USE OF EPA’S INDUSTRIAL WASTE MANAGEMENT EVALUATION 

MODEL (IWEM) TO SUPPORT BENIFICIAL 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 Kd 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 Kd 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 

ii
3.0.7 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 

iii
LIST OF TABLES 

Table 1. Annual production and use of recycled materials........................................2 

Table 2. Waste pile areas per 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 infiltration data for WisDOT and Maryland sites.................26 

Table 6a. Metals and corresponding initial 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 corresponding initial concentrations detected 

 in each secondary material for the Maryland study areas..........................27 

Table 7. EPA tabulated Kd values based on literature search...................................28 

Table 8. Comparison between output concentrations (ppm), as a 

 function of time, generated using MINTEQA2 and EPA Kds 

 with WisDOT data ......................................................................................49 

Table 9. Comparison between output concentrations (ppm), as a 

 function of time, generated using MINTEQA2 and EPA Kds 

 with North Carolina data.............................................................................50 

Table 10. IWEM modeling results for I-695 overpass ...............................................66 

Table 11. HYDRUS-2D metal concentrations at node 5 (water table).......................67 

Table 12. Metal output concentrations for each area and corresponding 

 factor increases............................................................................................68 

Table 13. Comparison between HYDRUS-2D and IWEM results for 

 bottom ash concentrations (mg/l) with time using USEPA 

 tabulated Kd 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 

iv
LIST 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 sections 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 area with instrumentation locations...................13 

Figure 7. Schematic cross-section of Rt. 213 site along A – A’ shown 

 on Figure 6 ................................................................................................14 

Figure 8. Map of I-695 overpasses with well and instrumentation 

 locations .....................................................................................................15 

Figure 9. Schematic cross-sections of I-695 site ......................................................16 

Figure 10. Sample WMU parameter input screen from IWEM..................................21 

Figure 11. Sample geologic/hydrogeologic data input screen from IWEM ...............23 

Figure 12. Sample infiltration data 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 concentration vs. time predicted by IWEM for 

 Cd leaching from recycled materials .....................................................41 

Figure 15B. Groundwater concentration vs. time predicted by IWEM for 

 Cr leaching from recycled materials......................................................41 

Figure 15C. Groundwater concentration vs. time predicted by IWEM for 

 Se leaching from recycled materials......................................................41 

Figure 15D. Groundwater concentration vs. time predicted by IWEM for 

v
 Ag 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 Kd values...................47 

Figure 18A. Groundwater concentration vs. varying WMU area for 

 Cd leaching from recycled materials .....................................................52 

Figure 18B. Groundwater concentration vs. varying WMU area for 

 Cr leaching from recycled materials......................................................52 

Figure 18C. Groundwater concentration vs. varying WMU area for 

 Se leaching from recycled materials......................................................52 

Figure 18D. Groundwater concentration 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 ash and coal fly ash at the WisDOT study area.................... 56-57 

Figure 20B. IWEM predicted Se concentrations over five years for 

 bottom ash and coal fly ash at the WisDOT study area.................... 56-57 

Figure 20C. IWEM predicted Ag concentrations over five years for 

 bottom ash and coal fly ash at the WisDOT study area.................... 56-57 

Figure 21A. As concentrations with time at MW1s using MINTEQA2 

 derived Kds..............................................................................................59 

Figure 21B. Pb concentrations with time at MW1s using MINTEQA2 

 derived Kds..............................................................................................59 

vi
Figure 22. Back-calculated field Kds (L/Kg) estimated for As and Pb 

 at the Swift Creek study area ...................................................................61 

Figure 23A. As concentrations with time at MW2s using MINTEQA2 

 derived Kds........................................................................................ 62-63 

Figure 23B. Pb concentrations with time at MW2s using MINTEQA2 

 derived Kds........................................................................................ 62-63 

Figure 24. Hydrogeologic cross-section of Swift Creek study area .........................64 

vii
1. INTRODUCTION 

1.0 Context

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

million miles of roads in the U.S. These roads require large volumes of materials for 

construction and maintenance purposes which generally are harvested from natural sources. 

Recently, increased interest in recycling has evolved as a measure of promoting sustainable 

construction and to alleviate issues surrounding the harvesting of natural resources in areas 

sensitive to environmental perturbations. There are promising results for equal or better 

engineering performance of recycled materials 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 foundry 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 roadway applications (Table 1). However, the 

majority of industrial byproducts are either stockpiled indefinitely or disposed of in landfills. 

According to the American Coal Ash Association (ACAA), 70 million tons of fly ash was 

produced in 2003 in the United States with only 39% of it being reused in a variety of 

applications. The remainder was disposed in waste 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). 

1
Table 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=flowablefill (Apul et al., 2003). a

Adapted from Collins and Ciesielski 1994, bAdapted 

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 costly, particularly if the rock needs to be hauled 

to the construction site. As a result, transportation agencies are seeking less costly methods to 

stabilize soft soils and construct working platforms. In some cases, industrial byproducts can be 

used to construct lower cost working platforms that provide equal support 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 natural resources. However, with the re-use of these industrial byproducts 

2
comes the concern of whether the leaching of contaminants (primarily heavy metals) contained 

in the materials will impact the underlying groundwater and if so, to what extent? 

 Secondary materials are generally the end-products of metal processing and coal 

combustion. For example, gray iron foundry sand is a byproduct from the metal processing 

industry that consists of impurities floating to the surface of molten 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 infiltration via precipitation. As a result, the 

metals sorbing to the individual material particles may leach into solution and be carried down 

into and through the subsurface as leachate. Depending on the concentrations of the metals in 

solution and the partitioning capabilities of the underlying soils, the leachate may eventually 

enter the groundwater with the threat of adversely impacting 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 past, field studies have been performed to understand the short term 

impacts secondary materials may or may not have on groundwater. However, few long term 

efforts have been made to address this topic thus little is known concerning the potential risks 

such materials may pose to human health and the environment. This is the limiting factor to the 

beneficial reuse of secondary materials. In order to evaluate the long term impacts of secondary 

materials, modeling becomes necessary. The remainder of this report addresses how the use of 

IWEM may aid in the determination of whether secondary materials are safe enough for 

beneficial-use applications in the highway environment. 

3
1.1 Significance and Objectives of this Study

 Groundwater is a crucial element in maintaining a sustainable world. According to the 

National Ground Water Association (NGWA), of the total 341 billion gallons of fresh water the 

United States uses each day, about 83.2 billion gallons, or 24 percent is groundwater. In the 

United States, 47 percent of the population relies on groundwater for drinking water. There are 

nearly 16 million water wells in the U.S., supplying groundwater for public supply, private 

supply, irrigation, livestock, manufacturing, mining, thermoelectric power, and other purposes 

(www.wellowner.org). Figure 1 illustrates 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 contaminating it or at least there must be tools 

available for a particular situation (e.g. leaching from secondary materials) that allow us to 

predict if groundwater contamination 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 impacts from recycled material use in a highway 

environment. It is hypothesized that the USEPA’s Industrial Waste Management Evaluation 

Model (IWEM) may aid in the evaluation of whether 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 those of another solute 

transport model (HYDRUS-2D) and actual field data to determine IWEM’s predictive accuracy. 

4
Additionally, this modeling provides 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 tools such as IWEM should support making 

scientific risk-based decisions concerning the appropriate recycling of secondary industrial 

materials. In other words, these types of models can be effective in promoting recycling or 

avoiding it if they can show groundwater 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 potential 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 aid them in determining whether a secondary 

material can be used in a particular situation. 

5
1.2 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 near Battleboro, North Carolina; 

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

All three sites provided sufficient data 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-transport of groundwater with respect to secondary 

material reuse. Methods of input and testing are discussed in the next chapter. 

1.2.1 Wisconsin State Highway 60

The majority of the project’s data came from a Wisconsin Department of Transportation 

(WisDOT) project along a 1.4 km stretch of Wisconsin State Highway 60 (STH 60) between 

6
Lodi and Prairie du Sac, WI in Columbia County (Figure 2). Information pertaining to this 

project was provided by The University of Wisconsin at Madison’s Department of Civil 

Engineering. At this location, four test sections, covering areas between 790 and 1600-m2, have 

been built during the re-construction of STH 60 in the summer of 2000. Each test section 

includes a sub-base layer composed of secondary byproduct materials (Figure 3). These 

materials consist of fly ash amended soil, bottom ash from coal-fired power plants, and foundry 

sand and foundry slag from gray iron casting industries. Two additional sections have also been 

constructed, each consisting of traditional highway support earthen materials for control 

purposes. Additionally, within each section, two 3.5 m x 4.8 m lysimeters 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 materials. The leachate was analyzed for the trace 

elements cadmium, chromium, selenium and silver. Additionally, throughout the monitoring, 

7
volumetric 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 also becomes important as it is required 

input into IWEM for the model to make an accurate 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 with 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 soils in the area consist of silt 

loam at the surface, but subsoils are generally 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 foundry slag, foundry sand, bottom ash, 

fly ash and crushed rock (control) and STH 60 near Lodi, WI (AC = asphalt concrete) (Lee and 

Benson, 2005). 

8
More 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 groundwater 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 Battleboro. NC (Figure 4). A site investigation in 

2002 followed by a subsequent groundwater analysis in June 2004 revealed groundwater 

concentrations of arsenic and lead above applicable 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 verify 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. 

9
Site Location

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

 The soils underlying the coal ash for this aquifer 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 surface. However, the 2002 site investigation 

showed that groundwater was present 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 groundwater because of the very slow travel time through this confining 

bed (Sherrill, 2003). 

10
All data for the site was provided by Sherrill Environmental, Inc. of Durham, NC who 

subcontracted ReUse Technology, Inc. of Rocky 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 Interstate 695 overpasses, Maryland

 During the 1990s, two projects were completed in Maryland in which Class F fly ash 

(CCPs) were used to form highway embankments (Figure 5). In 1993 and 1994, Baltimore Gas 

& Electric (BGE) (now Constellation Energy Group) and Delmarva Power (now Conectiv) 

provided approximately 40,000 tons and 20,000 tons of CCPs, respectively, to the Maryland 

State Highway Administration (SHA) to create the highway embankments for the Route 213 

overpass over Route 301 near Centerville on Maryland’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 overpass study areas, instrument clusters consisting 

of lysimeters and monitoring wells were installed 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 shoulder 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 

11
installed to a depth of 12 feet on the north embankment) 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: Route 213/301 and I-695 overpasses (ERM, 2004). 

12
13

Figure 6. Map of Route 213 study area with instrumentation locations (ERM, 2004). 
Figure 7. Schematic cross-section of Rt. 213 site along A – A’ shown on Figure 6 (ERM, 200

14
At the I-695 overpass (Figure 8), three monitoring instrument clusters consisting of two 

lysimeters and one well were installed on the shoulders of the overpass. Sample sites were 

labeled with the prefix 1 for the first cluster location (e.g., L1-18 for the lysimeter installed to a 

depth of 18 feet at first monitoring station), 2 for the second cluster location, and 3 for the third 

cluster location adjacent to Route 151 (ERM, 2004). Figure 9 displays a schematic cross-section 

of the study area. 

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

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

16
Between 1999 and 2003, samples were collected from the wells and lysimeters at both 

overpass sites and analyzed by Lancaster Laboratories for the following constituents: 

• Trace elements (e.g. arsenic, barium, beryllium, cadmium, chromium, copper, lead, 

manganese, nickel, selenium); 

• Major cation elements (e.g. aluminum, calcium, iron, magnesium, potassium, sodium) 

and; 

• Major anions (e.g. chloride, fluoride, sulfate, nitrite, and alkalinity) (ERM, 2004). 

For the purpose of this research, only trace elements 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 regulatory MCLs. Because Mn often showed up in the 

groundwater and not the lysimeter data, it is assumed that Mn preexists 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 secondary material leaching, regardless of MCL 

exceedences. Details of this modeling are presented in the next chapter. 

 A detailed description of this site, including environmental setting and construction 

details can be viewed in Environmental Resources Management, Inc.’s (ERM) 2004 technical 

report where the preceding data was obtained. 

17
2. METHODS 

2.0 Overview

 Data from the study areas outlined in Section 1.2 were used for input into IWEM and 

various other solute transport models to validate IWEM’s groundwater concentration predictive 

capabilities with respect to secondary material reuse. A two-step systematic approach was taken 

to accomplish this task. First, this involved running 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 performed 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 field data. After analyzing data comparisons 

between models and field studies, informative conclusions were made regarding IWEM’s ability 

to accurately predict groundwater concentrations 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 presented in Section 1.2 

as well as arbitrarily chosen values. Data used for model input can be divided into four 

categories: 1. WMU parameters, 2. site-specific geologic/hydrogeologic data, 3. infiltration data 

and 4. constituent parameters (e.g. metal distribution coefficients). 

18
2.1.1 WMU parameters (IWEM modeling specifically)

 For the purpose of this research, all IWEM modeling was performed using waste piles 

(WPs) as the representative WMU. It is felt a waste pile best exemplifies a real-life application 

of secondary materials in a roadway setting. IWEM 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 requires several WMU parameters. The following 

are parameters required for WPs: 

• distance to well (m) 

• area (m2) 

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

• operational life 

WP parameters were varied extensively throughout the course of IWEM modeling for 

each study area (especially distance to well and operational life). In most cases, distance to well 

and operational life values were arbitrarily 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 for a known time after implementation of the 

structural fill. In this case, IWEM simulations were performed using these known time and 

distances to evaluate whether the model would have predicted the concentrations detected in the 

well. As an example, data from the NC site shows elevated levels of As and Pb in a monitoring 

well located approximately 7 meters from the edge of the coal ash source. These concentrations 

19
were detected 10 years after the application of the 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 scenarios, ensuring outputs are as realistic as possible. An exception 

to this relates to various IWEM modeling with the WisDOT data. Many simulations were 

performed where WP areas for all four secondary material sections were arbitrarily chosen to be 

200 m2. This was used to provide a level ground for comparison purposes between the sections. 

However, runs were performed using the actual area of the bottom ash section (790 m2) in 

addition to an area representative of a one mile stretch of highway (8367 m2) 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 (m2) 

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 

20
feet below the surface because the fill was overlain 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 pertaining to a site’s geologic and hydrogeologic 

makeup is crucial when modeling groundwater and solute transport. How groundwater and 

constituents behave in the subsurface is largely dictated by the material through which it travels. 

IWEM, as well as most other groundwater models, requires geologic information which 

includes the type of subsurface environment (e.g. till over sedimentary 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 perform 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 subsurface 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/hydrogeologic 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 between them. A summary of geologic/hydrogeologic 

input data for each study area in presented in Table 3. 

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

Table 3. Geologic/hydrogeologic model input parameters for each study area (b = aquifer 

thickness; dh/dl = hydraulic conductivity; GW = groundwater; 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 

23
2.1.3 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 rainfall, 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 (flux) of the leachate through the bottom of the WMU 

(m/yr). Additionally, the type of soil that the leachate will encounter 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 data input screen from IWEM. 

24
 User-specified recharge rates are not allowed in IWEM. However, using the HELP 

model version 3.03, the IWEM database contains a list of average annual recharge rates for 97 

climate stations in the lower 48 contiguous states, representing 25 climate regions (EOA, 2002). 

Table 4 lists the climate station and corresponding recharge rate used for each study area. For 

continuity purposes, these values were used for all models. 

Table 4. Recharge rates by study area 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-Madison for the WisDOT study area for each test 

section. Additionally, infiltration 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 rates for the North Carolina study area were not available. 

Instead, pre-defined infiltration rates from the IWEM database were used. Based on the recycled 

material’s permeability (e.g. low, medium, high), IWEM provides a numerical value for 

infiltration. For North Carolina, the low permeability designation was chosen for coal fly ash 

due its poor water transmitting properties. This corresponded to an infiltration rate of 0.243 

m/yr. 

25
Table 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 related to secondary materials. Constituent data 

such as initial concentrations and distribution coefficients (Kds) are essential information 

required to perform accurate and successful groundwater modeling of metals. 

Site investigations from each study area yielded the types of metals detected in the reused 

materials as well as initial concentrations of each via laboratory testing. For the WisDOT and 

Maryland study areas, initial concentrations were 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 testing. Metals detected and corresponding 

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

million (ppm). 

26
Table 6a. Metals and corresponding initial concentrations detected 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 study area (Sherrill, 2003). 

Material Arsenic Lead 

Coal fly ash 0.11 0.353 

Table 6c. Metals and corresponding initial concentrations detected 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 described in the next sections. 

 In addition to initial concentrations, Kd values for each metal are required to perform 

accurate modeling simulations. The Kd 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 Kd

value, the more strongly a metal will attach itself to the soil, thus limiting its mobility through 

the subsurface and into the groundwater. 

27
 In IWEM, the modeler can either specify a user-defined Kd or rely upon the built-in 

USEPA developed chemical speciation model MINTEQA2 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 Kd

values for a variety of metals based on an extensive literature search. Mean values for metals 

considered in the research are presented in Table 7. These Kds were used for modeling with 

HYDRUS-2D. 

Table 7. EPA tabulated Kd values based on literature search (EPA, 1999). 

Metal Kd (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 simulating water and contaminant transport from heavy 

metal bearing secondary materials into the subsurface. 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 groundwater concentrations when the materials are 

reused as a base or sub-base in a roadway. 

28
Several steps were taken to accomplish the aforementioned objective. First, modeling 

was performed to evaluate how IWEM responds to varying input parameters. This included 

observing the model’s behavior while treating heavy metal transport as a function of: 

1. time/WMU operational life; 

2. receptor well distance secondary material source; 

3. varying distribution coefficient values (Kd); 

4. waste management unit (WMU) areas. 

Variable parameters 1, 2 and 4 were simulated 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 occurred. Additionally, the WisDOT project provides 

the greatest amount of data due to the use of four recycled materials at the study area (only fly 

ash is used at the North Carolina and Maryland sites). Input data from all three study areas was 

used to evaluate IWEM’s response to varying metal Kd values. 

2.2.1 Variable WMU operational life with fixed receptor well distance

 To evaluate how IWEM treats heavy metal transport as a function of leaching time, 

thirteen simulations were run for each test section at the WisDOT study area (52 total), where the 

operational life of the secondary material application was varied. Each simulation was 

performed with a receptor well located an arbitrarily fixed 50 meters 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 years at 20 year intervals. All other input 

values were held constant for each run. Distribution coefficient values derived from 

29
MINTEQA2 were applied. For the first 20 years, the model was run at 5 year intervals in order 

to determine when IWEM would recognize the presence 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 Kd values were again derived 

using MINTEQA2. For the purpose of this series of simulations, IWEM’s default operational 

life for a waste pile (40 years) was used as the fixed time. 

2.2.3 Variable Kd values

 A series of simulations were run where user-define Kds were varied to evaluate how 

IWEM responds to such changes. Twelve runs were executed using different Kd values for 

cadmium in bottom ash from the WisDOT data. IWEM’s default operational life of 40 years for 

a WP was chosen as the run time. The arbitrarily chosen Kd 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 held constant for each run. For the purpose of 

these series of simulations, an arbitrary well distance of 50 meters from the leachate source was 

30
selected. Simulations were not performed for the other test sections under the assumption they 

would yield the same trends. 

Upon initial evaluation of the IWEM modeling results where MINTEQA2 was used to 

derive Kd values, it appeared the final concentrations were higher than expected. It was felt that 

attenuating factors such as dispersion and dilution would have played a greater role in reducing 

concentrations over the transport distance specified. A hypothesis was made that the Kd values 

being used were smaller (possibly by several orders of magnitude) than those reported in the 

literature. However, IWEM does not produce an output file listing which Kd values were 

selected by MINTEQA2, thus these numbers were not known. To investigate this observation 

further, several simulations (using WisDOT data) were taken and used to back-calculate Kds. 

This was accomplished by randomly selecting user-defined Kd values and running simulations 

until final concentrations matched those produced by the MINTEQA2 Kd 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 Kd data listed in 

Section 2.1.4. The purpose of this was to determine what difference, if any, the low Kd values 

selected by MINTEQA2 had on the final groundwater concentrations observed at the receptor 

well by comparing them to the simulations using EPA reported Kd values. Based on 

comparisons of the EPA reported Kds 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 Kd values, then IWEM may be viewed as being too conservative and over 

predicting final groundwater output concentrations. 

 Runs were executed using input data from all three study areas. Using the WisDOT data, 

nine simulations were run for each test section (40 total). The simulations spanned a range from 

31
1 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 source was used keeping all other input values 

constant for each run. IWEM modeling using 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 changing 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 would yield the same trends. All 

other input values were held constant. 

Areas modeled include the arbitrarily chosen 200 m2 section discussed in Section 2.1.1, a 

one mile stretch of highway covering 8367 m2, and the 790 m2 bottom ash from the WisDOT 

site. Five simulations were run for each area using varying WP operational lives: 20, 60, 100, 

160, 200 years. A receptor well was located a fixed 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 years after the application 

of the materials. 

32
 Additional modeling was performed 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 m2 and 395 m2 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 concentrations had been observed, this study area 

presented a situation where the secondary material (coal fly ash) applied at the site caused 

groundwater concentrations to exceed regulatory standards (As and Pb in this case). Because of 

these exceedances, this data provided the perfect opportunity to model with IWEM in order to 

determine if the model would have predicted the contamination prior to coal ash reuse. 

 In a June 2004 groundwater investigation, 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 input data described earlier in this section, IWEM 

simulations were set up to replicate 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 Kds and 2. EPA reported Kds listed in Table 7. Seven simulations were performed for 

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

33
years. Following the modeling, observations were made to determine if IWEM was able to 

predict the groundwater contamination reported from field data, and if so, to what accuracy. 

 Additionally, modeling described in the previous paragraph was applied 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 contamination 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 absence of As and Pb at MW2s after 10 years. 

Furthermore, observations were made beyond 10 years 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 information from the Maryland sites was used 

for modeling with IWEM to further evaluate the model’s predictive accuracy with respect to 

secondary materials. While no metal MCL exceedances were found in the groundwater at the 

site, detections of As, Ba and Se were encountered in several areas of the Routes 213/301 and I695 overpasses. 

 Only the southern area of the Routes 213/301 overpass was modeled under the 

assumption that the north cluster would yield similar trends/results. Here 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. Additionally, lysimeter L102-9 was installed to the base of 

the coal ash to monitor groundwater solute concentrations entering the subsurface (ERM, 2004). 

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

34
 From 1999 to 2003, field sampling of MW-102 revealed detections of Ba which was used 

as the solute of concern for IWEM modeling. The 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 parameters described earlier in this chapter, 

simulations were run with both MINTEQA2 derived and EPA tabulated distribution coefficients 

with a coal ash operational life of 10 years (reflective 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 into account under the assumption 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 with 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 concentrations entering the subsurface (ERM, 2004). 

 From 2000 to 2003, field sampling of MW-3 and 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 the first sampling event in 2000 

were used for initial concentrations in IWEM. With the input parameters described earlier in this 

chapter, simulations were run with both MINTEQA2 derived and EPA tabulated distribution 

coefficients with a coal ash operational 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 

35
were compared to the As and Se concentrations of 0.01 and 0.023 ppm respectively 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 using 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 such as this requires the use of 

two models in order to effectively mimic the secondary material leaching scenario modeled by 

IWEM. 

HYDRUS-2D was used to simulate one dimensional (1D) vertical solute transport from 

the secondary material source through the unsaturated zone down 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. Input including initial metal 

concentrations and fluxes from the secondary material source, soil type, recharge rates, and 

hydrogeologic parameters (e.g. conductivity) remained unchanged from those used for IWEM 

simulations. Upon running HYDRUS, output concentrations and fluxes were read from the 

lower boundary of the modeled domain which is representative 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 m2

 WMU area ((14 

m)2 ~ 200 m2) used for most of the previous IWEM simulations; and 2. with a cross-section 

36
length of 1000 m to represent a real life application of secondary materials along a stretch of 

highway. The purpose of using these extreme lengths is to compare the output concentrations 

from both and determine if increasing the cross-sectional 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 had 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 Kd values from Table 7. 

Additionally, the 14 m cross-sectional scenario was run using a Kd value of zero for each 

solute (Ag, Cd, Cr, and Se). The majority of the IWEM simulations run to date have relied upon 

Kd values drawn from the MINTEQA2 database incorporated into the software. Earlier work has 

demonstrated that these values are relatively low (on the order of magnitude of one or less) and 

comparison to the HYDRUS-2D results using a Kd value of zero (i.e. low Kd) should help 

confirm this observation . Moreover, as discussed earlier, IWEM had been run as a function of 

time using the tabulated EPA values allowing once again for effective comparisons. 

 All HYDRUS simulations were run for 200 years using bottom ash data, which yielded 

the highest leachate concentrations. No runs were performed for the other materials in the 

research (e.g. foundry slag) under the assumption that 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 such 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 

37
of 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 fluxes could be read from these nodes. Figures 13 

and 14 display graphical representations of the boundary conditions and nodal arrangements for 

the 14 m cross-section respectively. 

Constant Flux Boundary

Constant Pressure 

Head Boundary

No-flow Boundary

5 meters

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

38
Figure 14. Graphical display of observation nodes used for 14 m cross-section in HYDRUS-2.

Ground Surface Observation Node

Water Table

39
40

3. RESULTS AND DISCUSSION 

3.0 IWEM Modeling

 After running 400+ simulations with IWEM, using various input data provided by select 

study areas, results were analyzed to help evaluate and form conclusions regarding IWEM’s 

performance as a fate-and-transport model with respect to the beneficial 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 determine if IWEM would have predicted such 

concentrations. Finally, comparisons were made between IWEM and HYDRUS-2D to evaluate 

agreement between the two models. 

3.0.1 Variable WMU operational life with fixed receptor well distance 

 To evaluate how IWEM treats heavy metal 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 meters 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 Kd

0.0E+00

1.0E-032.0E-033.0E-034.0E-035.0E-036.0E-03

0 20 40 60 80 100 120 140 160 180 200 220

Operational Life (years)

Concentration (ppm)

Bottom AshFly AshFoundry SlagFoundry Sand

MCL (0.005 ppm)

Fig. A [Cr] vs. t using MINTEQA2 derived Kd

0.0E+00

1.0E-042.0E-043.0E-044.0E-045.0E-04

0 20 40 60 80 100 120 140 1

Operational Life (years)

Concentration (ppm)

Bottom AshFly AshFoundry Slag

NOTE: MCL = 0.1 ppm

41

[Se] vs. t using MINTEQA2 derived Kd

0.0E+00

2.0E-034.0E-036.0E-038.0E-031.0E-021.2E-02

0 20 40 60 80 100 120 140 160 180 200 220

Operational Life (years)

Concentration (ppm) Bottom Ash Fly Ash Foundry Slag

NOTE: MCL = 0.05 ppm

Fig. C

[Ag] vs. t using MINTEQA2 derived Kd

0.0E+00

5.0E-041.0E-031.5E-032.0E-032.5E-033.0E-03

0 20 40 60 80 100 120 140 16

Operational Life (years)

Concentration (ppm) 

NOTE: HBN 

Figures 15A-D. Groundwater concentration vs. time predicted by IWEM for metals leaching from recycled mate
The plots in Figure 15 depict similar 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, where the difference between the 

input concentration and the plateau concentration demonstrates the magnitude of dilution that has 

occurred in the system. This shows that new 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 four 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 accounting for metal adsorption onto aquifer materials 

as the constituents travel through the unsaturated zone. Once breakthrough is achieved, the 

constituents eventually enter the saturated zone where they mix with the groundwater and flow to 

the receptor well. If adsorption occurs, then one would theoretically expect 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 (as with secondary material 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 where leaching occurs at a constant leachate 

concentration equal to the initial input. In other words, leachate is continually being introduced 

42
into 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 model 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. Although the average operational life of a waste pile 

is 40 years, the model was run for 200 years to simply gage its response to achieving equilibrium 

conditions. 

Based on the discussion in the preceding paragraphs, it appears that IWEM is accurately 

portraying the movement of metals within the subsurface as a function of time. Thus, keeping 

the MCL of a particular metal in mind, it seems one can determine, at least to a first 

approximation, if a certain material will contribute an appreciable 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 material would be safe enough to reuse for 200+ years 

under this scenario (i.e. fixed receptor well 50 meters from source) and using the MCL as 

criterion. 

It should also be noted that while Cr displays 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, therefore it reasonable to 

attribute the strange behavior to IWEM performance and not input parameter uncertainty. 

43
44

3.0.2 Variable receptor well distance from source with fixed WMU operational life

 To evaluate IWEM’s heavy metal transport capabilities as a function of receptor well 

distance from the source, nine simulations were run for each test section at the WisDOT study 

area (36 total), where the well distance 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 (average operational life for 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 clearly 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 unsaturated 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 particular operational life. Thus, an 
Figures 16A-D. Groundwater concentration vs. receptor well distance for metals leaching from recycled materia

[Cd] vs. d using MINTEQA2 devrived Kd

0.0E+00

1.0E-032.0E-033.0E-034.0E-035.0E-036.0E-03

10 60 110 160 210 260 310 360 410 460 510 560

Well Distance From Source (meters)

Concentration (ppm) Bottom Ash

Fly AshFoundry SlagFoundry Sand

EPA's MCL (0.005 ppm)

Fig. A [Cr] vs. d using MINTEQA2 derived Kd 

0.0E+00

5.0E-051.0E-041.5E-042.0E-042.5E-043.0E-04

10 60 110 160 210 260 310 360 410 

Well Distance From Source (meters)

Concentration (ppm)

NOTE

[Se] vs. d using MINTEQA2 derived Kd

0.0E+00

5.0E-031.0E-021.5E-022.0E-022.5E-023.0E-023.5E-02

10 60 110 160 210 260 310 360 410 460 510 560

Well Distance From Source (meters)

Concentration (ppm)

Bottom AshFly AshFoundry Slag

NOTE: EPA's MCL = 0.05 ppm

Fig. C [Ag] vs. d using MINTEQA2 derived Kd

0.0E+00

1.0E-032.0E-033.0E-034.0E-035.0E-036.0E-037.0E-03

10 60 110 160 210 260 310 360 410 

Well Distance From Source (meters)

Concentration (ppm) 

NOTE: HBN (n

45
informed decision can be made whether the material of interest is safe enough for beneficial use 

in a particular area based on the source/location for regional groundwater use (e.g. drinking 

water). In the case of all the metals modeled here, it would appear that each secondary material 

would be safe enough to reuse over a 40 year period if kept at least 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 dependent simulation (Section 3.0.1) would become 

necessary to run in conjunction with this data to truly determine the usability of the material. 

3.0.3 Variable Kd values

 As detailed in Section 2.2.3, twelve runs were executed using arbitrary user-defined Kd

values for cadmium in bottom ash from the WisDOT 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 Kd used as depicted in Figure 17. 

 Analysis of Figure 17 clearly shows that IWEM accurately portrays the inverse 

relationship between Kd and 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. However, it should be noted that over a longer period of time, 

after breakthrough has been achieved, concentrations would eventually become independent of 

the distribution coefficient. As a result, deviation from the trend depicted in Figure 17 would 

occur. 

46
0.0E+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 Kd 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 the proper concentration/Kd relationship for early time, the 

arbitrarily chosen Kd 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 Kd 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 Kds, the values chosen are unrealistically small compared to reported literature values. 

Recalling from Section 2.2.3, an attempt to back -calculate Kd values was performed to 

investigate the magnitude of the MINTEQA2 derived distribution coefficients. Results of this 

procedure did in fact show that MINTEQA2 was selecting Kd values much smaller than those 

reported in the literature (especially those reported by the EPA). It was determined that Kd

47
values used were on an order of magnitude of 1 (and often less than that). Thus, if Kd values 

used are lower than they should be, then IWEM is likely producing higher than expected output 

concentrations which would label the model as being conservative (over predicting 

concentrations in groundwater). 

However, it should be noted by the user that peak concentrations (regardless of the Kd

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 safe for reuse after a particular time, when in fact unacceptable 

concentrations in groundwater could result some time beyond the realm of modeling. For 

example, IWEM may determine that after 50 years an application of coal fly 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 cause 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 the groundwater concentration will be actually 

less than predicted. 

 As discussed in Section 2.2.3, to investigate IWEM’s conservatism further, the bottom 

ash simulations presented in Section 3.0.1 were rerun using USEPA Kd values. Simulations 

were not run for Cr because IWEM does not allow user specified Kd values for this particular 

metal. Table 8 shows comparisons between the output concentrations (ppm) generated using 

MINTEQA2 derived and EPA tabulated distribution coefficients. 

 An analysis of Table 8 shows that for each metal, the concentrations produced 

using MINTEQA2 derived Kds are larger than those calculated with the EPA tabulated values. 

48
Often, these numbers are higher by several orders of magnitude. Based on the inverse 

relationship between distribution coefficient and concentration, Table 8 clearly demonstrates that 

MINTEQA2 is drawing upon coefficients smaller than reported by the EPA which explains why 

final concentrations are higher due to less attenuation of the constituents by soil particles. 

Table 8. Comparison between output concentrations (ppm), as a function of time, generated 

using MINTEQA2 and EPA Kds 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 Kds are not known because IWEM lacks the production of an 

output file stating the selected values. The distribution coefficients could be back-calculated 

based on the iteration procedure described earlier. However, while this approach seems to yield 

accurate results, the procedure is very time consuming and can easily be avoided with the 

generation of an output file after each run. 

 Additionally, with the coal fly ash data from the North Carolina study area, a series of 

IWEM simulations were performed using MINTEQA2 derived and EPA tabulated Kds. 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 

49
distribution coefficients. In fact, the MINTEQA2 outputs quickly reach the level at which the 

concentrations are equal to the initial model input concentrations (0.11 and 0.353 ppm for As and 

Pb respectively). These results are further confirmation that MINTEQA2 is using Kds smaller 

than those normally expected from the literature. 

Table 9. Comparison between output concentrations (ppm), as a function of time, generated 

using MINTEQA2 and EPA Kds 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 simulations were run while varying the area of the 

WMU of interest (waste pile in this research) using 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 were plotted as a function of time for each area 

(Figures 22A to 22D). 

Analysis of each figure clearly shows a distinct increase in concentration as WMU area 

increases for each metal of concern. This apparent linear trend between output concentration and 

WMU footprint area is most likely the result of IWEM assuming WMUs to be square (EPA, 

50
51

2002). To investigate this observation further, peak Cr and Cd concentrations were plotted as a 

function of the square root of the WMU area (Figures 23A & B). As indicated by their r2

 values 

and slopes of approximately 1.4, both figures display a relatively high function of linearity, as 

seen in Figure 18, especially with larger areas. These figures suggest that IWEM, theoretically, 

is displaying the proper relationship between contaminant load and area for a square geometry. 

Analysis of Figures 19A & B shows that doubling 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 increases the length/width dimensions by a factor of 1.4 which 

should increase the mass loading, and thus groundwater concentrations, by the same factor. 

Again, Figures 19A & B demonstrate that IWEM is properly calculating concentrations as a 

function of area for square geometries. However, because square geometries are always 

assumed, the model does not allow the user to portray the true rectangular shape of a roadway 

which is desired in order to produce the most accurate results. 
[Cd] vs. t

Comparison Between WisDOT Provided and 

Arbitrarily Chosen WMU Areas for Bottom Ash

1.0E-031.0E-021.0E-01

20 40 60 80 100 120 140 160 180 200 220

Operational 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. t

Comparison Between WisDOT Provided and 

Arbitrarily Chosen WMU Areas for Bottom Ash

1.0E-051.0E-041.0E-031.0E-021.0E-01

1.0E+00

20 40 60 80 100 120 140 1

Operational Life (years)

Log Concentration (ppm)

EPA's MCL (0.1 ppm)

[Se] vs. t

Comparison Between WisDOT Provided and 

Arbitrarily Chosen WMU Areas for Bottom Ash

0.0E+00

1.0E-022.0E-023.0E-024.0E-025.0E-026.0E-027.0E-02

0 20 40 60 80 100 120 140 160 180 200 220

Operational Life (years)

Concentration (ppm)

WisDOT WMU Area (790 m2)Arbitrary WMU Area (200 m2)One Mile Stretch (8367 m2)

EPA's MCL (0.05 ppm) 10x One Mile Stretch

Fig. C

[Ag] vs. t for WP

Comparison Between WisDOT Provided and 

Arbitrarily Chosen WMU Areas for Bottom Ash

0.0E+00

2.0E-034.0E-036.0E-038.0E-031.0E-021.2E-021.4E-02

0 20 40 60 80 100 120 140 

Operational Life (years)

Concentration (ppm)

WisDOT WMU Area (790 Arbitrary WMU Area (200 One Mile Stretch (8367 m10x One Mile Stretch

NOTE: HBN 

Figures 18A-D. Groundwater concentration vs. varying WMU area for metals leaching from recycled material.

52
Peak [Cr] vs. WMU Area for Bottom Ash

R2 = 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

R2 = 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. Results depict relative linear 

trends between contaminant load and area for square geometries. 

53
This inability to account for varying geometries may greatly limit IWEM’s usefulness for 

modeling secondary material leaching from roadway settings, especially if a high degree of 

accuracy is desired. Assuming only a square geometry 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 recommendation 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 materials beneath a 6 m wide roadway, it may be 

useful to apply IWEM for modeling a 36 m2

 (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 the roadway were 

to be simulated, then factoring out mass loading factors 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 concentration to account for the 

additional loading produced with a square geometry that, in actuality, does not contribute to the 

groundwater. 

3.0.5 Additional Modeling with WisDOT Data

 As discussed in Section 2.2.5, additional modeling 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 observed 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 absence of) resulting from secondary material 

54
leaching 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 five 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 above the MDLs in several cases, MINTEQA2 

derived distribution coefficients were used (because actual field Kds 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 compared to the literature. Use of low Kds would 

effectively result in higher predicted concentrations 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 Kd values, poor geometry considerations, 

or a combination of both. However, without knowledge of the actual Kd 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 MINTEQA2 and/or actual field Kds are known to perform further 

evaluations. Still, these simulations can be viewed as worst-case scenarios due to the apparent 

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

than actual conditions. 

55
[Cd] vs. t using MINTEQA2 derived Kd

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 Kd

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 concentrations over five years for bottom ash and coal 

fly ash at the WisDOT study area. Concentrations measured in actual monitoring wells at the 

study area located 6 meters down gradient of each secondary material. 

3.0.6 Modeling with North Carolina data

 As outlined in Section 2.2.5, IWEM modeling was 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-dependent modeling after 200 years using Kds selected 

by MINTEQA2. Analyses of these figures show that within the first year, IWEM is predicting 

As and Pb well above their respective MCLs in MW1s. In fact, for As, concentrations reach the 

initial model input value of 0.11 ppm within 5 operational years. Similarly, the initial model 

57
input 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 where 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 Kds, IWEM was clearly able to predict the exceedances 

reported by field/laboratory testing. While the magnitude of these 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 coal fly ash as indicated from field studies. However, as shown 

with the WisDOT data, the stark contrast between MINTEQA2 and EPA Kd produced 

concentrations remains unexplained and is thus, worrisome. These results demonstrate the 

importance of selecting the appropriate Kd 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)

Actual concentration detected in 

MW1s after 10 years (0.068 ppm)

Figures 21A-B. As and Pb concentrations with time at MW1s using MINTEQA2 derived Kds. 

59
The two scenarios presented above appeared to be extreme cases with respect to the 

magnitude of Kd values used. For this reason, an attempt was made to estimate the actual soil Kd

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 procedure was performed where user-defined Kd values 

were repeatedly changed in order to produce an output concentration which matched the field 

concentrations of As and Pb. Results from this procedure produced Kd 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 Kds metals can have based on varying site conditions), they still in 

fact are within the literature ranges reported, (albeit on the lower end). It can be inferred that 

they must also be several orders of magnitude higher than the MINTEQA2 derived Kds since 

field concentrations are still considerably lower than those produced using MINTEQA2 (see 

Figure 21A & B). 

 Additionally, the same simulations executed for MW 1s were replicated for MW2s 

located 48 meters downgradient from the edge of the 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. 

60
Kd 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

Kd = 28 L/Kg @ 0.068 ppm 

(field concentration)

Kd = 21 L/Kg @ 0.028 ppm 

(field concentration)

Figure 22. Back-calculated field Kds (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 their initial input concentrations over 

200 years as seen in MW1s. After ten years (time between construction and 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 contrast to the non- detections reported 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 reuse of a seemingly non-threatening material. 

Instead, the user may just opt to landfill the coal ash where it is of no use. 

61
 To justify the preceding statements, the estimated site-specific Kd 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 demonstrates 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 with time at MW2s using MINTEQA2 derived Kds. 

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 considered by IWEM may also explain 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 shows 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 occurring 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 IWEM 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 lateral migration of the dissolved metal carrying 

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

the well screen for MW1s is located well above the Altavista soil which acts as a direct receptor 

for the lateral moving groundwater into the well. 

Figure 24. Hydrogeologic cross-section of Swift Creek study area (Sherrill, 2004). 

3.0.7 Modeling with Maryland data

3.0.7.1 Routes 213/301 Overpass

 IWEM modeling of the Routes 213/301 overpass (south cluster) produced Ba output 

concentrations of 0.0515 and 0.0002 ppm for the MINTEQA2 derived and EPA tabulated Kd

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 application 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 another demonstration of how 

64
MINTEQA2 uses Kd 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 Kd for Ba in order to predict a concentration two orders of 

magnitude greater. 

 Based on these results, the first impression would be to rely on MINTEQA2 in order to 

predict concentrations similar to those seen in reality. However, one can make the argument that 

using 100 L/Kg provides the most realistic situation because the value lies within the range 

reported from the literature Kds, thus the output concentration associated with it is most likely 

closer to the truth. Additionally, the input concentration used for these simulations was 

measured six years after the coal ash implementation. The material contains a finite amount of 

Ba and over the years, some of the Ba most likely was leached away from the ash thus lowering 

its concentration observed in the lysimeter. 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 been in the ash, it is difficult to predict what 

the model output would produce. However, based 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 the data are very similar to those observed in the 

Routes 213/303 groundwater. Again, MINTEQA2 results are comparable to the field data. 

Conversely, the EPA results are substantially lower. However, as with the Routes 213/303 data, 

65
the input concentration used for these simulations was measured several years (five) after the 

coal ash implementation, thus concentrations 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 likely greater than actual conditions due to apparent low Kd

selections which, again, makes the case 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 secondary material source 

through the unsaturated zone down to the water table. 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 

Kds, were compared for 200 years. Table 11 shows the concentrations of Cd, Cr, Se, and Ag at 

node 5 (bottom boundary/water table) for select years. 

66
Table 11. HYDRUS-2D metal concentrations at node 5 (bottom boundary/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 increasing the cross-sectional length of the 

domain (while keeping the width component fixed) has minimal effect on the solute output 

concentrations along the centerline. This is in contrast to the large increases in concentration as 

a function of WMU area observed with IWEM as shown in Figure 20. These 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 the waste piles are treated as square. Thus, increases in output 

concentrations with WMU area are overly exaggerated 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 groundwater concentrations 

for a square geometry. For instance, doubling the area of a square will increase the crosssectional length by a factor of 1.4, thus the groundwater impact should be approximately 1.4 

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

data bottom ash data for a 400 m2

 WMU area (two times the 200 m2 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 IWEM’s ability to account for the 1.4 factor 

increase in groundwater concentrations when the foot print area of a square geometry is doubled. 

However, the question remains whether IWEM can accurately calculate groundwater 

concentrations for a different geometry (e.g. a road). 

Table 12. Metal output concentrations for each area and corresponding factor increases. 

 Concentration (ppm) Factor 

Increase 

Metal 200 m2 400 m2 

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 Kd values. Again, concentrations of interest from HYDRUS were taken from 

the bottom node. Results are presented in Table 13. 

Analysis of Table 13 shows that the models predict similar concentrations for Cd and Ag. 

Because HYDRUS is only simulating transport through the unsaturated zone, it is likely the 

concentrations presented here would 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 

68
only representing transport through the vadose zone (while IWEM simulates transport through 

both unsaturated and saturated zones) 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 clear evidence that IWEM is over predicting 

concentrations and because equal user-defined Kd values were used between the models, this 

conservatism is likely the result of differing geometry 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 Kd 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 

69
 Finally, a comparison was made with IWEM results using MINTEQA2 Kd 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 confirm the notion that IWEM is drawing upon very low 

Kd 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 Kd 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 

70
4. 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 capabilities 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 provides 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 satisfactorily as a tool for predicting groundwater and solute 

flow at points down gradient from a source. With respect to varying WMU operational life, it 

was clearly able to demonstrate the non-linear relationship between 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 leachate is continually being introduced into the 

subsurface with no depletion of the constituent of concern occurring 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 

71
concentrations decrease over time. With this said, modeling over long time periods (100+ years) 

may not produce accurate/realistic results. Modeling particular WMUs is better left to the short 

term during which it is unlikely that metals within the subsurface will reach their peak 

concentrations. If IWEM is adapted to allow representation of recycled materials applications as 

finite sources, rather than continuous-type sources, 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 attenuation factors, IWEM shows the inverse 

relationship between concentration and well distance along the plume centerline, as would be 

expected. Using this, one can determine, at least 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 Kd of a constituent and its predicted 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 Kd is a 

critical parameter for predicting contaminant movement. 

One area of concern with respect to Kd values lies in the magnitude of the values derived 

by MINTEQA2 when user-defined values are unknown. Extensive modeling with various input 

data continually demonstrated that MINTEQA2-derived values appear to be much smaller than 

expected when compared to the literature. As a result, it is thought that IWEM may be too 

72
conservative 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 parameter output files are 

not provided so it is not known what values are selected by MINTEQA2; comparisons to other 

values cannot be made in order to asses the integrity 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 performance 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 operational lives and well distances. An apparent linear relationship was 

observed between increasing output concentration and increasing WMU footprint area. 

As discussed in Chapter 3, this situation is the 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 HYDRUS-2D modeling, increasing the cross-sectional 

length of an area should have a negligible effect on groundwater concentrations down gradient 

along a plume center line due to various attenuation factors. In this case the restrictions of the 

model may not allow accurate representation of a roadway section in relation to the underlying 

groundwater flow direction. 

73
A recommendation 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 materials beneath a 6 m wide roadway, it may be useful to apply 

IWEM for modeling a 36 m2

 (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 the 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 appropriate to use IWEM as 

a first-order approximation in determining whether secondary materials are safe for reuse in a 

roadway setting. However, factors including the model’s over predictive nature and inability to 

accurately represent the true geometry of a roadway may make it a liability for producing a final 

determination. As mentioned in Section 4.1, IWEM may be useful in a situation where the 

model predicts that, after long times (100+ years), adverse groundwater impacts will not result 

from secondary material leaching. 

On the other hand, completely relying on IWEM 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 impacts associated with mining, processing and 

transportation. Over-conservatism in assessing environmental risk from leaching can have many 

consequences related to human health risks, environmental degradation and ecosystem risks that 

should be recognized during any continued development of use of the IWEM model. 

74
Additionally, other factors were observed throughout this research which may hinder 

IWEM’s suitability for determining secondary material reuse. One factor of concern is IWEM’s 

lack of output files listing the non-user specified parameters selected by the model. Monte Carlo 

selected parameters (e.g. hydraulic conductivity, infiltration, etc.) as well as MINTEQA2 derived 

distribution coefficients are unknown 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 reflective 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 been repeatedly stated. But 

how conservative is IWEM? As of now, the answer to this is not clearly known but knowledge 

of such information could prove to be very beneficial when assessing whether 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 indication of actual conditions. 

 It should also be noted that IWEM is an extremely user-friendly program. Little to no 

groundwater modeling experience is required to execute the program. Ease of use make the 

model very accessible to sectors of the industry that may not be particularly proficient in 

modeling. 

75
4.4 Recommendations

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

beneficial in the form evaluated in this research. However, in order to promote IWEM as a tool 

that can be relied upon to justify the use or limitation 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 incidence of groundwater flow. 

• Kd 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 input (leachate) concentrations. This 

could take the form of mathematical functions or raw data (leachate 

concentrations could vary by month or year, 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 analysis. This information would improve 

the utility of the model and allow the user or decision maker to understand the 

uncertainty associated with the predictions. 

76
References 

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., Tanyu, B.F., Kim, W.H., and Senol, A. (2002). 

“Field Evaluation of Construction Alternatives 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. (ERM) (2004). “Technical Report: Evaluation of 

Water Quality with the use of Coal Combustion Products as Structural Fill at the Routes 

213/301 and Interstae 695 Overpasses.” Annapolis, Maryland. 

FHWA (2001) http://wwwcf.fhwa.dot.gov/ohim/hs01/tables/hm10.pdf. 

Fetter, C.W. (2001). Applied Hydrogeology, 4th 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 Behavior 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., Wittenborn, 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 from 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 Engineering Report No. 05-21, Dept. of 

Civil and Environmental Engineering, University of Wisconsin-Madison. 

Sherrill Environmental, Inc.(2003). “Site Investigation – Swift Creek Project, Highway 301.” 

Raleigh, North Carolina. 

77
Tanyu, B. F., Benson, C. H., Edil, T. B., and Kim, W. H. (2004). “Equivalency 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 Background 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