 6.719E‐01 0.6 R² = 0.6586, p‐value = 7 57E‐04 0.6 (mg (mg 0.4 0.4
0.2 0.2 Mercury Mercury 0 0 200 300 400 500 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0.0 0.0
‐0.2 ‐0.2 Hg/kg)) Hg/kg)) ‐0.4 ‐0.4 (mg (mg
‐0.6 ‐0.6
‐0.8 ‐0.8 y = ‐5.718E‐06x2 + 5.205E‐03x ‐ 1.700E+00 y = 0 0014x ‐ 1.1146 R² = 7.170E‐01 ‐1.0 R² = 0.6646, p‐value = 7.57E‐04 ‐1.0 Log10(Mercury 200 300 400 500 Log10(Mercury 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 1 1 0.9 0.9 2 0.8 0.8 y = ‐2.378E‐06x + 2.339E‐03x ‐ 1.423E‐02 R² = 7 003E‐01 0.7 y = 0.0008x + 0 2293 0.7 Hg/kg))^0.5 R² = 0.6692, p‐value = 7.57E‐04 Hg/kg))^0.5 0.6 0.6 (mg (mg 0.5 0.5 0.4 0.4 0.3 0.3 (Mercury (Mercury 200 300 400 500 200 300 400 500 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A57: White Sucker - Winokapau Lake, 1987
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.25 0.25 y = 0.0005x ‐ 0.0548 y = ‐2.023E‐06x2 + 1.801E‐03x ‐ 2.506E‐01 R² = 4.448E‐01 0.2 R² = 0.4217, p‐value = 1.20E‐02 0.2
Hg/kg) 0.15 Hg/kg) 0.15 (mg (mg 0.1 0.1
0.05 0.05 Mercury Mercury 0 0 200 250 300 350 400 450 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.6 ‐0.6 ‐0.7 ‐0.7 ‐0.8 ‐0.8 Hg/kg)) Hg/kg)) ‐0.9 ‐0.9 (mg (mg ‐1 ‐1 ‐1.1 ‐1.1 ‐1.2 ‐1.2 y = ‐1.636E‐05x2 + 1.274E‐02x ‐ 3.339E+00 y = 0.0024x ‐ 1.7559 ‐1.3 ‐1.3 R² = 6.207E‐01 ‐1.4 R² = 0.5293, p‐value = 1.20E‐02 ‐1.4 Log10(Mercury 200 250 300 350 400 450 Log10(Mercury 200 250 300 350 400 450 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.5 0.5 y = 0.0008x + 0.0618 y = ‐4.616E‐06x2 + 3.751E‐03x ‐ 3.848E‐01 0.45 R² = 0.4763, p‐value = 1 20E‐02 0.45 R² = 5.303E‐01 0.4 0.4
Hg/kg))^0.5 0.35 Hg/kg))^0.5 0.35 (mg (mg 0.3 0.3 0.25 0.25 0.2 0.2 (Mercury (Mercury 200 250 300 350 400 450 200 250 300 350 400 450 Length (mm) Length (mm) Figure A58: White Sucker - Winokapau Lake, 1992
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.45 0.45 y = 0 0009x ‐ 0.1438 y = 2.349E‐06x2 ‐ 7.402E‐04x + 1.325E‐01 R² = 0.593, p‐value = 1.01E‐07 R² = 6.155E‐01 0.35 0.35
Hg/kg) 0.25 Hg/kg) 0.25 (mg (mg
0.15 0.15
0.05 0.05 Mercury Mercury
‐0.05 200 300 400 500 ‐0.05 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed
2 ‐0.4 y = 0 0023x ‐ 1.6041 ‐0.4 y = 1.107E‐06x + 1 503E‐03x ‐ 1.474E+00 R² = 0.6511, p‐value = 1.01E‐07 R² = 6 519E‐01
Hg/kg)) ‐0.6 Hg/kg)) ‐0.6 (mg (mg ‐0.8 ‐0.8
‐1 ‐1
‐1.2 ‐1.2 Log10(Mercury 200 300 400 500 Log10(Mercury 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.7 0.7 y = 0.0011x + 0.0309 y = 1.660E‐06x2 ‐ 9.334E‐05x + 2.263E‐01 R² = 0.6336, p‐value = 1.01E‐07 R² = 6.421E‐01 0.6 0.6
0.5 0.5 Hg/kg))^0.5 Hg/kg))^0.5 0.4 0.4 (mg (mg
0.3 0.3
0.2 0.2 (Mercury (Mercury 200 300 400 500 200 300 400 500 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis. Figure A59: White Sucker - Winokapau Lake, 1996
Linear ‐ No Transformation Polynomial ‐ No Transformation 0.40 0.40 y = 0.0005x ‐ 0.005 0.35 0.35 y = 4.110E‐06x2 ‐ 1 912E‐03x + 3.003E‐01 R² = 0 3975, p‐value = 5.86E‐06 R² = 6 934E‐01 0.30 0.30
Hg/kg) 0.25 Hg/kg) 0.25
(mg 0.20 (mg 0.20
0.15 0.15 0.10 0.10
Mercury 0.05 Mercury 0.05 0.00 0.00 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed ‐0.3 ‐0.3 y = 0 0012x ‐ 1 2449 y = 9.880E‐06x2 ‐ 4 594E‐03x ‐ 5.112E‐01 ‐0.5 R² = 0.4, p‐value = 5 86E‐06 ‐0.5 R² = 6.969E‐01 Hg/kg)) Hg/kg)) ‐0.7 ‐0.7 (mg (mg
‐0.9 ‐0.9
‐1.1 ‐1.1
‐1.3 ‐1.3 Log10(Mercury 100 200 300 400 500 Log10(Mercury 100 200 300 400 500 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.70 0.70 y = 0.0006x + 0.1973 y = 4.746E‐06x2 ‐ 2.208E‐03x + 5.498E‐01 0.60 R² = 0.407, p‐value = 5.86E‐06 0.60 R² = 7.097E‐01 0.50 0.50 Hg/kg))^0.5 Hg/kg))^0.5 0.40 0.40 (mg (mg
0.30 0.30
0.20 0.20 (Mercury (Mercury 100 200 300 400 500 100 200 300 400 500 Length (mm) Length (mm) Figure A60: White Sucker - Winokapau Lake, 2004
Linear ‐ No Transformation Polynomial ‐ No Transformation
0.8 0.8 2 y = 0 001x ‐ 0.1467 y = 4.467E‐06x ‐ 2.206E‐03x + 4 051E‐01 0.7 R² = 0.3252, p‐value = 9.91E‐05 0.7 R² = 3.631E‐01 0.6 0.6
Hg/kg) 0.5 Hg/kg) 0.5
(mg 0.4 (mg 0.4
0.3 0.3 0.2 0.2
Mercury 0.1 Mercury 0.1 0 0 200 300 400 500 600 200 300 400 500 600 Length (mm) Length (mm)
Linear ‐ Log10 Transformed Polynomial ‐ Log10 Transformed 0 0 y = 0.002x ‐ 1.4306 y = 9.868E‐06x2 ‐ 5.114E‐03x ‐ 2.116E‐01 ‐0.2 R² = 0.3733, p‐value = 9.91E‐05 ‐0.2 R² = 4.281E‐01
Hg/kg)) ‐0.4 Hg/kg)) ‐0.4 ‐0.6 ‐0.6 (mg (mg
‐0.8 ‐0.8 ‐1 ‐1 ‐1.2 ‐1.2 ‐1.4 ‐1.4 Log10(Mercury 200 300 400 500 600 Log10(Mercury 200 300 400 500 600 Length (mm) Length (mm)
Linear ‐ Square Root Transformed Polynomial ‐ Square Root Transformed 0.9 0.9 y = 0.001x + 0.0725 y = 4 910E‐06x2 ‐ 2.479E‐03x + 6.790E‐01 0.8 R² = 0.3573, p‐value = 9.91E‐05 0.8 R² = 4.034E‐01 0.7 0.7 0.6 0.6 Hg/kg))^0.5 Hg/kg))^0.5 0.5 0.5 (mg (mg 0.4 0.4 0.3 0.3 0.2 0.2 (Mercury (Mercury 200 300 400 500 600 200 300 400 500 600 Length (mm) Length (mm)
Note: Squares indicate data identified as outliers and thus not included in the regression analysis.
Statistical Analysis of Mercury Concentrations in Fish From the Lower Churchill River and Estuary
APPENDIX B
Raw Data
Table B1: Analytical Results of Mercury Concentrations in Fish Tissue Samples
Brook Trout - Brook Trout - Brook Trout - Lake Chub - Brook Trout - Gull Brook Trout - Gull Brook Trout - Gull Brook Trout - Gull Brook Trout - Mud Burbot - Gull Lake, Estuary / Sandy Winokapau Lake, Winokapau Lake, Churchill River / Lake, 1992 Lake, 1996 Lake, 1999 Lake, 2004 Lake, 1977 2010 Point, 1999 1987 2010 Section 1, 2010 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 169 0.06 115 0 05 160 0 08 143 0 03 42 0.092 340 0.16 124 0.05 180 0.02 421 0.1 85 0.07 183 0.04 120 0 05 164 0 04 152 0 06 110 0.041 311 0.14 156 0.08 205 0.04 620 0.28 86 0.03 184 0.06 170 0.1 168 0 04 155 0 04 112 0.025 320 0 2 172 0.05 210 0.03 505 0.14 88 0.03 185 0.05 172 0 05 172 0 07 159 0 05 113 0.025 240 0.1 175 0.08 218 0.05 440 0.06 98 0.07 187 0.04 183 0 05 175 0 08 161 0 04 113 0.033 265 0.12 178 0.07 240 0.13 485 0.06 90 0.07 187 0.04 205 0 05 198 0 09 162 0 04 115 0.028 245 0.09 210 0 2 245 0.03 267 0.08 91 0.05 195 0.06 210 0 05 199 0 04 162 0 09 121 0.020 185 0 3 224 0.08 285 0.12 455 0.16 85 0.08 211 0.07 212 0 08 203 0 08 162 0 05 123 0.043 215 0.12 238 0.06 300 0.13 563 0.2 90 0.08 222 0.14 233 0 05 205 0 02 165 0 06 128 0.045 280 0.13 269 0.06 300 0.03 430 0.17 90 0.06 225 0.08 234 0 06 212 0 05 166 0 03 133 0.046 270 0.26 270 0.08 350 0.05 463 0.05 109 0.12 238 0.14 235 0 06 213 0 08 173 0 08 237 0.19 255 0.11 270 0.12 360 0.05 473 0.05 115 0.02 280 0.14 238 0 08 220 0 02 173 0 03 245 0.11 190 0.1 290 0.18 380 0.13 514 0.06 93 0.05 240 0.1 220 0.1 176 0 04 296 0.089 200 0.07 315 0.28 256 0.03 92 0.01 241 0 09 222 0 02 182 0 05 332 0.10 265 0.23 348 0.07 305 0.06 97 0.06 243 0 06 226 0 07 183 0 09 357 0.16 380 0.24 440 0.07 88 0.03 244 0 06 230 0 02 185 0 04 424 0.06 590 0.11 89 0.05 245 0.12 240 0 07 192 0.12 369 0.15 85 0.08 246 0 06 242 0 06 198 0 05 394 0.27 92 0.02 246 0 08 250 0 05 201 0 07 525 0.06 87 0.03 250 0 07 260 0 04 211 0.13 642 0.44 89 0.06 251 0 05 268 0 07 214 0 06 490 0.05 90 0.04 254 0 06 282 0 05 234 0 07 460 0.12 115 0.09 255 0 06 325 0 06 238 0 05 408 0.13 86 0.03 259 0 06 339 0 01 250 0 05 565 0.16 86 0.07 263 0 09 342 0 01 260 0.16 569 0.14 109 0.07 280 0.1 280 0.12 325 0.08 129 0.06 280 0 09 625 0.21 93 0.05 292 0 09 126 0.1 300 0 08 116 0.09 303 0 08 309 0 08 318 0.1 330 0.11 Table B1 (Cont.): Analytical Results of Mercury Concentrations in Fish Tissue Samples
Lake Trout - Lake Trout - Lake Whitefish - Lake Whitefish - Lake Whitefish - Lake Chub - Gull Lake Whitefish - Lake Whitefish - Lake Whitefish - Lake Whitefish - Winokapau Lake, Winokapau Lake, Churchill River / Churchill River / Estuary / Sandy Lake, 2010 Gull Lake, 1977 Gull Lake, 1992 Gull Lake, 1996 Gull Lake, 1999 1992 1999 Section 1, 1978 Section 1, 1999 Point, 1999 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 127 0.08 306 0 51 246 0 23 210 0 06 112 0.03 223 0.03 260 0.11 284 0.1 275 0.15 123 0.05 150 0.1 525 0 63 248 0 28 212 0.2 117 0.05 233 0.18 268 0.19 290 0.15 292 0.15 138 0.07 140 0.06 530 1 07 258 0 21 213 0 26 121 0.04 280 0.21 276 0.22 320 0.12 302 0.16 142 0.07 121 0.07 576 1.45 365 0.44 215 0.14 123 0.06 280 0.09 287 0.15 320 0.15 322 0.12 142 0.08 124 0.1 605 1 04 372 0.71 215 0.43 132 0.03 285 0.13 290 0.16 324 0.12 322 0.09 143 0.05 122 0.09 625 2.2 373 0 37 220 0.17 157 0.03 288 0.16 294 0.18 328 0.17 360 0.18 145 0.1 122 0.08 646 2.13 381 0.49 222 0.16 198 0.08 290 0.16 294 0.14 330 0.2 370 0.22 224 0.08 130 0.09 652 1 21 415 0 58 235 0.17 258 0.07 305 0.13 303 0.17 342 0.17 375 0.27 261 0.06 108 0.08 662 1.49 418 0.49 241 0.17 272 0.17 333 0.14 306 0.18 344 0.09 380 0.09 277 0.07 125 0.14 665 1 62 581 0 51 243 0 21 281 0.11 368 0.12 323 0.19 344 0.14 380 0.13 295 0.1 110 0.11 668 2 26 253 0.13 286 0.14 326 0.16 346 0.19 412 0.12 303 0.06 687 2.48 312 0 22 291 0.02 329 0.16 350 0.32 421 0.11 305 0.1 700 3 65 295 0.23 340 0.21 352 0.17 312 0.09 700 3.71 303 0.17 348 0.31 355 0.16 330 0.08 712 2.3 304 0.17 352 0.31 355 0.18 345 0.06 715 3 52 305 0.08 354 0 3 356 0.16 356 0.09 730 1 55 315 0.08 355 0.18 360 0.18 369 0.08 731 0 94 332 0.1 361 0.31 360 0.24 380 0.1 791 0 96 360 0.08 362 0.26 361 0.17 384 0.16 818 2 25 396 0.07 364 0.28 362 0.23 393 0.09 865 2 64 364 0.22 362 0.28 398 0.23 367 0.21 364 0.18 399 0.31 372 0 3 365 0.18 399 0.16 375 0.28 370 0.12 401 0.11 380 0.37 371 0.22 402 0.12 380 0.43 373 0.2 405 0.26 380 0.31 374 0.19 406 0.36 380 0.62 374 0.31 420 0.14 381 0.29 375 0.26 497 0.17 385 0.32 379 0.39 388 0.55 381 0.24 390 0.31 388 0.29 393 0.32 388 0.31 399 0.39 390 0.36 402 0.28 392 0.28 409 0.56 398 0.2 413 0 3 398 0.31 415 0.76 402 0.31 412 0.35 Table B1 (Cont.): Analytical Results of Mercury Concentrations in Fish Tissue Samples
Lake Whitefish - Lake Whitefish - Lake Whitefish - Lake Whitefish - Lake Whitefish - Lake Whitefish - Longnose Sucker - Longnose Sucker - Lake Whitefish - Lake Whitefish - Winokapau Lake, Winokapau Lake, Winokapau Lake, Winokapau Lake, Winokapau Lake, Winokapau Lake, Churchill River / Churchill River / Gull Lake, 2004 Gull Lake, 2010 1977 1987 1992 1996 1999 2004 Section 1, 1978 Section 1, 1999 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 233 0 061 263 0 07 288 0 39 100 0 22 292 0.23 75 0.1 133 0.07 104 0.16 221 0.11 109 0.03 284 0 048 316 0 06 295 0 38 107 0.2 335 0.18 115 0.07 143 0.08 129 0.097 235 0.08 115 0.1 304 0 054 328 0 04 302 0 33 109 0.19 347 0 2 115 0.1 143 0.11 275 0.11 235 0.08 117 0.05 312 0 088 336 0 08 321 0 52 110 0.15 370 0.18 118 0.05 189 0.12 282 0.082 244 0.11 118 0.06 313 0.100 358 0.1 322 0.47 112 0 26 370 0 3 132 0.05 194 0.17 287 0.11 254 0.09 120 0.02 347 0.22 360 0 09 334 1.18 115 0.15 378 0.28 138 0.12 197 0.14 295 0.11 256 0.13 122 0.07 359 0 083 368 0.16 335 1 53 116 0 23 384 0.33 210 0.06 202 0.16 305 0.12 278 0.23 125 0.06 365 0.12 370 0 08 344 1.1 118 0 21 386 0.21 260 0.22 232 0.1 320 0.12 330 0.04 131 0.1 368 0.13 370 0.1 350 2.19 128 0 08 422 0.26 268 0.09 238 0.12 327 0.14 336 0.11 137 0.05 375 0 074 373 0.13 350 1 143 0 07 440 0.61 269 0.15 248 0.09 347 0.15 346 0.21 170 0.07 375 0.27 406 0.13 350 1.1 185 0 04 450 0.44 280 0.18 255 0.08 351 0.14 353 0.34 172 0.04 376 0.31 354 2.4 190 0 04 460 1.09 289 0.26 282 0.15 367 0.20 180 0.12 384 0.15 355 1 37 190 0 08 480 1.13 320 0.14 302 0.13 370 0.13 184 0.03 385 0.20 355 1.3 192 0.11 342 0.16 312 0.11 377 0.18 209 0.11 386 0.27 357 1 82 217 0 06 358 0 2 320 0.12 389 0.17 219 0.07 396 0.17 358 1 55 220 0 09 395 0.34 325 0.12 444 0.23 240 0.16 415 0.28 362 0 69 230 0.16 415 0.28 333 0.13 254 0.13 415 0.15 366 0 63 245 0.11 435 0.85 375 0.16 258 0.13 430 0.20 368 0 89 248 0.11 386 0.13 274 0.05 370 2 04 270 0.16 393 0.23 283 0.12 373 0 85 270 0.15 396 0.15 288 0.07 375 1 06 290 0.14 402 0.26 291 0.05 375 0 84 325 0.12 405 0.34 297 0.13 375 0 93 328 0.11 406 0.17 300 0.06 376 1.12 328 0 26 418 0.23 302 0.12 389 1.12 330 0.16 482 0.45 302 0.04 399 0 91 339 0 08 307 0.12 346 0.11 310 0.04 360 0.18 321 0.06 380 0 32 326 0.18 380 0 68 410 0 54 Table B1 (Cont.): Analytical Results of Mercury Concentrations in Fish Tissue Samples
Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Longnose Sucker - Churchill River / Estuary / Sandy Winokapau Lake, Winokapau Lake, Winokapau Lake, Gull Lake, 1992 Gull Lake, 1996 Gull Lake, 1999 Gull Lake, 2010 Mud Lake, 1977 Section 1, 2010 Point, 1999 1977 1987 1992 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 82 0.03 228 0.12 221 0 08 163 0 06 115 0.04 115 0.17 260 0.06 228 0.47 150 0.09 97 0.09 84 0.03 199 0 02 241 0 21 163 0 06 120 0.08 167 0.05 186 0.06 267 0.3 159 0.05 110 0.12 88 0.02 205 0 02 284 0.15 170 0 06 123 0.1 174 0.05 310 0.16 267 0.44 162 0.1 150 0.15 90 0.02 168 0 07 292 0.16 170 0 08 123 0.05 187 0.18 320 0.15 310 0.8 162 0.09 160 0.09 94 0.02 301 0 08 336 0.18 170 0 09 123 0.09 187 0.1 280 0.08 319 0.31 163 0.05 206 0.13 94 0.02 302 0 09 340 0 21 172 0.13 130 0.05 207 0.19 320 0.12 321 0.96 175 0.07 210 0.09 95 0.02 283 0.11 340 0 29 173 0 08 145 0.05 215 0.17 270 0.05 323 0.89 190 0.08 218 0.17 95 0.04 163 0 04 346 0 29 173 0 07 173 0.06 217 0.17 260 0.31 324 0.69 200 0.11 245 0.08 96 0.03 268 0.1 354 0 36 174 0.1 175 0.12 220 0.15 190 0.12 327 1.1 205 0.09 257 0.07 99 0.01 242 0 02 373 0.13 175 0.11 181 0.08 225 0.25 250 0.07 330 1.18 210 0.04 265 0.16 99 0.02 274 0.12 374 0.18 175 0 08 188 0.13 231 0.12 275 0.08 330 0.92 216 0.5 281 0.13 100 0.02 220 0 05 394 0 58 177 0.1 200 0.07 241 0.17 245 0.07 330 0.51 222 0.49 284 0.12 104 0.02 120 0 04 402 0.41 178 0 08 214 0.12 248 0.18 190 0.16 330 0.99 260 0.1 292 0.12 105 0.03 126 0 03 180 0.11 223 0.14 264 0.12 245 0.08 335 1.33 260 0.46 298 0.12 109 0.03 119 0 06 180 0.13 244 0.15 270 0.14 190 0.08 338 0.6 270 0.42 304 0.13 110 0.04 180 0.12 265 0.13 278 0.1 352 1.04 280 0.42 305 0.1 123 0.02 180 0 09 287 0.05 285 0.05 354 0.87 288 0.58 311 0.1 128 0.03 183 0.11 299 0 2 290 0.07 356 0.62 290 0.47 329 0.21 128 0.03 183 0.14 311 0.09 300 0.08 356 1.05 300 0.16 380 0.19 143 0.02 185 0 09 337 0.12 310 0.06 432 0.29 310 0.52 460 0.59 185 0 08 354 0.11 320 0.08 456 0.48 315 0.07 465 0.52 185 0 09 365 0.19 330 0.16 467 0.59 330 0.83 485 0.81 190 0 06 380 0.15 331 0.19 475 0.48 330 0.72 495 0.57 190 0 08 393 0.42 359 0.17 492 0.29 330 0.68 500 0.93 190 0 07 412 0.13 374 0.1 496 0.4 330 0.5 503 0.72 190 0.15 414 0.23 385 0.38 540 0.65 340 0.76 190 0.1 418 0 55 400 0.25 340 1.03 195 0.12 421 0 28 412 0.16 350 0.32 202 0 08 440 0 2 430 0.33 360 0.65 210 0.1 455 0 52 456 0.14 222 0.17 222 0.1 222 0.17 222 0 07 225 0.12 228 0.16 230 0.17 230 0.19 250 0.1 252 0.11 265 0.12 278 0.11 301 0.1 322 0 28 343 0.18 372 0.19 382 0.2 390 0 23 390 0.19 413 0.42 415 0.4 Table B1 (Cont.): Analytical Results of Mercury Concentrations in Fish Tissue Samples
Longnose Sucker - Longnose Sucker - Longnose Sucker - Northern Pike - Northern Pike - Northern Pike - Ouananiche - Ouananiche - Rainbow Smelt - Northern Pike - Gull Winokapau Lake, Winokapau Lake, Winokapau Lake, Churchill River / Churchill River / Winokapau Lake, Winokapau Lake, Winokapau Lake, Estuary / Sandy Lake, 1977 1996 1999 2010 Section 1, 1978 Section 1, 2010 2004 1987 1999 Point, 1999 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 122 0.07 112 0 06 162 0.11 418 0 39 100 0.02 550 0.89 429 0.27 282 0.03 274 0.09 207 0.19 165 0.11 113 0 05 168 0.13 438 0.4 108 0.03 558 0.93 570 0.37 298 0.06 280 0.07 218 0.18 195 0.12 114 0 06 180 0.16 445 0 33 110 0.02 566 0.78 603 0.57 310 0.06 289 0.11 215 0.23 200 0.14 114 0 09 196 0 09 463 0.42 120 0.01 568 0.73 618 0.27 318 0.05 292 0.08 225 0.19 208 0.1 125 0.14 205 0.13 474 0 21 125 0.01 572 0.85 725 0.85 320 0.04 299 0.12 185 0.14 210 0.18 156 0.11 208 0 35 495 0 23 129 0.02 607 0.75 755 0.91 350 0.05 310 0.07 234 0.25 228 0.13 173 0 06 218 0 31 530 0 34 152 0.01 613 0 6 782 1 0 359 0.1 312 0.07 174 0.1 232 0.16 176 0.16 230 0.13 545 0.4 240 0.02 621 0.76 785 2.7 360 0.07 319 0.08 165 0.1 235 0.1 185 0 27 311 0.41 575 0 39 248 0.05 624 0.86 805 0.92 363 0.09 325 0.07 171 0.13 260 0.16 200 0 06 315 0.11 588 0 32 260 0.06 635 1.06 840 1 3 365 0.06 328 0.08 220 0.27 302 0.14 205 0.14 626 0 38 275 0.08 642 0.73 852 1 5 370 0.08 332 0.12 177 0.22 325 0.11 223 0.14 655 0.49 647 0.89 894 1 3 380 0.06 345 0.11 215 0.29 360 0.11 240 0 08 714 0 51 674 1.13 915 1 3 390 0.1 359 0.17 163 0.11 435 0.18 261 0 06 732 0 54 680 0 8 949 1.4 400 0.1 384 0.17 210 0.13 461 0.31 280 0 08 760 0 54 710 0.98 422 0.08 415 0.09 183 0.08 298 0 08 840 0 54 730 1.08 450 0.17 425 0.18 196 0.18 304 0.14 860 0 61 750 1.26 503 0.39 456 0.46 185 0.19 312 0 06 940 0 61 756 1 5 456 0.3 190 0.11 322 0.2 762 0.88 471 0.3 181 0.16 330 0.19 762 0.97 213 0.2 350 0.16 806 1.23 215 0.2 367 0.13 193 0.13 407 0.12 180 0.11 408 0.13 193 0.12 409 0.14 205 0.25 412 0.14 200 0.27 426 0.15 203 0.31 429 0 23 220 0.22 535 0.44 Table B1 (Cont.): Analytical Results of Mercury Concentrations in Fish Tissue Samples
Round Whitefish - Round Whitefish - White Sucker - White Sucker - White Sucker - White Sucker - White Sucker - White Sucker - Tom Cod - Estuary / White Sucker - Gull Churchill River / Winokapau Lake, Estuary / Sandy Winokapau Lake, Winokapau Lake, Winokapau Lake, Winokapau Lake, Winokapau Lake, Sandy Point, 1999 Lake, 2004 Section 1, 2010 1992 Point, 1977 1977 1987 1992 1996 2004 Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury Length Mercury (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) (mm) (mg Hg/kg) 107 0.02 242 0.12 280 0.16 310 0.1 122 0.089 202 0.16 220 0.05 214 0.07 83 0.08 230 0.18 149 0.02 252 0 07 261 0 36 315 0.18 124 0.037 223 0.17 240 0.05 234 0.1 114 0.14 231 0.15 152 0.06 254 0.1 176 0.17 330 0.14 225 0.14 223 0.11 323 0.14 255 0.14 115 0.13 237 0.17 110 0.02 254 0.16 173 0 06 320 0 08 237 0.11 228 0.15 325 0.14 260 0.1 131 0.16 262 0.11 112 0.03 255 0 09 195 0 08 330 0 06 242 0.13 233 0.14 340 0.13 269 0.09 166 0.15 292 0.13 104 0.02 255 0.13 178 0 09 310 0.12 242 0.085 282 0.24 345 0.13 276 0.08 172 0.1 316 0 091 162 0.03 258 0.19 170 0.14 320 0.12 243 0.056 322 0.21 348 0.16 280 0.13 220 0.06 325 0.15 117 0.03 264 0 22 269 0.18 330 0.1 247 0.12 334 0.29 378 0.08 300 0.12 220 0.12 326 0 067 125 0.02 266 0 23 230 0.13 330 0.19 256 0.13 336 0.27 379 0.19 300 0.08 228 0.1 330 0.12 173 0.03 268 0.11 238 0.11 320 0.14 257 0.12 408 0.28 380 0.13 305 0.18 256 0.11 335 0.17 115 0.03 270 0 33 255 0 21 334 0.16 257 0.14 425 0.31 380 0.13 309 0.11 262 0.08 337 0.15 103 0.02 270 0 21 250 0 23 325 0 08 257 0.15 458 0.21 394 0.1 310 0.16 288 0.08 343 0 093 110 0.02 272 0 22 142 0 06 345 0.19 259 0.10 469 0.42 397 0.11 315 0.09 292 0.06 345 0.16 110 0.04 272 0 24 217 0.2 310 0.18 277 0.098 422 0.21 320 0.09 294 0.12 347 0.35 115 0.03 277 0.19 208 0.18 330 0.17 317 0.15 336 0.15 305 0.12 350 0.12 120 0.03 286 0.19 219 0 27 320 0.15 334 0.20 342 0.21 310 0.09 353 0.22 110 0.04 292 0.1 190 0.16 310 0.19 356 0.19 346 0.2 312 0.13 361 0.14 118 0.02 295 0.3 172 0.14 330 0.12 357 0.20 351 0.14 320 0.13 380 0.12 105 0.02 296 0 09 193 0 07 345 0 22 363 0.16 352 0.15 320 0.13 393 0.47 105 0.03 300 0 21 192 0.16 320 0 27 387 0.13 362 0.13 320 0.08 395 0.18 100 0.03 300 0.14 158 0.16 335 0.16 419 0.43 380 0.14 325 0.16 397 0.17 308 0 24 180 0.14 310 0.14 430 0.46 380 0.2 330 0.12 400 0.16 309 0.13 172 0 05 335 0.2 434 0.67 385 0.17 350 0.09 412 0.22 310 0.16 165 0 09 320 0.16 448 0.64 386 0.25 360 0.13 412 0.24 318 0 33 195 0 06 330 0.11 472 0.35 402 0.13 385 0.16 413 0.26 320 0 32 259 0 24 330 0.13 408 0.28 392 0.13 414 0.46 338 0.3 249 0.15 410 0.21 395 0.11 415 0.23 245 0 23 410 0.23 410 0.18 419 0.25 251 0.15 411 0.27 411 0.15 419 0.20 423 0.24 412 0.12 420 0.28 432 0.16 413 0.17 429 0.24 446 0.43 420 0.14 432 0.30 467 0.33 425 0.27 434 0.34 488 0.25 430 0.36 441 0.47 435 0.2 442 0.24 442 0.23 445 0.24 445 0.39 448 0.51 450 0.26 459 0.46 450 0.28 462 0.38 453 0.33 472 0.27 460 0.38 494 0.19 462 0.29 470 0.3
INFORMATION RESPONSES LOWER CHURCHILL PROJECT CEAA REFERENCE NO.07‐05‐26178
JOINT REVIEW PANEL
Attachment C
Application of a Mechanistic Mercury Model to the Proposed Lower Churchill Reservoirs
IR# JRP.166
Application of a Mechanistic Mercury Model to the Proposed Lower Churchill Reservoirs:
Technical Memorandum in support of the Nalcor response to IR# JRP.166
Prepared for Nalcor
Prepared by.
Reed Harris and David Hutchinson Reed Harris Environmental Ltd.
Don Beals Beals and Associates
December 2010
TABLE OF CONTENTS
1 INTRODUCTION ...... 1
2 OBJECTIVES ...... 1
3 PROJECT DESCRIPTION ...... 2
4 MECHANISTIC MODEL OVERVIEW ...... 5
5 MODELING APPROACH ...... 7
6 RESMERC CALIBRATION TO THE PROPOSED LOWER CHURCHILL DEVELOPMENT ...... 8
7 MODEL SENSITIVITY TO FLOW AND EXTENT OF FLOODING ...... 12
8 SUMMARY ...... 15
9 REFERENCES ...... 16
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List of Figures Figure 1. Watersheds associated with Churchill River developments. Existing upper Churchill River development: Smallwood Reservoir. Proposed Lower Churchill River developments: Gull Island and Muskrat Falls. Figure from Newfoundland and Labrador Hydro (2006) ...... 2 Figure 2. Location of proposed Lower Churchill River hydroelectric developments. Figure from Newfoundland and Labrador Hydro 2006. Note that transmission corridor in image is not the current preferred option...... 3 Figure 3. Water elevations along the Lower Churchill River prior to and after the proposed development. Figure from Newfoundland and Labrador Hydro (2006), amended to indicate river reaches modeled with RESMERC model...... 3 Figure 4. Representation of Hg Cycling and Bioaccumulation in RESMERC ...... 5 Figure 5 Sediment zones in RESMERC...... 6 Figure 6 Predicted total Hg concentrations in surface waters in Gull Island and Muskrat Falls Reservoirs ...... 9 Figure 7. Predicted net MeHg production rates in Gull Island and Muskrat Falls Reservoirs...... 10 Figure 8 Predicted MeHg concentrations in surface waters in Gull Island and Muskrat Falls Reservoirs ...... 10 Figure 9 Predicted MeHg concentrations in zooplankton in Gull Island and Muskrat Falls Reservoirs ...... 11 Figure 10 Predicted MeHg concentrations in lake whitefish (age 4‐5 years) in Gull Island and Muskrat Falls Reservoirs ...... 11 Figure 11 Predicted MeHg concentrations in northern pike (age 9‐10 years) in Gull Island and Muskrat Falls Reservoirs ...... 12 Figure 12. Predicted peak MeHg concentrations in surface waters in Muskrat Falls Reservoir for different combinations of hydraulic residence time and extent of flooding...... 14 Figure 13. Predicted peak MeHg concentrations in age 4 northern pike in Muskrat Falls Reservoir for different combinations of hydraulic residence time and extent of flooding...... 14
List of Tables Table 1. Lower Churchill Hydroelectric Generation Project (*Labile carbon was defined as carbon in tree foliage, shrubs, herbs, mosses, lichens, and in the litter fungal/humic soil layer)...... 4 Table 2. Results of Muskrat Falls Reservoir simulations examining sensitivity to flow and extent of flooding ...... 13
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1 INTRODUCTION
This Technical Memorandum describes the application of a mechanistic model of mercury cycling and bioaccumulation in reservoirs to the proposed Lower Churchill Development in Labrador (Newfoundland and Labrador Hydro 2006). The document is intended as supporting information to Nalcor’s response to Information Request IR# JRP.166b. A regression model was previously applied to predict peak fish Hg levels for the Lower Churchill Development, as described elsewhere (Harris et al., 2009a; responses to IR# JRP.166 and 156). The regression model does not however predict methylmercury (MeHg) concentrations in water or plankton, which are important media in terms of downstream export of MeHg from reservoirs. A model known as RESMERC (Harris and Hutchinson, 2009; Harris et al., 2009b) uses a mass balance approach to predict time‐dependent concentrations for three forms of Hg (Hg) in up to 12 abiotic compartments and a seven level food web. RESMERC has been previously calibrated using results of experimental upland and wetland reservoirs in the Canadian Shield at the Experimental Lakes Area (ELA), Ontario (Harris et al., 2009b). While the model requires additional calibration to full scale reservoirs to enhance the ability to predict fish Hg levels, it is sufficiently developed to provide insights into the potential increases in methylmercury (MeHg) levels in the water column and zooplankton following the creation of Gull Island and Muskrat Falls Reservoirs, and to consider the potential effects of flow dilution. The model is being applied in this context in the current study.
2 OBJECTIVES
The primary objective of this study is to estimate concentrations of MeHg in the water column and zooplankton following the creation of Gull Island and Muskrat Falls Reservoirs, Labrador. While the model also predicts concentrations of Hg in other compartments (sediments, benthos and fish), an important aspect the application of this model emerged from the potential for downstream export of MeHg from the reservoirs, which occurs via the water column and plankton.
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3 PROJECT DESCRIPTION
The proposed hydroelectric development on the Lower Churchill River includes two generating stations: Gull Island and Muskrat Falls (Figure 1 through Figure 3). The construction period is scheduled to span approximately ten years. Construction activities will begin for Muskrat Falls Reservoir, followed three years later by the beginning of construction activities for Gull Island Reservoir. Flooding is expected to occur in year five in Muskrat Falls Reservoir and year 10 for Gull Island Reservoir.
Flooding will be associated with both generating stations: approximately 85 km2 for Gull Island and 41 km2 for Muskrat Falls ( Table 1), and fish Hg concentrations are expected to increase as a result.
Figure 1. Watersheds associated with Churchill River developments. Existing upper Churchill River development: Smallwood Reservoir. Proposed Lower Churchill River developments: Gull Island and Muskrat Falls. Figure from Newfoundland and Labrador Hydro (2006)
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Figure 2. Location of proposed Lower Churchill River hydroelectric developments. Figure from Newfoundland and Labrador Hydro 2006. Note that transmission corridor in image is not the current preferred option.
Figure 3. Water elevations along the Lower Churchill River prior to and after the proposed development. Figure from Newfoundland and Labrador Hydro (2006), amended to indicate river reaches modeled with RESMERC model.
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Table 1. Lower Churchill Hydroelectric Generation Project (*Labile carbon was defined as carbon in tree foliage, shrubs, herbs, mosses, lichens, and in the litter fungal/humic soil layer).
Gull Island Muskrat Falls Characteristic Data source Reservoir Reservoir Pre‐flood area (km2) 115 60 Minasquat (2008a) Flooded area (km2) 85 41 Minasquat (2008a) Total area post‐flood 200 101 Minasquat (2008a) (km2) Percent of reservoir 42 41 Minasquat (2008a) area that is flooded Percent of flood zone <1 6 Minasquat (2008b) that is wetland Reservoir length (km) 225 60 Hatch (2008) Estimated using Figure 3.8 from Hatch Maximum depth (m) 230 29 (2008) Water temperatures 0.5 ‐ 17 0.5 ‐ 17 Minasqaut (2007) (monthly means) (C) Calculated using volume data from Hydraulic residence Minasquat (2008a) and monthly mean flow 28 10 time (days) rates at Muskrat Falls gauging Station EC 03OE001 Expected to be Thermal stratification No Minasquat (2008a) intermittent Dissolved organic Current simulations use generic estimate for carbon in surface 6 6 boreal reservoirs. waters (mg L‐1) Current simulations use generic estimate for Surface water pH 6.5 6.5 boreal reservoirs. Suspended solids Current simulations use generic estimate for 1‐2 1‐2 (mg L‐1) boreal reservoirs. Surface water sulfate Current simulations use generic estimate for 40 40 (eq L‐1) boreal reservoirs. Based on fish caught during field studies Predatory fish Northern Pike Northern Pike
Total carbon pool Current simulations use general estimate for (upland, upper 11 cm) 2.72 x 104 2.72 x 104 boreal soils. Kg C ha‐1 Labile Carbon pool* Current simulations use general estimate for (upland, upper 11 cm) 2.48 x 103 2.48 x 103 boreal soils. Kg C ha‐1 Inflowing THg Estimated from limited observations from concentration in river 1.2 NA Jacques Whitford 2001 and Harris et al. (ng L‐1 unfiltered) (2009a) Inflowing MeHg Estimated from limited observations from concentration in river 1.2 NA Jacques Whitford 2001 and Harris et al. (ng L‐1 unfiltered) (2009a)
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4 MECHANISTIC MODEL OVERVIEW
RESMERC is a process‐based simulation model for reservoirs and lakes (Harris et al., 2009b). Model compartments include the water column, sediments, and a simplified food web that consists of several trophic levels (phytoplankton, zooplankton, benthos and up to four fish species) (Figure 4). Fish Hg concentrations tend to increase with age, and are therefore followed in each year class (up to 20 cohorts). The model predicts concentrations, mercury pools and major fluxes for each mercury form through time.
An overview of the major processes involved in the Hg cycle in reservoirs is shown in Figure 4. RESMERC Hg processes include atmospheric deposition, inflows and outflows (surface and groundwater), adsorption/desorption, particulate settling, particle decomposition at the sediment/water interface and within sediments, resuspension, burial, air/water gaseous exchange, industrial point sources, in‐situ transformations (e.g. methylation, demethylation, MeHg photodegradation, Hg(II) reduction and oxidation), Hg uptake kinetics in plankton and partitioning in benthos, and MeHg bioaccumulation in fish. A detailed description of model processes and equations in RESMERC is provided in the model user’s guide (Harris and Hutchinson 2009).
Figure 4. Representation of Hg Cycling and Bioaccumulation in RESMERC
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MeHg concentrations in fish are predicted using a bioenergetics approach described by Harris and Bodaly (1998). Hg fluxes are expanded from individual fish to entire fish populations by computing the fluxes for individual fish and then multiplying by the number of fish in each age class.
While many factors affect fish Hg concentrations in natural lakes, one process takes on special importance in reservoirs: decomposition. Flooding stimulates decomposition and more activity by microbes that convert inorganic Hg into MeHg. Special attention is devoted to these processes in RESMERC. Sediments are divided into a maximum of 5 zones in the model, based on terrain type and elevations set by the user (Figure 5). These zones can include littoral and profundal zones in the original lake, flooded uplands and flooded wetlands. Each sediment zone has two vertical sediments layers with thicknesses defined by the user (e.g. a surface layer of 1 cm and an underlying layer of 3 cm). Sediments below the 2nd layer are treated as a boundary condition. Each sediment layer has its own initial conditions, characteristics and inputs.
Additional information on the RESMERC model is available in the model user guide (Harris and Hutchinson 2009) and a report describing the model development (Harris et al., 2009b)
FloodedF e WetlandWetl nd – FloodedFl ed UplandU land shallowsh ow waters ater – shallows al watersw rs
Flooded Wetland FloodedF e UplandU l d – deep waters – deep waters
Original sediments
Figure 5 Sediment zones in RESMERC.
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5 MODELING APPROACH
Simulations were first carried out to calibrate the model to reflect estimates of inorganic Hg and MeHg concentrations in the Lower Churchill River prior to flooding, followed by simulations of the effects of reservoir creation. RESMERC is designed to simulate one reservoir at a time, while the potential exists for Hg in Muskrat Falls Reservoir to be influenced by upstream flooding in Gull Island Reservoir. To accommodate this possibility, simulations were first carried out for Gull Island Reservoir. Predicted MeHg concentrations in the outflow from Gull Island Reservoir were then used as inputs to the Muskrat Falls simulation, lagged by four years. Given some potential flexibility in the project schedule, a four year lag was assumed between the filling of the two reservoirs, as a conservative measure. Concentrations in Muskrat Falls would be expected to peak at slightly higher levels as the reservoirs are flooded at times closer to one another. Gull Island reservoir will include two primary basins. The first basin will consist of what is now Winokapau Lake, while the 2nd basin will exist from the existing outflow from Winokapau Lake to the Gull Island GS (Figure 3). As the RESMERC model does not simulate multiple basins in a reservoir, simulations were carried out assuming that Gull Island Reservoir consisted of two reaches (Figure 3). Overall, a set of 3 river reaches was simulated in series, first the upstream reach of Gull Island Reservoir, then the lower reach of Gull Island Reservoir, and finally Muskrat Falls Reservoir. For each river reach modeled, the calibration exercise was carried out by first simulating pre‐flood conditions, then the effects of flooding. For pre‐flood conditions the following sequence of steps was taken: i. Calibration of inorganic Hg concentrations in water (unfiltered) and sediments (on solids) to agree with observations, where available. ii. Calibration of MeHg concentrations in water (unfiltered) and sediments (on solids) to agree with observations, where available. iii. Adjusting model parameters, if necessary, so that MeHg concentrations in the lower food web agree with observations (where available). iv. Calibrating model predictions of fish Hg concentrations as necessary. Diet and, in rare instances, species‐specific bioenergetic parameters can be modified to improve agreement between the model and observed fish Hg concentrations.
For the simulations representing flooded conditions, it was initially assumed that the effects of the filling period were negligible in terms of affecting peak fish mercury concentrations (expected years later), and the reservoirs were assumed to be at full capacity when flooding occurred. Simulations were carried out for a period of 50 years, long enough for predicted fish mercury levels to reach peak values and then decline to background levels.
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6 RESMERC CALIBRATION TO THE PROPOSED LOWER CHURCHILL DEVELOPMENT
Input values for selected site conditions are shown in Table 1. For these simulations, generic estimates of carbon pools (readily degradable and refractory) in the flood zone were based on boreal shield data from the Experimental Lakes Area, Ontario (Harris et al., 2009b). A key aspect of simulating the effects of flooding is the duration of elevated decomposition rates after reservoir creation, an important influence on the production of MeHg in the reservoirs. A comparison was made between rates of accelerated decomposition associated with these simulations and rates of production of greenhouse gases estimated for the Lower Churchill Development (see Figures 2 and 3 of response to IR# JRP.148). In both cases, surges in decomposition/greenhouse gas production declined exponentially from initial peaks following flooding, towards stable long term values after approximately two decades. Predicted concentrations of total mercury (THg) in surface waters of the three modeled reaches are shown in Figure 6. Initial values at t=0 in the graphs represent the calibrated concentrations after 50 years of simulations for pre‐flood conditions, by which time concentrations year to year had stabilized. While some leaching of Hg from flooded soils occurs in the simulations, this process is predicted to have only a modest effect on surface water THg concentrations, due to dilution associated with rapid water throughput in the reservoirs. The small predicted increases in THg in surface waters would be difficult to identify given the natural variability of THg concentrations in the water column. Predicted MeHg production rates in sediments for the three modeled reaches are shown in Figure 7. Methylation rates in flooded soils are predicted to increase significantly above background levels in sediments after flooding, primarily due to a surge in microbial decomposition rates and the activity of microbes that methylation mercury. Decomposition and methylation rates then decline to background over a ~20 year period in the simulations. Predicted concentrations of MeHg in epilimnetic waters in the three modeled reaches are shown in Figure 8. MeHg concentrations in water are predicted to increase from 0.05 ng L‐ 1 by slightly less than a factor of two, to ~0.08 ng L‐1 in the two reservoirs, slightly higher in Muskrat Falls Reservoir. As will be shown below for sensitivity simulations, the predicted increase of MeHg concentrations in surface waters is significantly mitigated by dilution associated with rapid water throughput. The difference between background and peak predicted MeHg concentrations in the water column is modest in the context of natural variability of MeHg concentrations in surface waters (R. Harris, unpublished data from METAALICUS project at the Experimental Lakes Area, Ontario). Predicted concentrations of MeHg in zooplankton in the three modeled reaches are shown and Figure 9, mimicking the trends predicted for MeHg in the water column. This is because MeHg concentrations in phytoplankton and zooplankton in the simulations are effectively governed by water column MeHg concentrations in the simulations. The
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predicted background and peak MeHg levels in zooplankton are within the range reported for lakes (Kainz et al., 2008; Back et al., 2003, Garcia and Carignan 1999; Back and Watras 1995). Predicted concentrations of MeHg in lake whitefish (age 4‐5 years) and northern pike (age 9‐10 years) for the three modeled reaches are shown in Figure 10 and Figure 11. Peak fish mercury levels were predicted to be approximately 60‐90% above pre‐flood levels in the reservoirs, comparable but slightly less than increases predicted using a regression model (e.g. 2.3X for adult pike, see responses to IR# JRP.156 and IR# JRP.166). The RESMERC model also predicts the response of Hg concentrations through time. Fish MeHg levels are predicted to peak and return to background levels within approximately 3 decades. While this is consistent with observations from other reservoirs (Bodaly et al., 2007; Schetagne et al., 2003), these predictions should be treated as approximate given the current state of development of the model.
Figure 6 Predicted total Hg concentrations in surface waters in Gull Island and Muskrat Falls Reservoirs
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Figure 7. Predicted net MeHg production rates in Gull Island and Muskrat Falls Reservoirs.
Figure 8 Predicted MeHg concentrations in surface waters in Gull Island and Muskrat Falls Reservoirs
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Figure 9 Predicted MeHg concentrations in zooplankton in Gull Island and Muskrat Falls Reservoirs
Figure 10 Predicted MeHg concentrations in lake whitefish (age 4‐5 years) in Gull Island and Muskrat Falls Reservoirs
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Figure 11 Predicted MeHg concentrations in northern pike (age 9‐10 years) in Gull Island and Muskrat Falls Reservoirs
7 MODEL SENSITIVITY TO FLOW AND EXTENT OF FLOODING
Two factors expected to significantly affect the increase in fish mercury concentrations in reservoirs are the extent of flooding (flooded area/total area) and the extent of dilution associated with the hydraulic residence time of the reservoirs. Scenarios were simulated to test the influence of these two factors on predicted MeHg levels in Muskrat Falls Reservoir for the current calibrations of RESMERC for the Lower Churchill Development. Approximately 40% of the proposed reservoirs will be flooded terrain ( Table 1), and the water throughput of the reservoirs is rapid, with mean annual hydraulic residence times of 28 and 10 days for Gull Island and Muskrat Falls Reservoirs respectively. Two sensitivity simulations were carried out with all conditions kept the same as in the base calibration except for the hydraulic residence time, which was adjusted to 60 days (Scenario A in Table 2) and 365 days (Scenario B). Additional sensitivity simulations were also carried out for Muskrat Falls Reservoir with the same inputs as used for the base calibration, except that the extent of flooding was 10% in one scenario (Scenario C), and 90% in another (Scenario D). The extent of flooding in Gull Island Reservoir was not altered for this scenario. The bathymetry for these scenarios was hypothetical, constructed such that little change occurred to the overall reservoir volume and hydraulic residence
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time (to isolate the effects of flooding extent from the effects of residence time). A final scenario was simulated with both reduced flow and a greater extent of flooding than the base case for Muskrat Falls (Scenario E), and was expected to result in the highest MeHg concentrations for the group of sensitivity runs. Results are shown in Table 2, Figure 12 and Figure 13. These simulations suggest that flow and the extent of flooding are both important factors contributing to low to moderate predicted increases in MeHg in Muskrat Falls Reservoir waters and fish, and by extension the downstream export of MeHg in water and plankton. When the hydraulic residence time is short, as is the case for the proposed Lower Churchill Development (10 days in Muskrat Falls Reservoir), dilution mitigates predicted increases in MeHg concentrations in water and fish, for all scenarios tested, regardless of the extent of flooding. When hydraulic residence times are longer (e.g. 1 year), the extent of flooding is an important influence (Figure 12, Figure 13). The scenario with low flow and extensive flooding produced significantly higher peak MeHg levels than other scenarios. Table 2. Results of Muskrat Falls Reservoir simulations examining sensitivity to flow and extent of flooding
Scenario Flow Flooded Hyd. Residence Predicted Peak MeHg Concentration Area Time Water Sediments Lake Whitefish Northern Pike (age 4) (age 4) (km3/yr) % of (days) (ng/L) (ug/g) (ug/g wet) (ug/g wet) Total Base Case 57.4 40 10 0.06 9 0.10 0.42 A 10.3 40 60 0.11 9 0.11 0.52 B 1.7 40 365 0.29 10 0.18 1.02 C 57.4 10 10 0.06 9 0.08 0.37 D 57.4 90 10 0.17 9 0.15 0.73 E 1.7 90 365 1.52 17 0.61 4.04
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Figure 12. Predicted peak MeHg concentrations in surface waters in Muskrat Falls Reservoir for different combinations of hydraulic residence time and extent of flooding.
Figure 13. Predicted peak MeHg concentrations in age 4 northern pike in Muskrat Falls Reservoir for different combinations of hydraulic residence time and extent of flooding.
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8 SUMMARY
A mechanistic model of mercury cycling and bioaccumulation in reservoirs has been applied to the proposed Lower Churchill Development in Labrador. A regression model previously applied to predict peak fish Hg levels for the Lower Churchill Development, does not predict methylmercury (MeHg) concentrations in water or plankton, which are important media in terms of downstream export of MeHg from reservoirs. While the mechanistic model requires additional calibration to full scale reservoirs to enhance the ability to predict fish Hg levels, it is sufficiently developed to provide insights into the potential increases in methylmercury (MeHg) levels in the water column and zooplankton following the creation of Gull Island and Muskrat Falls Reservoirs, and to consider the potential effects of flow dilution. The following findings are presented in this context: MeHg concentrations in the water column of Gull Island and Muskrat Falls Reservoirs are predicted to increase approximately 60‐90% from an estimated background concentration of 0.05 ng L‐1 to roughly 0.1 ng L‐1, with Muskrat Falls concentrations being slightly greater than Gull Island. The predicted increases are significantly moderated by the rapid throughput of water, based on initial sensitivity simulations. These increases in MeHg concentrations in water are relatively low, remaining in the natural ranges observed for lakes and rivers. Zooplankton MeHg concentrations are predicted to mimic the relative increases estimated for MeHg in the water column in the reservoirs, rising perhaps by a factor of two in the reservoir a few years after flooding, declining thereafter towards background levels. Limited existing data suggest low baseline MeHg concentrations in zooplankton in the Lower Churchill River (<70 ng g‐1 dry), such that peak levels would likely remain within the range observed for natural waters. Concentrations of MeHg predicted for water and plankton in Muskrat Falls Reservoir have the potential to persist downstream until dilution occurs at the inflow to Goose Bay. This Technical Memorandum was prepared to support an Information Request (IR# JRP.166) from the Joint Review Panel for the proposed Lower Churchill Hydroelectric Generation Project. While the emphasis of this modeling exercise was on MeHg concentrations in water and plankton, which are subsequently transported downstream, the model also predicts MeHg concentrations in fish. Mercury concentrations in lake whitefish and northern pike are predicted to increase by roughly a factor of two in Gull Island and Muskrat Falls reservoirs, slightly less but comparable to predictions made with a regression model (see responses to IR# JRP.156 and IR # JRP.166). Overall the preliminary application of the RESMERC model suggests that the increases in MeHg levels in the water column and fish in Gull Island Reservoir, Muskrat Falls Reservoir, and downstream will be low to moderate in the context of observations from other
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reservoir systems. The low to moderate increases are predicted to be associated at least partly with short hydraulic residence times. These findings reflect the current state of development of the RESMERC model. Development of the model continues to evolve with the addition of new data from existing reservoirs. It is not expected that ongoing model development activities will substantively alter the prediction that dilution has a moderating effect on peak MeHg levels expected in fish, water and plankton in the reservoirs and downstream as expressed in this technical memorandum.
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