REEVALUATION OF CONTAMINANT TRANSPORT

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The Henry Aquifer in Gallatin County Illinois provides groundwater for municipal, ... the border of the mine site and low permeability caps over refuse areas ... Quantum Geographic Information System (GIS) software for the conceptual and numerical ...... withdrawing an average of two million liters per day from a single well.
REEVALUATION OF CONTAMINANT TRANSPORT IN THE HENRY AQUIFER, GALLATIN COUNTY ILLINOIS

by Joseph Micheal Krienert B.S., SOUTHERN ILLINOIS UNIVERSITY, 2015

A Thesis Submitted in Partial Fulfillment of the Requirements for the Master of Science

Department of Geology in the Graduate School Southern Illinois University Carbondale December 2017

THESIS APPROVAL

REEVALUATING CONTAMINANT TRANSPORT IN THE HENRY AQUIFER, GALLATIN COUNTY ILLINOIS

By JOSEPH MICHEAL KRIENERT

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the field of Geology

Approved by: Dr. Steve Esling, Chair Dr. Ken Anderson Dr. James Conder

Graduate School Southern Illinois University Carbondale August 7th 2017

AN ABSTRACT OF THE THESIS OF JOSEPH MICHEAL KRIENERT, for the Master of Science degree in Geology, presented on August 7th 2017, at Southern Illinois University Carbondale. TITLE: REEVALUATING CONTAMINANT TRANSPORT IN THE HENRY AQUIFER, GALLATIN COUNTY ILLINOIS MAJOR PROFESSOR: Dr. Steven P. Esling The Henry Aquifer in Gallatin County Illinois provides groundwater for municipal, irrigation, industrial, and household wells. The greatest annual withdrawal is by a water utility that serves over 40,000 persons in southeast Illinois. Buried coal refuse at a mine near the water utility has contaminated the groundwater. Remediation efforts, including source control wells on the border of the mine site and low permeability caps over refuse areas attempt to control the migration of contaminants offsite. Current mine land owners believe source control well pumping over 20 years has reduced contamination in the aquifer enough to stop pumping. However, some monitoring wells off the mine site have recently sampled high concentrations of contaminants. Previous studies failed to account for the elevated concentrations found offsite. The purpose of this research is to reevaluate contaminant transport in this region. Specific objectives include a new conceptual model of the hydrostratigraphy and hydrology, revised contaminant source locations and loading, and new groundwater models accurately calibrated to a comprehensive set of monitoring well data. The research included extensive review of prior studies and historical records from the past 50 years. Relevant information was combined in Quantum Geographic Information System (GIS) software for the conceptual and numerical models. A new groundwater modeling pre/post processor for MODFLOW and MT3DMS was

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created for Quantum GIS to calibrate the models and simulate future conditions for risk assessment. Observed hydraulic head and sulfate concentrations from 1984-2015 were used for calibration. In addition, modeled baseflow was compared with observed streamflow in 2017. The calibrated models were used for twelve unique scenarios that forecast contamination from 20172068. The scenarios tested model sensitivity to changes in groundwater management and environmental conditions. The results show that groundwater quality about 300 meters west of the mine deteriorates in all scenarios, water utility wells near the mine are at risk in most scenarios, and that the location and discharge of wells have a commanding effect on the regional groundwater flow and transport systems. This research offers important questions for further study, valuable tools for groundwater management in the region, and shows that without active source control wells, negative impacts to water quality near the mine will likely occur.

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ACKNOWLEDGMENTS I voice sincere respect to Dr. STEVEN P. ESLING for his patience as my committee chair. His time and insights were essential to completing this research. Appreciation continues to my committee members Dr. Ken Anderson for improving my ability to rationalize a scientific problem, and Dr. James Conder for teaching me analytical tools vital to this, and future works. I would also like to express my gratitude towards Dr. Justin Filiberto, Dr. Harvey Henson, Dr. Liliana Lefticariu, and Dr. Johnathan Remo for providing relevant technical expertise throughout my undergraduate and graduate studies at SIU. I am likewise indebted to the Saline Valley Conservancy District and the Illinois Groundwater Association, whose support helped make this research possible.

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DEDICATION

I dedicate this research to my mother Shirley J. Krienert, and my sister Jessica A. McCarty for providing unwavering support throughout my life.

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TABLE OF CONTENTS CHAPTER

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ABSTRACT ..................................................................................................................................... i ACKNOWLEDGMENTS ............................................................................................................. iii DEDICATION ............................................................................................................................... iv TABLE OF CONTENTS .................................................................................................................v LIST OF TABLES ....................................................................................................................... viii LIST OF FIGURES .........................................................................................................................x CHAPTERS CHAPTER 1 – Background .................................................................................................1 1.1 – Rationale 1.2 – Objectives 1.3 – Geography and Hydrology 1.4 – Bedrock Geology 1.5 – Surficial Geology 1.6 – Infrastructure 1.7 - Concerns CHAPTER 2 – Methods ....................................................................................................10 CHAPTER 3 – Conceptual Model .....................................................................................12 3.1 – Model Domain 3.2 – Hydrostratigraphy 3.3 – Hydraulic Conductivity 3.4 –Hydrologic Sources and Sinks

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3.5 – Contaminant Sources CHAPTER 4 – Numerical Models ....................................................................................18 Groundwater Flow 4.1 – Main Inputs 4.2 – Spatial Discretization 4.3 – Temporal Discretization 4.4 – Transient Parameters 4.5 – Hydraulic Conductivity 4.6 – Recharge 4.7 – Wells 4.8 – Drains 4.9 – General-Head Boundaries 4.10 – Changing-Head Boundaries Contaminant Transport 4.11 – Main Inputs 4.12 – Spatial and Temporal Discretization 4.13 – Advection and Porosity 4.14 – Dispersion and Diffusion 4.15 - Sources CHAPTER 5 – Calibration ................................................................................................28 5.1 – Hydraulic Head 5.2 – Groundwater Flux 5.3 – Contaminant Transport

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CHAPTER 6 – Scenario Analysis .....................................................................................31 6.1 – Assumptions 6.2 - Discussion CHAPTER 7 – Conclusions...............................................................................................38 7.1 – Scenario Implications 7.2 – Closing Thoughts and Recommendations TABLES AND FIGURES .............................................................................................................43 BIBLIOGRAPHY ..........................................................................................................................95 VITA ...........................................................................................................................................103

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LIST OF TABLES TABLE

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Table 1: Groundwater Pumping History of SVCD and Eagle No. 2 Mine....................................50 Tables 2A – 2D: Eagle No. 2 Regional Sulfate Monitoring Data Plots from 1991 - 2016 ...........52 Table 3: Hydraulic Conductivities of Present and Past Research ..................................................59 Tables 4A – 4B: Sediment Analysis of NPDES Outfall No. 001 Lining .....................................61 Table 5: MODFLOW Primary Inputs ............................................................................................62 Table 6: General Description of Stress-Periods Used....................................................................66 Table 7: MODFLOW Stress-Period Inputs ...................................................................................67 Table 8: MODFLOW Transient Condition Inputs.........................................................................67 Table 9: Monitoring and Pumping Well Attributes .......................................................................68 Table 10: Multi-Node Well (MNW2) MODFLOW Inputs ...........................................................68 Table 11: Recharge Analysis of Bedrock Uplands ........................................................................69 Table 12: Drain Package Inputs .....................................................................................................70 Table 13: General and Changing Head Package Inputs.................................................................71 Table 14: MT3DMS Primary Inputs ..............................................................................................71 Table 15: Dispersivity Values of Present and Past Research ........................................................72 Table 16: Hydrogeological Attributes for Present and Past Research ...........................................73 Table 17: Mass-Flux Source Loading Rates for Transport Model ................................................74 Table 18: Constant Concentration Sources of Cox (2013) ............................................................74 Table 19: Constant Concentration Sources of ESI (2003) .............................................................74 Table 20: Hydraulic Head Calibration Statistical Results .............................................................76 Table 21: Source Area 2015 Concentration Analysis ...................................................................80

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Table 22: Sulfate Calibration Statistical Results ...........................................................................82 Table 23: General Conditions of Forecast Scenarios .....................................................................84 Tables 24A – 24H: Detailed Conditions of Forecast Scenarios .............................................. 84-86

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LIST OF FIGURES FIGURE

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Figure 1: Physiography and Hydrology of Gallatin Co., IL ..........................................................43 Figure 2: Stratigraphic Column of Bedrock Geology ....................................................................44 Figure 3: Stratigraphic Column of Unconsolidated Geology ........................................................44 Figure 4: Cross-sections of Unconsolidated Lithology .................................................................45 Figure 5: Henry Aquifer Isopach in Gallatin Co., IL.....................................................................46 Figure 6: Henry Aquifer Semi-Confining Material Isopach in Gallatin Co., IL ...........................47 Figure 7: Agricultural Irrigation Groundwater Wells in Gallatin Co., IL .....................................48 Figure 8: Saline Valley Conservancy District Infrastructure .........................................................49 Figure 9: Regionally Defined Eagle No. 2 Mine Monitoring Wells..............................................51 Figure 10: SVCD and Eagle No. 2 Mine Monitoring and Pumping Wells ...................................53 Figure 11: Western Eagle No. 2 Mine Property Blueprint ............................................................54 Figure 12: Eastern Eagle No. 2 Mine Property Blueprint ..............................................................55 Figure 13: General Workflow of Research ....................................................................................56 Figure 14: Relief Map of Bedrock Topography in Gallatin Co., IL ..............................................57 Figure 15: Topographic Map of Henry Aquifer in Gallatin Co., IL ..............................................58 Figure 16: Modeled Groundwater Pumping Wells ........................................................................59 Figure 17: Timeseries Areal Images of Eagle No. 2 Mine ...........................................................60 Figure 18: Field Sampling Cores from Eagle No. 2 Mine NPDES Ditch .....................................61 Figure 19: Modeled Sulfate Contaminant Sources ........................................................................62 Figure 20: Groundwater Model Domains in Gallatin Co., IL........................................................63 Figure 21: Groundwater Divide Model Domain Reduction ..........................................................64

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Figure 22: Final Model Flow and Transport Domains ..................................................................65 Figure 23: Modeled Recharge Zones .............................................................................................67 Figure 24: Bedrock Upland Recharge to Henry Aquifer ...............................................................69 Figure 25: MODFLOW Drains ......................................................................................................70 Figure 26: Plan View of Dispersivity Analysis .............................................................................72 Figure 27: Cross-section of Dispersivity Analysis ........................................................................73 Figure 28: Hydraulic Head Calibration 2015 Residuals ................................................................75 Figure 29: Hydraulic Head Calibration Correlation Plots .............................................................77 Figure 30: Flux Calibration Features .............................................................................................78 Figure 31: Watersheds Associated with RORA Analysis and Flux Calibration ...........................79 Figure 32: Sulfate Calibration 2015 Concentrations .....................................................................80 Figure 33: Sulfate Calibration Correlation Plots ...........................................................................81 Figure 34: Sulfate Calibration 2015 Concentration Residuals ......................................................83 Figure 35: Scenario 1 Results ........................................................................................................87 Figure 36: Scenario 2 Results ........................................................................................................87 Figure 37: Scenario 3 Results ........................................................................................................88 Figure 38: Scenario 4 Results ........................................................................................................88 Figure 39: Scenario 5 Results ........................................................................................................89 Figure 40: Scenario 6 Results ........................................................................................................89 Figure 41: Scenario 7 Results ........................................................................................................90 Figure 42: Scenario 8 Results ........................................................................................................90 Figure 43: Scenario 9 Results ........................................................................................................91 Figure 44: Scenario 10 Results ......................................................................................................91

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Figure 45: Scenario 11 Results ......................................................................................................92 Figure 46: Scenario 12 Results ......................................................................................................92 Figure 47: Timeseries Sulfate Plume Movement Near Well WA0009A .....................................93 Figure 48: Timeseries Sampled Sulfate Concentrations of Well WA0009A ................................94

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CHAPTER 1 BACKGROUND 1.1 Rationale The Saline Valley Conservancy District (SVCD) provides potable water to over 16,000 households and businesses (> 40,000 persons) in Gallatin, Hamilton, Hardin, Johnson, Pope, and Saline Counties in southeastern Illinois. The district obtains its water from the Henry Aquifer, a semi-confined unconsolidated sand and gravel formation. Contamination from a nearby coal mine threatens the production wells. Peabody Coal Company operated the Eagle No. 2 Underground Mine from 1968 to 1993. Buried waste at the site leaches sulfate and other pollutants into the regional groundwater system, but since 1993 the transport of contaminants from the mine has been controlled in part by wells pumping along the property border. Several studies (GeoSyntec 1995, Prickett 1997, 2003, ESI 2003, Hoyal 2004, Cox 2013) have investigated the groundwater contamination near the mine. However, high concentrations of sulfate recently found in wells off site, and a desire by the owner of the mine to discontinue remediation pumping, warrant a reevaluation of the risks to the Henry Aquifer. 1.2 Objectives The main objectives of this research are to 1) develop a new conceptual model of the hydrostratigraphy and hydrology of the study area, 2) reassess contaminant source areas and loading rates, and 3) create a groundwater flow and transport model based on natural hydrologic boundaries that is calibrated to data collected from a comprehensive set of monitoring wells. The resulting flow and transport model will provide a tool for managing the groundwater resources in this region.

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1.3 Geography and Hydrology The study area is located within Gallatin County. The county encompasses two distinct physiographic provinces, the Mount Vernon Hill Country of the Central Lowland Province and the Shawnee Hills Section of the Interior Low Plateaus Province (Figure 1). The Mt. Vernon Hills include expansive, gently rolling plains covering much of the county's northeast and central regions. Towards the county's southern and western borders, the plains abruptly trend into the irregular and significantly higher elevation bedrock terrain of the Shawnee Hills Section (IPRI 2012). The county includes a variety of fluvial features (Figure 1). The most prominent are the major rivers of the region, the Ohio, the Lower Wabash, the North Fork and the Saline. The Lower Wabash River forms the northeastern border of the county until it joins the Ohio River. The Ohio then continues south defining the eastern border of the county. The North Fork River enters the northwestern corner of the county, flows south, and joins with the Saline River in the county’s east central region. The Saline then continues south, and discharges into the Ohio River along the county's southernmost tip. Throughout the county's interior there are many small streams, creeks, and agricultural ditches, the majority of which flow with a general trend from northeast to southwest. These features contribute to the North Fork and Saline Rivers’ watershed, covering the western two thirds of the county. The remaining fluvial drainage flows through various agricultural ditches and natural streams into the Wabash and Ohio Rivers along the county's eastern border. There are also several riparian marshes of the Lower Wabash and Ohio Rivers along this eastern margin. Aerial and satellite imagery indicates these areas occupy former flow paths of the Wabash and Ohio, and remain partially flooded perennially. The county also includes a few sparsely distributed ponds and small lakes which are anthropogenic in origin.

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1.4 Bedrock Geology The bedrock beneath Gallatin County is comprised of coals, sandstones, shales, siltstones, and limestones from the upper Pennsylvanian System. The Bond and Modesto Formations from the Pennsylvanian Mcleansboro Group make up most of the bedrock surface (Heinrich 1982, Nelson and Lumm 1984). Figure 2 depicts a stratigraphic column for the bedrock geology. The ancient Saline, North Fork, and Ohio Rivers flowed throughout the study area during the late Pennsylvanian, eroding valleys into the bedrock surface (Frye et al. 1972, Heinrich 1982, Smith 1976, Smith and Norris 1976). The North Fork and Saline River likely occupied their modern valleys (Heinrich 1982, Horburg 1950). The Ohio River, however, may have followed a different course in the past than it does now. Frye et al. (1972), Heinrich (1982), and Smith and Norris (1976) suggest that in the late Pennsylvanian the ancient Ohio River ran from southern Indiana into central Gallatin County, then back southeast towards Kentucky. The abrupt turn back east follows the Shawneetown Front Fault, and likely occurred because strata had become tectonically weakened (Heinrich 1982). Other faults contribute to regional displacement, including Junction Horst which is important to this research (Bristol 1975). This block vertically displaces approximately 10 to 15 meters in this study’s region of interest, and did not erode significantly before Cenozoic Age sediments were deposited (Bristol 1975, ESI 2003). 1.5 Surficial Geology Quaternary unconsolidated materials overlay the Pennsylvanian bedrock. Figure 3 summarizes the Quaternary geology, with the strata of interest highlighted in blue. The major glaciations which occurred throughout North America during the Pleistocene influenced this region’s depositional environment. The record of pre-Illinoian glaciation is sparse. During the

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Illinois Episode, ice advanced to the northern sections of Gallatin County and deposited the Glasford Formation, a glacial diamicton (Heinrich 1982). Wisconsin Episode glaciers stopped short of Gallatin County, but the outwash from these glaciers had a profound impact on the study area. The Ohio and Wabash Rivers were choked with sand and gravel, and aggraded their valleys. Often damns were created across tributaries, including that of the Saline River. Lakes filled, including paleo Lake Saline, and a complex sequence of interbedded fine-grained lacustrine and coarse-grained alluvial deposits formed. (Frye et al. 1972, Heinrich 1982). The lake sediments belong to the Equality Formation, while the alluvial deposits belong to the Henry Formation. Alluvium fills most of the Ancient Ohio River thalweg along the northern face of Shawneetown Hills, with thicknesses exceeding 50 meters in some places. Eolian silt veneers much of the regions topography, deposited during the Illinois, Sangamon, and Wisconsin Episodes. The Peoria Formation is the thickest of these sediments, ranging between 1.5 - 11 meters (Frye et al. 1972, Heinrich 1982). Late in the Wisconsin Episode, the breakup of aggraded valley dams caused massive flooding events (Frye et al. 1972, Heinrich 1982). One of these events, the Maumee Torrent, surged waters from northern Indiana down the ancient Wabash and Ohio Rivers through central Gallatin County. This flood is thought to have scoured a wide valley into the eolian, alluvial, and lacustrine deposits, trending northeast to southwest across the county (Heinrich 1982). As the Wisconsin glaciers receded, the rivers returned to a depositional environment similar to the one that exists today. Coarse-grained lateral accretion deposits are found below fine-grained overbank deposits up to 15 meters thick in the study area, comprising the Cahokia Formation. Figure 4 shows cross-sections of the unconsolidated materials in the study area, developed from well logs to highlight the complexity of the regions surficial geology. The sand

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and gravel of the Henry Formation along with the coarse-grained facies of the Cahokia define the Henry Aquifer. The fine grained overbank deposits partially confine the Henry Aquifer throughout the study area. Figure 5 shows an isopach map of the Henry Aquifer, and Figure 6 shows an isopach of these finer semi-confining deposits. 1.6 Infrastructure Agriculture, mining, and individual homeowners use groundwater in the study area in addition to the SVCD. The private homeowner wells are not considered in this research. This is mainly because household wells generally produce very little water compared to the SVCD and the mine. In addition, the recent expansion of the SVCD has prompted many landowners to become customers and close their private wells (Watson personal communication 2016, SVCD Staff 2016). Agriculture covers nearly eighty-seven percent of the land use in Gallatin County, making it the areas' most widespread industry (NASS 2007). Many farmers irrigate, and Figure 7 shows the wells that were distributed throughout the county's central and eastern lowland from 2012 to 2015 (Conner 2016, Watson personal communication 2016). Most irrigation only occurs for two months during the early and late parts of the summer season (Lamkey 2017). One forecast scenario in this thesis considered irrigation wells. The SVCD is currently managed out of Eldorado. In 1982, their first three wells were installed near the town of Junction. Well No. 1, 2 and 3 were installed to address the district’s initial need of six and a half million liters per day, and anticipated need of 13 million liters per day. Well No. 4 was added one km west-northwest of the original three wells in 1991. Increasing SVCD customers late in the 20th century triggered installation of well No. 5 in 1995. However, in 2000 this well was abandoned due to deteriorating water quality associated with contamination

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originating from the Eagle No. 2 Mine. More recently three new wells, No. 6 in 2001, No. 7 in 2005, and No. 8 in 2012, were installed approximately two kilometers directly north of the original three wells. In 2013, well No. 3 experienced complications during gravel pack cleaning and was shut down. Of the six operational SVCD wells, only four pump water simultaneously (SVCD Staff 2016). Since 2001, the district's daily withdrawals total between 12 million and 15 million liters per day, with well No. 4 pumping continuously in tandem with three additional wells used in rotation. The SVCD management considers the rotated use strategic because it limits stress on mechanical components (SVCD Records 2016). The water from the district wells is transported by buried pipeline approximately 13 km west-northwest along Illinois State Highway 13 to the water treatment and distribution facility near Equality. The delivery of treated water from the plant to customers covers an extensive region of southeastern Illinois. Figure 8 shows the SVCD wells, management facilities, and service area (SVCD Service 2005). The Eagle No. 2 Mine property was originally operated by Peabody Coal, but has changed owners on multiple occasions since closure. In 2016, ownership began transitioning from Heritage Coal to Virginia Legacy Fund. The mine was in service from 1968 to 1993 excavating and processing Springfield Coal from the upper Pennsylvanian System, highlighted red in Figure 2 and Figure 4. Illinois Springfield Coal incorporates varying amounts of poorly combustible, unwanted materials. Depending on the provenance, unfavorable elements might include metals such as iron and manganese, or sulfur and its byproduct sulfate [SO4-] (Affolter et al. 2001, Demir et al. 2001, Gluskater et al. 1968, Schubert 1979). In this research, sulfate is a physical tracer used to investigate contaminant transport throughout the study region. Furthermore, sulfate is of primary concern because its removal is costly when concentrations are high (SVCD Staff 2016).

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Documentation suggests that early in the mine’s operation, portions of the Henry Aquifer were partially exposed in the basins of large embankments onsite, notably Refuse No. 5, also known as West Refuse (IEPA 2016, ILWATER 2015, SVCD Records 2016, SVCD Staff 2016). Within these basins the mine used groundwater for production coal washing starting in 1982, withdrawing an average of two million liters per day from a single well. The production then increased to seven and a half million liters per day in 1987 distributed between three wells. Coal washing in the basins removed unfavorable components using the groundwater as a solvent. Sump pumps moved these contaminated waters to internment lakes, where the majority of particulates settled onto the lake beds. The lake waters eventually overflowed into a drainage ditch that discharges off site into Cypress Ditch, a prominent fluvial feature in Gallatin County. Eventually portions of the separated solid waste material from the slurry washing were buried within large embankments onsite. The meeting of the 92nd Congress (S. 630 Report 92-1162), the Clean Water Act (33 USC § 1251), and the Surface Mining Control and Reclamation Act (SMCRA), were important federal regulations that influenced cleanup at the mine (SVCD Records 2016). Installation of remediation and monitoring wells occurred at key locations on and near the mine property starting in the late 1980s. The purpose of these wells was to remove and monitor the contamination entering the aquifer. From the early 1990s to 2003 remediation pumping continued, and rates progressively increased to excess of nine and a half million liters per day distributed between six wells. Only three remediation wells remain, and pumping has since decreased gradually from the 2003 maximum to six and a half million liters per day, as recorded in February 2017. Table 1 summarizes the groundwater pumping chronology of the SVCD and the Eagle No. 2 Mine from 1982 to 2017 (Cox 2013, ESI 2003, Hoyal 2004, Prickett 1997, 2003,

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SVCD Archives 2016). Remediation also included the installation of low permeability caps covering many of the slurry and refuse burial areas in 1991 and 2001. These covers were built to reduce the amount of precipitation infiltrating the refuse. 1.7 Concerns Quarterly concentration measurements collected from a portion of the mine monitoring wells suggest 23 years of remediation pumping and 16 years of refuse caps have reduced contaminant transport (Figure 9 and Tables 2A-2D). However, some wells’ groundwater chemistry, in addition to uncertain bed material properties beneath the refuse, suggest negative impacts if the remediation wells were to be turned off. Monitoring well WA0009A is a well with elevated sulfate levels, situated approximately 90 meters beyond the northern edge of the mine property. The drill log for this well indicates a screened interval within the Henry Aquifer located at least 90 meters from any refuse area (SVCD Records 2016). In 2012 and 2013, sulfate concentrations of samples collected from WA0009A were consistently above 500 mg/l, exceeding the EPA drinking water standard of 250 mg/l. SMW4 is another monitoring well of concern, located in the eastern portion of the mine near the Shawneetown highland (Figure 10). This well has yielded groundwater samples with consistently high concentrations of sulfate (Figure 9, Table 2B). Quarter year samples from 2005 to 2015 measure an average of 3250 mg/l (IEPA 2016, SVCD Records 2016), and contamination of surrounding well samples appear to be originating from SMW4’s area (Figure 9, Tables 2A - 2B). Cox (2013), ESI (2003), and Prickett (2003) omitted SMW4 because it did not match model output. Prickett (2003) and ESI (2003) disagree on the location of SMW4’s well screen. Prickett (2003) suggested the screened interval either fully or partially penetrates the aquifer, while ESI (2003) believed that the screen is above the aquifer within Refuse No. 3

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(SVCD Records 2016). If the screened interval is within the refuse, its geochemical data could help estimate source loading functions. It could also indicate the potential for future contamination when comparing concentration of its samples against those of neighboring wells. Several Illinois State Geologic Survey (ISGS) well logs (ILWATER 2015) and two Peabody Coal engineering maps; shown in Figures 11 and 12 (SVCD Records 2016), indicate that portions of the basin linings are less than one meter thick. Some sections exposed gray sand and gravel during construction; presumably representing the Henry Aquifer (IEPA 2016, SVCD Staff 2016). Such thin and potentially absent barriers between the refuse and the Henry Aquifer poses an ongoing risk of leachate contamination emanating from the buried mine waste.

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CHAPTER 2 METHODS Figure 13 summarizes the workflow of this research. This project’s principal goal is to forecast contaminant transport within the Henry Aquifer under a variety of interpreted environmental conditions and scenarios. The conceptual model had to be thorough enough to address these objectives, while remaining simple enough to build reliable and efficient numerical models (Anderson et al. 2015). The modular finite-difference groundwater flow software MODFLOW (McDonald and Harbaugh 2005), was chosen for constructing the groundwater flow model. The flows produced from this model were then united with a contaminant transport model. The software chosen for this was the modular transport in three-dimensions of multiple species, or MT3DMS, (Zheng and Wang 2010). These numerical modeling programs were selected for three main reasons; they are open source software packages maintained by US federal agencies, they meet the requirements of this research, and they have been widely adopted by professional hydrogeologists. In order to construct the numerical models, the graphical pre and post processor Graphic Groundwater for Geographic Information Systems (G3IS) was used (Krienert et al. 2015). G3IS was adapted from educational software produced in the early 1990s (Esling 2000) to function as a plugin in the open-source GIS software Quantum-GIS (QGIS). The capacity to explore and superimpose large amounts of information via a series of maps during groundwater modeling makes QGIS and G3IS valuable tools for the hydrogeologist. Properly deciding when and where groundwater stresses occur in the conceptual and numerical models is essential (Anderson et al. 2015). This is especially important in this study

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because well locations, well pumping rates, and contaminant sources changed frequently. Many of the pumping stresses between 1968 and 2015 occur for less than eight years. Analysis of a preliminary groundwater flow model suggested that the water table takes at least eight years to recover from a change in discharge. In this case, recovery represents a 10 centimeter or less change of hydraulic head near the cone of depression surrounding a pumping well. Therefore, the groundwater flow and transport models were predominantly transient. These numerical models were then calibrated to historical measurements of hydraulic head and sulfate concentration measured in several Eagle No. 2 Mine and SVCD wells. Calibration for the flow and transport models was mainly based on measurements in 2015, however, additional adjustments were made to match observations in 1984, 1992-1994, 2001, 2007, and 2011. This research fused aspects of model sensitivity testing with a forecast based scenario analysis. This was accomplished by including a suite of scenarios that account for reasonable environmental possibilities in the study area. Combining these modeling steps was determined to be an efficient means of addressing the regions hydrogeology, and potential groundwater management options.

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CHAPTER 3 CONCEPTUAL MODEL 3.1 Model Domain Initially, the conceptual model extended to natural hydrologic and geologic boundaries; reaching the Lower Wabash and Ohio Rivers in the east, the North-Fork and Saline Rivers in the west and south, and to a northern boundary defining a change from predominantly coarse grained to fine grained materials. Preliminary numerical models based on this conceptual model then helped reduce the model domain to a central portion of the county. 3.2 Hydrostratigraphy Approximately 5,000 ISGS well logs distributed throughout Gallatin County were used to evaluate the Henry Aquifer's thickness and lithology. About 30-50% of this library was reliable, whereas the remaining 50-70% had vague material descriptions. The majority of logs document the geology below the county lowlands. Logs from boreholes on Shawneetown Hills and Gold Hills indicate a bedrock to loess interface with no substantial alluvial deposits. Because of this, these areas were not included in this study. Logs near the town of Junction indicate nearly continuous sand and gravel of the Henry Aquifer. However, the logs also indicate a complex stratigraphy of interlayered sand, and finegrained materials within 20 meters of the surface. The fine-grained materials are probably associated with paleo Lake Saline and overbank deposits (Frye et al. 1972, Heinrich 1982, Poole and Sanderson 1981). In the north, these fine grained materials become more dominant as the Henry Aquifer pinches out. The logs suggest that the Henry Aquifer has a maximum thickness of 60 meters located within the Ancient Ohio River thalweg. This thickness doesn’t change

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substantially in the east towards the Ohio River, but it does decrease to between 30 and 15 meters in the southern portion of the study area. Many wells located in the immediate proximity of the mine log stratigraphy that was summarized into three horizons; 1) semi-confining finegrained silts, muds, and clays, over 2) medium to finer sands, over 3) coarser sands and gravels that extend to bedrock. The Pennsylvanian bedrock surface beneath the aquifer represents a no-flow boundary in the aquifer system. The lower bedrock possibly communicates groundwater with the unconsolidated deposits, however these flow rates are expected to be significantly less than those occurring within the Henry Aquifer from precipitation recharge. The Shawneetown and Gold Hills rise abruptly compared to the topography elsewhere in the region. This relief exposes fractured limestones and sandstones. Contact between the fractured bedrock uplands and the aquifer may influence the groundwater budget and was considered in the numerical model. Only 550 well logs reached the bedrock surface. Those logs included shales, coals, sandstone, or limestones of the Pennsylvanian. For each of these wells, the depth to bedrock was subtracted from a LiDAR Digital Elevation Model (DEM) coterminous elevation (IPRI 2012). Then the results were combined with bedrock surface topography in Illinois, Indiana, and Kentucky (Smith and Norris 1976). Smith and Norris's (1976) maps contour the bedrock surface from a dense sample of well logs. The logs used for this map were not available, so the map was georeferenced and point sampled with QGIS. These point samples were added to the ISGS bedrock logs. The ESI (2003) model used high resolution bedrock surface elevations in the immediate vicinity of the mine. Their map included an important aspect of the bedrock topography; Junction Horst's vertical offset directly beneath the mine property (ESI 2003, Prickett 1997, 2003). Unconsolidated overlying materials are likely thinner above this offset,

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possibly affecting flow and transport. This study did not have access to all of the well logs used by ESI. Instead, the map was georeferenced, point sampled, and added to the growing database of bedrock surface elevations. A new bedrock surface was created through inverse distance weighted interpolation of the acquired data points. This surface was then adjusted by correcting regions derived from sparse data by examining the ISGS logs of deep wells that did not reach bedrock. Bedrock must lie below a surface defined by these deep wells, herein referred to as the minimum depth to bedrock. The elevations from the bedrock surface map and the minimum depth to bedrock map were compared pixel by pixel. If the bedrock map had an elevation higher than the minimum depth to bedrock map, the bedrock map was corrected. The final eight meter pixel resolution produced a more detailed bedrock topographic map over that of previous work, improving the conceptual model (Figure 14). The top of the Henry Aquifer was determined through a similar approach as the bedrock map. Approximately 1200 logs were individually reviewed to find the contact that separates silts, mud, and clay from predominantly sand and gravel. A map of the elevation at the top of the aquifer was found through inverse distance interpolation from these known contact data points. This map of the Henry Aquifer topography is shown in Figures 15. 3.3 Hydraulic Conductivity Table 3 compares values for hydraulic conductivity from previous work with those used in this research. Hydraulic conductivity from aquifer tests by Poole and Sanderson (1981) are close to the conductivity values used in this research. Conductivity values associated with the alluvial deposits in the American Bottoms (Esling 2008, Keller 1999, Schicht et al. 1995), and

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other locations that include the Henry Formation discussed by Kempton et al. (1982) are close to those used in this model. The acceptable ranges for shallow alluvial aquifers discussed by Domenico and Schwartz (1990), and Fetter (2001) also support the values used. 3.4 Hydrologic Sources and Sinks Several creeks, streams, and agricultural ditches are located within the model domain. These fluvial reaches were characterized by sampling elevation data from LiDAR. The accuracy of the LiDAR helped establish detailed stream gradients for each feature, a characteristic missing from Cox's (2013) and ESI's (2003) work. An important attribute of the fluvial features are bed material and thickness, which affect flow to and from the Henry Aquifer below. The average thickness of the bed material for each fluvial feature was determined from the isopach map of fine grained surficial materials. The results were compared to data collected from a short field survey of four streams in the study area. 2.5 centimeter cores were collected of streambed sediments at a depth of one half meter. Field measured bed material thicknesses compared favorably to those determined from the isopach map. The USGS Groundwater Toolbox (GWT) was used to analyze time series data measured in the middle 20th century near the town of Junction (Barlow 2015). A recession-curvedisplacement method for estimating recharge (RORA) was used. The analysis indicated recharge ranged from 2-20 centimeters per year. These results agree with Walton's (1965) findings that 10-20% of annual rainfall, which is 100 to 140 cm in Gallatin Co., recharges shallow unconsolidated aquifers in Illinois. The results of the RORA analysis are similar to recharge values used in previous work.

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Three man-made hydrologic features on the mine property likely influence groundwater flow; the Makeup Lake, the Freshwater Lake, and the National Pollutant Discharge Elimination System (NPDES) ditch draining these lakes. The Eagle No. 2 Mine has pumped groundwater since 1968, discharging it into the lakes and ditch after use. The lakes and ditch are full of water year round and represent perennial features in the conceptual model (Google 2016, SVCD Staff 2016). Cox (2013), Prickett (1997, 2003) and ESI (2003) showed that the SVCD and Eagle No. 2 groundwater pumping wells can substantially affect the regional flow system. Table 1 shows the detailed pumping activity for these wells. Precise location of the SVCD wells was determined with LiDAR data and a short GPS survey of the well fields. A representative of the ISGS provided a Public Land Survey System (PLSS) sub-grid used by Peabody Coal's engineering staff to locate the wells on the mine property (Keefer 2017). Figure 16 shows the locations of all pumping wells considered in this research, including those simulated during forecast scenario analysis. 3.5 Contaminant Sources Historical records of mine operations were infrequent or limited in detail. Because of this, the contaminant source locations are mostly derived from previous research by Cox (2013), ESI (2003), and Prickett (1997, 2003). However, the locations were adapted from mine-site engineering blueprints (Figures 11 and 12), the NPDES Permit No. IL0044661 and historical orthographic imagery (Figure 17) (Google 2016, SVCD Archives 2016). The present research adjusted the horizontal extent of many of the sources and added a small washing basin located on the western border of the mine. The northern NPDES drainage ditch (Outfall No. 001) was an

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important source included in the conceptual model based on work by Prickett (2003) and Cox (2013). To further investigate the NPDES ditch as a source, a bed material bulk analysis of residual sulfate was made. The analysis indicated bulk concentrations exceeding water quality samples from regions of the aquifer not known to be affected by the Mine’s contaminant plume. This suggests these sediments are potential sources of sulfate greater than any background levels present. Figure 18 documents these cores and shows their quick transition from dark loose silty bed materials into gray sands which are likely the Henry Aquifer. The NPDES permit for discharge to the ditch allows sulfate concentrations as high as 3500 mg/l. A ditch carrying high concentrations of sulfate, with bed materials capable of producing sulfate leachate, suggests the ditch should be treated as a contaminant source. Figure 19 shows all the contamination sources included in this study.

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CHAPTER 4 NUMERICAL MODELS Groundwater Flow Model 4.1 Main Inputs This study used the following MODFLOW packages; Basic, Discretization (DIS), Layer Property Flow (LPF), Link-MT3DMS (LMT7), Preconditioned Conjugate-Gradient (PCG), Output Control (OC), Changing Head (CHD), Drain, General Head Boundary (GHB), Head Observation (HOB), Multi-node Well Revised (MNW2), and Well. Table 5 shows the main inputs for the core packages; DIS, LPF, and PCG. 4.2 Spatial Discretization GeoSyntec (1995) created the original flow and transport model of the study area, which included the mine and the immediate vicinity around the mine. Prickett (1997, 2003) and ESI (2003) developed similar models. All of these models used specified discharge in cells along the boundary to simulate flow of groundwater into and outside of the model domain. Cox (2013) significantly expanded the previous model domain with a telescoping grid, ranging from 30 meter cells at the mine, to 60 meter cells at the model boundary. The present study initially used the same boundaries as Cox. This model had one layer containing uniform cells measuring 150 meters on a side, and its domain was defined by natural fluvial and geologic boundaries. This preliminary model was used to delineate a smaller model for a more detailed calibration and scenario analysis. Figure 20 shows the domain of each of the models.

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A series of simulations varied recharge, hydraulic conductivity, and pumping by the SVCD wells in the preliminary model domain in order to establish a detailed model grid. The SVCD pumping rates were based on the daily average from 2005 to 2015 of 12.8 million liters per day equally distributed between the district's seven functioning wells. The SVCD's withdrawals are historically significant and caused simulated hydrogeological boundaries to shift if they were not included. Two stable groundwater divides were identified in the east and southeast. The detailed model incorporates these no flow boundaries, and also shares portions of the geologic and hydrologic boundaries used in the preliminary model. The low recharge, low conductivity, active SVCD pumping simulation illustrated in Figure 21 shows this detailed model domain in cyan, with geological pinch outs and groundwater divides highlighted in orange and violet respectively. The detailed model has three layers; the top of layer 1 is defined by the top of the Henry Aquifer (Figure 15), the elevation at the base of layer 3 is defined by the bedrock surface (Figure 14), and the top and bottom of layer 2 is defined by boundaries that split the aquifer into three layers of equal thickness. All layers in the model have grid cells measuring 20 meters on a side. This model (herein referred to simply as the model) included 207,824 horizontally uniform cells per layer, 142,521 of which were active or specified head cells. A grand total of 427,563 active or specified cells were simulated in the three model layers. Figure 22 shows the study area in plan-view with the active cells of the model grid. 4.3 Temporal Discretization The model included 16 stress periods (Table 6) in order to define changes of the study area wells location and discharge. The first stress period has a duration of ten years. Flow model simulations suggest that the aquifer reaches steady state eight years after a change in a stress, therefore the first stress period is steady state. The subsequent stress periods are transient.

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Anderson et al. (2015) recommended starting a transient model with steady-state conditions. For the transient stress periods, time steps and time step multipliers were based on the period duration (Table 7). 4.4 Transient Parameters Transient flow requires specific storage and specific yield. These were derived from literature and based on the different unconsolidated materials represented by the conceptual model. Table 8 shows the values used for specific storage based on the works of Batu (1998), Fetter (2001), and Domenico and Mifflin (1965), and the values of specific yield based on the works of Heath (1983), and Morris and Johnson (1967). 4.5 Hydraulic Conductivity Horizontal hydraulic conductivity was homogeneous for each model layer, but different in each layer. Vertical hydraulic conductivity was also unique for each layer. The values used for hydraulic conductivity were based on this projects three-layer hydrostratigraphy of the Henry Aquifer interpreted from ISGS logs. In the study area, the logs indicate contrasting materials with depth, beginning just below the surficial soils with 1) silty, sandy clays that transition into 2) fine and medium grained sands, 3) terminating near or at the bedrock surface with a mixture of coarse sands and gravels. Table 3 lists all of the hydraulic conductivity values used, with comparative modeling inputs for the region from Cox (2013), ESI (2003), Prickett (1997), and GeoSyntec (1995).

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4.6 Recharge From 1968 through 1990 (stress periods 1 through 5), recharge was set to 7.0 x 10-9 meters per second (22 centimeters per year) for the top most active cell. This value was slightly higher than the results of the range determined with the USGS Groundwater Toolbox, but agrees well with values used in previous models. SVCD archival records (2016) indicate that by 1991, the South 40 Refuse area was reclaimed and covered with a low permeability layer. From 1991 (stress period 6+) onward, recharge was set to 2.0 x 10-9 meters per second (6 centimeters per year) for the top most active cells located below the cover. Reclamation and covering of Slurry No. 5, Slurry No. 1a, Refuse No. 3, and Slurry No 2 was completed in 2001 (stress period 10+). From then on recharge was adjusted for the top most active cells directly beneath these areas to the value used for the South 40 Refuse cap, 2.0 x 10-9 meters per second (6 centimeters per year). Figure 23 shows all recharge zones located in the study area. 4.7 Wells The length and elevation for each well screen are different, as shown in Table 9 (IEPA 2016, SVCD Archives 2016). Because many screens bridge multiple model layers, the MNW2 package was implemented. Table 10 summarizes inputs for the MNW2 package. Gold and Shawneetown Hills were omitted from the model, yet likely contribute a recharge flux to the adjacent unconsolidated materials of the Henry Aquifer. The recharge rate used elsewhere in the model of 7.0 x 10-9 meters per second was a flux source for the watersheds located on top of the highland areas. Each watershed’s total volumetric flow rate from recharge was divided among its perimeter cells as a positive discharge (injection) into layer two with the Well package shown Table 11 and Figure 24. This discharge was constant for all stress-periods.

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4.8 Drains Nearly all small streams and ditches were simulated with the Drain package. The locations and input values are shown in Figure 25 and Table 12. The drains incorporated a gradient to the stage based on starting and ending elevations that were sampled from the 2012 LiDAR DEM, then linearly interpolated along the drain reach. Cypress Ditch Lower, Cypress Ditch Upper, and Little Cypress Ditch had conductance that changed along their length. Conductance is calculated from; C = KA / L (Equation 1, adapted from McDonald et al. 2005) Where; A represents the models uniform cell edge length of 20 meters multiplied by the stream width based on geospatial and field estimates at mean annual stage In these calculations, the stream width and length are hypothesized to remain constant, while bed thickness (L), or conductivity (K) change over the drain length. This approach was taken because the isopach map of the surficial low permeability materials shows a change in thickness along the drains. The previously discussed streambed field survey of the region generally corroborates these assumptions. 4.9 General-Head Boundaries The GHB package was used for three features in the flow model; the Makeup Lake, Freshwater Lake, and NPDES drainage ditch. The lakes have static stages, while the ditch has a graded stage determined from the 2012 LiDAR DEM. Sampling of the NPDES ditch bed along New Market Road indicated a decrease in the thickness of the low permeability sediments with

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increased distance from the mine. The conductance of these materials in the model reflected this change. The input values for these three GHB features are indicated in Table 13. 4.10 Changing-Head Boundaries The CHD package was used for one feature, a segment of the Middle-Fork Saline River in the far southwestern region of the model. CHD was used to represent the river for three reasons; a sizable fourth level hydrological unit watershed of 30 square kilometers suggests it is perennial, field observations during each season of 2016 confirmed flow in the channel, and the isopach map of bed thickness suggests that the channel at least partially incises into the Henry Aquifer. The beginning and ending stage elevations of the river were sampled from the LiDAR DEM (2012) and values along the stream reach were interpolated. The attributes used for the Middle-Fork Saline River CHD are shown in Table 13.

Contaminant Transport 4.11 Main Inputs The MT3DMS packages used included the Advection, Basic, Dispersion, Sink Source Mixing (SSM), Transport Observation (TOB), and Generalized Conjugant Gradient solver (GCG). The main inputs for these packages are indicated in Table 14. 4.12 Spatial and Temporal Discretization The transport model has the same cell dimensions as the flow model. However, the domain of the transport model was reduced by experimentally observing the maximum extent of the contaminant plume. The transport model domain contains 69,351 active cells from the 207,824 total cells per layer. Thus, the total active cells of the transport model from all three

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model layers is 208,053. A plan view comparison of the active flow and transport model domains is shown in Figure 22. The transport and flow models have the same temporal discretization, with the exception that the number of transport steps is automated by MT3DMS. 4.13 Advection and Porosity The finite-difference (FD) upstream-weighting method was used for advective transport. The FD method was selected for three main reasons; the Peclet number fell within recommended limits for the FD method (see Equation 2); the FD method reduces simulation times, and has better calibration statistics than the Hybrid Method of Characteristics (HMOC) and Third-Order Total-Variation-Diminishing (TVD) tracking options. Cox (2013) and ESI (2003) also used the FD method for their transport models. Equation 2, adapted from Anderson et al. (2015) Pe = vΔx / D = Δx / ɑL Where; Pe is the Peclet number, v is the average groundwater velocity in the region of interest, D is the effective diffusion coefficient, Δx is the cell dimension, and ɑL is the longitudinal dispersivity The longitudinal dispersivity values used in previous work range from 30 to 100 meters. These values and the 20-meter grid cells in this work produce Peclet numbers between 0.2 and 0.67. The Peclet numbers for the model fall within the range suggested by Zheng and Bennett (2002), and Huyakorn and Pinder (1983); values less than or equal to two eliminate unwanted oscillatory behavior and reduce numerical dispersion when using the FD method.

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The effective porosity values used (Table 3) were unique for each layer, and homogeneous throughout each layer. This approach is consistent with the simplified hydrostratigraphy, and the values were literature derived (Anderson et al. 2015, Domenico and Schwartz 1990, Fetter 2012, Kempton et al. 1982, Morris and Johnson 1967, Poole and Sanderson 1981). 4.14 Dispersion and Diffusion The dispersion package was included because certain regions of the flow model have low relative groundwater velocity, suggesting that dispersion influences plume morphology. Table 15 details the per layer dispersivity and diffusion values from previous work and determined during calibration. Gelhar (1992) and Lovanh et al. (2000) suggested that longitudinal dispersivity is one tenth of the distance between maximum concentration, and a location where the concentration is 50% of the maximum along a transect in the direction of groundwater flow. 23 years of remediation pumping have likely transformed the plume significantly, suggesting that an analysis of historical data would provide the best dispersivity values. Figure 26 shows an interpolated map of the concentration plume based on average observed values from 1992 to 1994. At this point in time, sufficient concentration measurements were recorded before heavy production and remediation pumping began. Figure 26 also shows the concentration transect used to determine dispersivity (white line with three tick points). The transect begins at the highest average concentration of 1117 mg/l sampled near the Makeup Lake, and ends ~500 meters west of SVCD Wells 6, 7, and 8 at a concentration of 50 mg/l based on their average sampled concentrations from 2005 to 2015. Figure 27 shows a profile view of the transect, with the locations of the

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maximum concentration, 50% of maximum concentration, and terminus. The distance between maximum concentration and 50% of maximum concentration is approximately 1050 meters. Ten percent of this distance is 105 meters, which was increased to 130 meters for longitudinal dispersivity in all model layers during calibration. A value of 42.9 meters for transverse horizontal dispersivity (33% of longitudinal dispersivity) was determined during calibration. Gelhar (1992), Lovanh et al. (2000), and Zheng (2010) recommended setting transverse horizontal dispersivity at least an order of magnitude less than longitudinal dispersivity. However, values from literature support the value selected for this study. Pinder (1973) and Vaccaro et al. (1983) investigated transport in glaciofluvial sand and gravel aquifers similar to the Henry Aquifer, and used transverse horizontal dispersivity at 30% of longitudinal. Additionally, Gelhar (1992) estimated that the coarse unconsolidated alluvial aquifers of the Hastings City Rubbish Dump Site and Roya Hill Talus site have horizontal dispersivities around 20% of longitudinal. Table 16 shows the values from these studies compared with those of this work (adapted from Gelhar 1992). The model has a transverse dispersivity in the vertical plane of 4 meters (3% of longitudinal dispersivity). Although Table 16 suggests this value is slightly higher than similar studies, Gelhar (1992), Lovanh et al. (2000), and Zheng (2010) recommend a transverse vertical dispersivity that is less than horizontal transverse dispersivity, and roughly two orders of magnitude less than the longitudinal dispersivity. The bulk diffusion coefficient of sulfate in open deionized water is approximately 10.7 x 10-8 m2/s (Domenico and Schwartz 1998). The effective diffusion coefficient input for each model layer can be determined by multiplying the bulk diffusion coefficient by half the porosity

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in each layer (Helfferich 1966). The modeled value for all three layers in Table 15 is low, suggesting advection and dispersion dominate transport of sulfate, and diffusion can be considered negligible. 4.15 Sources Using constant source loading values for 50 years was hypothesized to not sufficiently define the historically dynamic concentrations sampled in the field. Therefore, six different phases of mass-flux source loading were used between 1968 and 2016, shown in Table 17. Cox (2013) utilized two distinct source loading phases pre and post of the 2001 remediation efforts, shown in Table 18. ESI (2003) modeled 10 source loading phases, shown in Table 19.

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CHAPTER 5 CALIBRATION 5.1 Hydraulic Head The model was calibrated to hydraulic head from Eagle No. 2 Mine monitoring wells for seven separate years. SVCD and Eagle No. 2 Mine pumping wells were not included because of non-static water level error during periods of pumping. The focus annum was 2015. Five additional years assured model characterization of the groundwater systems response to historical stresses. The supplementary years were 1984, 1992-1994 averaged, 2001, 2007, and 2011. Approximately 120 manually adjusted simulations were necessary to reach calibration. Correlation plots and a variety of statistics were used to determine calibration. The statistics were based on the residual difference between simulated vs observed values each year for each well. A visual adaptation of the main calibration year (2015) head residuals is shown in Figure 28. The statistics used included root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe model efficiency coefficient (NSE), correlation coefficient (CC), coefficient of determination (CD), and standard deviation (SD). The suite of statistics for all calibration years are shown in Table 20. The correlation plots, RMSE, and SD were the chief gauges used to manually adjust values between simulations. The correlation plots were used as a visual aid. Linear trend lines were added to the plotted data. Adjustment to model parameters was influenced by the trend line positions after each run. The goal was for the trend line y-intercepts to equal zero, maintain a slope close to one, and yield minimal autocorrelation. The final head correlation plots are shown in Figure 29. The acceptable RMSE in all calibration years was less than or equal to one meter. One meter was

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appropriate because it generally accounts for the yearly transient change in hydraulic head in the study region. A yearly ranking (Table 20) of each wells target quality was based on observed fluctuation in hydraulic head. The deviation of simulated annual value from the observed annual average was the classifier. The resulting ranks included five levels; Rank 1: +/- less than or equal to one standard deviation from year mean, Rank 2: +/- less than or equal to two standard deviations from year mean, etc. 5.2 Groundwater Flux A comparison of the simulated vs observed discharge flux from the Cypress Ditch drainage basin was made to complement the hydraulic head calibration. Figure 30 shows the locations of all fluvial features included in this analysis, and Figure 31 shows the associated contributing watersheds. A field measurement of stream discharge was taken at a sample point across Cypress Ditch as it nears its confluence with the Saline River in the southern part of the model. This measurement was taken in late April of 2017 following 20 – 25 cm precipitation that occurred over 4 weeks; seasonably higher than average. For the first stress-period, simulated baseflow into the Cypress Ditch Drainage system was compared to observed stream discharge at the field measurement site. The simulated baseflow, was approximately 20% of the observed discharge. O'Hearn et al. (1980), and Santhi et al. (2008) suggest in this region of the US, a range of 10-20% of total streamflow comes from groundwater baseflow.

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5.3 Contaminant Transport The transport model calibration included the same years as the flow calibration. The concentration contours for the primary year of 2015 are shown in Figure 32. In total 150 manual adjustments were made to produce the calibrated transport model. The mass-flux source loading was the most sensitive parameter, motivating a post-calibration analysis of contaminant loading entering the aquifer. Simulated concentrations from 2016 were sampled from layer one with 60% point density within each source area. Each sample set was averaged, and converted to milligrams per liter based on average associated cell volume. The results of this analysis are summarized in Table 21. The values share some similarities with the constant concentration source methods of Cox (2013) shown in Table 18, and ESI (2003) shown in Table 19, which further supports the final calibrated mass-flux loading rates in Table 17. Correlation plots, RMSE, and SD were the primary tools used to adjust the transport model during calibration. All other statistics were included after calibration to summarize the results. The correlation plots and statistics are shown in Figure 33 and Table 22 respectively. A visual adaptation of the main calibration year (2015) sulfate residuals is shown in Figure 34. The correlation plots and SD were referenced in the same way as they were in the flow calibration. The RMSE was less than or equal to 100 milligrams per liter sulfate concentration for 2015. This 100 milligrams per liter level is based on measurement error, average annual concentration change observed in the target wells, and potential regional variation in background sulfate. The acceptable error for RMSE was adjusted from 100 to 200 milligrams per liter for the remaining six hindcast years. This increase accounts for decreasing reliability of older concentration measurement records.

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CHAPTER 6 SCENARIO ANALYSIS A total of 12 forecast scenarios were simulated. These scenarios show model sensitivity to parameter adjustments such as recharge and dispersivity, providing inferences on the groundwater flow/transport system. These scenarios also represent preliminary risk assessments for regional water managers. Table 23 and the supporting sub tables 24A-24H provide details about the conditions for each scenario. Figures 35 through 46 show the sulfate concentration contours in model layer one after each scenario was simulated for 100 years (beginning in 1968 and ending in 2068). All model conditions before 2017 were based on the calibrated model, and conditions after 2017 were based on each unique scenario’s conditions. In the forecast scenarios, only parameters specifically included in Table 23 were changed, and all others matched the calibrated model. 6.1 Assumptions The four main assumptions of the scenario forecasts include 1) constantly pumping wells when active, 2) constant climatic conditions with the exception of scenario 11, 3) 0 mg/l background concentration of sulfate in the Henry Aquifer, and 5) sulfate transport without geochemical reactions. Future pumping by the SVCD was based on the 2017 estimated need of 12.8 million liters per day, except for scenario 12 which hypothesizes an increased total of 15 million liters per day. These withdrawals were equally distributed among the included SVCD wells in each scenario. In reality, the SVCD does not pump each well continuously, and occasionally wells are temporarily shut down for maintenance. However, a constant distribution of pumping likely

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averages out the daily and monthly rotational changes with minimal error. One scenario (No. 4) includes remediation pumping for the first 15 of the 50 forecast years. Over this duration, the pumping rates used by the remediation wells in 2017 were sustained. No other scenarios include remediation pumping. Scenario 3 includes six regional agricultural irrigation wells constantly pumping at rates distributed annually, derived from their estimated total withdrawal over two months of the summer season. As with the hindcast model, all scenarios except for No. 11 maintained constant areal recharge and constant positive flux along the Shawneetown and Gold Hills escarpment boundaries. Using constant values avoided an overly complicated model. Scenario 11 was included to evaluate the sensitivity of the model to variable recharge. The model assumes a background concentration of sulfate in the Henry Aquifer of 0 mg/l. However, the northern SVCD wells No. 6, 7, and 8 yielded water samples between 15 and 25 mg/l sulfate from 2005 to 2015. Therefore, the results of the forecast scenarios could potentially increase based on the actual background concentration in a particular region of the aquifer. The biogeochemical alteration of sulfate in the groundwater was not considered in the forecast, or hindcast simulations. These secondary processes could potentially influence the fate of sulfate in the regional groundwater system.

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6.2 Discussion Scenario 1: Natural plume development without SVCD or Eagle No. 2 Mine wells pumping (Figure 35) Groundwater flow from the vicinity of the mine trends north-northwest when the SVCD and mine wells do not pump. This flow direction is significantly different from the westerly trending flows from the calibration year of 2015. When pumping stresses are not present, such a change in flow is likely influenced by two model factors, groundwater influx from the Shawneetown Hills bedrock, and the conductance of Cypress Ditch north-northwest of the mine. Scenario 2: Only SVCD wells pumping (Figure 36) Several differences are evident in the morphology of the transport plume when comparing scenario 2 against scenario 1. The main plumes in scenario 2 hook from the northeast to southwest, likely influenced by the pumping stress associated with the three SVCD wells in the southeast. The two separate SVCD pumping centers appear to spread the front of the plume, lowering overall concentrations when compared to scenario 1. The concentrations near the SVCD’s north wells No. 6 and 7, and the south well No. 2 approach 100 mg/l, nearly 60% of the concentrations sampled by well No. 5 in 2005 that lead to its shutdown. Scenario 3: SVCD and agricultural irrigation wells pumping (Figure 37) Six agricultural irrigation wells pumping in tandem with six SVCD wells alter the plumes extent from the previous. In this scenario, the number of SVCD wells (6) and Irrigation wells (4) located near the northern/southern borders of the mine appear to create a broadly dispersed plume front. In comparison with scenario 2, concentrations at the northern SVCD well field decreased while those at the southern well field increased. This difference is potentially due the

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southern SVCD wells No. 1, 2, 4 and Irrigation well No. 2 magnifying the general gradient of groundwater flow towards the Saline River in the southeast. Only the immediate vicinity of Well No. 2 has a risk of high concentration, reaching a value around 100 mg/l. Scenario 4: SVCD wells constantly pump while remediation wells pump temporarily (Figure 38) With only 15 years’ of continued remediation pumping, the overall plume morphology is very similar to scenario 2. Yet, sulfate concentrations transported off the mine site west of Slurry No. 5 and Slurry No. 3 markedly decline as much as 100 mg/l. The temporary remediation pumping reduces sulfate concentrations in the region of the SVCD wells No. 2 and 8 by 10 to 20 mg/l from those shown in scenario 2. In this simulation, all SVCD well centers have concentrations at or less than 50 mg/l. However, wells No. 2 and 8 are less than 100 meters from areas with concentrations above 100 mg/l. Scenario 5: SVCD uses three new western wells plus one existing well (Figure 39) Existing SVCD Well No. 4 in addition to hypothesized wells No. 9, 10, and 11 (Figure 39) equally distribute a combined withdrawal of 15 million gal/day. This scenario had a plume morphology significantly different that the other scenarios considered. No longer are pumping stresses dispersing the plume to the north and south as it moves offsite. In the northeast, the sharp concentration front expressed in scenario 1 returns, suggesting that natural stresses dominate transport in that region. The new tight cluster of only three pumping SVCD wells two and a half kilometers west of the mine stretches the southern plume out a substantial distance compared to all other scenarios. Although the three new SVCD wells do not experience concentrations above 50 mg/l by 2068, their stress on the system significantly decreases groundwater quality in between their location and the mine.

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Scenario 6: Mass-flux loading for all sources is increased by 5 mg/s (Figure 40) Sulfate loading that is greater than that of the calibrated model substantially increases concentrations on and off the mine property. This result better defines the hook of the northeastern plume, and indicates a greater risk to private (agricultural and homeowner) groundwater users in that area. SVCD Wells No. 6 and 8 in the north become encircled with concentrations near, or in excess of 80 mg/l. SVCD Well No. 2 in the south captures sulfate and helps protect SVCD Well No. 1. Well No. 2 is within a region with concentrations exceeding 150 mg/l. This scenario indicates that the model is sensitive to small changes in contaminant loading, and also reinforces the importance of accurately estimating source loading values that represent those occurring in the field. Scenario 7: All dispersivity values are decreased by 50% (Figure 41) With decreased values of dispersivity, plume fronts become sharper and move less distance from the mine than with calibrated values. The concentrations throughout the study area are similar to those shown when source loading is increased in scenario 6. Well No. 2 in the south is surrounded with concentrations equal to or greater than 100 mg/l by 2068. In the northern SVCD well field, only the immediate vicinity of Well No. 6 is affected with concentrations rising marginally above the 50 mg/l. Scenario 8: All dispersivity values are increased by 50% (Figure 42) When dispersivity is set greater than the calibrated values, plume morphology remains similar to scenarios 6 and 7, but the concentrations decrease significantly throughout the model domain. The only active SVCD well with concentrations reaching a magnitude of concern is No. 2 at approximately 80 mg/l sulfate.

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Scenario 9: All hydraulic conductivity, conductance, and porosity values are decreased by 50% (Figure 43) In this scenario, plume morphology is similar to that expressed in scenarios 2, 3 and 4, but simulated sulfate concentrations are 20 to 30 mg/l lower along the plume margins. The plume morphology also maintains a relatively steep front throughout. These results appear to suggest that the model is sensitive to parameters commonly known to influence, in this case decrease, groundwater velocity. This scenario shows that the only SVCD wells with concentrations of concern are No. 2 and 8, at or exceeding approximately 60 mg/l. Scenario 10: All hydraulic conductivity, conductance, and porosity values are increased by 50% (Figure 44) With higher conductance and conductivity, the northeastern section of Cypress Ditch becomes a more dominant sink for groundwater flow than SVCD Well No. 6, 7, and 8. This appears to result in a more northerly trend of transport from Slurry No. 2 and Refuse No. 3. For the southwestern section, the transverse horizontal width of the plume is increased and concentrations are reduced compared to previous scenarios. This is likely due to Cypress Ditch’s increased conductance gradient northeast to southwest. These results reinforce the observation from scenario 9, that the model is sensitive to parameters that influence groundwater velocity. The only well indicating a concentration risk is No. 2 at approximately 100 mg/l.

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Scenario 11: Alternating 12.5 year cycles of drought by varying recharge (Figure 45) Long term climatic trends simulated in this scenario suggest that keeping a static recharge throughout the entire hindcast simulation (1968 – 2016) is a reasonable approach. This is because even with long 12.5-year cycles alternating between 8 and 22 centimeters per year recharge, the model plume morphology and concentrations are similar to scenario 2. Only SVCD Wells No. 2 and No. 8 show concentration risk, at or near 100 mg/l. Scenario 12: Four old east, and three new west SVCD wells increase total pumping by 15% (Figure 46) This scenario presents a potential management option for the SVCD, which includes strategic pumping to moderate the plume. The effects of dividing pumping between the existing northeast (No. 6 and 7) and southeast (No. 1 and 4) wells stretches the plume horizontally transverse to the primary direction of flow, resulting in decreased concentrations along the front. Although some simulated concentrations increase as much as 150 mg/l when compared to Scenario 5, these elevated values are limited to the region immediately west of Slurry No. 5. Only SVCD well No. 8 shows a risk of simulated concentrations in excess of 50 mg/l.

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CHAPTER 7 CONCLUSIONS 7.1 Scenario Implications All scenarios indicate that groundwater wells within about 300 meters immediately north or west of the mine property are at risk for sulfate concentrations in excess of the EPA water quality standard without remediation pumping for 50 years after 2017. Most scenarios, aside from No. 6, do not show concentrations near SVCD wells No. 1, and 2 in excess of those currently observed in the field. One potential aspect influencing this is omission of the NPDES mine ditch as a source once remediation pumping hypothetically stops in 2017 (e.g., the ditch no longer carries contaminated runoff). However, preliminary laboratory analysis suggests that sulfate within the NPDES bed sediments may move into the Henry Aquifer if the sediments continue to be saturated. Thus, saturation of the NPDES ditch via natural recharge, even without remediation pumping, could potentially increase concentrations offsite, especially in the vicinity of SVCD wells No. 1, 2, and 4. The scenarios also show that carefully managing the timing and pumping rates of the various SVCD wells could be an option to control the plume if remediation pumping ceases. This was made evident by scenarios 2, 3, 5, and 12, where the distribution of pumping among active wells dispersed the plume front over a greater area when compared to the results of scenario 1. This resulted in concentrations for scenarios 2, 3, 5, and 12 that were less than or equal to 50 mg/l near the SVCD wells. These simulations point out that future management actions by the SVCD will influence contaminant transport in the aquifer. Where, when, and how much the SVCD pumped over the

39

simulated 50 years greatly altered the morphology of the plumes. The private household and agricultural users in close proximity to the mine were often in areas where simulated concentrations exceeded EPA standards. This suggests that stopping remediation pumping could pose a potential risk to groundwater wells near the mine. 7.2 Closing Thoughts and Recommendations A post audit assessment of this research is recommended. Plans to install new wells (SVCD Staff 2016), and the possible shutdown of the mine remediation wells, underscores the importance of this effort. Further work should consider the following four important possibilities: 1) A detailed study of water interactions between the internment lakes, settling basins, NPDES ditches, and the Henry Aquifer. 2) Continued evaluation of the mine's operations between 1968 and 1993 to improve understanding of potential sources. 3) Increasing detail to modeled hydrostratigraphy as more logs become available. 4) Extensive testing of a variety of SVCD pumping management plans in order to minimize future contaminant transport. This research consistently showed that there is a transfer of water between the mine lakes, NPDES drainage channel, and groundwater in the vicinity of the mine. Similar relationships were discussed by Cox (2013), ESI (2003), and Prickett (1997, 2003). These structures were constructed to facilitate control and removal of contamination from the Henry Aquifer. However, field observations and modeling suggest sulfate is actually being discharged

40

back into the groundwater system through the lakes and ditch. The proposed causes of this contaminant reintroduction are poorly engineered low permeability bed materials, and the SVCD and Eagle No. 2 wells pumping in the region of the mine. These wells put stress on the aquifer, and promote recharge from these sources. An example of this relationship is illustrated by samples from SVCD Well No. 3 near Cypress Ditch, with sulfate concentrations exceeding 80 mg/l sulfate (2007-2011 average), while SVCD Wells No. 1 and 2 closer to the mine sampled water at 50 mg/l or less. The west Settling Basin No. 008 was a source that previous research did not include. Its presence in this study was based off three pieces of evidence; the 1980s Eagle No. 2 Mine engineering blueprint (Figure 11), the Eagle No. 2 NPDES permit No. IL0044661, and areal images of the mine (Figure 17). Historical documents suggest that the West Refuse and Slurry No. 5 areas have insufficient low permeability linings. The field study of the NPDES Outfall No. 001 during this research also indicated permeable bed linings. Therefore, the neighboring settling basin was deemed an important source area to include. Use of this basin as a source area could be further verified with a sulfate bulk analysis of the bed material and assessment of precipitation runoff retention in the basin. The hindcast simulation may provide an explanation for the 2012 spike in sulfate concentration at mine well WA0009A. Concentration contours for the years of 2002 through 2016 shown in Figure 47 depict a high concentration plume limb traveling southwest away from Slurry No. 2 and Refuse No. 3 towards remediation well SCW1. The simulation appears to show that SCW1 is drawing this plume through the screened interval of monitoring well WA0009A. The timing of these simulation results are supported by field observations (Figure 48), which show a similar concentration pulse between 2007 and 2016. If such a hypothesis is correct and

41

SCW1 were to be shutdown, remnants of this sulfate limb might become a threat to the region west and north of the mine. This analysis shows that contaminant transport in the aquifer can move in an unexpected manner due to the influence of pumping stresses and source areas. If the mine’s remediation pumping ceases, additional pockets of concentration throughout the region currently being captured could mobilize towards nearby municipal wells in a similar fashion. This suggests that continued scrutiny of pumping and source changes through time is essential. Two sets of monitoring wells provide data on contamination at different depths. SW1S/SW1D and SW2S/SW2D are located west, and northwest of the mine respectively. The suffix S represents shallow, and the suffix D represents deep. For each pair, the screens are vertically separated by approximately 20 meters. Observations from each pair frequently show a stratification in sulfate concentration between 30 to 300 mg/l quarterly. This model could not reproduce a vertical gradient of such magnitude. Layers of high conductivity materials with limited horizontal extent common to alluvial deposits were not included in this model, but might contribute to this stratification. Future investigation on these aspects is recommended as more well logs in the region become available. From 2005 to 2015 the measured water table elevation was 10 meters or less from the land surface throughout the study area. These observations also showed a maximum change of head +/- one meter annually. The simulated thickness of layer one in the immediate vicinity of the mine averages 20 meters, and is separated from the land surface by less than 5 meters of semi-confining material. Simulations showed that changes in hydraulic head occur within layer one, but significantly above the interface between layer one and layer two. The rewetting feature in MODFLOW was not used because of this. The rewetting option might be reconsidered in

42

future models if the SVCD increases pumping, which could potentially drop the water table elevation near the bottom elevations of layer one. The SVCD's water usage has a commanding effect on the regional groundwater flow and transport systems; a point made evident by the significant change in the location of the groundwater divide when pumping is considered in the preliminary model, and the manipulation of the contaminant plume by the SVCD wells shown during scenario analysis. This suggests that the SVCD needs to take a cautious approach to managing their well fields especially if the remediation wells cease pumping. One potential mitigation effort would be to consider mixing water sourced from well fields with high and low concentrations of sulfate, in an effort to dilute and continue meeting water quality standards. This body of research offers an alternative examination of the groundwater flow and contaminant transport conditions in the Henry Aquifer of Gallatin County Illinois than that of previous studies. Several questions arose during the research, which provide important opportunities for future studies in the region. This work further enhanced our knowledge of flow and transport in the Henry Aquifer, and provides a resource to help regional groundwater managers make more informed decisions in the future.

43

Figure 1: Physiographic Provinces and Hydrology of Gallatin County, Illinois.

44

Figure 2: Stratigraphic column of the bedrock geology in the study area. The Eagle No. 2 Mine extracted the Springfield Coal Member, highlighted in red. (adapted from IBS 2001 and Bristol 1975)

Figure 3: Stratigraphic column of the Quaternary sediments in the study area. Formations important to this study are highlighted in blue. (adapted from Heinrich 1982 and Hansel et al. 1997)

45

Figure 4: Three geologic cross-sections of the unconsolidated lithology in the study area.

46

Figure 5: Isopach of the Henry Formation and other coarse-grained alluvial sediments, e.g. the Henry Aquifer; hash regions indicate predominantly bedrock and/or loess.

47

Figure 6: Isopach of the surficial fine grained materials semi-confining the Henry Aquifer; hash regions indicate predominantly bedrock and/or loess.

48

Figure 7: Center post agricultural irrigation wells in Gallatin County from 2012 to 2016; based on orthographic imagery (Conner 2016), health department records (Watson 2016), and a regional field survey.

49

Figure 8: Current and planned SVCD service areas (2005), with current groundwater well field.

50

Table 1. Groundwater pumping history by the SVCD and Eagle No. 2 Mine.

Eagle No. 2 Mine and SVCD Well Pumping Rates (m3/sec) 78-81 82-84 85-86 87-90 91 92-94 95-97 98-00 01 02-03 04-08

SVCD Wells

Mine Wells

Years 09-10 11-12 13-14 15 Stress2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Period Active Wells 1 4 4 5 6 7 8 10 11 11 10 9 10 9 9 WA9 -0.025 -0.025 -0.025 -0.025 -0.025 -0.025 -0.025 0 0 0 0 0 0 0 0 WA19 0 0 0 -0.063 -0.0189 -0.013 -0.006 0 0 0 0 0 0 0 0 WA21 0 0 0 0 0 -0.025 -0.025 -0.028 0 0 0 0 0 0 0 WA26 0 0 0 0 0 0 0 -0.008 -0.008 -0.008 0 0 0 0 0 WA27 0 0 0 0 0 0 0 -0.01 -0.01 -0.01 0 0 0 0 0 SCW1 0 0 0 0 0 0 0 -0.034 -0.034 -0.034 -0.035 -0.035 -0.036 -0.037 -0.037 SCW2 0 0 0 0 0 0 0 0 -0.025 -0.028 -0.025 0 0 0 0 SCW3 0 0 0 0 0 0 0 -0.023 -0.023 -0.023 -0.014 -0.014 -0.014 -0.013 -0.011 SCW4 0 0 0 0 0 0 0 0 -0.025 -0.025 -0.013 -0.028 -0.025 -0.028 -0.028 SVCD1 0 -0.028 -0.028 -0.03 -0.03 -0.023 -0.027 -0.022 -0.024 -0.024 -0.0255 -0.0248 -0.02 -0.0244 -0.0244 SVCD2 0 -0.025 -0.025 -0.028 -0.03 -0.019 -0.026 -0.019 -0.026 -0.026 -0.0255 -0.0248 -0.02 -0.0244 -0.0244 SVCD3 0 -0.027 -0.027 -0.029 -0.031 -0.023 -0.029 -0.024 -0.025 -0.025 -0.0255 -0.0248 -0.02 0 0 SVCD4 0 0 0 0 -0.04 -0.035 -0.035 -0.047 -0.033 -0.033 -0.0255 -0.0248 -0.02 -0.0244 -0.0244 SVCD5 0 0 0 0 0 0 -0.016 -0.022 0 0 0 0 0 0 0 SVCD6 0 0 0 0 0 0 0 0 -0.044 -0.044 -0.0255 -0.0248 -0.02 -0.0244 -0.0244 SVCD7 0 0 0 0 0 0 0 0 0 0 -0.0255 -0.0248 -0.02 -0.0244 -0.0244 SVCD8 0 0 0 0 0 0 0 0 0 0 0 0 -0.02 -0.0244 -0.0244

51

Figure 9: Eagle No. 2 Mine monitoring and pumping wells associated with Tables 2A – 2D.

52 Tables 2A-2D. Time-series sulfate measurements from water samples obtained by Eagle No. 2 wells, color indexed to Figure 9. SCW1

Table 2A: Central Region Observed Sulfate Concentrations (mg/l)

SCW4

SMW3

SMW5

WA0005

WA0007

WA0009

WA0009A

1000

800

600

400

200

0

Table 2B:: Eastern Region Observed Sulfate Concentrations (mg/l)

SCW3

SMW4

SMW4A

SMW4B

SW3S

SW3SA

SW3SB

SW3SC

WA0017

5000 4500 4000 3500 3000 2500 2000 1500

1000 500 0

Table 2C: North Eastern Region Observed Sulfate Concentrations (mg/l)

WA0002

WA0003

WA0029A

1000

800

600

400

200

0

Table 2D: Western Region Observed Sulfate Concentrations (mg/l) 1000

800

600

400

200

0

SCW2

SMW1

SMW2

SW1S

SW1D

SW2D

SW2S

WA0010

WA0011

53

Figure 10: SVCD and Eagle No. 2 Mine wells included in this study.

54

Figure 11: 1980 era Eagle No. 2 Mine engineering blueprint for the southwestern property (SVCD Records 2016).

55

Figure 12: 1980 era Eagle No. 2 Mine engineering blueprint for the northeastern property; note Sub-Area 5’s high basal permeability (SVCD Records 2016).

56

Figure 13: General workflow of this research.

57

Figure 14. Relief map of the bedrock topography in Gallatin County, IL (data from ILWATER 2015, IPRI 2012, Smith and Norris 1976, and ESI 2003).

58

Figure 15: Topographic map of the Henry Aquifer in Gallatin County, IL; hash regions indicate predominantly bedrock and/or loess (interpreted from ILWATER 2015, IPRI 2012).

59 Table 3. Hydraulic conductivity and porosity for current and historical models. Model Comparison of Calibrated Hydraulic Conductivity (m/s) and Porosity Model Current

Cox (2013)

ESI (2003)

Prickett (1997)

GeoSyntec (1995)

Axis Horizontal Vertical Porosity Horizontal and Vertical Porosity Horizontal and Vertical Porosity Horizontal and Vertical Porosity Horizontal and Vertical Porosity

Layer 1 3.0E-04 10 0.20 5.78E-04 to 7.5E-04 0.25 7.52E-04 to 5.29E-04 0.30

Layer 2 4.5E-04 6.5 0.25 5.78E-04 to 7.5E-04 0.25

Layer 3 6.0E-04 4.0 0.30

--

--

--

--

8.96E-04

--

--

0.20 2.15E-06 to 8.47E-04 --

--

--

--

--

--

--

---

* Vertical conductivity is calculated by MODFLOW as the ratio of (Horizontal/Vertical)

Figure 16: Groundwater pumping wells considered in this research.

60

Figure 17: Orthographic areal imagery from 1994 to 2016 showing mine site during closure and remediation (Google 2016).

61 Tables 4A and 4B. NPDES ditch lining bulk analysis.

Feature

NPDES Drainage Ditch

Approx. Length (m)

2720

Average Average Bulk Volume Bed Material Effective Bed Bed of Bed Volume of Bed Average Width Thickness Materials (m3) Porosity (%) Materials (m3) (m) (m) 10

0.75

20400

17%

16932

Estimated Bed Material Density (kg/m3)

Dry Bed Material

Estimated Total

[SO4-]

Average Mass %

1600

0.91%

Estimated MassLoading of [SO4-] from Ditch Bed

(kg/m )

Estimated Infiltration Rate of Clay Bed Material (m/sec)

246529.92

9.00E-05

22.188

Bed

[SO4-] 3

Observed Bed-Only

Initial Dry Mass (g) Recovered Dry BaSO4 Sample ID *[ +/- 0.005 g ] Precipitate Mass* (g) 1A 1B 2A 2B Averages =

5.4792 3.3079 5.7480 3.4031 4.4846

0.0832 0.0727 0.1143 0.1075 0.0944

-

Total [SO4-] mg/m2 sec

-

Corrected Dry SO4 Precipitate Mass* (g) 0.0342 0.0299 0.0470 0.0442 0.0389

Percent SO40.62% 0.90% 0.82% 1.30% 0.91%

(kg/m2 sec)

22187692.80

Observed Cell BedOnly (Total/323) [SO4-]

68692.55

mg/m2 sec Modeled Cell Bed+H2O [SO4-]

142454.56

mg/m2 sec Modeled Total (x323 Cells) [SO4-] Bed+H2O

46012821.84

mg/m2 sec

Figure 18: 2.5 cm diameter by 0.3 m deep cores sampling bed materials of Eagle No. 2 Mine NPDES Ditch Outfall No. 001 near New Market Road in central Gallatin Co., IL (note shallow transition from saturated loose silts to gray poorly sorted sands, inferred to be the Henry Aquifer).

62

Figure 19: Contamination sources in the numerical transport model. Table 5. MODFLOW inputs for the Discretization, Layer Property Flow, and Preconditioned Conjugate-Gradient Method Packages. MODFLOW Main Package Options Setting Value Layers 3 Rows 496 Columns 419 Uniform Grid True Discretization Cell Dimensions 20 Top Elevations Heterogeneous Bottom Elevations Heterogeneous Stress Periods 15 Units Meters per second Wetting All Inactive Layer Types All Convertible Layer Property Flow Anisotropy False Interblock Transmissivity Average Harmonic Maximum Number of Calls 2000 Maximum Iterations per Call 30 Matrix Recondition 1 Preconditioned ConjugateRelaxation Factor 1 Gradient Method Preconditioning Method Modified Incomplete Cholesky Residual Criterion for Convergence 0.0001 Temporal Damping Parameter 1 Eigenvalue Upper Bound Calculated Package

63

Figure 20: Prickett (1997), ESI (2003), Cox (2013), and this study’s model domains.

64

Figure 21: Preliminary steady state model results. Note reduced model domain (cyan) based on geologic pitchout (orange) and groundwater divides (violet).

65

Figure 22: Final model domains; inset on West Refuse/Slurry No. 5 with 20 meter uniform grid.

66

Table 6. General description of model stress-periods.

StressPeriod

Temporal Condition

Historical Duration Range in Years

1

Steady State

1968-1977

10

Mine opens; Slurry No. 1a, West Refuse (Early Slurry No. 5), West Basin 8, Makeup/Fresh Lakes, and NPDES ditch become active contaminant sources

2

Transient

1978-1981

4

Slurry No. 2, and South 40 Refuse become active contaminant sources, Mine production well WA0009 begins pumping

3

Transient

1982-1984

3

SVCD production wells No. 1, 2, and 3 begin pumping

4

Transient

1985

1

Slurry No. 5 (formerly West Refuse), and Refuse No. 3 become active contaminant sources

5

Transient

1986-1990

5

Mine production well WA0019 begins pumping

6

Transient

1991

1

SVCD prodcution well No. 4 begins pumping, Mine reclamation reduces South 40 Refuse concentration and associated surfical recharge

7

Transient

1992-1994

3

Mine production well WA0021 begins pumping

8

Transient

1995-1997

3

SVCD production well No. 5 begins pumping

9

Transient

1998-2000

3

Mine production well WA0019 ends pumping; Mine source control wells WA0026, WA0027, WA0028 begin pumping

10

Transient

2001

1

Mine source control well WA0021 and SVCD production well No. 5 end pumping; Replacement mine source control wells WA0019R / WA0021R and SVCD production well No. 6 begin pumping; Mine reclamation eliminates West Refuse,

11

Transient

2002-2003

2

Mine source control wells WA0026 and WA0027 end pumping

12

Transient

2004-2008

5

SVCD production well No. 7 begins pumping

13

Transient

2009-2010

2

Mine source control well WA0019R ends pumping

14

Transient

2011-2012

2

SVCD production well No. 8 begins pumping

15

Transient

2013-2015

3

SVCD production well No. 3 ends pumping

16

Transient

2016

1

Mine source control well SCW3 changes rate

17

Transient

2017-2068

50

Scenario Forecasts (See Table 23)

Events

67

Table 7. MODFLOW stress period inputs. Hindcast Model Stress-Period Attributes StressPeriod 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Duration (sec) 3.16E+08 1.26E+08 9.47E+07 3.16E+07 1.58E+08 3.16E+07 9.47E+07 9.47E+07 9.47E+07 3.16E+07 6.31E+07 1.58E+08 6.31E+07 6.31E+07 9.47E+07 3.16E+07

Number of Time Step Time Steps Multiplier 1 20 15 10 23 11 15 15 15 11 13 23 13 13 15 11

1 1.2 1.3 1.3 1.1 1.3 1.3 1.3 1.3 1.3 1.3 1.2 1.3 1.3 1.3 1.3

Temporal Type Steady State Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient Transient

Table 8. MODFLOW transient condition parameters. Layer 1 2 3

MODFLOW Transient Parameters Specific Storage (1/m) Specific Yield (%) 9.00E-06 20 1.00E-05 22 2.00E-05 24

Figure 23: Model recharge zones.

68

Table 9. Monitoring and pumping well screen attributes. Observed Screen MSL Top

Bottom

Model Layer(s)

SMW1

96.59

93.54

1

Table 10. Multi-Node Well (MNW2) MODFLOW package inputs.

SMW2 SMW3 SMW4 SMW4A SMW4B SMW5 SW1D SW1S SW2D SW2S SW3S SW3SA SW3SB SW3SC WA0002 WA0003 WA0005 WA0007 WA0009 WA0009A WA0010 WA0011 WA0017 WA0029A Pumping Wells WA19 WA21 WA26 WA27 SCW1 (WA9) SCW2 (WA19R) SCW3 (WA28) SCW4 (WA21R) SVCD1 SVCD2 SVCD3 SVCD4 SVCD5 SVCD6 SVCD7 SVCD8

90.43 109.17 102.30 99.25 95.20 103.81 75.69 95.45 72.64 96.06 102.75

78.24 104.60 99.25 96.20 92.15 102.29 69.60 93.93 66.54 93.01 99.71

2 1 2 2 3 1 3 1 3 1 1 1 3 2 1 1 2 1 3 3 1 2 1 2

Hindcast and Forecast MNW2 Well Attribute Inputs (All wells use THIEM option) Well Screen Screen Pumping Well Row Column Radius (m) Top MSL Bottom MSL WA0009 0.110 86.150 82.900 254 235 WA0019 0.110 81.860 76.360 272 209 WA0019R 0.110 81.860 76.360 272 209 WA0021 0.130 84.470 78.300 282 228 WA0021R 0.130 84.470 78.300 282 228 WA0026 0.130 96.620 85.950 269 222 WA0027 0.130 94.840 84.170 268 240 WA0028 0.130 98.700 87.800 246 269 SCW1 0.110 85.730 82.900 254 235 SCW2 0.130 81.860 76.480 272 209 SCW3 0.130 98.150 87.800 246 269 SCW4 0.130 84.470 78.400 282 227 SVCD1 0.229 80.390 65.500 313 179 SVCD2 0.229 79.520 64.600 300 186 SVCD3 0.229 80.180 65.300 302 167 SVCD4 0.229 87.500 72.500 281 126 SVCD5 0.229 77.500 62.900 268 187 SVCD6 0.229 84.640 69.700 207 188 SVCD7 0.229 85.370 70.370 199 173 SVCD8 0.229 84.410 69.500 218 179 SVCD9 0.229 90.000 75.000 262 84 SVCD10 0.229 91.000 76.000 244 85 SVCD11 0.229 89.000 74.000 244 106 IRR1 0.130 85.000 80.000 152 259 IRR2 0.130 85.000 80.000 313 91 IRR3 0.130 85.000 80.000 155 368 IRR4 0.130 85.000 80.000 187 181 IRR5 0.130 85.000 80.000 135 373 IRR6 0.130 85.000 80.000 210 218

Monitoring Wells

? ? ? 99.06 103.77 98.35 102.70 75.73

96.01 94.63 95.30 99.65 72.07 ?

101.61 95.82 107.44

95.51 83.63 98.30 ?

81.86 79.47 96.62 94.84 75.73 81.86 97.15 79.47 80.39 79.52 80.18 87.50 79.51 84.64 85.37 84.41

76.38 73.68 85.95 84.17 72.07 76.38 86.48 73.68 65.39 64.52 65.18 72.50 64.51 69.64 70.37 69.41

3 3 2,3 2,3 3 3 2,3 3 2,3 3 2,3 2,3 3 2,3 2,3 2,3

69

Table 11. Bedrock upland recharge to Henry Aquifer (see Figure 24); source area refers to upland watershed. Bedrock Flux (via WELL package injection in Layer 2, with Source Recharge of 7.00E-09 m/s) Source Flux Perimeter Calculated Per-Cell Calibrated Per-Cell Terrain Source Cells Source Area (m2) Cells (m3/s) Discharge (m3/s) Discharge (m3/s) Shawneetown Hills 14,600 5,840,000 0.04088 510 8.02E-05 4.80E-05 Gold Hill 5,900 2,360,000 0.01652 326 5.07E-05 4.80E-05

Figure 24: Model perimeter influenced by recharge from bedrock upland to Henry Aquifer (see Table 11).

70

Table 12. MODFLOW Drain package inputs; locations shown by ID in Figure 25.

Drain Package Inputs for Modeled Streams, Creeks, and Ditches Name Figure ID Cypress Ditch Lower (CDL) 1 Cypress Ditch Upper (CDU) 2 CDU North Fork 1 3 CDU North Fork 2 4 CDU North Fork 3 5 CDU East Branch (CDUEB) 6 CDUEB Fork 1 7 CDUEB Fork 2 8 Little Cypress Ditch West (LCDW) 9 LCDW Fork-1 10 Eagle No.2 Mine North Agri. Drain 11 Eagle No.2 Mine East Drain 1 12 Eagle No.2 Mine West Drain 2 13 Unknown South Drain-1 14 Unknown South Drain-2 15 Unknown South Drain-3 16 Unknown South Drain-4 17 Unknown South Drain-5 18

Figure 25: MODFLOW Drain package features; IDs are associated with attributes listed in Table 12.

Conductance (m2/s) Stage Trend (MSL) 1.00E-04 to 4.00E-04 105.03 to 104.61 9.10E-05 to 9.70E-05 107.37 to 105.03 7.0E-05 106.91 to 105.06 5.0E-05 106.24 to 105.81 8.0E-05 108.7 to 106.63 8.0E-05 107.21 to 105.25 8.0E-05 114.34 to 107.02 7.0E-05 111.67 to 106.94 9.00E-05 to 1.50E-04 106.97 to 104.68 7.0E-05 106.71 to 106.07 4.0E-05 108.19 to 107.06 1.0E-05 107.81 to 106.06 4.0E-05 108.2 to 106.97 7.0E-05 108.55 to 105.8 7.0E-05 108.68 to 106.82 7.0E-05 119.39 to 108.09 7.0E-05 117.45 to 110.36 7.0E-05 116.08 to 110.47

71

Table 13. General and Changing Head Boundary (GHB and CHD) MODFLOW package inputs.

General and Changing Head Boundry Package Inputs Feature Eagle No.2 Mine NPDES Ditch Eagle No.2 Makeup-Lake Eagle No.2 Freshwater-Lake Saline River (Middle-Fork)

Type GHB GHB GHB CHD

Conductance (m2/s) 8.00E-05 to 1.00E-04 8.0E-06 8.0E-06 --

Stage (MSL) 112.8 to 104.6 112.32 112.32 104.62 to 104.59

Table 14. MT3DMS Basic, Generalized Conjugant-Gradient, and Advection package inputs.

Package Basic Transport

Generalized ConjugantGradient Method

Advection

MT3DMS Main Package Options Setting Value Discretization Concurrent with Flow Model True Number of Mobile Components 1 Units Milligrams per second Maximum Outer Iterations 1 Maximum Inner Iterations 100 Relaxation Factor 1 Preconditioning Method Modified Incomplete Cholesky Dispersion Tensor Lumped to Right-Hand-Side Residual Criterion for Convergence 0.001 Initial Transport Step Size 0 Maximum Transport Step Size 0 Maximum Transport Steps per Timestep 30000 Tracking Method Hybrid (Euler and Runge-Kutta) Solver Upstream Finite Difference Courant Number 0.6 Particle Origins Random Initial Particle Count per Cell 3 Minimum Particle Count per Cell 2 Maximum Particle Count per Cell 5 Maximum Moving Particles 700,000 Minimum Concentration Gradient 1.00E-06

72

Table 15. Dispersivity and Effective Molecular Diffusion Coefficient for current and previous work; -- indicates unknown parameters.

Dispersivities (meters) and Effective Molecular Diffusion Coefficient (Values are uniform for all model layers) Effective Molecular Study Longitudinal Horizontal Vertical Diffusion Coefficient Current 130.0 42.9* 3.9*