FINAL

Baseline Ecological Risk Assessment WORK PLAN

Bonita Peak Mining District and Durango Reach

San Juan County, CO La Plata County, CO

October 2016

Prepared for: United States Environmental Protection Agency, Region 8 Ecosystem Protection and Remediation-Program Support 1595 Wynkoop St. Denver, Colorado 80202

Prepared by:

Region 8 Environmental Services Assistance Team (ESAT) TechLaw, Inc.

16194 W. 45th Drive Golden, Colorado

Contract No. EP-W-13-028 DCN: EP8 – 4 - 1103

Table of Contents

SECTION 1: GENERAL INTRODUCTION ........................................................................................... 1 

1.1 Site description and history ................................................................................................................. 3 1.2 Exposure units for the future BERA and BERA Addendum ............................................................ 45 1.2.1 BPMD exposure units and reference locations ................................................................................ 5 1.2.2 Durango reach exposure units and reference location ..................................................................... 6 1.3 Work plan organization ....................................................................................................................... 6 

SECTION 2: SUMMARY OF THE 2016 BERA ..................................................................................... 8 

2.1 Introduction ......................................................................................................................................... 8 2.2 2016 BERA conclusions ..................................................................................................................... 9 2.2.1 Risk conclusions for benthic macroinvertebrates ............................................................................. 9 2.2.2 Risk conclusions for fish ................................................................................................................ 10 2.2.3 Risk conclusions for wildlife receptors .......................................................................................... 11 

SECTION 3: CONCEPTUAL SITE MODEL ....................................................................................... 12 

3.1 Contaminant fate and transport ......................................................................................................... 12 3.2 Sources of contamination .................................................................................................................. 12 3.3 Release and transport mechanisms .................................................................................................... 12 3.4 Contact point and exposure media .................................................................................................... 13 3.5 Routes of entry .................................................................................................................................. 13 3.6 Key receptor groups .......................................................................................................................... 13 3.7 Exposure pathways ........................................................................................................................... 13 3.8 Conceptual site model ....................................................................................................................... 14 

SECTION 4: ASSESSMENT AND MEASUREMENT ENDPOINTS ................................................ 15 

4.1 Introduction ....................................................................................................................................... 15 4.2 Selecting representative assessment endpoint species or communities ............................................ 15 4.3 Endpoint selection ............................................................................................................................. 17 

SECTION 5: SELECTION OF CONTAMINANTS OF POTENTIAL ECOLOGICAL CONCERN

.................................................................................................................................................................... 20 

5.1 Introduction ....................................................................................................................................... 20 5.2 COPEC selection process for aquatic community-level receptor groups ......................................... 20 5.2.1 Introduction .................................................................................................................................... 20 5.2.2 Conservative ESVs to select COPECs for fish and aquatic invertebrates ...................................... 22 5.3 COPEC selection process for the wildlife receptors ......................................................................... 23 5.4 COPEC selection summary tables .................................................................................................... 24 

SECTION 6: CHARACTERIZATION OF EFFECTS ......................................................................... 25 

6.1 Introduction ....................................................................................................................................... 25 6.2 Selection of toxicity benchmarks ...................................................................................................... 25 6.2.1 Fish and aquatic invertebrates ........................................................................................................ 25 6.2.2 Wildlife receptors ........................................................................................................................... 26 6.3 Toxicity testing ................................................................................................................................. 26 6.4 Ecotoxicity of select metals to trout .................................................................................................. 27 

SECTION 7: EXPOSURE ANALYSIS .................................................................................................. 30 

7.1 Introduction ....................................................................................................................................... 30 7.2 Exposure to fish and aquatic invertebrates ........................................................................................ 31 7.3 Wildlife food chain modeling ........................................................................................................... 31 

SECTION 8: RISK CHARACTERIZATION ....................................................................................... 34 

8.1 Introduction ....................................................................................................................................... 34 8.2 Risk estimation methods ................................................................................................................... 34 8.3 Uncertainty analysis .......................................................................................................................... 36 

SECTION 9.0 REFERENCES ................................................................................................................. 37 

Tables

Table 1: No-effect and low-effect sediment ecological screening values Table 2: Surface water chronic and acute ecological screening values Table 3: No-effect and low-effect TRVs for birds Table 4: No-effect and low-effect TRVs for mammals Table 5: Measurement endpoints and related sampling activities Table 6: Exposure parameters for the wildlife receptors used in food chain modeling

Figures Figure 1: Exposure units along Mineral Creek and Animas River Figure 2: Durango reach exposure units Appendices Appendix 1: EPA responses to comments to Sunnyside Gold Corporation review comments to

the draft BERA WP Appendix 2: Steps and background for developing trout-specific hardness-dependent acute

toxicity thresholds (DRAFT)

List of Acronyms and Abbreviations

Ag silver Al aluminum As arsenic AUF area use factor Ba barium Be beryllium BERA baseline ecological risk assessment BLM bureau of land management BMI benthic macroinvertebrate BPMD Bonita Peak Mining District BW body weight Cd cadmium CDOW Colorado division of wildlife Co cobalt CO Colorado COPEC contaminant of potential ecological concern Cr chromium CSM conceptual site model CTE central tendency exposure Cu copper DL detection limit DR Durango reach EcoSSL ecological soil screening levels EDD estimated daily dose EPC exposure point concentration ER-M effect range-medium ESAT environmental service assistance team ESV ecological screening value EU exposure unit Fe iron gpm gallons per minute Hg mercury HQ hazard quotient LEL low effect level MDL maximum detection limit MMI multi-metric index Mn manganese Mo molybdenum mg/Kg milligram per kilogram mg/L milligram per liter Ni nickel Pb lead PEC probable effect concentration

PEL probable effect level QAPP quality assurance project plan RME reasonable maximum exposure SAP sampling and analysis plan Sb antimony Se selenium SEL severe effect level SGC Sunnyside Gold Corporation T&E threatened and endangered Th thallium TRV toxicity reference value UCL upper confidence level US EPA United States Environmental protection agency USFWS United States fish and wildlife service V vanadium WP work plan Zn zinc

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SECTION 1: GENERAL INTRODUCTION This document presents the Work Plan (WP) to perform a Baseline Ecological Risk Assessment (BERA) using analytical, biological, and toxicological data collected from the Bonita Peak Mining District (BPMD) located in San Juan County, CO. Note that in this report, the term “Bonita Peak Mining District” refers to 48 named mines with their associated receiving waters at and upstream of the town of Silverton, CO, which consists of the following elements (a) the mainstem Animas River from Silverton up to Animas Forks, (b) the West Fork, South Fork, and North Fork of the Animas River, and (c) Mineral Creek and select tributaries. Mineral Creek flows into the Animas River in Silverton. Importantly, Cement Creek, which is another major tributary which also flows into the Animas River in Silverton, is not included in this WP. The reason is that previous investigations have shown that Cement Creek is largely devoid of aquatic life from its source to its confluence with the Animas River. This WP originated in response to the listing of the BPMD on the National Priorities List in September of 2016 and the need to write a remedial investigation and feasibility study, which includes a BERA. It was developed with substantial input from the Biological Technical Assistance Group (BTAG) at a meeting held in Durango, CO on July 25 and 26, 2016. Members of the BTAG include federal, state, and tribal representatives, among others. It was also modified in response to comments provided by one of the stakeholders (see Appendix 1). The 2016 BERA (TechLaw, 2016) focused on the Animas River flowing between Silverton and Bakers Bridge, which is located about 33 river miles further downstream. The future BERA will specifically investigate the potential for aquatic ecological risk in the BPMD. The BTAG recommended including a stretch of the Animas River running from James Ranch (below Bakers Bridge but upgradient of Durango) to Purple Cliffs (downgradient of Durango) in the BPMD BERA report. This stretch, called the “Durango reach”, will be part of a comprehensive plan to address potential aquatic ecological risk in the Animas River below Bakers Bridge. No new sampling is proposed for this reach in support of the BERA Addendum; only existing or historical data will be used in the risk analysis. However, quantitative Benthic Macroinvertebrate (BMI) community surveys and BMI tissue collection will occur in the Durango reach as part of on-going monitoring efforts through an existing EPA contract. Note that the Durango reach is physically separated from the BPMD by 50+ river miles. The outcome of the Durango reach BERA will be presented as a separate, stand-alone addendum to the BPMD BERA report. The future BERA and BERA Addendum will follow the general principles as laid out in the following United States Environmental Protection Agency guidance documents: USEPA. 1997. Ecological risk assessment for Superfund: process for designing and

conducting ecological risk assessments, interim final. Environmental Response Team, Edison, NJ. EPA/540/R-97/006.

USEPA. 1998. Guidelines for ecological risk assessment. EPA/630/R-95/002F. Surface water, sediment, and pore water samples will be collected at each of 19 BPMD Exposure Units (EUs) in October 2016 to complement the existing analytical chemistry datasets for use in

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the future BERA. Qualitative fish surveys will be conducted at each of the 19 EUs to determine the presence/absence of fish. In addition, quantitative BMI community surveys and toxicity testing are proposed in the Mineral Creek and Animas River watersheds. Due to concerns about the sustainability of fish populations in the smaller creeks of the BPMD, only a subset of locations (specifically EU-3, EU-5 and EU-9 and associated reference locations) will be sampled for fish to generate tissue residue data for use in wildlife food chain modeling. The future BERA Addendum for the Durango reach will incorporate existing and historical (i.e., from 2010 to the present) surface water and sediment analytical data, porewater, BMI tissue data, toxicity testing results and biological community surveys for BMIs and fish; some of this information was collected in the summer and fall of 2015 after the Gold King Mine spill of August 5, 2015. In addition, Mountain Studies Institute will perform BMI community surveys and collect BMI tissue samples from the Durango reach through a separate EPA contract. Metals in BMI tissues will be determined through chemical analyses and these data will be used in the BERA Addendum for food chain modeling. Surface water acute and chronic benchmarks (hardness-adjusted, as appropriate) and sediment no-effect and low-effect benchmarks will be used to quantify toxicity of metals to aquatic community-level receptor groups exposed to surface water, sediment and pore water. Wildlife no-effect and low-effect Toxicity Reference Values (TRVs) will be used to assess the toxicity of metals to birds and mammals through ingestion of surface water, sediment, and prey items via the food web. The community-level and wildlife risks will be reported in terms of metal-specific Hazard Quotients (HQs) calculated by comparing exposures to published toxicity values. The BPMD has multiple mining-related features all the way into the upper-most reaches of its watershed. Additionally, naturally-occurring metals concentrations occur at levels that limit aquatic life in some but not all tributaries. Hence, a background location representative of the entire river systems is not available for use in the future BERA. The BTAG identified three creeks in the Animas River and two creeks in the Mineral Creek watersheds that will serve as local reference waterways for the BPMD. Hermosa Creek was selected as the local reference waterway for the Durango reach in the BERA Addendum. EPA recognizes the distinct challenge of identifying true background conditions at the BPMD, representative of both “anthropogenic background” (i.e., the presence of natural and human-made substances in the environment from human activities but not specifically related to Superfund-related releases) and “naturally-occurring” background (i.e., substances present in the local environment but not influenced by human activity). It is understood that some limited mining-related activities may have occurred within the watersheds of one or more of the reference waterways. It is hoped that sampling more than one reference stream will provide a range of results for evaluation and interpretation in the future BPMD BERA and further understand the complexity of the various dynamics of the district. The 2016 BERA (TechLaw, 2016), together with the future BPMD BERA and future Durango reach BERA addendum, will comprehensively address the potential for aquatic ecological risk from the headwaters of Mineral Creek and the Upper Animas River downstream to Durango.

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1.1 Site description and history The information summarized in this subsection was obtained from Church et al. (2007) and EPA (2012). The BPMD is located in the northernmost headwaters of the Animas River watershed in San Juan County, CO. It covers the drainage basin of the Animas River at and upstream of the town of Silverton and its two main tributaries (i.e., Cement Creek and Mineral Creek). Elevations in the watershed range between about 9,000 ft and 13,500 ft. The discovery of gold and silver (Ag) brought miners to the area in the early 1870’s. The discovery of Ag in the base-metal ores was the major factor in establishing Silverton as a permanent settlement. Between 1870 and 1890, the richer ore deposits were discovered and mined. Not until 1890 was a serious attempt made to mine and concentrate the larger low-grade ore bodies in the area. Twelve concentration mills operated in the valley by 1900. All sent their products to the Kendrick and Gelder Smelter near the mouth of Cement Creek in Silverton. Mining and milling operations slowed down around 1905, and mines were consolidated into fewer and larger operations with the facilities for milling large volumes of ore. After 1907, mining and milling continued in the basin whenever prices were favorable. Gladstone, located about eight miles upstream of Silverton on Cement Creek, is the site of an historic mining town developed in the 1880s in response to the onset of mining. The town was the central location and railroad terminus for milling and shipping mine ores from the surrounding valley. Gladstone declined in the 1920’s and no remnants of it remain visible today. The Sunnyside Mine was the only active year-round mine left in the county by the 1970’s. This mine ceased production in 1991, and underwent extensive reclamation. The Gold King Mine’s permit with the Division of Reclamation, Mining and Safety was revoked by the Colorado Mined Land Reclamation Board and the financial warranty bond was forfeited in 2005. The headwaters and tributaries of Cement Creek, Mineral Creek, and the Animas River originate in treeless alpine regions. With a few exceptions, the streams follow high-gradient, narrow glaciated valleys. The vegetation along those valleys is rather sparse in the presence of extensive areas of exposed rock and talus (i.e., a sloping mass of rock debris at the base of a cliff). Howardsville, located between Silverton and Eureka at the mouth of Cunningham Creek, was established in 1874 by the Bullion City Company. The town became the base for many mines up Cunningham Gulch, including: the Old Hundred Mine, Buffalo Boy, Green Mountain, Pride of the West, Shenandoah-Dives Mine, Gary Owen Mine, and Emma Mine. The Pride of the West mill was built in 1940 as a 50-ton capacity mill and was expanded in 1967 by the Dixilyn Corporation to a 400-ton capacity mill. The town of Eureka is located about eight miles northeast of Silverton at the confluence of Upper Animas and Eureka Gulch. Some of the mines located up Eureka Gulch include: the Sunnyside Mine, the Clipper Mine, the Ben Franklin Mine, the Bavarian Mine, the Midway Mine, the Moonbeam Mine, and the Ransom Mine. The Sunnyside Flotation Mill in Eureka was built in

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1917 with a 600 tons per day capacity. Two settling ponds were built in the Animas River Valley but when the mill was abandoned in 1949, the tailings dams were partially washed out and tailings were washed down the Animas River. Animas Forks, named for the three forks of the Animas River, is located twelve miles northeast of Silverton and was first established in 1874. Numerous mines were located upstream of Animas forks. The town, which started to decline in 1910 when the Gold Prince Mill ceased operation, became a ghost town in the 1920’s. As early as 1874, prospectors found mineralized veins along both the middle and main forks of Mineral creek. However, the drainage did not attract much attention because these formations were scattered and offered low-grade ores. The Silver Crown mine on Mill Creek was the most promising mine in the late 1870’s and saw some development. A settlement at the base of Red Mountain Pass known as Sweetville was begun in 1882 to allow access to the rich veins found on the north side of Red Mountain, and to help explore the Mineral Creek basin. The rival camp of Chattanooga was located next to Sweetville. The two camps merged under the name of Chattanooga in 1883 and secured a post office. The Mineral Creek district became prominent in San Juan County in the early 1890’s, with the North Star and Victoria mines and mills close to Silverton the most significant producers. The Bandora mine had rich ores, but production didn’t start until 1893, and the silver crash effectively shuttered the doors on the operation for the next few years. The most prominent mines on Mineral Creek were the Northstar, Hercules, Victoria, Bandora, Brooklyn, and Bonner mines. The Mineral Creek district never experienced the intense mining development that occurred on the Upper Animas and Cement Creek. Past surveys of fish and benthic invertebrate communities showed that the headwaters of the Animas River above Silverton, the main stems of Cement and Mineral Creeks, and several smaller tributaries support little or no aquatic life due to the presence of naturally-occurring and/or site-related contamination. On the other hand, South Fork Mineral Creek and several tributaries of the upper Animas River drain basins that provide substantial acid-neutralizing capacity and support viable trout populations. The Animas River between Maggie Gulch and the mouth of Cement Creek in Silverton supports brook trout and a moderately-impaired invertebrate community (see Chapters D and E18 in Church et al., 2007), which suggests substantial improvements in surface water quality since the 1970’s. Note, however, that sections of the Animas River further upstream from Maggie Gulch are still severely impacted by past mining activities. The stream biota in the Animas River downstream from Silverton are also degraded due to input from Cement and Mineral Creeks (see Chapters A, D, E18, and E19 in Church et al., 2007). 1.2 Exposure units for the future BERA and BERA Addendum

1.2.1 BPMD exposure units and reference locations

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The future BERA will subdivide the BPMD into 19 EUs to facilitate sample collection, exposure calculations, toxicity testing, aquatic community evaluations, data interpretation, risk characterization, and future risk management decisions. The EUs are as follows (from Silverton upstream to the source of the Animas River) (see Figure 1):

EU-01 Mineral Creek - from the confluence with the Animas River upstream to the confluence with South Fork of Mineral Creek

EU-02 Mineral Creek - from the confluence with the South Fork of Mineral Creek upstream to the confluence with the Middle Fork of Mineral Creek

EU-03 Mineral Creek - from the confluence with the Middle Fork of Mineral Creek upstream to the confluence with Mill Creek

EU-04 Mineral Creek - from the confluence with Mill Creek upstream to the source EU-05 Mineral Creek - South Fork of Mineral Creek from the confluence with Mineral Creek

upstream to the source EU-06 Mineral Creek - Middle Fork of Mineral Creek from the confluence with Mineral Creek

upstream to the source EU-07 Animas River - from the confluence with Arrastra Creek upstream to the confluence with

Cunningham Creek in Howardsville EU-08 Cunningham Creek - from the confluence with the Animas River upstream to the source EU-09 Animas River - from the confluence with Cunningham Creek in Howardsville upstream to

the confluence with Minnie Gulch EU-10 Animas River - from the confluence with Minnie Gulch upstream to the confluence with

mainstem South Fork of the Animas River in Eureka EU-11 Upper South Fork of the Animas River - from the confluence with Eureka Gulch upstream

to the source EU-12 Eureka Gulch - from the confluence with the Upper South Fork of the Animas River

upstream to the source EU-13 Mainstem South Fork of the Animas River - from the confluence with the Animas River

in Eureka upstream to the confluence of Eureka Gulch and the Upper South Fork of the Animas River

EU-14 Animas River - from the confluence with mainstem South Fork of the Animas River in Eureka upstream to the confluence with mainstem West Fork of the Animas River in Animas Forks

EU-15 Upper West Fork of the Animas River - from the confluence with Placer Gulch upstream to the source

EU-16 Placer Gulch – from the confluence with the Upper West Fork of the Animas River upstream to the source

EU-17 Mainstem West Fork of the Animas River - from the confluence with the Animas River in Animas Forks upstream to the confluence with Placer Gulch and the Upper West Fork of the Animas River

EU-18 North Fork of the Animas River - from Animas Fork upstream to the confluence with Burrows Creek

EU-19 Burrows Creek - from the confluence with the North Fork of the Animas River upstream to its source

The following tributaries are retained as reference areas that will be evaluated separately from the EUs, as follows:

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Animas River - Maggie Gulch Animas River – Picayne Gulch Animas River – North Fork Animas River Mineral Creek - Mill Creek Mineral Creek – Bear Creek

Note that one sampling location in Mineral Creek, positioned just upstream of the confluence of this creek with the Animas River, was included in the 2016 BERA. The results of that evaluation, plus other historical data, showed that the lower reach of Mineral Creek was severely impaired. This major tributary to the BPMD is included in this BERA WP so that its entire watershed can be further evaluated for the presence of ecological risk. The details of the proposed sampling efforts on these various EUs in support of the future BERA are described in the Sampling and Analysis Plan/Quality Assurance Project Plan (SAP/QAPP).

1.2.2 Durango reach exposure units and reference location

The Durango reach will be split into two EUs to facilitate exposure calculations, BMI community evaluations, and risk characterization. This large reach consists of the stretch of the Animas River flowing between James Ranch and Purple Cliffs over a distance of about 22 river miles. Additional surface water and sediment sampling, fish population surveys or toxicity testing will not occur on the Durango Reach in support of the BERA Addendum. Instead, existing and recent historical data will be used, as available and appropriate, together with the results of on-going periodic BMI surveys. In addition, Hermosa Creek will serve as the local reference waterway for this reach of the Animas River. The two Durango reach EUs are as follows (see Figure 2): EU-DR1: Animas River – James Ranch to 32nd Street EU-DR2: Animas River – 32nd Street to Purple Cliffs 1.3 Work plan organization

This BERA WP is organized as follows:

Section 2: Summary of the 2016 BERA Section 3: Conceptual Site Model (CSM) Section 4: Assessment and measurement endpoints Section 5: Selection of chemicals of potential concern Section 6: Characterization of effects Section 7: Exposure analysis Section 8: Risk characterization Section 9: References

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SECTION 2: SUMMARY OF THE 2016 BERA

2.1 Introduction The 2016 BERA evaluated the potential for ecological risk in the Animas River flowing between Silverton and Bakers Bridge. The EUs considered in that BERA were as follows: The Animas River above mainstem Cement Creek: this reach of the Animas River covered

about two river-miles between sampling locations A60 and A68. All the sampling locations from this reach of the river were combined into a single EU.

The Animas River between mainstem Cement Creek and mainstem Mineral Creek: this reach of the Animas River covered about one river-mile between the confluences of the two creeks. The two sampling locations within this reach were combined into one EU.

The Animas River below mainstem Mineral Creek: this reach of the Animas River covered about 30 river-miles between sampling locations A71B, and Bakers Bridge (BBridge). Each of the five sampling location on this reach of the river were considered as distinct EUs due to the large distances separating A71B and Baker’s Bridge.

Mainstem Cement Creek: the section evaluated in the BERA was represented by sampling

locations CC48 and CC49 found on the creek within one mile of the confluence with the Animas River. Both sampling locations were combined into one EU.

Mainstem Mineral Creek: the section evaluated in the BERA was represented by sampling location M34 found on the creek just upstream of the confluence with the Animas River.

These five reaches (and the five sampling locations within the Animas River below mainstem Mineral Creek) were treated as separate EUs to derive Reasonable Maximum Exposure (RME) and Central Tendency Exposure (CTE) Exposure Point Concentrations (EPCs) for use in the baseline evaluation. The main goal of the 2016 BERA was to derive risk estimates for the following receptor groups: benthic invertebrates exposed to sediment and pore water,

fish exposed to surface water, and

four wildlife species representing different trophic levels exposed via ingestion of surface

water, sediment, and food items. The surface water data represented dozens of samples collected from the EUs between May 2009 and September 2014. The sediment data set was substantially smaller and consisted of analytical data collected from those same waterways during five sampling events in May 2012, October

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2012, May 2013, April 2014, and September 2014. The pore water data set consisted of analytical data collected in April and September 2014. The effects evaluation used chronic surface water benchmarks (hardness-adjusted, as appropriate), plus no-effect and low-effect sediment benchmarks, to quantify toxicity to aquatic community-level receptor groups exposed to surface water, sediment, and pore water. No-effect and low-effect TRVs for birds and mammals were used to assess the toxicity of metals via ingestion by the four wildlife receptor species. In addition, surface water and sediment toxicity tests were performed in the laboratory on samples collected from mainstem Cement Creek, mainstem Mineral Creek, and the Animas River above Cement Creek and below Mineral Creek to measure effects to benthic invertebrates (the freshwater amphipod Hyalella azteca) and juvenile rainbow trout (Oncorynchus mykiss). EPA and others also assessed the benthic and fish community structure and function in the EUs, and obtained benthic invertebrate samples for tissue residue analysis.

Exposure to the four wildlife receptor species was quantified using a food chain model which calculated RME and CTE Estimated Daily Doses (EDDs) based on ingesting surface water, sediment, and food items. The food items consisted of benthic invertebrates (measured metal levels), fish (estimated metal levels based on sediment analytical data), and aquatic plants (estimated metal levels based on sediment analytical data), depending on the target wildlife species. Risk was quantified using HQs, which compared measured exposures (i.e., RME and CTE surface water, sediment and pore water EPCs) or estimated exposures (RME and CTE wildlife EDDs) to chronic surface water benchmarks, no-effect and low-effect sediment benchmarks, and wildlife no-effect and low-effect TRVs. Finally, toxicity data from fish and benthic invertebrates exposed to surface water and sediment in the laboratory were evaluated statistically to determine which of the observed responses were significantly different from the laboratory control sample (note: an upstream reference sample was not available for the statistical comparison due to a lack of unimpacted reference locations). 2.2 2016 BERA conclusions 2.2.1 Risk conclusions for benthic macroinvertebrates

The four independent measurement endpoints indicated that the BMI community was impacted in sections of the Animas River between A60 and BBridge, and in mainstem Cement and Mineral Creeks. The two creeks were the most impaired. In addition, comparing Multi-Metric Index (MMI) scores obtained from the Animas River starting in the early 1990’s indicated that the benthic invertebrate community at sampling location A68 (located in the Animas River above mainstem Cement Creek) and at sampling locations A72 and A73 (located in the Animas River below mainstem Mineral Creek) had not consistently improved over time. The MMI scores at locations further downstream on the Animas River (i.e., A75D and James River [located below Baker’s Bridge]) showed a largely unimpaired benthic invertebrate community. 2.2.2 Risk conclusions for fish

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Mainstem Cement Creek:

The chemical conditions in surface water were highly toxic to fish, particularly due to low pH and high aluminum (Al), and to a lesser extent by the presence of cadmium (Cd), copper (Cu), and zinc (Zn). The toxicity tests showed that surface water collected in November 2012 (i.e., post-runoff period) was acutely toxic to juvenile rainbow trout. The preponderance of evidence suggested that the fish community (if present) in mainstem Cement Creek would experience lethal stress under current conditions. Mainstem Mineral Creek:

Serious pH drops during the pre-runoff period coupled with high Al levels during the pre-runoff and post-runoff periods suggested that fish may experience high stress in the winter as well as summer and fall, but that survivors could possibly recover during the rest of the year (spring). The toxicity tests showed that surface water samples collected in November 2012 (i.e., post-runoff period) and April 2013 (pre-runoff period) were acutely toxic to juvenile rainbow trout. The preponderance of evidence suggested that the fish community (if present) in mainstem Mineral Creek would likely experience high stress under current conditions. Animas River above mainstem Cement Creek: The chemical conditions indicated the presence of one or more sources of metal contamination located further upstream in the watershed. The chemical signature of the surface water suggested that long-term toxicity to the fish community was possible, particularly due to Al, Cd, and Zn. Low pH was not an issue in this reach. The presence of high toxicity in juvenile rainbow trout acutely exposed to surface water further confirmed the results of the chemical analyses. The evidence suggested that the fish community in this reach could be stressed during much of the year. This conclusion was supported by the fact that daily surface water samples collected between April and July 2014 using “MiniSipper” sampling devices at location A56 (upstream of A60) showed the presence of potentially severe chronic toxicity from dissolved Al, Cd, Cu, lead (Pb), and Zn during the pre-runoff and runoff periods. Animas River between mainstem Cement Creek and mainstem Mineral Creek Little chemical data was available because only two samples were collected and no acute toxicity testing was performed. The limited data suggested that the surface water in this reach was likely lethal to fish, mostly due to low pH and high levels of Al, with secondary stress caused by Cd and Zn. Animas River below mainstem Mineral Creek The chemical signature of the surface water in this reach reflected the major inputs from mainstem Mineral and Cement Creeks, and the reach of the Animas River above mainstem Cement Creek. Surface water samples collected from sampling location A72 during the pre- and post-runoff periods were acutely toxic to juvenile rainbow trout. Surface water samples collected during the same two hydrologic periods from the EUs further downstream did not show acute toxicity,

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suggesting that the effect had been “diluted out”. However, the preponderance of evidence showed that Al, Cd, and Zn in surface water could exert chronic effects on the fish community to at least the Baker’s Bridge EU located about 30 miles downstream from Silverton. This conclusion was supported by two additional lines of evidence: o Daily surface water samples collected between April and July 2014 using “MiniSipper”

sampling devices positioned at locations A73, A75D and Baker’s Bridge showed the presence of low-grade and multi-week chronic toxicity associated with dissolved Al, Cd, and Zn during the pre-runoff and runoff periods.

o A fisheries survey performed by the Colorado Division of Wildlife (CDOW) in 2010 on the Animas River in the vicinity of sampling locations A72, A73, and A75D/A75B showed a severe decline of the trout populations at all three locations between 2005 and 2010. CDOW ascribed this collapse to a drastic reduction in surface water quality apparently associated with the discontinuance of a water treatment project in the Gladstone area on Cement Creek upgradient from Silverton. A 2014 follow-up fisheries survey by CDOW in the vicinity of sampling location A75D/A75B showed a continued decline in the local brook trout population.

2.2.3 Risk conclusions for wildlife receptors

Animas River above mainstem Cement Creek Potential for minimal risk to wildlife receptors was identified for Zn (for the American dipper) and Pb (for the belted kingfisher). The American dipper was also used as a surrogate species to perform a conservative assessment of risk for the southwestern willow flycatcher—a federally and state-listed bird species. The evidence did not suggest that this species was at substantial risk from foraging in the Animas River above mainstem Cement Creek between sampling location A60 and A68. Animas River below mainstem Mineral Creek The potential for risk to wildlife receptors in this reach of the Animas River was restricted to Cu in the American dipper at sampling locations A73B and A75B, with minor risk from Cu to the mallard (100% diet only) at the same two locations. The remaining metals were of no concern to any of the wildlife receptors because the HQs fell below 1.0 for those metals. Benthic invertebrates were not collected for tissue residue analysis from sampling locations A73B and A75B. Hence, the levels of metals in benthic tissues at these two locations were estimated using conservative published sediment-to-benthic invertebrate regression models and uptake factors for use in the food chain model. It is noteworthy that the only two sampling locations with excessive risk from Cu are A73B and A75B. Given this pattern, the conclusion was that the risk from Cu was hypothetical and unlikely to be realized in the field.

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SECTION 3: CONCEPTUAL SITE MODEL 3.1 Contaminant fate and transport The available information was reviewed to determine which fate and transport mechanisms might result in complete exposure pathways to aquatic community-level receptors and aquatic-dependent wildlife receptors that live and feed in the BPMD, as well as the Durango reach. The goal was to identify the major elements of a complete exposure pathway, which consists of the following components:

- sources of contamination, - release and transport mechanisms, - contact points and exposure media, - routes of entry - key receptors - exposure pathways

Each component is briefly discussed below. 3.2 Sources of contamination The primary sources of contamination in the BPMD are the numerous mining-related features (e.g., mine shafts, tailings piles, waste rock piles, adits, drainage tunnels, former smelter sites, slag piles, etc.) which continuously release metal-enriched water and substrate into the dozens of waterways that make up the watershed. The Durango reach, however, is located outside of the direct influence of the mining-related features but does receive this material as it is carried downstream via the gulches and creeks into the Animas River or as the material settles down along the way and becomes incorporated into the river substrate. This transported material serves as a secondary source of contamination to the surface water and pore water. 3.3 Release and transport mechanisms

Metals-contaminated groundwater may sporadically or continuously emerge from the numerous adits, mine shafts and drainage tunnels located throughout the BPMD. Snow melt or rain water may interact with the tailings piles, waste rock piles, or slag piles, thereby becoming acidified. This acidified drainage water solubilizes high levels of metals which are then carried into the local waterways. Rapid snow melting or heavy rains may directly erode the various waste piles, thereby physically entraining metals-contaminated soil particles into the local waterways and depositing them into the substrate. Subsequent high-flow conditions during the annual snowmelt may entrain these particles and move them further downstream or deposit them along the banks of the waterways, depending on the local topography. Metals present in the waste material mixed in with the native river sediment can be released into the water column either as dissolved or particulate metals.

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3.4 Contact point and exposure media The BPMD and Durango reach EUs represent the points of contact that will be evaluated in the future BERA and BERA Addendum. The exposure media consist of surface water, pore water, sediment, and two types of aquatic biota (i.e., BMIs and fish), depending on the location of the specific EUs. 3.5 Routes of entry

The major routes of entry for metals in waterways of the BPMD and the Durango reach to the receptors retained for evaluation in the future BERA and BERA Addendum are as follows:

- Direct contact with surface water (fish and water-column invertebrates) - Direct contact with pore water (benthic invertebrates), - Direct contact with sediment (benthic invertebrates) - Drinking of surface water (wildlife receptors) - Incidental ingestion of sediment during feeding (wildlife receptors) - Ingestion of contaminated prey items (all receptor groups, but only quantified for the

aquatic-dependent wildlife receptors via food chain modeling).

3.6 Key receptor groups The future BERA and BERA Addendum will evaluate exposures and risk to aquatic community-level receptor groups consisting of BMIs, water-column invertebrates and fish, and to selected aquatic-dependent bird and mammal species. The future BERA and BERA Addendum will assume that the BPMD and the Durango reach support the receptor groups listed below, or comparable receptors to the ones used as surrogate wildlife species. BMIs are expected to live either on top of or within the substrate in the EUs of the BPMD

and Durango reach.

Water-column invertebrates and fish are expected (depending on species) to live either on top of the substrate or within the water column in the EUs of the BPMD and Durango reach.

Aquatic-dependent carnivorous birds and mammals are assumed to feed on fish or BMIs that live in the EUs of the BPMD and Durango reach.

3.7 Exposure pathways Exposure pathways are the means by which contaminants can be transferred from a contaminated medium to the target receptors. The future BERA and BERA Addendum will evaluate the following exposure pathways:

BMIs: direct contact with sediment and pore water.

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Water-column invertebrates and fish: direct contact with surface water.

Aquatic-dependent invertivorous birds: ingestion of surface water, sediment and BMIs.

Aquatic-dependent piscivorous birds: ingestion of surface water and fish.

Aquatic-dependent piscivorous mammals: ingestion of surface water, sediment and fish. 3.8 Conceptual site model The CSM is the culmination of the problem formulation process. The model will show how mining-related contaminants are expected to move from their source(s) to the various receptor groups of concern via the release and transport mechanisms, the contact points and exposure media, and the routes of entry. The future BERA and BERA Addendum will include a detailed CSM pertaining to the BPMD and Durango reach EUs, respectively.

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SECTION 4: ASSESSMENT AND MEASUREMENT ENDPOINTS 4.1 Introduction Assessment endpoints represent explicit expressions of the key ecological resources to be protected from mining-related contaminants. They generally reflect sensitive populations, communities, or trophic guilds. Listed below are four general criteria for selecting the assessment endpoints that will be used in the future BERA and BERA Addendum. The ecological resource should:

have relevance, be susceptible to the stressors of concern, have biological, social, and/or economic value, and be relevant to the risk management goals for the site.

By considering these selection criteria, risks identified to one or more of the assessment endpoints will support the future risk management decision process.

Measurement endpoints represent measurable ecological characteristics, quantified through laboratory or field studies, which can be related back to the valued ecological resources chosen as the assessment endpoints. Measurement endpoints are required because it is often not possible to directly quantify risk to an assessment endpoint. The measurement endpoints should represent the same exposure pathway(s) and mechanisms of toxicity as the assessment endpoints in order to be relevant and useful in supporting risk-based decision making.

Risk questions establish a link between assessment endpoints and their predicted responses. The risk questions (see below) should provide a basis to develop the study design and evaluate the results of the site investigation in the analysis phase and during risk characterization (USEPA, 1997). 4.2 Selecting representative assessment endpoint species or communities It is neither practical nor possible to evaluate the potential for ecological risk to all of the individual parts of the local aquatic ecosystem affected by the mining-related contaminants. Instead, key components are identified to select those species or groups most likely to experience exposure to contaminants. BMIs Contaminants with the potential to bioaccumulate can be transferred from sediment, surface water and/or pore water into the BMIs and up the local aquatic food chain, thereby harming the invertebrates themselves, the local fish, or higher trophic-level receptors that feed on them. Significant alterations in BMI communities could also impact the energy cycling at the base of the aquatic food chain. Another potential effect associated with high levels of metals

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(particularly aluminum and iron) in surface water may be habitat changes caused by the formation of metal deposits that concrete onto the substrate and smother BMIs. Water-column invertebrates The water-column invertebrate community encompasses zooplankton as well as other key species that could include but are not limited to beetles, copepods, and cladocerans. These types of organisms play a role in energy and nutrient transfer to higher trophic levels and also represent a potential food resource for other aquatic receptors. The presence of site-related contaminants in the surface water could result in direct mortality or decreased reproduction in water-column invertebrates. Fish The presence of metals in the surface water and sediment in local waterways can impair the fish community in two general ways: (1) mortality due to direct exposure to dissolved metals in the water column, or (2) high metal concentrations in aquatic biota via food chain uptake which could affect reproduction and the long-term survival of the exposed fish. Another potential effect associated with high levels of metals (particularly aluminum and iron) in surface water may be changes in habitat caused by the formation of metal deposits that concrete onto the substrate and smother trout sac fry in the spring. Wildlife receptors Several aquatic-dependent bird and mammal species can be expected to feed on aquatic prey obtained from the EUs in the BPMD and Durango reach. The following target wildlife receptors will be evaluated in the future BPMD BERA and the Durango reach BERA Addendum belted kingfisher (Ceryle alcyon) The belted kingfisher is an aquatic-dependent piscivorous bird typically found along the edges of rivers, streams, lakes and ponds. The kingfisher requires shallow water (typically < 60 cm deep) which is free of vegetation and remains relatively clear in order to be able to spot its prey. It feeds predominantly on small fish (< 18 cm), which it captures near the surface of the water. These feeding habits place this receptor high in the food chain. This species was selected for use in food chain modeling to represent piscivorous birds with relatively small home ranges. American dipper (Cinclus mexicanus) The American dipper is an aquatic-dependent invertivore that forages on the bottom of fast-moving rocky streams in mountainous regions of the western U.S. It dives to the bottom of the stream where it seeks out mainly aquatic insects and their larvae. This species was selected for use in food chain modeling to represent birds which feed on benthic invertebrates. raccoon (Procyon lotor)

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The raccoon is a nocturnal omnivore that lives in mixed forests, coastal marshes and urban areas. It feeds on a wide variety of food items, including but not limited to invertebrates, plants, fish, amphibians, small birds and mammals. This species is retained for use in food chain modeling as an aquatic-dependent piscivorous mammal. 4.3 Endpoint selection

The following assessment endpoints will be used to evaluate the potential for ecological risks to the targeted receptor groups in the BPMD and in the Durango reach. A risk question is appended to each assessment endpoint. It is assumed that evaluating and protecting these assessment endpoints will also protect all other similar aquatic receptors in the BPMD and Durango reach EUs. Maintain a stable and healthy BMI community: Are the contaminant levels in sediment and

pore water high enough to affect survival, growth or reproduction in the BMIs?

Maintain a stable and healthy water-column invertebrate and fish community: Are the contaminant levels in surface water high enough to affect survival, growth or reproduction in the water-column invertebrates and fish?

Maintain stable and healthy aquatic-dependent invertivorous bird populations: Are

the contaminant levels in surface water, sediment and BMIs high enough to affect survival, growth, or reproduction in aquatic-dependent invertivorous birds?

Maintain stable and healthy aquatic-dependent piscivorous bird populations: Are

the contaminant levels in surface water and fish high enough to affect survival, growth, or reproduction in aquatic-dependent piscivorous birds?

Maintain stable and healthy aquatic-dependent piscivorous mammal populations:

Are the contaminant levels in surface water, sediment, and fish high enough to affect survival, growth, and reproduction in aquatic-dependent carnivorous mammals ?

Measurement endpoints: Assessment endpoint # 1: Maintain a stable and healthy BMI community: Are the contaminant levels in sediment and

pore water high enough to affect survival, growth or reproduction in the BMIs? The future BERA and BERA Addendum will use up to four measurement endpoints to assess the potential impacts of metals to the BMI community: Compare the metal levels in bulk sediment samples to sediment no-effect and low-effect

Ecological Screening Values (ESVs).

Compare the dissolved metals levels in pore water samples to surface water chronic and acute ESVs.

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Assess the acute toxicity of bulk sediment samples to the freshwater amphipod Hyalella

azteca after 10 days of exposure in the laboratory.

Assess the structure and function of the BMI community in the field. The quality of the physical habitat will also be evaluated in each EU to assess its suitability

to support a BMI community. Assessment endpoint #2: Maintain a stable and healthy water-column invertebrate and fish community: Are the

contaminant levels in surface water high enough to affect survival, growth or reproduction in the water-column invertebrates and fish?

The future BERA and BERA Addendum will use three measurement endpoints to assess the potential impacts of metals to the water-column invertebrate and fish communities:

Compare the dissolved metal levels in surface water to surface water chronic and acute

ESVs. Assess the acute toxicity of surface water to juvenile rainbow trout (O. mykiss) after 96

hour of exposure in the laboratory.

Assess the presence or absence of fish in the field. The quality of the physical habitat will also be evaluated in each EU to assess its suitability

to support fish populations. Assessment endpoint #3:

Maintain stable and healthy aquatic-dependent invertivorous bird populations: Are

the contaminant levels in surface water, sediment and BMIs high enough to affect survival, growth, or reproduction in aquatic-dependent invertivorous birds?

The future BERA and BERA Addendum will use one measurement endpoint to assess the potential impact of metals ingested by this receptor group:

Use metal levels measured in surface water, sediment and BMIs in a food chain model to

calculate metal-specific EDDs for comparison against avian no-effect and low-effect TRVs.

Assessment endpoint #4:

Maintain stable and healthy aquatic-dependent piscivorous bird populations: Are

the contaminant levels in surface water and fish high enough to affect survival, growth,

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or reproduction in aquatic-dependent piscivorous birds? The future BERA and BERA Addendum will use one measurement endpoint to assess the potential impact of metals ingested by this receptor group:

Use metal levels measured in surface water and fish in a food chain model to calculate

metal-specific EDDs for comparison against avian no-effect and low-effect TRVs.

Assessment endpoint #5: Maintain stable and healthy aquatic-dependent piscivorous mammal populations:

Are the contaminant levels in surface water, sediment, and fish high enough to affect survival, growth, and reproduction in aquatic-dependent piscivorous mammals?

The future BERA and BERA Addendum will use one measurement endpoint to assess the potential impact of metals ingested by this receptor group:

Use metal levels measured in surface water, sediment, and fish in a food chain model to

calculate metal-specific EDDs for comparison against no-effect and low-effect mammalian TRVs.

The measurement endpoints and associated sampling activities are presented in Table 5 for the BPMD and the Durango reach EUs.

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SECTION 5: SELECTION OF CONTAMINANTS OF POTENTIAL ECOLOGICAL CONCERN

5.1 Introduction

Contaminants of Potential Ecological Concern (COPECs) are analytes present at concentrations that could negatively affect the ecological receptors under consideration and therefore require further investigation. The future BERA will use analytical data from surface water, sediment and pore water samples collected from the various EUs in the BPMD (i.e., 2010 to present) to identify the COPECs for aquatic community-level receptor groups and aquatic-dependent wildlife receptors. The 2016 BERA already identified COPECs in surface water and sediment. However, that report evaluated conditions in the Animas River at and downstream from Silverton, CO. The future BERA will be based on samples collected throughout the BPMD. As a result, the future BERA will re-select the surface water and sediment COPECs to ensure that all metals that should undergo a baseline evaluation are in fact identified and included. The surface water and pore water analytical data will consist of both total metals (i.e., unfiltered) and dissolved metals (i.e. filtered). Exposure by fish and aquatic invertebrates to metals in surface water and pore water will be quantified using only the dissolved metals because these data represent the fraction which is bioavailable, and therefore toxic, to these community-level groups. Exposure by wildlife receptors via drinking of the surface water will be quantified using only total metals data because they represent the full fraction of metals ingested by birds and mammals. The future BERA Addendum for the Durango reach will use existing and historical (i.e., 2010 to present) metals data to select surface water, pore water and sediment COPECs for aquatic community-level receptor groups and aquatic-dependent wildlife receptors. No new sampling will occur in the Durango reach to augment the existing surface water, pore water and sediment datasets. 5.2 COPEC selection process for aquatic community-level receptor groups

5.2.1 Introduction The COPECs for the fish and aquatic invertebrates will be identified by calculating HQs based on dividing a conservative EPC, represented by the maximum-detected concentrations (or ½ the Maximum Detection Limit [MDL] for non-detects) for each metal measured in the surface water, pore water and sediment data sets, by the conservative ESVs discussed further below, as follows:

HQ = exposure toxicity

Where:

HQ = hazard quotient (unitless)

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Exposure = the EPC (i.e., maximum concentration or ½ the MDL for non-detect) for a metal measured in surface water (µg/L), pore water (µg/L) and sediment (mg/kg)

Toxicity = the surface water chronic ESV (µg/L) or no-effect sediment ESV (mg/kg) for that metal

The following decision criteria will be used to select surface water, pore water, and sediment COPECs.

Decision Criterion 1: A metal is retained as a COPEC if one of the following conditions is met:

The maximum-detected concentration, or ½ the MDL for a non-detected metal, equals or

exceeds its ESV (i.e., HQ > 1.0).

A metal is present above its DL at least once but lacks an ESV. Decision Criterion 2: A metal is excluded as a COPEC if the following condition is met:

The maximum concentration, or ½ the MDL for a non-detected metal, falls below the ESV

(HQ <1.0). Calcium, magnesium, potassium, and sodium will automatically be eliminated as COPECs because they represent essential physiological electrolytes. The surface/pore water COPEC selection process will evaluate the dissolved metals in two ways, depending on whether toxicity is hardness-independent or hardness-dependent, as follows: Hardness-independent toxicity The aquatic toxicity of several metals (e.g., antimony (Sb), arsenic (As), cobalt (Co), iron (Fe), mercury (Hg), molybdenum (Mo), selenium, (Se), thallium (Th), and vanadium (V)) does not depend on water hardness. COPEC selection for these metals will consist of comparing maximum metal levels to their published surface water chronic ESVs. Hardness-dependent toxicity The Colorado Department of Public Health and Environment (CDPHE) has determined that the aquatic toxicity of Al, Ag, Cd, chromium (Cr), Cu, manganese (Mn), nickel (Ni), Pb, and Zn depend on hardness (note: as per the CDPHE (undated) regulation 31, the toxicity of Al also depends on surface water pH). In addition, the Michigan Department of Environmental Quality (MIDEQ, 2015) has determined that the aquatic toxicity of barium (Ba) and beryllium (Be) is also hardness-dependent. All else being equal, the toxicity of these metals drops in hard water but increases in soft water at a rate which is metal-specific. It would therefore be inaccurate to automatically select the highest concentration of each of these metals to select surface/pore water COPECs because a lesser concentration could be more toxic if the hardness is much lower.

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Under those circumstances, the only reliable way to identify the most-toxic surface/pore water concentration is as follows: (1) calculate hardness-adjusted HQs for each target metal in each surface/pore water sample (note: A hardness-adjusted HQ is obtained by dividing a metal concentration by its toxicity benchmark adjusted for the hardness of the water sample associated with that metal), (2) identify the highest HQ for a target metal in all of the surface/pore water samples, and (3) select the metal concentration associated with that HQ as the concentration for use in COPEC selection. The future BERA and BERA Addendum will use this approach to ensure that the metal concentration associated with the highest HQ will be used in the pore water and/or surface water COPEC selection process. 5.2.2 Conservative ESVs to select COPECs for fish and aquatic invertebrates 5.2.2.1 Sediment no-effect ESVs

The published sources of sediment no-effect ESVs used for selecting sediment COPECs are described below. The order of preference (from highest preference to lowest preference) will be as follows:

MacDonald, D.D., C.G. Ingersoll, and T.A. Berger. 2000. Development and evaluation of

consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39:20-31.

Persaud, D., R. Jaagumagi and A. Hayton. 1993. Guidelines for the protection and

management of aquatic sediment quality in Ontario. Ontario Ministry of Environment and Energy.

Jones, D.S., G.W. Suter and R.N. Hull. 1997. Toxicological benchmarks for screening contaminants of potential concern for effects on sediment-associated biota: 1997 revision. Oak Ridge National Laboratory. ES/ER/TM-95/R4.

US EPA Region 5 RCRA ecological screening levels, August 22, 2003, available at: http://epa.gov/region5/waste/cars/pdfs/ecological-screening-levels-200308.pdf

Ingersoll, C.G., P.S. Haverland, E.L. Brunson, T.J. Canfield, J.F. Dwyer, C.E. Henke, N.E. Kemble, D.R. Mount, and R.G. Fox. 1996. Calculation and evaluation of sediment effect concentrations for the amphipod Hyalella azteca and the midge Chironomus riparius. J. Great Lakes Res. 22(3): 602-623.

Thompson, P.A., J. Kurias and S. Mihok. 2005. Derivation and use of sediment quality guidelines for ecological risk assessment of metals and radionuclides released to the environment from uranium mining and milling activities in Canada. Environ. Monitor. Assess. 110:71-85.

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Long, E.R., D.D. Macdonald, S.L. Smith and F.D. Calder. 1995. Incidence of adverse biological effects with ranges of chemical concentrations in marine and estuarine sediments. Environ. Manag. 19:81-97.

The Long et al. (1995) reference is included, even though its benchmarks pertain specifically to estuarine and marine environments. The reason for inclusion is that this reference is the only one that provides a sediment benchmark for Ag. Table 1 summarizes the sediment no-effect ESVs retained for selecting sediment COPECs in the BPMD and the Durango reach EUs. 5.2.2.2 Surface water ESVs The published sources of surface water chronic ESVs used in selecting surface water and pore water COPECs are described below. The order of preference (from high preference to low preference) will be as follows: Colorado Department of Public Health and Environment (CDPHE). Undated. Water

Quality Control Commission. Regulation No. 31. The basic standards and methodologies for surface water (5 CCR 1002-31), available at https://www.colorado.gov/pacific/sites/default/files/31_2016%2806%29hdr.pdf)

Michigan Department of Environmental Quality (MIDEQ). 2015. Rule 57 Water Quality Values

based on Rule 323.1057 (Toxic Substances) of the Part 4. Water Quality Standards gives procedures for calculating water quality values to protect humans, wildlife and aquatic life. http://www.michigan.gov/documents/deq/wrd-swas-rule57_372470_7.pdf

Note that MIDEQ (2015) also contains Tier II secondary chronic values derived using the methods promulgated by the Great Lakes Water Quality Initiative. Table 2 summarizes the surface water chronic ESVs retained for selecting surface water COPECs in the BPMD and the Durango reach EUs. 5.3 COPEC selection process for the wildlife receptors The approach outlined above does not apply to the aquatic-dependent wildlife receptors evaluated using food chain modeling. The reason is that exposure to these receptor groups is not associated with direct contact to surface water or sediment, but instead reflects ingestion of surface water, sediment, and aquatic food items combined. Therefore, a metal will automatically be retained as a wildlife COPEC for evaluation in the future BERA and BERA Addendum food chain models if it meets both of the following two conditions: 1) the metal is present above its analytical DL in at least one surface water sample or one sediment sample, and 2) the metal is identified as an “important bioaccumulative compound” in Table 4-2 in EPA (2000). Metals that fall into the bioaccumulative category consist of As, Cd, hexavalent chromium (CrVI), Cu, Pb, methylmercury (MeHg), Ni, Se, Ag, and Zn. CrVI and MeHg are not expected to be present in surface water or sediment collected from the BPMD and the Durango reach. However, as a conservative measure, oxidized Cr (i.e., CrIII) and inorganic Hg, if detected, will be retained for evaluation in the wildlife food chain models.

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5.4 COPEC selection summary tables The COPEC selection process will be presented in tables for surface water, sediment, pore water and wildlife receptors. Each table will present the frequency of detection of each metal, the minimum and maximum detected concentrations (or 1/2 the MDL if the metal is not detected), the location of the maximum-detected concentration, the concentration used for screening and the medium-specific ESV used to calculate the HQ, followed by the calculated HQ and whether the metal is selected as a COPEC and why. Each metal will be identified as a COPEC or not with a “yes” or “no” followed by a reason; (a) the maximum detected concentration exceeds the ESV (b) the metal lacks an ESV, (c) the maximum-detected concentration does not exceed the ESV, or (d) the metal has the potential to bioaccumulate in food chains.

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SECTION 6: CHARACTERIZATION OF EFFECTS

6.1 Introduction The characterization of effects occurs after the COPEC-selection process. It consists of quantifying the toxicity of the COPECs to the various receptor groups under different exposure conditions.

The following two general approaches will be used to determine the toxicity of the COPECs to the aquatic community-level receptor groups: (a) use published ESVs developed for different matrices, and (b) performing surface water and sediment toxicity tests.

On the other hand, the toxicity of the COPECs ingested by the aquatic-dependent bird and mammal wildlife receptors foraging in the BPMD and the Durango reach will be assessed based on published TRVs, as explained further below. 6.2 Selection of toxicity benchmarks 6.2.1 Fish and aquatic invertebrates The potential effects to fish and aquatic invertebrates exposed to metals in the waterways from the BPMD and the Durango reach will be assessed using the sediment no-effect ESVs and the surface water chronic ESVs (hardness-adjusted, as needed) listed in the previous section (see Tables 1 and 2). In addition, the metals detected in the sediment samples will be further evaluated for effects to BMIs using published sediment low-effect ESVs, which consist of Probable Effect Concentrations (PECs), Probable Effect Levels (PELs), Effect Range-Medium (ER-M) concentrations, and/or Severe Effect Levels (SELs). The following hierarchy (in order of preference) will be used to obtain these sediment low-effect ESVs (see also Table 1):

MacDonald et al. (2000); consensus-based PECs, Long et al. (1995); ER-Ms, Persaud, et al. 1993; SELs Ingersoll et al. (1996); PELs, Thompson et al. (2005); SELs, and Los Alamos National Laboratory; LELs. The future BERA and BERA Addendum will assess the toxicity of metals in surface water and pore waterto fish and aquatic invertebrates based on both surface water chronic and acute ESVs. The latter will serve to identify dissolved metal levels that are immediately hazardous to aquatic organisms, although it is anticipated that future clean-up decisions will be primarily focused on chronic exposure and toxicity once source control has been achieved

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6.2.2 Wildlife receptors The following hierarchy of published sources will be used to obtain the avian and mammalian no-effect and low-effect TRVs for comparison against the EDDs in the wildlife risk characterization:

EPA’s Ecological Soil Screening Level (EcoSSL) reports, available at http://www.epa.gov/ecotox.ecossl/

Table C-8 in the Remedial investigation report for Lower Darby Creek Area Site, Clearview

Landfill Operable Unit 1 (OU-1), Delaware and Philadelphia Counties, PA. May 2010. Prepared by TetraTech NUS, Inc. under EPA contract No. EP-S3-07-04 (available at https://semspub.epa.gov/work/03/2156095).

Sample et al., 1996, Toxicological benchmarks for wildlife: 1996 Revision, ES/ER/TM-

86/R3, available at http://www.esd.ornl.gov/programs/ecorisk/documents/tm86r3.pdf Tables 3 and 4 summarize the no-effect and low-effect TRVs selected for birds and mammals, respectively. 6.3 Toxicity testing Effect to BMIs and fish will also be measured directly in the laboratory by exposing the amphipod H. azteca and juvenile rainbow trout (O. mykiss) to field-collected samples of sediment and surface water, respectively. Table 5 shows the locations selected for surface water and sediment toxicity testing in the BPMD. Toxicity testing will be performed at Hermosa Creek as a reference location, but will not be performed for the Durango reaches. Historical toxicity testing data will be used for the evaluation of the remaining Durango reaches. The amphipod test lasts for 10 days and is considered an acute test. The rainbow trout test lasts for 96 hours and is also considered an acute test. The end points are mortality (H. azteca and O. mykiss), growth, and biomass (H. azteca only). The results will be compared statistically to mortality,growth, and biomass observed in the surface water and sediment samples collected from the reference locations. The field-collected surface water and sediment samples used in the toxicity tests will also be analyzed for metals. The chemistry data will be available to help interpret the test results. The SAP/QAPP provides more details on the proposed toxicity testing program. EPA recognizes the inherent limitations of assessing the acute toxicity of surface water to juvenile rainbow trout, but nonetheless feels that these bioassays will provide useful data to support risk characterization and risk management decision making. The lack of a toxic response (i.e., mortality) after 96 hours does not mean that trout populations in the field are protected from harm. That limitation will be fully discussed in the future uncertainty analysis. Also, the lack of acute toxicity will be weighed against the surface water analytical chemistry results. Conversely, the presence of acute toxicity to juvenile rainbow trout is a powerful signal with direct risk management implications EPA has also used acute trout tests to derive site-specific water-effect ratios. These bioassays have shown that site water spiked with a known level of Zn can be up to four times less toxic

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than laboratory control water spiked by the same amount of zinc. This pattern implies that some of the zinc spiked in the site water becomes bound up to particulate matter or subject to competitive inhibition at the gill surface which effectively results in reduced bioavailability. Based on these insights, EPA will perform acute trout toxicity tests. 6.4 Ecotoxicity of select metals to trout Trout are considered to be keystone species in the Animas River watershed, including the BPMD and the Durango reach. The ability of the Animas River and its major tributaries to maintain stable and self-sustaining trout populations is considered a highly-desirable management goal. Acidity and metals are believed to be the two major chemical stressors that affect trout populations in the BPMD and the Durango reach. Acidity/low pH

Sulfuric acid is released when water and oxygen interact with sulfide-rich materials. Low pH is toxic to aquatic receptors. Sensitive species of fish and aquatic invertebrates experience increased mortality at a pH of around 6.0. For example, brook trout populations are known to disappear from streams when pH drops to the low 5s for an extended period of time. Other trout species (e.g., rainbow trout or brown trout) are considered more sensitive to increased acidity and are therefore affected sooner than brook trout.

Metals

High acidity solubilizes metals, resulting in metals-enriched surface water runoff. Dissolved metals are of the highest concern because, unlike metals associated with the particulate fraction, they are bioavailable to exert direct toxicity to aquatic community-level receptors.

The relative sensitivity of four trout species (namely, brook trout, brown trout, rainbow trout and cutthroat trout) to Cd, Cu, and Zn was determined in support of the 2016 BERA (see Appendix 2. The four trout species included in this evaluation may be found in the Animas River above and below Silverton. The three metals of concern are known to be associated with past and current releases in the BPMD. Al is another key surface water contaminant. This metal is not included in Appendix 2 because not enough trout-specific toxicity data were found to derive acute and chronic toxicity threshold values. The freshwater aquatic life criteria for Al provided in Parametrix (2009) will be reviewed and considered as they present an updated hardness-based derivation of both chronic and acute aluminum benchmarks. If appropriate, the Parametrix criteria may be used as secondary, less-conservative risk characterization thresholds to assess the uncertainty in risk estimates based on the CDPHE values.

A literature search was performed in the spring of 2015 to obtain 96-hour acute toxicity data on juvenile life stages to derive acute toxicity thresholds for Cd, Cu, and Zn. These thresholds were standardized to a hardness of 50 mg/L CaCO3 to allow for a direct comparison of species sensitivity to the three metals.

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The table below summarizes the results of this effort. Appendix 2 provides additional details on the literature search criteria and statistical analysis of the data.

Relative sensitivity of four trout species to three metals in surface water

Trout Species Target metal Acute toxicity

thresholds Relative sensitivity Brook trout cadmium 1.15 µg/L 1 brown trout cadmium 1.21 µg/L 2 rainbow trout cadmium 1.33 µg/L 3 rainbow trout copper 13.4 µg/L 1 brown trout copper 18.1 µg/L 2 brook trout copper 22.7 µg/L 3 cutthroat trout copper 24.4 µg/L 4 rainbow trout zinc 121 µg/L 1 cutthroat trout zinc 141 µg/L 2 brown trout zinc 283 µg/L 3 brook trout zinc 732 µg/L 4

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The information provided in the table above can be summarized as follows:

Cadmium

Acute toxicity data for brook trout, brown trout and rainbow trout were available to calculate Cd acute toxicity thresholds. It is not known how much more or less sensitive cutthroat trout may be compared to these three species.

The difference in acute toxicity thresholds between the three trout species was minimal and unlikely to be significant.

Cd was the most acutely toxic of the three target metals to trout.

Copper

Acute toxicity data were available to calculate Cu acute toxicity thresholds for all four trout species.

The rainbow trout was over two times more sensitive to Cu than the cutthroat trout. The sensitivities of brown trout and brook trout fell between these extremes.

The acute toxicity of Cu fell in between that of Cd and Zn.

Zinc

Acute toxicity data were available to calculate Zn acute toxicity thresholds for all four trout species.

The rainbow trout was six times more sensitive to Zn than the brook trout. The sensitivities of cutthroat trout and brown trout fell between these extremes.

Zn was the least acutely toxic of the three target metals.

Based on this information, it can be concluded that the rainbow trout appears to be consistently very sensitive to the three metals. The relative sensitivities of the other three species to Cu and Zn are less consistent and vary by species. The trout toxicity data summarized above will be used to support the risk characterization in the future BERA and BERA Addendum.

29

SECTION 7: EXPOSURE ANALYSIS 7.1 Introduction The exposure analyses for the future BPMD BERA and Durango Reach BERA addendum will use all of the data collected in the fall of 2016, together with recent (2010 to 2016) monitoring data (when available/applicable), to (a) quantify the direct exposures in fish and aquatic invertebrates to COPECs measured in surface water, pore water, and sediment samples collected from the BPMD and (b) quantify the exposures to COPECs in wildlife receptors feeding on aquatic biota, drinking surface water and ingesting sediment from the BPMD. The future BERA and BERA Addendum will assess two different sets of exposures for each COPEC, namely more-conservative RMEs and less-conservative CTEs. Whenever possible, the ProUCL software (EPA, 2015) will be used to calculate 95% Upper Confidence Levels (UCLs) for use as the RMEs. Some of the RMEs may have to be represented by their maximum-detected concentrations if the available data sets are too small to calculate reliable 95% UCLs. The CTEs will always be represented by arithmetic means. To the extent possible, and following the approach used in the 2016 BERA, the future BERA and BERA addendum will compile, present and interpret the available surface water analytical results in terms of three distinct hydrologic periods, as follows: pre-runoff data (collected between January and April), runoff data (collected in May and June), and post-runoff data (collected between July and December). Unlike surface water, however, it is not anticipated that enough analytical data will be available for pore water and sediment to compile robust datasets representative of the three hydrologic periods. Instead, following the example set in the 2016 BERA, the sediment and pore water analytical data will be combined across hydrologic periods into single datasets for calculating RMEs and CTEs. As explained in Section 1 of this WP, the future BERA will subdivide the BPMD into 19 EUs. All the analytical data for surface water, pore water, sediment, fish, and BMIs pertaining to a particular EU will be combined within each matrix to calculate the matrix-specific RMEs and CTEs. Hence, these EPCs will represent the conditions prevailing within each of the BPMD EUs. The future BERA Addendum for the Durango reach covers the Animas River from James Ranch downstream to Purple Cliffs. This reach will be subdivided into two separate EUs (i.e., EU-DR1 and EU-DR2). All available 2010 to 2016 analytical data for surface water, sediment, pore water, BMI tissues, and fish tissues for each EU will be combined within each matrix to calculate the matrix-specific RMEs and CTEs. Hence, these EPCs will represent the conditions prevailing within the two Durango reach EUs. 7.2 Exposure to fish and aquatic invertebrates

30

The EU-specific RMEs and CTEs will be calculated for each surface water, pore water, and sediment COPEC using the analytical chemistry datasets. The future BERA will assume that these two sets of exposure values represent the range of concentrations experienced by fish and aquatic invertebrates living in each of the BPMD EUs. Similarly, the EU-specific RMEs and CTEs for the Durango reach will be calculated for each surface water and sediment COPEC using the available analytical chemistry datasets. The future BERA Addendum will assume that these two sets of exposure values represent the range of concentrations experienced by fish and aquatic invertebrates living in the Durango reach EU. Quantitative BMI community surveys will occur at all the EUs in the Mineral Creek and Animas River watersheds, which includes collecting tissue samples to generate chemical analytical data. Qualitative fish surveys will be conducted at each of the 19 EUs in the BPMD to determine the presence/absence of fish. In addition, due to concerns regarding the sustainability of fish populations in the small creeks of the BPMD, brook trout will be collected from only a small subset of locations (i.e. EU-3, EU-5 and EU-9 and their associated reference locations) to obtain tissue samples for chemical analyses (see Table 5). Exposure by aquatic community-level receptor groups to the prevailing in-situ conditions will also be assessed by performing quantitative BMI and qualitative (presence/absence) fish community surveys in all the EUs of the BPMD and the Durango reach. Results of past community surveys that may have been performed at one or more of these EUs will also be included in the future BERA and future BERA Addendum, if appropriate and available. The results of past and future BMI surveys will be summarized in the form of a standardized MMI, as was done for the 2016 BERA. BMI community surveys and tissue collection efforts are on-going in the Durango reach through an existing monitoring contract with EPA. Qualitative fish community surveys are not planned in this EU, but historic fish tissue data are available and will be incorporated into the ecological risk assessment food-chain modeling as described below. 7.3 Wildlife food chain modeling Section 4.2 of this WP outlines the proposed aquatic-dependent wildlife receptors to be evaluated in the future BERA. These receptors are the American dipper (representing aquatic-dependent invertivorous birds feeding on aquatic invertebrates), belted kingfisher (representing aquatic-dependent piscivorous birds feeding on fish), and the raccoon (representing aquatic-dependent piscivorous mammals feeding on fish). Food web models will be used to obtain CTE and RME EDDs for these wildlife species by calculating exposure via ingestion of surface water, sediment (when applicable) and aquatic prey. The future BERA will have location-specific tissue residue data for BMIs from all 19 EUs and fish from three select locations (i.e., EU-3, EU-5 and EU-9) in the BPMD. Those results will provide measured tissue levels for EU-specific input to the EDD calculations. The BERA Addendum will have location-specific BMI and fish tissue residue levels from each of the two Durango reaches (i.e. EU-DR1 and EU-DR2) for use in food-chain modeling. Those results will provide direct EU-specific input to the EDD calculations.

31

The EDDs represent a dose of a COPEC that an aquatic-dependent wildlife receptor may obtain while foraging in the BPMD and Durango Reach EUs. The receptor-specific EDDs will be calculated using the analytical data for the surface water, sediment, BMIs, and fish samples collected from select EUs, if available. The EDDs also incorporate receptor-specific exposure parameters and food chain model assumptions (see Table 6). The wildlife exposures associated with ingesting surface water will be quantified using total metals concentrations only. The reason is that the full amount of metal in the ingested water becomes part of the daily dose for a wildlife receptor. Note that Table 6 also provides home ranges, but for information purposes only. The Area Use Factor (AUF), which represents the fraction of a receptor species’ home range covered by the surface area of an EU, will be set at “1.0” in all the exposure equations. Assuming an AUF of 1.0 can be conservative because it indicates that the entire daily dose ingested by a wildlife receptor is obtained entirely within a particular EU, which may be unrealistic. The approach for generating more representative AUFs will be included in future risk management discussions within the BTAG if actionable risk is identified to one or more of the wildlife receptors foraging in certain EUs. This issue will also be discussed in the uncertainty section of the future BPMD BERA and Durango reach BERA addendum. The incidental ingestion of sediment will be included for the aquatic-dependent wildlife receptors likely to experience this exposure pathway, namely the American dipper and the raccoon. The belted kingfisher, on the other hand, is assumed not to ingest sediment because it feeds on fish captured directly from within the water column and consumed without contact to sediments. Other important parameters in the model include receptor Body Weight (BW); food, water, and sediment ingestion rates; and diet composition. The total EDD (EDDtotal) experienced by the aquatic-dependent wildlife receptors foraging in the BPMD and Durango EUs is the sum of the doses obtained from the three major routes of exposure, as follows:

EDDtotal = EDDdiet + EDDwater + EDDsediment

The dose associated with each exposure route will be calculated as follows:

Dose from feeding on aquatic biota (BMIs and fish):

EDDdiet = FIR X Cbiota X DFi X AUF/BW

Where:

EDDdiet = Dose of COPEC from feeding on aquatic biota (mg COPC/kg BW-day)

FIR = food ingestion rate (kg food/kg BW-day, dw [dry weight]) Cbiota = RME or CTE COPEC level in each food item (mg/kg, dw for fish and

BMIs) DFi = dietary fraction of each food item (unitless; proportion of food type in the

diet)

32

AUF = area use factor (unitless; assumed 1.0 for all wildlife receptors) BW = body weight of the adult receptor (kg, ww) Dose from ingesting surface water: EDDwater = WIR X Cwater X AUF/BW Where: EDDwater = Dose of COPEC obtained from drinking river water (mg COPEC/kg BW-

day) WIR = water ingestion rate (L/kg BW-day) Cwater = RME or CTE COPEC level in drinking water (mg COPEC/L water) AUF = area use factor (unitless; assumed 1.0 for all wildlife receptors) BW = body weight of the adult receptor (kg, ww)

Dose from ingesting sediment: EDDsediment = SIR X Csediment X AUF/BW Where: EDDsediment = Dose of COPEC obtained from ingesting sediment (mg COPEC/kg BW-

day) SIR = sediment ingestion rate (kg/kg BW-day, dw) Csediment = RME or CTE COPEC level in sediment (mg COPEC/kg sediment, dw) AUF = area use factor (unitless; assumed 1.0 for all wildlife receptors) BW = body weight of the adult receptor (kg, ww)

33

SECTION 8: RISK CHARACTERIZATION 8.1 Introduction

The potential for ecological risk will be quantified during risk characterization. This phase, which represents the last stage of the risk assessment, is built around three sequential steps: 1) risk estimation; 2) uncertainty analysis; and 3) risk description. The exposure analysis and characterization of effects presented in the previous two sections of this BERA WP are integrated during risk estimation to determine the potential for adverse effects to each of the assessment endpoints, given the assumptions inherent in the analysis phase. The uncertainty analysis provides context for the influences of those assumptions on the risk estimation process. Finally, the risk findings are summarized, interpreted, and discussed in the risk description section using various lines of evidence which address the risk estimates as well as the uncertainties associated with them. 8.2 Risk estimation methods The BPMD BERA will focus on providing an “integrated” risk characterization for the two aquatic community-level receptor groups (i.e., BMIs and fish) which will be evaluated using multiple lines of evidence. Such an approach can provide a better understanding of the potential for ecological risk compared to a measurement endpoint by measurement endpoint evaluation. Some lines of evidence (e.g., MMIs) are expected to provide more powerful insights than other more qualitative lines of evidence (e.g., comparing analytical data to generic published benchmarks). However, no attempts will be made to assign formal “weights” to each measurement endpoint as these weighing values can be subjective, controversial, and open to interpretation. Given the nature of the data, risk will be quantified mostly using the HQ method, which compares measured exposures (i.e., surface water, sediment, and pore water EPCs) or estimated exposures (wildlife EDDs) to corresponding toxicity values (i.e., acute and chronic surface water ESVs or no-effect and effect sediment ESVs, plus wildlife no-effect and low-effect TRVs). COPEC-specific HQs will then be calculated using the following general equation:

HQ = exposure ÷ toxicity For the evaluation of the sediment analytical data and the wildlife food chain modeling results, this risk calculation approach will generate four complementary HQs for each combination of receptor and COPEC, as follows:

HQ = RME exposure ÷ no-effect toxicity value (more conservative) HQ = CTE exposure ÷ no-effect toxicity value HQ = RME exposure ÷ low-effect toxicity value HQ = CTE exposure ÷ low-effect toxicity value (less conservative)

34

For the evaluation of the surface water and pore water analytical data, this risk calculation approach will also generate four complementary HQs for each combination of receptor group and COPEC, as follows: HQ = RME exposure ÷ surface water chronic ESV(more conservative) HQ = CTE exposure ÷ surface water chronic ESV HQ = RME exposure ÷ surface water acute ESV HQ = CTE exposure ÷ surface water acute ESV (less conservative) The approach outlined above generates four sets of HQs for a given receptor and COPEC in order to provide a broader context in support of future risk management decision making. To help with data interpretation, the HQ-based risk characterization will use the terms “minor/low risk” (CTE effect HQs < 2.0), “moderate risk” (CTE effect HQs > 2 but < 5.0), or “high risk” (CTE effect HQs > 5.0), even though it is understood that risk does not necessarily increase linearly with increasing HQs. However, this terminology will be used to qualitatively highlight differences in risk. For the surface water evaluation, this terminology will be expanded to include the words acute or chronic in front of the terms “minor/low risk”, “moderate risk” or “high risk” to clearly differentiate between acute and chronic effects. Besides assessing the potential for ecological risk associated with RME and CTE surface water and sediment exposures, the risk characterization for community-level aquatic receptor groups will also view each surface water and sediment sample as representing an individual event in which organisms are exposed to COPECs. This particular approach was also used in the 2016 BERA. As such, individual HQs will be calculated for each surface water and sediment sample within an EU and will be plotted by sampling station and period. It is assumed that community-level risks are unlikely to occur if all the HQs measured within a particular EU fall below 1.0. On the other hand, community-level risks are more likely to occur if most or all of the individual HQs exceed 1.0. Some impact may occur, but without resulting in community-wide effects, if only a fraction of the HQs exceeds 1.0. The BMI and fish habitat appraisals to be performed at each of the EUs do not represent separate measurement endpoints. Instead, the habitat data will be used to help interpret the chemical, toxicological, and biological data collected for each of the measurement endpoints. For example, if the available habitat at an EU is not suitable to maintain a fish population, then the outcome of the surface water HQ calculations and the surface water toxicity tests in support of the fish assessment endpoint for that EU will be interpreted accordingly. The surface water and sediment toxicity tests, and the BMI community surveys represent separate lines of evidence which do not rely on calculating HQs. The results of the toxicity tests will be compared statistically to their reference samples to

identify significant differences in mortality and/or growth in the test organisms. Significantly higher mortality or lower growth in one or more samples will be interpreted as indicating the presence of impairment.

35

The MMI scores for each biotype will be compared against aquatic life thresholds to

determine attainment threshold values or impairment threshold values. For those values that fall between the two threshold values or “gray area”, additional metrics will be used to determine attainment or impairment. The additional metrics used as auxiliary metrics consist of the Hilsenhoff Biotic Index and the Shannon Diversity Index. A site is considered impaired if a Class 1 waters fails to meet the criteria shown below for either auxiliary metric.

Auxiliary metric thresholds for Class 1 waters with MMI scores between the attainment and

impairment thresholds

Biotype Hilsenhoff Biotic

Index Shannon Diversity

Index 1 transition <5.4 >2.4 2 mountains <5.1 >3.0

Finally, based on previous decisions pertaining to the 2016 Animas River BERA, the risk characterization will not quantify “incremental risk” by subtracting reference HQs from their corresponding EU-specific HQs. Hence, the EU-specific risks provided in the two future BERAs will represent “total” risk, and not “incremental” risk. However, the risk measured at the reference locations will be discussed qualitatively in the uncertainty analysis. EPA believes that the best way forward is to present and discuss total risk in the risk characterization and then determine in post-BERA risk management discussions with the BTAG how best to account for local reference conditions. This approach recognizes the unique difficulty of reaching an up-front consensus of what constitutes true “background” in the BPMD.

8.3 Uncertainty analysis

Uncertainty is inherent in a BERA because numerous assumptions need to be made in order to proceed with the assessment. These assumptions can affect all aspects of the BERA, including the CSM, the characterization of effects, the exposure analysis, and the risk characterization. The uncertainty analysis will identify and discuss the major assumptions made in the future BERA and the BERA Addendum. It will also provide a short description to determine if each assumption is likely to have overestimated or underestimated the potential for ecological risk. If the risk interpretation at a particular EU is inconclusive, then EPA may be open to collecting more site-specific data from that EU at a later date to narrow down the uncertainties and strengthen the conclusions. The end result will be a balanced overview of uncertainty to help risk managers understand the full extent of potential ecological risk to the aquatic environment in the BPMD and the Durango reach EUs.

36

SECTION 9.0 REFERENCES Church, S.E., P. von Guerard, and S.E. Finger, eds., 2007. Integrated investigations of environmental effects of historical mining in the Animas River watershed, San Juan County, Colorado. U.S. Geological Survey Professional Paper 1651, 1,096 p. Colorado Department of Public Health and Environment (CDPHE). Undated. Water Quality Control Commission. Regulation No. 31. The basic standards and methodologies for surface water (5 CCR 1002-31) Ingersoll, C.G., P.S. Haverland, E.L. Brunson, T.J. Canfield, J.F. Dwyer, C.E. Henke, N.E. Kemble, D.R. Mount, and R.G. Fox. 1996. Calculation and evaluation of sediment effect concentrations for the amphipod Hyalella azteca and the midge Chironomus riparius. J. Great Lakes Res. 22(3): 602-623. Jones, D.S., G.W. Suter and R.N. Hull. 1997. Toxicological benchmarks for screening contaminants of potential concern for effects on sediment-associated biota: 1997 revision. Oak Ridge National Laboratory. ES/ER/TM-95/R4. Long, E.R., DD Macdonald, S.L. Smith and F.D. Calder. 1995. Incidence of adverse biological effects with ranges of chemical concentrations in marine and estuarine sediments. Environ. Manag. 19:81-97. Los Alamos National Laboratory ecological screening levels, available at https://lanl.gov/environment/protection/eco-risk-assessment.php MacDonald, D.D., C.G. Ingersoll, and T.A. Berger. 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39:20-31. Michigan Department of Environmental Quality (MIDEQ). 2015. Rule 57 Water Quality Values based on Rule 323.1057 (Toxic Substances) of the Part 4. Water Quality Standards gives procedures for calculating water quality values to protect humans, wildlife and aquatic life. http://www.michigan.gov/documents/deq/wrd-swas-rule57_372470_7.pdf Parametrix. 2009. Updated freshwater aquatic life criteria for aluminum (Exhibit 2 of direct testimony of Robert W. Gensemer, Ph.D.). Prepared for the Los Alamos National Laboratory. 25 pp. Persaud, D., R. Jaagumagi and A. Hayton. 1993. Guidelines for the protection and management of aquatic sediment quality in Ontario. Ontario Ministry of Environment and Energy. Sample et al., 1996, Toxicological benchmarks for wildlife: 1996 revision, ES/ER/TM-86/R3, available at http://www.esd.ornl.gov/programs/ecorisk/documents/tm86r3.pdf

37

Suter, G.W.II, and C.L. Tsao. 1996. Toxicological benchmarks for screening of potential contaminants of concern for effects on aquatic biota on Oak Ridge Reservation: 1996.

Table C-8 in the Remedial investigation report for Lower Darby Creek Area Site, Clearview Landfill Operable Unit 1 (OU-1), Delaware and Philadelphia Counties, PA. May 2010. Prepared by TetraTech NUS, Inc. under EPA contract No. EP-S3-07-04 (available at https://semspub.epa.gov/work/03/2156095). TechLaw, Inc. 2016. Final draft baseline ecological risk assessment. Upper Animas Mining District. San Juan County, Colorado. Prepared under contract for U.S. Environmental Protection Agency Region 8, 1595 Wynkoop Street, Denver, CO 80202. Thompson, P.A., J. Kurias and S. Mihok. 2005. Derivation and use of sediment quality guidelines for ecological risk assessment of metals and radionuclides released to the environment from uranium mining and milling activities in Canada. Environ. Monitor. Assess. 110:71-85 U.S. EPA EcoSSL reports, available at http://www.epa.gov/ecotox.ecossl/ U.S. EPA. 1997. Ecological risk assessment for Superfund: process for designing and conducting ecological risk assessments, interim final. Environmental Response Team, Edison, NJ. EPA/540/R-97/006. USEPA. 1998. Guidelines for ecological risk assessment. EPA/630/R-95/002F. EPA, 2000. Bioaccumulation testing and interpretation for the purpose of sediment quality assessment, Status and needs. EPA-823-R-00-001, February 2000. U.S. EPA, Region 5, RCRA Ecological Screening Levels, August 22, 2003, available at http://epa.gov/region05/waste/cars/pdfs/ecological-screening-levels-200308.pdf. U.S. EPA. 2012. Final Sampling and Analysis Plan/Quality Assurance Project Plan. 2012 sampling events. Upper Animas Mining District, Gladstone, San Juan County, Colorado (May 2012). U.S EPA. 2015. ProUCL version 5.1. user guide. Statistical software for environmental applications of data sets with and without nondetect observations, available at https://www.epa.gov/land-research/proucl-software

TABLES

Table 1No-effect and low-effect sediment ecological screening

values Baseline ecological risk assessment and addendumBonita Peak Mining District Superfund Site and Durango

reach San Juan County, CO

Metal

no-effect ESVs (mg/kg) ESV source

low-effect ESVs (mg/kg) ESV source

Aluminum 14000 f 60000 fAntimony 12 d -- --Arsenic 9.79 a 33 aBarium 48 h -- --Beryllium -- -- -- --Cadmium 0.99 a 4.98 aChromium 43.4 a 111 aCobalt 50 e -- --Copper 31.6 a 149 aIron 20000 c 40000 cLead 35.8 a 128 aManganese 460 c 1100 cMercury 0.18 a 1.06 aMolybdenum 8.3 g 540 gNickel 22.7 a 48.6 aSelenium 0.9 g 4.7 gSilver 1.0 b 3.7 bThallium -- -- -- --Vanadium 27.3 g 77 gZinc 121 a 459 aESV = ecological screening value

Benchmark sources:

h. Los Alamos National Laboratory (LANL). ECORISK Database. Available at: http://www.lanl.gov/community-environment/environmental-stewardship/protection/eco-risk-assessment.php

Prepared by: EC 6/9/16Reviewed by: SJP 6/13/16

g. Thompson, P.A., J. Kurias and S. Mihok. 2005. Derivation and use of sediment quality guidelines for ecological risk assessment of metals and radionuclides released to the environment from uranium mining and milling activities in Canada. Environ. Monitor. Assess. 110:71-85

a. MacDonald, D.D., C.G. Ingersoll, and T.A. Berger. 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39:20-31.b. Long, E.R., D.D. MacDonald, S.L. Smith and F.D. Calder. 1995. Incidence of adverse biological effects with ranges of chemical concentrations in marine and estuarine sediments. Environ. Manag. 19:81-97.c. Persaud, D., R. Jaagumagi and A. Hayton. 1993. Guidelines for the protection and management of aquatic sediment quality in Ontario. Ontario Ministry of Environment and Energy.d. Jones, D.S., G.W. Suter and R.N. Hull. 1997. Toxicological benchmarks for screening contaminants of potential concern for effects on sediment-associated biota: 1997 revision. Oak Ridge National Laboratory. ES/ER/TM-95/R4.e. U.S. EPA, Region 5, RCRA Ecological Screening Levels, August 22, 2003, available at http://epa.gov/region05/waste/cars/pdfs/ecological-screening-levels-200308.pdff. Ingersoll, C.G., P.S. Haverland, E.L. Brunson, T.J. Canfield, J.F. Dwyer, C.E. Henke, N.E. Kemble, D.R. Mount, and R.G. Fox. 1996. Calculation and evaluation of sediment effect concentrations for the amphipod Hyalella azteca and the midge Chironomus riparius . J. Great Lakes Res. 22(3):602-623.

Table 2Surface water chronic and acute ecological

screening values Baseline ecological risk assessment and addendum

Bonita Peak Mining District Superfund Site and Durango reach San Juan County, CO

MetalSurface water

chronic ESV (µg/L) Surface water acute

ESV (µg/L) ESV source

Aluminum# 73* 512 a

Antimony 240 2300 b

Arsenic 150 340 a

Barium# 100 572 b

Beryllium# 0.65 11.6 b

Cadmium# 0.15 0.5^ a

Chromium# 24 183 a

Cobalt 100 740 b

Copper# 2.7 3.6 a

Iron 1000 not available a

Lead# 0.5 14 a

Manganese# 1040 1881 a

Mercury 0.01 2.8 a (chronic); b (acute)

Molybdenum 3200 58000 b

Nickel# 16 145 a

Selenium 4.6 18.4 a

Silver# 0.01& 0.19 a

Thallium 15 94 a (chronic); b (acute)

Vanadium 27 160 bZinc#

34 45 aESV = ecological screening value

Prepared by: EC 6/9/16

Reviewed by: SJP 6/13/16

Modified by: SJP 7/18/16Modified by: SJP 10/7/16Reviewed by: DH 10/13/16

a. Colorado Department of Public Health and Environment. Water Quality Control Commission. Regulation No. 31. The basic standards and methodologies for surface water (5 CCR 1002-31)

b. Michigan Department of Environmental Quality (MDEQ). 2015. Rule 57 Water Quality Values based on Rule 323.1057 (Toxic Substances) of the Part 4. Water Quality Standards gives procedures for calculating water quality values to protect humans, wildlife and aquatic life. http://www.michigan.gov/documents/deq/wrd-swas-rule57_372470_7.pdf

Benchmark Sources:

^ This acute value is derived based on the "trout" equation& this chronic value is derived based on the "trout" equation

* As per the CO guidelines, the final benchmark for Al may have to be recalculated to take hardness into account depending on the pH of the surface water

# The benchmark for these hardness-dependent metals are presented for a generic surface water hardness of 25 mg/L, but will be adjusted to a site-specific hardness in the BERA.

Table 3No-effect and low-effect TRVs for birds

Baseline ecological risk assessment and addendumBonita Peak Mining District Superfund Site and Durango reach San Juan

County, CO

Eco-SSL TRVsa

1996 toxicological benchmarks for

wildlifeb

Lower Darby Creek Area Superfund Site

BERAc

1996 toxicological benchmarks for

wildlifeb

Arsenic 2.24 5.14 4.51 12.8

Cadmium 1.47 1.45 6.35 20

Chromium III 2.66 1.0 15.6 5.0

Copper 4.05 47 34.9 61.7

Lead 1.63 1.13 44.6 11.3

Mercury (inorganic) -- 0.45 -- 0.9

Nickel 6.71 77.4 18.6 107

Selenium 0.29 0.5 0.82 1.0

Silver 2.02 -- 60.5 --

Zinc 66.1 14.5 171 131

EcoSSL – ecological soil screening level

TRV – toxicity reference value

Footnotes:

All units are mg/kg bw-day

Shading identifies the TRVs selected for use in the BERA a EPA Eco SSL reports (http://www.epa.gov/ecotox/ecossl), as follows:

EPA, 2005. Ecological soil screening levels for arsenic. Interim final. OSWER Directive 9285.7-62.

EPA, 2005. Ecological soil screening levels for cadmium. Interim final. OSWER Directive 9285.7-65.

EPA, 2008. Ecological soil screening levels for chromium. Interim final. OSWER Directive 9285.7-66.

EPA, 2007. Ecological soil screening levels for copper. Interim final. OSWER Directive 9285.7-68.

EPA, 2005. Ecological soil screening levels for lead. Interim final. OSWER Directive 9285.7-70.

EPA, 2007. Ecological soil screening levels for nickel. Interim final. OSWER Directive 9285.7-76.

EPA, 2007. Ecological soil screening levels for selenium. Interim final. OSWER Directive 9285.7-72.

EPA, 2006. Ecological soil screening levels for silver. Interim final. OSWER Directive 9285.7-77.

EPA, 2007. Ecological soil screening levels for zinc. Interim final. OSWER Directive 9285.7-73.

-- not available

prepared by: EC 6-8-16

reviewed by: SJP 6-13-16modified by: SJP 10-7-16

c Table C-8 in the Feasibility Study (FS) prepared for the Lower Darby Creek Area Superfund Site (https://semspub.epa.gov/work/03/2156095).

Metal*

no-effect TRVs low-effects TRVs

* Only those metals identified as "important bioaccumulatice compounds " in Table 4-2 of EPA (2000) are included in this table.

b Sample et al. , 1996, Toxicological Benchmarks for Wildlife: 1996 Revision, ES/ER/TM-86/R3, http://www.esd.ornl.gov/programs/ecorisk/documents/tm86r3.pdf (values are the toxicities measured in the test species)

Table 4No-effect and low-effect TRVs for mammals

Baseline ecological risk assessment and addendumBonita Peak Mining District Superfund Site and Durango reach San Juan

County, CO

Eco-SSL TRVsa

1996 toxicological benchmarks for

wildlifeb

Lower Darby Creek Area Superfund Site

BERAc

1996 toxicological benchmarks for

wildlifeb

Arsenic 1.04 0.126 4.55 1.26Cadmium 0.77 1.0 6.87 10.0

Chromium III 2.4 2737e 58.2 --

Copper 5.6 11.7 82.7 15.4Lead 4.7 8.0 186.4 80Mercury (inorganic) -- 1.0 -- 3.0d

Nickel 1.7 40 14.8 80Selenium 0.14 0.2 0.66 0.33Silver 6.02 -- 119 --Zinc 75.4 160 298 320EcoSSL – ecological soil screening level

TRV – toxicity reference value

Footnotes:

All units are in mg/kg bw-day

Shading identifies TRVs selected for use in the SLERAa USEPA Eco SSL reports (http://www.epa.gov/ecotox/ecossl), as follows:

EPA, 2005. Ecological soil screening levels for arsenic. Interim final. OSWER Directive 9285.7-62.

EPA, 2005. Ecological soil screening levels for cadmium. Interim final. OSWER Directive 9285.7-65.

EPA, 2008. Ecological soil screening levels for chromium. Interim final. OSWER Directive 9285.7-66.

EPA, 2007. Ecological soil screening levels for copper. Interim final. OSWER Directive 9285.7-68.

EPA, 2005. Ecological soil screening levels for lead. Interim final. OSWER Directive 9285.7-70.

EPA, 2007. Ecological soil screening levels for nickel. Interim final. OSWER Directive 9285.7-76.

EPA, 2007. Ecological soil screening levels for selenium. Interim final. OSWER Directive 9285.7-72.

EPA, 2006. Ecological soil screening levels for silver. Interim final. OSWER Directive 9285.7-77.

EPA, 2007. Ecological soil screening levels for zinc. Interim final. OSWER Directive 9285.7-73.

d The reference did not provide a low-effect benchmark. The value represents the no effect benchmark X 3e The no-effect TRV for CrIII is as reported in the reference

-- not available

prepared by: EC 6-8-16

reviewed by: SJP 6-13-16

modified by: SJP 10-7-16

c Table C-8 in the Feasibility Study (FS) prepared for the Lower Darby Creek Area Superfund Site (https://semspub.epa.gov/work/03/2156095).

Analyte*

no-effect TRVs low-effects TRVs

b Sample et al ., 1996, Toxicological Benchmarks for Wildlife: 1996 Revision, ES/ER/TM-86/R3, http://www.esd.ornl.gov/programs/ecorisk/documents/tm86r3.pdf (values are the toxicities measured in the test species)

* Only those metals identified as "important bioaccumulatice compounds " in Table 4-2 of EPA (2000) are included in this table.

Table 5Measurement endpoints and related sampling activities

Baseline ecological risk assessment and addendumBonita Peak Mining District Superfund Site and Durango reach

Toxicity testing Biological surveys Sampling activities Toxicity testing Biological surveys Sampling activities Biological surveys Biological surveys

at mouth of Mineral Creek before confluence with Animas River M34 √ √ √ √ √ √ √ √ √ √Mineral Creek below confluence with South Fork M29A √ √ √ √Mineral Creek above confluence with South Fork M27 √ √ √ √ √ √ √ √ √ √Mineral Creek below confluence with Middle Fork M27A √ √ √ √Mineral Creek above confluence with Middle Fork M14B √ √ √ √ √ √ √ √ √ √ √ √ √ √ √Mineral Creek below confluence with Mill Creek M11 √ √ √ √

Mineral Creek ‐ upstream of Mill Creek EU‐4 Mineral Creek above confluence with Mill Creek M10A √ √ √ √ √ √ √ √ √ √

Mineral Creek ‐ South Fork EU‐5 at mouth of South Fork before confluence with Mineral Creek M28 √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Mineral Creek ‐ Middle Creek EU‐6 towards mouth of Middle Creek before confluence with Mineral Creek M20 √ √ √ √ √ √ √ √ √ √

above confluence with Arrastra Creek A56 √ √ √ √ √ √ √ √ √ √below confluence with Cunningham Creek A55 √ √ √ √

Cunningham Creek  EU‐8 at mouth of Cunningham Creek before confluence with Animas River  A48 √ √ √ √ √ √ √ √ √ √

above confluence with Cunningham Creek and upstream of the Colorado Gold Fields holding ponds

A45 √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

below confluence with Minnie Gulch A41A √ √ √ √above confluence with Minnie Gulch A40 √ √ √ √ √ √ √ √ √ √

below confluence with mainstem South Fork Animas River A35 √ √ √ √

Upper South Fork Animas River up to confluence with Eureka Gulch

EU‐11 at mouth of Upper South Fork before confluence with Euraka Gulch A36 √ √ √ √ √ √ √ √ √ √

Eureka Gulch up to confluence with Upper South Fork Animas River

EU‐12 at mouth of Eureka Gulch before confluence with Upper South Fork A37 √ √ √ √ √ √ √ √ √ √

mainstem South Fork Animas River up to confluence with Animas River in Eureka

EU‐13 at mouth of mainstem South Fork before confluence with Animas River  A34 √ √ √ √ √ √ √ √ √ √

above confluence with mainstem South Fork A33 √ √ √ √ √ √ √ √ √ √below confluence with mainstem West Fork A14 √ √ √ √

Upper West Fork Animas River to confluence with Placer Gulch

EU‐15 at mouth of Upper West Fork before confluence with Placer Gulch A10 √ √ √ √ √ √ √ √ √ √

Placer Gulch to confluence with Upper West Fork Animas River

EU‐16 at mouth of Placer Gulch before confluence with Upper West Fork A20 √ √ √ √ √ √ √ √ √ √

mainstem West Fork Animas River to confluence with Animas River in Animas Forks

EU‐17at mouth of mainstem West Fork above confluence with Animas River along California Gulch

A15 √ √ √ √ √ √ √ √ √ √

North Fork Animas River below confluence with Burrows Creek A08 √ √ √ √ √ √ √ √ √ √

North Fork Animas River above confluence with mainstem West Fork Animas River 

A09 √ √ √ √

Burrows Creek  EU‐19 at mouth of Burrows Creek before confluence with North Fork A07 √ √ √ √ √ √ √ √ √ √

EU‐DR01 From James Ranch to 32nd Streetmultiple locations

√ √ √ √ √ √ √ √ √ √ √ √ √

EU‐DR02 from 32nd Street to Purple Cliffsmultiple locations

√ √ √ √ √ √ √ √ √ √ √ √ √

Mill Creek‐ Ref ‐‐ towards mouth of Mill Creek before confluence with Mineral Creek M08 √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Bear Creek ‐ Ref ‐‐ at mouth of Bear Creek before confluence with Mineral Creek M30 √ √ √ √ √ √ √ √ √ √

Maggie Gulch ‐ Ref ‐‐ at mouth of Maggie Gulch before confluence with Animas River A43 √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Picayne Gulch ‐ Ref ‐‐ at mouth of Picayne Gulch before confluence with Animas River A26 √ √ √ √ √ √ √ √ √ √

North Fork Animas River ‐ Ref ‐‐ Animas River upstream of confluence with Burrows Creek A05 √ √ √ √ √ √ √ √ √ √

Hermosa Creek ‐‐Hermosa Creek upstream of irrigation diversion approximately 20 meters

√

NotesSampling is scheduled to occur in October 2016

Animas River ‐ Eureka to Animas Forks EU‐14

Measurement endpoint #1  Measurement endpoint #2 Measurement Endpoint #3

Dissolved metals in pore water

Total recoverable metals in sediment

Sediment (Hyallela aztecta )

Dissolved metals in surface water

Surface water (O. mykiss)

Total metals in surface water

Total recoverable metals in sediment

Animas River ‐ Howardsville to Minnie Gulch EU‐9

Animas River ‐ Minnie Gulch to Eureka EU‐10

Measurement Endpoint #4 Measurement Endpoint #5

Sampling activities Biological surveys Sampling activities Sampling activities

Total metals in surface water

Total recoverable metals in sediment

Quantitative bentic inverts Quantitative fish Qualitative fishTissue concs in benthic inverts

Mineral Creek ‐ Animas River to South Fork EU‐1

Mineral Creek ‐ South Fork to Middle Fork EU‐2

Mineral Creek ‐ Middle Fork to Mill Creek EU‐3

Exposure units EU name Proposed sample locations

Sample location name

Tissue levels in fishTotal metals in surface water

Tissue levels in fish

Quantitative refers to the assessment of the structure and function of the benthic macroinvertebrate and fisheries communities in the field.  Also, at these locations, benthic macroinvertebrate and fish tissue samples will be collected to measure tissue metal concentrations

Animas River  ‐ Durango reach

EXPOSURE UNITS FOR THE BONITA PEAK MINING DISTRICT AND DURANGO REACH AQUATIC BERA & BERA ADDENDUM

Upper Animas River

Mineral Creek

Animas River  ‐ Durango reach

REFERENCE LOCATIONS FOR THE BONITA PEAK MINING DISTRICT AND DURANGO REACH

Mineral Creek

North Fork Animas River ‐ Burrows Creek to Animas Forks   

EU‐18

Animas River

Animas River ‐ confluence with Arrastra Creek to Howardsville

EU‐7

Durango Reach 

The qualitative fish assessment will consist of a one‐pass backpack electroshocking run to assess the presence/absence of fish at each sampling location

Measurement endpoint #1: Maintain a stable and healthy benthic macroinvertebrate community:  Are the contaminant levels in sediment and pore water high enough to cause biologically‐significant changes or impair the function of the benthic macroinvertebrate community in the Bonita Peak Mining District and Durango Reach EUs ?Measurement endpoint #2: Maintain a stable and healthy water column invertebrate and fish community:  Are the contaminant levels in surface water high enough to cause biologically‐significant changes or impair the function of the water column invertebrate and fish communities in the Bonita Peak Mining District and Durango Reach EUs? Measurement endpoint #3: Maintain stable and healthy aquatic‐dependent invertivorous bird populations: Are the contaminant levels in surface water, sediment and benthic invertebrates high enough to cause biologically‐significant changes or impair the function of aquatic ‐dependent invertivorous bird populations foraging in the Bonita Peak Mining District and Durango Reach EUs?Measurement endpoint #4: Maintain stable and healthy piscivorous bird populations: Are the contaminant levels in surface water and fish high enough to impair paquatic‐dependent iscivorous bird populations foraging in the Bonita Peak Mining District  and Durango reach EUs?Measurement endpoint #5: Maintain stable and healthy aquatic‐dependent carnivorous mammal populations: Are the contaminant levels in surface water, sediment, and fish high enough to impair aquatic ‐dependent carnivorous mammal populations foraging in the Bonita Peak Mining District  and Durango reach EUs?

Table 6Exposure parameters for the wildlife receptors selected for use in food chain modeling Baseline

ecological risk assessment and addendumBonita Peak Mining District Superfund Site and Durango reach

San Juan County, CO

sediment(kg/kg BW-

day, dw)

--h 100g

0.003414j 100g

d Calculated using IRfood (g dw/day) = 0.153*BW(g)0.834, adjusted to 1.0 kg of receptor (see Table 2 [carnivores] on p. 28R in Nagy, 2001)

f EPA. 1993. Wildlife exposure factors handbook. EPA/600/R-93/187a created by: EC (6/9/16)f1 the number is the average of three mean BW values for adult male and female belted kingfishers (EPA, 1993) reviewed by: SJP (6/14/16)f2 the number is the average of seven mean BW values for adult male and female raccoons (EPA, 1993) modified by: SJP (10/6/16)gbest profesional judgment based on the needs of the BERA reviewed by: DH 10/13/16h the belted kingfisher is assumed not to ingest sediment i best professional judgment (value represents 10% of food intake on a dry-weight basis) j The estimated % soil in the diet (dw) of raccoons is estimated at 9.4% as shown in Table 4-4 on p. 4-20 in EPA (1993)

l Montana Field Guide. Available at http://fieldguide.mt.gov/speciesDetail.aspx?elcode=ABPBH01010BW - Body weightdw - dry weight

ingestion ratesdietary

composition

home range (kg)

food water

aqu

atic

in

vert

.

fish

(kg/kg BW-day, dw)

(L/kg BW-day)

(Cinclus mexicanus )American dipper

0.054k 0.176a 0.155c 0.0176i 100g --from 50 yards up to half a

milel

aquatic insectivorous birdswildlife species

body weight

piscivorous birdsbelted kingfisher (Ceryle alcyon ) 0.147f1 0.158b 0.11c -- 2.25f km

piscivorous mammalsraccoon(Procyon lotor) 5.78f2 0.036d 0.0831e -- 9.19f km

k mean BW of average adult male (57 g) and adult female (51 g) American dippers; Alaska Department of Fish and Game (http://www.adfg.alaska.gov/static/education/wns/american_dipper.pdf)

a Calculated using IRfood (g dw/day) = 0.63*BW(g)0.683, adjusted to 1.0 kg of receptor (see Table 3 [passerines] on p. 29R in Nagy, 2001)b Calculated using IRfood (g dw/day) = 0.849*BW(g)0.663, adjusted to 1.0 kg of receptor (see Table 3 [carnivorous birds] on p. 29R in Nagy, 2001)c Calculated using IRwater (L/day) = 0.059*BW(kg)0.67, adjusted to 1.0 kg of receptor (see eq. 3-15 [all birds] on p. 3-8 in EPA, 1993)

Nagy, K. A. 2001. Food requirements of wild animals: Predictive equations for free-living mammals, reptiles, and birds. Nutr. Abs. and Rev. Series B. Livestock Feeds and Feeding. Vol. 71, No. 10. Pp 21 – 32.

e Calculated using IRwater (L/day) = 0.099*BW(kg)0.90, adjusted to 1.0 kg of receptor (see eq. 3-17 [all mammals] on p. 3-10 in EPA, 1993)

Figures

Area of Interest

Figure A.7-1Bonita Peak Mining DistrictExposure Units and Reference Areas

Map Date: August 4, 2016Data Sources: Rivers and Streams: CDOW (2004) Proposed Sample Locations: U.S. EPA (2016) Exposure Units and Reference Areas: U.S. EPA (2016) Image: Microsoft Bing Hybrid (2016)Map Projection: UTM Zone 13 N, NAD83, Meters

0 21Miles

0 52.5Kilometers

µ

!.ProposedSample LocationsEU-01(Mineral Creek)EU-02(Mineral Creek)EU-03(Mineral Creek)EU-04(Mineral Creek)EU-05(South Fork ofMineral Creek)EU-06(Middle Fork ofMineral Creek)EU-07(Animas River)EU-08(Cunnigham Creek)EU-09(Animas River)EU-10(Animas River)

EU-11(Upper South Forkof Animas River)EU-12(Eureka Gulch)EU-13(South Fork ofAnimas River)EU-14(Animas River)EU-15(West Fork ofAnimas River)EU-16(Placer Gulch)EU-17(Upper West Forkof Animas River)EU-18(North Fork ofAnimas River)EU-19(Burrows Creek)Reference Areas

Area of Interest

Figure A.7-2Bonita Peak Mining DistrictExposure Units and Reference Area

Map Date: September 6, 2016Data Sources: Rivers and Streams: CDOW (2004) Proposed Sample Locations: U.S. EPA (2016) Exposure Units and Reference Areas: U.S. EPA (2016) Image: Microsoft Bing Hybrid (2016)

Map Projection: UTM Zone 13 N, NAD83, Meters

0 21Miles

0 52.5Kilometers

µ

EU-DR01(Animas River)

EU-DR02(Animas River)

Reference AreaPreviously Studied(~45 Miles Downstreamto Bakers Bridge)

 

 

 

Appendix 1

Response to Comments

GENERAL COMMENTS: Sunnyside Gold Corp. general comment 1: The Work Plan (WP) includes the required risk assessment elements but lacks detail and explanation. This detail may be present in the Sampling and Analysis Plan (SAP) and Quality Assurance Project Plan (QAPP), however, those documents were received only recently and have not yet been reviewed. Considering the scope of the 2015 Baseline Ecological Risk Assessment (BERA), the WP does not provide information about whether the various measurement endpoint data will be compiled into a focused Weight-of-Evidence (WOE) approach to assess risks rather than narrowly focus on surface water and sediment chemistry exceedance of effects thresholds. For example, the 2015 BERA evaluated pore water and bulk sediment Hazard Quotients (HQs) relative to site-specific toxicity test results. The BERA authors also conducted benthic community surveys to derive Multi-Metric Indices (MMIs). Some of these elements were brought together in the 2015 BERA, such as the toxicity data relative to the bulk sediment and pore water HQs. The MMI data were evaluated separately. Bringing the sediment toxicity data together with the sediment and pore water concentration/HQ data, and the community metric information into a WOE for risk characterization and conclusions would be beneficial, particularly if there are instances when effects thresholds are exceeded, but toxicity is not apparent. Combining the information into a sediment quality WOE approach would enhance the risk conclusions. There are opportunities in this 2016 BERA to provide a more complete WOE approach that considers the effects threshold information together with toxicity data and community/population data to consider if ecological risks are present. Finally, how will the results of this BERA be used? We know from the 2015 BERA that significant impairment exists (e.g., high HQs, results of toxicity tests) from Silverton to Bakers Bridge as well as for the Animas River in proximity to Mineral Creek and Cement Creek. Where risks are characterized as significant, will any additional analyses be conducted to refine risk estimates from an Exposure Unit (EU)-specific assessment to a location-specific assessment within each EU? EPA response to general comment 1: The 2017 Bonita Peak Mining District (BPMD) BERA will focus on providing an “integrated” risk characterization for the two aquatic community-level receptor groups (i.e., Benthic Macroinvertebrates [BMI] and fish). It is acknowledged that this approach provides more powerful insights compared to a disconnected measurement-endpoint by measurement-endpoint evaluation. We also recognize that some lines of evidence (e.g., the MMIs) may provide more powerful insights than other more qualitative lines of evidence (e.g., comparing analytical data to generic published benchmarks). However, the Agency will avoid assigning formal “weights” to each measurement endpoint as these weighing values can be subjective, controversial and open to interpretation. As stated at the start of this response, we accept the need to fully integrate the evaluation of the various measurement endpoints for the aquatic receptor groups. Also, if the risk interpretation at a particular EU is inconclusive, then the Agency is open to collecting more site-specific data from that EUs to narrow down the uncertainties and strengthen the conclusions.

SPECIFIC COMMENTS Sunnyside Gold Corp. specific comment 1: Page 1, last paragraph, states, “The Bonita Peak Mining District has multiple mining-related features all the way into the uppermost reaches of its watershed. Additionally, there are naturally occurring metals concentrations at levels that limit aquatic life in some but not all tributaries. Hence, a true background location representative of the river itself will not be available for use in the future BERA. However, several creeks are identified further below that will serve as local reference waterways.” In this paragraph from the WP, it is not clear what is meant by the statement, “Hence, a true background location representative of the river itself will not be available for use in the future BERA.” Further it is not clear why or how the statement above distinguishes between “background” and “local reference waterways”. In the context of CERCLA, the USEPA (2000) defines background as constituents or locations that are not influenced by the releases from a site, and is usually described as naturally occurring or anthropogenic: 1) Anthropogenic – natural and human-made substances present in the environment as a result of human activities (not specifically related to the CERCLA release in question); and, 2) Naturally occurring – substances present in the environment in forms that have not been influenced by human activity. In the BPMD, both types of background areas are likely present. Mast et al. (2007) confirms that metals released from weathering of altered rock from unmined areas are significant in some stream reaches and must be taken into account when water quality standards and remediation goals are established for the Animas River watershed study area. Differentiating between the impact from significant mining-related features and effect of natural sources of metals or acidic waters is a critical component of a CERCLA Remedial Investigation, and deserves considerable thought relevant to the locations chosen and how those data will be used in the ERA process. On Page 32, the WP states that the risk measured at the reference locations may be discussed qualitatively in the uncertainty analysis. We suggest that the conditions attributable to background be evaluated quantitatively for the risk characterization so that background conditions can be considered in the risk management actions consistent with EPA policy (USEPA 2002). EPA response to specific comment 1: The Agency recognizes the distinct challenge of identifying local background conditions in the BPMD which represent the “Animas River” or “Mineral Creek”. In order to avoid confusion, both the BERA WP and the SAP/QAPP will be revised to ensure that the term “reference” is used consistently throughout these reports, and not “background”. The BTAG identified five reference locations that will be sampled in the fall of 2016 in support of the 2017 BPMD BERA. Those locations are Mill Creek and Bear Creek in the Mineral Creek watershed, and Maggie Gulch, Picayne Gulch, and the north fork of the Animas River upstream of the confluence with Burrows Creek in the Animas River watershed. It is understood that some limited mining-

related activities may have occurred within the watersheds of one or more of these reference waterways. It is hoped that sampling more than one reference stream will provide a range of results for evaluation and interpretation in the BPMD BERA. However, based on previous decisions pertaining to the 2015 Animas River BERA, we do not propose to quantitatively evaluate reference risk in the risk characterization. Hence, the BERA risk tables will provide “total” risk, and not “incremental” risk. Instead, as explained in the BERA WP, the reference conditions will be fully evaluated and explained in the uncertainty section. The Agency believes that the best way forward is to present and discuss total risk in the risk characterization and then determine in post-BERA risk management discussions within the BTAG how best to account for local reference conditions. This softer approach recognizes the unique difficulty of reaching an up-front consensus on what constitutes true “background” in the BPMD. Sunnyside Gold Corp. specific comment 2.a The WP states in Section 4.3 that assessment endpoints will be based on “biologically-significant” changes in receptor categories. How will this be determined? Will habitat/benthic/fish data be collected from background locations? How will biologically significant changes in bird populations be determined from food chain food-chain modeling? EPA response to specific comment 2.a: We agree that the term “biologically-significant” is open to interpretation. To avoid any confusion, all the assessment endpoints will be rephrased as follows (the example below is for the BMI community):

“Maintain a stable and healthy benthic macroinvertebrate community: Are the contaminant levels in sediment and pore water high enough to affect survival, growth, and/or reproduction or impair the function of the benthic macroinvertebrate community […].”

Sunnyside Gold Corp. specific comment 2.b Assessment endpoints #1 Site-specific sediment toxicity tests will be conducted as part of the 2017 BPMD BERA. Since they are being conducted, results of those tests should take priority as the primary measurement endpoint. Ten day Hyalella. azteca tests are not acute tests, but rather sub-chronic tests that form the highest percentage of tests used to derive the consensus-based sediment quality guidelines being used as effects thresholds for this future BERA. Therefore, site-specific, ten-day H. azteca tests are entirely adequate and should be the preferred measure for assessment of sediment toxicity. EPA response to specific comment 2.b: As explained in the response to general comment 1, the 2017 BPMD BERA will assess all measurement endpoints in an integrative fashion. The results of the sediment toxicity tests will be given more importance in the risk interpretation than the HQs calculated based on published sediment benchmarks. However, EPA feels that the MMIs, which integrate community-level responses over long-term exposures to local conditions, may be better site-specific indicators of

risk than the laboratory-based sediment toxicity tests. One of several reasons for this opinion is the Agency’s view that surface water is likely to be an important exposure medium to BMIs in the field due to the preponderance of coarse substrate which may allow partial or even full contact with the overlaying surface water. For the record, EPA considers the 10-day H. azteca sediment test to represent an acute bioassay (e.g., EPA, 1996) Sunnyside Gold Corp. specific comment 2.c The 2016 BERA WP also indicates that assessments of habitat quality will be conducted, which should also be incorporated into the overall assessment. A quantitative evaluation in each reach of the substrate distribution is important particularly when large substrates are the dominant substrate present, but risks are being evaluated based on fine sediment deposition. For example, if bulk sediment concentrations exceed effects thresholds or sediment toxicity is defined in sediments comprised of fine materials, what is the relative contribution of fines to the overall sediment substrate of the reach being evaluated? In high gradient streams, fines are often transient subjected to annual scour and deposition. EPA response to specific comment 2.c: The Agency agrees that the results of the habitat quality evaluation to be performed at all the EUs will play an important role to help inform the risk characterization, on issues such as the representativeness of fines in the substrate. Note, however, that habitat quality does not represent a separate measurement endpoint. Instead, the habitat quality data will be used as suggested in the comment, i.e., help interpret the chemical, toxicological, and biological data collected for each of the measurement endpoints. For example, if the available habitat at an EU is not suitable to maintain a fish population, then the outcome of the surface water toxicity tests in support of the fish assessment endpoint for that EU will be interpreted accordingly. Sunnyside Gold Corp. specific comment 2.d Assessment Endpoint #2 The surface water data and pore water data are compared to chronic effects thresholds, and acute toxicity thresholds for trout have been recalculated from the 2015 BERA, and could be used to compare ambient surface water concentrations to revised thresholds. It appears from the 2015 BERA and the work from Church et al. (2007) that runoff and episodic pulses of acutely toxic COC concentrations exist which should be captured by the surface water sampling program. Further, once source control has been achieved, clean up decisions will be primarily focused on chronic exposure and toxicity. EPA response to specific comment 2.d: The proposed sampling and analysis effort in support of the 2017 BPMD BERA will occur in the early fall of 2016. Every attempt will also be made to identify and obtain chemical, biological, and toxicological data collected over the last five years from the target EUs in the fall in order to generate EU-specific datasets as representative as possible of fall conditions. However, keep in mind that flows in the Animas and Mineral Creek watersheds in early fall typically is close to or at base flow. Also, all surface water sampling in the fall is “punctual”, meaning that the samples represent conditions at one particular moment in time, which may or may not coincide with a run-off event or an episodic pulse of acutely toxic surface water. Nonetheless, the BERA WP

will be modified to include acute toxicity Ecological Screening Values (ESVs) for use in the risk characterization. Sunnyside Gold Corp. specific comment 3 Section 5.2.1 identifies that ½ the Method Detection Limit (MDL) for non-detects will be used. As per USEPA guidance, the more technically-defensible calculation of Central Tendency Exposure (CTE) and Reasonable Maximum Exposure (RME) is to use the routines for non-detected data in USEPA's Pro UCL version 5.1. EPA response to specific comment 3: Section 5.2.1 pertains specifically to selecting Contaminants of Potential Ecological Concern (COPECs). That process does not rely on CTEs or RMEs, but only on the maximum-detected value for detected compounds, or half the method detection limit for non-detected compounds. As explained in Section 7.1 of the BERA WP, EPA will use the ProUCL software, whenever possible, to calculate CTEs and RMEs for use in the actual exposure analysis. Sunnyside Gold Corp. specific comment 4: Section 5.2.1, page 20, Bullet – Hardness dependent toxicity, 2nd Paragraph The paragraph states that “the only reliable way to identify the most toxic surface/pore water concentration…”. Exposure Point Concentrations (EPCs) will be represented by RMEs and CTEs. Criteria thresholds based on hardness should have a similar level of consideration when the hardness used represents the conditions of exposure. The goal of the ERA is to estimate risks based on representative conditions, not maximum-only conditions. As stated below, the representative low-flow criteria would be more appropriately derived using hardness values from those conditions. The Colorado Department of Public Health and Environment (CDPHE) Regulation 31 (5 CCR 1002-31) states the following related to derivation of hardness for hardness-dependent metal evaluations: Hardness values to be used in equations are in mg/l as calcium carbonate and shall be no greater than 400 mg/l. The exception is for aluminum, where the upper cap on calculations is a hardness of 220 mg/l. For permit effluent limit calculations, the hardness values used in calculating the appropriate metal standard should be based on the lower 95 percent confidence limit of the mean hardness value at the periodic low-flow criteria as determined from a regression analysis of site-specific data. Where insufficient site-specific data exists to define the mean hardness value at the periodic low-flow criteria, representative regional data shall be used to perform the regression analysis. Where a regression analysis is not possible, a site-specific method should be used, e.g., where hardness data exist without paired flow data, the mean of the hardness during the low-flow season established in the permit shall be used. In calculating a hardness value, regression analyses should not be extrapolated past the point that data exist. For determination of standards attainment, where paired metal/hardness data is available, attainment will be determined for individual sampling events. Where paired data is not available, the mean hardness should be used.

EPA response to specific comment 4: As with the previous comment, Section 5.0 of the BERA WP deals with selecting COPECs, not calculating CTEs and RMEs for use in the risk characterization. COPEC selection, by definition, identifies “worst-case” exposure conditions to ensure that a contaminant is not inadvertently removed when it should be kept for further assessment. As explained in the BERA WP, when it comes to the hardness-dependent metals and retaining the highest values to select COPECs but without also accounting for surface water hardness may not necessarily identify the most- toxic concentrations since toxicity depends on hardness. That approach was also implemented in the 2015 Animas River BERA. As to the second half of the comment, note that the goal of the BERA is not to calculate permit effluent limits but to assess the potential for ecological risk at particular locations under existing exposure conditions. The latest version of CDPHE’s Regulation 31 (5 CCR 1002-31) will be used to obtain the surface water benchmarks for the hardness-independent and hardness-dependent metals. As noted in the comment, the case of aluminum is unique because its benchmark not only depends on surface water hardness, but also on surface water pH. This nuance was fully captured in the 2015 Animas River BERA and will again be addressed in the 2017 BPMD BERA. The latter will follow the same two-pronged approach as used in the 2015 Animas River BERA to quantify the toxicity of metals in surface water. The first approach consists of calculating a COPEC- specific HQ (hardness-adjusted, if necessary) for each surface water sample collected at a particular sampling location. The ensuing COPEC-specific HQs are then plotted to see if the majority fall above or below unity (i.e., HQ = 1.0). The second approach computes COPEC-specific CTEs and RMEs by combining all analytical data in a single dataset for each sampling location. Following the method used in the 2015 Animas River BERA, the lower 95% confidence limit of the available hardness values will be derived for each dataset and used to calculate conservative exposures for the hardness-dependent metals. Sunnyside Gold Corp. specific comment 5: Section 5.2.2.2 Surface water chronic ESVs Suter et al. (1996) data are out of date and have never been updated. The Michigan Department of Environmental Quality (MDEQ) Rule 57 values (MDEQ 2015) are regularly updated when new data become available as part of the Great Lakes Water Quality Initiative. Potential COPECs should be evaluated against the most relevant and current information. EPA response to specific comment 5: We agree with the comment. The Suter et al., (1996) reference will be removed from the final BERA WP and replaced with the MDEQ Rule 57 reference. The surface water ESVs foe use in the 2017 BPMD BERA will be changed accordingly. Sunnyside Gold Corp. specific comment 6: Section 5.3. Selection of COPECs for Wildlife Receptors.

Rather than simply identifying all metals that fall into the USEPA important bioaccumulative compound category and are detected in surface water as COPECs, a conservative screening step could easily be completed that uses the maximum concentrations detected in abiotic and biotic media in the default exposure model to estimate a screening-level exposure estimate. That estimate could be compared to the NOAEL Toxicity Reference Values (TRVs) and those metals that have screening level exposure estimates could then be carried forward as COPECs. By using the exposure model, the risk assessment may be able to eliminate additional metals at the screening step which would streamline the risk characterization. EPA response to specific comment 6: EPA recognizes that the proposed method represents an alternative way to identify wildlife COPECs. However, it entails performing (simplified) food chain modeling in order to identify the wildlife COPECs for use in the definitive food chain modeling. We are concerned that this approach may create more confusion than necessary. Also, we aim to pattern the 2017 BPMD BERA after the 2015 Animas River BERA since those two complementary documents should be harmonized as much as possible. The method proposed in the BERA WP, and used in the 2015 Animas River BERA, is admittedly conservative but simple and fully transparent. Even though it may identify more wildlife COPECs than strictly necessary, the food chain models are spreadsheet-driven. Hence, including extra COPECs in the calculations does not affect the overall effort. We propose to leave unchanged the COPEC-selection process for the wildlife receptors in the BERA WP. Sunnyside Gold Corp. specific comment 7: Section 6.2.2 Wildlife receptors, Tables 3 and 4 Tables 3 and 4. Selection of Wildlife TRVs. The table indicates that for most metals, the ‘Effects TRV’ selected is an Ecological Soil Screening Level (Eco-SSL) TRV yet the footnotes identify the source of the TRVs as from an ERA in Pennsylvania. We have been unable to verify the source or veracity of the cited Effects TRVs in the EcoSSL documents as shown in the column header. Additional information on the source and derivation details of the actual TRV shown in the table should be provided in this document rather than citing another risk assessment. In addition, it is recommended that the range of available effects-based TRVs be discussed for each metal in the table and be used in the selection and justification of the effects-based TRVs selected for this assessment. EPA response to specific comment 7: Tables 3 and 4 provide the no-effect and low-effect TRVs for birds and mammals, respectively. The reviewer identified an inconsistency with the table headings which will be corrected in the final BERA WP. The no-effect TRVs were obtained mainly from EPA’s Eco-SSL reports, whereas the low-effects TRVs (wrongly labeled as Eco-SSL TRVs in the two tables) were mostly obtained from Table C-8 in the Feasibility Study (FS) prepared for the Lower Darby Creek Area Site (https://semspub.epa.gov/work/03/2156095). Unfortunately, the EcoSSL reports only provide no-effect TRVs. The low-effect TRVs presented in Table C-8 of the FS were derived from bird and mammal toxicity data presented in the Eco SSL reports.

At this point in time, the Agency does not see the need to discuss COPEC-specific ranges of TRVs to justify the low-effects TRVs summarized in Tables 3 and 4 of the BERA WP. The low-effect TRVs presented in these two tables were obtained using EPA-approved toxicity datasets summarized in the Eco SSL reports. These values are appropriate for use in BERAs. If the need arises as part of the risk management decision making process, the Agency is open to revisit some of the low-effect TRVs to determine if acceptable alternative values could be substituted in the risk calculations. Other TRV sources may be obtained from the Los Alamos National Laboratory or the EPA Region 8 RCRA program, among others. If necessary, this decision will be made after the BERA has been reviewed by the BTAG. Sunnyside Gold Corp. specific comment 8: Section 6.3 Toxicity testing See comment above on the need and lack of value added of conducting acute toxicity testing for trout. EPA response to specific comment 8: Comment 8 is unclear but appears to be referring to the first paragraph of comment 11. EPA fully recognizes the inherent limitations of the acute trout toxicity tests but nonetheless feels that these bioassays provide useful data. It is clear that the lack of a toxic response (i.e., mortality) after 96 hours does not necessarily mean that trout populations in the field are therefore automatically protected from the COPEC exposures experienced in the test. That limitation was fully described in the uncertainty analysis of the 2015 Animas River BERA, and will be re-iterated in the 2017 BPMD BERA. Note also that a lack of acute toxicity will be weighed against other available lines of evidence (e.g., surface water analytical chemistry). Conversely, the presence of acute toxicity to juvenile rainbow trout is a powerful signal with direct risk management implications. EPA has also used acute trout tests to derive site-specific Water Effect Ratios (WERs). These bioassays have shown that site water spiked with a known level of Zn can be up to four times less toxic than laboratory control water spiked by the same amount of zinc. This pattern implies that some of the zinc spiked in the site water becomes bound up to particulate matter or subject to competitive inhibition at the gill surface which effectively results in reduced bioavailability. Based on these insights, we will continue to perform acute trout toxicity tests. Sunnyside Gold Corp. specific comment 9: Section 6.4 Ecotoxicity of select metals to trout Derivation of trout-specific criteria, given their importance as keystone sensitive species in the aquatic system, makes good sense because it eliminates data for non-trout species that don’t exist at the site. These data can and should be used to compare not only acute but also chronic thresholds to ambient surface water concentrations to examine if the CDPHE standards are representative and accurate for the different species. These trout-specific thresholds can also be

compared to the previous toxicity data generated by Besser and Leib (2007) and the 2015 BERA data to evaluate if these thresholds provide realistic estimates of acute toxicity for cadmium, copper, and zinc. Aluminum toxicity is also cited as a concern, not only for trout but also for aquatic life as a whole. CDPHE Regulation 31 incorporates a hardness-based equation for derivation of aluminum criteria in waters above a pH of 7. We recommend that Parametrix (2009) be reviewed as it presents an updated hardness-based derivation of both the acute and chronic aluminum criteria. Since the ERA process allows for less-conservative assumptions following the selection of COPECs, the Parametrix aluminum chronic criterion provides a scientifically-defensible value that could be used as a secondary, less-conservative risk characterization threshold to assess the uncertainty in risk estimates using the CDPHE threshold. EPA response to specific comment 9: The 2017 BPMD BERA will assess the potential for ecological risk to fish based on the species-specific trout toxicity surface water thresholds and the more generic CDPHE surface water benchmarks. The same approach was used in the 2015 Animas River BERA. The 2015 BPMD BERA will also review the Parametrix (2009) approach to derive an alternative aluminum chronic criterion to consider for inclusion as an additional line of evidence for use in the risk characterization. Sunnyside Gold Corp. specific comment 10.a: Section 7.3 Wildlife food chain modelling. 4th paragraph. While the sediment ingestion pathway is an important and potentially-significant exposure pathway that should be evaluated, the risk assessment should also acknowledge that metals ingested via sediment are generally less than 100% bioavailable. The incorporation of metals bioavailability in sediments is recommended as either part of the risk characterization or in the uncertainty analysis for those COPECs with HQs greater than 1.0 under the CTE exposure scenario. The potential effects of sediment bioavailability should then also be considered in the conclusions of the risk characterization for each receptor. EPA response to specific comment 10.a: The comment points to a critical uncertainty embedded within the food chain models, i.e., the assumption that metals in sediment are 100% bioavailable. EPA is fully aware that this assumption is probably quite conservative and results in higher HQs. However, without site-specific data on metals bioavailability, it is customary practice to calculate the wildlife HQs assuming 100% bioavailability. This issue was fully discussed in the uncertainty section of the 2015 Animas River BERA and will also be included in the 2017 BPMD BERA. It is expected that bioavailability will receive much scrutiny during future risk management discussions with the BTAG if the food chain models identify risk to one or more wildlife receptors. Based on the outcome of these discussions, EPA will consider collecting more

sediment from targeted EUs to assess site-specific bioavailability using chemical extraction techniques which mimic the gastrointestinal tract of birds or mammals. Sunnyside Gold Corp. specific comment 10.b: Table 5. Food ingestion rates estimated using the equations provided in EPA (1993) should be updated to use the more recent models provided in Nagy (2001), or other sources of updated information. EPA response to specific comment 10.b: The final BERA WP will update the food ingestion rates using the more recent models provided in Nagy (2001). Sunnyside Gold Corp. specific comment 10.c: Table 5. Additional information regarding the derivation of the exposure parameters shown in the table should be provided. For example, does the body weight represent a mean of both males and females, juvenile or adult, etc.? EPA response to specific comment 10.c: Table 5 will be updated by including a foot note explaining how the body weights were obtained. Sunnyside Gold Corp. specific comment 10.d: Table 5. The 4th paragraph of section 7.3 indicates that the belted kingfisher is not assumed to ingest sediment. We agree with that assumption, however, Table 5 provides a sediment ingestion rate for the belted kingfisher. The discrepancy should be discussed or Table 5 should be corrected. EPA response to specific comment 10.d: Table 5 will be adjusted to show that the belted kingfisher does not ingest sediment. Sunnyside Gold Corp. specific comment 10.e: Section 7.3 and Table 5. Please discuss how the home range estimates provided in Table 5 with units of miles and/or kilometers will be incorporated into the exposure estimates as part of the unitless Area Use Factor (AUF). EPA response to specific comment 10.e: The home range of the wildlife receptors is provided in Table 5 as supporting information but will not be applied to the food chain models, i.e., the food chain models will assume an AUF of 1.0, meaning that the entire exposure to a wildlife receptor will come from within each EU. Section 7.3 of the final BERA WP will be amended to clarify this point. If necessary, the AUFs – and how best to incorporate them in the food chain models – will be included in risk management discussions within the BTAG if risk is present to one or more

wildlife receptors foraging in certain EUs. This issue will also be discussed in the uncertainty section of the 2017 BPMD BERA if risk to one or more wildlife receptors is identified. Sunnyside Gold Corp. specific comment 10.f: Table 5. Please provide clarification as to whether or not the crayfish prey items shown in the table will be measured from crayfish samples collected or whether the crayfish EPCs will be estimated using the benthic invertebrate samples to be collected. EPA response to specific comment 10.f: Table 5 was in error and will be corrected by removing crayfish and mussels. The food chain models will assess risk to the aquatic-dependent wildlife piscivores by assuming that they forage exclusively on fish. The current text in Section 7.3 of the BERA WP is accurate in that respect. Sunnyside Gold Corp. specific comment 11.a: Section 8.2 Risk estimation methods Page 31, 1st paragraph The WP states that risks will be quantified mostly using the HQ method, which is consistent with risk assessment protocols. We want to clarify that (1) the HQ method is just one line of evidence, and (2) where site data include site-specific toxicity, results of those tests should be considered more reliable than comparisons to literature-based benchmarks. Site specific toxicity results integrate exposure of multiple COPECs through time and thus provide the best exposure estimate possible. Further, if resources are diverted from the acute trout toxicity testing with trout to more sediment toxicity testing, a more complete and representative assessment of toxicity in sediment should be achieved. We are not suggesting abandoning the use of sediment quality guidelines as part of the assessment, rather utilizing the best and most direct measures of exposure and effects as the primary line of evidence. EPA response to specific comment 11.a: See EPA responses to general comment 1 and specific comment 8. Sunnyside Gold Corp. specific comment 11.b: Page 32, 1st paragraph While we do not have an issue with using qualitative statements to describe HQs as low, moderate, or high; however, such an approach would be useful if integrated into a WOE approach that includes HQs, toxicity test results, and field metrics/population information. EPA response to specific comment 11.b: See EPA responses to general comment 1 Sunnyside Gold Corp. specific comment 11.c: Page 32, 4th paragraph

See previous comment related to background and the need for identifying and discussing background in the risk characterization section. While incremental risks will not be derived as noted in the WP, the importance and influence of background in this system should be fully vetted in the risk characterization section. EPA response to specific comment 11.c: See EPA responses to specific comment 1 Sunnyside Gold Corp. specific comment 11.d: Page 32, 1st bullet We agree that toxicity test data should be compared to reference samples as opposed to laboratory control samples. EPA response to specific comment 11.d: Comment noted. Sunnyside Gold Corp. specific comment 11.e: Page 32, 2nd bullet We assume the published ‘reference MMIs” include those outlined in CDPHE’s Policy 10-1 (CDPHE 2010), which indicates that two scores are ultimately derived, an attainment threshold and an impaired threshold. There is also a range between these two thresholds or “gray area”. EPA response to specific comment 11.e: The MMI scores for each biotype will be compared against aquatic life thresholds to determine attainment threshold values or impairment threshold values. For those values that fall between the two threshold values or “gray area”, additional metrics will be used to determine attainment or impairment. The additional metrics used as auxiliary metrics consist of the Hilsenhoff Biotic Index and the Shannon Diversity Index. A site is considered impaired if a Class 1 waters fails to meet the criteria shown below for either auxiliary metric.   

 Auxiliary Metric Thresholds for Class 1 Waters with MMI Scores Between the Attainment and

Impairment Thresholds

Biotype Hilsenhoff Biotic Index Shannon Diversity

Index 1 Transition <5.4 >2.4 2 Mountains <5.1 >3.0

References Besser, J.M. and K.K. Leib. 2007. Toxicity of metals in water and sediment to aquatic biota. Chapter E19 in Church, S.E., von Guerard, Paul, and Finger, S.E., eds., 2007, Integrated

investigations of environmental effects of historical mining in the Animas River watershed, San Juan County, Colorado: U.S. Geological Survey Professional Paper 1651, 1,096 p. plus CDROM. [In two volumes.]. Church, S.E., von Guerard, Paul, and Finger, S.E., eds., 2007, Integrated investigations of environmental effects of historical mining in the Animas River watershed, San Juan County, Colorado: U.S. Geological Survey Professional Paper 1651, 1,096 p. plus CD-ROM. [In two volumes.] Colorado Department of Public Health and Environment (CDPHE). 2010. Aquatic Life Use Attainment, Methodology to Determine Use Attainment for Rivers and Streams. Policy Statement 10-1. Colorado Department of Public Health and Environment, Denver, CO. Colorado Department of Public Health and Environment (CDPHE). Undated. Water Quality Control Commission. Regulation No. 31. The basic standards and methodologies for surface water (5 CCR 1002-31) EPA. 1996. Ecological effects test guidelines. OPPTS 850.1735. Whole sediment acute toxicity invertebrates, freshwater. EPA-712-C-96-354). Mast, M.A., P.L. Verplanck, W.G. Wright, and D.J. Bove. 2007. Characterization of Background Water Quality, Chapter E7 in Church, S.E., von Guerard, Paul, and Finger, S.E., eds., 2007, Integrated investigations of environmental effects of historical mining in the Animas River watershed, San Juan County, Colorado: U.S. Geological Survey Professional Paper 1651, 1,096 p. plus CD-ROM. [In two volumes.] Michigan Department of Environmental Quality (MDEQ). 2015. Rule 57 Water Quality Values based on Rule 323.1057 (Toxic Substances) of the Part 4. Water Quality Standards gives procedures for calculating water quality values to protect humans, wildlife and aquatic life. http://www.michigan.gov/documents/deq/wrd-swas-rule57_372470_7.pdf Nagy, K. A. 2001. Food requirements of wild animals: Predictive equations for free-living mammals, reptiles, and birds. Nutr. Abs. and Rev. Series B. Livestock Feeds and Feeding. Vol. 71, No. 10. Pp 21 – 32. Parametrix. 2009. Updated Freshwater Aquatic Life Criteria for Aluminum (Exhibit 2 of Direct Testimony of Robert W. Gensemer, Ph.D.). Prepared for Los Alamos National Laboratory. 25 pp. USEPA. 2002. Role of Background in the CERCLA Cleanup Program. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Emergency and Remedial Response. OSWER 9285.6-07P

Appendix 2

Trout Sensitivity Analysis

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Steps and Background for Developing Trout Specific Hardness-Dependent Toxicity Thresholds

1) BACKGROUND:

This project evaluates acute and chronic laboratory toxicity studies to provide evidence of a stressor-response relationship between water hardness and key metals (i.e., cadmium [Cd], copper [Cu], and zinc [Zn]) associated with past mining activities in Colorado. The literature search focused on four trout species (i.e. brook trout, brown trout, cutthroat trout, and rainbow trout) with the objective of establishing protective threshold metal concentrations in surface water to inform risk management decisions at mining sites.

Data sources used for this evaluation include existing surface water criteria documents, EPA’s Ecotox database (http://cfpub.epa.gov/ecotox/), peer-reviewed journal articles, and high-quality secondary literature. Pertinent information from each study was captured in a database and included organism parameters (length, weight, life stage), water quality characteristics (pH, hardness, alkalinity, temperature), design specifications (exposure duration, exposure type, method of chemical analysis), and toxicity endpoints. This trout sensitivity project evaluated trout-specific acute and chronic toxicity data from exposure to aluminum. EPA issued aluminum water quality standards (i.e. 750 µg/L for acute exposure; 87 µg/L for chronic exposure) in 1988, which are protective of aquatic life including trout (USEPA, 1988). In 2011, Colorado promulgated new aluminum standards which take into account the hardness and pH of the receiving water (CDPHE, 2013).1 As part of the 2011 Colorado recalculation effort, several aquatic species (i.e., midge, perch, snail, amphipod, daphnid, and trout) were evaluated and ranked for sensitivity to aluminum. The project described here, however, is exclusively focused on trout, for which not enough data points are available (i.e., 4 acute rainbow trout and 3 chronic brook trout) to confidently derive new toxicity thresholds. A literature search for post-2011 aluminum toxicity data on trout also did not uncover new information. Therefore, trout-specific acute and chronic toxicity threshold values for aluminum could not be calculated. Instead, the current pH- and hardness-dependent Colorado aluminum standard is adopted as the default standard.

2) SCREENING CRITERIA:

Acute toxicity data were screened to retain only studies conducted using the most acceptable procedures (Stephan et al., 1985) (Appendix A).

a. Only results from 96-hr LC50 tests were retained. Exposure periods less than or greater

than 96 hours were excluded. b. Results from flow-through tests were retained. Static tests were excluded. c. Only results from exposures with a pH within the defined range for aquatic life use (6.5 <

pH < 9) were retained. d. Results from tests in which the concentrations of the test material were measured were

retained. Tests in which concentrations were estimated or unreported were not retained. e. Only data from trout younger than 1 year (~10 grams) were retained because

smaller/younger trout are, to a limit, known to be more sensitive than large/older fish.

1 Aluminum water quality standards adopted in 2011, as cited in Regulation – 31 (effective as of January 31, 2013).

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Early life stage fish, which are still in the yolk sac (i.e. 1 day post hatch (dph)), were excluded from the data evaluation, as were eggs.

f. Results of acute toxicity tests in which organisms were fed during the 96-hour exposure period were not retained.

g. Results of tests conducted in unusual dilution waters were not retained. Stephan et al. (1985) defines unusual dilution waters as those, for example, with total organic carbon (TOC) > 5 mg/L. Other examples include those dilution waters with excessively high relative concentrations of cations and anions, (e.g. Na+, Cl-, Mg +2)

h. Acute values that appeared to be questionable in comparison with other acute and chronic data for the same species were not retained.

i. Retained toxicity values were converted to dissolved values using EPA metal-specific conversion factors if total metal concentration was reported, or if it was not stated.

j. Toxicity values from studies where fish were acclimated, or pre-exposed to the metal toxicant, were excluded from the data evaluation.

3) RATIONALE:

Geometric means and natural logarithms were calculated and used throughout this analysis because the most-widely used relationship is between hardness and acute toxicity of metals in fresh water. In addition, a log-log relationship fits these data (Stephan et al., 1985).

4) METHODOLOGY:

The literature search data were retained and entered in an Excel spreadsheet (Filename: TroutMetalDataEvaluation) for data evaluation. The data for Cd, Cu and Zn were entered in two separate tabs, namely Summary Metal and Metal Acute Trout. The pooled and species-specific trout slopes, species mean acute values (SMAV), Y-intercepts, and predicted toxicity thresholds (i.e. acute, chronic, LC50s) are outlined in Steps 5-13 below and cross-referenced in each of the Metal Acute Trout tabs in the Excel spreadsheets.

5) DETERMINATION OF SPECIES-SPECIFIC SLOPE:

The purpose of the species-specific slope is to decide whether the data for each species is useful, and the degree of agreement within, and between, species (Stephan et al., 1985). This summary data is compiled in Appendix A. A least-squares regression was developed in the Summary Metal tab for each species and each metal for which comparable 96-hour acute LC50 toxicity values were available for two or more different values of water quality characteristics (i.e. hardness). Regressions were plotted with the x-y graphing function in Excel. Slopes and R2 values were determined for each trout species and each metal using the natural logarithms of acute toxicity values vs. the corresponding natural logarithms of the hardness values to calculate a species-specific slope for each metal. The species- and metal-specific information needed for data evaluation was entered in the Summary Metal tab, and includes the following information:

a. 96-hour LC50 (µg/L) b. Hardness (mg/L CaCO3) c. Citation or source

6) DETERMINATION OF POOLED SLOPE FOR SALMONIDS:

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The pooled slope of the regression line is the best estimate of the trout species relationship between toxicity and hardness, and required for Step 7. Data is entered into the Metal Acute Trout tab for each metal, as follows:

a. Trout species b. Metal of concern c. Fish mass (grams) d. Fish length (mm) e. pH f. Hardness (mg/L CaCO3) g. Alkalinity (mg/L CaCO3) h. Temperature (C°) i. Reported or dissolved 96-hour toxicity endpoint value (µg/L), with a separate column

indicating if the value is total (“T”) or dissolved (“D”). This parameter is important as total values are converted to dissolved concentrations based on the methodology in Stephan et al., (1985).

j. Citation or source k. Notes

A least-squares regression of the natural logarithms of acute toxicity values vs. the corresponding natural logarithms of the hardness was performed on all retained data cumulatively for all trout species to obtain a pooled salmonid slope (V) and R2 for each metal (Figures 1 to 3). The pooled salmonid slope is calculated in the Metal Acute Trout tab and entered into cell K9.

7) DETERMINATION OF SPECIES MEAN ACUTE VALUE:

The data are pooled and averaged using the geometric mean to provide the SMAV and required for Step 8. The following equation was used to calculate the SMAV for each trout species as per the methodology described in Stephan et al., (1985):

SMAV50 = e (ln W) – V (ln X – ln 50) where: SMAV50 = species mean acute value at hardness of 50 mg/L CaCO3 W = geometric mean of dissolved acute toxicity values V = pooled acute slope X = geometric mean of hardness values Ln = natural logarithm E = exponent

8) DETERMINATION OF Y-INTERCEPT FOR LC50 TOXICITY THRESHOLDS: The y-intercept for LC50 toxicity thresholds is a statistical term that is required to calculate the LC50 toxicity threshold in Step 9. The following equation was used to calculate species-specific y-intercepts for each metal (Table 1):

Y-Intercept = ln(SMAV50) – (V x ln50)

where: SMAV50 = species mean acute value at hardness of 50 mg/L CaCO3 V = pooled acute slope

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Ln = natural logarithm of 50 mg/L CaCO3 hardness value

9) DETERMINATION OF LC50 TOXICITY THRESHOLDS: The calculation of LC50 toxicity thresholds, as reported in Table 1, is the initial step for determining acute and chronic toxicity thresholds in Steps 11 and 13 below. Species-specific LC50 equations were defined for each metal using the following equation: Predicted LC50 = e (V * ln (hardness) + Y-intercept) where: V = pooled acute slope Ln = natural logarithm of 50 mg/L CaCO3 hardness value

10) DETERMINATION OF Y- INTERCEPT FOR ACUTE TOXICITY THRESHOLDS:

The y-intercept for acute toxicity thresholds (Table 1) is a statistical term that is required to calculate the predicted acute toxicity thresholds in Step 11. The y-intercept was calculated using a safety factor of 2 (i.e., SMAV50/2 ) for each metal using the equation in Step 8.

11) DETERMINATION OF ACUTE TOXICITY THRESHOLDS:

The calculation of acute toxicity thresholds (Table 1) is the final step in the derivation process, if an acute/ chronic ratio (ACR) is unavailable. The predicted acute toxicity threshold equation (with a safety factor) was reported in the same manner as in Step 9, but with the intercept calculated in Step 10.

12) DETERMINATION OF Y-INTERCEPT FOR CHRONIC TOXICITY

THRESHOLDS: The y-intercept for chronic toxicity thresholds (Table 1) is a statistical term that is necessary for calculation of the predicted chronic toxicity thresholds in Step 13. The y-intercept was calculated for each metal using the equation in Step 8. The SMAV50 was divided by the species-specific ACR (Table 2).

13) DETERMINATION OF CHRONIC TOXICITY THRESHOLDS:

The predicted species-specific chronic toxicity threshold equation (with appropriate ACR applied) is reported in the same manner as in Step 9, however, with the intercept calculated in Step 12 (Table 1).

14) SELECTION OF METAL TOXICITY THRESHOLD VALUE: The lesser of two values calculated via Steps 11 and 13 was used as the species specific metal toxicity threshold values (Table 1).

15) UNCERTAINTY: Professional judgment is needed to determine the uncertainty associated with information taken from scientific literature, and any extrapolations used in developing the trout toxicity thresholds. In order to standardize the data selection process and reduce the number of potential confounding factors, data points from 96-hour acute toxicity tests were screened

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5 | P a g e

against the criteria outlined in Steps 2a-j. This screening process produces more “standardized” datasets, which reduces the overall level uncertainty of the data analysis, except as noted below. The toxic action and bioavailability of the three target metals is influenced by the pH, hardness, and DOC of the surface water. Hardness is a critical data point as natural logarithms of the 96-hour toxicity values and water quality characteristics (i.e. hardness) are plotted to determine a species-specific slope. A weak statistical relationship between hardness and LC50 values is likely if the entire data set consists of a narrow range of hardness concentrations (e.g. 45 – 55 mg/L CaCO3). Note that the reported brown trout correlation coefficient (R2 = 0.19) for Cu and the brook trout species specific slope (i.e. 11.08) for Cu (Tables 4b and 4c, respectively) indicate a weak relationship based on the limited range of hardness values, and therefore, introduce some level of uncertainty into the data evaluation for this metal.

Much scientific literature is available to determine the effects of Zn exposure to trout in a wide range of hardness values (Tables 3a – 3d), and therefore, confidence is high when calculating the trout toxicity thresholds for this metal. No Cd data were available for brook trout that satisfied the flow-through criterion outlined in Step 2b. In the absence of flow-through data, static toxicity data points were incorporated into the data evaluation as outlined in Stephan et al. (1985). Therefore, the inclusion of static 96-hour Cd data points deviates from the accepted methodology, and introduces some degree of uncertainty.

The published data do not fully capture the sensitivity of all trout species to Cd. These missing data represent an uncertainty in the current evaluation. Future research efforts should concentrate on obtaining acute and chronic data endpoints (i.e. 96-hour LC50s, NOECs, LOECs, etc.) in both soft and hard dilution waters with special emphasis on cutthroat and brook trout species.

References: Colorado Department of Public Health and the Environment (CDPHE), 2013. Regulation No. 31. CDPHE Water Quality Commission. Basic standards and methodologies for surface water (5 CCR 1002-31). Aluminum standards adopted in 2011. Most recent Regulation – 31 version: January 21, 2013. Stephan, C.E., D.I. Mount, D.J. Hansen, J.H. Gentile, G.A. Chapman, and W.A. Brungs. 1985. Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. PB5-227049. Duluth, MN. US Environmental Protection Agency (USEPA). 1988. Ambient water quality criteria for aluminum. Office of Water. EPA-440/5-88-008.

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6 | P a g e

Figure 1: Zinc regression

y = 1.1712x + 1.3715R² = 0.4835

4

5

6

7

8

9

10

2 3 4 5 6 7

ln (LC50)

ln (Hardness)

Zinc Pooled Reponse - Trout Species

BKT

BT

RBT

CUTT

Linear (Pooled slope)

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7 | P a g e

Figure 2: Copper regression

y = 1.109x - 0.9455R² = 0.7744

0

1

2

3

4

5

6

7

2 3 4 5 6 7

ln (LC50)

ln (Hardness)

Copper Pooled Response - Trout Species

BKT

BT

RBT

CUTT

Linear (Pooled Slope)

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8 | P a g e

Figure 3: Cadmium regression

y = 0.8103x - 2.2355R² = 0.6395

0

0.5

1

1.5

2

2.5

3

3.5

4

2 3 4 5 6 7

ln (LC50)

ln (Hardness)

Cadmium Pooled Response - Trout Species

BT

RBT

BKT

Linear (Pooled slope)

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9 | P a g e

Table 1: Parameters for species-specific metal toxicity equations. Red values indicate an estimate of a protective species-specific threshold to be used at hardness 50 for individual metals of concern.

LC50 Toxicity Thresholds (c)

Acute Toxicity Thresholds (d)

Chronic Toxicity

Thresholds (e)LC50 Toxicity Thresholds (f)

Acute Toxicity Thresholds (g)

Chronic Toxicity

Thresholds (h)Brown Trout Cadmium 53.11 2.55 2.44 0.64 0.8103 -2.283978729 -2.97712591 -3.177000834 2.43 1.21 0.99Rainbow Trout Cadmium 68.25 3.43 2.06 0.64 0.8103 -2.189457258 -2.882604439 -2.910608635 2.67 1.33 1.30Brook Trout Cadmium 227.23 7.87 ACR (i) 0.64 0.8103 -2.333165987 -3.026313167 ACR (a) 2.31 1.15 ACR (i)

Brook Trout Copper 51.08 46.53 ACR (i) 0.77 1.109 -0.522060013 -1.215207193 ACR (a) 45.44 22.72 ACR (i)Brown Trout Copper 50.72 36.67 2.17 0.77 1.109 -0.752304666 -1.445451847 -1.5282753 36.09 18.05 16.61Cutthroat Trout Copper 62.22 62.21 ACR (i) 0.77 1.109 -0.450493193 -1.143640373 ACR (a) 48.81 24.41 ACR (i)Rainbow Trout Copper 83.27 47.04 2.48 0.77 1.109 -1.053054191 -1.746201372 -1.960465618 26.72 13.36 10.78

Brook Trout Zinc 79.69 2528.70 2.34 0.48 1.1712 2.707789444 2.014642264 1.859649082 1464.91 732.46 627.29Brown Trout Zinc 57.31 663.90 1.63 0.48 1.1712 1.756526041 1.063378861 1.266107227 565.83 282.91 346.50Cutthroat Trout Zinc 67.33 399.50 2.633 (j) 0.48 1.1712 1.059889126 0.366741946 0.091765246 281.93 140.96 107.07Rainbow Trout Zinc 62.49 314.76 1.87 0.48 1.1712 0.908783827 0.215636646 0.283915487 242.39 121.19 129.76

(i) ACR is unavailable

Notes:

(j) Zinc Acute-to-Chronic Ratio for Cutthroat Trout was pre-populated for calculation of chronic endpoint.

Pooled R 2 (b)

Y-Intercepts

Calculated Values at Hardness of 50 mg/L

CaCO3

(c) Step 8 - equation and methodology for determining the y-intercept for LC50 toxicity thresholds

(d) Step 10 - methodology for determining the y-intercept for acute toxicity thresholds(e) Step 12 - methodology for determining the y-intercept for chronic toxicity thresholds(f) Step 9 - methodology and equation for calculating LC50 toxicity thresholds(g) Step 11 - methodology for calculating acute toxicity thresholds(h) Step 13 - methodology for calculating chronic toxicity thresholds

Values in red under Calculated Values at Hardness of 50 mg/L CaCO 3 indicate an estimate of a protective, species-specific, dissolved toxicity applicable to waters of hardness values of 50 for individual metals of concern.

(a) See Table 2: Derivation of Acute to Chronic Ratios (b) Step 6 - methodology for determining pooled slope and R2

Refer to pages 3-5, steps 6-13, Steps and Background for Developing Trout Specific Hardness-Dependent Toxicity Thresholds

Geomean of Hardness Values (X)

Geomean of Acute

Dissolved LC50s (W)

Acute Chronic

Ratio (ACR) (a)

Pooled Slope (V)

(b)MetalSpecies

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10 | P a g e

Table 2: Derivation of Acute to Chronic Ratios (ACRs) Metal Species Hardness Acute Chronic Ratio ACR ReferenceCadmium Brown Trout 37.6 2.4 0.4 5.9250 2.4425 Davies and Brinkman 1994a

Brown Trout 29.0 1.2 1.0 1.2059 Brinkman and Hansen 2004Brown Trout 68.0 3.9 1.8 2.1311 Brinkman and Hansen 2004Brown Trout 151.0 10.1 6.5 1.5443 Brinkman and Hansen 2004Brown Trout 39.8 1.9 1.3 1.4060 Davies and Brinkman 1994bRainbow Trout 47.0 2.6 1.5 1.7959 2.0568 Davies, Gorman, Carlson and Brinkman 1993Rainbow Trout 49.0 3.1 1.5 2.0952 Davies, Gorman, Carlson and Brinkman 1993Rainbow Trout 281.0 13.1 9.2 1.4317 Davies and Brinkman 1994cRainbow Trout 29.0 2.7 1.3 2.1190 Davies and Brinkman 1994cRainbow Trout 101.0 5.4 1.9 2.8421 Besser et al 2007

Copper Brown Trout 50.0 30.2 13.9 2.1727 2.1727 Davies et al 2002

Rainbow Trout 120.0 80.0 27.8 2.8808 2.4779 Seim et al 1984Rainbow Trout 101.0 83.0 40.0 2.0750 Besser et al 2007

Zinc Brook Trout 45.9 1996.0 854.7 2.3353 2.3353 Holcombe and Andrew 1978

Brown Trout 50.0 392.0 194.0 2.0206 1.6331 Davies and Brinkman 1999Brown Trout 39.0 550.0 457.0 1.2035 Davies and Brinkman 1994Brown Trout 27.3 367.0 251.0 1.4622 Davies et al 2003Brown Trout 131.0 1104.0 598.0 1.8462 Davies et al 2003Rainbow Trout 33.2 125.0 74.0 1.6892 1.8681 Brinkman and Hansen 2004Rainbow Trout 145.4 588.0 325.0 1.8092 Brinkman and Hansen 2004Rainbow Trout 25.5 430.0 276.7 1.5540 Sinley et al 1974Rainbow Trout 101.0 530.0 219.0 2.4201 Besser et al 2007

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APPENDIX A

Summary of the calculation of Zn, Cu, and Cd thresholds of four salmonid species

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Table 3a: Reported values of acute Zn toxicity (and corresponding test hardness) to rainbow trout. These studies remain from the screening of a larger dataset with 5

criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln

hardness Source

Rainbow Trout 850 6.75 120.0 4.79 1 Oncorhynchus 158 5.06 20.0 3.00 1 mykiss 2257 7.72 120.0 4.79 2 175 5.16 101.0 4.62 8

263 5.57 101.0 4.62 8 125 4.83 33.2 3.50 13

Slope= 0.9874 588 6.38 145.4 4.98 13

R2 = 0.5691 222 5.40 56.8 4.04 14 242 5.49 56.8 4.04 14 228 5.43 100.0 4.61 26 253 5.53 100.0 4.61 26 269 5.59 100.0 4.61 26 282 5.64 100.0 4.61 26 346 5.85 100.0 4.61 26 449 6.11 100.0 4.61 26 91 4.51 23.0 3.14 29 133 4.89 23.0 3.14 29 200 5.30 32.0 3.47 35 130 4.87 29.2 3.37 46 171 5.14 30.2 3.41 46 194 5.27 29.1 3.37 46 209 5.34 29.6 3.39 46 370 5.91 46.8 3.85 57 517 6.25 47.0 3.85 57 2510 7.83 178.0 5.18 57 2960 7.99 179.0 5.19 57 333 5.81 58.0 4.06 80 108 4.68 34.2 3.53 86 583 6.37 146.4 4.99 86 Geometric mean 313 62.5

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Table 3b: Reported values of acute Zn toxicity (and corresponding test hardness) to brown trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln

hardness Source

Brown Trout 642 6.46 43.7 3.78 14 Salmo trutta 381 5.94 44.5 3.80 14 617 6.42 45.6 3.82 14

757 6.63 54.2 3.99 14 392 5.97 54.1 3.99 18 642 6.46 37.6 3.63 37

Slope = 1.0158 392 5.97 51.8 3.95 40

R2 = 0.7931 871 6.77 51.9 3.95 40 1033 6.94 54.4 4.00 41 484 6.18 52.6 3.96 42 603 6.40 54.6 4.00 42 382 5.95 45.3 3.81 43 508 6.23 49.5 3.90 43 367 5.91 27.3 3.31 45 1104 7.01 131.0 4.88 45 6259 8.74 411.4 6.02 45

Geometric mean 665 57.3

Table 3c: Reported values of acute Zn toxicity (and corresponding test hardness) to brook trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln

hardness Source

Brook Trout 1700 7.44 50.0 3.91 23 Salvelinus fontinalis 1900 7.55 50.0 3.91 23 738 6.60 54.6 4.00 42 1550 7.35 46.8 3.85 57

Slope= 1.0568 2120 7.66 47.0 3.85 57

R2 = 0.7822 4980 8.51 170.0 5.14 57 6140 8.72 178.0 5.18 57

6980 8.85 179.0 5.19 57 Geometric mean 2529 79.7

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Table 3d: Reported values of acute Zn toxicity (and corresponding test hardness) to cutthroat trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln

hardness Source Cutthroat trout 140 4.94 31.1 3.44 13 Oncorhynchus 1645 7.41 149.4 5.01 13 clarkii 185 5.22 47.4 3.86 17 1420 7.26 144.0 4.97 17

Slope = 1.6229 314 5.75 47.4 3.86 17

R2 = 0.9347 1500 7.31 144.0 4.97 17 184 5.21 47.4 3.86 17 142 4.96 41.7 3.73 17 1040 6.95 144.0 4.97 17 130 4.87 40.8 3.71 42 411 6.02 51.3 3.94 42 Geometric mean 400 67.3

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Table 3e: Values of acute Zn toxicity (and corresponding test hardness) to trout eliminated through screening of a larger dataset with 5 criteria known to influence Zn toxicity.

Excluded studies Species Hardness LC50 Rationale Source RBT 10 101 c 2 RBT 33.0 170 a,e 4 RBT 33.0 177 e 4 RBT 33.0 187 e 4 RBT 33.0 200 e 4 RBT 33.0 207 e 4 RBT 33.0 221 e 4 RBT 33.0 241 e 4 RBT 33.0 245 e 4 RBT 33.0 265 e 4 RBT 33.0 290 e 4 RBT 33.0 336 e 4 RBT 33.0 459 e 4 RBT 33.0 93 e 4 RBT 33.0 105 e 4 RBT 33.0 129 e 4 RBT 33.0 138 e 4 BKT 170.0 1900 a 7 BKT 181.0 5400 a 7 RBT 97.5 1120 b,e 9 RBT 30.9 4530 a 10 RBT 30.2 170 a 10 RBT 31.2 190 a 10 RBT 31.3 110 a 10 RBT 31.4 880 a 10 RBT 387.0 4460 a 10 RBT 389.0 11100 a 10 RBT 389.0 5160 a 10 RBT 394.0 9950 a 10 RBT 390.0 7260 a 11 RBT 390.0 4850 a 11 RBT 390.0 4200 a 11 RBT 390.0 3960 a 11 RBT 390.0 5210 a 11 BT 54.1 871 g 18 RBT 5.6 40 b,e 19 RBT 41.3 169 b,e 20 RBT 41.3 2170 b,e 20 RBT 100.0 571 f 26 RBT 23.0 651 d 29 RBT 23.0 815 d 29 RBT 83.0 1755 d 30 RBT 9.2 65 e 32 RBT 9.2 95 e 32

DRAFT April 14, 2015

BT 54.0 690 c 40 BT 206.7 2267 c 40 BKT 52.6 1178 d 42 RBT 330.8 730 c 46 RBT 30.0 441 c 46 RBT 105.6 1170 c 46 RBT 190.0 1470 c 46 RBT 399.0 2560 c 46 RBT 102.3 904 c 46 RBT 396.3 2280 c 46 RBT 45.1 153 c 46 RBT 139.0 214 c 46 RBT 228.3 283 c 46 RBT 332.3 483 c 46 RBT 29.1 1510 c 46 RBT 28.7 548 c 46 RBT 28.4 610 c 46 RBT 20.0 90 b,e 49 RBT 30.0 810 d,e 51 RBT 30.0 410 d,e 51 RBT 30.0 430 d,e 51 RBT 314.0 7210 d,e 54 RBT 312.0 4520 d,e 54 RBT 23.0 560 d,e 54 RBT 30.0 830 d,e 54 RBT 102.0 1000 d,e 54 RBT 312.0 1164 d,e 54 RBT 22.0 235 d,e 54 RBT 28.5 26.8 a 55 RBT 29.1 33.3 a 55 RBT 29.6 124 a 55 RBT 30.8 109 a 55 RBT 30.9 23.9 a 55 RBT 31.3 53.3 a 55 RBT 87.1 184 a 55 RBT 90.0 257 a 55 RBT 44.4 756 d 57 RBT 170.0 1910 d 57 BKT 44.4 2420 d 57 RBT 250.0 5300 b,e 60 RBT 250.0 1600 b,e 60 RBT 250.0 590 b,e 60 RBT 5.0 280 b,e 66 RBT 16.0 117 b 67 RBT 24.0 130 b 67 RBT 137.0 2600 b 68 RBT 143.0 2400 b 68 RBT 504.0 4760 a 81 RBT 14.0 560 b 83 RBT 14.0 670 b 83

DRAFT April 14, 2015

a Exposure longer than 96 h or unspecified b Static or static renewal exposure c Unusual dilution waters d Fish too large, too old e Water chemistry estimated or unreported f Fish too young, still in yolk sac g Fish pre-exposed to metals

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Table 4a: Reported values of acute Cu toxicity (and corresponding test hardness) to rainbow trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln LC50 hardness ln

hardness Source

Rainbow Trout 192 5.26 125.0 4.83 5 Oncorhynchus mykiss 58 4.06 101.0 4.62 8 42 3.74 101.0 4.62 8

Slope= 1.131 6 1.77 20.4 3.02 22

R2 = 0.8196 17 2.84 41.9 3.74 22 38 3.63 45.2 3.81 22

47 3.84 44.1 3.79 22 24 3.19 44.6 3.80 22

20 2.98 36.1 3.59 22 19.1 2.95 100.0 4.61 26 56.6 4.04 100.0 4.61 26 59.9 4.09 100.0 4.61 26 59 4.08 100.0 4.61 26 42.4 3.75 100.0 4.61 26 60.6 4.10 100.0 4.61 26 162 5.09 194.0 5.27 28 82 4.41 194.0 5.27 28 80 4.38 194.0 5.27 28 99 4.59 194.0 5.27 28 263 5.57 194.0 5.27 28 123 4.81 194.0 5.27 28 212 5.36 194.0 5.27 28 16 2.79 23.0 3.14 30 17.28 2.85 23.0 3.14 30 52.224 3.96 104.0 4.64 56 296 5.69 220.0 5.39 56 34 3.54 98.2 4.59 56 97 4.57 214.0 5.37 56 89 4.49 105.0 4.65 56 33 3.50 99.0 4.60 59 31 3.42 102.0 4.62 59 309 5.73 360.0 5.89 59 30 3.40 31.0 3.43 59 48 3.87 100.0 4.61 59 46 3.84 101.0 4.62 59 48 3.87 99.0 4.60 59

DRAFT April 14, 2015

516 6.25 371.0 5.92 59 298 5.70 361.0 5.89 59 81 4.40 100.0 4.61 59 39 3.67 107.7 4.68 61 30 3.41 107.7 4.68 61 17 2.85 25.1 3.22 63 9 2.22 22.2 3.10 71 9 2.14 14.0 2.64 71 7 1.90 14.2 2.65 71 19 2.95 54.0 3.99 80 13 2.57 35.0 3.56 90 16 2.78 43.0 3.76 90

Geometric mean 47 83.3 Table 4b: Reported values of acute Cu toxicity (and corresponding test hardness) to brown trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln LC50 hardness ln

hardness Source

Brown Trout 39.4 3.67 57.1 4.04 18 Salmo trutta 33.9 3.52 52.6 3.96 42 57.3 4.05 53.3 3.98 42

Slope = 1.2413 29.4 3.38 44.7 3.80 42

R2 = 0.1973 39.4 3.67 48.7 3.89 42

30.2 3.41 51.2 3.94 42 35.8 3.58 54.5 4.00 42

Geometric mean 37 50.7

DRAFT April 14, 2015

Table 4c: Reported values of acute Cu toxicity (and corresponding test hardness) to brook trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln LC50 hardness ln

hardness Source

Brook Trout 74.6 4.31 53.3 3.98 42 Salvelinus fontinalis 45 3.81 50.0 3.91 23

Slope= 11.08 30 3.40 50.0 3.91 23

R2 = 0.8027

Geometric mean 46.5 51.1

DRAFT April 14, 2015

Table 4d: Reported values of acute Cu toxicity (and corresponding test hardness) to cutthroat trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln LC50 Hardness ln

hardness Source

Cutthroat trout 15.072 2.71 26.4 3.27336 28 Oncorhynchus 155.52 5.05 83 4.41884 28 clarkii 352.32 5.86 205 5.32301 28

Slope= 1.6308 23.6 3.16 41.2 3.71844 42

R2 = 0.9507 47.8 3.87 50.4 3.91999 42

Geometric mean 62.2 62.2

DRAFT April 14, 2015

Table 4e: Values of acute Cu toxicity (and corresponding test hardness) to trout eliminated through screening of a larger dataset with 5 criteria known to influence Cu toxicity.

Excluded studies Species Hardness LC50 Source Rationale

RBT 33.0 7 4 e RBT 125 190 5 d RBT 125 210 5 d BKT 170.0 64 7 a BKT 181.0 38 7 a RBT 97.5 110 9 b,e BT 57.1 30.2 18 g RBT 41.3 13.8 20 b,e RBT 41.3 36 20 b,e RBT 36.2 12.9 22 c RBT 45.4 25.1 22 c RBT 300 890 24 d RBT 52.2 62.9 26 f CUTT 222.72 69.9 28 c RBT 194.0 165 28 d RBT 194.0 197 28 d RBT 194.0 514 28 d RBT 194.0 243 28 d RBT 23 29 29 d RBT 23 28 29 d RBT 42.0 57 30 e RBT 132.5 120 31 b RBT 9.2 2.8 32 e RBT 9.2 4.2 32 e RBT 112.0 160 33 e BKT 52.6 48.2 42 d RBT 169.0 110 47 b,e RBT 169.0 100 47 b,e RBT 169.0 50 47 b,e RBT 169.0 60 47 b,e RBT 362.5 102 48 d RBT 33.0 400 50 b RBT 36.0 52 54 e RBT 100.0 56 54 e

DRAFT April 14, 2015

RBT 350.0 150 54 e RBT 98.0 85.9 59 c RBT 101.0 176 59 c RBT 370.0 232 59 c RBT 364.0 111 59 c RBT 366.0 70 59 c RBT 32.0 22.4 59 c RBT 371.0 82.2 59 c RBT 31.0 28.9 59 c RBT 101.0 40 59 c RBT 30.0 30 59 c RBT 30.0 19.9 59 c RBT 250.0 930 60 b,e RBT 250.0 1150 60 b,e RBT 250.0 430 60 b,e RBT 120.0 11.3 62 b,e RBT 120.0 11.3 62 b,e RBT 120.0 14.3 62 b,e RBT 120.0 15.9 62 b,e RBT 120.0 23.9 62 b,e RBT 44.0 135 64 b,e BKT 45.4 105.6 65 d BKT 45.4 86.4 65 d RBT 18.3 94 69 e RBT 23.7 93 69 e RBT 24.4 89 69 e RBT 31.0 90 69 e RBT 172.0 67.9 70 b RBT 176.0 35.5 70 b RBT 176.0 18.1 70 b RBT 176.0 52.5 70 b RBT 176.0 18.1 70 b RBT 177.0 27.7 70 b RBT 178.0 53.9 70 b RBT 178.0 30.7 70 b RBT 179.0 37.3 70 b RBT 180.0 46.2 70 b RBT 180.0 17.9 70 b RBT 180.0 21.2 70 b RBT 13.0 19.4 71 c RBT 12.2 5.9 71 c

DRAFT April 14, 2015

RBT 170.0 80 75 b,e RBT 52.2 100 78 a RBT 120 80 77 d RBT 284.0 650 84 e RBT 120.0 66.7 85 e RBT 57.0 40 87 b RBT 57.0 21 87 b RBT 57.0 22 87 b RBT 57.0 24 87 b RBT 39.0 8.1 89 b RBT 42.0 3.4 89 b RBT 90.0 17.2 89 b RBT 90.0 32 89 b RBT 38.0 21 90 b RBT 45.0 17.2 90 b

a Exposure longer than 96 h or unspecified b Static or static renewal exposure c Unusual dilution waters d Fish too large or too old e Water chemistry estimated or unreported f Fish too young, still in yolk sac g Fish pre-exposed to metals

DRAFT April 14, 2015

Table 5a: Reported values of acute Cd toxicity (and corresponding test hardness) to rainbow trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln hardness Source

Rainbow Trout 3.70 1.31 101.0 4.62 8 Oncorhynchus mykiss 5.20 1.65 101.0 4.62 8 2.77 1.02 100.0 4.61 26

Slope= 0.8249 2.89 1.06 100.0 4.61 26

R2 = 0.6659 4.83 1.57 100.0 4.61 26 3.71 1.31 100.0 4.61 26 4.54 1.51 100.0 4.61 26 2.96 1.09 100.0 4.61 26 1.31 0.27 23.0 3.14 29 1.01 0.01 23.0 3.14 29 1.83 0.60 28.0 3.33 39 2.67 0.98 29.0 3.37 39 13.10 2.57 281.0 5.64 39 2.58 0.95 47.0 3.85 36 3.00 1.10 49.0 3.89 36 17.79 2.88 120.0 4.79 72 2.93 1.08 44.4 3.79 74

Geometric mean 3.43 68.2 Table 5b: Reported values of acute Cd toxicity (and corresponding test hardness) to brown trout. These studies remain from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln hardness Source

Brown Trout 1.23 0.21 29.2 3.37 13 Salmo trutta 3.90 1.36 67.6 4.21 13

Slope = 1.2018 10.10 2.31 151.4 5.02 13

R2 = 0.9755 1.17 0.16 54.1 3.99 18

1.87 0.63 36.9 3.61 38 2.37 0.86 37.6 3.63 37

Geometric mean 2.51 53.1

DRAFT April 14, 2015

Table 5c: Reported values of acute Cd toxicity (and corresponding test hardness) to brook trout. This study remains from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln hardness Source

Brook trout 1.5 0.41 42 3.74 27 Salvenius fontinalis 2.4 0.88 44 3.78 27 26 3.26 340 5.83 27

Slope = 0.8336 29 3.37 350 5.86 27

R2 = 0.5189 3.8 1.34 356 5.87 27

4.4 1.48 325 5.78 27

Geometric mean 5.97 171.5 Table 5d: Reported values of acute Cd toxicity (and corresponding test hardness) to cutthroat trout. This study remains from the screening of a larger dataset with 5 criteria known to influence metal toxicity.

Dissolved

LC50 ln

LC50 hardness ln hardness Source

Cutthroat Trout 2.40 0.88 44.9 3.80 12 Oncorhynchus clarkii No slope

Geometric mean 2.4 44.9

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Table 5e: Values of acute Cd toxicity (and corresponding test hardness) to trout eliminated through screening of a larger dataset with 5 criteria known to influence Cu toxicity.

Excluded studies Species Hardness LC50 Source Rationale

RBT 33.0 7 4 e RBT 125 190 5 d RBT 125 210 5 d BKT 170.0 64 7 a BKT 181.0 38 7 a RBT 97.5 110 9 b,e BT 57.1 30.2 18 g RBT 41.3 13.8 20 b,e RBT 41.3 36 20 b,e RBT 36.2 12.9 22 c RBT 45.4 25.1 22 c RBT 300 890 24 d RBT 52.2 62.9 26 f CUTT 222.72 69.9 28 c RBT 194.0 165 28 d RBT 194.0 197 28 d RBT 194.0 514 28 d RBT 194.0 243 28 d RBT 23 29 29 d RBT 23 28 29 d RBT 42.0 57 30 e RBT 132.5 120 31 b RBT 9.2 2.8 32 e RBT 9.2 4.2 32 e RBT 112.0 160 33 e BKT 52.6 48.2 42 d RBT 169.0 110 47 b,e RBT 169.0 100 47 b,e RBT 169.0 50 47 b,e RBT 169.0 60 47 b,e RBT 362.5 102 48 d RBT 33.0 400 50 b RBT 36.0 52 54 e RBT 100.0 56 54 e

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RBT 350.0 150 54 e RBT 98.0 85.9 59 c RBT 101.0 176 59 c RBT 370.0 232 59 c RBT 364.0 111 59 c RBT 366.0 70 59 c RBT 32.0 22.4 59 c RBT 371.0 82.2 59 c RBT 31.0 28.9 59 c RBT 101.0 40 59 c RBT 30.0 30 59 c RBT 30.0 19.9 59 c RBT 250.0 930 60 b,e RBT 250.0 1150 60 b,e RBT 250.0 430 60 b,e RBT 120.0 11.3 62 b,e RBT 120.0 11.3 62 b,e RBT 120.0 14.3 62 b,e RBT 120.0 15.9 62 b,e RBT 120.0 23.9 62 b,e RBT 44.0 135 64 b,e BKT 45.4 105.6 65 d BKT 45.4 86.4 65 d RBT 18.3 94 69 e RBT 23.7 93 69 e RBT 24.4 89 69 e RBT 31.0 90 69 e RBT 172.0 67.9 70 b RBT 176.0 35.5 70 b RBT 176.0 18.1 70 b RBT 176.0 52.5 70 b RBT 176.0 18.1 70 b RBT 177.0 27.7 70 b RBT 178.0 53.9 70 b RBT 178.0 30.7 70 b RBT 179.0 37.3 70 b RBT 180.0 46.2 70 b RBT 180.0 17.9 70 b RBT 180.0 21.2 70 b RBT 13.0 19.4 71 c RBT 12.2 5.9 71 c

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RBT 170.0 80 75 b,e RBT 52.2 100 78 a RBT 120 80 77 d RBT 284.0 650 84 e RBT 120.0 66.7 85 e RBT 57.0 40 87 b RBT 57.0 21 87 b RBT 57.0 22 87 b RBT 57.0 24 87 b RBT 39.0 8.1 89 b RBT 42.0 3.4 89 b RBT 90.0 17.2 89 b RBT 90.0 32 89 b RBT 38.0 21 90 b RBT 45.0 17.2 90 b

a Exposure longer than 96h or unspecified b Static or static renewal exposure c Unusual dilution waters d Fish too large or too old e Water chemistry estimated or unreported f Fish too young, still in yolk sac g Fish pre-exposed to metals

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