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THE TRANSPORT AND FATE OF CHLORIDE WITHIN THE GROUNDWATER OF
A MIXED URBAN AND AGRICULTURAL WATERSHED
Jessica J. Ludwikowski
56 Pages
Groundwater modeling plays an important role in quantifying solute transport in
watersheds. Many watersheds contain developed or urbanized lands. Urbanized settings
contain impervious surfaces that are highly prone to pollutant run off such as road salt.
Road salt runoff can affect the quality of surface and groundwater resources in addition to
having severe impacts on ecosystems and all ecosystem components. Subsequently,
surface waters and groundwater within Illinois have shown elevated concentrations of
chloride. In a typical winter season in Illinois, about 471,000 tons of road salt are
deposited. About 45% of the deposited road salt will infiltrate through the soils and into
shallow aquifers. A small percentage of chloride remains in the subsurface feeding
shallow aquifers during the non-salting season. Chloride has the potential to reside within
groundwater for years based on the pathway, the geologic material, and the recharge rate
of the aquifer system. However, the relationship between road salt application rates,
residence times, and net mass accumulation of chloride have not been studied.
The transport and fate of chloride in Little Kickapoo Creek watershed (LKCW)
was modeled utilizing MODFLOW, MODPATH, and MT3D. An increase in application
rate showed increases in the mass of chloride within LKCW. Chloride concentrations
reached maximum contaminant level of 250 mg/L after 10 years of deposition exhibiting
how quickly the solute builds up within LKCW. Allowing the solute to return to safe
drinking levels within the watershed took 30 years. Steady-state times varied based on
application rates, lower rates of 1,000 mg/L to 2,500 mg/L took about 60 years, higher
rates never achieved steady-state conditions. Model simulations reveal a positive
relationship between application rate and residence time, the average time a molecule
resides within the reservoir, of chloride. At steady-state conditions, the Cl- residence time
reflects that of groundwater (~1,000 days). Prior to steady-state conditions, residence
time varies from 1,123 to 1,288 days based on application rate.
KEYWORDS: Chloride, Groundwater Modeling, Land use, MODFLOW, MODPATH,
MT3D, Road salt, Watershed, Residence Time
THE TRANSPORT AND FATE OF CHLORIDE WITHIN THE GROUNDWATER OF
A MIXED URBAN AND AGRICULTURAL WATERSHED
JESSICA J. LUDWIKOWSKI
A Thesis Submitted in Partial
Fulfillment of the Requirements
for the Degree of
MASTER OF SCIENCE
Department of Geography-Geology
ILLINOIS STATE UNIVERSITY
2016
© 2016 Jessica J. Ludwikowski
THE TRANSPORT AND FATE OF CHLORIDE WITHIN THE GROUNDWATER OF
A MIXED URBAN AND AGRICULTURAL WATERSHED
JESSICA J. LUDWIKOWSKI
COMMITTEE MEMBERS:
Eric Peterson, Chair
Walt Kelly
David Malone
Catherine O’Reilly
i
ACKNOWLEDGMENTS
I would like to express the deepest appreciation to my committee chair, Professor
Eric Peterson, who has been a supporting and patient advisor through the whole process.
Without his guidance and persistent help this thesis would not have been possible.
I would like to thank my committee members. Professor David Malone, who has
guided me through my graduate academic career and support with the Plate 1. Professor
Catherine O’Reilly for helping me through the graduate program at Illinois State
University. Lastly, Walton Kelly and Daniel Abrams for their very helpful perspective in
groundwater modeling.
Thanks also goes to my fellow graduate students in the Hydrogeology program
with their help providing feedback for the writing process. In addition, I would like to
thank Bill Shields for his technical support in constructing the surficial map.
Finally, I would like to thank my future husband whose encouragement kept me
moving at times when I was beyond overwhelmed. Without his unconditional love and
support I would not have even applied for graduate school nor complete this thesis.
J. J. L.
ii
CONTENTS
Page
ACKNOWLEDGMENTS i
CONTENTS ii
TABLES iv
FIGURES v
CHAPTER
I. INTRODUCTION AND BACKGROUND 1
Introduction 1
Statement of the Problem 2
Sources of Chloride 3
Previous Work 4
Objective 7
Hypotheses 8
II. METHODS AND MATERIALS 9
Watershed Characteristics 9
Conceptual Model 12
Model Setup 13
Sensitivity Analysis 20
III. RESULTS 22
Sensitivity Analysis 22
Flush Scenario Results 25
Build-up Scenario Results 30
MODPATH Results 38
iii
IV. DISCUSSION 41
Residence Time 41
Mass Accumulation – Flush Scenario 42
Mass Accumulation – Build-up Scenario 43
Further Consideration 44
V. CONCLUSION 46
VI. FUTURE CONSIDERATION 48
REFERENCES 49
APPENDIX A: Plate 1 56
iv
TABLES
Table Page
1. Values Used for Model Parameters 17
2. Build-up Scenarios and their Assigned Application Rate 19
v
FIGURES
Figure Page
1. Land Use within the Little Kickapoo Creek Watershed 10
2. Distribution of Geologies Used in Model Simulations 13
3. Boundary Conditions Used in Model 15
4. Sensitivity Analysis MODFLOW Results 23
5. Sensitivity Analysis MT3D Results 24
6. Flush Scenario Results 26
7. Flush Scenario Results 27
8. Flush Scenario Color Flood Map 29
9. Build-up Scenario Results 31
10. Build-up Scenario Results 31
11. Build-up Scenario Results 32
12. Build-up Scenario Results 33
13. Build-up Scenario Results 34
14. Build-up Scenario Results 35
15. Build-up Scenario Results Color Flood Map 36
16. Build-up Scenario Results Color Flood Map 37
vi
17. MODPATH Results 39
18. MODPATH Results 40
1
CHAPTER I
INTRODUCTION AND BACKGROUND
Introduction
The rapid urbanization of the human population in metropolitan areas can have
severe effects on local groundwater resources (Fitzhugh and Richter, 2004; Jenerette and
Laresen, 2006; Oiste, 2014; Villholth, 2006). With urbanization comes impervious
surfaces that are highly prone to run off of pollutants such as road salt (Kelly, 2008).
Road salt is a compound containing chloride (Cl-) and is most commonly found with
cations such as calcium, sodium, or magnesium. Chloride is relatively unreactive on the
Earth’s surface and is considered conservative in nature. Therefore, the properties of Cl-
deem it the perfect tracer of contamination in the environment. The goal of this study is
to model the transport and fate of Cl- in the shallow groundwater of the Little Kickapoo
Creek watershed (LKCW). Numerical simulations can reveal the residence time, the
average time a molecule resides within the reservoir, of Cl- within an aquifer system and
the capacity of an aquifer to store a solute. Upon completion of this model, a better
understanding of contaminant transport in an urbanized watershed will be gained.
2
Statement of the Problem
Road salt runoff can affect the quality of surface and groundwater resources in
addition to having severe impacts on an ecosystem and all its components. The most
obvious effect of Cl- is the degradation of groundwater and surface water quality (Huling
and Hollocher, 1972; Pilon and Howard, 1987; Amrhein et al., 1992; Howard and
Haynes, 1993; Williams et al., 2000; Bester et al., 2006). Organisms that live within
surface waters are impacted by Cl-. Different toxicity thresholds exist among aquatic
organisms, and it is common to see negative effects on aquatic life from levels ranging
from as little as 150 mg/L to as high as 30,330 mg/L depending on the species (Siegel,
2007). Elevated Cl- concentrations have been found to obstruct the reproduction of
aquatic organisms (Beggel and Geist, 2015). The presence of Cl- in lakes is toxic to
plankton, which is a vital food source to fish and amphibians and contributes to the
eutrophication of waters (Evans and Frick, 2001). Of even greater consequence, Cl- in
stream water changes the sign of net nitrogen mineralization from negative, which
consumes inorganic nitrogen, to positive in debris dam material, which results in dams
becoming sources of inorganic nitrogen (Hale and Groffman, 2006). This results in
bacteria becoming unable to help break down nitrogen in the waters making the water
more dangerous and uninhabitable for a variety of aquatic species (Hale and Groffman,
2006). The elevated Cl- concentrations coupled with the presence of acetate, also found in
deicers, can mobilize heavy metals by ion exchange (Granato et al., 1995). Thus, even if
a species is resistant to Cl- the release of heavy metals could be toxic. While Cl- in
3
drinking water is not toxic to humans, water with concentrations exceeding the secondary
drinking water standard of 250 mg/L is classified as non-potable (U.S. EPA, 2011).
Sources of Chloride
Although the focus of this project is contamination via road salt it should be
acknowledged that other sources contribute Cl- to watersheds. There are two categories
from which Cl- can be introduced into the environment: natural sources and
anthropogenic sources (Mullaney et al., 2009). Natural sources consist of oceans,
bedrock, soils, geologic deposits, and volcanic activity (Mullaney et al., 2009).
Anthropogenic sources include landfills, agricultural practices, the chloralkali industry,
septic system discharge, treated wastewater, and road salt (Kelly, 2008; Mullaney et al.,
2009). These anthropogenic sources of Cl- are borne from humans and are primarily
associated with waste disposal. For instance, leachate from landfills is high in Cl- due to
the disposal of cleaning products and food and beverages high in salt and can leak Cl-
into surrounding waters as was witnessed in seven landfills around Illinois resulting in a
median groundwater concentration of 1,284 mg/L. Another anthropogenic source is
human waste associated with septic systems and water treatment plants (Mullaney et al.,
2009). Panno et al reported septic tank effluent can discharge Cl- concentrations as high
as 5,620 mg/L, which could be quite a significant source if the area’s population relies on
this type of disposal (Panno et al., 2005). Other anthropogenic sources of chloride-rich
waste include agriculture production, both row crops production and animal husbandry.
Fertilizers, such as KCl-, deposited on agricultural lands can raise Cl- content in
groundwater and surface waters by as much as 20 mg/L in comparison to the 10 mg/L
4
background levels found in Illinois (Kelly, 2008). Livestock waste, such as animal feed
and urine, contributes Cl- to local waters. Panno et al. (2005) found a median Cl-
concentration of 57 mg/L in groundwater wells near livestock.
In the mid to upper latitudes, the largest contributor of Cl- is road deicers (Kelly,
2008; Mullaney et al., 2009). To maintain safe roadways in the winter, humans have used
salt compounds that decrease the freezing point of water to prevent ice from forming.
These compounds contain Cl-, and when applied to winter roads, a surplus of Cl-
develops. Application rates can vary from 1 to 74 ton(s) per road mile in the Midwest and
eastern states (Jones and Sroka, 1997; Heisig, 2000; Mullaney et al., 2009). In the
northeastern United States, a range of 545 to 23,716 kg/km2/year of road salt is deposited,
much of which will runoff into local surface waters or infiltrate into groundwater (Panno
et al., 2005). Water sources close to roads can contain Cl- concentrations ranging from
2000 mg/L to as high as 14,175 mg/L (Lax and Peterson, 2008; Panno et al., 2005).
Cassanelli and Robbins (2013) report that throughout Connecticut Cl- concentrations in
groundwater have increased by more than an order of magnitude in the last century, an
increase proportionate with Connecticut’s use of road salt.
Previous Work
In groundwater, Cl- is transported differently than in surface waters such as rivers,
streams, lakes, ponds, and wetlands. In rivers and streams, the travel time of Cl- is much
shorter in comparison to groundwater systems. Church and Friesz (1993) concluded that
an estimated 55% of deposited road salt enters local surface water bodies. This results in
a seasonal variance in which spikes of Cl- are observed in surface waters during winter
5
storm events (Williams et al., 2000; Kelly, 2008; Perera et al., 2013; Corsi et al., 2014).
River and stream Cl- loads are detected within hours of road salt deposition unlike the
yearlong increased levels observed in groundwater (Kelly et al., 2012). Chloride can be
retained in surface waters, primarily reservoirs. In the cities of Minneapolis and St. Paul,
a local watershed retained annually 77% of road salt deposited within lakes, wetlands,
ponds, and groundwater (Novotny et al., 2009). However, runoff is not the only pathway
in which Cl- can travel into rivers and streams; it can also be fed from Cl- impacted
groundwater (Cantafio and Ryan, 2014).
In the snow-laden areas of the United States, aquifer systems have been studied
exclusively with respect to Cl-. Since the 1960s, waters within some shallow Chicagoland
aquifers have seen a Cl- increase of 1 mg/L/year or more due to increased road salt
application (Kelly, 2008). This annual increase indicates that Cl- can be stored within
soils and groundwater. While residing within soils, Cl- travels vertically via molecular
diffusion and dispersion feeding groundwater (Lax and Peterson, 2008). Multiple studies
have found that each year a percentage of deposited road salt remains within the
unsaturated zone, supplying Cl- to groundwater through the non-salting season (Mason et
al., 1999; Lax and Peterson, 2008; Kelly et al., 2012; Corsi et al., 2014). As a result, there
is an accumulation of Cl- in groundwater systems. Once in groundwater, Cl- residence
time can be decades to thousands of years, similar to residence times for groundwater,
and therefore, the transport of Cl- will reflect these residence times (Daley et al., 2009).
A host of factors can influence the transport of Cl- through a system. Howard and
Haynes (1993) modeled the inflows and outflows of Cl- in a Toronto watershed utilizing
6
aquifer parameters to find when the watershed would reach steady state. In the model,
parameters such as recharge, aquifer thickness, specific yield, and groundwater flow
velocities played a crucial role in determining when the watershed would reach steady
state with Cl- (Howard and Haynes, 1993). Soil type can also influence the transport of
Cl- (Ramakrishna and Viraraghavan, 2005; Findlay and Kelly, 2011). Finally, hydraulic
conductivity of aquifers, if low, can retard or slow the transport of Cl- within an aquifer
system (Findlay and Kelly, 2011).
Land use in hydrologic systems can similarly affect the transport of Cl-.
Urbanized areas are known to have impervious surfaces that contaminants cannot seep
through. Daley et al. (2009) discovered that areas with high percentages of road
pavement and impervious surfaces have a positive correlation with Cl- concentrations in
surface and groundwater due to runoff from road salt. They conclude that runoff from
impervious surfaces can transport Cl- faster than rural settings (Daley et al., 2009). Lax et
al. (in review) suggest that urban land use as low as 10% results in elevated Cl-
concentrations in stream waters.
Temporal factors can impact the travel of Cl- in hydrologic systems. Obviously,
road salt deposited during winter storm events is one of the leading factors. During
winter-storm season, the frequency and severity of road salt application dramatically
increases temporal Cl- concentrations in streams, lakes, and groundwater (Godwin et al.,
2003; Robinson et al., 2003; Kaushal et al., 2005; Rosfjord et al., 2007; Kelly, 2008;
Daley et al,. 2009). Granato et al. (1995) found in southeastern Massachusetts that winter
recharge rates contributed 75% of the annual subsurface recharge due to low
7
evapotranspiration during winter months. The combination of road salt application and
higher infiltration result in the transport of Cl- into groundwater.
Numerical modeling can quantify the residence time of Cl-, the total solute
storage, and the time when steady state will be reached. Huling and Hollocher (1972)
conducted a study in eastern Massachusetts, which concluded Cl- residence time in
groundwater, at a minimum, exceeded 1 year (Huling and Hollocher, 1972). Boutt et al.
(2001) estimated that transport distances can exceed 10 km in a 50 year period within a
Michigan watershed. This same study also found that the watershed will reach steady-
state conditions in 50 years (Boutt et al., 2001). Howard (1993) completed a mass
balance model that revealed Cl- concentrations in Toronto, Candida would reach steady
state within 60 years of initial salt application. Bester et al. (2006) completed a 3D model
that exhibited Cl- would take decades to flush out of groundwater in an Ontario glacial
moraine aquifer system. Model estimates are site specific but these studies indicate how
Cl- would transport in aquifers of shallow glacial systems. However, none of the models
have compared different application rates and their relationship with Cl- residence time
and mass accumulation.
Objective
The goal of this study is to develop a coupled groundwater-solute transport model
of a shallow aquifer in a central Illinois watershed. Utilizing this model, the relationship
between Cl- and road salt application rates will be investigated. The goal is to examine
the solute residence time and solute storage by simulating different application rates.
Solute storage is the watersheds capacity to accumulate Cl-, which will be referred to as
8
storage from herein. Based on the stability and conservative nature of Cl-, one would
expect that increasing the application rate of road salt will cause Cl- to accumulate within
the aquifer. With Cl- mass accumulating within the watershed the residence time of Cl-
will increase. For this study, residence time is the amount of time the solute, Cl-, travels
from source to sink along a flow path. Thus, if the application rate of road salt increases
then the residence time of Cl-increases. As a result, higher application rates will equate to
a higher total mass of Cl- within the system and longer residence times. In brief, a
positive relationship is posited between road salt application rate and both residence time
and mass of Cl- within an aquifer.
Hypotheses
1. The relationship between the rate of road salt application and the residence time of Cl-
within a shallow aquifer is positive.
2. The relationship between the rate of road salt application and the mass of Cl-
accumulating within a shallow aquifer is positive.
9
CHAPTER II
METHODS AND MATERIALS
Watershed Characteristics
The watershed in this study was located in Bloomington-Normal, McLean
County, Illinois (Figure 1). In 2014, Bloomington’s total population is 78,902 and
Normal has a total population of 54,664; both cities have grown at rates of 3.0% and
4.1% respectively (U.S. Census Bureau, 2014). Little Kickapoo Creek watershed, the
focus of this study, is a part of the greater watershed Kickapoo Creek. The Little
Kickapoo Creek watershed (LKCW) covered a total area of approximately 70 km2, with
the stream running through the center of the watershed and tributaries extending
throughout (Figure 1). Little Kickapoo Creek (LKC) originates in the southeast of
Bloomington, IL and flows to the south-southwest of Bloomington-Normal metropolitan
area. The stream leaves the highly urbanized area and flows through a low-density urban
setting before transitioning into agricultural and forested areas (Figure 1). The land use
was 27% urban, 69% agricultural, and 4% forested/wetland/surface water areas;
classifying the watershed as mixed urban and agricultural (U.S Geological Survey, 2011;
Figure 1).
10
Figure 1: Land Use within the Little Kickapoo Creek Watershed. Data obtained from the
U.S Geological Survey (2011).
A 1:24,000–scale (Plate 1) of the watershed did not exist. To accurately model the
hydrogeology, a surficial geology quadrangle map of the Bloomington-East 7.5 Minute
Quadrangle was completed. The map was constructed using McLean County Soil Survey
11
data, Illinois State Geologic Survey well log data, previous geologic investigations, and
field investigations. Soils data provided information on the parent material of the soil.
ESRI’s ArcGIS 10.2 was utilized for grouping soils based on the parent material. In
addition, ArcGIS 10.2 was used to analyze well logs that provided unit thicknesses; to be
considered mappable, units had to be at least 5 m thick. With geologic units delineated,
the shapefile was imported and redrafted in ACD’s Canvas 11. The completed map was
combined with contour elevations into a GeoPDF, provided by the USGS, using Adobe
Illustrator.
The interpretations of the surficial geology map are consistent to the entirety of
McLean County in which the deposits are glacially borne. The surficial geology is
influenced by the Wisconsin glacial episode wherein two moraines and an outwash plain
are present (Plate 1). The two moraines are the Normal Moraine in the north and the
Bloomington Moraine in the south. Each moraine is comprised of Wedron Group tills;
with the Normal Moraine being of the Lemont Formation and the Bloomington Moraine
consisting of the Tiskilwa Formation (Plate 1). Both tills can be described as red to
grayish-blue diamict clays and gravel, with a hydraulic conductivity (K) of 1.0 x 10-8 m/s
and a thickness of 70 meters (Hensel and Miller, 1991; Plate 1). South of the
Bloomington Moraine is an outwash plain of the Mason Group Henry Formation, which
is a sand and gravel mix with a K of 1.0 x 10-4 m/s and a thickness of 8 to 10 meters
(Ackerman et al., 2015; Plate 1).
12
Conceptual Model
The conceptual model for LKCW was developed using available hydrogeologic
information. Based on the subsurface geologic map, it was decided to construct a one-
layer model that accounts for flow through the glacial sediments. The bedrock underlying
the glacial sediments is mostly low-conductivity Pennsylvanian shale, thus limiting
vertical movement of water in the aquifer. To differentiate between the outwash and till
geologies (Figure 2), the respective cells had assigned hydraulic conductivities to
represent the respective units. Till units identified within Plate 1 were combined into one
major unit. Outwash units are typically thinner than till but it is assumed that the outwash
thickness does not affect transport. The till unit could have been multiple layers due to
the lenses of sand and gravel; it was decided that the lenses extent across the region was
small, and thus, the influence of the lenses on the model was assumed to be negligible. In
this particular region, till units can be as thick as 70 m so cell thickness was set a uniform
100 m among both geologies. The model was classified as 2-D. The till and outwash are
represented as homogeneous and isotropic, but K values differ between the units.
13
Figure 2: Distribution of Geologies Used in Model Simulations.
Model Setup
Boundary conditions were assigned utilizing hydrography data from the National
Hydrography Dataset (U.S. Geological Survey, 2014). The hydrography data included the
boundary of the Kickapoo Creek watershed, parts of which was used to delineate the
14
domain of the LKCW. The domain of the model was limited to the surface water
drainage basin for LKC, assuming that the surface water divide serves as a groundwater
divide for the shallow groundwater system (Figure 3). A no flow boundary existed along
the perimeter and bottom of the domain; recharge was applied across the surface of the
model domain. The surface boundary along the LCK and tributaries was represented with
constant head and solute conditions (Figure 3). Roadways from the National
Transportation Dataset characterized the cells that represent sources of Cl- due to road
salt (Figure 3). Cells that contained roadways had increased Cl- application over the
winter stress period; in the summer the cells returned to background levels. Chloride was
applied to non-urban cells at a constant background rate, 10 mg/L, throughout the entire
simulation. Flow of the system was assumed to be steady-state flow, but the solute
transport (Cl-) was transient due to the seasonal depositional rates.
15
Figure 3: Boundary Conditions Used in Model. The model domain represents the
groundwater divide (black), LKC constant head and constant Cl- concentration (blue),
and roadways constant fluid flux (recharge) and variable rate of for salt application (red).
Groundwater Vistas Version 6 preprocessor allowed the conversion of GIS-based
data to be put into model input files. Incorporating GIS databases into the watershed-
scale model facilitated the ability to apply specific parameters to model cells. The
16
National Hydrologic Dataset provided a shapefile for LKC and the watershed that were
imported in Groundwater Vistas to outline constant head and no-flow boundary cells.
Next, elevation data were applied to the model to depict the top elevation of cells and to
aid the direction of groundwater flow (US Geological Survey, 2013). The National Land
Cover Database assisted in the classification of cells in the model by revealing urban and
agricultural land use locations (Figure 1). Urbanized cells had an increased Cl- value that
reflect those after winter storm events; while agricultural and forested areas had low
constant concentrations through the whole simulation (Figure 1).
Upon completing the application of boundary conditions, parameter values were
added to model cells. Values for each parameter were chosen from previous studies and
literature on the study area (Table 1). Hydrogeologic parameters were applied based on
the geologic unit the cell represented. Other parameters, such as the recharge rate, were
applied uniformly across the watershed despite the varying cell geologies. A recharge
rate of 0.026 cm/day, representing 10% of the annual precipitation, was used along the
top boundary. The recharge value is consistent with other models of the area (Lax and
Peterson, 2008; Van der Hoven et al., 2008). Aquifer test data from wells located in the
modeled area were used to measure storage values for the tills and outwash (Table 1). To
simulate conditions after a winter storm event, urbanized cells (Figure 1) were assigned
elevated Cl- levels of ≥1,000 mg/L provided from shallow monitoring wells installed
along an interstate (Kelly and Roadcap, 1994; Lax and Peterson, 2008; Table 2). The
1,000 mg/L is lower than the measured concentrations within infiltration near a road (Lax
and Peterson, 2008) but given the size of the model cells will be more representative. The
17
non-urban cells had an initial concentration of 10 mg/L simulating background conditions
(Kelly, 2008), and the recharge maintained a constant 10 mg/L concentration over the
duration of the simulation. The model, composed of one layer, simulated transport in the
horizontal dimensions. Model cells are 100 mx100 m, generating a finite-difference grid
with 164 rows, 72 columns, and a total of 7,136 active cells.
Table 1: Values Used for Model Parameters
Stress periods were used to divide the model simulation into seasons. Each year is
broken into two stress periods: 1) winter and 2) summer through fall. The winter stress
period lasted 84 days while the summer through fall spanned the remaining 281 days or
time steps. Following Lax and Peterson (2008), the elevated concentrations of Cl- were
Parameter Value Used Source
Outwash K 1.0 x 10-4
m/s Ackerman et al., 2014
Outwash Porosity 0.35 Ackerman et al., 2014
Outwash Sy 0.021 Field Test (data unreported)
Outwash Ss 0.0007 Field Test (data unreported)
Till K 1.0 x 10-8
m/s Hensel & Miller, 1991
Till Porosity 0.25 Ackerman et al., 2014
Till Ss 0.00056 Field Test
Till Sy 0.01 Field Test
Recharge Rate 2.3 x 102 m/s Lax & Peterson, 2008
Cl- Dispersivity Longitude 1.78 m Giadom et al., 2015
Cl- Dispersivity Latitude 1.64 m Giadom et al., 2015
Cl- Concentration Winter ≥1,000 mg/L Lax & Peterson, 2008
Cl- Concentration Summer 10 mg/L Kelly, 2008
18
applied through the 84 day winter season. As previously stated, the elevated Cl- level was
applied to urban cells, to simulate road salt deposition. Non-urban cells maintained a
constant background level during the entire simulation. Succeeding the winter stress
period, deposition decreased to background levels in the urban cells for the summer
through fall period.
To test the hypotheses, there was seven scenarios. Scenarios 1 and 2 simulated 10
cycles of winter and summer seasons (or 10 years) wherein at the end of year 10, road
salt application ceases and background levels are applied at a constant rate. From years
11 through 60, all cell types have background application. Scenario 1 used Cl- application
rates of 1,000 mg/L whereas Scenario 2 employed 10,000 mg/L. At the end of each
decade, the maximum Cl- concentration and net mass was recorded. Utilizing a basic
mass balance equation the amount of Cl- entering and leaving the system was calculated.
The purpose of the two scenarios was to observe how the watershed flushes out Cl- after
50 years of no deposition and its relationship to the different application rates. Scenarios
3 - 7 simulated a constant, but different, deposition rate across a 60-year span (Table 2).
Varying the application rate provided insight to its relationship with mass build up and
Cl- residence time. For each year, the residence time was calculated using the Equation as
presented by Dingman (2002), where total solute storage and the amount of solute
leaving the system. The maximum concentration and net mass were recorded at the end
of each 5-year period. Herein, Scenarios 1 and 2 are referred to as the “Flush Scenarios”,
while Scenarios 3 – 7 are referred to as the “Build-Up Scenarios”.
19
𝑇𝑟 =𝑇𝑜𝑡𝑎𝑙 𝑀𝑎𝑠𝑠
𝑠𝑜𝑙𝑢𝑡𝑒𝑀𝑎𝑠𝑠 𝑂𝑢𝑡
𝑠𝑜𝑙𝑢𝑡𝑒 Equation 1
Table 2: Build-up Scenarios and their Assigned Application Rate
MODFLOW is a program developed by the United States Geological Survey that
numerically solves the three dimensional groundwater flow equation using the finite
difference method thus simulating groundwater flow (Harbaugh et al., 2000).
MT3D simulates advection, dispersion/diffusion, and chemical reactions of
contaminants in groundwater flow systems using boundary conditions and external
sources or sinks (Zheng and Wang, 1999). To accurately model Cl- movement a
dispersivity coefficient of 1.78 m for longitude and 1.64 m for latitude was employed
(Giadom et al., 2015). Porosity values of the till and outwash units were 0.25 and 0.35
respectively. The solute was distributed via the recharge parameter by assigning a
constant Cl- concentration of ≥1,000 mg/L to cells containing a road or urbanization.
Therefore, the source or Cl-in was recharge and the constant head boundary was the sink
or Cl-out. The constant head boundary was assigned a Cl- concentration of 0 mg/L; no
temporal data existed to apply Cl- that is representative of winter and proceeding seasons.
Scenario Winter Application Rate (mg/L)
3 1,000
4 2,500
5 5,000
6 7,500
7 10,000
20
The concentration remained constant over the 84-day period. Since Cl- is conservative no
retardation factors or reactions were simulated.
MODPATH is a particle tracking post-processing model that computes three-
dimensional flow paths using output from MODFLOW (Pollock, 2012). MODPATH
tracks a particle’s path from cell to cell until it reaches the sink/source or a boundary.
Particles were added to roadways to simulate road salt to observe flow pathways and
particle travel times.
Sensitivity Analysis
Sensitivity analyses were conducted for the groundwater flow model
(MODFLOW) and for the solute transport model (MT3D). Since no data were available
to calibrate the model, the sensitivity analysis was completed to reveal which parameters
the model was sensitive and which had no effect on the model output. The sensitivity
analyses examined how increases and decreases of 25, 50, and 75% for each model
parameter (Table 1) affected the head or concentration within the system. After a percent
change, the model was run and the simulated head or concentration was recorded. In
order to quantify which parameters were sensitive, the observed and simulated values
were compared and the root mean squared error (RMSE) was calculated. Actual observed
values do not exist, therefore values of the base model were used as a comparison
instead. The base model uses an application rate of 1,000 mg/L and the parameter values
listed in Table 1. Base model values were then compared to adjusted simulated values.
Eleven targets were randomly placed within the model domain to track base model and
adjusted values. To choose target locations, the model was broken intro 10 sections. Each
21
section was assigned a set of rows and columns. In Excel, a random number generator
was used to determine the set of rows and columns of each section. If the number pulled
did not lie within the model domain the process was repeated.
22
CHAPTER III
RESULTS
Sensitivity Analysis
With no available field data, the model was not calibrated; instead a sensitivity
analysis was performed. The MODFLOW model was sensitive to recharge, till Kx, and
till Ky (Figure 4). When increasing percent intervals to 75%, the model was most
sensitive to recharge leading to a RMSE of 6.7 m while till Kx was the most sensitive
when decreased 75% with a RMSE of 10.4 m. Although the model was most sensitive to
recharge and till, the initial values of 2.3 x 102 m/s and 1.0 x 10-8 m/s are widely
supported in other studies (Ackerman et al., 2015; Curry, 2007; Lax and Peterson, 2008;
Van der Hoven et al., 2008). Other parameters such as the outwash Kx and Ky did affect
the head of the model but only by 0.3 m after adjusting the parameter by ±75% (Figure
4). The outwash conductivities have a minimal effect on the model head because outwash
cells cover a small area; therefore, outwash conductivities do not have as much of an
influence on the groundwater flow as the till (Figure 4). The MODFLOW model was not
sensitive to changes in porosity, specific yield, or specific storage.
23
Figure 4: Sensitivity Analysis MODFLOW Results. Mean squared error represents head
values (m).
The MT3D model was most sensitive to recharge and till porosity in comparison to
all other parameters (Figure 5). Percent decreases resulted in the model to be most sensitive
to till porosity with an RMSE of 22 mg/L at -75% which represents 11% of the maximum
concentration. Lowering the porosity results in faster groundwater flow and therefore faster
solute transport, which decreases the solute concentration. Increases in the parameters
caused the model to be most sensitive to the recharge volume with an RMSE of 6 mg/L at
75%, which is 3% of the maximum concentration. As per the MODFLOW model, the final
values used for each of these parameters are supported in other studies (Ackerman et al.,
0.00
2.00
4.00
6.00
8.00
10.00
-75 -50 -25 0 25 50 75
RM
SE (
m)
Change from initial value (%)
Outwash Ky Till Kx Outwash Kx Till Ky Recharge
24
2015; Curry, 2007; Lax and Peterson, 2008; Van der Hoven et al., 2008). None of the other
parameters affected the solute transport as much as recharge and till porosity, with the
highest concentration change of 1 mg/L at ±75% (Figure 5). The MT3D model was
insensitive to changes in till and outwash Kx and Ky values. The final values did not change
from those selected as initial parameter values (Table 1).
Figure 5: Sensitivity Analysis MT3D Results. Mean squared error represents Cl-
concentration (mg/L).
0.00
5.00
10.00
15.00
20.00
25.00
-75 -50 -25 0 25 50 75
RM
SE (
mg/
L)
Change from inital value (%)
Till Porosity Outwash Porosity Till SyOutwash Sy Outwash Ss Till SsTill Kx Outwash Kx Recharge
25
Flush Scenario Results
The flush scenarios simulates road salt application of 1,000 or 10,000 mg/L for 10
winter seasons. At year 10, the application of Cl- is shut off, and the model is run for 50
additional years. Upon simulation completion, mass balance data and the maximum Cl-
concentration across the model domain from each decade were recorded. For each
scenario, the maximum Cl- level increases and then peaks right at year 10 for both rates
(Figure 6). After year 10, the levels decrease but never return to initial concentrations as
used in the simulations (Figure 6). For both rates, maximum Cl- levels are a percentage of
the deposition rate even after 10 winter seasons. For instance, when salt was applied at
1,000 mg/L the maximum Cl- level is 85 mg/L or 9% of the rate (Figure 6). The same is
true when applied at 10,000 mg/L, by the end of year 10 the maximum concentration is
767 mg/L or 8% of the application rate (Figure 6). Chloride does not completely return to
background levels of 10 mg/L either. By the end of the simulation, 50 years after
application, the maximum Cl- concentration was 166 and 25 mg/L for 10,000 and 1,000
mg/L rates (Figure 6).
26
Figure 6: Flush Scenario Results. Where road salt was applied for 10 winter seasons and
shut off at end of year 10. Reported is the maximum Cl- concentration (mg/L) at the end
of each decade and the background levels (black).
The flush scenarios mass balance data were used to calculate the net mass of Cl-.
The net mass of Cl- increases and peaks at year 10 for both the 1,000 and 10,000 mg/L
application rates with a mass of 11,800 Kg/L and 127,000 Kg/L respectively (Figure 7).
Thereafter, the mass drops by a little more than half by the simulation end with 6,200
Kg/L for the 1,000 mg/L application rate, which is 53% of mass peak, and 73,500 Kg/L
for the 10,000 mg/L application rate, which is 58% of mass peak (Figure 7).
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
0 10 20 30 40 50 60
Max
Cl-
(mg/
L)
Time (years)
Application of 1,000 mg/L Application of 10,000 mg/L Background 10 mg/L
27
Figure 7: Flush Scenario Results. Where road salt was applied for 10 winter seasons and
shut off at end of year 10. Reported is the net mass of Cl- (Kg/L) at the end of each
decade.
A color flood map of both application rates displays the Cl- concentration across
the watershed (Figure 8). In both scenarios, salt builds up within roadways and urbanized
areas (Figure 8). After the application of chloride is ceased, the salt slowly dissipates
from roadways and urbanized areas into surrounding sediments and LKC. This can be
seen by how the Cl- (hotter colors) migrate from deposition areas, especially at year 60
(Figure 8). Still at the end of the 60-year period, Cl- concentrations are highest along
roadways, especially those within areas comprised of till material, and lowest in
agricultural areas (Figure 8). Geologic material influences Cl- removal from the
0.00E+00
2.00E+04
4.00E+04
6.00E+04
8.00E+04
1.00E+05
1.20E+05
1.40E+05
0 10 20 30 40 50 60
Net
Mas
s o
f C
l-(K
g/L)
Time (years)Application of 1,000 mg/L Application of 10,000 mg/L
28
watershed. Cells representing tills have increased Cl- concentrations and continue to store
Cl- despite 50 years of no application (Figure 8 Panel B). The southern tip of the
watershed is comprised mostly of the highly conductive outwash material and therefore,
displays lowest Cl- concentrations despite being near the primary source (Figure 8). The
10,000 mg/L rate has more Cl- in storage than the 1,000 mg/L rate due to Cl- loading in
the low conductivity tills. The decadal color change in the 10,000 mg/L map show the
concentrations and storage depleting through the years (Figure 8).
29
Figure 8: Flush Scenario Color Flood Map. Panel (A) 1,000 mg/L application rate and
panel (B) 10,000 mg/L application rate. Both panels show models in which road salt was
applied for 10 winter seasons; shut off at end of year 10 and then ran at background
levels for 50 years after.
30
Build-up Scenario Results
Build-up scenarios simulate a constant road salt application for 60 winter seasons,
with each scenario having a specific application rate (Table 2). Similar to the flush
scenarios, mass balance data and the maximum Cl- concentrations at five-year intervals
were recorded. As the application rate increases so do the Cl- concentrations within the
system, a linear relationship between the two is implied (Figure 9). For each individual
application rate, the maximum Cl- level increases every year (Figure 10). Application
rates of 7,500 mg/L and 10,000 mg/L show no signs of reaching steady state, but the
lower rates appear to be nearing a plateau by the end of the 60-year simulation (Figure
10). The point at which the watershed reaches steady state is relative to the application
rate; severe application rates such as 10,000 mg/L show the watershed as continually
storing Cl-. Even after a 60-year period, the maximum Cl- levels are only about 19% of
input for all rates. For example, the 10,000 mg/L application rate results in a maximum
Cl- concentration of 1,900 mg/L (Figure 10).
31
Figure 9: Build-up Scenario Results. Relationship between the application rate and the
maximum Cl- concentration at the end of the 60-year simulation.
Figure 10: Build-up Scenario Results. Where road salt was applied for 60 winter seasons.
Reported are the maximum Cl- concentrations (mg/L) at the end of each five-year period.
0
200
400
600
800
1000
1200
1400
1600
1800
2000
- 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000
Max
Co
nce
ntr
atio
n (
mg/
L)
Application Rate (mg/L)
0100200300400500600700800900
10001100120013001400150016001700180019002000
0 10 20 30 40 50 60
Max
Cl-
(mg/
L)
Time (years)1,000 mg/L 2,500 mg/L 5,000 mg/L 7,500 mg/L 10,000 mg/L
32
The net mass of Cl- was also computed for build-up models at the end of each
five-year period. Application rate and net mass presents a linear relationship; as the
application rate increases so does the net mass (Figure 11). From the start to year 60, each
simulation shows Cl- mass accumulating annually, with the 1,000 mg/L and 2,500 mg/L
rates exhibiting some plateauing (Figure 12). At the end of year 60, the net mass is
596,000 Kg/L for the 10,000 mg/L application rate and 58,000 Kg/L for the 1,000 mg/L
(Figure 12). As expected, increasing road salt application also increases the net mass of
Cl-.
Figure 11: Build-up Scenario Results. Relationship between the application rate and the
net mass of Cl- at the end of the 60-year simulation.
0.00E+00
1.00E+05
2.00E+05
3.00E+05
4.00E+05
5.00E+05
6.00E+05
7.00E+05
- 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000
Net
Mas
s C
l-(K
g/L)
Application Rate (mg/L)
33
Figure 12: Build-up Scenario Results. Where road salt was applied for 60 winter seasons.
Reported are the maximum net mass of Cl-at the end of each five-year period.
The residence time was calculated every year for each application rate using
Equation 1. Application rate and residence time display a positive relationship with a
range of 1,123 to 1,288 days for the rates of 1,000 and 10,000 mg/L (Figure 13). The
residence time of each application rate peaks around year 5 and then levels out thereafter
(Figure 14). In Figure 14, the exponential increase during the years 0 to 5 is due to mass
Cl-out being lower than mass Cl-
in once the peak is reached mass Cl-out begins to equal
mass Cl-in. From years 10 to 60 the residence times start to converge around 1,000 days
reflecting the groundwater residence time (Figure 14).
0.00E+00
1.00E+05
2.00E+05
3.00E+05
4.00E+05
5.00E+05
6.00E+05
7.00E+05
0 5 10 15 20 25 30 35 40 45 50 55 60
Net
Mas
s o
f C
l-(K
g/L)
Time (years)
1,000 mg/L 2,500 mg/L 5,000 mg/L 7,500 mg/L 10,000 mg/L
34
Figure 13: Build-up Scenario Results. Where road salt was applied for 60 winter seasons.
Shown is the maximum solute residence time (days) and application rate (mg/L)
1100
1120
1140
1160
1180
1200
1220
1240
1260
1280
1300
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Max
imu
m R
esi
de
nce
Tim
e (
day
s)
Application Rate (mg/L)1,000 mg/L 2,500 mg/L 5,000 mg/L 7,500 mg/L 10,000 mg/L
35
Figure 14: Build-up Scenario Results. Where road salt was applied for 60 winter seasons.
Shown is the solute residence time (days) recorded at the end of each simulated year.
Color flood maps of model scenario 2 were constructed to demonstrate the
distribution of Cl- across the watershed. Both map’s roadways and urbanized areas have
the highest concentration of Cl- and the lowest concentrations are found in LKC (Figure
15 and Figure 16). Unlike the first set of color flood maps (Figure 8), the spreading of
higher concentrations surrounding the deposition areas settings exhibit salt overflowing
and storing in the adjacent sediments (Figure 15 and Figure 16). As LKC represents a
point of groundwater discharge, Cl- transport is directed towards LKC. For both
application rates, the agricultural lands have the lowest concentrations due to their
distance from urban areas and roadways (Figure 15 and Figure 16). With both application
0
200
400
600
800
1000
1200
1400
0 10 20 30 40 50 60
Re
sid
en
ce T
ime
(d
ays)
Time (years)1,000 mg/L 2,500 mg/L 5,000 mg/L 7,500 mg/L 10,000 mg/L
36
rates, the Cl- concentration increases over time in the agricultural areas (Figure 15 and
Figure 16). The 10,000 mg/L map uses a different color scale due to reaching
concentrations over 200 mg/L only after 10 years of application (Figure 16).
Figure 15: Build-up Scenario Results Color Flood Map. Chloride concentration color
flood map of model scenario 2 at the 1,000 mg/L application rate. Shown is the model in
which road salt was applied for 60 winter seasons and LKC (white).
37
Figure 16: Build-up Scenario Results Color Flood Map. Chloride concentration color
flood map of model scenario 2 at the 10,000 mg/L application rate. Shown is the model in
which road salt was applied for 60 winter seasons and LKC (blue). White areas indicate
concentrations at or below 200 mg/L.
38
MODPATH Results
As a conservative solute, Cl- will not degrade or react with the environment;
MT3D was therefore applicable to model the transport of this conservative species. The
MT3D solute model shows the transport of Cl- from roadways into the surrounding
sediments and LKC. To be certain of the solute path, MODPATH was used to show
particles traces and relative residence times. Particles were applied to roadways and
tracked for 100 years to see where they traveled. Particle travel time varied from 10 years
to 100 years depending upon how close the starting point was to LKC (Figure 17 and
Figure 18). Even after 100 years, some particles never left the watershed via LKC (Figure
17). To test if all particles ventured into LKC an east-west line was drawn across the
northern watershed to track travel paths (Figure 18). In Figure 18, the eastern most
particle flowed towards LKC and not directly south. In addition, all other particles in
Figure 18 traveled directly to the stream as well.
39
Figure 17: MODPATH Results. Particle trace results; the time at each arrow is time
(years). Within the red box: black lines are roads, red dots are particle starting points, red
lines are particle paths, and the solid blue is LKC.
40
Figure 18: MODPATH Results. Particle trace results; the time at each arrow is time
(years). Within the red box: black lines are roads, red dots are particle starting points, red
lines are particle paths, and the solid blue is LKC.
41
CHAPTER IV
DISCUSSION
Residence Time
The relationship between application rate and Cl- residence time is positive; as the
application rate increases so increases the residence time (Figure 13). This relationship is
especially prevalent in the first 5 years of application where mass is accumulating (Figure
14). At year 5, residence time peaks for all model simulations; thereafter, the residence
time decreases and simulations begin to converge to a value of ~1,000 days (Figure 14).
The decreases observed from years 5 to 60 are due to Cl- mass beginning to reach steady-
state conditions. By year 60, models 3 and 4 are at steady-state conditions (Figure 12),
and the Cl- residence time of ~1,000 days reflects that of the groundwater similar to
Daley et al. (2009) (Figure 14). The Cl- residence time of ~3 years is supported by
MODPATH results (Figure 17 and Figure 18); groundwater residence times of 3 years is
also supported by previous studies (Kelly et al., 2012). Models 5 to 7 are close to steady-
state conditions but have yet to reach the point of diminishing returns (Figure 14). By
graphing the residence time through the entire model simulation we were able to
visualize the ability of the watershed to store and flush Cl- based on each application rate.
Due to Cl- mass still accumulating in higher rates the watershed had yet
42
to reach steady-state conditions thus providing insight into the relationship of application
rate and residence time.
Mass Accumulation – Flush Scenario
Flush models were assigned specific application rates that were applied for 10
winter seasons then shut off. The estimated flush time is relative to application rate with
the application rate of 1,000 mg/L having 47% of its mass flush away while the 10,000
mg/L saw 42% flushed away (Figure 7). The 10,000 mg/L rate took 30 years to return to
the EPA maximum contaminant level of 250 mg/L (Figure 6). Bester et al. (2006)
simulated the transport of a Cl- plume in an industrial/urban aquifer setting; model
simulations indicated Cl- would flush out of the aquifer after four decades of no
application. For both application rates, the simulations show that after 15 years the
maximum Cl- concentrations are half of the peak concentrations, similar to Bester et al
(2006) (Figure 6). Bester et al. (2006) stated that remaining Cl- resided in the low-
conductivity material. The MT3D color flood maps indicate Cl- is stored within the low
conductivity roadside sediments (Figure 8). Solute within the highly conductive outwash
sediments have lower concentrations despite being near the source (Figure 8). This
suggests that the geologic material present influences the flush time, supported by
Howard and Haynes (1993) which suggest that aquifer conductivity influence the
transport of Cl- through a system.
43
Mass Accumulation – Build-up Scenario
Build-up models were assigned application rates that were held constant for 60
years (Table 2). Application rate has a linear relationship with mass accumulation and
groundwater concentration of Cl-, validating the stated hypothesis (Figure 9 and Figure
11). The maximum Cl- concentration within all simulations rose annually at a rate of 1
mg/L (Figure 10), similar to rates reported by Kelly (2008). By year 60, maximum Cl-
concentrations ranged of 197 mg/L to 1,900 mg/L, which are similar to measured Cl-
concentrations in previous studies (Kelly and Roadcap, 1994; Panno et al., 2005; Kelly,
2008; Figure 10). Alarmingly, all models except rates of 1,000 mg/L and 2,500 mg/L
exceeded the MCL after 10 years of deposition (Figure 10). The net mass accumulation is
dependent upon application rate; final net mass ranges from 58 million metric tons to 596
million metric tons, exhibiting a linear relationship with application rates (Figure 12).
Lower rates of 1,000 mg/L and 2,500 mg/L reached steady-state conditions at year 60
contrasting higher rates, suggesting steady-state estimates are dependent on application
rates (Figure 12). For the scenarios examining the lower application rates, estimates of
time to reach steady state matches those of previous studies (Howard and Haynes, 1993;
Boutt et al., 2001). None of those studies took into consideration the relationship
application rates have with accumulation of mass and concentration in the watershed.
This study’s simulations reveal that the watershed exhibits a linear relationship between
with Cl- storage and application rate, which affects steady-state estimates.
44
Further Consideration
At higher rates, 5,000 mg/L to 10,000 mg/L, the simulations exhibit no sign of
reaching steady state after six decades, which is inconsistent with previous estimates
(Howard and Haynes, 1993; Boutt et al., 2001). The estimates differ due to a few reasons.
First, previous studies model transport in different media (i.e. outwash) wherein
groundwater travel times are higher than those of till. As seen in the color flood maps, Cl-
concentrations are highest in tills and lowest in outwash material (Figure 8; Figure 15;
and Figure 16). Other sensitive parameters, such as recharge, can vary depending upon
the region. Unlike the models of Howard and Haynes (1993) and Boutt et al. (2001), the
developed model was not calibrated. While calibration could deem the higher application
rates unrealistic, the recharge rate used in this study was drawn from calibrated flow
models developed for the locale area (Ackerman et al., 2015; Lax and Peterson, 2008).
The application rates greater than 2,500 mg/L would be considered unrealistic when
observing the Cl- concentrations in groundwater wells surrounding a Illinois interstate
(Kelly, 2008; Lax and Peterson, 2008). Land use and the amount of urbanized land within
an area is correlated to higher Cl- levels (Lax et al., in review; Peterson and Benning,
2013). The LKCW could have more urbanized land compared to former models. A
majority of urbanized land occurs in the northern model domain and the headwaters of
LKC, which will extend the amount of time Cl- is in the system. Finally, dissimilar to the
Howard and Hynes (1993) model, the application rate was held constant for the winter
period. Inputting road salt at a constant rate provides only a preliminary estimate of
45
steady-state times. The variation in snowfall would dictate Cl- application rates and affect
the steady-state time.
Color flood maps of the watershed display the distribution of Cl- concentrations
throughout the watershed (Figure 8; Figure 15; and Figure 16). The Cl- concentration is
influenced by the land use of that area. The LKCW is 27% urbanized and 69%
agricultural land uses both of which have associated Cl- concentrations (Figure 1).
Urbanized areas (i.e. roadways) exhibit the highest Cl- concentrations, which is analogous
with (2005) Panno et al. study. Agricultural land use have low Cl- concentrations that
range from 10 mg/L to 50 mg/L which is supported by previous studies (Panno et al.,
2005; Kelly, 2008; Figure 8; Figure 15; and Figure 16). A limitation of the model is the
lack of temporal Cl- concentrations within LKC; the stream was set as a 0 mg/L constant
head boundary, which is why we observe the lowest concentrations along those cells
(Figure 15 and Figure 1). Lax et al. (in review) found that during winter months Cl-
concentrations in an urban stream range between 65 to 1,350 mg/L and for an agricultural
stream between 20 and 60 mg/L. In addition, a seasonal variance in which spikes of Cl-
are observed in surface waters during winter storm events (Williams et al., 2000; Kelly,
2008; Perera et al., 2013; Corsi et al., 2014). Summer Cl- concentrations can also spike
due to contaminated groundwater leaching into LKC (Benning and Peterson, 2012).
Thus, the model lacked simulating LKC seasonal Cl- concentration changes.
46
CHAPTER V
CONCLUSION
Modeling of the watershed revealed 1) the relationship between road salt
application rates and mass solute storage and 2) the relationship between road salt
application rate and solute residence time. A positive relationship was observed between
application rate and mass accumulation. In addition, a positive relationship was observed
between application rate and residence time. The time it takes for the watershed to return
to safe drinking levels is dependent upon the application rate; as the application rate
increases the flush time increases. Steady-state time was also dependent on application
rates, wherein a positive relationship was observed.
The modeling of Cl- transport in this study reveals the proficiency in which a
watershed can store and cleanse road salt. At high application rates, the watershed takes
30 years of no application to return to safe drinking levels, which would not be
achievable due to human dependency on deicers. Lower application rates reached steady-
state conditions after 60 years of deposition. Presently, watersheds within the Midwest
could have reached steady-state conditions with road salt considering application started
in the 1960s. Kelly et al (2012) demonstrated that shallow aquifers within the Chicago
metropolitan area have increased in Cl- concentrations since the 1970s. The Cl-
contaminated groundwater then feeds local streams wherein we observed elevated surface
water Cl- concentrations through non-salting seasons (Kelly et al., 2012). The results of
47
this study display that elevated Cl- concentrations in the groundwater can sustain high
surface water concentrations through the non-salting season. Therefore, with a
continuance of application in the proceeding winter it is possible that surface water Cl-
concentrations will continue to increase through the decades as shown in Kelly et al
(2012) and Kelly et al (2007). Elevated surface waters and groundwater could lead to
detrimental effects on the watershed ecosystem.
48
CHAPTER VI
FUTURE CONSIDERATION
In continuing this studies endeavor, it is necessary to refine the model. A
calibration of the model is essential to ensure accuracy. Layers should be added
effectively simulating the glacial sediments and the geology of the region. The addition
of layers will also introduce surface water-groundwater interactions within the watershed.
Stream data should be pulled from the stream gage station along LKC. Extensive spatial
water sampling of LKC should be conducted over multiple years to model accurately Cl-
levels in the stream. The model should be ran for additional decades to see when
background levels are reached. Lastly, the model should be run until steady state is
reached for all application rates. Many factors can be introduced into the model to help
ensure the validity but nonetheless it must be realized that models are always flawed.
49
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56
APPENDIX A
PLATE 1
See Supplemental File