### USE OF EPA’S INDUSTRIAL WAST E MANAGEMENT EVALUATION MODEL (IWEM) TO SUPPORT BENI FICIAL USE DETERMINATIONS A Final Report Submitted to: The US EPA Office of Solid Waste and Emergency Response (OSWER)

Submitted by: Jeffrey S. Melton and Kevin H. Gardner

Recycled Materials Resource Center Gregg Hall, University of New Hampshire Durham, New Hampshire, 03824

This project was funded by a grant from The US EPA Office of Solid Waste and Emergency Response (OSWER)TABLE OF CONTENTS

LIST OF TABLES.........................................................................................................iv LIST OF FIGURES ........................................................................................................v CHAPTER PAGE CHAPTER 1 – INTRODUCTION .................................................................................1 1.0 Context.........................................................................................1 1.1 Significance and Objectives of this Study ...................................4 1.2 Study Areas..................................................................................6 1.2.1 Wisconsin State Highway 60...........................................6 1.2.2 U.S. Highway 301, North Carolina..................................9 1.2.3 Routes 213/301 and Interstate 695 overpasses, Maryland...................................................................................11

CHAPTER 2 – METHODS..........................................................................................18 2.0 Overview.....................................................................................18 2.1 Study Data....................................................................................18 2.1.1 WMU Parameters (IWEM modeling specifically ................................................................................19 2.1.2 Site-specific geologic/hydrogeologic data.....................21 2.1.3 Infiltration Data..............................................................24 2.1.4 Constituent Data.............................................................26 2.2 IWEM Modeling..........................................................................28 2.2.1 Variable WMU operational life with fixed receptor well distance ...............................................................29 2.2.2 Variable receptor well distance from source with fixed WMU operational life..............................................30 2.2.3 Variable K d values .........................................................30 2.2.4 Variable WMU areas .....................................................32 2.2.5 Additional Modeling with WisDOT Data......................32 2.2.6 Modeling with North Carolina data ...............................33 2.2.7 Modeling with Maryland data........................................34 2.3 HYDRUS-2D Modeling ..............................................................36

CHAPTER 3 – RESULTS AND DISCUSSION..........................................................40 3.0 IWEM Modeling..........................................................................40 3.0.1 Variable WMU operational life with fixed receptor well distance ...............................................................40 3.0.2 Variable receptor well distance from source with fixed WMU operational life..............................................44 3.0.3 Variable K d values .........................................................46 3.0.4 Variable WMU areas .....................................................50 3.0.5 Additional Modeling with WisDOT Data......................54 3.0.6 Modeling with North Carolina data ...............................57

ii3.0.7 Modeling with Maryland data........................................64 3.0.7.1 Routes 213/301 Overpass .....................................64 3.0.7.2 I-695 Overpass......................................................65 3.1 HYDRUS-2D Modeling .............................................................66

CHAPTER 4 – CONCLUSIONS AND FUTURE WORK..........................................71 4.0 Objectives ...................................................................................71 4.1 Basic Groundwater Modeling Performance................................71 4.2 Modeling Performance of Secondary Material Reuse................73 4.3 Conclusions on IWEM’s Suitability for Determining Secondary Material Reuse ....................................................................74 4.4 Recommended Future Work.......................................................76

REFERENCES CITED ...............................................................................................77

iiiLIST OF TABLES

Table 1. Annual production a nd use of recycled materials........................................2 Table 2. Waste pile areas pe r study site used for IWEM input................................20 Table 3. Geologic/hydrogeologic model input parameters for each study area ...................................................................................................23 Table 4. Recharge rates by study area obtained from IWEM..................................25 Table 5. Site-specific infiltrati on data for WisDOT and Maryland sites.................26 Table 6a. Metals and corresponding in itial concentrations detected in each secondary material for the WisDOT study area ............................27 Table 6b. Metals and corresponding initial concentrations detected in each secondary material for the North Carolina study area...................27 Table 6c. Metals and correspondin g initial concentrations detected in each secondary material for the Maryland study areas..........................27

Table 7. EPA tabulated K d values based on literature search...................................28 Table 8. Comparison between out put concentrations (ppm), as a function of time, generated using MINTEQA2 and EPA K d s with WisDOT data ......................................................................................49 Table 9. Comparison between out put concentrations (ppm), as a function of time, generated using MINTEQA2 and EPA K d s with North Carolina data.............................................................................50 Table 10. IWEM modeling results for I-695 overpass ...............................................66

Table 11. HYDRUS-2D metal concentr ations at node 5 (water table).......................67 Table 12. Metal output concentrations for each area and corresponding factor increases............................................................................................6 8 Table 13. Comparison between HYDRUS-2D and IWEM results for bottom ash concentrations (mg/l) with time using USEPA tabulated K d values for both models ...........................................................69 Table 14. Comparison between HYDRUS-2D and IWEM results for bottom ash concentrations (mg/l) with time using low Kds .......................70

ivLIST OF FIGURES

Figure 1. Groundwater usage by category for U.S. in 2000 .......................................5 Figure 2. Site location: Wisconsin State Highway 60 ................................................7 Figure 3. Profiles of the test se ctions constructed using foundry slag, foundry sand, bottom ash, fly ash and crushed rock (control) for STH 60 near Lodi, WI............................................................................8 Figure 4. Site location: U.S. highway 301 near Battleboro, NC...............................10 Figure 5. Site location map: Route 213/301 and I-695 overpasses...........................12 Figure 6. Map of Route 213 study ar ea with instrumentation locations...................13 Figure 7. Schematic cross-secti on of Rt. 213 site along A – A’ shown on Figure 6 ................................................................................................14 Figure 8. Map of I-695 overpa sses with well and instrumentation locations .....................................................................................................15 Figure 9. Schematic cro ss-sections of I-695 site ......................................................16 Figure 10. Sample WMU parameter input screen from IWEM..................................21 Figure 11. Sample geologic/hydrogeologi c data input screen from IWEM ...............23 Figure 12. Sample infiltration da ta input screen from IWEM ....................................24 Figure 13. Graphical display of boundary conditions used for 14 m cross-section in HYDRUS-2D..................................................................38 Figure 14. Graphical display of observation nodes used for 14 m cross-section in HYDRUS-2D..................................................................39 Figure 15A. Groundwater concentrati on vs. time predicted by IWEM for Cd leaching from recycled materials .....................................................41 Figure 15B. Groundwater concentrati on vs. time predicted by IWEM for Cr leaching from recycled materials......................................................41 Figure 15C. Groundwater concentrati on vs. time predicted by IWEM for Se leaching from recycled materials......................................................41 Figure 15D. Groundwater concentrati on vs. time predicted by IWEM for

vAg leaching from recycled materials .....................................................41 Figure 16A. Groundwater concentration vs . receptor well distance for Cd leaching from recycled materials after 40 years.........................45 Figure 16B. Groundwater concentration vs . receptor well distance for Cr leaching from recycled materials after 40 years..........................45 Figure 16C. Groundwater concentration vs . receptor well distance for Se leaching from recycled materials after 40 years..........................45 Figure 16D. Groundwater concentration vs . receptor well distance for Ag leaching from recycled materials after 40 years.........................45 Figure 17. Cadmium concentrations as a function of variable K d values...................47 Figure 18A. Groundwater concentr ation vs. varying WMU area for Cd leaching from recycled materials .....................................................52 Figure 18B. Groundwater concentr ation vs. varying WMU area for Cr leaching from recycled materials......................................................52 Figure 18C. Groundwater concentr ation vs. varying WMU area for Se leaching from recycled materials......................................................52 Figure 18D. Groundwater concentr ation vs. varying WMU area for Ag leaching from recycled materials .....................................................52 Figure 19A. Peak Cr concentrations vs. WMU area...................................................53 Figure 19B. Peak Cd concentrations vs. WMU area ..................................................53 Figure 20A. IWEM predicted Cd concentrations over five years for bottom as h and coal fly ash at th e WisDOT study area.................... 56-57 Figure 20B. IWEM predicted Se con centrations over five years for bottom as h and coal fly ash at th e WisDOT study area.................... 56-57 Figure 20C. IWEM predicted Ag concentrations over five years for bottom as h and coal fly ash at th e WisDOT study area.................... 56-57 Figure 21A. As concentrations w ith time at MW1s using MINTEQA2 derived K d s..............................................................................................59 Figure 21B. Pb concentrations w ith time at MW1s using MINTEQA2 derived K d s..............................................................................................59

viFigure 22. Back-calculated field K d s (L/Kg) estimated for As and Pb at the Swift Creek study area ...................................................................61 Figure 23A. As concentrations w ith time at MW2s using MINTEQA2 derived K d s........................................................................................ 62-63 Figure 23B. Pb concentrations w ith time at MW2s using MINTEQA2 derived K d s........................................................................................ 62-63 Figure 24. Hydrogeologic cross-se ction of Swift Creek study area .........................64

vii1. INTRODUCTION

1.0 Context

According to the Federal Highway Admini stration (FHWA, 2001) there are nearly 4

million miles of roads in the U.S. These ro ads require large volumes of materials for construction and maintenance purposes which generally are harvested from natural sources

. Recently, increased interest in recycling has e volved as a measure of promoting sustainable construction and to alleviate issues surrounding the harvesting of natura l resources in areas sensitive to environmental perturbations. There are promising results for equal or better engineering performance of recycled material s at comparable or less costs and without significant environmental impact (Apul et al., 2003). Every year millions of tons of industrial byproducts (secondary materials) are produced in the United States. Such byproducts include f oundry sands and slags, as well as coal fly and bottom ashes. In some cases, these “left over” materials are reused in various facets of the construction sector, primarily in relation to ro adway applications (Table 1). However, the majority of industrial byproducts are either stockp iled indefinitely or disp osed of in landfills. According to the American Coal Ash Associ ation (ACAA), 70 million tons of fly ash was produced in 2003 in the United States with onl y 39% of it being reus ed in a variety of applications. The remainder was disposed in wast e containment facilities such as landfills. In Wisconsin alone, more than 800,000 tons of gray iron foundry sand is landfilled annually with little or no hope of being reused (Lee and Benson, 2005).

1Table 1. Annual production and use of recycled materials. U=undermined, MF=mineral filler, ACM=asphalt cement modifier, A=aggregate, CM=cementitious material, E=embankment or fill, and F=flowablef ill (Apul et al., 2003). a Adapted from Collins and Ciesielski 1994, b Adapted from Schroeder 1994, c Adapted from Chesner et. Al 1998.

In the transportation industry, soft soils encountered during road construction are removed and replaced with crushed rock to form a sturdy working platform for pavement construction. This construction practice can be cos tly, particularly if the ro ck needs to be hauled to the construction site. As a result, transporta tion agencies are seeking less costly methods to

stabilize soft soils and construc t working platforms. In some cases, industrial byproducts can be used to construct lower cost working platforms that provide equal suppor t as those constructed with crushed rock (Tanyu et al. 2004). Use of industrial byproducts in this manner also facilitates sustainable construction by reusing materials currently being landfilled and reducing the use of virgin natura l resources. However, with the re -use of these industrial byproducts

2comes the concern of whether the leaching of co ntaminants (primarily heavy metals) contained in the materials will impact the underlying groundwater and if so, to what extent? Secondary materials are generally the e nd-products of metal processing and coal combustion. For example, gray iron foundry sa nd is a byproduct from the metal processing industry that consists of impur ities floating to the surface of mo lten material (Proctor et al. 2000). These impurities often contain metals such as As, Cd, Cr, Pb, Hg, Se and Ag. Fly ash is a fine-textured particulate that is removed from the exhaust during coal combustion. This

material also often contains metals that include As, Cd, Cr, Cu, Pb, Ni and Zn. When placed in a roadway setting, these byproducts are subject to in filtration via precipitation. As a result, the metals sorbing to the individual material partic les may leach into solution and be carried down into and through the subsurface as leachate. De pending on the concentrations of the metals in solution and the partitioning capabilities of th e underlying soils, the leachate may eventually enter the groundwater with th e threat of adversely impac ting regional groundwater quality. The extent to which leachate produced from industrial byproducts will effect subsurface soils and groundwater is poorly understood. This is primarily due to the lack of studies concerning the topic. In the pa st, field studies have been perf ormed to understand the short term impacts secondary materials may or may not have on groundwater. However, few long term

efforts have been made to addre ss this topic thus little is know n concerning the potential risks such materials may pose to human health and the e nvironment. This is the limiting factor to the beneficial reuse of secondary materials. In orde r to evaluate the long te rm impacts of secondary materials, modeling becomes necessary. The remai nder of this report addresses how the use of IWEM may aid in the determin ation of whether secondary materials are safe enough for beneficial-use applications in the highway environment.

31.1 Significance and Objectives of this Study

Groundwater is a crucial element in maintain ing a sustainable worl d. According to the National Ground Water Association (N GWA), of the total 341 billi on gallons of fresh water the United States uses each day, about 83.2 billion ga llons, or 24 percent is groundwater. In the United States, 47 percent of the population relies on groundwater for drinki ng water. There are nearly 16 million water wells in the U.S., supplying groundwater for public supply, private supply, irrigation, livestock, ma nufacturing, mining, thermoelect ric power, and other purposes (www.wellowner.org). Figure 1 i llustrates groundwater usage by category for the U.S. in 2000. Based on this data, it is clear that groundwater is essential to maintaining everyday life.

Thus extreme care must be taken to avoid cont aminating it or at least there must be tools available for a particular situation (e.g. leachi ng from secondary materials) that allow us to predict if groundwater contaminatio n is going to occur in order to take the necessary precautions to minimize damage to human health and the environment. Groundwater models provide us

with such tools. While a great deal is already known about groundwater contamination, minimal research has been conducted concerning modeling of impact s from recycled material use in a highway environment. It is hypoth esized that the USEPA’s Industrial Waste Management Evaluation

Model (IWEM) may aid in the evaluation of whet her secondary materials are safe enough for beneficial-use applications in a roadway setting. The primary objective of this research is to investigate IWEM’s potential benefits with proper input from field and laboratory testing. Validation of IWEM was tested using data from field studies from sites in Wisconsin and North Carolina. In this research, outputs from IWEM have been compared with t hose of another solute transport model (HYDRUS-2D) and actual field data to determine IWEM’s predictive accuracy.

4Additionally, this modeling provi des an assessment of groundwater impact in the scenarios investigated.

Figure 1. Groundwater usage by category for U.S. in 2000. http://ga.water.usgs.gov/edu/ wugw.html

. Proper use of groundwater modeling tool s such as IWEM should support making scientific risk-based decisions concerning the appropriate recycling of secondary industrial materials. In other words, th ese types of models can be effective in promoting recycling or avoiding it if they can show gr oundwater contamination will result. With this objective, it was hoped that IWEM could accurately predict the fate and transport of leachate from these secondary materials in order to evaluate potenti al adverse effects on groundwater. The ultimate long term goal upon validating IWEM is its adoption by State DOTs, State environmental agencies, and construction companies to help ai d them in determining whether a secondary material can be used in a particular situation.

51.2 Study Areas Testing of IWEM was conducted using field and laboratory data from three U.S. sites where secondary material applications are currently being used with respect to roadway settings (i.e. structural sub-base support):

1. Wisconsin State Highway 60 near Lodi, Wisconsin;

2. U.S. Highway 301 at Swift Creek n ear Battleboro, North Carolina;

3. Routes 213/301 and Interstate 695 overpasses in Maryland.

All three sites provided sufficient da ta for IWEM input which includes:

• site geology/hydrogeology

• initial secondary material leachate concentrations

• groundwater sampling data for comparison purposes

• regional climate data

The information from these locations was used for input into IWEM in order to obtain groundwater concentrations at a point down gradient from the secondary material source.

Detailed observations and conclusions were made on model results to interpret IWEM’s capabilities in predicting the fate-and-transpor t of groundwater with respect to secondary material reuse. Methods of input and te sting are discussed in the next chapter.

1.2.1 Wisconsin State Highway 60 The majority of the project’s data came fr om a Wisconsin Department of Transportation (WisDOT) project along a 1.4 km stretch of Wi sconsin State Highway 60 (STH 60) between

6Lodi and Prairie du Sac, WI in Columbia Count y (Figure 2). Information pertaining to this project was provided by The Univ ersity of Wisconsin at Madi son’s Department of Civil Engineering. At this location, four test sections, covering ar eas between 790 and 1600-m 2 , have been built during the re-construction of STH 60 in the summer of 2000. Each test section includes a sub-base layer com posed of secondary byproduct materials (Figure 3). These materials consist of fly ash amended soil, botto m ash from coal-fired power plants, and foundry sand and foundry slag from gray ir on casting industries. Two additi onal sections have also been constructed, each consisting of traditional hi ghway support earthen materials for control purposes. Additionally, within ea ch section, two 3.5 m x 4.8 m lysime ters have been installed to collect leachate draining from the bottom of the sub-base layers (Lee and Benson, 2005).

Site Location Site Location

Figure 2. Site location: Wisconsin State Highway 60 (www.mapquest.com).

Between 2000 and 2004, leachate samples were collected periodically from the lysimeters to characterize the secondary material s. The leachate was analyzed for the trace elements cadmium, chromium, selenium and si lver. Additionally, th roughout the m onitoring,

7volumetric leachate fluxes were also recorded. Element analytical and leachate flux data was used for input into IWEM for initial characterization of the model’s fate-and-transport capabilities (see Chapter 3). Knowledge of the region’s subsurface geology al so becomes important as it is required input into IWEM for the model to make an accu rate assessment of how water will flow through the specified domain. Review of USGS logs and maps show a bedrock geology dominated by

Silurian dolomite and Ordovician dolomite w ith some limestone, sandstone, and shale. Cambrian sandstone, with some dolomite and shale, is present to a lesser extent in the area. Bedrock is overlain by drift usually less than 50 feet thick and soil s in the area consist of silt loam at the surface, but subsoils are generall y calcareous loam (till) or calcareous sand and gravel outwash. The loess cap is typically about 2 feet thick ( www.npwrc.usgs.gov ).

Figure 3. Profiles of the test sections constructed using f oundry slag, foundry sand, bottom ash, fly ash and crushed rock (contro l) and STH 60 near Lodi, WI (AC = asphalt concrete) (Lee and Benson, 2005).

8More recently, a groundwater-monitoring program has been implemented at the site. In January 2004, groundwater monitoring wells were installed adjacent to the bottom ash and fly

ash test sections. Both wells were installed 6 meters from the edge of the highway shoulder. Continuous monitoring of these wells via ground water samples and water-table measurements

has been conducted since the installation of the wells with laboratory analyses of the groundwater samples revealing no concentrations of Ag, Cd, Cr, or Se above the method detection limit (MDL) to date (Lee and Benson, 2005).

1.2.2 U.S. Highway 301, North Carolina In the early 1990s, coal ash was reused on a 12-acre portion of commercial property along U.S. Highway 301 at Swift Creek near Battle boro. NC (Figure 4). A site investigation in 2002 followed by a subsequent groundwater an alysis in June 2004 revealed groundwater concentrations of arsenic and lead above appli cable limits (0.28 and 0.068 ppm respectively) in a monitoring well located approximately 25 feet from the edge of the fill (Sherrill, 2003). This scenario provided the perfect opportunity to ve rify whether IWEM could have successfully identified the contamination during planning stages so a scientific risk-based decision could have been made as to whether the material was safe enough for reuse.

9Site Location

Figure 4. Site location: U.S. highway 301 near Battleboro, NC (www.mapquest.com)

The soils underlying the coal ash for this aqui fer consist largely of Altavista. Altavista soils are alluvial deposits that formed on flood plains. This layer of soil under the ash is approximately 6.5 ft thick and is characterized as a dense sandy clay alluvial material that has a very low permeability of about 7x10 -8 cm/sec. Prior to construction of the coal ash structural fill the water table was at least 1.5 ft below ground su rface. However, the 2002 site investigation showed that groundwater was presen t within the majority of the coal ash (4 meters below ground surface). This is likely a result of the impermeable nature of the Altsvista which acts as a barrier to vertical migration of groundwat er because of the very slow tr avel time through this confining bed (Sherrill, 2003).

10All data for the site was provided by Sherri ll Environmental, Inc. of Durham, NC who subcontracted ReUse Technology, Inc. of Roc ky Mount, NC to perform the investigative procedures. Additional site-specific parameters used for IWEM input are outlined in the next chapter.

1.2.3 Routes 213/301 and Intersta te 695 overpasses, Maryland During the 1990s, two projects were comple ted in Maryland in which Class F fly ash (CCPs) were used to form highway embankme nts (Figure 5). In 1993 and 1994, Baltimore Gas & Electric (BGE) (now Conste llation Energy Group) and Delmarva Power (now Conectiv) provided approximately 40,000 tons and 20,000 tons of CCPs, re spectively, to the Maryland State Highway Administration (SHA) to creat e the highway embankments for the Route 213 overpass over Route 301 near Centerville on Ma ryland’s eastern shore. Between 1996 and 1998, BGE provided 320,000 tons of CCPs to support the Maryland Transportation Authority (MdTA) with the construction of three overpasses during the reconstruction of a portion of I-695 near Sparrows point (ERM, 2004). Following coal ash application at the overpa ss study areas, instrument clusters consisting of lysimeters and monitoring wells were inst alled on the shoulders of the overpasses to characterize the water quality in the unsaturated and saturated zones within several feet of the embankments. The purpose of the lysimeters was to monitor leachate produced by the CCPs and the wells to monitor groundwater. At the Route 213/301 overpass, two monitoring instrument clusters consisting of three lysimeters and one well were installed on the should er of each side of the overpass. Sample sites were labeled with the prefix 101 for the north embankment (e.g., L101-12 for the lysimeter

11installed to a depth of 12 feet on the north em bankment) and 102 for the south embankment as shown on Figure 6 (ERM, 2004). Figure 7 displays a schematic cross-section of the study area.

Figure 5. Site location map: Rout e 213/301 and I-695 overpasses (ERM, 2004).

1213 Figure 6. Map of Route 213 study area with instrumentation locations (ERM, 2004).Figure 7. Schematic cross-section of Rt. 213site along A – A’ shown on Figure 6 (ERM, 2004).14At the I-695 overpass (Figure 8) , three monitoring instrument clusters consisting of two lysimeters and one well were in stalled on the shoulders of the overpass. Sample sites were labeled with the prefix 1 for th e first cluster location (e.g., L1-18 for the lysimeter installed to a depth of 18 feet at first monito ring station), 2 for the second clus ter location, and 3 for the third cluster location adjacent to Route 151 (ERM, 2004). Figure 9 di splays a schematic cross-section of the study area.

Figure 8. Map of I-695 overpasses with well and instrumentation locations (ERM, 2004).

15Figure 9. Schematic cross-sections of I-695 site (ERM, 2004).

16Between 1999 and 2003, samples were collected fr om the wells and lysimeters at both overpass sites and analyzed by Lancaster Laboratories for the following constituents:

• Trace elements (e.g. arsenic, barium, bery llium, cadmium, chromium, copper, lead, manganese, nickel, selenium);

• Major cation elements (e.g. aluminum, cal cium, iron, magnesium, potassium, sodium) and;

• Major anions (e.g. chloride, fluoride, sulf ate, nitrite, and al kalinity) (ERM, 2004). For the purpose of this research, only trace elem ents were considered as IWEM cannot model the

other constituents. With the exception of manganese (Mn), none of the trace elements detected in the lysimeters and wells ever exceeded MD regulator y MCLs. Because Mn often showed up in the groundwater and not the lysimeter da ta, it is assumed that Mn pr eexists in the groundwater, thus is not a result of the CCP leachate. However, several detections of arsenic and barium, did appear in both the lysimeter and well data implying there may be some connections between CCP leachate and groundwater concentrations. As with the North Carolina data, this scenario provided another opportunity to evaluate if IWEM could have successfully predicted groundwater concentrations resulting from s econdary material leach ing, regardless of MCL exceedences. Details of this modeling are presented in the next chapter. A detailed description of this site, in cluding environmental setting and construction

details can be viewed in Environmental Reso urces Management, Inc.’s (ERM) 2004 technical report where the preceding data was obtained.

172. METHODS

2.0 Overview Data from the study areas outlined in Sec tion 1.2 were used for input into IWEM and various other solute transport models to valida te IWEM’s groundwater concentration predictive capabilities with respect to seconda ry material reuse. A two-step systematic approach was taken to accomplish this task. First, this involved r unning numerous (400+) simulations with IWEM to obtain groundwater concentrations at various points down gradient from the leachate source over a time distribution ranging from 1 to 200 years (maximum time allowed by IWEM). Secondly, using the same input, simulations were perfor med with HYDRUS-2D. The results of the two models were compared to determine IWEM’s accuracy. In addition to other models, IWEM outputs were compared to actual groundwater fi eld data. After analyzing data comparisons between models and field studies, informative co nclusions were made regarding IWEM’s ability to accurately predict groundwater concentratio ns resulting from secondary material leaching, particularly focused on the highway environment.

2.1 Study Data The study described here uses data collected from the three areas pr esented in Section 1.2 as well as arbitrarily chosen values. Data us ed for model input can be divided into four categories: 1. WMU parameters, 2. site-specific geologic/hydrogeol ogic data, 3. infiltration data and 4. constituent parameters (e.g. me tal distribution coefficients).

182.1.1 WMU parameters (IWE M modeling specifically) For the purpose of this research, all IW EM modeling was performed using waste piles (WPs) as the representative WMU. It is felt a wa ste pile best exemplifies a real-life application of secondary materials in a roadway setting. IW EM considers a WP to be a temporary source with an average operational life of 40 years which is similar to fill used for structural support in a

road. In addition to the WMU type, IWEM requi res several WMU parameters. The following are parameters required for WPs:

• distance to well (m)

• area (m 2 )

• depth of base of WP below ground surface (m)

• operational life

WP parameters were varied extensively th roughout the course of IWEM modeling for each study area (especially distance to well and operat ional life). In most cases, distance to well and operational life values were ar bitrarily chosen. An exception to this is when actual field data presented a monitoring well specified a certain distance from the material source where groundwater sampling results are available fo r a known time after implementation of the structural fill. In this case, IWEM simula tions were performed using these known time and

distances to evaluate whether th e model would have predicted the concentrations detected in the well. As an example, data from the NC site s hows elevated levels of As and Pb in a monitoring well located approximately 7 meters from the edge of the coal ash source. These concentrations

19were detected 10 years after the application of th e fill. Thus, IWEM was run with a well located 7 meters from the waste pile for ten years. The final output concentrations for As and Pb from the model were then compared to the field data. For each site, the reported WP areas were used for modeling (Table 2). Using these values helps mimic real-life scenar ios, ensuring outputs are as realistic as possible. An exception to this relates to various IWEM modeling wi th the WisDOT data. Many simulations were performed where WP areas for all f our secondary material sections were arbitrarily chosen to be

200 m 2 . This was used to provide a level ground fo r comparison purposes between the sections. However, runs were performed using the actual area of the bottom ash section (790 m 2 ) in addition to an area representative of a one mile stretch of highway (8367 m 2 ) and one ten times

that. Additional information on this is provided in Section 2.2.

Table 2. Waste pile areas per study site used for IWEM input.

Study Sites Area (m 2 ) Wisconsin (bottom ash) 790.4 North Carolina 46450 Maryland (Rts. 213/301) 2160 Maryland (I-695)) 2000

Lastly, the depth of the WP below the ground surface is required for input into IWEM. This value for this parameter is generally zero because the secondary material usually applied over the top of the ground surface. However, for the NC study area, the depth was specified at 4

20feet below the surface because the fill was overlai n by earthen material (Sherrill, 2003). Figure 10 displays a sample WMU parameter input screen from IWEM.

Figure 10. Sample WMU parameter input screen from IWEM.

2.1.2 Site-specific geologic/hydrogeologic data In addition to WMU parameters, data pertai ning to a site’s geol ogic and hydrogeologic makeup is crucial when modeling groundwater a nd solute transport. How groundwater and

constituents behave in the subsur face is largely dictated by the ma terial through which it travels. IWEM, as well as most other groundwater m odels, requires geologic information which includes the type of subsurface environment (e.g. till over sedi mentary rock, sand and gravel, alluvial and floodplain with overbank deposits, etc. ) and soil type (e.g. sandy loam, silty clay loam, etc.). Additionally, the user is prompted to input various hydrogeologic parameters including:

21• groundwater pH

• depth to water table (m)

• hydraulic conductivity (m/yr)

• hydraulic gradient

• aquifer thickness (m)

For some study areas (e.g. WisDOT), not all site-specific geologic/hydrogeologic data was available. To compensate for these unknown parameters, IWEM relies upon the EPACMTP

Monte Carlo module to derive the data, allowing the model to perf orm probabilistic analyses of constituent fate and transport. A Monte Carlo simulation “is a statistical technique by which a quantity is calculated repeatedly, using randomly selected parameter values for each calculation” (EPA, 2002). Simply speaking, based on the site ’s subsurface environment, a Monte Carlo simulation is able to approximate the full range of possible outcomes for a particular unknown parameter, and its likelihood. Additionally, the Monte Carlo module in EPACMTP “makes it possible to incorporate variability into the subs urface pathway modeling analysis and to quantify the impact of parameter variability on well concentrations” (EPA, 2002). More detailed information pertaining to the Monte Carlo module is described in the EPACMTP Technical Background Document (U.S. EPA, 2002a)

. All available geologic/hydroge ologic data was either obtained through the reports described in Chapter 1 or the USGS. Additionally, information was kept consistent between all models to ensure accurate comparisons betw een them. A summary of geologic/hydrogeologic input data for each study area in presented in Table 3.

22Figure 11. Sample geologic/hydrogeologi c data input screen from IWEM.

Table 3. Geologic/hydrogeologic model input pa rameters for each study area (b = aquifer thickness; dh/dl = hydraulic c onductivity; GW = groundw ater; K = hydraulic conductivity; MC = Monte Carlo; WT = water table).

Study Area Subsurface Environment Soil Type GW pH Depth to WT

(m) K (m/yr) dh/dl b (m)

Wisconsin till over sedimentary rock silt loam 6.5 5 MC MC MC

North Carolina alluvial & flood plain with overbank deposits silty clay loam MC 4 0.022 0.0125 MC Maryland (Rts 213/301) unconsolidated/ semiconsolidated shallow aquifer sandy loam 5 6.1 MC MC 18.3

Maryland (I-695) unconsolidated/ semiconsolidated shallow aquifer silty clay loam 6.5 0.75 MC MC 36.6

232.1.3 Infiltration data

In order for a constituent to be leached from a source and carried down through the subsurface, an aqueous mechanism must exist to promote its mobility. In most cases, when considering WMUs, this mechanism refers to rain fall, or recharge. IWEM has a separate input screen which requires the user to specify the average annual recharge rate (m/yr) for the study area of interest, as well as the infiltration (f lux) of the leachate thr ough the bottom of the WMU (m/yr). Additionally, the type of soil that the leachate will en counter through the bottom of the WMU is required (e.g. silty loam). Figure 12 is a sample infiltration input screen for the WisDOT study area.

Figure 12. Sample infiltration da ta input screen from IWEM.

24User-specified recharge rates are not a llowed in IWEM. However, using the HELP model version 3.03, the IWEM databa se contains a list of average annual recharge rates for 97 climate stations in the lower 48 contiguous stat es, representing 25 climate regions (EOA, 2002). Table 4 lists the climate station and correspondin g recharge rate used for each study area. For continuity purposes, these values were used for all models.

Table 4. Recharge rates by study ar ea obtained from IWEM. Rates were used in all simulations for all models.

Study Area Climate Station Recharge Rate (m/yr) Wisconsin Madison, WI 0.091 North Carolina Greensboro, NC 0.326 MD: Rt.213/I-695 Seabrook, NJ 0.243/0.143

IWEM does allow the user to specify site-specific infiltration data. Infiltration values were provided by The University of Wisconsin-M adison for the WisDOT study area for each test section. Additionally, infiltrati on rates for the coal ash used for construction of the routes 213/301 and I-695 overpasses in Maryland were provided by Environmental Resources Management, Inc. of Annapolis, MD. These values were used in all simulations for all models (Table 5). Site-specific infiltration rate s for the North Carolina study area were not available. Instead, pre-defined infiltration rates from the IW EM database were used. Based on the recycled material’s permeability (e.g. low, medium, hi gh), IWEM provides a numerical value for infiltration. For North Carolina, the low permeab ility designation was chosen for coal fly ash due its poor water transmitting properties. This corresponded to an infiltration rate of 0.243 m/yr.

25Table 5. Site-specific infiltration data for WisDOT and Maryland sites.

Test-Section Material WisDOT Infltr. (m/yr) MD Infltr. Rts. 213/301 (m/yr) MD Infltr. I-695 (m/yr) Bottom Ash 0.0949 --- --- Fly Ash 0.0584 0.178 0.131 Foundry Sand 0.0110 --- --- Foundry Slag 0.0803 --- ---

2.1.4 Constituent data For the purpose of this project, only the fate and transport of metals (e.g. Cd and Pb) were modeled as they are the primary constituents rela ted to secondary materials. Constituent data

such as initial concentrations and distribution coefficients (K d s) are essential information required to perform accurate and succe ssful groundwater modeling of metals. Site investigations from each study area yielded the types of metals detected in the reused

materials as well as initial con centrations of each via laboratory testing. For the WisDOT and Maryland study areas, initial concentrations we re measured from the leachate collected in lysimeters located directly below the fills (see Chapter 1). For the North Carolina data, initial

concentrations were measured via TCLP te sting. Metals det ected and corresponding

concentrations are listed in Tables 7a – c for each area. All concentrations are listed in parts per million (ppm).

26Table 6a. Metals and correspondi ng initial concentrations detect ed in each secondary material for the WisDOT study area (Edil et al., 2003).

Material Cadmium Chromium Selenium Silver Bottom Ash 0.0212 0.0151 0.0412 0.0118 Fly Ash 0.0032 0.0143 0.0263 0.0038 Foundry Sand 0.0118 -- -- -- Foundry Slag 0.0166 0.0319 0.0178 0.0039

Table 6b. Metals and corresponding initial concentrations detected in each secondary material for the North Carolina st udy area (Sherrill, 2003).

Material Arsenic Lead Coal fly ash 0.11 0.353 Table 6c. Metals and correspondi ng initial concentrations detect ed in each secondary material for the Maryland study areas (ERM, 2004).

Material Location Arsenic Barium Coal fly ash Routes 213/301 --- 0.052 Coal fly ash I-695 0 .037 ---

The above data was used for input in all modeling scenarios and simulations. Detailed procedures for each model are de scribed in the next sections. In addition to initial concentrations, K d values for each metal are required to perform accurate modeling simulations. The K d is a constituent-specific parameter which is a measure of how strongly the leached constituent will bind to soil in the subsurface. The greater the K d value, the more strongly a metal will attach itself to the soil, thus limiting its mobility through the subsurface and into the groundwater.

27In IWEM, the modeler can either specify a user-defined K d or rely upon the built-in USEPA developed chemical speciation model MI NTEQA2 to derive a value if not known. IWEM modeling was performed both with using user-defined and MINTEQA2 values. Userdefined numbers were obtained from a 1999 USEPA document which reported average K d values for a variety of metals based on an extens ive literature search. Mean values for metals considered in the research are presented in Table 7. These K d s were used for modeling with

HYDRUS-2D.

Table 7. EPA tabulated K d values based on litera ture search (EPA, 1999).

Metal K d (L/Kg) Ag (I) 398.1 As 1584.9 Ba (II) 100 Cd (II) 501.2 Cr (VI) 6.3 Pb (II) 5011.9 Se (IV) 20

2.2 IWEM Modeling Comprehensive modeling (400+simulations) has been performed with IWEM to determine how the model responds when simulati ng water and contaminant transport from heavy metal bearing secondary materials into the subsurf ace. The objective of this work is to evaluate whether IWEM can be used as a predictive tool to accurately determine whether leaching from materials will result in significant changes in gr oundwater concentrations when the materials are reused as a base or sub-base in a roadway.

28Several steps were taken to accomplish the aforementioned objective. First, modeling was performed to evaluate how IWEM responds to varying input parame ters. This included observing the model’s behavior while treati ng heavy metal transport as a function of:

1. time/WMU operational life;

2. receptor well distance secondary material source;

3. varying distribution coefficient values (K d );

4. waste management unit (WMU) areas. Variable parameters 1, 2 and 4 were si mulated primarily using WisDOT data for model input, largely because only information from this study area was available earlier on in the validation process when this modeling occurr ed. Additionally, the Wi sDOT project provides the greatest amount of data due to the use of four recycled mate rials at the study area (only fly ash is used at the North Carolina and Maryland site s). Input data from all three study areas was used to evaluate IWEM’s response to varying metal K d values.

2.2.1 Variable WMU operational life w ith fixed receptor well distance To evaluate how IWEM treats heavy meta l transport as a function of leaching time,

thirteen simulations were run for each test sect ion at the WisDOT study area (52 total), where the operational life of the secondary material a pplication was varied. Each simulation was performed with a receptor we ll located an arbitrarily fixed 50 me ters from the leachate source. The thirteen simulations spanned a range from 1 to 200 years (max input value for IWEM) which included: 1, 5, 10, 15, and 20 to 200 year s at 20 year intervals. All other input

values were held constant for each run. Di stribution coefficient values derived from

29MINTEQA2 were applied. For the first 20 years, the model was run at 5 year intervals in order to determine when IWEM would recognize the presen ce of the constituent at the receptor well.

2.2.2 Variable receptor well distance from source with fixed WMU operational life The objective of this portion of research was to evaluate how IWEM predicts constituent mobility in the subsurface as a function of leaching distance from the source. Again, the WisDOT information was used as model input for the same reasons discussed earlier.

Nine simulations were run for each section (36 total) varying the distance of the groundwater receptor well from the leachate source. The nine simulations spanned a range of distances from 10 to 500 meters which included: 10, 25, 75, 100, 150, 200, 300, 400, and 500 meters. All other input values were held constant for each run and K d values were again derived using MINTEQA2. For the purpose of this seri es of simulations, IWEM’s default operational life for a waste pile (40 years) was used as the fixed time.

2.2.3 Variable K d values

A series of simulations were run where user-define K d s were varied to evaluate how IWEM responds to such changes. Twelve runs were executed using different K d values for

cadmium in bottom ash from the WisDOT data. IW EM’s default operational life of 40 years for a WP was chosen as the run time. The arbitrarily chosen K d values used ranged from 0 to 8 which included: 0, .001, .01, .05, .1, .5, .75, 1, 2, 3, 5, and 8. All other input values (e.g. hydrogeologic and infiltration parameters) were he ld constant for each run. For the purpose of these series of simulations, an arbitrary well distance of 50 meters from the leachate source was

30selected. Simulations were not performed for th e other test sections und er the assumption they would yield the same trends. Upon initial evaluation of th e IWEM modeling results wher e MINTEQA2 was used to

derive K d values, it appeared the final concentrations were higher than expected. It was felt that attenuating factors such as disp ersion and dilution would have pl ayed a greater role in reducing

concentrations over the trans port distance specified. A hypothesis was made that the K d values being used were smaller (possibly by several or ders of magnitude) than those reported in the literature. However, IWEM does not produce an output file listing which K d values were selected by MINTEQA2, thus thes e numbers were not known. To investigate this observation further, several simulations (using WisDOT data ) were taken and used to back-calculate K d s. This was accomplished by randomly selecting user-defined K d values and running simulations

until final concentrations matched those produced by the MINTEQA2 K d derived runs.

The results and conclusions from the work above (presented in Section 2.0.3) prompted the running of time-dependent IWEM simulations using the EPA reported K d data listed in Section 2.1.4. The purpose of this was to de termine what difference, if any, the low K d values selected by MINTEQA2 had on the final groundwate r concentrations observed at the receptor well by comparing them to the si mulations using EPA reported K d values. Based on comparisons of the EPA reported K d s with other literature values, confidence in the accuracy of these numbers is strong. Thus, it stands to reason that if MINTEQA2 is drawing upon unrealistically low K d values, then IWEM may be viewed as being t oo conservative and over predicting final groundwater output concentrations. Runs were executed using input data from all three study area s. Using the WisDOT data, nine simulations were run for each test section (40 total). The simulations spanned a range from

311 to 200 years which included: 1, 5, 10, 15, 20, 50, 100, 150 and 200 years. For all runs, a fixed receptor well distance of 50 meters from the s ource was used keeping all other input values constant for each run. IWEM modeling usin g the North Carolina and Maryland data are

discussed in Sections 3.2.5 and 3.2.6 respectively.

2.2.4 Variable WMU areas Lastly, to evaluate IWEM’s response to ch anging input parameters, multiple simulations were run while varying the area of the WMU of interest (waste pile in this research). As discussed earlier in this chapter, data from WisDOT was used for this portion of the research. In particular, metals were simulated leaching from the bottom ash section of the site. The other

three materials were not modeled based on the assumption they woul d yield the same trends. All other input values we re held constant. Areas modeled include the arbitrarily chosen 200 m 2 section discussed in Section 2.1.1, a one mile stretch of highway covering 8367 m 2 , and the 790 m 2 bottom ash from the WisDOT

site. Five simulations were run for each ar ea using varying WP operational lives: 20, 60, 100, 160, 200 years. A receptor well was located a fixe d distance of 50 meters from the source.

2.2.5 Additional Modeling with WisDOT Data As described in Chapter 1, two monitoring wells were installed adjacent to the bottom ash and fly ash test sections, 6 meters from the Wisconsin State Highway 60 shoulder. Continuous groundwater sampling and subsequent laboratory analyses have demonstrated no concentrations of Ag, Cd, Cr and Se above the MDL at these wells 5.5 year s after the application

of the materials.

32Additional modeling was performe d for both test sections to evaluate if IWEM predicts similar observations. The model was run for 1, 2, 3, 4, 5, and 5.5 years using the actual material areas for each test section (790 m 2 and 395 m 2 for bottom and fly ash respectively) with fixed receptor well distances of 6 meters. All other input data (e.g. initial metal concentrations and leachate fluxes) was held constant. Additionally , distribution coefficients were derived using MINTEQA2.

2.2.6 Modeling with North Carolina data Data from the Highway 301 at Swift Creek project near Battleboro, NC (discussed in Chapter 1) was introduced midway through the research portion of this project. Unlike the WisDOT data where no elevated groundwater con centrations had been ob served, this study area presented a situation where the secondary material (coal fly ash) applied at the site caused groundwater concentrations to exc eed regulatory standards (As and Pb in this case). Because of these exceedances, this data provided the perfec t opportunity to model w ith IWEM in order to determine if the model would have predicted the contamination prior to coal ash reuse. In a June 2004 groundwater investigati on, As and Pb exceedances (0.028 and 0.068 ppm respectively) were detected in monitoring well MW1s located approximately 7 meters from the east edge of the reused coal ash. Using the i nput data described earlier in this section, IWEM simulations were set up to replicat e actual conditions at the site in order to model the transport of As and Pb to MW1s. Two sets of time-dependent runs were executed using: 1. MINTEQA2

derived K d s and 2. EPA reported K d s listed in Table 7. Seven simulations were performed for

each K d scenario at 1, 5, 10 (time between coal ash reuse and investigation), 20, 50, 150 and 200

33years. Following the modeling, observations were made to determine if IWEM was able to predict the groundwater contaminat ion reported from field data, and if so, to what accuracy. Additionally, modeling described in the previ ous paragraph was appl ied to MW2s at the site located approximately 48 meters east from the edge of the coal ash fill and 41 meters downgradient of MW1s. No As or Pb contamina tion was detected at this well. Modeling was performed to evaluate IWEM’s ability to account for attenuation factors (e.g. dispersion and adsorption) which would be responsible for the ab sence of As and Pb at MW2s after 10 years. Furthermore, observations were made beyond 10 year s to analyze if As and Pb would eventually

be introduced into MW2s.

2.2.7 Modeling with Maryland data As with the North Carolina data, leaching in formation from the Maryland sites was used for modeling with IWEM to further evaluate th e model’s predictive accuracy with respect to secondary materials. While no metal MCL excee dances were found in the groundwater at the site, detections of As, Ba and Se were encount ered in several areas of the Routes 213/301 and I695 overpasses. Only the southern area of the Rout es 213/301 overpass was modeled under the assumption that the north cluster would yield sim ilar trends/results. He re MW-102 was installed in a pre-existing exploratory borehole through the coal ash and extended 10 feet below the water

table with a 5-foot screened interval. Additiona lly, lysimeter L102-9 was in stalled to the base of the coal ash to monitor groundwater solute conc entrations entering the subsurface (ERM, 2004).

The concentrations detected here were used as initial input co ncentrations into IWEM.

34From 1999 to 2003, field sampling of MW-102 revealed detections of Ba which was used as the solute of concern for IWEM modeling. Th e last round of sampling in 2003 indicated Ba at 0.06 ppm. In 1999, Ba was detected at 0.052 ppm in L102-9 which was used as the initial input concentration (ERM, 2004). Using input parame ters described earlier in this chapter,

simulations were run with both MINTEQA2 derived and EPA tabulat ed distribution coefficients with a coal ash operational life of 10 years (ref lective of the time between coal ash application and Ba detection in MW-102 in 2003). Once modeling was complete IWEM results were compared to the Ba concentrations detected in MW-102. For the I-695 overpass, transport of As and Se in the groundwater to MW-3 (in cluster 3) was modeled. Again, only one area was taken in to account under the assu mption that the other clusters would yield similar trends /results. Additionally, the highest concentrations of As and Se were found in this vicinity. As with MW-102, MW -3 was installed in a pre-existing exploratory borehole through the coal ash and extended 10 feet below the water table w ith a 5-foot screened interval. Adjacent to the well, lysimeter L3-30 was installed to the base of the coal ash to monitor groundwater solute concentrat ions entering the subsurface (ERM, 2004). From 2000 to 2003, field sampling of MW-3 a nd L3-30 continuously showed detections of As and Se in the groundwater (although never exceeding the MCL). Concentrations of As and Se (0.037 and 0.029 ppm respectively) detected in L3-30 during th e first sampling event in 2000

were used for initial concentrati ons in IWEM. With th e input parameters desc ribed earlier in this chapter, simulations were run with both MI NTEQA2 derived and EPA tabulated distribution coefficients with a coal ash opera tional life of 5 years (reflective of the time between coal ash application and As/Se detection in MW-3 in 2003). Once modeling was complete IWEM results

35were compared to the As and Se concentratio ns of 0.01 and 0.023 ppm resp ectively detected in MW-3 in 2003. These results are reported in Chapter 3.

2.3 HYDRUS-2D Modeling

Following IWEM modeling, the next phase of research involved comparing IWEM’s

results to those of other solute transport groundwater models us ing the same input. Doing so allowed for the analysis of whether IWEM can accurately predict groundwater concentrations at a point down gradient from a secondary material source. IWEM simulates 1-D flow in the

unsaturated zone and 3-D flow in the saturated zone. A situation su ch as this requires the use of two models in order to effectively mimic the se condary material leaching scenario modeled by

IWEM.

HYDRUS-2D was used to simulate one dimensi onal (1D) vertical solute transport from the secondary material s ource through the unsaturated zone do wn to the water table. Not only can HYDRUS provide 1D flow to mimic that modeled by IWEM, but it can do so through variably saturated media representative of the unsaturated zone. Inputincluding initial metal concentrations and fluxes from the secondary ma terial source, soil type , recharge rates, and hydrogeologic parameters (e.g. conductivity) rema ined unchanged from those used for IWEM simulations. Upon running HYDRU S, output concentrations and fluxes were read from the lower boundary of the modeled domain which is repres entative of the top of the water table (i.e. 0 pressure head). Two scenarios were run with HYDRUS using the WisDOT data for input: 1. with a cross-sectional length of 14 meters (m) to mimic the arbitrarily chosen 200 m 2 WMU area ((14 m) 2 ~ 200 m 2 ) used for most of the previous IWEM simulations; and 2. w ith a cross-section

36length of 1000 m to represent a real life applic ation of secondary materials along a stretch of highway. The purpose of using these extreme leng ths is to compare the output concentrations from both and determine if increasing the cross-s ectional horizon has any dramatic effect on the concentration observed at the water table along the plume centerline. In previous IWEM work, it was observed that increasing the WMU area ha d an almost linear effect on the output groundwater concentrations. It was thought that this is an unrealistic result that may be attributed to IWEM assuming its WMUs to be square which is not representative of roadway geometry (rectangular). Chapter 3 discusses this topic in greater detail. Both scenarios were run using the average EPA tabulated K d values from Table 7. Additionally, the 14 m cross-secti onal scenario was run using a K d value of zero for each solute (Ag, Cd, Cr, and Se). The majority of th e IWEM simulations run to date have relied upon

K d values drawn from the MINTEQA2 database inco rporated into the software. Earlier work has demonstrated that these values are relatively lo w (on the order of magnitude of one or less) and comparison to the HYDRUS-2D results using a K d value of zero (i.e. low K d ) should help confirm this observation . Moreover, as discusse d earlier, IWEM had been run as a function of time using the tabulated EPA values allo wing once again for effective comparisons. All HYDRUS simulations were run for 200 y ears using bottom ash data, which yielded the highest leachate concentratio ns. No runs were performed for the other materials in the research (e.g. foundry slag) under the assumption th at these would yield identical trends. The unsaturated zone was represented by a simple rectangular geometry. The boundary conditions (BC) of the modeled domain were set up su ch that the top was defined by a daily constant flux

BC representing the incoming water leaching from the secondary material as reported by Sauer et. al (2005); the bottom as a constant pressure head BC set equal to zero which is characteristic

37of the water table; and the vertical sides as no-flow BCs . Additionally, five observation nodes were set along the center of the domain extending from the surface to the water table. Solute concentrations and fluxes, as well as water fluxe s could be read from these nodes. Figures 13 and 14 display graphical representations of th e boundary conditions and nodal arrangements for the 14 m cross-section respectively.

Constant Flux Boundar y

Constant Pressure He a d Bounda r y No-flow Boundar y 5 meters

Figure 13. Graphical display of boundary conditi ons used for 14 m cross-section in HYDRUS2D.

38Figure 14. Graphical display of observation nodes used for 14 m cross-section in HYDRUS-2. Obse rv at i o n N ode Ground Surface

Water Table

3940 3. RESULTS AND DISCUSSION

3.0 IWEM Modeling After running 400+ simulations with IWEM, us ing various input data provided by select study areas, results were analy zed to help evaluate and form conclusions regarding IWEM’s performance as a fate-and-transport model with respect to the benefici al use of secondary

materials in a road environment. Initial modeling was performed to observe how IWEM responded to varying input parameters, primarily using data from the WisDOT study area. Next, using input data from sites with elevated groundwater concentrations caused by secondary material leaching, the model was tested to de termine if IWEM would have predicted such concentrations. Finally, comparisons were ma de between IWEM and HYDRUS-2D to evaluate agreement between the two models.

3.0.1 Variable WMU operational life w ith fixed receptor well distance To evaluate how IWEM treats heavy meta l transport as a function of leaching time, thirteen simulations (spanning 1 to 200 years) were run for each test at the WisDOT study area (52 total), where the operational life of the secondary material application was varied. The metals simulated were Ag, Cd, Cr, and Se. Each simulation was performed with a receptor well located an arbitrarily fixed 50 mete rs from the leachate source. Once all the runs were complete, output concentrations of each constituent for each test section were plotted verse time (Figures 15A through 15D).[Cd] vs. t using MINTEQA2 derived Kd0.0E+001.0E-032.0E-033.0E-034.0E-035.0E-036.0E-030 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)Bottom AshFly AshFoundry SlagFoundry SandMCL (0.005 ppm)Fig. A[Cr] vs. t using MINTEQA2 derived Kd0.0E+001.0E-042.0E-043.0E-044.0E-045.0E-040 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: MCL = 0.1 ppmFig. B41[Se] vs. t using MINTEQA2 derived Kd0.0E+002.0E-034.0E-036.0E-038.0E-031.0E-021.2E-020 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: MCL = 0.05 ppmFig. C[Ag] vs. t using MINTEQA2 derived Kd0.0E+005.0E-041.0E-031.5E-032.0E-032.5E-033.0E-030 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: HBN (non-cancer) = 0.12 ppmFig. DFigures 15A-D. Groundwater concentrationvs. time predicted by IWEM for metalsleaching from recycled materials.The plots in Figure 15 depict sim ilar trends for the transport of metals in the subsurface. Initially, as leaching time increases , metal concentrations increase quickly in the receptor well down gradient. However, as time progresses, concentration increases diminish resulting in steady state behavior caused by dilution. In some cases (e.g. Cr and Se), the concentrations eventually plateau indicating no net sorption is taking place, wh ere the difference between the input concentration and the plateau concentration demonstrates the magnitude of dilution that has occurred in the system. This shows that ne w contaminant entering the system is no longer increasing apparent groundwater concentrations as a result of dilution by surrounding freshwater. Furthermore, in the cases of Cr for all f our materials and Cd for bottom ash and foundry slag, detectable groundwater concentrations do not appear in the receptor well until approximately five years after the material is applied. This phenomenon illustrates that, in addition to dilution, IWEM is effectively accoun ting for metal adsorption onto aquifer materials

as the constituents tr avel through the unsaturated zone. Once breakthrough is achieved, the constituents eventually enter th e saturated zone where they mix with the groundwater and flow to the receptor well. If adsorption occurs, then one would theoretically e xpect to observe this delayed response. Normally, after a certain period of time, it would be expected for the concentration of a particular constituent to decrease with dilution as a result of a source being finite. In an actual field situation, waste within a finite source (a s with secondary materi al applications) would eventually be depleted and, thus, would no longer contribute to the formation of leachate into the subsurface. IWEM does not depict such a scenario here because a waste pile (selected WMU) is assumed to be a continuous-type source wher e leaching occurs at a constant leachate concentration equal to the initial input. In other words, leachate is continually being introduced

42into the unsaturated and saturated zones with no depletion of the constituent of concern (COC) occurring within the waste pile material (e.g. bottom ash). This assumption is acceptable however, because IWEM considers a waste pile to be a temporary source with an average operational life of 40 years (even though mode l simulations were run for 200 years). The leaching of metals over 40 years is not considered to be a significant amount of time due to their slow moving nature within the subsurface. A lthough the average operational life of a waste pile is 40 years, the model was run for 200 years to s imply gage its response to achieving equilibrium

conditions. Based on the discussion in the preceding paragr aphs, it appears that IWEM is accurately portraying the movement of metals within the s ubsurface as a function of time. Thus, keeping the MCL of a particular metal in mind, it se ems one can determine, at least to a first approximation, if a certain materi al will contribute an apprecia ble concentration to groundwater a particular distance from the source. An informed decision can then be made whether the material of interest is safe enough for beneficial use or not. In the case of all the metals modeled here, it would appear that each secondary materi al would be safe enough to reuse for 200+ years under this scenario (i.e. fixe d receptor well 50 meters from source) and using the MCL as

criterion.

It should also be noted that while Cr di splays an unusual step -like behavior with increasing time (for reasons unknown) , the overall trend is similar to the other metals, thus it is viewed as acceptable data for this research. All simulations performed for Cr throughout this research exhibited the same trend regardless of input concentration, theref ore it reasonable to attribute the strange behavior to IWEM perf ormance and not input pa rameter uncertainty.

4344 3.0.2 Variable receptor well distance from source with fixed WMU operational life To evaluate IWEM’s heavy metal transport capabilities as a func tion of receptor well distance from the source, nine simulations were run for each test sect ion at the WisDOT study area (36 total), where the well dist ance was varied from 10 to 500 meters. The metals simulated were Ag, Cd, Cr, and Se. Each simulation was performed for a fixed leaching time (WMU operational life) of 40 years (avera ge operational life fo r waste piles). Once all the runs were complete, output concentrations of each constituent for each test section were plotted versus well distance (Figures 16A through 16D). An evaluation of Figures 16A through 16D clea rly illustrates IWEM’s ability to show the inverse relationship between increasing well distance and decreasing concentration along the plume centerline, as would be expected. With increasing transport distance (i.e. increased well distance) attenuation and dilution factors dominate in the subsurface and act to reduce the metal concentrations in the groundwater. IWEM successfully accounts for these factors via solving the advection-dispersion equation within the unsatur ated and saturated zones (see Chapter 2). Based on the work presented here, it appears the IWEM is accurately portraying the

movement of metals within the subsurface as a function of receptor well distance from the leachate source. Keeping the MCL of a particular metal in mind, it seems one can determine, at least to a first order degree, if a certain recycled material will contribute an appreciable concentration to groundwater for a part icular operational life. Thus, anFigures 16A-D. Groundwater concentration vs.receptor well distance for metals leachingfrom recycled materials after 40 years.[Cd] vs. d using MINTEQA2 devrived Kd0.0E+001.0E-032.0E-033.0E-034.0E-035.0E-036.0E-0310 60 110 160 210 260 310 360 410 460 510 560Well Distance From Source (meters)

Concentration (ppm)Bottom AshFly AshFoundry SlagFoundry SandEPA's MCL (0.005 ppm)Fig. A[Cr] vs. d using MINTEQA2 derived Kd0.0E+005.0E-051.0E-041.5E-042.0E-042.5E-043.0E-0410 60 110 160 210 260 310 360 410 460 510 560Well Distance From Source (meters)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: EPA's MCL = 0.1Fig. B[Se] vs. d using MINTEQA2 derived Kd0.0E+005.0E-031.0E-021.5E-022.0E-022.5E-023.0E-023.5E-0210 60 110 160 210 260 310 360 410 460 510 560Well Distance From Source (meters)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: EPA's MCL = 0.05 ppmFig. C[Ag] vs. d using MINTEQA2 derived Kd0.0E+001.0E-032.0E-033.0E-034.0E-035.0E-036.0E-037.0E-0310 60 110 160 210 260 310 360 410 460 510 560Well Distance From Source (meters)

Concentration (ppm)Bottom AshFly AshFoundry SlagNOTE: HBN (non-cancer) = 0.12 ppmFig. D45informed decision can be made whether the material of interest is safe enough for beneficial use in a particular area based on the source/loca tion for regional groundwat er use (e.g. drinking water). In the case of all the metals modeled he re, it would appear that each secondary material would be safe enough to reuse over a 40 year period if kept at l east 35 meters from the nearest source of usable groundwater (as determined from Figure 16A. However, peak concentrations may occur beyond 40 years, thus a time depende nt simulation (Section 3.0.1) would become necessary to run in conjunction with this data to truly determine the usab ility of the material.

3.0.3 Variable K d values As detailed in Section 2.2.3, twelve runs were executed using arbitrary user-defined K d values for cadmium in bottom ash from the Wis DOT data to evaluate how IWEM responds to such changes. After the twelve simulations were complete, the output concentrations were plotted as a function of the corresponding K d used as depicted in Figure 17. Analysis of Figure 17 clearly shows that IWEM accurately portrays the inverse relationship between K d and concentration. This result es tablishes user confidence in IWEM’s ability to account for adsorption of metals onto aquifer materials in order to help provide an accurate output concentration. However, it shou ld be noted that over a longer period of time, after breakthrough has been achieved, concentrati ons would eventually become independent of the distribution coefficient. As a result, deviat ion from the trend depicted in Figure 17 would

occur.

460.0E+00 1.0E-03 2.0E-03 3.0E-03 4.0E-03 5.0E-03 6.0E-03

0.001 0.01 0.1 1 10 Kd (L/Kg)Concentration (ppm) EPA's MCL (0.005 ppm)

NOTE: [Cd] = 0.0046 ppm at Kd = 0.0

Figure 17. Cadmium concentrations as a function of variable K d values. The simulation was run for bottom ash from the WisDOT data with a receptor well located 50 meters from the source over a leaching period of 40 years. Initial Cd concentration equal to 0.0212 ppm.

While Figure 17 depicts th e proper concentration/K d relationship for early time, the arbitrarily chosen K d values are noticeably smaller (by several orders of magnitude) than

distribution coefficient values normally reported in the literature for cadmium (as well as other heavy metals). For example, the average K d for Cd reported by USEPA earlier in Chapter 3 is 598 L/Kg. The coefficients presented in the above figure suggest that when MINTEQA2 is used

to derive K d s, the values chosen are unrealistically sma ll compared to reported literature values. Recalling from Section 2.2.3, an at tempt to back -calculate K d values was performed to investigate the magnitude of the MINTEQA2 derive d distribution coefficients. Results of this procedure did in fact show th at MINTEQA2 was selecting K d values much smaller than those reported in the literature (especially those re ported by the EPA). It was determined that K d

47values used were on an order of magnitude of 1 (and often less than that). Thus, if K d values used are lower than they should be, then IWEM is likely producing higher than expected output

concentrations which would label the mode l as being conservative (over predicting concentrations in groundwater). However, it should be noted by the user that peak concentrations (regardless of the K d value used) may not occur until beyond that maximum allowed modeling time (even if the material has been removed). This could result in a situation where the user is mislead into believing a certain material is sa fe for reuse after a particular time, when in fact unacceptable concentrations in groundwater could result some time beyond the real m of modeling. For example, IWEM may determine that after 50 years an application of coal fl y ash will not result in As levels above the MCL in groundwater. Thus the user may be confident to apply the ash unaware that an element could potentially cau se adverse conditions beyond 200 years (IWEM maximum allowed modeling time) and affect future generations. Conversely, if IWEM determines a material is clean enough for reuse, this conservatism can be viewed as a confirmation, knowing that th e groundwater concentrati on will be actually less than predicted. As discussed in Section 2.2.3, to investigat e IWEM’s conservatism further, the bottom ash simulations presented in Sect ion 3.0.1 were rerun using USEPA K d values. Simulations were not run for Cr because IWEM does not allow user specified K d values for this particular metal. Table 8 shows comparisons between th e output concentrations (ppm) generated using MINTEQA2 derived and EPA tabulat ed distribution coefficients. An analysis of Table 8 shows that for each metal, the concentrations produced using MINTEQA2 derived K d s are larger than thos e calculated with the EPA tabulated values.

48Often, these numbers are higher by several or ders of magnitude. Based on the inverse relationship between distribution co efficient and concentration, Tabl e 8 clearly demonstrates that MINTEQA2 is drawing upon coeffici ents smaller than reported by the EPA which explains why final concentrations are higher due to less attenuation of the cons tituents by soil particles.

Table 8. Comparison between output concentrati ons (ppm), as a function of time, generated using MINTEQA2 and EPA K d s with WisDOT data.

Time (yrs) MINTEQA2 EPA MINTEQA2 EPA MINTEQA2 EPA 1 0 0 1.80E-03 5.65E-06 1.67E-05 0 5 2.00E-04 0 6.00E-03 2.82E-05 2.00E-04 0 10 7.00E-04 0 7.30E-03 5.65E-05 7.00E-04 0 15 1.10E-03 0 8.00E-03 8.47E-05 1.10E-03 0 20 1.40E-03 0 8.30E-03 1.00E-04 1.30E-03 0 50 2.50E-03 0 9.40E-03 3.00E-04 2.00E-03 0 100 3.40E-03 0 9.80E-03 6.00E-04 2.50E-03 0 150 3.80E-03 0 9.70E-03 8.00E-04 2.60E-03 0 200 4.10E-03 0 9.60E-03 1.10E-03 2.70E-03 0 Cd Se Ag

Again, the values of these K d s are not known because IWEM lacks the production of an output file stating the selected values. The distribution coeffici ents could be back-calculated based on the iteration procedure desc ribed earlier. However, while this approach seems to yield accurate results, the procedure is very time c onsuming and can easily be avoided with the generation of an output file after each run. Additionally, with the coal fl y ash data from the North Carolina study area, a series of IWEM simulations were performed using MINTEQA2 derived and EPA tabulated K d s. Results from these runs are presented in Table 9 and again it can be seen that the concentrations generated using MINTEQA2 are significantly larger than those produced with the EPA

49distribution coefficients. In fact, the MINTEQA2 outputs quickl y reach the level at which the concentrations are equal to th e initial model input concentrat ions (0.11 and 0.353 ppm for As and Pb respectively). These results are furthe r confirmation that MI NTEQA2 is using K d s smaller than those normally expected from the literature.

Table 9. Comparison between output concentrati ons (ppm), as a function of time, generated using MINTEQA2 and EPA K d s with North Carolina data.

Time (yrs) MINTEQA2 EPA MINTEQA2 EPA 1 5.11E-02 0 3.52E-02 0 5 1.10E-01 0 1.18E-01 0 10 1.10E-01 0 1.50E-01 0 20 1.10E-01 0 3.53E-01 0 50 1.10E-01 0 3.53E-01 0 100 1.10E-01 0 3.53E-01 0 150 1.10E-01 0 3.53E-01 0 200 1.10E-01 0 3.53E-01 0 As Pb

3.0.4 Variable WMU areas As discussed in Section 2.2.4, multiple simulatio ns were run while varying the area of the WMU of interest (waste pile in this research) us ing the WisDOT data. Again, the purpose of this was to gain a better understanding of how IWEM responds to varying input parameters. After all simulations were complete, metal concentrations we re plotted as a function of time for each area (Figures 22A to 22D). Analysis of each figure clearly shows a distin ct increase in concentration as WMU area increases for each metal of concern. This appare nt linear trend between output concentration and WMU footprint area is most likely the result of IWEM assuming WMUs to be square (EPA,

5051 2002). To investigate this observation further, pe ak Cr and Cd concentrations were plotted as a function of the square root of the WMU area (Figures 23A & B). As indicated by their r 2 values and slopes of approximately 1.4, both figures disp lay a relatively high fu nction of linearity, as seen in Figure 18, especially wi th larger areas. These figures suggest that IWEM, theoretically, is displaying the proper relations hip between contaminant load and area for a square geometry. Analysis of Figures 19A & B shows that doublin g the area of a square geometry results in approximately a 1.4x factor increase in concentration which is the expected outcome. In theory, doubling the area of a square in creases the length/width dimens ions by a factor of 1.4 which should increase the mass loading, and thus groun dwater concentrations, by the same factor. Again, Figures 19A & B demonstrate that IWEM is properly calculating concentrations as a function of area for square geometries. Howe ver, because square geometries are always assumed, the model does not allow the user to po rtray the true rectangular shape of a roadway which is desired in order to produce the most accurate results.[Cd] vs. tComparison Between WisDOT Provided andArbitrarily Chosen WMU Areas for Bottom Ash1.0E-031.0E-021.0E-0120 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Log Concentration (ppm)WisDOT WMU Area (790 m2)Arbitrary WMU Area (200 m2)One Mile Stretch (8367 m2)10x One Mile Stretch (83670 m2)EPA's MCL (0.005 ppm)Fig. A[Cr] vs. tComparison Between WisDOT Provided andArbitrarily Chosen WMU Areas for Bottom Ash1.0E-051.0E-041.0E-031.0E-021.0E-011.0E+0020 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Log Concentration (ppm)WisDOT WMU Area (790 m2)Arbitrary WMU Area (200 m2)One Mile Stretch (8367 m2)10x One Mile StretchEPA's MCL (0.1 ppm)Fig. B[Se] vs. tComparison Between WisDOT Provided andArbitrarily Chosen WMU Areas for Bottom Ash0.0E+001.0E-022.0E-023.0E-024.0E-025.0E-026.0E-027.0E-020 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)WisDOT WMU Area (790 m2)Arbitrary WMU Area (200 m2)One Mile Stretch (8367 m2)10x One Mile StretchEPA's MCL (0.05 ppm)Fig. C[Ag] vs. t for WPComparison Between WisDOT Provided andArbitrarily Chosen WMU Areas for Bottom Ash0.0E+002.0E-034.0E-036.0E-038.0E-031.0E-021.2E-021.4E-020 20 40 60 80 100 120 140 160 180 200 220Operational Life (years)

Concentration (ppm)WisDOT WMU Area (790 m2)Arbitrary WMU Area (200 m2)One Mile Stretch (8367 m2)10x One Mile StretchNOTE: HBN (non-cancer) = 0.12 ppmFig. DFigures 18A-D. Groundwater concentration vs. varying WMU area for metalsleaching from recycled material.52Peak [Cr] vs. WMU Area for Bottom Ash

R 2 = 0.9842 slope = 1.41

0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030 0.0035 0.0040 0.0045 0.0050

0 50 100 150 200 250 300 350 Square Root of WMU Area (m)Concentration (ppm) Fig. A

Peak [Cd] vs. WMU Area for Bottom Ash

R 2 = 0.9605 slope = 1.36

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018

0 50 100 150 200 250 300 350 Square Root of WMU Area (m)Concentration (ppm) Fig. B

Figures 19A-B. Peak Cr and Cd concentrations vs. WMU area. Result s depict relative linear trends between contaminant load and area for square geometries.

53This inability to account for varying geometri es may greatly limit IWEM’s usefulness for modeling secondary material leaching from road way settings, especially if a high degree of accuracy is desired. Assuming only a square ge ometry appears to be another key factor contributing to IWEM’s over predicting of output concentrations in relation to secondary material reuse in a roadway setting. Based on the above discussion, a recommendati on for the appropriate use of IWEM may be to use the model for simulating water and solute transport from only representative squares of roadways. For instance, given secondary mate rials beneath a 6 m wide roadway, it may be useful to apply IWEM for modeling a 36 m 2 (6m x 6m) section of the road. This appears to be a

case where IWEM’s conservatism could be minimize d. If a larger portio n of the roadway were to be simulated, then factoring out mass loading f actors may need to be considered in order to produce more accurate predictions. In other words, each time the area is doubled, then a factor of 1.4 needs to be subtracted from the output groundwater concentra tion to account for the additional loading produced with a square geometry that, in actua lity, does not contribute to the groundwater.

3.0.5 Additional Modeling with WisDOT Data As discussed in Section 2.2.5, additional m odeling was performed with IWEM using the WisDot groundwater data. The purpose was to determine if IWEM would predict metal groundwater concentrations below the MDLs as obs erved at the site during the first five years following secondary material applications of bottom and coal fly ash. Once modeling was completed, groundwater concentrations (or abse nce of) resulting from secondary material

54leaching were plotted as a function of time and compared to the MDLs of the corresponding metal of concern (Figures 20A-C). Analysis for Figures 20A-C shows that after fi ve years, concentrations of Cd for bottom ash and Se for both bottom and fly ash exceeding the MDLs. However, Cd concentrations for fly ash and Ag for bottom and fly ash remained below the MDLs as observed at the study area. Additionally, IWEM predicted 0 ppm of Cr in groundwater after five years for both materials which corresponds to the field measurements. Although IWEM predicted concentrations abov e the MDLs in several cases, MINTEQA2

derived distribution coefficients we re used (because actual field K d s have not been measured at the site) which, as previously discussed in this chapter, appear to be considerably lower than what actual field values may be when comp ared to the literature. Use of low K d s would effectively result in higher predicted concentrat ions which could explain the reason why some of

the values are shown to exceed the MDLs. Based on the exceedences, it can be assumed that this is merely a case where IWEM is over predicting concentrations as a result of using low K d values, poor geometry considerations, or a combination of both. However, without knowledge of the actual K d values, establishing confidence in this assumption becomes difficult. However, because several scenarios predicted metal concentrations above MDLs, for the time being, IWEM still needs to be considered

conservative at least until MIN TEQA2 and/or actual field K d s are known to perform further evaluations. Still, these simulations can be view ed as worst-case scenar ios due to the apparent

low K d values selected by MINTEQA2. As a result, predicted concentrations are likely higher

than actual conditions.

55[Cd] vs. t using MINTEQA2 derived K d Receptor Well 6 Meters from Source

0.0E+00 1.0E-03 2.0E-03 3.0E-03 4.0E-03 5.0E-03 6.0E-03

01234567 Operational Life (years)Concentration (ppm) Bottom Ash Fly Ash MCL (0.005 ppm) Fig. A

MDL = 0.001 ppm

[Se] vs. t using MINTEQA2 derived Kd Receptor Well 6 Meters from Source

0.0E+00 1.0E-02 2.0E-02 3.0E-02 4.0E-02 5.0E-02 6.0E-02

01234567 Operational Life (years)Concentration (ppm) Bottom Ash Fly Ash Fig. B

MCL (0.05 ppm)

MDL = 0.001 ppm

56[Ag] vs. t using MINTEQA2 derived K d Receptor Well 6 Meters from Source

0.0E+00 5.0E-04 1.0E-03 1.5E-03 2.0E-03 2.5E-03 3.0E-03

01234567 Operational Life (years)Concentration (ppm) Bottom Ash Fly Ash

NOTE: MDL = 0.01 ppm Fig. C

Figures 20 A-C. IWEM predicted metal concentra tions over five years for bottom ash and coal fly ash at the WisDOT study area. Concentratio ns measured in actual monitoring wells at the study area located 6 meters down grad ient of each secondary material.

3.0.6 Modeling with North Carolina data As outlined in Section 2.2.5, IWEM modeling wa s performed to simulate the transport of As and Pb (leaching from coal fly ash) into monitoring well MW1s where field sampling has

detected exceedances of these metals at 0.028 and 0.068 ppm respectively. Again, modeling was performed using both MINTEQA2 and EPA tabulated distribution coefficients. Figures 21 A and B depict time-depende nt modeling after 200 years using K d s selected by MINTEQA2. Analyses of these figures show th at within the first year, IWEM is predicting As and Pb well above their respective MCLs in MW 1s. In fact, for As, concentrations reach the initial model input value of 0.11 ppm within 5 ope rational years. Similarly, the initial model

57input value of 0.353 ppm for Pb is predicting to occur before 20 years. These results imply that no dilution is occurring indicating that IWEM may not be properly solving the advectiondispersion equation for this particular situation wh ere the water table is present at the bottom of

the fill. Conversely, using average EPA tabulated distribution coefficients of 1,585 and 5,012 L/Kg for As and Pb respectively, IWEM predicts zero impact to groundwater in MW1s after 200 years. With MINTEQA2 specified K d s, IWEM was clearly able to predict the exceedances reported by field/laboratory testing. While the magnitude of thes e predicted values appears to be another case of IWEM’s conservatism when compared to test results, something positive can be

taken from the fact that the model was able to show groundwater contamination would result in conjunction with the reuse of the co al fly ash as indicated from fi eld studies. However, as shown with the WisDOT data, the stark c ontrast between MI NTEQA2 and EPA K d produced concentrations remains unexplained and is thus , worrisome. These results demonstrate the importance of selecti ng the appropriate K d to represent a site when modeling and, thus, must be considered extremely carefully when using IWEM in order to produce the most accurate predictions.

58[As] vs. t using MINTEQA2 Kd Receptor Well (MW1s) 7 Meters from the Soucre

1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 20 40 60 80 100 120 140 160 180 200 220 Operational Life (years)Concentration (ppm)

NCAC 2L Standard = 0.01 Fig. A

Initial model input value (0.11 ppm)

Actual concentration detected in MW1s after 10 years (0.028 ppm)

[Pb] vs. t using MINTEQA2 Kd Receptor Well (MW1s) 7 Meters from the Soucre

1.0E-02 1.0E-01 1.0E+00

0 20 40 60 80 100 120 140 160 180 200 220 Operational Life (years)Concentration (ppm)

EPA's MCL = 0.015 ppm Fig. B

Initial model input value (0.353 ppm)

A ctual concentration detected in MW1s after 10 years (0.068 ppm)

Figures 21A-B. As and Pb concentrations wi th time at MW1s using MINTEQA2 derived K d s.

59The two scenarios presented above appeared to be extreme cases with respect to the magnitude of K d values used. For this reason, an attemp t was made to estimate the actual soil K d values for the site based on the concentrations of As and Pb detected in MW1s to get an estimate of IWEM’s conservatism. An iteration pro cedure was performed where user-defined K d values were repeatedly changed in order to produce an output concentration which matched the field

concentrations of As and Pb. Resu lts from this procedure produced K d values of approximately 21 L/Kg and 28 L/Kg for As and Pb respectively (Figure 22). While these values are several orders of magnitude lower than the average EPA tabulated numbers (which is not unusual considering the wide range of K d s metals can have based on varying site conditions), they still in fact are within the literature rang es reported, (albeit on the lower e nd). It can be inferred that they must also be several orders of ma gnitude higher than the MINTEQA2 derived K d s since field concentrations are still considerably lower than those produced using MINTEQA2 (see Figure 21A & B). Additionally, the same simulations execute d for MW 1s were replicated for MW2s located 48 meters downgradient from the edge of th e fly ash (41 meters from MW1s). At this location, field sampling and subsequent laboratory testing showed no detection of As and Pb over regulatory standards. It should be noted that field samples were collected 10 years following the application of the fly ash.

60K d vs. Concentration for As & Pb Back-calculation for Swift Creek Study Area

1 10 100 1000 10000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 Concentration (ppm)Kd(L/Kg) As Pb

K d = 28 L/Kg @ 0.068 ppm (field concentration)

K d = 21 L/Kg @ 0.028 ppm (field concentration)

Figure 22. Back-calculated field K d s (L/Kg) estimated for As and Pb at the Swift Creek study area.

Figures 23A and B again show levels of As and Pb quickly (within 5 years) rising above their respective MCLs in MW2s, although never achieving th eir initial input c oncentrations over 200 years as seen in MW1s. After ten years (tim e between construction a nd the data collection), IWEM predicts concentrations of 0.088 and 0.0721 ppm for As and Pb respectively in MW2s.

These values are a stark contra st to the non- detections repor ted by the field data and again illustrate another case were IWEM appears to be over predicting. A scenario such as this would lead the user to believe the coal ash (at least in the short term) poses a threat to subsurface conditions and may result in abandoning the reus e of a seemingly non-threatening material. Instead, the user may just opt to landf ill the coal ash where it is of no use.

61To justify the preceding statements, the estimated site-specific K d values for As and Pb (21 and 28 L/Kg respectively) were used to simulate transport to MW2s. Results from this

simulation demonstrated no detectable As or Pb concentrations in MW2s which is consistent

with the field data. This result further demons trates the importance of using the appropriate Kd for a given situation.

[As] vs. t using MINTEQA2 Receptor Well (MW2s) 47.5 Meters from the Source

1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+01

0 20 40 60 80 100 120 140 160 180 200 220 Operational Life (years)Concentration (ppm)

EPA's MCL = 0.05 ppm Fig. A

Initial model input value (0.11 ppm)

Detection limit (0.01) ; actual value reported below limit at MW2s after 10 years.

Figures 23A-B. As and Pb concentrations wi th time at MW2s using MINTEQA2 derived K d s.

62[Pb] vs. t using MINTEQA2 Kd Receptor Well (MW2s) 47.5 Meters from the Source

1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 20 40 60 80 100 120 140 160 180 200 220 Operational Life (years)Concentration (ppm)

EPA's MCL = 0.015 ppm Fig. B

Initial model input value (0.353 ppm)

Detection limit (0.01) ; actual value reported below limit at MW2s after 10 years.

Figures 23A-B continued

Actual site conditions cons idered by IWEM may also expl ain why the model is over predicting by such a large magnitude for the North Carolina study area. Recall from Section 1.2.2 that the coal ash is underlined by a highly impermeable layer of Altavista soil (hydraulic conductivity ~ 7x10 -8 cm/sec). Additionally, Figure 24 show s that groundwater levels in MW1s and MW2s are located above this material (up into the coal ash) indicated that pooling of rain

water and water used for dust control is occurr ing because the soil is acting as a barrier to vertical migration of groundwater. Due to the impermeability of the soil and IWEM’s apparent conservative nature, it makes sense to assume IW EM is allowing very little of the water to penetrate through the confining bed. This situation, coupled with the moderate regional hydraulic gradient, may be resulting in the late ral migration of the di ssolved metal carrying

63groundwater over the Altavista soil and directly into the down gradient shallow wells. In fact,

the well screen for MW1s is located well above th e Altavista soil which acts as a direct receptor for the lateral moving groundwater into the well.

Figure 24. Hydrogeologic cros s-section of Swift Creek study area (She rrill, 2004).

3.0.7 Modeling with Maryland data 3.0.7.1 Routes 213/301 Overpass IWEM modeling of the Routes 213/301 ove rpass (south cluster) produced Ba output concentrations of 0.0515 and 0.0002 ppm for the MINTEQA2 derived and EPA tabulated K d simulations respectively. These values are in comparison to the analytical detection of 0.06 ppm at MW-102 ten years after the coal fly ash applic ation at the site. Analysis of these results clearly shows that the MINTEQA2 results are most comparable to the actual field data while the EPA concentration is several orders of magnitude less. This is anothe r demonstration of how

64MINTEQA2 uses K d values much smaller than those normally reported in the literature. In this case, 100 L/Kg was the user-defined distribution coefficient chosen for Ba. Thus MINTEQA2

must have selected a much lower K d for Ba in order to predict a concentration two orders of magnitude greater. Based on these results, the first impression w ould be to rely on MINTEQA2 in order to predict concentrations similar to those seen in re ality. However, one can make the argument that using 100 L/Kg provides the most realistic situ ation because the value lies within the range reported from the literature K d s, thus the output concentration a ssociated with it is most likely

closer to the truth. Additiona lly, the input concentration used for these simulations was measured six years after the coal ash implementa tion. The material contai ns a finite amount of Ba and over the years, some of the Ba most lik ely was leached away from the ash thus lowering its concentration observed in the ly simeter. In other words, the true initial input concentration would be higher which in turn would produce a greater output. Because it is not known how much higher the initial concentration would have b een in the ash, it is difficult to predict what the model output would produce. However, base d on results in the preceding sections, one can infer that the model output would be higher than actual site conditions would likely show, which again demonstrates IWEM’s over predictive nature.

3.0.7.2 I-695 Overpass Table 10 contains the I-695 overpass modeling results in comparison to the As and Se

concentrations detected in MW-3. Trends in th e data are very similar to those observed in the Routes 213/303 groundwater. Again, MINTEQA2 resu lts are comparable to the field data. Conversely, the EPA results are substantially lower. However, as with the Routes 213/303 data,

65the input concentration used for these simulations was measured several years (five) after the coal ash implementation, thus con centrations are most likely greater than what the actual values would have been for the same reasons discussed in the previous section. Additionally, the MINTEQA2 results are most lik ely greater than actual conditi ons due to apparent low K d selections which, again, makes the cas e for IWEM being overpredictive.

Table 10. IWEM modeling results for I-695 overpass.

MW-2 MINTEQA2 EPA As (ppm) 0.01 0.0085 0 Se (ppm) 0.023 0.0285 0.0002

3.1 HYDRUS-2D Modeling As described in Section 2.3, the objective of modeling with HYDRUS-2D was to validate IWEM’s one dimensional (1D) vertical solute transport from the sec ondary material source through the unsaturated zone down to the water tabl e. Again, simulations were performed using bottom ash data from the WisDOT study area for 14 and 1000 m cross-sectional length

scenarios.

Solute concentrations from the five nodes for the 14 and 1000 m scenarios, using USEPA

K d s, were compared for 200 years. Table 11 shows the concentrations of Cd, Cr, Se, and Ag at node 5 (bottom boundary/water ta ble) for select years.

66Table 11. HYDRUS-2D metal concentrations at node 5 (bottom bounda ry/water table).

Cd Cr Time (yrs) 14m 1000m 14m 1000m 1 0 0 8.35E-15 9.12E-15 10 4.70E-20 5.07E-20 1.04E-10 1.12E-10 40 4.78E-17 5.21E-17 9.58E-08 1.04E-07 100 4.64E-15 5.09E-15 7.60E-06 8.25E-06 150 3.53E-14 3.86E-14 4.87E-05 5.25E-05 200 2.60E-13 1.62E-13 1.71E-04 1.84E-04 Se Ag Time (yrs) 14m 1000m 14m 1000m 1 7.38E-17 8.01E-17 0 0 10 8.48E-13 9.15E-13 8.27E-20 8.92E-20 40 8.38E-10 9.14E-10 8.40E-17 9.17E-17 100 7.69E-08 8.43E-08 8.15E-15 8.95E-15 150 5.58E-07 6.09E-07 6.19E-14 6.78E-14 200 2.24E-06 2.43E-06 2.60E-13 2.84E-13 Analysis of Table 11 clearly shows that in creasing the cross-sectional length of the domain (while keeping the width component fi xed) has minimal effect on the solute output concentrations along the cente rline. This is in contrast to th e large increases in concentration as

a function of WMU area observed with IWEM as shown in Figure 20. Th ese results support the

notion discussed in Section 3.0.4 that IWEM can not accurately portray the true geometry of a roadway since the footprint of th e waste piles are treated as squa re. Thus, increases in output concentrations with WMU area are overly exaggera ted which may lead to false determinations of whether a secondary material is appropriate for reuse. Theoretically, a longer source will have a greater impact on groundw ater concentrations for a square geometry. For instance, doubling the area of a square wi ll increase the crosssectional length by a factor of 1.4, thus the groundwater impact should be approximately 1.4

67times greater. To illustrate this point, an IWEM simulation was perf ormed with the WISDOT

data bottom ash data for a 400 m 2 WMU area (two times the 200 m 2 arbitrary area reported earlier) for a period of 100 years with a receptor well 50 m down gradient. Metal output concentrations for each area and corresponding factor increases are reported in Table 12. Results presented in Table 12 illustrate IW EM’s ability to account for the 1.4 factor increase in groundwater concentrat ions when the foot print area of a square geometry is doubled. However, the question remains whether IW EM can accurately calculate groundwater

concentrations for a differ ent geometry (e.g. a road).

Table 12. Metal output concentrations for each area and corresponding factor increases. Concentration (ppm) Factor Increase Metal 200 m 2 400 m 2 Cd 2.30E-03 3.60E-03 1.6 Cr 3.54E-05 5.69E-05 1.6 Se 9.40E-03 1.40E-02 1.5 Ag 1.90E-03 2.80E-03 1.5

Next, comparisons between HYDRUS-2D and IWEM results were performed using USEPA reported K d values. Again, concentrations of in terest from HYDRUS were taken from

the bottom node. Results are presented in Table 13. Analysis of Table 13 shows that the models predict similar c oncentrations for Cd and Ag. Because HYDRUS is only simulating transport th rough the unsaturated zo ne, it is likely the concentrations presented here w ould decrease due to dispersion and further retardation in the saturated zone, bringing them closer to the zero values reported by IWEM. However, for Cr and Se, concentrations predicted by HYDRUS are considerably lower than those of IWEM (especially in early time). This raises concern because, again, HYDRUS is

68only representing transport through the vadose z one (while IWEM simulates transport through

both unsaturated and saturated z ones) and one would expect the concentrations to be larger because the solutes have not yet been subject to further transport, dilution and attenuation through the saturated zone. This provides cl ear evidence that IWEM is over predicting concentrations and because equal user-defined K d values were used between the models, this conservatism is likely the result of differing geomet ry considerations (i.e. square vs. rectangle). Table 13. Comparison between HYDRUS-2D and IWEM results for bottom ash concentrations (mg/l) with time using USEPA tabulated K d values for both models.

Cd Cr Time (yrs) IWEM Hydrus IWEM Hydrus 1 0 0 0 8.35E-15 10 0 4.70E-20 6.55E-04 1.04E-10 20 0 1.50E-18 1.00E-03 3.21E-09 40 0 4.78E-17 2.50E-03 9.58E-08 80 0 1.52E-15 2.82E-03 2.67E-06 100 0 4.64E-15 3.00E-03 7.60E-06 150 0 3.53E-14 9.00E-03 4.87E-05 200 0 2.60E-13 1.20E-02 1.71E-04 Se Ag Time (yrs) IWEM Hydrus IWEM Hydrus 1 5.65E-06 7.38E-17 0 0 10 5.65E-05 8.48E-13 0 8.27E-20 20 1.00E-04 2.68E-11 0 2.64E-18 40 3.22E-04 8.38E-10 0 8.40E-17 80 5.06E-04 2.58E-08 0 2.68E-15 100 6.00E-04 7.69E-08 0 8.15E-15 150 8.00E-04 5.58E-07 0 6.19E-14 200 1.10E-03 2.24E-06 0 2.60E-13

69Finally, a comparison was made with IWEM results using MINTEQA2 K d values and HYDRUS results using values equal to zero (Table 14). Observations from Table 14 show similar results in magnitude between the models for each solute and time. This appears to confir m the notion that IWEM is drawing upon very low

K d values (less than or equal to 1 L/Kg) from MINTEQA2 since HYDRUS is using a value of

zero in this situation. Table 14. Comparison between HYDRUS-2D and IWEM results for bottom ash concentrations (mg/l) with time. Results based on using K d values of zero and MINTEQA2 derived for HYDRUS and IWEM respectively.

Cd Cr Time (yrs) IWEM Hydrus IWEM Hydrus 1 0 1.24E-07 0 9.70E-08 10 7.00E-04 1.07E-03 4.53E-05 8.40E-04 20 1.40E-03 1.12E-02 1.84E-04 8.17E-03 40 2.30E-03 2.29E-02 3.54E-04 1.61E-02 80 3.10E-03 2.11E-02 8.68E-04 1.52E-02 100 3.40E-03 2.15E-02 2.00E-03 1.50E-02 150 3.90E-03 2.08E-02 2.00E-03 1.51E-02 200 4.10E-03 2.09E-02 2.00E-03 1.51E-02 Se Ag Time (yrs) IWEM Hydrus IWEM Hydrus 1 1.80E-03 2.41E-07 1.67E-05 6.90E-08 10 7.30E-03 2.10E-03 7.00E-04 6.01E-04 20 8.30E-03 2.18E-02 1.30E-03 6.24E-03 40 9.40E-03 4.45E-02 1.90E-03 1.28E-02 80 9.50E-03 4.11E-02 2.30E-03 1.18E-02 100 9.80E-03 4.17E-02 2.50E-03 1.19E-02 150 9.70E-03 4.04E-02 2.60E-03 1.16E-02 200 9.60E-03 4.06E-02 2.70E-03 1.16E-02

704. CONCLUSIONS AND RECOMMENDATIONS

4.0 Objectives The objectives of this study were to:

• Evaluate IWEM’s basic performance as a groundwater model by observing how it responds to varying input parameters;

• Evaluate IWEM’s groundwater modeling ca pabilities with respect to heavy metal leaching from reused secondary materials in comparison to other model results and actual field data and;

• Form a conclusion as to whether IWEM is suitable for predicting groundwater concentrations resulting from secondary material leaching. In addition, this section provide s recommendations for changes to IWEM that would improve its utility as a model to evaluate groundwater impacts associated with recycled materials use in roadways.

4.1 Basic Groundwater Modeling Performance Overall, IWEM performed sa tisfactorily as a tool for pr edicting groundwater and solute flow at points down gradient from a source. With respect to varying WMU operational life, it was clearly able to demonstrat e the non-linear relationship betw een increasing leaching time and solute concentration. However, the majority of the WMUs modeled in IWEM (e.g. waste pile) are treated as continuous-type sources where leac hate is continually being introduced into the subsurface with no depletion of th e constituent of concern occurr ing within the waste material (e.g. bottom ash). Because of this, concentrations never peak and appear to keep increasing or plateau with time. In reality, materials provide finite sources of leachate and leached metals

71concentrations decrease over time. With this said, modeling over long time periods (100+ years) may not produce accurate/realistic results. Modeling particular WMUs is be tter left to the short term during which it is unlikely that metals within the subsurface will reach their peak concentrations. If IWEM is adap ted to allow representation of r ecycled materials applications as finite sources, rather than continuous-type sour ces, it should be feasible to run the model for longer time periods. IWEM’s ability to successfully solve the advection-dispersion equation allows it to accurately portray the movement of metals within the subsurface as a function of receptor well distance from the leachate source (an exception to this may occur when the water table is present at or above the bottom of a particular material as is the case for the North Carolina study area (Section 4.0.5)). Accounting for various at tenuation factors, IWEM shows the inverse relationship between concentra tion and well distance along the pl ume centerline, as would be expected. Using this, one can determine, at leas t to a first order degree, if a certain recycled material will contribute an appreciable mass to groundwater for a particular operational life. As with varying well distance from leaching source, IWEM accurately portrays the inverse relationship between the K d of a constituent and its predic ted concentration. This result establishes user confidence in IWEM’s ability to account for adsorption of metals onto aquifer materials in order to help provide an accurate output concentration and makes it clear that K d is a critical parameter for predicting contaminant movement. One area of concern with respect to K d values lies in the magnitude of the values derived by MINTEQA2 when user-defined values are u nknown. Extensive modeling with various input data continually demonstrated that MINTEQA2-der ived values appear to be much smaller than expected when compared to the literature. As a result, it is thought that IWEM may be too

72conservative in nature and will over predict output concentrations. This could result in a situation where the user is mislead into believing a certain material is not safe for reuse, when no groundwater contamination is likely to occur. As a result, the material may be unnecessarily landfilled. Conversely, if IWEM determines a material is clean enough for reuse, this conservatism can be viewed positively, providing confidence that the output concentration will be less than predicted. The major problem with this, however, is that pa rameter output files are not provided so it is not known wh at values are selected by MI NTEQA2; comparisons to other

values cannot be made in order to asses the in tegrity of the distribution coefficient chosen and the degree of conservatism is unknown.

4.2 Modeling Performance of Secondary Material Reuse For the purposes of evaluating IWEM’s pe rformance with respect to leaching from reused secondary materials in a roadway setting, waste piles were used as the representative WMU. In addition to the parameters described in the preceding section, WMU (waste pile) areas were varied with fixed operati onal lives and well distances. An apparent linear relationship was observed between increasing output concentrat ion and increasing WMU footprint area. As discussed in Chapter 3, this situation is th e result of an assumed square waste pile area by IWEM, which may not allow the modeler to accurately portray the true geometry of a roadway section. As demonstrated with HYDR US-2D modeling, increasing the cross-sectional length of an area should have a negligible e ffect on groundwater concen trations down gradient along a plume center line due to various attenuation factors. In this case the restrictions of the model may not allow accurate re presentation of a roadway secti on in relation to the underlying groundwater flow direction.

73A recommendation for the appropriate use of IWEM may be to use the model for simulating water and solute transport from onl y representative square s of roadways. For instance, given secondary materials beneath a 6 m wide roadway, it may be useful to apply IWEM for modeling a 36 m 2 (6m x 6m) section of the road. This appears to be a case where

IWEM’s conservatism could be minimized. If a larger portion of th e roadway were to be simulated, then factoring out mass loading factors may need to be considered in order to produce more accurate predictions.

4.3 Conclusions on IWEM’s Suitability for Determining Secondary Material Reuse

Based on the information collected in this study, it would be appropria te to use IWEM as a first-order approximation in determining whethe r secondary materials are safe for reuse in a roadway setting. However, factors including the m odel’s over predictive nature and inability to accurately represent the true geometry of a road way may make it a liability for producing a final determination. As mentioned in Section 4.1, IW EM may be useful in a situation where the model predicts that, after long times (100+ y ears), adverse groundwater impacts will not result from secondary material leaching. On the other hand, completely relying on IW EM may result in the unnecessary disposal of materials and prohibit safe and effective recycled materials use. To the extent that the model is overly conservative, the user may opt to use virgin materials which not only depletes natural resources but has many other environmental impa cts associated with mining, processing and transportation. Over-conservatism in assessing environmental risk from leaching can have many consequences related to human health risks, en vironmental degradation and ecosystem risks that should be recognized during any continued de velopment of use of the IWEM model.

74Additionally, other factors were observed th roughout this research which may hinder IWEM’s suitability for determining secondary materi al reuse. One factor of concern is IWEM’s lack of output files listin g the non-user specified parameters se lected by the model. Monte Carlo selected parameters (e.g. hydrau lic conductivity, infiltra tion, etc.) as well as MINTEQA2 derived

distribution coefficients are unknow n to the modeler at the completion of a simulation. Having knowledge of these unknown values would allow the user to compare them to other sources to determine their credibility. For instance, if a Monte Carlo simulation produced a hydraulic conductivity of 10 -6 cm/s for a well-sorted sand and gravel aquifer, the user could make the

determination that this value is inaccurate and more representative of an aquifer composed of silt and clay (glacial till) instead (Fetter, 2001). This would allow the user to decide it would be more plausible to enter his/her own conductivity more re flective of a sand and gravel aquifer (10 -

3 – 10 -1 cm/s) in order to produce a more accurate output concentration (even though the true hydraulic conductivity value may still not be known). Throughout this research, IWEM’s conservative nature has b een repeatedly stated. But how conservative is IWEM? As of now, the an swer to this is not clearly known but knowledge of such information could prove to be very bene ficial when assessing whet her a certain material is safe enough for reuse. Having an idea of how much IWEM over predicts may allow the

modeler to formulate calculations which could give a better indicati on of actual conditions.

It should also be noted that IWEM is an ex tremely user-friendly program. Little to no groundwater modeling experience is required to ex ecute the program. Ease of use make the model very accessible to sectors of the industry that may not be particularly proficient in modeling.

754.4 Recommendations Users seeking first-order approximations on materials may find the program to be

beneficial in the form evaluated in this researc h. However, in order to promote IWEM as a tool that can be relied upon to justify the use or lim itation of secondary applications, some changes

should be made to the model.

• The model should allow representation of more complex geometries with variation of the angle of in cidence of groundwater flow.

• K d values, and all other parameters determined by IWEM through the MonteCarlo procedures, should be clearly output in files.

• The model should allow for time-variant i nput (leachate) conc entrations. This

could take the form of mathemati cal functions or raw data (leachate concentrations could vary by month or y ear, and could represent data collected from the field or from laboratory experiments).

• The model should output distributions rather than single values, particularly when the model is conducting a Monte-Carlo anal ysis. This information would improve the utility of the model and allow the us er or decision maker to understand the uncertainty associated with the predictions.

76References Apul, D.S., Gardner K.H., and Eighmy T.T. ( 2003). “A Probablistic Source Framework for Leaching from Secondary Materials in Highway Construction.” Clean Technology Environmental Policy , 5, 120-127. Edil, T.B., Benson, C.H., Bin Shafique, S., Ta nyu, B.F., Kim, W.H., and Senol, A. (2002). “Field Evaluation of Construction Alternativ es for Roadway Over Soft Subgrade.” Geo Engineering Report No. 02-04, Dept. of Civil and Environmental Engineering, University of Wisconsin-Madison. Environmental Resources Management, Inc. (E RM) (2004). “Technical Report: Evaluation of Water Quality with the use of Coal Combusti on Products as Structural Fill at the Routes 213/301 and Interstae 695 Overpasse s.” Annapolis, Maryland. FHWA (2001) http://wwwcf.fhwa .dot.gov/ohim/hs01/tables/hm10.pdf

. Fetter, C.W. (2001). Applied Hydrogeology, 4 th Edition, Prentice Hall, Upper Sadle River, NJ. HYDRUS-2D (2004) http://www.pc -progress.cz/Fr_Hydrus2D.htm

. Lee, T. and Benson, C. (2005), “Leaching Beha vior of Green Sands from Gray-Iron Foundries Used for Reactive Barrier Applications”, Environmental Engineering Science , 23(1), 153-167. National Ground Water Association (2004) http://www.ngwa.org

. Proctor, D. M., Fehling, K. A., Shay, E. C., W ittenborn, J. L., Green, J. J., Avent, C., Bigham, R. D., Connolly, M., Lee, B., Shepker, T. O., and Zak, M. A. (2000). “Physical and Chemical Characteristics of Blast Furnace, Basic Oxygen Furnace, and Electric Arc Furnace Steel Industry Slags.” Environmental Science & Technology , 34, 15761582. Recycled Materials Resource Center, University of New Hampshire (2004). “Research Project 32: Monitoring and Analysis of Leaching fr om Subbases Constructed with Industrial Byproducts.” http://www.rmrc.unh.edu/Research/ Rprojects/ Project32/ p32summary.pdf

. Sauer, J.J., Benson, C.H., and Edil, T.B. (2005). “Metals Leaching from Highway Test Sections Constructed with Industrial Byproducts.” Geo Engineeri ng Report No. 05-21, Dept. of Civil and Environmental Engineering, University of Wisconsin-Madison. Sherrill Environmental, Inc.(2003). “Site Inves tigation – Swift Creek Project, Highway 301.” Raleigh, North Carolina.

77Tanyu, B. F., Benson, C. H., Edil, T. B., and Kim, W. H. (2004). “Equiva lency of Crushed Rock and Three Industrial By-Products Used for Working Platforms During Pavement Construction.” Transportation Research Record, 1874, 58-69. U.S. EPA (2002a). “ Industrial Waste Management Evaluation Model (IWEM) Technical Background Document ”, Office of Solid Waste and Emergency Response, Washington, D.C. U.S. EPA (2002b). “ Industrial Waste Management Evaluation Model (IWEM) User's Guide ”, Office of Solid Waste and Emergency Response, Washington, D.C. U.S. EPA (2002c) EPACMTP Technical Backgr ound Document. Office of Solid Waste, Washington, DC. U.S. EPA (1999). “Partition Coefficients for Metals in Surface Water, Soil, and Waste.” USGS (2000) http://ga.water.usgs.gov/edu/wugw.html

. USGS: Northern Praire Wildlife Research Center (2001) http: //www.usgs.gov/npwrc.html

.

78