&EPA
EPA/600/R-20/143 | June 2020 | www.epa.gov/research
United States
Environmental Protection
Agency
The Influence of Stormwater
Management Practices and
Wastewater Infiltration on
Groundwater Quality:
Case Studies
Office of Research and Development
Center for Environmental Solutions & Emergency Response | Groundwater Characterization & Remediation Division
-------
EPA/600/R-20/143
June 2020
The Influence of Stormwater
Management Practices and
Wastewater Infiltration on
Groundwater Quality:
Case Studies
Doug Beak
ORD/CESER/GCRD/SRB - Ada, OK 74820
Michael Borst
ORD/CESER/WIDD/SMB - Edison, NJ 08837
Steve Acree
ORD/CESER/GCRD/TSERB - Ada, OK 74820
Randall Ross
ORD/CESER/TSCD - Ada, OK 74820
Ken Forshay
ORD/CESER/GCRD/TSERB - Ada, OK 74820
Robert Ford
ORD/CESER/LRTD/CSSB - Cincinnati, OH 45268
Junqi Huang
ORD/CESER/GCRD/SRB - Ada, OK 74820
Chunming Su
ORD/CESER/GCRD/SRB - Ada, OK 74820
Jessica Brumley
NRC Post-Doctoral Researcher — Ada, OK 74820
Alexis Chau
ORAU Contractor — Ada, OK 74820
Cassie Richardson
ERAP Student Contractor — Ada, OK 74820
Office of Research and Development
Center for Environmental Solutions & Emergency Response | Groundwater Characterization & Remediation Division
-------
Notice/Disclaimer Statement
This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved
for publication. Approval does not signify that the contents necessarily reflect the views and policies of the
Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation
for use.
Acknowledgments
The authors would like to acknowledge the beneficial comments from one internal and four external reviewers.
We would like to acknowledge Mr. Russell Neill and Mr. Justin Groves for their field sampling and equipment
installation activities at the Fort Riley and Louisville study sites. We would also like to acknowledge Mr. Mark
White, Ms. Kristie Rue, Ms. Molly Sexton, Ms. Lisa Costantino, Ms. Claire Wadler, and Ms. Lynda Callaway for
their sample analysis at the GCRD labs. We also thank Ms. Maily Pham and the CMTB Laboratory for their
sample analysis in Cincinnati. We would also like to acknowledge Dr. Mustafa Bob, Mr. Steve Vandegrift, and Mr.
John Olszewski for improving QA/QC aspects of this research effort, review comments, and QAPP review. We are
also grateful to Dr. Amy Shields and Ms. Brenda Groskinsky for supporting our research efforts through a RARE
project at Fort Riley. We would also like to express gratitude to Mr. Daniel Dorn and EPA Region 7 Laboratory
staff for help with the organic analysis and with sampling during the Fort Riley study. The authors would also
like to acknowledge the support from the Directorate of Public Works, particularly Mr. Chris Otto. Thank you to
Dr. Marc Kodack, and Assistant Secretary of the Army Katherine Hammack, and the U.S. Army who championed
the project. The Army Corps of Engineers for parking lot design, construction, and on-site support. We would
also like to thank USD 475 (Geary County Unified School District) and especially the faculty, staff, and students
of Seitz Elementary School for site access and accommodations during sampling at Fort Riley. We would also
like to acknowledge Dr. Bill Shuster and the U.S. Geological Survey for the initial borings and analysis of soils for
the Fort Riley study. We would also like to thank Ms. Ardra Morgan and the Net Zero program for support of the
Fort Riley study, and we would like to thank Elgin Sweeper. Co. and Key Equipment and Supply for maintenance
vacuuming of the permeable pavement. We would like to express our gratitude to the Louisville and Jefferson
County Metropolitan Sewer District for partnership in the CRADA, design and construction of the SCM's, and
sample collection through AECOM. We would also like to thank Mr. Michael Price (City of Yakima) and Mr.
Joel Freudenthal (County of Yakima) for their support and advice on the Yakima project. We would also like to
acknowledge Mr. Robert Elleman, Mr. Brian Nickel, Ms. Karen Burgess and EPA Region 10 staff for their support,
comments, and advice for the Yakima study. Finally, the authors would like to thank Ms. Kathy Tynsky with
AttainX for her graphic development, editing and formatting of this report.
-------
Foreword
The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the Nation's land,
air, and water resources. Under a mandate of national environmental laws, the Agency strives to formulate
and implement actions leading to a compatible balance between human activities and the ability of natural
systems to support and nurture life. To meet this mandate, EPA's research program is providing data and
technical support for solving environmental problems today and building a science knowledge base necessary
to manage our ecological resources wisely, understand how pollutants affect our health, and prevent or reduce
environmental risks in the future.
The Center for Environmental Solutions and Emergency Response (CESER) within the Office of Research and
Development (ORD) conducts applied, stakeholder-driven research and provides responsive technical support
to help solve the Nation's environmental challenges. The Center's research focuses on innovative approaches
to address environmental challenges associated with the built environment. We develop technologies and
decision-support tools to help safeguard public water systems and groundwater, guide sustainable materials
management, remediate sites from traditional contamination sources and emerging environmental stressors,
and address potential threats from terrorism and natural disasters. CESER collaborates with both public and
private sector partners to foster technologies that improve the effectiveness and reduce the cost of compliance,
while anticipating emerging problems. We provide technical support to EPA regions and programs, states, tribal
nations, and federal partners, and serve as the interagency liaison for EPA in homeland security research and
technology. The Center is a leader in providing scientific solutions to protect human health and the environment.
As part of the Safe and Sustainable Water Resources Research Program (SSWR) this report focuses on field
investigations of potential impacts to groundwater quality from the use of Green Infrastructure (Gl) for
stormwater and wastewater management. The project was initiated as part of the SSWR 5.02 research effort.
The goal of this report is to provide initial results from three field studies (Fort Riley, Kansas; Louisville, Kentucky,
and Yakima, Washington) examining the potential impacts to groundwater resulting from the use of Gl. Potential
impacts to groundwater quality are of at least three general types: 1) direct contamination of groundwater
by contaminants (e.g., metals, oil and gas, pesticides); 2) indirect contamination through changing aquifer
conditions that allow a potential contaminant to be mobilized (e.g., arsenic mobilization due to redox changes);
3) interaction of infiltrated water with existing subsurface contaminants (in either soil, subsoil, or groundwater)
that could alter the spatial extent of existing contamination and equilibria. In addition, the effort will provide the
basis for long-term research on the potential for changes in groundwater quality and potential mechanisms for
these changes if encountered as the result of the use of Gl.
Ann Keeley, Director
ORD/CESER/GCRD
-------
Table of Contents
Foreword iii
Executive Summary xv
1.0 Introduction 1
2.0 Analytical Methods 4
2.1 Field Parameters 4
2.2 Dissolved Metal, Nutrient, Anion, Carbon Species, and Water Isotope 4
2.3 Organic Parameters 4
3.0 Water Quality Sampling Methods 5
3.1 Fort Riley 5
3.1.1 Monitoring Wells 5
3.2 Louisville 8
3.2.1 Groundwater Sampling 8
3.2.2 Soil Porewater Sampling 8
3.3 Yakima 8
3.3.1 Groundwater Sampling 8
3.3.2 Outfall Sampling 8
4.0 Hydrogeologic Methods 9
4.1 Water-Level Measurements 9
4.2 Hydraulic Conductivity 9
4.3 Borehole Flowmeter 10
4.4 Borehole Geophysical Methods 11
4.5 Direct-Push Soil Electrical Conductivity Profiling Survey 12
5.0 Data Analysis Methods 13
5.1 Background Data 13
5.2 Anion Cation Balances 14
-------
5.3 Statistical Analysis 14
5.4 Water Quality Index (WQI) 15
5.5 Chloro-alkaline Index 17
5.6 Geochemical Modeling 17
6.0 Louisville Study 18
6.1 Louisville Study Summary 18
6.2 Overview 18
6.3 Louisville Climate 20
6.4 Underground Storage Galleries and Bioinfiltration Areas 21
6.5 Subsurface Monitoring Network 22
6.5.1 Groundwater Monitoring Wells and Piezometers 22
6.5.2 Porewater Samplers 24
6.6 Louisville Hydrogeology Characterization Results 24
6.6.1 Hydraulic Conductivity Structure 24
6.6.2 Groundwater Flow Field Characterization 24
6.6.3 Water Table Mounding Due to Gallery Infiltration 24
6.7 Louisville Geochemical Characterization Study Results 25
6.7.1 Fate and Transport of Constituents through the Vadose Zone 25
6.7.2 Groundwater 50
6.7.3 Summary of Geochemical Analysis 68
6.7.4 Impacts to Groundwater Quality 68
7.0 Yakima Study 72
7.1 Yakima Study Summary 72
7.2 Overview 72
7.3 Climate 73
7.4 Gl Design 74
7.5 Monitoring Network Yakima, Washington 75
7.5.1 Subsurface Monitoring Network - Yakima, Washington 76
7.5.2 Outfall Monitoring Network - Yakima, Washington 76
7.6 Yakima Hydrology 76
7.7 Yakima Study Results 77
-------
7.7.1 Background Groundwater Quality 77
7.7.2 Major Anions and Cations, pH, Specific Conductivity 78
7.7.3 Major Anion and Cation Geochemical Processes 78
7.7.4 Summary of Major Anion and Cation Analysis 85
7.7.5 Other Chemical Constituents 85
7.8 Impacts to Groundwater Quality 89
7.8.1 Regulatory Standards 89
7.8.2 Water Quality Index 92
8.0 Fort Riley Study 94
8.1 Fort Riley Study Summary 94
8.2 Introduction 96
8.3 Climate 97
8.4 Regional Geology/Hydrogeology 97
8.5 Hydrogeologic Setting 98
8.6 Parking Lot and Storage Gallery Design 99
8.7 Subsurface Monitoring Network 101
8.7.1 Groundwater Monitoring Wells and Piezometers 102
8.7.2 Tensiometers and Porewater Samplers 102
8.7.3 Infiltration Gallery Water Level 103
8.7.4 Infiltration Gallery Monitoring Wells 103
8.7.5 Vadose Zone Temperature Profilers 103
8.7.6 Weather Station 104
8.7.7 Fort Riley Subsurface Monitoring Network Summary 104
8.8 Fort Riley Study Results 104
8.8.1 Subsurface Characterization 104
8.8.2 Aquifer Hydraulic Conductivity Structure 113
8.8.3 Groundwater Flow Field Characterization 114
8.8.4 Water-Table Mounding due to Gallery Infiltration 119
8.8.5 Infiltration Gallery Side Wall Temperature Monitoring 119
8.8.6 Vadose Zone Properties 123
8.8.7 Precipitation Patterns During the Study 126
8.8.8 Movement of Water Through the Vadose Zone 127
-------
8.8.9 Fate and Transport of Constituents through the Vadose Zone 138
8.8.10 Vadose Zone Summary 150
8.8.11 Groundwater 151
9.0 Summary, Conclusions, and Future Research Needs 176
9.1 Types of Green Infrastructure 176
9.2 Vadose Zone 176
9.3 Stable Isotopes as Water Movement Tracers 177
9.4 Groundwater 178
9.5 Conclusions 179
9.6 Research Needs 180
References 181
-------
Figures
Figure 1-1. Generalized depictions of Gl surface infiltration structures 2
Figure 1-2. Generalized depictions of Gl subsurface infiltration structures 2
Figure 3-1. Discrete interval sampler 6
Figure 3-2. Temporary well locations at the Fort Riley study site 7
Figure 4-1. Typical equipment used to perform pneumatic slug tests 9
Figure 4-2. Geophysical logging trailer with natural gamma logging tool at FRGW12 12
Figure 6-1. Map of CSO190 sewershed in the Louisville Study 19
Figure 6-2. Louisville temperature and precipitation data from NOAA (2019) 20
Figure 6-3. Bumpouts with bioinfiltration basins used in the Louisville study 21
Figure 6-4. Louisville storage gallery design shown as a cross section 21
Figure 6-5. Louisville storage gallery design shown in a transverse section 22
Figure 6-6. Aerial view of the Louisville study monitoring network 23
Figure 6-7. Groundwater elevation recorded using a pressure transducer/data logger at well MW1 25
Figure 6-8. Plots of major anions and SPC over the course of the study south of Main Street 26
Figure 6-9. Plots of major anions and SPC over the course of the study north of Main Street 27
Figure 6-10. Plots of major cations and SPC over the course of the study north of Main Street 27
Figure 6-11. Plots of major cations and SPC over the course of the study south of Main Street 28
Figure 6-12. Plots of A. Sodium vs. chloride, and B. Sodium + potassium vs. total cations 30
Figure 6-13. A. Plot of calcium + magnesium vs. bicarbonate + sulfate. B. Plot of magnesium vs. calcium.
C. Plot of dolomite SI vs. calcite SI. D. Plot of gypsum SI for soil porewater samples collected south
of Main Street 31
Figure 6-14. Plots indicating the significance of ion exchange reactions 32
Figure 6-15. Plots of A. Sodium vs. chloride. B. Sodium + potassium vs. total cations for soil porewater samples
north of Main Street in the Louisville study 34
Figure 6-16. A. Plot of calcium + magnesium vs. bicarbonate + sulfate. B. Plot of magnesium vs. calcium.
C. Plot of dolomite SI vs. calcite SI. D. Plot of Gypsum SI for soil porewater samples collected
north of Main Street 35
Figure 6-17. Plots indicating the significance of ion exchange reactions 36
Figure 6-18. Plots of fluoride and chloride concentration with time 37
Figure 6-19. Plots of iodide and chloride concentration with time 38
Figure 6-20. Changes in nitrate + nitrite concentration with respect to time for the SPWs north of Main Street.. 39
Figure 6-21. Changes in nitrate + nitrite concentration with respect to time for the SPWs south of Main Street.. 40
Figure 6-22. Changes in phosphate concentration with respect to time for the SPWs north of Main Street 41
Figure 6-23. Changes in phosphate concentration in relationship to time for the SPWs south of Main Street 42
Figure 6-24. Changes in dissolved organic carbon concentrations with respect to time for the SPWs
north of Main Street 43
-------
Figure 6-25. Changes in DOC concentrations in relationship to time for the SPWs south of Main Street 44
Figure 6-26. Changes in barium concentrations with respect to time for the SPWs north of Main Street 45
Figure 6-27. Changes in barium concentrations with respect to time for the SPWs south of Main Street 46
Figure 6-28. A Piper Diagram showing the February 2016, August 2018 extrapolations 53
Figure 6-29. Plots of A. Sodium vs. chloride. B. Sodium + potassium vs. total cations for the Louisville
study site 54
Figure 6-30. Plots of A. Magnesium vs. calcium. B. Calcium + magnesium vs. bicarbonate + sulfate. C. Dolomite SI
vs. calcite SI. D. Gypsum SI 55
Figure 6-31. Plots of the A. Chloro-alkaline index 1 vs. chloro-alkaline index 2 and
B. Na+K-CI vs. Ca+Mg-HC03-S04 56
Figure 6-32. Plots showing the changes in dissolved organic carbon concentrations with time 57
Figure 6-33. Plots showing the changes in fluoride concentrations with time 58
Figure 6-34. Plots showing the changes in iodide concentrations with time 59
Figure 6-35. Plots showing the changes in nitrate + nitrite concentrations with time 60
Figure 6-36. Plots showing the changes in phosphate concentrations with time 61
Figure 6-37. Plots showing the changes in barium concentrations with time 62
Figure 6-38. Plots showing the changes in chromium concentrations with time 63
Figure 6-39. Plots showing the changes in nickel concentrations with time 64
Figure 6-40. Percentage of samples exceeding regulatory standards for analytes which have regulatory
standards 69
Figure 6-41. Water quality indices plotted over time for the Louisville study 70
Figure 7-1. Temperature and precipitation data for the Yakima study site 73
Figure 7-2. Yakima study site aerials before outfall construction and after outfalls were constructed 74
Figure 7-3. Pictures of the outfall at the Yakima study site 75
Figure 7-4. The monitoring network at the Yakima study site 75
Figure 7-5. Groundwater flow directions at the Yakima study site 76
Figure 7-6. Plots of A. Sodium vs chloride. B. Sodium + potassium vs total cations for the Yakima study site 79
Figure 7-7. Plots of A. Magnesium vs calcium. B. Calcium + magnesium vs bicarbonate + sulfate. C. Dolomite
SI vs calcite SI. D. Gypsum SI 81
Figure 7-8. Plots of the A. Chloro-alkaline index 1 vs chloro-alkaline index 2 and
B. Na+K-CI vs Ca+Mg-HC03-S04 82
Figure 7-9. Piper diagram for BCF836 groundwater during the Yakima study 83
Figure 7-10. Piper diagram for MW-06 groundwater during the Yakima study 84
Figure 7-11. Time series plots for dissolved organic carbon concentrations 85
Figure 7-12. Time series plots for nitrate + nitrite concentrations 86
Figure 7-13. Time series plots for fluoride concentrations 87
Figure 7-14. Time series plots for phosphate concentrations 88
Figure 7-15. Percentage of groundwater samples exceeding regulatory standards for analytes which
have regulatory standards 90
-------
Figure 7-16. Percentage of treated wastewater samples from outfalls exceeding regulatory standards
for analytes which have regulatory standards 91
Figure 7-17. Water quality indices plotted over time for the Yakima Study using the Federal Drinking
Water Standards 93
Figure 7-18. Water quality indices plotted over time for the treated wastewater in the outfall in the
Yakima Study using the New York Effluent Limitations for Discharges to Groundwater 93
Figure 8-1. Location of parking lot and the storage gallery at the Fort Riley study site 96
Figure 8-2. Summary of temperature and precipitation data obtained from NOAA for the Fort Riley study 97
Figure 8-3. Location of Seitz Elementary School in relation to surface water features in the area 98
Figure 8-4. A picture of the Fort Riley parking lot showing the PCIP section 99
Figure 8-5. Storage gallery design at the Fort Riley study site 100
Figure 8-6. Photo showing the installation of the storage gallery at the Fort Riley study site 100
Figure 8-7. Aerial view of the soil porewater samplers, piezometers, and monitoring wells at the Fort Riley
study site 101
Figure 8-8. Approximate locations and results of ERI surveys 105
Figure 8-9. Results for transect TIL08-1 adjacent to infiltration gallery 105
Figure 8-10. Responses from natural gamma and induction conductivity logging tools for FRPW04 106
Figure 8-11. Locations of soil EC profiles, monitoring wells and piezometers in relation to the permeable
pavement 107
Figure 8-12. Soil EC profile results for north (TNS1-1) to south (TNS1-8) transect 108
Figure 8-13. Soil electrical conductivity profile (TNS1-7) compared to natural gamma and induction log
from adjacent well (FRPW10) 109
Figure 8-14. Soil EC profiles from north (TEW1-3) to south (TEW4) transect, and natural gamma for
FRGW11 110
Figure 8-15. EC profile results for TNS1-7 agree favorably with the RIL09-1 ERI survey results Ill
Figure 8-16. Percent change in resistivity profile from ERI surveys conducted before and after high intensity
precipitation event on October 8, 2018 112
Figure 8-17. Saturated hydraulic conductivity distribution 113
Figure 8-18. Groundwater elevation recorded using a pressure transducer/data logger at well FRGW01 114
Figure 8-19. Shallow potentiometric surface interpreted from groundwater elevation measurements
obtained using an electronic water level indicator on March 29, 2018 114
Figure 8-20. Shallow potentiometric surface interpreted from groundwater elevation measurements
obtained using an electronic water level indicator on October 9, 2018 115
Figure 8-21. Location of wells FRGW02, FRGW05, and FRGW07 used for automated calculation of temporal
variations in the magnitude and direction of the hydraulic gradient 116
Figure 8-22. Temporal variation in the magnitude and direction of the hydraulic gradient calculated using
groundwater elevation measurements 116
Figure 8-23. Rose diagram depicting the azimuthal distribution of the hydraulic gradient vectors 117
Figure 8-24. Distribution of variation in inferred groundwater flow direction 117
Figure 8-25. Temporal variability of hydraulic gradient and inferred groundwater flow direction 118
Figure 8-26. Trends in monitored precipitation, temperature, and gallery water height 120
-------
Figure 8-27. Trends in monitored vadose zone temperature and gallery water height 121
Figure 8-28. Trends in the rate of vadose zone temperature change as a function of infiltration magnitude 122
Figure 8-29. Comparison of sand, silt and clay content from the continuous cores 123
Figure 8-30. The relationship of vadose zone textural properties to chemical properties 124
Figure 8-31. Concentration profiles vs depth for total aluminum, total calcium, total iron, total manganese,
total silicon, and total organic carbon 125
Figure 8-32. Daily precipitation during the Fort Riley study 126
Figure 8-33. Annual and mean monthy precipitation for the Fort Riley study 126
Figure 8-34. Depths and locations of the soil pore samplers 127
Figure 8-35. Changes in water level elevation inside the infiltration gallery and the daily precipitation
during the study at the Fort Riley study site 128
Figure 8-36. A single precipitation event in April 2016 showing the changes in water level in the infiltration
gallery and rainfall intensity and duration 129
Figure 8-37. A single precipitation event in July 2016 showing the changes in water level in the infiltration
gallery and rainfall intensity and duration 129
Figure 8-38. Surface clogging of a permeable paver infiltration gallery 130
Figure 8-39. Example of tensiometer output from tensiometer T2A 131
Figure 8-40. Soil moisture tension data during the November 17, 2015 precipitation event 132
Figure 8-41. Soil moisture tension data during the May 26, 2016 precipitation event 133
Figure 8-42. Temperature data during the November 17, 2015 precipitation event 136
Figure 8-43. Temperature data during the May 26, 2016 precipitation event 137
Figure 8-44. Chloride, Sodium, and SPC with respect to time for soil porewater clusters 1 and 2 139
Figure 8-45. Chloride and SPC data with time in soil porewater cluster 3 139
Figure 8-46. SPC, chloride and sodium data with time for soil porewater cluster 4 140
Figure 8-47. A. Sodium vs. chloride. B. Sodium + potassium vs. total cations 141
Figure 8-48. A. Plot of calcium + magnesium vs. bicarbonate + sulfate. B. Plot of magnesium vs. calcium.
C. Plot of dolomite SI vs. calcite SI. D. Plot of Gypsum SI 142
Figure 8-49. Plots indicating the significance of ion exchange reactions 143
Figure 8-50. Changes in barium concentration in relationship to time for the Fort Riley SPWs 144
Figure 8-51. A plot of barium vs. chloride 145
Figure 8-52. Changes in uranium concentration in relationship to time for the Fort Riley SPWs 146
Figure 8-53. Plot comparing fluoride concentration and chloride concentration vs time for the SPW data
in the Fort Riley Study 147
Figure 8-54. Changes in nitrate + nitrite concentration in relationship to time for the Fort Riley SPWs 148
Figure 8-55. Plot of 6180 versus 62H for the groundwater collected at the Fort Riley Gl study site 152
Figure 8-56. Plots of the changes in 6180 and 62H with time for the A. Downgradient samples and
B. SPW samples FRLW01 and FRLW09 153
Figure 8-57. Changes in 6180 with time for the SPW and downgradient groundwater in the Fort Riley study 154
Figure 8-58. Changes in 62H with time for the SPW and downgradient groundwater in the Fort Riley study 155
Figure 8-59. Concentration Maps for the October 2018 upgradient sampling at the Fort Riley Gl study site 157
-------
Figure 8-60. Plots of A. Sodium versus chloride and B. Sodium + potassium versus total cations for the
Fort Riley study 159
Figure 8-61. Plots of A. Magnesium versus calcium. B. Calcium + magnesium versus bicarbonate + sulfate.
C. Dolomite Si versus calcite SI. D. Gypsum SI 160
Figure 8-62. Plots of the A. Chloro-alkaline index 1 versus chloro-alkaline index 2 and
B. Na+K-CI versus Ca+Mg-HC03-S04 161
Figure 8-63. Piper diagram examining potential mixing in the groundwater 163
Figure 8-64. Time series plots for phosphate concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 165
Figure 8-65. Time series plots for arsenic concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 166
Figure 8-66. Time series plots for barium concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 167
Figure 8-67. Time series plots for nickel concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 168
Figure 8-68. Time series plots for vanadium concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 169
Figure 8-69. Time series plots for Uranium concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells 170
Figure 8-70. Percentage of groundwater samples exceeding regulatory standards for analytes which have
regulatory standards 174
Figure 8-71. Water Quality indices plotted over time for the Fort Riley Study 175
-------
Tables
Table 5-1. Water quality index scores and water quality assessments 15
Table 5-2. Human health-based standards used for the WQI analysis in this study 16
Table 6-1. Saturated hydraulic conductivity of aquifer materials estimated using pneumatic slug testing
techniques in monitoring wells installed near the stormwater control measures 24
Table 6-2. Summary of trace inorganic data for the Louisville soil porewater samples 48
Table 6-3. Study specific background ranges for select analytes determined for the Louisville Gl Study 51
Table 6-4. Slopes from the trend analysis that are being used for rate of change in parameter concentrations.... 52
Table 6-5. Summary of trace inorganic data for Louisville groundwater samples 66
Table 6-6. Rates of change for parameters north and south of Main Street for the Louisville Study 67
Table 6-7. Initial parameter concentrations, last sampling concentrations, and concentrations extrapolated
5 and 10 years into the future assuming rates in Table 6-6 67
Table 7-1. Study specific background ranges determined using the 2-sigma method on NWIS data
for the Yakima Study 78
Table 8-1. Saturated hydraulic conductivity of aquifer materials estimated using pneumatic slug testing
techniques in wells and piezometers 113
Table 8-2. Estimated lag times of water exiting the infiltration gallery to reaching tensiometers for the
November 17, 2015 and May 26, 2016 precipitation events 135
Table 8-3. Summary of trace components from the Fort Riley soil porewater samples 149
Table 8-4. SVOC compounds detected in the soil porewater samples collected at the Fort Riley study site 150
Table 8-5. Study specific background ranges determined for the Fort Riley Gl Study 156
Table 8-6. Summary of trace components for the Fort Riley groundwater samples 172
-------
Acronyms and Abbreviations
°c
Degrees Celsius
AASHTO
American Association of State Highway and
Transportation Officials
ANOVA
Analysis of Variance
ASR
Aquifer Storage and Recovery
bgs
Below Ground Surface
CAI
Chloro-Alkaline Indices
cm
Centimeter
CRADA
Cooperative Research and Development
Agreement
CSO
Combined Sewer Overflows
CSS
Combined Sewer Systems
d
Day
DIC
Dissolved Inorganic Carbon
DOC
Dissolved Organic Carbon
DWEL
Drinking Water Equivalent Level
EC
Electrical Conductivity
Eh
Measurement of redox state
ERI
Electrical Resistivity Image
GCRD
Groundwater Characterization and
Remediation Division
Gl
Green Infrastructure
h
Hour
HDPE
High-Density Polyethylene
L
Liter
lymin
Liters per minute
km2
Square kilometer
m
Meter
m3
Cubic meter
m3/s
Cubic meter per second
m/s
Meters per second
MCL
Maximum Contaminant Level
meq/L
Milliequivalents per liter
min
Minute
ML
Megaliter
mL
Milliliter
mL/min
Milliliter per minute
mm
Millimeter
msl
Mean Sea Level
MOU
Memorandum of Understanding
MSD
Metropolitan Sewer District
mV
Millivolt
MW
Monitoring Well
NOAA
National Oceanic and Atmospheric
Administration
NSQD
National Stormwater Quality Database
NURP
Nationwide Urban Runoff Program
ORD
Office of Research and Development
ORP
Oxidation-Reduction Potential
PICP
Permeable Interlocking Concrete Pavers
PVC
Polyvinyl chloride
PW
Piezometer
RARE
Regional Applied Research Effort
SCM
Stormwater Control Measures
SI
Saturation Index
a
Standard deviation
sMCL
Secondary Maximal Contaminant Level
SPC
Specific conductivity (nS/cm)
SPW
Soil Porewater
SSWR
Safe and Sustainable Water Resources
SVOC
Semi-volatile Organic Compound
TDS
Total Dissolved Solids
TOC
Total Organic Carbon
UIG
Underground Infiltration Gallery
USACE
United States Army Corps of Engineers
U.S. EPA
United States Environmental Protection
Agency
VOC
Volatile Organic Compound
WQI
Water Quality Index
WQU
Water Quality Unit
xiv
-------
Executive Summary
In the 1800s, engineering solutions for stormwater management were developed in urban areas (Burian and
Edwards, 2002; Dietz, 2007; Fletcher et al., 2015; Brumley et al., 2018). Pipes, sewers, and drainage networks
collect runoff and convey stormwater to treatment facilities or move the stormwater from the urban areas
to surface water bodies. The impermeable surfaces that characterize urban areas do not allow infiltration of
stormwater to the subsurface decreasing access to subsurface storage capacity and increasing the flood risks.
Green Infrastructure (Gl) systems are designed to address these concerns by mimicking natural systems by
infiltrating runoff, facilitating groundwater recharge, and increasing storage volume. Additionally, Gl systems
reduce stress on wastewater systems, decrease combined sewer overflows, modulate peak flow during storm
events, restore impaired urban waters, and improve watershed health (Bedan and Clausen, 2009; U.S. EPA, 2010;
Tedoldi et al., 2016).
In 2010, the United States Environmental Protection Agency (U.S. EPA) produced a report that analyzed several
case studies on the use of Gl throughout the United States (U.S. EPA, 2010). This report promoted the use of Gl
technology; however, the effects of Gl on groundwater quantity or quality were not addressed. Other reviews on
the use of Gl focused on other parts of the local hydrologic cycle, surface and underdrain water contamination,
or studied only one type of Gl system (Brattebo and Booth, 2003; Bedan and Clausen, 2009; Eckart et al.,
2017). Generally, the studies did not address whether Gl systems could potentially create a significant risk to
groundwater by creating new pathways for contaminants to be infiltrated, mobilized and transported to the
subsurface and then to groundwater resources. Contaminants may include nutrients (e.g., nitrate), metals (e.g.,
arsenic), dissolved anions (e.g., chloride), pathogens (e.g., fecal coliforms), pesticides (e.g., atrazine), and other
organic compounds (e.g., benzene) that would come from automobiles, lawn fertilizers and pesticides, sewer
overflows, and road de-icing salts.
In 2015, U.S. EPA initiated a study to examine the potential impacts to groundwater quality from the use of Gl
and the results will be presented in this report. This study was conducted under the Safe and Sustainable Water
Resources program (SSWR). An overarching objective of the SSWR program is to support increased adoption of
green infrastructure into community stormwater management plans and watershed/sewershed sustainability
goals by providing information and guidance. As part of this SSWR project, three diverse locations were selected
to study based on climate, geology, type of infrastructure used, and geographic location. The three sites are
Louisville, Kentucky, Fort Riley, Kansas, and Yakima, Washington. The Louisville site is located in the CSO-190
sewershed and primarily uses underground storage galleries as the Gl to capture the stormwater runoff. The
Louisville study site has a humid sub-tropical climate. The Yakima site has an arid climate and the study is
unique because wastewater is being infiltrated on the flood plain of the Yakima River. The Fort Riley site has a
permeable pavement system that infiltrates stormwater runoff and is located at Seitz Elementary School on U.S.
Army Fort Riley post. Fort Riley is in north eastern Kansas which has a humid sub-tropical climate. The purpose
of this report is to draw conclusions from these studies to provide an initial assessment on the influence of Gl
on groundwater quality. This assessment will look at the individual site monitoring data through the autumn of
2018.
In general, all three studies measured a common set of geochemical and groundwater parameters and fall into
the following general categories: field parameters, carbon species, nutrients, anions, dissolved metals, and stable
isotopes of water. With a few exceptions within any category the measured parameters were identical. The
Fort Riley study had additional organic parameters measured that were not measured in the other two studies.
For the two studies that used engineered Gl systems, Louisville and Fort Riley, the hydrology of the system was
investigated. The hydrology investigations of the system used sensors, hydrogeological methods, and geophysical
tools to understand the movement of water in the system.
XV
-------
The results of the Louisville study indicated that there were changes to groundwater quality in wells near the Gl,
however, the traditional stormwater contaminants do not appear to be accumulating in the groundwater. Based
on the analysis of the soil porewater and groundwater in this study, major anions and cations concentrations are
decreasing with time. Ion exchange and dissolution/precipitation reactions are likely the dominant geochemical
processes occurring. One process that could be problematic is reverse ion exchange reactions that may cause
sodium loading on the fine-grained materials in the vadose zone and aquifer. Plugging could occur if these
particles disperse and lead to lower stormwater infiltration rates. Phosphate groundwater concentrations
increased during this study which is problematic if groundwater is used as an irrigation source. Additionally,
surface water could be threatened if these sediments were allowed to erode into surface water body.
At the Louisville site, chromium groundwater concentrations may have begun to increase. If the groundwater at
the site was used as a drinking water source, it could be problematic for chromium concentrations to increase.
In the case of the Louisville study the groundwater is not being used as a drinking water source. Analysis of the
groundwater compared to regulatory standards indicated the groundwater receiving the stormwater runoff is
only of minor risk. The Water Quality Index (WQI) analysis also indicated that the groundwater quality in most
cases was suitable for human consumption. The greatest risk to groundwater quality is the dilution effect caused
by the infiltration of stormwater into the groundwater. The dilution is changing water from a calcium-bicarbonate
type water to a sodium bicarbonate type water. If the groundwater was being used as a water supply the dilution
could have negative effects to water treatment because most water treatment plants are designed to treat water
over a narrow range of water quality. Changes in water type could also have negative effects to the water supply
distribution system in older systems that have scale build up.
The results of the Yakima study indicated that the groundwater near the outfalls had changed as the result of the
infiltration of treated wastewater. Based on the analysis of the major anions and cations, geochemical processes,
and other constituents (DOC, nitrate + nitrite, fluoride, and phosphate) leads to the conclusion that mixing of
the upgradient groundwater with the infiltrating treated wastewater from the outfall is likely the major process
changing the water quality. The analysis of the water quality in relationship to regulatory standards suggested
that the contaminants of concern in the mixed groundwater and treated wastewater were nitrate + nitrite and
arsenic. These contaminants were present in the upgradient groundwater. Nitrate + nitrite would be the bigger
concern in the groundwater if the State of Washington arsenic regulatory limits were not considered. When the
State of Washington arsenic regulatory limits are considered, then all the samples collected which had detectable
arsenic would be of concern. The WQI analysis determined that the groundwater and treated wastewater had
excellent to good water quality during the study. WQI analysis of the treated wastewater effluent with the New
York effluent limits showed that the treated wastewater was good. Reverse ion exchange did not seem to be an
important geochemical process in the wells near the outfall. This suggested that sodium loading is not an issue
in this study. There was evidence that phosphate concentrations in the groundwater were increasing and if the
increase continues, it could be problematic to surface water at some point. Finally, the infiltration of treated
wastewater could be an important management practice for the disposal of wastewater.
The Fort Riley study was the most comprehensive Gl study in this report. The vadose zone and aquifer materials
were characterized for chemical and physical properties, the hydrogeology was characterized, and water quality
samples were taken. The characterization of the vadose zone and aquifer showed that there was decrease in
finer grained materials with depth in the vadose zone. There was also heterogeneity at the study site, and in
general, the clay content decreased from north to south across the study site. The decreases in fine-grained
materials were also related to the chemical properties of the vadose zone and aquifer sediments. The cation
exchange capacity, total calcium, total sodium, total aluminum, total manganese, total iron, total silicon,
total organic carbon, and total carbon concentrations decrease with depth. This means that the sorptive and
reactive properties of the vadose zone materials decrease with depth. The ability of the vadose zone to remove
contaminants and modify the infiltrated groundwater decreases with depth.
-------
The hydrologic analysis suggested that the measured changes in water table elevation were not related to the
infiltrated stormwater. The water table elevations were a function of the natural infiltration and changes in
height of water in the stormwater retention basin south of the Gl site. There was little evidence for mounding.
Analysis of temperature data and tensiometer performance suggest that recharge water moved through the
vadose zone in a matter of hours to a day. The tensiometers also showed that there was an effective radius in
which the water moves from the bottom and sides of the infiltration gallery. The infiltrated water only moved
out from the infiltration gallery to a radius of less than 3.1 m. The stable isotopes of water can also be used to
track the mixing of the infiltrated water in the groundwater.
The analysis of water in the vadose zone indicated that major anions and cations were transported through
the vadose zone. The vadose zone data collected, indicated that ion exchange and precipitation/dissolution
reactions were the most important. In the later sampling dates (in 2018), there was indication that reverse ion
exchange may be occurring. Reverse ion exchange could potentially be problematic since sodium could build up
on the vadose zone fine-grained particles. Excess sodium on these fine-grained particles could lead to dispersion
and dispersion could lead to plugging in the aquifer solids leading to reduced infiltration. In the vadose zone,
it was demonstrated that chloride (a conservative species) moved through the vadose zone faster than other
analytes. This also shows that the movement of nonconservative species is retarded by interactions with the
vadose zone solid phases. Another important point that chloride showed was that as chloride concentrations
increased, barium concentrations also increased and would be more mobile because of complexation of barium
with chloride forming a soluble barium chloride complex in solution. Other metals of concern can similarly be
mobilized by chlorides.
The groundwater analysis showed that the upgradient concentrations of some chemical species (e.g., uranium)
were larger than the background groundwater and the groundwater downgradient. The upgradient source
for these chemical species is not known. Sulfate concentrations in general in the downgradient wells were
decreasing similar to what was observed in the soil porewater samples. Background wells and upgradient
wells showed no trends in sulfate concentrations or increasing sulfate concentrations suggesting that the
decreasing trends in the downgradient wells were from dilution caused by the infiltrated stormwater. There
was also evidence of dilution in barium, strontium, arsenic, and uranium. There was also evidence for mixing
causing changes to groundwater quality downgradient of the infiltration gallery. The increasing phosphate
concentrations in the wells immediately downgradient of the infiltration gallery were not related to infiltrating
water. The increasing phosphate concentrations appeared to be from upgradient groundwater moving under the
infiltration gallery to these wells.
The dominant geochemical process affecting groundwater in this study varied. In some cases, such as sodium
and potassium, the dominant process appeared to be ion exchange or reverse ion exchange. In other cases,
such as with calcium and magnesium the dominant process was likely precipitation/dissolution reactions. In this
study, there was not one dominant mechanism affecting the groundwater quality downgradient. The analysis
of the organic contaminants in the study indicated that most of the organic contaminants measured were not
detectable. There was no detected organic compound that had concentration of regulatory concern. The results
of the water quality in reference to regulatory standards or advisory limits suggested that the groundwater was
potentially a risk to human health. The main drivers of risk were the MCL exceedance of uranium and arsenic
that were identified in the upgradient wells. The WQI analysis indicated that the background well water quality
and downgradient water quality was excellent too good, using the criteria discussed in Table 5.1. The upgradient
wells' groundwater quality was rated as good to poor based on the WQI analysis.
-------
Key observations and findings:
• All three studies showed changes in groundwater quality.
• Mixing infiltrate water with groundwater can cause groundwater changes downgradient of the Gl
infrastructure.
• The concentration of traditional stormwater contaminants did not pose a concern with the potential
exception of chromium in the groundwater in Louisville.
• One of the greatest risks to groundwater quality is the dilution effect caused by the infiltration of
stormwater into the groundwater.
• Dilution of the groundwater by the infiltrated water could change the drinking water source chemistry
and impact water treatment. Dilution can be problematic if the groundwater is used as a source of
drinking water.
• Phosphate concentrations increased once infiltration of water began although the phosphate
concentration increases in the Fort Riley study may not be related to Gl.
• Reverse ion exchange is potentially occurring at the Louisville and Fort Riley study sites. Increased sodium
loading on the fine-grained particles in the vadose zone could lead to clogging of the pore spaces and
diminish infiltration and water movement.
• The solubility and mobility of barium was affected by the concentrations of chloride. When chloride
concentrations increased the barium concentrations also increased. The converse was also true. This
relationship between chloride and barium can also happen with other metals such as cadmium.
The enhanced mobility of some metals could be potentially problematic when chloride salts are applied
as de-icing agents and the stormwater is infiltrated.
-------
1.0
Introduction
Engineering solutions for stormwater management in urban areas have been undertaken since the late 1800s
(Burian and Edwards, 2002; Dietz, 2007; Fletcher et al., 2015; Brumley et al., 2018). In built environments, the
removal of vegetation and soil is a common practice. The vegetation and soil are replaced with impervious
surfaces such as roads, buildings, parking lots, and driveways (Konrad, 2003; U.S. EPA, 2010; Brumley et al.,
2018). The replacement of natural vegetation and soil with impervious surfaces leads to an increase in flood
frequency (Konrad, 2003; Brumley et al., 2018). Because of the impermeable surfaces in built environments,
there is less infiltration and an increase in flood risk (Brumley et al., 2018). Runoff from urban development leads
to unnaturally high volumes of stormwater that move quickly over the paved surfaces to erode stream banks;
thus, large amounts of sediment can enter downstream water bodies (U.S. EPA, 2010; Brumley et al., 2018).
Traditional stormwater management relies on engineered infrastructure such as pipes, sewers, and drainage
networks that transport water to treatment facilities or rapidly move the water from the urban area and
downstream into receiving waters (Berland et al., 2017; Konrad, 2003; Brumley et al., 2018). This upsets the
natural hydrologic equilibrium by disrupting groundwater and stream recharge in favor of moving water out
of the watershed (U.S. EPA, 2010; Brumley et al., 2018). In contrast, after a precipitation event in undisturbed
settings, stormwater largely infiltrates, flowing through the vadose zone as a subsurface flow until the storage
capacity is reached and recharges aquifers or discharges into stream networks (Konrad, 2003; Brumley et al.,
2018).
Green Infrastructure (Gl) systems have been developed to mimic natural infiltration and hydrologic processes
analogous to undisturbed settings. These Gl systems mimic natural systems by infiltrating runoff, facilitating
groundwater recharge, restoring and retaining storage volume and correcting the impacts of urbanization
(Shuster et al., 2014; Shuster et al., 2015; Berland et al., 2017). Additional advantages of Gl systems involve
reducing stress on wastewater systems, decreasing combined sewer overflows (CSOs) to receiving water,
decreasing peak flow during storm events, restoration of impaired urban waters, and improving watershed
health (Bedan and Clausen, 2009; U.S. EPA, 2010; Tedoldi et al., 2016).
Green Infrastructure generally falls into two categories: surface infiltration and subsurface infiltration (Pitt et
al., 1999). Surface infiltration involves water infiltrating from the surface, mimicking natural environmental
processes. Examples of surface infiltration structures include bioretention basins and grass swales (Figure
1-1). Subsurface infiltration is when water is directly infiltrated into the vadose zone (subsurface). Examples of
subsurface infiltration are permeable pavement and dry wells (Figure 1-2). The effectiveness of Gl technologies
is determined by the local hydrogeological conditions. Gl can be beneficial in residential areas, commercial
areas, industrial areas, parking lots, roads and highways (Tedoldi et al., 2016). Gl generally is a more spatially
distributed approach to stormwater management (Dietz, 2007).
The United States Environmental Protection Agency (U.S. EPA) examined several case studies on the use of Gl
throughout the United States, including the policies, goals, and incentives for Gl stormwater management (U.S.
EPA, 2010). U.S. EPA promoted the use of Gl technology; however, the effects of Gl on groundwater quantity
and quality were not addressed. A variety of reviews on the use of Gl have been done, but these reviews mainly
focused on the hydrology, surface and underdrain water contamination, or on one type of Gl system (Brattebo
and Booth, 2003; Bedan and Clausen, 2009; Eckart et al., 2017). The limited studies that have been done on
Gl designs generally rely on assumptions from local government surveys and models. Most do not incorporate
monitoring after installation for groundwater impacts (Bedan and Clausen, 2009). Generally, the studies did not
address whether Gl systems would be a source or sink for stormwater contaminants, or whether they pose a
risk for groundwater contamination. Gl technologies could potentially pose a risk to groundwater because these
systems create new pathways for contaminants from stormwater, anthropogenic activities, and wastewater to
-------
UfflWUafi#/|7r*vrt
Mfitoilkw
pa
Figure 1-1. Generalized depictions of Gl surface infiltration structures. A. Bioretention basin, and
B. Grass swale. (After Brumley et al., 2018).
InHltrat&in
Figure 1-2. Generalized depictions of Gl subsurface infiltration structures. A. Permeable pavement, and
B. Dry well. (After Brumley et al., 2018).
Gravel
B
ftjjrmH
(HP*
be infiltrated to groundwater that did not exist before Gl installation (Schirmer et al., 2013). These contaminants
include nutrients, metals, dissolved minerals, pesticides, other organics, and pathogens. Sources can include
residues from automobiles, lawn treatments such as fertilizers and pesticides, sewer overflows, and road deicing
salts (U.S. EPA, 1983; NSQD, NSQ.D, 2015).
Another potential mechanism for groundwater contamination occurs when the captured stormwater runoff
interacts with constituents in the soil and vadose zone mobilizing existing contamination or naturally occurring
constituents. These interactions include acid-base reactions, mineral precipitation and dissolution, sorption, ion
exchange, oxidation-reduction reactions, biodegradation, mixing relationships, and dissolution and evolution of
gases. Colloidal transport is also a concern—something that happens when the contaminant sorbs onto a benign
colloid and moves through the substrate (de Jonge et al., 2004). These interactions potentially create risks to
groundwater quality (Tedoldi et al., 2016).
Brumley et al., (2018) published a literature review of contamination to groundwater as the result of Gl use. The
findings of this review were inconclusive; impacts, potential impacts, or no impacts were found. In most cases,
the review found that the study results were extrapolated to predict what may occur to the groundwater. These
results overlooked other mechanisms that could enable contaminant transport to the groundwater. In other
cases, there was no monitoring of contaminants in aquifers or deeper in the vadose zone and no groundwater
quality assessments.
-------
According to Brumley et al. (2018) when groundwater monitoring was conducted, it was unknown if the
sampling strategies used would detect changes in groundwater quality. In most cases information on
groundwater flow direction was limited and the relationship of monitoring points to the potential transport of
contaminants could not be determined. In addition, many of the studies did not account for lag between water
infiltration and the time it takes to transport the infiltrated water through the vadose zone to the aquifer. Lag
time is an important consideration because it is critical in understanding the transient changes to groundwater
quality. The only systems that consistently showed impacts to groundwater quality were the aquifer storage
and recovery (ASR) systems. With ASR unintended consequences were found (Brumley et al., 2018) and these
unintended consequences generally occur when the mixing of two waters with different composition and
characteristics happens. This mixing results in contamination of the aquifer from natural occurring constituents.
Urban riparian zones function as Gl systems (Brumley et al., 2018). Only a few studies have been conducted on
their influence on groundwater, and there are no studies that specifically look at urban riparian zone Gl as a
stormwater infiltration technology.
Due to the absence of understanding the potential risks posed to groundwater by stormwater infiltration, U. S.
EPA initiated a study examining the potential impacts to groundwater quality in 2015. Three study locations were
selected based on climate, geology, type of infrastructure used, and geographic location. The Fort Riley study is a
permeable pavement system that infiltrates stormwater runoff and is located on the Fort Riley Post. Fort Riley is
in northeastern Kansas, which has a humid sub-tropical climate. The second study site is in Louisville, Kentucky,
in the CSO-190 sewershed and primarily uses underground storage galleries to capture and infiltrate stormwater
runoff. Louisville has a humid sub-tropical climate. The final study is the Yakima study. This study is located
in Yakima, Washington, an arid climate. The Yakima study differs from the previous two studies since treated
wastewater is being infiltrated on the flood plain of the Yakima River. These studies are part of the Gl research
program in the EPA's Office of Research and Development (ORD) Safe and Sustainable Water Resources (SSWR)
research program.
The purpose of this report is to provide an initial assessment on the influence of Gl on groundwater quality. This
assessment will look at the individual site monitoring data through autumn 2018 for each site.
-------
2.0
Analytical Methods
In general, all three studies measured a common set of parameters and fall into the following general categories:
field parameters, carbon species, nutrients, anions, dissolved metals, and water isotopes. With a few exceptions
within any category the measured parameters were the same. The Fort Riley study had additional organic
parameters not measured in the other two studies.
2.1 Field Parameters
Field parameters measured were temperature, alkalinity, pH, oxidation-reduction potential (ORP), specific
conductivity, dissolved oxygen, turbidity, and total dissolved solids (TDS). Table A-l in Appendix A lists the field
parameters measured as part of this study, the method used, and the studies in which the parameter was
measured.
2.2 Dissolved Metal, Nutrient, Anion, Carbon Species, and Water Isotope
The carbon species measured in all three studies were dissolved organic carbon (DOC), dissolved inorganic
carbon (DIC), dissolved carbon dioxide, bicarbonate, and carbonate. The nutrients analyzed were nitrate + nitrite,
ammonia, total nitrogen (for Fort Riley and Louisville), total Kjeldahl nitrogen (for Yakima), phosphate, and total
phosphorous. In all three studies, the following anions were measured: bromide, chloride, sulfate, fluoride,
and iodide. For the dissolved metals, analysis in all three studies measured the following metals: aluminum,
antimony, arsenic, barium, beryllium, boron, calcium, cadmium, cobalt, chromium, copper, iron, potassium, lead,
lithium, magnesium, manganese, molybdenum, nickel, selenium, silicon, silver, sodium, strontium, titanium,
thallium, vanadium and zinc. Two additional dissolved metals were measured in the Fort Riley and Louisville
studies: thorium and uranium. Finally, all three studies measured the stable water isotopes 62H and 6180. Table
A-2 in Appendix A gives the analytical methods, preservation, and holding times for these parameters collected
in soil porewater and groundwater samples.
2.3 Organic Parameters
The Fort Riley study also included organic parameters. The organic parameters were volatile organic compounds
(VOC) listed in Table A-3 and other organic compounds listed in Table A-4 in Appendix A.
-------
3.0
Water Quality Sampling Methods
3.1 Fort Riley
Sampling for the Fort Riley study consisted of sampling the groundwater, and soil porewater (SPW).
3.1.1 Monitoring Wells
Groundwater was collected by a groundwater sampling network of monitoring wells and piezometers described
later in individual study sites (Section 6, 7, and 8). Water level measurements were taken before placing the
pump into the well. The water level measurements followed the K-GCRD-SOP-1132-O standard operating
procedure. With an exception of the discrete interval sampling, the groundwater was collected using a
submersible centrifugal pump. For the monitoring wells, the pump intake was set 0.30 m below the water table
when the water table was below the top of the well screen and at the top of the screen when the water table
was more than 0.30 m above the top of the screen. For piezometers, the pump was placed 0.30 m below the
top of screen (in the middle of the screened interval). Attached to the pump is a dedicated piece of high-density
polyethylene (HDPE) tubing to convey the water to the surface. The pumping rate of less than 0.5 L/min was
used to limit drawdown.
Initially, the end of the HDPE tubing was connected to a flow cell equipped with an YSI 5600 multiparameter
probe to monitor the stabilization of geochemical parameters pH, ORP, specific conductivity, dissolved oxygen,
and temperature. The stabilization of geochemical parameters indicates that formation water is being extracted
from the well (Table A-5, Appendix A). Once geochemical parameter stability had been achieved, the HDPE
tubing was disconnected from the flow cell and a series of unfiltered samples was collected for VOCs, turbidity
and alkalinity (Table A-l and Table A-3, in Appendix A). Turbidity and alkalinity were analyzed in the field. Once
the unfiltered samples were collected, a series of filtered samples were collected for dissolved metals, anions,
nutrients, carbon species, and stable isotopes of water (Table A-2, Appendix A).
3.1.1.1 Discrete Interval Sampling of Monitoring Wells
To better understand the aquifer hydrology and the potential chemical stratification in the aquifer, the
monitoring wells were sampled at discrete intervals along the 3.1 m screen. The 3.1 m screen was divided
into 0.6-m sections with a 0.15-m interval between sections. This allowed for up to four discrete samples to
be taken if the well screen was fully saturated or fewer depending on the depth to water in the well. Discrete
interval sampling took place in three events, August 2016, June 2017, and November 2017 to capture changes in
groundwater elevation at the site.
The discrete sampler was a 0.6-m section of screen sandwiched between two packers used to isolate 0.6-m
sections of the aquifer for sampling (Figure 3-1). The pump placed in the discrete sampler screen segment
was either a submersible centrifugal pump or a 0.03 m bladder pump. The pump intake was located within
the screened interval of the discrete interval sampler. To ensure that water entering the discrete sampler was
from the packed off interval, a low flow rate was applied to the pump (less than 0.25 L/ min). Water level
measurements were taken before placing the pump in the well. The water level measurements were used to
determine the depth of the first sampling interval. The discrete interval sampler was lowered into the well to
the desired depth of the first sampling interval which was at the water level determined. A dedicated piece of
HDPE tubing was connected to the pump. The other end of the tubing was connected to a flow cell equipped
with an YSI 5600 multiparameter probe. The collection of samples followed the procedure outlined for the
monitoring wells (above). Once the samples were collected at the first interval, the sampler was lowered to the
next sampling interval. The next sampling interval was 0.15 m from the bottom of the previous sampling interval,
or the sampler was lowered 0.71 m. The sampler was lowered until all desired sampling intervals in a well were
obtained.
-------
Figure 3-1. Discrete interval sampler. A. shows the sampler and B. is a close-up of the upper packer.
3.1.1.2 Upgradient Sampling
Preliminary data collected at the Fort Riley Study Site indicated the potential for higher concentrations of some
analytes upgradient at the study site. Therefore, an upgradient sampling plan at the site was developed to
characterize groundwater moving into the site. As part of this sampling plan, seventeen temporary wells were
installed (See Figure 3-2 for temporary well locations) upgradient of the existing groundwater network.
Temporary monitoring wells were installed in October 2018. The wells were installed approximately 1.5 m
below the potentiometric surface observed at the time of drilling (9.1 to 12.2 m below land surface). Table D-l,
Appendix D provides the location of the temporary wells.
Temporary monitoring wells were constructed with 5.08-cm, threaded PVC screens and casings with O-rings
installed by hand using sand and bentonite packs as required. Upon completion, the wells were developed
by pumping and surging with a Proactive stainless-steel submersible pump until the produced water cleared.
Once the temporary well was developed, samples were collected (as outlined in Section 3.1.1). After sample
collection, the temporary well was decommissioned.
3.1.1.3 Soil Porewater Sampling
Soil porewater was sampled using soil porewater samplers (UMS SIC20 and SICS20). The K-GCRD-SOP-1153-O
describes the detailed procedure used. To extract soil water in the unsaturated zone, the soil water tension must
be surpassed by the sampler's potential - that is, a vacuum must be applied.
This method uses discontinuous evacuation method with a portable vacuum pump. The collection bottle was
attached to the porewater sampler tube using an airtight connection. The collection bottle was then evacuated
to approximately -0.85 bar. If the soil water tension is more than -0.85 bar, SPW will be extracted until vacuum
and SPW tension equilibrate. The collection bottle was evacuated again, and the cycle repeated until the desired
volume of soil porewater is collected. The collection flask was placed in a cooler with ice for preservation. When
the temperature was below freezing, samples were not collected to avoid water freezing in the collection tubing
and damaging the sampler.
Once the collection of the porewater sample was finished, the collection bottle was disconnected from the
porewater sampler tubing. The porewater sample was then passed through a 0.45 [im filter using a 60 mL
syringe to collect the samples for the desired analytes (see Section 3.1.1). Because the volume of porewater
sample collected was sometimes limited, sample parameter collection priority was established. During samplings
in which organic parameters were collected, the priority was to collect a sample for organic compounds. Then,
depending on the remaining sample volume, the samples were prioritized based on established procedure.
-------
mm
TEW4
AFRGW08
TEW1-1
w O,
TEW2-1 TEW2-2
n £
TEW2-3
TEW3-1
Figure 3-2. Temporary well locations at the Fort Riley study site. Red balloons indicate the locations where the temporary wells were installed. The
cyan circles show existing monitoring wells, the red area is the location of the infiltration gallery, and the black outlined area is the outline of the
parking lot. (Source: Google Earth)
-------
3.2 Louisville
3.2.1 Groundwater Sampling
The groundwater sampling methods for the Louisville study site were the same as those used in the Fort Riley
study with the exception that VOCs and organic compounds were not sampled in this study. See Section 3.1.1.1
for sampling method description.
3.2.2 Soil Porewater Sampling
Soil porewater sampling at the Louisville study site was the same as the Fort Riley study, again except for the
organic compounds which were not sampled as part of this study. See Section 3.1.1.4 for sampling method
description.
3.3 Yakima
Sampling for the Yakima study consisted of sampling the groundwater and outfalls. The methods by which each
of these was sampled are described below.
3.3.1 Groundwater Sampling
Groundwater was collected from a groundwater sampling network described later (Section 7.3.1). Water
level measurements were taken prior to placing the pump in the well or pumping the well. The water level
measurements followed the K-GCRD-SOP-1132-O standard operating procedure. The water level obtained was
used to determine the purge volume using Equation 3.1 and using three well volumes for the purge.
Purge Volume (mL) = 3xnxr2xH Equation 3.1
where H = water level in cm from the bottom of the well, r = radius of the well casing in cm.
Once the purge volume was calculated, new polyethylene tubing was inserted into the well with the inlet of the
tubing about 15 cm from the bottom of the well. The surface end of the polyethylene tubing was attached to a
peristaltic pump. The pumping rate was set to less than 500 ml/min. The well was pumped until water was clear
of all sediment and three well volume purged the sample collection followed the same procedures as Fort Riley.
For samples that require filtering, filtered with 2.5-cm or 4.7-cm diameter 0.7 pim GF/F filters using filter cups
under low vacuum, peristaltic pump, or with a 60 mL syringe and syringe filter holders.
3.3.2 Outfall Sampling
Outfall samples at the Yakima study site consisted of a series of grab samples for the parameters of interest
(Section 2) or by use of a peristaltic pump (Narr et al., 2019). Grab samples were collected by directly submerging
the sample bottles into the water body and allowing the bottle to fill. Sample collection using the peristaltic
pump was accomplished by placing HDPE tubing in the water body. Once the tubing was in place, the pump was
used to transfer water to fill the sample bottles. Geochemical parameters were collected in the field by filling
the cup of the multi-parameter sonde (Hydrolab) and allowing the probes to stabilize. Once probes stabilized the
values of the geochemical parameters were recorded.
8
-------
4.0
Hydrogeologic Methods
4.1 Water-Level Measurements
Manual water-level measurements in wells and piezometers were routinely performed as described in
standard operating procedure K-GCRD-SOP-1132-O and automated measurements using pressure transducers
were performed according to procedure K-GCRD-SOP-1134-O. Hydraulic gradients were estimated using
both potentiometric surfaces interpreted from manual groundwater elevation measurements and the 3PE
spreadsheet application (Beljin et al., 2014). This application allowed gradients to be estimated on a frequent
basis which provided more representative information regarding the range of hydrologic conditions at the site.
4.2 Hydraulic Conductivity
Field measurements of hydraulic conductivity at both the Fort Riley Kansas, and Louisville, Kentucky sites were
made using pneumatic slug testing techniques as described in procedure K-GCRD-SOP-1103-1. Due to the high
hydraulic conductivity of the aquifer materials, pneumatic slug tests were performed using equipment like that
depicted in Figure 4-1. The method was based on recommendations derived from Butler (1997) and used air
pressure and vacuum to initiate near-instantaneous changes in hydraulic head within the well combined with
high frequency monitoring of the aquifer response using a data logger and pressure transducer. This method was
needed for meaningful slug tests in the high-hydraulic conductivity media at these sites. The response data were
analyzed using the methods of Bouwer and Rice (1976) and Springer and Gelhar (1991).
Computer
I'nemuatic Manifold Assembly
/ Regulator
Pressure Gauge
Data
Logger
Compressor
Quick Release Valve Wellhead ******
Land Surface
Water Table
— Pressure Transducer
"Well Casing
- Well Screen
Figure 4-1. Typical equipment used to perform pneumatic slug tests.
-------
4.3 Borehole Flowmeter
A sensitive electromagnetic borehole flowmeter was used to define the hydraulic conductivity distribution of
aquifer materials at the Fort Riley site using procedure K-GCRD-SOP-1092-1. The studies consisted of measuring
the vertical component of groundwater flow at fixed intervals in wells under undisturbed (ambient) and pumping
conditions. Measurements made during constant-rate groundwater extraction defined the distribution of
groundwater flow to the well. The rate of water flow to the well from an individual interval was proportional to
the hydraulic conductivity of the materials adjacent to the screen. Therefore, knowledge of the contribution of
flow from each measurement interval allows interpretation of the hydraulic conductivity of each interval relative
to the average hydraulic conductivity of materials screened by the well (Molz et al., 1994; Young et al., 1998).
The electromagnetic borehole flowmeter used in these studies was a commercially available system
manufactured by Tisco, Inc., consisting of a 1.3-cm ID downhole probe, a 2.5-cm ID downhole probe, and an
uphole electronics module. Probe design is based on Faraday's Law that states that the voltage induced by an
electrical conductor moving through a magnetic field is directly proportional to the velocity of the conductor.
The major components of the probe include an electromagnet and a pair of electrodes mounted at right angles
to the poles of the magnet. The downhole probe is designed as a hollow iron core through which water flows.
The electromagnet surrounding the core produces a strong magnetic field. A voltage that is proportional to the
average water velocity is generated as the conductor (i.e., groundwater) flows through the magnetic field. An
uphole electronics package connected to the probe amplifies and displays the voltage signal. Real-time data
acquisition is controlled by an associated computer. The system is capable of measuring flow rates ranging from
less than 0.040 L/min to 40 L/min.
Before conducting the study, the flowmeter was calibrated in a test cell constructed of materials identical to
those of well construction. Calibration was performed by measuring water discharge from the test cell and
comparing the measured discharge rate with the associated voltage measured by the flowmeter. Flow rates were
chosen to span the range of rates that would be used in the field. Data obtained during the calibration phase
indicated that the meter responses for both the 1.3-cm ID probe and the 2.5-cm ID probe were linear over the
range of potentially applicable flow rates.
The field tests were conducted using a procedure based on the methods of Molz et al. (1994) and Young et al.
(1998). Flow rate measurements using the electromagnetic borehole flowmeter were made under ambient and
constant-rate pumping conditions at a measurement interval of approximately 30 cm within the well screen. The
use of the 1.3-cm ID probe was attempted for flow measurements under ambient conditions, however, extreme
variability, likely due to electromagnetic interference, required using the less sensitive, 2.5-cm ID probe that
was used for all measurements under pumping conditions. The tests were performed at constant pumping rates
ranging from 3.4 L/min to 4.3 L/min. The rates were chosen to induce flow from each test interval with negligible
head loss across the downhole probe. Each test was performed using the following general protocol:
1. Ambient vertical flow rates were measured from total depth to the top of the screen or water table
under static conditions.
2. The flowmeter probe was lowered to the bottom of the well. A submersible pump was installed
at the water table and pumping was initiated to establish a horizontal flow field. The discharge rate
from the pump was measured using a graduated cylinder and a stopwatch at intervals not exceeding
approximately 30 min.
3. The flowmeter was then used to measure vertical flow rates at each of the elevations occupied
during the ambient flow profile.
10
-------
Measurements of vertical flow rates under ambient and constant-rate pumping (induced flow) conditions were
analyzed using methods described by Molz and Young (1993) and Young et al. (1998). The sign convention
used in this study was positive for upward flow and negative for downward flow within the well. The ambient
flow rate at each measurement point was subtracted from the flow rate measured at that elevation under
constant-rate pumping to obtain the portion of total flow due only to pumping. The differences between these
pumping-induced flow rates at different elevations represent the differences in horizontal flow toward the well
due to differences in hydraulic conductivity of aquifer materials and hydraulic gradients. It was assumed that the
hydraulic head distribution along the well screen was essentially uniform under the low flow rate conditions of
these tests. This allowed the relative hydraulic conductivity distribution to be estimated using Equation 4-1:
where: K is the average hydraulic conductivity of screened materials, K. is the horizontal hydraulic conductivity
of interval /', AQi is induced flow from interval i, Aqi is ambient flow from interval i, Azi is the thickness of interval
i, QP is the total extraction rate, and b is the aquifer thickness influenced by the test. For each measurement
interval, the unitless hydraulic conductivity relative to the average hydraulic conductivity was then converted
to an estimate of the hydraulic conductivity of each measurement interval in units of length per time using an
estimate of the average or bulk hydraulic conductivity obtained from pneumatic slug tests.
4.4 Borehole Geophysical Methods
Two borehole geophysical tools, natural gamma (Century Geophysical Corporation tool number 9012) and
induction conductivity (Century Geophysical Corporation tool number 9510), were used to characterize site
lithology using wells and piezometers. Natural gamma tools contain a sensor that detects gamma radiation
produced primarily from isotopes of potassium, thorium and uranium found naturally in materials surrounding
the borehole. Shale and sediments derived from the weathering of shale have a high clay content derived
from the weathering of potassium rich minerals (e.g., potassium feldspar and mica) that often concentrate
uranium and thorium (Keys, 1989). Consequently, shales, and clay rich sediments yield relatively high levels of
gamma radiation. The differences in clay content allow natural gamma tools to detect changes in lithology and
determine relative clay content.
The induction conductivity tool measures the electrical conductivity of subsurface materials. This is done by
generating a magnetic field in materials around a well with a transmitter coil, which results in a current (e.g.,
current loop or eddy current). The current loop generates a secondary, out of phase magnetic current in the
material, which induces in-phase voltage in the receiver coil. The conductivity of the material is a function of the
ratio of the current circulating in the material to the voltage induced in the receiver coil (Keys, 1989). Generally,
clean sands and gravels have lower electrical conductivity (e.g., higher resistivity) values than sediments with
silt and clay. The vertical resolution of the 9510 tool is a function of the coil spacing and may not be capable of
resolving beds less than 1.5 m).
These wire line tools were used with the GCRD geophysical logging trailer (Century Geophysical Corporation).
Each sonde was lowered to the bottom of the borehole and allowed to equilibrate to the ambient groundwater
temperature before logging. To log the wells, each tool was moved upward at 0.1 m/s, while computer software
recorded responses from the sensors (Figure 4-2). Calibration checks were performed on the induction
conductivity tool at the beginning and end of each day.
£ W y V ;
K
Equation 4-1
-------
Figure 4-2. Geophysical logging trailer with natural gamma logging tool at FRGW12.
4.5 Direct-Push Soil Electrical Conductivity Profiling Survey
The electrical conductivity (EC) of subsurface material was measured using the GeoProbe 6610T rig to advance
a direct-push electrical conductivity probe. The probe consists of a four-electrode Wenner array with a 0.02
m inner electrode spacing. As the probe is advanced, current is applied to the outer electrodes and voltage is
measured by the inner electrodes (Schulmeister, et. al, 2003). As with the induction conductivity tool, clean
sands and gravels are generally characterized by low EC and increased clay content results in increases in EC.
Vertical resolution of the probe is approximately equal to the inner electrode spacing (i.e., 20 cm). The higher
resolution of soil EC probe allows the identification of thinner stratigraphic units than the induction tool.
-------
5.0
Data Analysis Methods
5.1 Background Data
Background groundwater data for the Fort Riley, Louisville, and Yakima study sites were collected from various
databases and the literature. Secondary data from these sources were screened based on various evaluation
criteria, such as: (1) did the organization that collected the data have a quality management system in place; (2)
were the secondary data collected under an approved Quality Assurance Project Plan or other similar planning
document; (3) were the analytical methods used comparable to those used for the primary data; (4) did the
analytical laboratories have demonstrated competency (such as through accreditation) for the analysis they
performed; (5) were the data accuracy and precision control limits stated and similar to the primary data; (6) are
the secondary data source method detection limits (MDLs) and quantification limits (QLs) comparable to those
associated with the primary data; and (7) were sampling methods comparable to those used for the primary
water quality data collected for this study. In general, the necessary accompanying metadata are unavailable for
the secondary water quality data sources to fully assess these evaluation criteria; thus, the secondary data are
used with the understanding that they are of an indeterminate quality relative to the requirements specified for
this study.
Sampling is not always required to establish background, as some anthropogenic constituents can only be
attributed to a source and therefore, there would not be background concentrations. Some constituents (e.g.,
metals) may occur either naturally or result from human activities. In these studies, generally all wells in each
sampling round were sampled for the full suite of inorganic constituents so that if anthropogenic or natural
background levels were present, they would be accounted for.
EPA also located published information about the aquifers in the area as well as any water quality data available
from local, state, or federal entities. Where available, well construction records were also reviewed. EPA
used a systematic approach to plan and implement well purging and sampling and analytical procedures, as
documented in each case study QAPP, to ensure consistent results, comparability, and usefulness of data.
The use of historical data to determine background water quality has several limitations (Battelle, 2013; Reiman
et al., 2008; Matschullat et al., 2000). Battelle (2013) highlighted the QA issues and sample collection methods
regarding the use of secondary data. Battelle (2013) also discussed the intended purpose of the database being
considered. Therefore, it is possible that some databases also contain data that are not background.
A conservative approach to comparing the data collected during this study was used initially as a screening
method for determining whether there were potential impacts on water quality. As previously described, the
process will use limited filtering of the historical data, and all the historical data will be compared with the study
data. If suspected contamination has occurred, it is discussed further in this report.
Other approaches can be used to help delineate background in geochemical data (Matschullat et al., 2000;
Reimann et al., 2008). Matschullat et al. (2000) proposed several other means of determining background
concentrations, specifically, methods designed to predict the upper limits of the threshold of background. These
methods include the Iterative 2a technique (o is the standard deviation), the 4o outlier test, the calculated
distribution function, and the Inflection points on a cumulative frequency curve. Reimann et al. (2008) suggested
the use of the mean ± 2a. Matschullat et al. (2000) concluded that the iterative 2o-technique and the calculated
distribution function provide realistic approximations of the background condition; however, they further point
out that no single method can provide absolute results due to the inherent complexity of geochemical data sets.
13
-------
5.2 Anion Cation Balances
The software package AqQA (version 1.5.0; Rockware) was used to evaluate internal consistency of water
composition by calculating cation/anion balances. Major-ion charge balances were calculated by comparing the
summed milliequivalents of major cations (calcium, magnesium, sodium, and potassium) with major anions
(chloride, sulfate, and bicarbonate) in filtered samples using the Equation 5-1.
Charge Balance (%) . * 100% Equation 5-1
Where the charge balance is the cation/anion balance, locations are the sum of the major cations and ^anions
are the sum of the major anions.
For the study and background datasets, samples with a charge balance error less than about 15% were used for
water-type analysis and for constructing geochemical plots such as Piper or Schoeller diagrams. Charge balance
errors greater than 15% would result in erroneous water-types and geochemical plots. In most cases, charge
balance errors exceeding the 15% criterion were due to missing concentrations of major cations or anions.
Charge balance criteria were not used to screen data for use in summary statistic calculations and for plotting
box and whisker diagrams.
5.3 Statistical Analysis
Intra-site variability of the data collected in this study was examined by evaluating time dependent concentration
trends at specific locations. Summary statistics were calculated for selected parameters (e.g., mean, median,
standard deviation, minimum, and maximum values). Parameters with non-detect values were set at half
the MDL. Summary statistics determined for parameters that showed mixed results, both larger than QL and
less than QL, were generally determined only when at least half the concentration data were larger than the
censoring level (US EPA, 2000). In rare cases data censoring was required (e.g., for iron and manganese) and
these are noted in the tabulated data.
Statistical evaluations were carried out using the Systat (version 13) and Origin (version 2019b) software
packages. Hypothesis testing for the water quality data was performed using parametric and nonparametric
methods. An assumption underlying parametric statistical procedures is that datasets are normally distributed
or can be transformed to a normally distributed form; data transformations in some cases included logarithmic
functions. Post-hoc tests were performed to determine significant differences among water quality datasets
for particular analytes, including the Tukey multiple comparison tests for parametric method and Dunn's Test
for nonparametric methods. A p-value < 0.05 was interpreted as a significant difference between compared
datasets. Given the exploratory nature of this study, p-value adjustments were not incorporated (e.g., Bonferroni
or Sidak correction factors) and the traditional significance threshold of 0.05 was applied for these data
comparisons (Milliken and Johnson, 2009).
Trend analysis performed on time series data was performed using the Mann-Kendall test. The Mann-Kendall
test is a nonparametric test that identifies a trend in a time series, which is useful for situations where a seasonal
component is present. The null hypothesis HQ for the Mann-Kendall test is that there is no trend in the data. The
alternative hypotheses are that there is an increasing or decreasing trend. The Mann-Kendall test also gives an
estimation of the slope of the trend. A positive slope indicates an increasing trend and a negative slope for a
decreasing trend. A Mann-Kendall analysis that had a confidence level of > 0.85 was used as an indication that a
trend was present in the data. However, a confidence level used to indicate a significant trend was set at 0.90
(p = 0.10).
Regression and correlations of data were performed using least squares linear regressions. The null hypothesis,
H0 for a regression is that the slope of the line is equal to zero and the F-test is used to determine whether or not
the regression is significant. If the Prob > F is less than 0.05 than the null hypothesis is rejected, and the slope
is significantly different than zero. The r2 value tells us how much of the variability of the fit is explained by the
response. The larger the r2 the more variability is explained and the more reliable the fit is.
-------
5.4 Water Quality Index (WQI)
A Water Quality Index (WQI) is an attempt to take complex water quality data and information and transform it
into a score that is easily understood and usable for decision makers and the public. The concept of a WQI was
first proposed by Horton in 1965 (Horton, 1965; Tyagi et al., 2013) and the WQI has been recognized by several
organizations or governmental bodies such as the National Sanitation Foundation (NSF), Canadian Council of
Ministers of the Environment (CCME) and the State of Oregon (Tyagi et al., 2013). Several different methods for
calculating the WQI have been proposed (Brown et al., 1970; Willis and Irvine, 1996; Cude, 2001; Stigter et al.,
2006; Saeedi et al., 2010; Terrado et. al., 2010; Vasanthavigar et al., 2010; Gebrehiwor et al., 2011; Tyagi et al.,
2013; Varola and Davraz, 2015; Bora and Goswami, 2017; Shah and Joshi, 2017).
The approach taken in this research was the weighted arithmetic approach. This approach was taken because it
is simpler than the alternatives and does not require complicated statistics, considers health hazards, is easy to
communicate, and considers the influence of different parameters on water quality. The drawback to the use of a
WQI is that a single value does not reflect the story of water quality and a flawed number for a parameter could
over-emphasize or diminish its real contribution to the actual water quality. Although there are drawbacks to the
WQI approach, it is still a useful approach to understanding water quality and the impact that changes in water
quality could have. However, one still should use health-based water quality criteria to assess water quality.
The WQI approach used in this research only considered human health-based parameters to judge water quality.
The human health-based criteria used to judge the WQI were the National Primary Drinking Water Regulations
and National Secondary Drinking Water Regulations (US EPA, 2017), State Drinking Water Standards, and New
York Effluent Limitations for Discharges to Groundwater (used for wastewater only; 56 CRR-NY §706.6, 2019).
Equation 5-2 shows the equation for the calculation of the WQI.
WQI = Equation 5-2
Where Q is the quality rating for the /th parameter and W; is the weighting factor for the /th parameter. The
calculation for Q is given in Equation 5-3.
Qi = 100 Equation 5-3
Where V is the actual value of the parameter for Q, V is the ideal value (V = 0 for all parameters used in this
research), and Vs is the value of the regulatory limit for parameter Q. The calculation for the weighting factor is
given in Equation 5-4.
fc
W, — ^ Equation 5-4
Where k is the constant of proportionality and is calculated in Equation 5-5.
^ = zrr Equation 5-5
The water quality assessment based on the WQI is given in Table 5-1. Parameters and standards used in the WQI
assessments are listed in Table 5-2.
Table 5-1. Water Quality Index Scores and Water Quality Assessments
WQI Score
Water Quality Assessment
0-25
Excellent
26-50
Good
51-75
Poor
76 -100
Very Poor
>100
Unsuitable for drinking
-------
Table 5-2. H uman health-based standards used for the WQI analysis in this study.
Parameter
Units
National Primary
and Secondary
Drinking Water
Standards1
Kansas Surface
Water Quality
Standards
Domestic Water
Supply2
Kentucky
Domestic
Water Supply
Standards3
Washington
CWA
Effective
Criteria4
New York
Groundwater
Effluent
Limitations for
Discharges5
Total Dissolved Solids
mg/L
500
500
250
500
1000
Nitrate + Nitrite
mg N/L
10
10
10
10
20
Chloride
mg/L
250
250
250
250
500
Sulfate
mg/L
250
250
250
250
500
Fluoride
mg/L
4
4
4
4
3
Silver
Hg/L
100
100
100
50
100
Aluminum
Hg/L
50
50
50
50
Arsenic
Hg/L
10
10
10
10
50
Barium
Hg/L
2000
2000
1000
1000
2000
Beryllium
Hg/L
4
4
4
4
Cadmium
Hg/L
5
5
5
10
10
Chromium
Hg/L
100
100
100
50
Copper
Hg/L
1000
1000
1300
1000
400
Iron
M-g/L
300
300
300
300
600
Manganese
M-g/L
50
50
50
50
600
Nickel
M-g/L
610
610
200
Lead
M-g/L
15
15
15
15
50
Antimony
Hg/L
6
6
5.6
6
6
Selenium
M-g/L
50
50
50
50
20
Thallium
M-g/L
2
2
0.24
1.7
Uranium
M-g/L
30
30
30
30
'US EPA, 2017
2KDHE, 2015
3Commonwealth of Kentucky, 2016
"State of Washington, 2016
56 CRR-NY §706.6, 2019
-------
5.5 Chloro-alkaline Index
Chloro-alkaline indices (CAI) are useful in determining the importance of ion exchange reactions (Schoeller, 1965;
Schoeller, 1967; Zaidi et al., 2015, Batabyal and Gupta, 2017; Ndoye et al. 2018). There are two CAIs that are
used, and the equations are listed below (Equations 5-6 and 5-7).
, CF-(Na++K+) r
CAI 1 = Equation 5-6
riI1 cr- (Na++K+) r
2 = „ Equation 5-7
SOj +ilC03+C0j +no3 m
All concentrations in these equations are meq/L. Positive CAIs signifies reverse ion exchange reactions where
sodium and potassium in the soil porewater replaces calcium and magnesium on the solid phases and releases
calcium and magnesium to the water (Schoeller, 1965; Schoeller, 1967; Zaidi et al., 2015, Batabyal and Gupta,
2017; Ndoye et al. 2018). Negative CAIs denotes ion exchange reactions where calcium and magnesium in the
water replaces the sodium and potassium on the solid phases, releasing sodium and potassium into the water
(Schoeller, 1965; Schoeller, 1967; Zaidi et al., 2015, Batabyal and Gupta, 2017; Ndoye et al. 2018).
5.6 Geochemical Modeling
The geochemical modeling used the Geochemist's Workbench Professional Software Package Release 12 (Bethke
et al., 2018a). Geochemical modeling was used to calculate the solution activities of select chemical species and
to calculate the saturation indices of common mineral species potentially occurring at the study sites or which
may form during the infiltration of stormwater or wastewater and subsequent mixing with the groundwater.
Another use for geochemical modeling was the creation of activity diagrams and Eh - pH diagrams. The final
geochemical modeling application was reaction path modeling using the React application in Geochemist
Workbench (Bethke et al., 2018b). Details of geochemical modeling can be found in Section 1.3 in Appendix A.
17
-------
6.0
Louisville Study
6.1 Louisville Study Summary
Based on the analysis of the soil porewater and groundwater in this study, it is indicated that major anions and
cations are decreasing with time. Ion exchange and dissolution/ precipitation reactions are likely the dominant
geochemical processes occurring. One process that could be problematic is reverse ion exchange reactions that
could cause sodium loading on the fine-grained materials in the vadose zone and aquifer and lead to these
particles dispersing. Dispersion over time could cause plugging and lower the infiltration rates of stormwater.
Phosphate concentrations also increased with time. Increasing phosphate in the groundwater could degrade
water quality, especially if the groundwater would be used as an irrigation source.
Chromium concentration, if it continues to increase, could be problematic if the groundwater was used as a
drinking water source. In the case of the Louisville study the groundwater is not being used as a drinking water
source.
Analysis of the groundwater compared to Federal regulatory standards indicates the groundwater receiving the
stormwater runoff is only of minor human health risk. The WQI analysis also indicates that the groundwater
quality in most cases was suitable for human consumption. In one well, MW-03 during 2018 the water would not
be suitable for human consumption.
The greatest risk to groundwater quality is the dilution effect caused by the infiltration of stormwater into the
groundwater. If the groundwater was being used as a water supply the dilution could potentially have negative
effects to water treatment. These negative effects could have negative effects on the water supply distribution
system in older systems.
6.2 Overview
The Louisville study is part of the Gl research program in the EPA's Office of Research and Development
(ORD) Safe and Sustainable Water Resources (SSWR) research program. The project was conducted under a
Cooperative Research and Development Agreement1 (CRADA) between EPA's CESER and Louisville and Jefferson
County Metropolitan Sewer District (MSD).
The Louisville study was conducted in the Portland neighborhood in Louisville, Kentucky. The study site is
located in a 58.7 hectare sewershed known as CSO190 (Figure 6-1). CSO190 is a mixed land use sewershed with
residential, light industrial, and commercial areas; and the Ohio River is approximately 1 km to the north of MW-
01. MSD, under terms of the negotiated consent decree2, will use Gl to reduce the annual overflow frequency
(from 54 to 8) and overflow volume (from 136 ML to 13.8 ML). Both the frequency and volume were based on a
statistical rain year outlined in the consent decree.
To meet the consent decree goals, the MSD opted to use Gl stormwater control measures (SCMs). The MSD
selected a combination of underground infiltration galleries (UIGs) and bioinfiltration areas (planted bumpouts)
to intercept stormwater runoff before it entered the combined sewer system (CSS). During rain events, part
of the runoff volume drained to the planted bumpouts through curb cuts with some water infiltrating into the
media and subsurface. When the runoff exceeded the infiltration capacity (as is expected in most rain events),
the excess water ponded in the bump out until water levels reached the rim elevation of the overflow. The
excess water then bypassed to catch basin and entered the pipe network that routed water to the water quality
'CRADA #834-14 became effective on March 3, 2015.
2Civil Action 3:08-cv-00608-CRD
18
-------
s*«thzt
"Knurr
LEGEND —
OVERALL BOUNDARY
SUB-BASIN WATERSHEDS
» EXISTING DOWNSPOUT
(Disconnected)'
• EXISTING DOWNSPOUT
(Connected) / Lr
> INTERSECTIONS /*\/ \. ^
jR>
Figure 6-1. Map of CSO190 sewershed in the Louisville Study. The blue shaded area is the initial construction phase and the other shaded sections
followed. (Source: MSD)
19
-------
unit (WQU) and the UIGs. The other portion of the area draining entered the WQU serviced by a curb-and-gutter
design, which directed flow to the UIG. Sediment settled in the sump storage area, and oils, floating trash, and
debris rose to the surface. The surface runoff that exceeded the receiving capacity of Gl SCMs drained to the
sewer basin directly,
6.3 Louisville Climate
Louisville is in the Bluegrass Region of Kentucky and has a humid subtropical climate (Plantmap.com, 2019;
Weatherbase, 2019). This climate type is described by relatively high temperatures and evenly distributed
precipitation. Figure 6-2 shows monthly temperature and precipitation data obtained from the NOAA (NOAA,
2019c). As is shown on Figure 6-2A, the hot season ranges from beginning in mid-May and lasts until mid-
September. The mean temperature extremes during this time are about 17 °C and 33 °C (NOAA, 2019c). The
cold season typically runs from December through February. The mean temperature extremes range from -3
°C to 9 °C during this period (NOAA, 2019c). The mean yearly precipitation for Louisville is 1140.7 mm (NOAA,
2019c). Between March and July, there is typically the greatest precipitation of the year. The most snowfall
occurs between December and February (Figure 6-2B). Louisville typically receives 317 mm of snow each year.
The average wind speeds in Louisville show differences ranging from 2.2 to 4.0 m/s (Weatherspark, 2019b). The
windier part of the year is typically from mid-October to February. The average wind speeds during this period
are more than 4.0 m/s. The wind direction from mid-March to June and from mid-August to mid-December is
most often out of the south. From July to mid-August, and from mid-December to mid-March, the wind is most
often out of the west (Weatherspark, 2019b).
45
40
35
30
^ 25
0
Q
S! 2 0
3
CD
1 15
e
0)
|- 10
5
0
-5
-10
—•— Maximum Daily
-•—Mean Dafy
—Minimum Daily ;
A
1—
c
TO
"I
0?
LL
<0
¦£Z
CL
<
~r~
200
eft
fij
0)
a>
£)
CD
b
O
t-
t-
<
£
CL
0
10
u
O
%
o
8
(j
Precipitation!
S*KW
160 -
£ 100
Figure 6-2. Louisville temperature and precipitation data from NOAA (2019). Panel A shows temperature
data and Panel B shows precipitation data.
-------
6.4 Underground Storage Galleries and Bioinfiltration Areas
The bumpouts that captured the stormwater
runoff used relatively simple bioinfiltration
basins (Figure 6-3). These bumpouts used 0.6
m of engineered media placed over the native
soil and were planted with suitable vegetation
that required minimal maintenance. As is
shown in Figures 6-4 and 6-5, the bumpouts
were installed between the street and
sidewalk area. The stormwater runoff was then
conveyed from the bumpout and entered WQU
(Hydro International First Defense Enhanced
Vortex Separator) that captured floatables
and sediment (Figure 6-5). From the WQU,
the water then entered the storage gallery
through 0.92 m diameter perforated HDPE
pipe. This pipe was installed in large uniform
double washed aggregate. The pipe effectively
increased the porosity of the storage gallery
and distributed the influent water volume. The
DIG excavation extended to about 4.3 m below
grade to reach soils with larger infiltration rates
based on the geotechnical study (7NT, 2015).
Figure 6-3. Bumpouts with bioinfiltration basins used in the
Louisville study.
Pavement
Cap Stone
Sidewalk
Planted Bum
Asphalt
KTC #3
Unwashed
Crushed Stone
0.91 m diameter HDPE
Pipe AASHTO 294
Geotextile Fabric
MIRAFI RS280i
KTC #3
Double Washed
Crushed Stone
H
0.61 m
14.00 m
Figure 6-4. Louisville storage gallery design shown as a cross section.
21
-------
Cap Stone and
KTC #3 Unwashed
Crushed Stone
KTC #3 Double Washed
Crushed Stone
1.22 m
Hydro International First Defense
0.91 m diameter HDPE
Geotextile Fabric
Perforated
Enhanced Vortex Separator Pipe AASHTO 294 End Cap MIRAFI RS280i
Figure 6-5. Louisville storage gallery design shown in a transverse section.
6.5 Subsurface Monitoring Network
The subsurface monitoring system was designed to monitor the chemical and hydrological interaction of
the stormwater control measures with groundwater. The network consisted of a series of soil porewater
samplers, piezometers, and wells installed near the stormwater infiltrations features. The location of the wells,
piezometers, and porewater sampler clusters adjacent to the stormwater control measures are depicted in
Figure 6-6. This network consisted of sixteen soil porewater samplers, ten piezometers, and ten monitoring wells.
6.5.1 Groundwater Monitoring Wells and Piezometers
The groundwater monitoring wells and piezometers were installed in boreholes drilled using hollow-stem
auger techniques and constructed using 5.08-cm diameter, schedule 40 PVC casing and machine-slotted PVC
screens. Wells were constructed with screens 3.1 m in length and piezometers used screens 0.6 m in length.
Locations of wells, piezometers and screened intervals can be found in Table B-l in Appendix B. A piezometer
was installed adjacent to the monitoring wells along the SCMs and screened such that the bottom of the screen
was approximately equivalent to the bottom of the monitoring well screen. A silica sand filter pack was installed
adjacent to the screen in each monitoring well or piezometer. For both wells and piezometers, the annular space
above the filter pack was sealed with bentonite. The wells and piezometers were completed at the surface in
concrete vaults. For purposes of determining groundwater elevations, the designated measuring point on the top
of the well/piezometer casing was surveyed to 0.30 cm relative to the site vertical datum using a total station.
The water level and temperature in all wells and piezometers were monitored using pressure transducers. Wells/
piezometers within approximately 75 m of a logging station were equipped with Campbell Scientific, Inc., model
CS451 differential pressure transducers and hard wired to battery operated data loggers. Wells and piezometers
that could not be connected to a central data logger either because of distance or infrastructure barriers were
fitted with standalone loggers (model 3001 produced by Solinst Canada Ltd.). The water levels and temperature
were time-date stamped and this data was routinely recorded at a 10 min interval. For wells and piezometers
equipped with Campbell Scientific pressure transducers, the monitoring frequency was increased to 1 min during
storm events.
22
-------
PW09]
mi Cluster 6
L°»RW10
Google Earth
- ¦
Figure 6-6. Aerial view of the Louisville study monitoring network. Red circles are the monitoring wells, cyan circles are the piezometers, the
yellow squares are the soil porewater samplers and the colored rectangular shaded areas are the infiltration galleries. (Source; Google Earth)
23
-------
6.5.2 Porewater Samplers
Six vertical clusters of SPWs (UMS model SICS20) were installed in the vadose zone near the SCMs. The soil
porewater samplers were installed at a 25° angle following procedures described in UMS GmbH Miinchen
(2007). Cuttings from the borehole were slurried using potable water and used to backfill the hole. Tubing from
the porewater samplers was terminated in vaults to allow sample collection. Two clusters were installed north of
Main Street and four south of Main Street. The locations and probe elevations of the SPWs are given in Table B-2
in Appendix B.
6.6 Louisville Hydrogeology Characterization Results
6.6.1 Hydraulic Conductivity Structure
Pneumatic slug tests were performed in five of the monitoring wells to estimate the saturated hydraulic
conductivity of the aquifer materials near the stormwater control measures. Results (Table 6-1) of these tests
ranged from approximately 24 m/d to 76 m/d. These results indicate that the hydraulic conductivity of aquifer
materials near the water table at the study site is relatively high and generally increased moving from north to
south across the network.
Table 6-1. Saturated hydraulic conductivity of aquifer materials estimated
using pneumatic slug testing techniques in monitoring wells installed near
the stormwater control measures.
Well
Estimated Saturated Hydraulic Conductivity
(m/d)
L-190-1-MW-02
24.4
L-190-1-MW-05
42.7
L-190-1-MW-06
64.0
L-190-1-MW-08
76.2
L-190-1-MW-10
70.1
6.6.2 Groundwater Flow Field Characterization
The elevation of the water table at the Louisville study site varied approximately 0.65 m (Figure 6-7) between
December 2015 and February 2018. Due to the high hydraulic conductivity of the aquifer at the site and the
linear configuration of the monitoring network, potentiometric surfaces could not be constructed.
6.6.3 Water Table Mounding Due to Gallery Infiltration
Groundwater elevation data obtained from pressure transducers installed in the wells adjacent to the
storm water infiltration structures were used to determine the rise in water table elevation associated with
precipitation events. During the period for which groundwater elevation data was available, three rainfall
events with a daily magnitude of 50 mm or greater (as recorded at the National Oceanic and Atmospheric
Administration weather station USC00154955) located about 2.4 km from the study site were identified. These
events occurred on February 2-3 and December 17-18 of 2016 and September 1-2 of 2017. Data from the
pressure transducers installed in wells MW-01, MW-06, MW-08, MW-09, and MW-10 (Figure 6-7) indicated
that in each case, the rise in the water table elevation associated with these rainfall events was less than 0.1 m.
Therefore, it appears that groundwater mounding associated with these stormwater infiltration structures was
minimal. This result is expected given the relatively high hydraulic conductivity of the aquifer in this area which
would allow any mounding from concentrated recharge features to dissipate rapidly.
24
-------
130.5 -I
r 100
60 E
-------
Figure 6-8 shows the major anion and SPC data for the data collected south of Main Street. Bicarbonate did not
show a spike in any of the SPW samplers in February 2018 (Figure 6-8A). Except for SPW cluster 6, the spikes in
sulfate concentrations mainly occurred in the shallowest porewater samplers (LW-4A, LW-5A, and LW-7A; Figure
6-8B). In LW-6A there was no spike in sulfate concentrations in February 2018 (Figure 6-8B). This may be related
to the distance LW-6A was from the infiltration gallery, since it is the farthest away from the gallery. The deeper
SPW samplers (LW-4C, LW-5C, LW-6C, and LW-7C) did not show a spike in sulfate concentrations in February
2018 (Figure 6-8B). With the exception of LW-4C, spikes in chloride concentrations were observed in all the SPW
samples in February 2018 (Figure 6-8C). No SPC spikes were observed in all the SPW samples in February 2018
except for LW-4C (Figure 6-8D).
O
*•'
03
c
o
-2
03
O
CD
CO
o
0
1
CD
E
>
¦4—*
o
^ E
o o
O CO
o ^
200-
100-
400
300
200
100
0
600
400
200
0
-------
Major anion data north of Main Street is plotted in Figure 6-9. North of Main Street, in general, there do not
appear to be distinct spikes in concentrations of major anions or SPC during the study. In general, the major
anions do not follow the same pattern as SPC (Figure 6-9). The major cations (calcium, magnesium, sodium, and
potassium) do not show any concentration spikes during the study (Figure 6-10).
& 3
a "a
3 E
to —
a
3 .—.
c E
O ^
O to
a 3
"o
aj
a.
in
300
200
100
QJ
"?5
c
o
¦e
ro
o a*
-------
Figure 6-11 shows the major cation and SPC data for SPW samples south of Main Street. Spikes in calcium
concentrations were found in all SPW samples, regardless of depth, during February 2018 (Figure 6-11A).
Except for SPW cluster 4, the concentration of calcium was larger in the deepest soil porewater sampler. In SPW
cluster 4, the shallowest SPW samplers had a larger calcium concentration (Figure 6-11A). In most of the SPW
clusters south of Main Street, the spike in magnesium concentration was similar to the calcium concentration
(Figure 6-11B). In cluster 4, the shallowest SPW sampler, LW-4C, it was not apparent if there was a spike in
magnesium concentration (Figure 6-11B). Either way the spike in the deepest SPW sampler was larger. Potassium
concentrations south of Main Street are plotted in Figure 6-11C, Only SPW cluster 6 showed spikes in potassium
concentrations in February 2018 at both depths in the cluster. In the shallowest SPW sampler (LW-6A), potassium
concentration was larger than in the shallow SPW sampler (LW-6C; Figure 6-11C). Soil porewater sampler clusters
4 and 5 had a similar pattern in February 2018. In both cases, the shallowest SPW samplers (LW-4A and LW-5A)
showed a spike in potassium concentration; whereas, there was no spike in the deepest SPW (LW-4C and LW-5C;
Figure 6-11C). In SPW cluster 7, in February 2018, the spike in potassium concentration occurred in the deepest
SPW sampler (LW-7C), and there were no spikes in the shallower SPW sampler (LW-7A; Figure 6-11C). Finally,
sodium concentrations south of Main Street are plotted in Figure 6-11D In February 2018, sodium spikes were
only observed in the shallowest SPW samplers in the four clusters.
I?
£ E
o
200
100-
£
¦i £
w en
£ E
o
CL
15
10-
5 -i
0
o
c ^
o -y
O CO
o 3
iff
"o
0)
Q.
CO
5000
4000 -i
3000
2000
1000
A
B
D
N\
&
-40 -i
-20
E
13 A
cfl —J
^ CT)
o> £
03
300 £ _
200 % B)
100 CO —
/
/
-\\N nS
Figure 6-11. Plots of major cations and SPC over the course of the study south of Main Street. A. Calcium,
B. Magnesium, C. Potassium, D. Sodium, and E. Specific Conductivity. The gray box highlights the February
2018 data points, LW-4C are the green circles and lines, LW-4A are the green triangles and lines, LW-5C are the
blue circles and lines, LW-5A are the blue triangles and lines, LW-6C are the cyan circles and lines, LW-6A are
the cyan triangles and lines, LW-7C are the magenta circles and lines, and LW-7A are the magenta triangles
and lines. All soil porewater samplers that are labeled as C are the shallowest soil porewater samplers and the
deepest soil porewater samplers are labeled as A.
28
-------
Specific conductivity can be a good surrogate for the movement of dissolved constituents through the vadose
zone. Chloride was the only major anion that correlated well with SPC both north of Main Street (r2= 0.739) and
south of Main Street (r2= 0.735). Chloride is a conservative species and should not have any major interactions
with the media in the vadose zone, thus a good correlation with SPC is likely. Bicarbonate and sulfate did not
correlate well with SPC north of Main Street as is indicated by the poor correlation coefficients, r2= 0.037 and
r2= 0.018, respectively. South of Main Street SPC was also poorly correlated with bicarbonate (r2= -0.008) but was
somewhat correlated with sulfate (r2= 0.417). Bicarbonate and sulfate are not conservative and can participate in
geochemical reactions and interact with the vadose zone materials. Calcium did not correlate well with SPC north
of Main Street (r2= 0.251) but correlated better south of Main Street (r2= 0.530). The correlation of SPC with
magnesium was similar to calcium, north of Main Street (r2= 0.163) and south of Main Street (r2= 0.659). The
correlation of SPC with sodium and potassium was the converse of what was shown for calcium and magnesium.
Sodium and potassium correlated with SPC better north of Main Street than they did south of Main Street. North
of Main Street, the correlation coefficient for sodium and potassium was r2= 0.659 and r2= 0.666, respectively.
South of Main Street, the correlation coefficients for sodium and potassium were r2= 0.440 and r2= 0.295,
respectively. Calcium, magnesium, potassium and sodium are not conservative and are expected to participate
in other geochemical processes such as dissolution/precipitation, ion exchange, sorption and interactions with
biota in the vadose zone. This would suggest that some of the differences between north and south of Main
Street would be differences in the geochemical processes that may be occurring.
6.7.1.1.2 Geochemical Processes for Major Anions and Cations in the Vadose Zone South of Main Street
Since it has been shown that there are spikes in SPC, chloride, and sodium concentration south of Main
Street, and road salt (halite, NaCI) is commonly used as a deicing agent, the dissolution of halite is potentially
a geochemical process controlling the sodium concentrations in the vadose zone. Figure 6-12A is a plot of
sodium verses chloride. If halite is controlling the Na concentration, then all the data points would lie on or
near the 1:1 (halite dissolution line) in this figure (Schoeller, 1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al.,
2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). Most of the data lies near or on the 1:1 line indicating
that halite dissolution may be controlling the sodium concentrations. However, there are points that lie slightly
above and below the halite dissolution line. Points that lie above the halite dissolution line suggest that there
is an excess of sodium compared to chloride in the soil porewater and that ion exchange or aluminosilicate
weathering is controlling the sodium concentrations (Schoeller, 1965; Schoeller, 1967; Kortatsi, 2007; Zaidi et
al., 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). Some of the data do lie below the 1:1 line suggesting
that chloride is enriched with respect to sodium in the porewater. This suggests that reverse ion exchange is
potentially happening in these samples - that is sodium in the porewater replacing calcium and magnesium
on the solid surfaces in the vadose zone (Schoeller, 1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al., 2015;
Batabyal and Gupta, 2017; Ndoye et al., 2018). Figure 6-12B is a plot of sodium + potassium verses total cations.
When y = x, then the total cations equal sodium + potassium, so this would be the maximum value. Likewise, y
= 0.5x means that sodium + potassium is equal to calcium + magnesium. Silicate weathering is likely when the
data falls between y = x and when y is above 0.5x (Batabyal and Gupta, 2017; Ndoye et al., 2018). Except for
one porewater sample, all the data are below the y = 0.5X trendline suggesting that silicate weathering is not
a dominant geochemical process affecting sodium concentrations in the vadose zone (Figure 6-12B) and ion
exchange reactions would likely be controlling sodium concentration in SPW solution.
29
-------
^ / Silicate
/ Weather
Dominant
Silicate
Weather
Not Dominant
Exchange
Chloride (meq/L)
Total Cations (meq/L)
Figure 6-12. Plots of A. Sodium vs. chloride. B. Sodium + potassium vs. total cations for soil porewater
samples south of Main Street in the Louisville study. In both plots the green circles are LW-4C data, green
triangles are LW-4A data, blue circles are LW-5C data, blue triangles are LW-5A data, cyan circles are LW-6C
data, cyan triangles are LW-6A data, magenta circles are LW-7C data, and magenta triangles are LW-7A data.
Figure 6-13A is a plot of calcium + magnesium versus bicarbonate + sulfate. Data falling on the 1:1 trend line
indicates that calcite (CaC03), dolomite (CaMgC03), and gypsum (CaS04-2H;0) are likely controlling the calcium
and magnesium concentrations in the soil porewater (Schoeller, 1965; Schoeller, 1967; Kortatsi, 2007; Zaidi
et al., 2015; Batabyai and Gupta, 2017; Ndoye et al.; 2018). Most of the soil porewater data in the study falls
on this line, which likely means that the dominant geochemical process controlling calcium and magnesium
concentrations in the soil porewater is precipitation/ dissolution of calcite, dolomite and gypsum. Several
samples lie above the 1:1 trend line which implies that ion exchange or mineral weathering is potentially
responsible for the excess calcium and magnesium in the soil porewater (Zaidi et al., 2015; Batabyai and Gupta,
2017; Ndoye et al., 2018). In very few cases, bicarbonate + sulfate was in excess and there was a depletion of
calcium + magnesium in those porewater samples. This would potentially indicate that reverse ion exchange was
occurring (Schoeller, 1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al., 2015; Batabyai and Gupta, 2017; Ndoye
et al., 2018). Reverse ion exchange reactions are when sodium and potassium in solution replace calcium and
magnesium on the solids in the vadose zone and release calcium and magnesium into solution (Schoeller, 1965;
Schoeller, 1967; Zaidi et al., 2015, Batabyai and Gupta, 2017; Ndoye et al. 2018). Increased sodium on solids can
lead to dispersion of fine-grained solids and this could lead to plugging and decreased infiltration rates.
A plot of magnesium verses calcium is shown in Figure 6-13B. Data that plots above the 1:1 trend line indicate
that dolomite is dominant over calcite. Conversely data that plots below the 1:1 trend line indicates that calcite is
dominant to dolomite (Ndoye et al., 2018). All the study data south of Main Street plot below the 1:1 trend line
indicating that calcite is the dominant carbonate phase controlling the calcium and magnesium concentrations
in the porewater samples. Figure 6-13C is a plot of the dolomite saturation index (SI) versus the calcite SI.
-------
Equilibrium with a solid phase is indicated when the SI is near 0. When the SI is less than 0, the solution is
undersaturated with respect to the solid phase. This means dissolution is possible until the solution reaches
equilibrium with the solid phase. Precipitation of a solid phase can occur when the SI is greater than 0 and
precipitation could occur until the solution reaches equilibrium with the solid phase (Sposito, 1989; Hounslow,
1995; Batabyal and Gupta, 2017). Most of the soil porewater samples are likely in equilibrium with either calcite
or dolomite solid phases present in the vadose zone (Figure 6-13C). There are also a few samples that are
oversaturated with respect to calcite and dolomite (Figure 6-13C). The excess calcium or magnesium in this case
would be available to participate in other geochemical processes, such as ion exchange (occurring in the vadose
zone) or potentially be transported to the groundwater if precipitation does not occur. Gypsum (CaS04-2H„0)
can also control calcium concentrations if gypsum is present in the vadose zone. Figure 6-13D shows a plot of
the gypsum SI for the porewater data. The soil porewater is undersaturated with respect to gypsum. If gypsum is
present in the vadose zone it would tend to dissolve releasing additional calcium into the soil porewater.
Bicarbonate + Sulfate (meq/L)
Calcium (meqA.)
to
d
B
~
c
1
1
C*k»tderwrti*r*fred i
DrtkMBlte GvtfMl ufMfcd l
i
i
i
i
i
i takrtc Qver5
-------
Chloro-alkaline indices (CAI) are useful in determining the importance of ion exchange reactions (Schoeller,
1965; Schoeller, 1967; Zaidi et al., 2015, Batabyal and Gupta, 2017; Ndoye et al. 2018). There are two CAIs that
are used, and the equations are given in Section 5.5. Positive CAIs imply reverse ion exchange reactions where
sodium and potassium in the soil porewater replace calcium and magnesium on the solids in the vadose zone
and release calcium and magnesium to the porewater solution (Schoeller, 1965; Schoeller, 1967; Zaidi et al.,
2015, Batabyal and Gupta, 2017; Ndoye et al. 2018). Negative CAIs indicate that ion exchange reactions are likely,
where calcium and magnesium in the porewater replace the sodium and potassium on the vadose zone solids,
releasing sodium and potassium into the porewater solution (Schoeller, 1965; Schoeller, 1967; Zaidi et al., 2015,
Batabyal and Gupta, 2017; Ndoye et al. 2018).
Figure 6-14A is a plot of CAI1 verses CAI2 for SPW samplers south of Main Street. As is shown in this figure, most
of the CAIs are negative which suggests ion exchange reactions are the dominant process overall. However, there
are also some positive CAIs mainly in the deeper portion of the vadose zone. These positive CAIs indicate that
reverse ion exchange is potentially occurring. If this trend continues, it suggests that Na loading of the vadose
zone materials could occur. This could become problematic. Excess Na can cause dispersion of the vadose zone
fine-grained solids, which could limit the infiltration of the stormwater into the subsurface.
A plot of sodium + potassium - chloride verses calcium + magnesium - bicarbonate - sulfate is shown in Figure
6-14B. Data that plots on or close to the trend line y = -x indicates that ion exchange reactions are the dominant
process (Zaidi et al., 2015, Batabyal and Gupta, 2017; Ndoye et al. 2018). Most of the soil porewater data plot is
close to this line indicating that ion exchange reactions (including reverse ion exchange reactions) are a dominant
geochemical process in the vadose zone south of Main Street.
10
¦10
<
u
¦20
-30
-40
A
j
Reverse Ion Exchange
1
d>
CB
TO
o
LU
c
o
•
•
Q
+
-2
2 3
CAI 2
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14
Ca+Mg-HC03-S04
Figure 6-14. Plots indicating the significance of ion exchange reactions. A. Plot of CAI1 vs. CAI2. B. Plot of
Na+K-Cl vs. Ca+Mg-HC03-S04. The dashed black lines in both plots represent the origin of the plots and divides
the negative and positive values. In all plots the green circles= LW-4C data, green triangles= LW-4A data, blue
circles= LW-5C data, blue triangles= LW-5A data, cyan circles= LW-6C data, cyan triangles= LW-6A data, magenta
circles= LW-7C data, and magenta triangles= LW-7A data. The light green shaded area indicates samples where
ion exchange reactions may be dominant, and the pink shaded areas indicate where reverse ion exchange may
be dominant.
32
-------
6.7.1.1.3 Geochemical Processes for Major Anions and Cations in the Vadose Zone North of Main Street
Although there were no apparent spikes in SPC, chloride, and sodium concentration north of Main Street,
influxes of sodium and chloride are still likely occurring. The background sodium and chloride concentrations
are likely large enough that the increase in sodium and chloride only changes the observed sodium and
chloride concentrations minimally. Because halite (road salt) dissolution is potentially a geochemical process
that could control sodium concentrations in the vadose zone. Figure 6-15A is a plot of sodium verses chloride.
Much of the data lies near or on the 1:1 line, which indicates that halite dissolution may be controlling the
sodium concentrations; however, there are points that lie above the halite dissolution line. Points above the
halite dissolution line suggest that there is an excess of sodium compared to chloride in the soil porewater.
It also suggests that ion exchange or aluminosilicate weathering is playing a role in controlling the sodium
concentrations (Schoeller, 1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al., 2015; Batabyal and Gupta,
2017; Ndoye et al., 2018). Another potential source would be urban fill and the potential to have associated
contaminants leaching through the vadose zone (Swanson and Lamie, 2010) because of the increased infiltration
in urban soils (Boudreault et al., 2010; Shuster et al., 2014; Shuster et al., 2015; McGrane, 2016). The deepest
SPW samplers (LW-1E and LW-2E) are randomly distributed along the halite dissolution line. In cluster 1, the next
two depths, LW-1A and LW-1C, respectively, lie on the halite dissolution line with roughly equivalent sodium and
chloride concentration. Beyond the concentration differences from the deep SPW sampler, these move along
the halite dissolution line in chronologic order, with the initial sampling having the highest concentrations of
sodium and chloride and steadily decreasing sodium concentration to the final sampling time. This suggests that
leaching from a source is a potential mechanism. The concentrations in the shallowest SPW (LW-1D) begin with
concentrations similar to the middle SPWs, but, with time, both sodium and chloride concentrations decreased
with chloride concentration decreasing faster than sodium concentration moving the points away from the
halite dissolution line. This suggests that with time, the mechanism controlling the sodium concentrations could
potentially shift from dissolution to ion exchange or silicate weathering. SPW cluster 2, on the other hand,
shows a somewhat different pattern. LW-2E is randomly distributed along the halite dissolution line. The middle
SPW (LW-2A) has a pattern like LW-1D, where both chloride and sodium concentrations are decreasing, and the
chloride concentration is decreasing faster than the sodium concentration. The shallowest SPW (LW-2D) shows
decreasing sodium and chloride concentrations along the halite dissolution line. Figure 6-15B is a plot of sodium
+ potassium verses total cations. Most deep SPW (LW-1E and LW-2E) fall below the y = 0.5X trend line suggesting
that silicate weathering is not the dominant process at these depths. In cluster 1 (LW-1C), the data falls on the
line y = 0.5x suggesting that silicate weathering is possible. The shallower SPW samplers, LW-1A, LW-1D, LW-2A,
and LW-2D show the sample data for the most part falls between the trend lines y = 0.5x and y = x, which could
suggest that silicate weathering is an important process or leaching of a source as the result of urban fill (Figure
6-15B).
-------
20
Silicate
Weather
Dominant
00
~~
10
Silicate
Weather
Not Dominant
¦m
•m
0
0
10
20
30
1000
J2?
100
CD
O"
LU
tO
Tj
Reverse Ion
Exchange
0.01
001
0 1
1
10
100
1000
Chloride (meq/L) Total Cations (meq/L)
Figure 6-15. Plots of A. Sodium vs. chloride. B. Sodium + potassium vs. total cations for soil porewater
samples north of Main Street in the Louisville study. In both plots the black circles are LW-1E data, black
triangles are LW-1C data, black diamonds are LW-1A data, black stars are LW-1D data, red circles are LW-2E
data, red diamonds are LW-2A data, and red stars are LW-2D data.
Figure 6-16A is a plot of calcium + magnesium verses bicarbonate + sulfate. Unlike the data south of Main Street,
most of the SPW data north of Main Street does not fall on this trend line, which suggests that the dominant
geochemical process controlling calcium and magnesium concentrations in the soil porewater may not be the
precipitation/dissolution of calcite, dolomite and gypsum. In this case, most of the samples lie above the 1:1
trend line. This implies that ion exchange or mineral weathering is potentially responsible for the excess calcium
and magnesium in the soil porewater (Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). In many
cases bicarbonate + sulfate was in excess and there was depletion of calcium + magnesium in those porewater
samples. This would potentially indicate that reverse ion exchange was occurring (Schoeller, 1965, Schoeller,
1967, Kortatsi, 2007; Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). In general, there is
no trend in the deep SPW data (LW-1E and LW-2E); however, for shallower samples there is a trend showing
decreasing calcium and magnesium concentrations with time. The observed chemical behavior of calcium and
magnesium could also suggest the potential of urban fill having a role in affecting calcium and magnesium
concentrations.
A plot of magnesium verses calcium is shown in Figure 6-16B. As was the case with data south of Main Street,
the data north of Main Street plots below the 1:1 trend line indicating that calcite is the dominant carbonate
phase controlling the calcium and magnesium concentrations in the porewater samples.
34
-------
Figure 6-16C is a plot of the dolomite SI versus the calcite SI. Approximately one third of the SPW samples are
likely in equilibrium with either calcite or dolomite solid phases present in the vadose zone. About a third of the
SPW samples are oversaturated with respect to calcite and dolomite. The excess calcium or magnesium in this
case would be available to participate in other geochemicai processes, such as ion exchange, occurring in the
vadose zone or potentially be transported to the groundwater if precipitation does not occur. The remainder of
the samples were undersaturated with respect to calcite and dolomite.
Figure 6-16D shows a plot of the gypsum SI for the porewater data. The soil porewater is undersaturated with
respect to gypsum. If gypsum is present in the vadose zone, it would dissolve releasing additional calcium into
the soil porewater.
Bicsrbcmsie + Sulfate (meqj'l)
Calcium (meq/l_)
2-
co
-2-
c
1
1
Calcite uodersaturaied i
Dolomite ovfTvntumicd i
i
i
i
'j
»
i
i
i
Calotte overs-aturatetf
* Dolomite ovmasurated
/
EaulkMim
Etgu libriurn
A
Calcffp urutervi*uratf*d .J ,
Dolomite unde^sAiuifated i
** I
**
•
# i
•
t L-nlcitc Dvrrsn* iiratpd
Dolomite undefsarturarted
!
1
1
D
o
¦2-
-3-
' » ! i
Calcite SI
Figure 6-16. A. Plot of calcium + magnesium vs. bicarbonate + sulfate. B. Plot of magnesium vs. calcium.
C. Plot of dolomite SI vs. calcite SI. D. Plot of Gypsum SI for soil porewater samples collected North of Main
Street. In all plots the black circles are LW-1E data, black triangles are LW-1C data, black diamonds are LW-
1A data, black stars are LW-5A data, red circles are LW-2E data, red diamonds are LW-2A data, and red stars
are LW-2D data. In plot C the gray shaded areas indicate equilibrium with calcite or dolomite, yellow shaded
area indicates that calcite is undersaturated and dolomite is oversaturated, green shaded area indicates
both calcite and dolomite are oversaturated, blue shaded area indicates both calcite and dolomite are
undersaturated, and purple shaded area is oversaturated with respect to calcite and undersaturated with
respect to dolomite. In D the gray shaded area indicates equilibrium with gypsum.
35
-------
Figure 6-17A is a plot of CAI1 verses CAI2 for SPW samplers north of Main Street. About half of the CAIs are
negative which suggests ion exchange reactions are the dominant process overall. The other half of the samples
had positive CAIs. These positive CAIs indicate that reverse ion exchange is potentially occurring. Cluster 1,
LW-1D (the shallowest SPW sampler), had ail negative CAIs which suggest that ion exchange was the dominant
near-surface mechanism. Initial samples from LW-1E were all positive, but after October 2017 they became
negative. This would suggest that initially, reverse ion exchange was important; however, with time, it shifted
to ion exchange. With L.W-1A, all the samples were positive suggesting reverse ion exchange was the important
mechanism. In LW-1C, the data suggests cycling between ion exchange and reverse ion exchange is occurring
over time. In cluster 2, LW-2E and LW-2A (with the exception of July 2016), all the CAIs were negative. This
indicates ion exchange is important. LW-2D, however, showed the opposite trend. Initially the CAIs were positive
and in the last two samples the CAIs were negative.
A plot of sodium + potassium - chloride verses calcium + magnesium - bicarbonate - sulfate is shown in Figure
6-17B. Most of the soil porewater data plot along this line, which indicates that ion exchange reactions including
reverse ion exchange reactions are a dominant geochemical process in the vadose zone north of Main Street.
Reverse Ion Exchange
10 -8-6-4-2 0 2 4 6 S 10 12 14
Ca+Mg-HC03-S04
Figure 6-17. Plots indicating the significance of ion exchange reactions. A. Plot of CAI1 vs. CAI2. B. Plot of
Na+K-CI vs. Ca+Mg-HC03-S04. The dashed black lines in both plots represent the origin of the plots and
divides the negative and positive values. In all plots the black circles are LW-1E data, black triangles are
LW-1C data, black diamonds are LW-1A data, black stars are LW-5A data, red circles are LW-2E data, red
diamonds are LW-2A data, and red stars are LW-2D data. The light green shaded area indicates samples
where ion exchange reactions may be dominant, and the pink shaded areas indicate where reverse ion
exchange may be dominant.
36
-------
6.7.1.2 Other Soil Porewater Constituents
6.7.1.2.1 Fluoride
The chloride data showed spikes in February 2018 and spikes were also shown in some of the fluoride data.
Figure 6-18 is a plot of fluoride data and chloride data during the study for SPW samples north and south of Main
Street. There were three SPW samples that showed fluoride concentration spikes in February 2018 south of Main
Street (Figure 6-18A upper panel). All SPW samples, except LW-4C, showed spikes in chloride data in February
2018 (Figure 6-18A lower panel). The fluoride and chloride data in most cases did not have the same patterns
during the study. North of Main Street (Figure 6-18B), there were no spikes in the chloride or fluoride data in
February 2018 and the patterns of chloride and fluoride concentrations were not similar during the study. This
suggests that chloride/fluoride ratios would not be useful in tracking source of chloride at this study location.
u.
02-
00
soo-
200-
100-
1/1/2016 7/1/2016 1M/2017 7/1/2Q17 1/1/2018 7/1/2018
¦&,
£ 0.6 -
OA -
It.
00
100 -
U1/2016 7/1/2016 1/1/2017 7/1/2017 1/1/2018 7/1/201B
Date Date
Figure 6-18. Plots of fluoride and chloride concentration with time comparing the fluoride and chloride
A. South of Main Street and B. North of Main Street. Black circles and lines are LW-1E, black triangles and
lines are LW-1C, black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2,
red circles and lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D, green
circles and lines are LW-4C, green triangles and lines are LW-4A; B. SPW cluster 5, blue circles and lines are
LW-5C, blue triangles and lines are LW-5A; C. SPW cluster 6, cyan circles and lines are LW-6C, cyan triangles
and lines are LW-6A; D, SPW cluster 7, blue circles and lines are LW-7C, blue triangles and lines are LW-7A.
-------
6.7.1.2.2 Iodide
The importance of iodide in this study was its potential use to help track sources of chloride (Vengosh and
Pankratov, 1998; Panno et al., 2005; Panno et al., 2006). Figure 6-19 shows the iodide concentrations (with time
in comparison) with chloride concentration with time for SPW samples north and south of Main Street. South
of Main Street there were no spikes in iodide concentration in February 2018 (Figure 6-19A). In addition, the
pattern of iodide concentrations differed from the pattern of chloride concentrations. Figure 6-19B shows the
iodide and chloride data north of Main Street. There were no spikes in chloride or iodide concentrations and
the pattern of iodide and chloride concentrations were not similar. As was the case with fluoride, iodide in the
vadose zone would likely not be useful in tracking chloride sources.
iOC
ao -
s
2 40
500
400-
OD
£
o
300 -
200
100
1/1/201® 7/1/2016 1/1/2017 7/1/3017 1/1/2016 7/1/2016
Date
a 50 H
3
a
--
U
¦100 -
1/1/2016 7/1/2016 1/1/2017 7/1/2017 1/1/2016 7/1/2016
Dale
Figure 6-19. Plots of iodide and chloride concentration with time comparing the fluoride and chloride
A. South of Main Street and B, North of Main Street. Black circles and lines are LW-1E, black triangles and
lines are LW-1C, black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2,
red circles and lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D, green
circles and lines are LW-4C, green triangles and lines are LW-4A; B. SPW cluster 5, blue circles and lines are
LW-5C, blue triangles and lines are LW-5A; C. SPW cluster 6, cyan circles and lines are LW-6C, cyan triangles
and lines are LW-6A; D. SPW cluster 7, blue circles and lines are LW-7C, blue triangles and lines are LW-7A.
-------
6.7.1.2.3 Nitrate + Nitrite
Figures 6-20 and 6-21 show the nitrate + nitrite concentrations for the SPW samples north and south of Main
Street.
Nitrate + nitrite concentrations in SPW clusters north of Main Street are shown in Figure 6-20. In cluster 1 (Figure
6-20A), only LW-1D had a trend, and in this case the nitrate + nitrite concentrations increased (p < 0.001). Cluster
2 showed decreasing trends in nitrate + nitrite concentrations in LW-2E and LW-2A (Figure 6-20B), and in both
cases the trend was significant (p = 0.020 and p < 0.001, respectively). The nitrate + nitrite concentration in all
other SPW samplers in both clusters had no statistical trend.
Figure 6-20. Changes in nitrate + nitrite concentration with respect to time for the SPW's north of Main
Street. A. SPW cluster 1 and B. SPW cluster 2. Black circles and lines are LW-1E, black triangles and lines are
LW-1C, black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2, red circles
and lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D. The red dashed
line shows the nitrate + nitrite MCL (10 mg N/L).
39
-------
Figure 6-21 shows the nitrate + nitrite data for SPW clusters south of Main Street. There is no significant
difference in the nitrate + nitrite concentrations in these clusters. In SPW clusters 4, 5, and 6 (Figures 6-21A-
C), there was a decreasing trend in nitrate + nitrite concentrations. These were ail significant (p's ranged from
< 0.001 to < 0.011). In cluster 7 (Figure 6-21D), LW-7A had a decreasing trend and was almost significant (p =
0.054). There was no trend for LW-7C.
WlfiOlfl H1/2Q16 in«!17 7'1i2017 1.1/2018 7/1/2018
Dale
M1.2018 7.1,2018 1/1,'2017 7/1<2017 1/1/2018 7,'1/2018
Date
51
1
z
1/1.2016 mffiQIi 1/1.2017 7/1/2017 1/1/2018 7/1/2018
Dale
7'- 2C to
1.'1.2017
7/1 2017
1 * '2018
Figure 6-21. Changes in nitrate + nitrite concentration in relationship to time for the SPW's south of Main
Street. A. SPW cluster 4, B. SPW cluster 5, C. SPW cluster 6, and D. SPW cluster 7. Green circles and lines are
LW-4C, green triangles and lines are LW-4A; B. SPW cluster 5, blue circles and lines are LW-5C, blue triangles
and lines are LW-5A; C. SPW cluster 6, cyan circles and lines are LW-6C, cyan triangles and lines are LW-6A;
D. SPW cluster 7, blue circles and lines are LW-7C, blue triangles and lines are LW-7A. The red dashed line
shows the nitrate + nitrite MCL (10 mg N/L).
-------
6.7.1.2.4 Phosphate
Figure 6-22 shows the time series data for P04 north of Main Street and Figure 6-23 shows the time series data
for phosphate south of Main Street.
Phosphate time series data north of Main Street is plotted in Figure 6-22. The phosphate concentrations in
cluster 1, LW-1A, LW-1C, LW-1D, and LW-1E showed increasing phosphate concentrations during the study
(Figure 6-22A) and these trends were significant (p < 0.001, p = 0.001, p = 0.001, and p = 0.049, respectively).
Cluster 2 (Figure 6-22B), LW-2A, LW-2D, and LW-2E ail had significantly increasing phosphate concentrations
during the study (p = 0.001, p = 0.030, and p = 0.005). There was no pattern to phosphate increases with depth
in SPW data north of Main Street.
0.35-
CL 0.25 -
0.10-
0.05-
0.40
A iS) iA A ~$> A
<\\n ^
0.35
0.30-
CL 0.25
0> 0.20
CL
CO
o 0.15-
/
0.10-
0.05
'iii i|iii iijiiii—i—|—i—i i i i—|—i—i—i—i—i—|—i—i » » i
A jA (A A A
# ^
n\n n\n \\ ^\n
Figure 6-22. Changes in phosphate concentration with respect to time for the SPW's north of Main Street.
A. SPW cluster 1 and B. SPW cluster 2. Black circles and lines are LW-1E, black triangles and lines are LW-1C,
black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2, red circles and
lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D.
41
-------
South of Main Street time series data is plotted in Figure 6-23. The phosphate concentrations were significantly
increasing at all depths (LW-4C and LW-4A, p < 0.001) in cluster 4 (Figure 6-23A). In cluster 7 (LW-7C and LW-7A,
p < 0.001 and p = 0.004, respectively) the phosphate significantly increased (Figure 6-23D). Cluster 7 was the
SPW cluster nearest the infiltration gallery, and cluster 4 was the next SPW cluster moving south from the
infiltration gallery. The shallower depths in clusters 5 and 6 (LW-5A and LW-6A) had statistically increasing
phosphate concentration (p < 0.001 in both; Figure 6-23B and C). However, the deeper depths, LW-5C and LW-6C,
had decreasing phosphate concentration and were significant (p = 0.075 and p = 0.054, respectively).
0«D
a j 2C
"
1/1/2016 7/1/2016 1/1/2017 7/1.2017 1/1/2018 7/1/2018
Date
0.40
B J 90
1/1/2016 7/1/2016 1/1/2017 7/1/2017 1/1/2016 7/1/2018
Date
8 0 20
1/1/2016 7/1*2016 1/1/2017 7/1/2017 1/1(2018 7/1.2018
Date
¦ 0 20
1/1/2016 7/1/2016 1/1/2017 7/1/2017 1/1/2018 7/1/2018
Date
Figure 6-23. Changes in phosphate concentration in relationship to time for the SPW's south of Main Street.
A. SPW cluster 4, B. SPW cluster 5, C. SPW cluster 6, and D. SPW cluster 7. Green circles and lines are LW-4C,
green triangles and lines are LW-4A; B. SPW cluster 5, blue circles and lines are LW-5C, blue triangles and
lines are LW-5A; C. SPW cluster 6, cyan circles and lines are LW-6C, cyan triangles and lines are LW-6A;
D. SPW cluster 7, blue circles and lines are LW-7C, blue triangles and lines are LW-7A.
42
-------
6.7.1.2.5 Dissolved Organic Carbon
Figures 6-24 and 6-25 are plots of the DOC concentrations north and south of Main Street during the duration
of the study, respectively. Figure 6-24A shows the changes in DOC concentration in SPW cluster 1 north of Main
Street. The DOC concentration was decreasing at three depths in this cluster, LW-1E, LW-1C, and LW-1A. LW-1C
was not a significant decreasing trend (p = 0.082), whereas LW-1E and LW-1A were significant trends (p = 0.003
and p = 0.034, respectively). The shallowest depth, LW-1D did not have a trend in DOC concentration during
the study. In cluster 2 (Figure 6-24B), the deepest depth, LW-2D did not have a trend in DOC concentration but
the shallower two depths (LW-2A and LW-2E) did show nonsignificant (p = 0.118 and p = 0.105, respectively)
decreasing concentrations with depth. The decreasing DOC concentration trend suggests that the soluble
carbon pool in the vadose zone is being depleted. More time is needed to determine if the soluble carbon pool
continues to be depleted or if equilibrium with the DOC in the porewater and carbon pools will be obtained.
Figure 6-24. Changes in dissolved organic carbon concentrations with respect to time for the SPW's north of
Main Street. A. SPW cluster 1 and B. SPW cluster 2. Black circles and lines are LW-1E, black triangles and lines
are LW-1C, black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2, red
circles and lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D.
43
-------
Plots of DOC concentrations versus time for clusters south of Main Street are shown in Figure 6-25. There were
significant differences in DOC concentration between clusters 4 and 7 (p = 0.015) and between clusters 5 and 7
(p = 0.023). The concentrations of DOC in cluster 4 are larger than in cluster 7 and in cluster 7 are larger than
in cluster 5. Cluster 7 is the closest group to the infiltration gallery followed by cluster 4 then cluster 5, Figure
6-25A shows how DOC concentrations changed with time in Cluster 4. Both LW-4C and LW-4A had decreasing
DOC concentration with time and the trends were significant (p = 0.011 and p = Q.016, respectively). For cluster
5 (Figure 6-25B), both depths had significantly decreasing DOC concentrations (LW-5C, p = 0.003 and LW-5A, p =
0.001). The DOC trends in cluster 6 (Figure 6-25C) followed those of clusters 4 and 5 with LW-6C (p = 0.046) and
LW-6A (p = 0.028) had significantly decreasing concentrations of DOC. In cluster 7 (Figure 6-25D), the deepest
SPW sampler (LW-7C) had a significantly decreasing concentration of DOC (p = 0.006), but the shallower SPW
sampler (LW-7A) did not show a trend in DOC concentration based on the statistical trend analysis. As was the
case with SPWs north of Main Street, the soluble carbon pools in the vadose zone are being depleted. More
sampling is needed to determine if this trend will continue.
7/1.2016 M«017 7.'1.-2017 V1/201S 7/1/2016
uiftoie 7/ircoie 1/1,2017 7/12017 1/ihoh 7/i20ie
1/1,2016 7/12016 1.'1|2017 7/12017 1/1/2018 7,'12016
1/1,2016 7/1.2016 1/1.2017 7/1.2017 1i1.2018 7/1/2016
Figure 6-25. Changes in DOC concentration in relationship to time for the SPWs south of Main Street.
A. SPW cluster 4, B. SPW cluster 5, C. SPW cluster 6, and D. SPW cluster 7. Green circles and lines are LW-4C,
green triangles and lines are LW-4A; B. SPW cluster 5, blue circles and lines are LW-5C, blue triangles and
lines are LW-5A; C. SPW cluster 6, cyan circles and lines are LW-6C, cyan triangles and lines are LW-6A;
D. SPW cluster 7, blue circles and lines are LW-7C, blue triangles and lines are LW-7A.
44
-------
6.7.1.2,6 Barium
Barium is a contaminant listed on the National Primary Drinking Water Regulations (EPA, 2017). In addition,
barium can be used as surrogate for other metals that have increased solubility because of its ability to complex
with chloride (Kabata-Pendias and Mukherjee, 2007; Madejon, 2013). The concentrations of barium with respect
to time north of Main Street are plotted in Figure 6-26 and south of Main Street are plotted in Figure 6-27.
North of Main Street, the barium concentrations for SPW clusters 1 and 2 are plotted in Figure 6-26. For cluster
1, LW-1E, LW-1C, LW-1A, and LW-1D (Figure 6-26A), the barium concentration overtime were all decreasing
significantly (p = 0.046, p = 0.006, p = 0.001, and p < 0.001, respectively). Similarly, in cluster 2, LW-2E, LW-2A,
and LW-2D (Figure 6-26B), the barium concentrations have decreasing trends with respect to time. However, in
LW-2E the barium trend was significant (p = 0.054), but the trends in LW-2A and LW-2D were significantly
decreasing with time (p < 0.001 for both).
Figure 6-26. Changes in barium concentrations with respect to time for the SPWs north of Main Street.
A. SPW cluster 1 and B. SPW cluster 2. Black circles and lines are LW-1E, black triangles and lines are LW-1C,
black diamonds and lines are LW-1A, and black stars and lines are LW-1D; B. SPW cluster 2, red circles and
lines are LW-2E, red diamonds and lines are LW-2A, and red stars and lines are LW-2D.
45
-------
Barium concentration versus time south of Main Street are plotted in Figure 6-27. In cluster 4, both SPW
samplers showed decreasing barium concentration during the study (Figure 6-27A). This decreasing trend was
significant in LW-4C (p = 0.054) and in LW-4A (p = 0.003). Cluster 5 also showed a decreasing trend in barium
concentrations (Figure 6-27B). In the case of cluster 5 the decreasing trends in barium concentration for both
LW-5C and LW-5A were significant (p = 0.038 and p = 0.007, respectively). The barium concentration trend in
SPW cluster 6 was different than in clusters 4 and 5 (Figure 6-27 C). LW-6C had a significant decreasing trend
in barium concentration (p = 0.006). LW-6A on the other hand showed no trend in barium concentrations over
time. In cluster 7, the barium concentration trends were the inverse of cluster 6 (Figure 6-27D). In cluster 7, the
deepest SPW sampler (LW-7C) does not show a trend in barium concentrations but the shallower SPW sampler
(LW-7A) does have a significantly decreasing trend (p = 0.006).
V1W1IS 7/1/301 fl 1,'1.-a017 7,'1/2QI7 1,'1i20l8 T/IB01S
1,'1'2016 T/iaOIG 1>'1ffl)17 7/11^2017 W1J2018 7/IO018
lfl/2016 1/1/Sdi 7 7/i/3
-------
6.7.1.2.7Stormwater Contaminants
The Nationwide Urban Runoff Program (NURP) and the National Stormwater Quality Database (NSQD) have
identified several inorganic contaminants and organic contaminants that are important to evaluate when
infiltrating stormwater in a Gl system (U.S. EPA, 1983; NSQD, 2015). Summary of NURP and NSQD inorganic
compounds as well as other trace metals are shown in Table 6-2.
Geogenic3 background concentrations of these parameters are unknown in the soil and soil porewater in this
study. For most parameters in this table the concentration range would likely be consistent with background
concentrations. There were a few parameters, chromium, copper, manganese, molybdenum, nickel, antimony,
vanadium, and zinc, in which the maximum concentrations were considerably greater than the concentration
in the 75th percentile. For manganese and nickel (LW-2D); molybdenum and zinc (LW-1D); antimony (LW-1C);
and vanadium (LW-5A) the maximum concentrations were in the initial sampling (July 2016) and decreased in
subsequent events. The maximum concentration for copper (LW-6A) occurred in September 2016 and decreased
in subsequent events. The maximum concentration for chromium was found in LW-4C. Initially the chromium
concentrations were low in the SPW sampler but increase to the maximum concentration in July 2017 and
October 2017. After October 2017 the concentrations of chromium decreased in LW-4C. This suggests that the
increased infiltration of stormwater likely initially flushed these trace components through the vadose zone. This
data does not indicate whether or not there is sequestration of these trace components in the vadose zone.
Resulting from geological processes.
47
-------
Table 6-2. Summary of trace inorganic data for the Louisville soil porewater samples.
Ammonia
Total
Nitrogen
Phosphate
Ag
Al
As
Ba
Be
Cd
Co
Cr
Cu
Units
mg N/L
mg N/L
mg P/L
Mg/L
Mg/L
Mg/L
Mg/L
Mg/L
Hg/L
Hg/L
Hg/L
Hg/L
Total Number of Analyses
130
130
130
130
130
130
130
130
130
130
130
130
Number of Detects
10
127
18
1
71
75
127
8
15
35
78
74
Percent Detects
8
98
14
1
55
58
98
6
12
27
60
57
Mean
0.04
1.52
0.163
0.5
25
0.9
63
0.8
0.7
1.1
2.3
4.5
Standard Deviation
0.02
1.27
0.052
29
0.3
40
0.5
0.2
0.6
2.7
21
Minimum
0.02
0.08
0.111
1
0.5
8
0.5
0.5
0.6
0.5
0.5
25th Percentile
0.02
0.80
0.140
7
0.7
33
0.6
0.5
0.8
0.8
0.7
Median
0.03
1.21
0.148
0.5
16
0.8
56
0.6
0.6
1.1
1.0
0.8
75th Percentile
0.04
1.73
0.163
26
1.0
87
0.8
0.8
1.2
2.3
1.2
Maximum
0.07
8.51
0.341
132
2.8
210
1.8
1.1
4.1
13
132
Fe
Mn
Mo
Ni
Pb
Sb
Se
Th
Tl
U
V
Zn
Units
Hg/L
Mg/L
Mg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Total Number of Analyses
130
130
130
130
130
130
130
130
130
130
130
130
Number of Detects
0
43
88
81
0
36
57
1
2
40
109
8
Percent Detects
0
33
68
62
0
28
44
1
2
31
84
6
Mean
254
3.0
5.2
1.9
5
0.5
0.3
0.9
59
99
Standard Deviation
761
2.2
12
5.1
5
0.2
0.4
68
72
Minimum
0.5
0.3
0.5
0.5
1
0.2
0.5
3
52
25th Percentile
0.9
1.7
0.8
0.5
1
0.3
0.6
12
61
Median
3.7
2.7
1.2
0.7
3
0.5
0.3
0.7
26
64
75th Percentile
120
3.5
3.5
0.8
6
0.4
0.9
87
98
Maximum
4170
14
69
24
23
0.5
1.9
339
250
-------
6.7.1.3 Louisville Soil Porewater Summary
Soil porewater data indicated that there was a difference in soil porewater quality depending on if the porewater
was collected north or south of Main Street. Overall, the major anion and cation data and SPC showed larger
values north of Main Street. North of Main Street, many of these same parameters showed decreasing
concentrations with time. This trend was similar south of Main Street, except that chloride concentrations did
not show a trend overall.
North of Main Street, the dominant geochemical processes that were indicated were precipitation/dissolution
and exchange reactions based on major anion and cation data. However, in the shallower depths, silicate
weathering could also be a major process affecting porewater chemistry. Weathering (in deeper depths),
redox process, and sorption are likely occurring, but these are likely minor geochemical processes based on
major anion and cation chemistry. Exchange reactions in the deeper depths were predominantly ion exchange
reactions. In the middle depths the exchange reactions shifted from reverse ion exchange reactions to ion
exchange reactions, and in the shallower depths the exchange reactions cycled between ion exchange and
reverse ion exchange reactions. Urban fill could also be contributing to the changes shown in the major anions
and cations because of the increased leaching that resulted from the installation of Gl (Boudreault et al., 2010;
Swanson and Lamie, 2010; Shuster et al., 2014; Shuster et al., 2015; McGrane, 2016).
South of Main Street, the dominant geochemical processes that were indicated were precipitation/dissolution
and exchange reactions based on major anion and cation data. Exchange reactions were shown to be either ion
exchange or reverse ion exchange. The reverse ion exchange process was mainly found in the deeper soil depths
and ion exchange in the shallow depths.
Nitrate + nitrite, DOC, barium, and strontium concentrations decreased with time; however, phosphate
concentrations were increasing with time. Based on the study data, fluoride and iodide would not be useful
in tracking road salt applications (source delineation) because of the significant delay in the peak fluoride and
iodide concentrations relative to the peak chloride concentrations.
There were a few parameters, chromium, copper, manganese, molybdenum, nickel, antimony, vanadium, and
zinc, that had greater than expected concentrations in a few events. For manganese, nickel, molybdenum, zinc,
antimony, and vanadium, the maximum concentrations were in the initial sampling (July 2016) and decreased
in subsequent events. The maximum concentrations for copper occurred in September 2016 and decreased in
subsequent events. The maximum concentrations for chromium were in 2017 and then the concentrations of
chromium decreased. This suggests that urban fill and the potential to have associated contaminants may in part
be why we are seeing these patterns in trace metal behavior (Swanson and Lamie, 2010). The other part is that
Gl has been shown to increase infiltration in urban soils (Boudreault et al., 2010; Shuster et al., 2014; Shuster et
al., 2015; McGrane, 2016). This suggests that the increased infiltration of stormwater likely flushed these trace
components through the vadose zone.
49
-------
6.7.2 Groundwater
All groundwater quality data can be found in Appendix E, Table E-2.
6.7.2.1 Background Groundwater Quality
To assess changes in groundwater quality, it is important to understand the groundwater quality before
implementing the Gl infrastructure. A background sampling event was undertaken in February 2016. No single
sampling event can account for seasonal changes or ongoing trends in groundwater chemistry. Therefore, a
literature and database search was conducted to gather more groundwater quality information for this study
site.
The USGS NWIS (USGS, 2016) was the only data source located that provided any background water quality data
for this study. Only groundwater data from outwash aquifers and wells within a 10 km radius of the study site
south of the Ohio River were used. The total number of sampling points was 269.
Concentrations from both the monitoring wells (MW) and the piezometers (PW) were compared against the
NWIS groundwater quality data from the NWIS database. All parameters in the NWIS database that could be
compared with the study data were significantly different. Because of this the NWIS data is not suitable for use
in understanding the background water quality in this study. The results of statistical analysis, which compares
NWIS groundwater quality data to MW and PW data can be found in Appendix B Table B-3.
Other methods can be used to help define background in geochemical data (Matschullat et al., 2000; Reimann
et al., 2008). Matschullat et al. (2000) proposed several other means of determining background concentrations,
specifically, methods designed to predict the upper limits of the threshold of background. These methods include
the Iterative 2a (o= Standard Deviation) technique, the 3o outlier test, the calculated distribution function, and
the inflection points on a cumulative frequency curve. Reimann et al. (2008) suggested the use of the mean ±
2a. Matschullat et al. (2000) concluded that the iterative 2o-technique and the calculated distribution function
provide realistic approximations of the background condition; however, they further point out that no single
method can provide absolute results due to the inherent complexity of geochemical data sets. Finally, Reimann
et al., 2018 proposed the use of the interquartile range and median ± 2MAD (MAD= Median Absolute Deviation).
All these techniques were applied to the study data and based on this analysis the iterative 2a technique was
selected to define the background ranges. This choice was based on not being appropriately conservative and
yielding consistent results. The background ranges for parameters that will be discussed in this study are given
in Table 6-3 and for all parameters in Table B-4, Appendix B. The lower and upper critical values define the
background concentration ranges in Table 6-3 and the percentage of samples included in the background range
is also shown.
6.7.2.2 Monitoring Wells and Piezometers
Only chromium and iodide concentrations showed significant differences (p = 0.014 and p = 0.020, respectively)
between samples collected from the MWs and PWs (See Appendix B, Table B-5 for other statistical comparisons).
The MWs and the PWs are completed approximately at the same depths. The difference in these is that the MWs
have a 3.1 m screened interval and the PWs have a 0.6 m screened interval. This analysis also suggests that the
aquifer is likely not stratified. Therefore, since it is expected that the top of the aquifer will be a better indicator
of potential changes to groundwater quality and the MW wells are the only wells that are screened across the
top of the aquifer, only the MWs will be used in subsequent analysis.
6.7.2.3 Major Anions and Cations, pH, Specific Conductivity
Detailed analysis of time series and trend analysis for major anions and cations, pH and specific conductivity can
be found in Appendix B, Section 1.6.
50
-------
Table 6-3. Study specific background ranges for select analytes determined for the Louisville Gl Study.
Parameter
Units
Total
Number of
Analyses
Number of
Analyses
Used
Mean
Std Dev
Median
Min
Max
Lower
Critical
Value
Upper
Critical
Value
Percent of
Samples
Included
Specific Conductance
US/cm
220
200
1024
139
1040
746
1302
746
1302
90.9
PH
220
202
7.01
0.12
7.03
6.76
7.26
6.77
7.25
91.8
Dissolved Organic Carbon
mg/L
220
200
0.54
0.12
0.53
0.36
0.86
0.3
0.78
90.9
Bicarbonate
mg HCO3/L
220
208
381
45
285
263
496
291
471
94.5
Chloride
mg/L
220
203
71.6
23.7
73.8
25.6
122
24.2
119
92.3
Sulfate
mg/L
220
206
75.7
16.8
76.9
39.8
112
42.0
109
93.6
Fluoride
mg/L
220
198
0.20
0.05
0.20
0.08
0.31
0.10
0.30
90.0
Iodide
Mg/L
120
115
5.61
3.50
5.40
0.75
17.5
BDL1
12.6
95.8
Nitrate + Nitrite
mg N/L
220
210
4.20
1.50
4.10
1.31
7.23
1.20
7.20
95.5
Phosphate
mg P/L
220
211
0.064
0.037
0.055
0.004
0.167
BDL
0.138
95.9
Total Phosphorous
mg P/L
110
102
0.058
0.016
0.056
0.028
0.093
0.026
0.09
92.7
Aluminum
Mg/L
171
164
4.6
8.1
1
0.5
51
BDL
21
95.9
Barium
Mg/L
220
206
64
15
64
25
121
34
94
93.6
Calcium
mg/L
220
198
113
15
115
79.6
144
83.8
142
90.0
Cobalt
Mg/L
195
183
0.4
0.2
0.3
0.3
1.3
BDL
0.8
93.8
Chromium
Mg/L
195
192
1.9
3.4
1.0
0.3
29
1.4
5.4
98.5
Copper
Mg/L
170
163
1.5
2.5
1.0
0.3
16
BDL
6.6
95.9
Iron
Mg/L
220
212
31
21
25
25
156
BDL
73
96.4
Potassium
mg/L
220
197
5.45
1.00
5.55
3.21
7.84
3.45
7.45
89.5
Magnesium
mg/L
220
206
37.2
5.64
37.9
24.9
48.6
25.9
48.5
93.6
Manganese
Mg/L
195
183
17
25
6.0
0.3
124
BDL
67
93.8
Sodium
mg/L
220
208
43.9
11.3
43.7
17.0
73.6
21.2
66.6
94.5
Nickel
Mg/L
195
194
1.2
1.4
0.6
0.3
6.4
BDL
4.0
99.5
'BDL = Below Detection Limit.
-------
6.7.2.4 Dilution Effects on Major Cations and Anions
The Gl infrastructure at the Louisville study site is designed to infiltrate very large quantities of water into the
subsurface. Although the stormwater runoff itself is not sampled in this study, the concentrations of major
anions and cations are likely much lower than what has been measured in the groundwater. Because of this, it
is likely that when the infiltrated stormwater mixes with the groundwater, the infiltrated stormwater dilutes the
major anions and cations in groundwater locally. Based on this analysis, the slopes of the trend lines can be used
as rough indicators of changes in water quality caused by the dilution of the major anions and cations. The slopes
are given in Table 6-4.
Table 6-4. Slopes from the trend analysis that are being used for rate of change in parameter
concentrations. Locations' rates of change are the average of the wells north and south of Main Street.
Well/ Location
Slopes
Bicarbonate
Chloride
Sulfate
Calcium
Magnesium
Sodium
Potassium
mg/L/yr
mg/L/yr
mg/L/yr
mg/L/yr
mg/L/yr
mg/L/yr
mg/L/yr
MW-01
-28.6
-10.4
-14.2
-10.5
-10.5
-1.85
-0.87
MW-02
-16.7
-0.43
-13.8
3.71
3.71
-5.38
0.04
MW-03
-27.0
-10.8
-12.9
-5.78
-5.78
-3.90
1.06
MW-04
-18.5
-22.4
-9.03
-7.96
-7.96
-5.86
-0.35
MW-05
-56.8
-6.87
-15.4
-16.2
-16.2
4.89
-0.88
MW-06
-20.2
-8.81
-9.48
-5.40
-5.40
-1.88
-0.10
MW-07
-20.0
1.14
-10.5
-5.48
-5.48
5.33
0.15
MW-08
-14.3
-3.90
-14.6
-4.30
-4.30
-1.94
-0.42
MW-09
-29.5
-11.4
-23.8
-13.4
-13.4
-6.31
-0.67
MW-10
-6.33
-23.0
-13.8
-7.04
-7.04
-6.54
-0.47
North of Main St
-29.2
-10.8
-17.5
-7.99
-4.63
-3.16
-0.39
South of Main St
-14.2
-6.97
-14.6
-7.12
-4.75
-3.54
-0.30
Changes in groundwater quality caused by the major anion and cation composition can be extrapolated using
the rates of change for the major anions and cations along with the concentrations of these parameters in the
August 2018 samples. This analysis assumes that the rates of change will remain constant, the composition of
the stormwater remains constant, and that the geochemical processes currently happening will not change
with dilution. Even though these assumptions will not hold, the extrapolations will provide insight into potential
changes in groundwater quality that could arise because of changes in major anion and cation compositions.
52
-------
Figure 6-28 is a Piper diagram showing data from the background sampling (February 2016), the August 2018
sampling, extrapolations five years into the future, and extrapolations 10 years into the future. In the cation
trilinear diagram (bottom left) of the Piper diagram, the black arrow shows the path of how the major cation
composition changes with time. The relative amounts of magnesium are decreasing rapidly, and the relative
amounts of calcium are decreasing slowly. The relative amount of sodium is increasing. The extrapolation in five
years shows that magnesium composition would be a minor component, and calcium has decreased slightly
more than was in August 2018. In ten years, the groundwater is still dominated by calcium, but the contribution
of sodium has increased. In the case of wells south of Main Street, it is approaching being the major cation. The
anion trilinear plot (lower right Figure 6-28) of the piper diagram shows that sulfate and chloride amounts are
decreasing and the extrapolation to five and ten years shows these to only be trace components. The mixing
diamond, on the other hand, shows that water type is shifting, and the extrapolations would indicate that water
is becoming softer - that is calcium and magnesium are decreasing. The ten-year extrapolation indicates that the
water is still a calcium-bicarbonate type water but is shifting toward a more sodium-bicarbonate type water.
O
/Ca-Cl\ ^
&
Mixed
a
Na-C!
O
Mixed
o
Na-HCO-
S04-Type
Mg-Type
Mixed-Type
Mixed-Type
cP
Na-Type
Ca-
Cl-Type
¦o
0.8 0.6
0.0
1.0
0.4
0.2
0.0 0.2
0.4
0.6 0.8
1.0
Ca CI
Figure 6-28. A Piper Diagram showing the February 2016 (circles), August 2018 (triangles), extrapolation
5 years into the future (diamonds), and extrapolation 10 years into the future (stars). Black symbols are
data for north of Main Street and red symbols are for data south of Main Street. The black arrows in
each field indicate the direction data would trend in the 5 year and 10 year extrapolations.
53
-------
6.7.2.5 Major Anion and Cation Geochemicai Processes
Geochemical processes that can modify the composition of groundwater quality need to be identified and their
contribution and importance need to be identified. Several geochemical processes that may participate in the
altering of groundwater quality for major anions and cations are oxidation/reduction reactions, precipitation/
dissolution reactions, and ion exchange reactions. These processes will be discussed below.
The relationship of sodium compared to chloride is plotted in Figure 6-29A. The halite dissolution line indicates
that neither sodium nor chloride are in excess. This suggests that equilibrium of a solid such as halite is
controlling the solubility of sodium and chloride or that there is a potential influx of water that was influenced
by deicing chemicals. The groundwater data from this study falls on the halite dissolution line (Figure 6-29A).
This suggests that exchange reactions and silicate weather are not likely dominant processes controlling sodium
and chloride; however, this does not distinguish between halite controlling the concentrations of sodium and
chloride or the infiltration of water that is heavily influenced by deicing compounds.
Figure 6-29B is a plot showing the relationship of sodium + potassium to total cations. All the groundwater data
collected falls below the y=0.5x line indicating that silicate weathering is not a dominant geochemical process
controlling sodium and potassium.
20-
Silicate
Weather
Dominant
10-
"D
Silicate
Weather
Not Dominant
o
10
20
30
100
o»
cr
a>
E
O)
E
'¦5
o
if)
0 1
Reverse Ion
Exchange
0.01
TTTT—
0.1
0.01
1
100
1000
Chloride (meq/L) Total Cations (meq/L)
Figure 6-29. Plots of A. Sodium versus chloride. B. Sodium + potassium versus total cations for the Louisville
study site. Black circles and lines show data for MW-01, red circles and lines show data for MW-02, green
circles and lines show data for MW-03, blue circles and lines show data for MW-04, cyan circles and lines
show data for MW-05, magenta circles and lines show data for MW-06, dark yellow circles and lines show
data for MW-07, purple circles and lines show data for MW-08, wine-colored circles and lines show data for
MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas in B indicate region where
silicate weathering is dominant and yellow shaded areas indicate region where silicate weathering is not
dominant.
54
-------
Calcium arid magnesium relationships are shown in Figure 6-30. Calcite would be the important carbonate solid
phase based on the data collected (Figure 6-30A). This inference is based on calcium being in excess.
Figure 6-30B is a plot of calcium + magnesium versus bicarbonate + sulfate. Although the study data falls on
or near the 1:1 line, most of the data plots above the line. This suggests that both precipitation/dissolution
reactions and reverse ion exchange reactions could be dominant processes. There are a very few points that fall
below the 1:1 line which would indicate that ion exchange reactions are the dominant process.
Figures 6-30C and 6 30D are plots indicating which solid phases are potentially controlling the calcium and
magnesium concentration in the samples. Figure 6-30C indicates that all the samples are in equilibrium with
calcite and dolomite, suggesting that dissolution and precipitation reactions are important processes. However,
Figure 6-30D indicates that all the samples are undersaturated with respect to gypsum.
s.s
10
s
r
2
B
D
10
1
3
4
6
9
2
12
0
4
e
s
14
Calcium (meqyL) Bicarbonate * Sulfate (rneqfl_)
c
I
II
Cakiiie underwiuratedi .
Dolomtfe overwtuiaied *
¦
«
¦
i
i
i
ji
i Cateitc owiialurated
i Dofcmnite ovwiilurattcS
i
laiil.Erium
Faiilli&rTunn
urvders-akirated
urKleirSiluMUieP »
1
i CakJte everutiffatcd
, Beterfrute ur»ctefs*iufated
i
i
i
i
i
i
•64-2024 6
Calcite SI
Figure 6-30. Plots of A. Magnesium versus calcium. B. Calcium + magnesium versus bicarbonate + sulfate.
C. Dolomite SI versus calcite SI. D. Gypsum SI. Black circles and lines show data for MW-01, red circles show
data for MW-02, green circles show data for MW-03, blue circles show data for MW-04, cyan circles show
data for MW-05, magenta circles show data for MW-06, dark yellow circles show data for MW-07, purple
circles show data for MW-08, wine-colored circles show data for MW-09, and dark cyan circles show data
for MVV-10. In C the gray shaded region indicates equilibrium between the sample and solids, the yellow
shaded region indicates the sample is undersaturated with respect to calcite and oversaturated with respect
to dolomite, green shaded region is where the sample is oversaturated with respect to calcite and dolomite,
blue shaded region is where the sample is undersaturated with respect to both calcite and dolomite, and the
purple shaded region is where the sample is oversaturated with respect to calcite and undersaturated with
respect to dolomite. In D the gray shaded region indicates equilibrium with gypsum.
55
-------
The plots in Figure 6-31 indicate the role ion exchange process plays in the groundwater at the Louisville study
site. Figure 6-31A is a plot of the chloro-alkaline indices. Based on this plot, both types of ion exchange reactions
are possible. There were no temporal patterns, but most of the samples indicate ion exchange reactions. Even
though ion exchange reactions are suggested in most samples, there is still considerable data that suggests
reverse ion exchange is important. Figure 6-31B is a plot of sodium + potassium - chloride versus calcium +
magnesium - bicarbonate - sulfate, in this plot data, the plots on the y=-x trend line suggest that ion exchange
reactions are important. The study data did not plot on the y=-x trend line. An ordinary least squares regression
of the data shows that the slope of a trend line would be -0.535, and this indicates that there is an ion imbalance
in solution. In this case, the data suggests that in most samples, calcium and magnesium are in excess. This
would suggest that ion exchange reactions are an important process.
A
j
Reverse Ion
Exchange
. *
*
r
•
•
Ion Exchange
- I,,,,,
¦ i | ¦ ¦ ¦ i | ¦ ¦ ¦ I ¦ ¦ ¦ ¦ i ¦ ¦ ¦ ¦ i
-0.4 -0.2 0.0 0.2 04
CAI2
4
3
2
1
0
1
-2
3
-4
5-4 -3 -2 -1 0 1 2 3 4 5 6
Ca+Mg-HC03-S04
Figure 6-31. Plots of the A. Chloro-alkaline index 1 versus chloro-alkaline index 2 and B. Na+K-CI versus
Ca+Mg-HC03- S04. Black circles and lines show data for MW-01, red circles and lines show data for MW-02,
green circles and lines show data for MW-03, blue circles and lines show data for MW-04, cyan circles and
lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow circles and lines
show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles and lines show
data for MW-09, and dark cyan circles and lines show data for MW-10. In A the green shaded areas represent
the region where ion exchange reactions would be important, and the magenta shaded areas represent the
region where reverse ion exchange reactions would be important.
56
-------
6.7.2.6 Other Chemical Constituents
6.7.2.6.1 Dissolved Organic Carbon
Figure 6-32 shows plots of DOC concentrations versus time. North of Main Street (Figure 6-32A), there were no
trends in DOC concentrations in wells MW-01, MW-03, and MW-04. In MW-02 there was a significant decreasing
trend in DOC concentration (p = 0.036). However, MW-05 showed a significant increasing trend in DOC
concentrations (p = 0.010). South of Main Street (Figure 6-32B), there were no trends in DOC concentrations
in MW-06, MW-07, and MW-10. For wells MW-08 and MW-09, there were significant decreasing DOC
concentrations (p = 0.010 and p = 0.015, respectively).
9 2.0 -
0.5
TTTTTTTTTTJTTTTTTTTTTTpTTTTl
1/1/2016 1/1/2017 1/1/2018
| i i t i » i i i i i i | r i i i t i » i i i !
1/1/2016 1/1/2017 1/1/2018
Data
Date
Figure 6-32. Plots showing the changes in dissolved organic carbon concentrations with time for A. Wells
north of Main Street and B. Wells south of Main Street. Black circles and lines show data for MW-01, red
circles and lines show data for MW-02, green circles and lines show data for MW-03, blue circles and lines
show data for MW-04, cyan circles and lines show data for MW-05, magenta circles and lines show data for
MW-06, dark yellow circles and lines show data for MW-07, purple circles and lines show data for MW-08,
wine-colored circles and lines show data for MW-09, and dark cyan circles and lines show data for MW-10.
Gray shaded areas represent the site-specific background.
57
-------
6.7.2.6,2 Fluoride
The time series plots for fluoride are shown in Figure 6-33. Fluoride concentrations plotted against time for
samples north of Main Street are shown in Figure 6-33A. There were no trends in fluoride concentrations north
of Main Street. South of Main Street (Figure 6-33B), there were no trends in fluoride concentrations in wells,
MW-06, MW-07, and MW-08. Fluoride concentrations in MW-09 and MW-10 had a significant increasing trend in
fluoride concentrations (p = 0.019 and p = 0.003, respectively).
Although there were, except for MW-09 and MW-10, no trends in fluoride concentrations, the groundwater
fluoride concentrations could still be affected by de-icing. There was overall no correlation between fluoride and
chloride (r2= 0.06). The correlations with chloride in just MW-09 and MW-10 showed slightly better correlation
(r= 0.25). It is unlikely that de-icing agents play much of a role in fluoride concentrations.
O)0.3 -
O) 0.3
TV
/
2 0.2 -
§ 0.2-
1/1/2016 1/1/2017 1/1/2018
Date
1/1/2016 1/1/2017 1/1/2018
Date
Figure 6-33. Plots showing the changes in fluoride concentrations with time for A. Wells north of Main Street
and B. Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines show
data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for MW-04,
cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow
circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles
and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas
represent the site-specific background.
58
-------
6.7.2.6,3 Iodide
Figure 6-34 shows plots of the time series iodide concentration data. There were no trends in iodide
concentration for MW-01, MW-03, MW-04, and MW-05 wells (Figure 6-34A) north of Main Street. MW-02 did
show an increasing trend in iodide concentrations, and it was significant (p = 0.063). There were no trends in
iodide concentration in any of the wells south of Main Street during the study (Figure 6-34B). There was no
correlation in iodide and chloride concentrations (r2 = -0.01). This is not surprising since the data from the vadose
zone suggested that iodide did not correlate with chloride. This means that it is unlikely that iodide would be
useful in determining if de-icing agents were used at this site.
70:
60-
50
\
(J — I I I I I I I iT I I | I I I I I ! I I I I I | I I I I I I I I I I
1/1/2016 1/1/2017 1/1/2018
Date
\
i
i i | I
1/1/2016 1/1/2017 1/1/2018
Date
Figure 6-34. Plots showing the changes in iodide concentrations with time for A. Wells north of Main Street
and B. Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines show
data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for MW-04,
cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow
circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles
and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas
represent the site-specific background.
59
-------
6.7.2.6,4 Nitrate + Nitrite
Time series data for nitrate + nitrite is plotted in Figure 6-35. All wells north of Main Street (Figure 6-35A), had
decreasing nitrate + nitrite concentrations with time. The wells with significant trends are MW-01 (p = 0.010),
MW-02 (p = 0.019),, MW-03 (p = 0.063), MW-04 (p = 0.016) and MW-05 (p = 0.002). South of Main Street (Figure
6-35B), also showed decreasing nitrate + nitrite concentrations in MW-06, MW-07, and MW-09. The decreasing
trends were significant in MW-06 and MW-07 (p = 0.016 and p = 0.037, respectively). MW-08 and MW-10 did not
show any concentration trends during the study.
11
1/1/2016 1/1/2017 1/1/2018
Date
10--
9-
8
7
v 6
O)
E
5-
+
-------
6.7.2.6,5 Phosphate
Figure 6-36 shows plots of the time series phosphate data for the groundwater at the Louisville study site.
Groundwater north of Main Street (Figure 6-36A), shows increasing phosphate concentrations in all wells except
MW-04. These increasing phosphate concentration trends were all significant (p-values range 0.006 - 0.025).
South of Main Street (Figure 6-36B), three of the wells showed increasing phosphate concentrations. These wells
were MW-06, MW-07, and MW-09. The increasing trends were significant in MW-06, MW-07, and MW-09 (p =
0.010, p = 0.006, and p = 0.089, respectively). The other two wells, M W-08 and MW-1Q did not show trends in
phosphate concentrations.
0.35
A
0.30-
0.25 -
CL
CO
£ 0.20 -
0)
co
a.
(/)
o
0.15 -
CL 0.10-
0.05
0.00
w 1
I I I I I I I I I ! I I I I I I I I I I I I I I I I I I I I I I I
1/1/2016 1/1/2017 1/1/2018
0.35
0.30-
0.25-
£ 0.20 -
0.15-
/
CL 0.10 -
i i | i i i i i i i i i i r | i i i i i
1/1/2016 1/1/2017 1/1/2018
Date
Date
Figure 6-36. Plots showing the changes in phosphate concentrations with time for A. Wells north of Main
Street and B. Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines
show data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for
MW-04, cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark
yellow circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored
circles and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded
areas represent the site-specific background.
61
-------
6.7.2.6,6 Barium
Figure 6-37 shows how the barium concentrations varied during the study period. The barium concentrations
north of Main Street during the study are shown in Figure 6-37A. In weils MW-01 and MW-05, there was a
trend of decreasing barium concentrations. In both cases these trends were significant (p = 0.037 and p = 0.003,
respectively). There were no trends in barium concentration in wells MW-02, MW-03, and MW-04. South of Main
Street (Figure 6-37B), four of the wells had decreasing trends in Ba concentration. These wells were MW-06,
MW-08, MW-09, and MW-10. There was no trend in barium concentrations in MW-07. The decreasing barium
concentration trends were significant in MW-06 (p = 0.063), MW-08 (p = 0.030), MW-09 (p = 0.076), and MW-10
(p = 0.004).
200-
150 -
O)
I 100
i
TO
CO
50 -
A
•
•
1\
1 *
M-*\A ^ •
u
A:
V
•
I 100
1/1/2016 1/1/2017 1/1/2018
Date
p 11 "1
1/1/2016 1/1/2017 1/1/2018
Date
Figure 6-37. Plots showing the changes in barium concentrations with time for A. Welis north of Main Street
and B. Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines show
data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for MW-04,
cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow
circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles
and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas
represent the site-specific background.
Another potential mechanism controlling barium is ion exchange as was indicated during the analysis of major
cations. There is a weak correlation between barium concentrations and chloride concentrations (r2= 0.39)
which would suggest that when the concentration of chloride increases, barium is complexed, and the barium
concentration increases. There is also a weak correlation between barium and sodium (r2= 0.38). As was the case
with chloride, as the sodium concentration increases the barium concentration also increases. This suggests that
at least some of the barium is weakly held by the solids in the aquifer. Chloride can complex with barium and
form a soluble and stable solution complex. Sodium on the other hand can participate in reverse ion exchange
reactions and replace barium weakly bound to the solids. In both cases, this provides evidence that at least some
of the barium is weakly bound to aquifer solids and ion exchange would be an explanation for this weak bonding.
62
-------
6.7.2.6.7 Chromium
Chromium time series information is plotted in Figure 6-38. There were no trends in chromium concentrations
north of Main Street in wells MW-01, MW-02, and MW-03 (Figure 6-38A). However, in wells MW-04 and MW-
05, the chromium concentrations were increasing with time. These trends were significant (p = 0.010 and p
= 0.006, respectively). The data for chromium concentrations with time are plotted in Figure 6-38B for wells
south of Main Street. There were no chromium concentration trends in wells MW-09 and MW-10. In wells MW-
06, MW-07, and MW-08, the chromium concentrations were increasing with time. These trends in chromium
concentration were significant in MW-06 (p = 0.002) and MW-07 (p = 0.008) and in MW-08 (p = 0.063). Both
north and south of Main Street, the wells with increasing chromium concentrations are the wells closest to the
infiltration gallery.
A
225
200 -|
100
75-
50-
25-
0 - HTu vrv i n 11111
1/1/2016 1/1/2017 1/1/2018
TIE
200 -
MCL>
r~t i h i
Date
1/1/2016 1/1/2017 1/1/2018
Date
Figure 6-38.Plots showing the changes in chromium concentrations with time for A. Wells north of Main
Street and B, Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines
show data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for MW-
04, cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow
circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles
and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas
represent the site-specific background. The red dashed line represents the MCL for chromium (100 ng/L).
63
-------
6.7.2.6,8 Nickel
Figure 6-39 shows plots of nickel concentrations versus time collected north and south of Main Street. Nickel
concentrations during the study north of Main Street are plotted in Figure 6-39A. There were increasing trends
in nickel concentrations in wells MW-01, MW-03, MW-04, and MW-05. These increasing trends were significant
(p-values ranged from 0.030 - 0.097). In well MW-02, there was no trend in nickel concentrations with time
during the study. South of Main Street, only the two wells closest to the infiltration gallery show any trends in
nickel concentrations (Figure 6-39B). In both MW-06 and MW-07 these trends in increasing nickel concentrations
were significant (p = 0.016 and p = 0.048, respectively). In wells MW-08, MW-09, and MW-10 there were no
trends in nickel concentrations.
1/1/2016 1/1/2017 1/1/2018
Date
-yrrT-r-r-TTT-r-r-i -j -
1/1/2016 1/1/2017 1/1/2018
Date
Figure 6-39. Plots showing the changes in nickel concentrations with time for A. Wells north of Main Street
and B. Wells south of Main Street. Black circles and lines show data for MW-01, red circles and lines show
data for MW-02, green circles and lines show data for MW-03, blue circles and lines show data for MW-04,
cyan circles and lines show data for MW-05, magenta circles and lines show data for MW-06, dark yellow
circles and lines show data for MW-07, purple circles and lines show data for MW-08, wine-colored circles
and lines show data for MW-09, and dark cyan circles and lines show data for MW-10. Gray shaded areas
represent the site-specific background. The red dashed line represents the MCL for chromium (100 jag/L).
64
-------
6.7.2.6.9 Stormwater Inorganic Contaminants and Select Trace Metals in Groundwater
The NURP and NSQD have identified several inorganic contaminants and organic contaminants that are
important to evaluate when infiltrating stormwater in a Gl system (U.S. EPA, 1983; NSQD, 2015). Summary
of NURP and NSQD inorganic compounds as well as other trace metals are shown in Table 6-5. Site-specific
background concentrations of these parameters are given in Table 6-4 and these will be used to compare data to.
Phosphate, barium, chromium, and nickel were discussed in the previous sections and in Appendix B Section 1.7.
Initially the concentrations for aluminum, iron, and manganese were greater than the site-specific background
for these metals. The reasons for this were likely that initially the wells were not fully developed and the
sediment in the water caused the higher concentrations observed. Total phosphorous, cobalt, and copper
had concentrations greater than the site-specific background. Total phosphorous concentrations increased
throughout the study similar to what was observed for phosphate. The concentrations of cobalt initially were
within the site-specific background concentration range but the concentrations near Main Street began to
increase in October 2017 and were still increasing in August 2018. The source of the cobalt is not known. Initially,
copper concentrations were within the range of the site-specific background for copper. In September 2016, the
copper concentrations were greater than the site-specific background in the wells near Main Street. The copper
concentrations then decreased and were within the site-specific background for the remainder of the study. The
source of copper is not known.
-------
Table 6-5. Summary of trace inorganic data for Louisville groundwater samples.
Ammonia
Total
Nitrogen
Phosphate
Total
Phosphorous
Ag
Al
As
Ba
Be
Cd
Co
Cr
Cu
Units
mg N/L
mg N/L
mg P/L
mg P/L
Mg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Total Number of Analyses
100
100
90
100
100
100
100
100
100
100
100
100
100
Number of Detects
29
100
90
90
0
33
83
100
28
1
29
78
63
Percent Detects
29
100
100
90
0
33
83
100
28
1
24
78
63
Mean
0.11
4.15
0.077
0.061
18
1.0
61
0.9
0.5
1.0
5.6
8.1
Standard Deviation
0.15
1.74
0.055
0.020
33
0.2
20
0.3
0.6
24
25
Minimum
0.03
0.51
0.009
0.024
1
0.5
19
0.5
0.5
0.5
0.5
0.5
25th Percentile
0.04
2.72
0.038
0.049
4
0.8
51
0.6
0.5
0.6
0.8
0.8
Median
0.05
4.20
0.062
0.057
6
1.0
60
0.8
0.5
0.8
1.1
1.2
75th Percentile
0.08
5.50
0.096
0.072
16
1.1
67
1.1
0.5
1.6
1.9
2.2
Maximum
0.67
7.64
0.325
0.136
136
1.6
177
1.8
0.5
2.2
209
129
Fe
Mn
Mo
Ni
Pb
Sb
Se
Th
Tl
U
V
Zn
Units
Mg/L
Mg/L
Mg/L
Mg/L
Mg/L
Mg/L
Mg/L
Mg/L
Hg/L
Hg/L
Hg/L
Hg/L
Total Number of Analyses
100
100
100
100
100
100
100
100
100
100
100
100
Number of Detects
6
85
59
54
7
24
85
5
3
46
11
7
Percent Detects
6
85
59
54
7
24
85
5
3
46
11
7
Mean
159
31
1.2
1.1
0.8
0.6
2.9
0.5
0.46
0.7
0.6
108
Standard Deviation
136
109
0.6
0.6
0.4
0.1
1.2
0.0
0.18
0.2
0.1
54
Minimum
51
0.5
0.5
0.5
0.5
0.5
1.0
0.5
0.26
0.5
0.5
56
25th Percentile
92
1.5
0.9
0.6
0.5
0.5
2.2
0.5
0.40
0.6
0.5
70
Median
121
3.7
1.0
0.8
0.6
0.6
2.6
0.5
0.53
0.6
0.5
86
75th Percentile
146
9
1.2
1.3
1.0
0.7
3.6
0.5
0.57
0.9
0.5
142
Maximum
428
735
3.8
3.0
1.4
0.9
6.6
0.5
0.60
1.1
0.8
194
-------
6.7.2.6.10 Changes in Other Chemical Constituents
From the above analysis it was shown that in general the parameters had increasing or decreasing trends in at
least some of the wells. Table 6-6 shows the mean rate of change in the parameters with the wells grouped as
north or south of Main Street. Except for DOC, the rate of change north and south of Main Street had the same
sign, meaning they were either increasing or decreasing trends. DOC north of Main Street had increasing rate
but south of Main Street there was a decreasing rate. Using the rates obtained, an extrapolation can be made
assuming that these rates will not change in the future. This assumption is likely not the reality because there
are other potential geochemical processes that could modify these rates or changes in the kinetics of the system.
Table 6-7 shows the initial concentrations (February 2016), the concentration in the last sampling (August 2018),
and extrapolations five and ten years into the future using the rates derived in Table 6-6 north and south of Main
Street.
Table 6-6. Rates of change for parameters north and south of Main Street for the Louisville Study.
Parameter
Dissolved
Organic Carbon
Nitrate+
Nitrite
Phosphate
Barium
Chromium
Nickel
Units
mg/L-y
mg N/L-y
mg P/L-y
ng/L-y
ng/L-y
ng/L-y
North of Main Street
0.019
-1.60
0.046
-6.39
22.3
0.34
South of Main Street
-0.078
-0.68
0.032
-6.31
13.9
0.42
Table 6-7. Initial parameter concentrations, last sampling concentrations, and concentrations extrapolated 5 and
10 years into the future assuming rates in Table 6-6.
Parameter
Units
February 2016
August 2018
August 2023 (5 y)
August 2028 (10 y)
North of
Main St.
South of
Main St.
North of
Main St.
South of
Main St.
North of
Main St.
South of
Main St.
North of
Main St.
South of
Main St.
Dissolved
Organic Carbon
mg/L
0.46
0.65
0.58
0.49
0.68
0.10
0.77
<0.50
Nitrate + Nitrite
mg N/L
6.67
6.13
2.70
2.44
<0.10
<0.10
<0.10
<0.10
Phosphate
mg P/L
0.069
0.048
0.155
0.137
0.387
0.299
0.619
0.461
Barium
Hg/L
70
76
69
48
37
16
5
<1.0
Chromium
Hg/L
1.0
0.8
2.5
15
114
84
226
154
Nickel
Hg/L
0.7
0.6
1.1
1.1
2.8
3.2
4.5
5.3
Based on the extrapolations in Table 6-6, phosphate, chromium, nickel, and DOC north of Main Street would be
expected to increase in concentration in five and ten years. Nickel and chromium are both naturally occurring
elements in soils and sediments; however, both are associated with automobile parts and fluids (Minnesota
Stormwater Manuel, 2019). In addition, chromium has been associated with fossil fuel combustion. The
source of the increasing nickel and chromium concentrations is not known at this time, but urban fill could be
the source. Phosphate is also a naturally occurring compound. In urban setting sources of phosphate include
atmospheric deposition, plant and leaf litter, fertilizers, and road salt (Minnesota Stormwater Manuel, 2019).
Any of these sources of phosphate are possible at this study site, along with urban fill.
In contrast, nitrate + nitrite, barium, and DOC south of Main Street would be expected to decrease in
concentrations. Within 5 years, nitrate + nitrite concentrations would be negligible, and within 10 years, barium
and strontium concentrations would also be negligible. A likely reason for the decreasing concentrations
observed is dilution effects caused by the infiltration of stormwater.
Trends can be affected by many subsurface processes. These subsurface processes could change the rates of
increase or decrease, reverse the trends, or stop the trends. An example of a process that could potentially
mitigate the current rates is given in Appendix B Section 1.8.
-------
6.7.3 Summary of Geochemical Analysis
Most of the major anions and cations, regardless of whether north or south of Main Street, showed decreasing
concentrations. Calcium and magnesium concentrations were decreasing, and sodium showed no trends.
Because of this, the water type is gradually becoming less calcium dominant shifting towards sodium dominant.
This is likely caused by the dilution effect from the influx of infiltrated stormwater.
The data indicated that the dominant geochemical processes were precipitation/dissolution and exchange
processes. Reverse ion exchange processes were also shown to be an important type of exchange process.
Weathering processes, redox processes, and sorption were likely happening though these were minor processes.
Nitrate + Nitrite, strontium, barium concentrations were all decreasing overtime; however, barium
concentrations did increase in relationship to chloride concentrations. This was similar to what was seen in the
soil porewater samples.
Phosphate and total phosphorous concentrations were shown to increase. The increase in phosphate
concentrations is likely caused by the infiltration of stormwater. Nickel, cobalt, and chromium concentrations
were also found to increase with time in the wells nearest to the infiltration gallery. The relationship of
stormwater to nickel, cobalt and chromium is not understood at this time, but it is likely that chromium
concentrations are not related to stormwater because of the magnitude of the concentration increase.
Aluminum, iron, and manganese initially had concentrations greater than the site-specific background but
decreased in concentration throughout the study.
6.7.4 Impacts to Groundwater Quality
6.7.4.1 Regulatory Standards
One measure of water quality are federal and state drinking water standards such as the National Primary
Drinking Water Standards (MCL; EPA, 2017), National Secondary Drinking Water Standards (sMCL; EPA, 2017)
and Commonwealth of Kentucky Drinking Water Standards (401 KAR 10:031). Figure 6-40 gives the number of
exceedances and percent of samples that exceed these drinking water standards for each regulated contaminant
analyzed for. Only 1.1% of samples exceeded that MCL for Cr in the study. All other samples were less than the
MCL. There were several exceedances in the sMCL for TDS, aluminum, iron, and manganese. Eighty-four percent
of the samples analyzed exceeded the sMCL for TDS. For aluminum 3.8% of samples exceeded the sMCL, 1.0% of
samples exceeded the sMCL for iron, and 7.8% of samples exceeded the sMCL for manganese.
The Commonwealth of Kentucky water quality standards are slightly different than the Federal standards
(Figure 6-40). First, some of the sMCLs are not regulated in Kentucky such as aluminum, silver, and manganese.
Kentucky does regulate TDS, chloride, sulfate, and iron as an enforceable standard. Kentucky includes nickel as a
water quality parameter that is regulated but the Federal standards do not. Finally, there are differences in the
regulatory limits for TDS, barium, antimony, and thallium. In all cases, the Kentucky standards are more stringent
than the Federal standards (Figure 6-40). Using the Kentucky standards, all the samples exceed the contaminant
limits for TDS. The exceedances for chromium and iron are the same as MCLs discussed earlier. Thallium
exceeded the contaminant limit in 2.2% of samples.
Another set of criteria that could be applied to the groundwater at this study site are those of the Drinking Water
Standards and Health Advisories (DWSHA; EPA, 2018). There were no exceedances of the One-Day or Ten-Day
Health Advisories during the study. The Drinking Water Equivalent Level (DWEL) did have exceedances in 1.1%
of the samples analyzed for chromium (Figure 6-40). The Lifetime Health Advisory had 3.3% of the samples
analyzed for manganese exceeding this advisory value (Figure 6-40). The Taste Threshold was exceeded in this
study in 89% of the samples for sodium (Figure 6-40).
68
-------
;~ 100
O
^ 90
OS
cn
80
cn 14
c
TJ
(D
0
o 10
LU
>.2 E E § E -g
cr O P 9 — O F n
7 E ^ ® >CD ^ —
IE?
-------
6.7.4.2 Water Quality Index
One can compare regulatory standards to water quality data to determine if impacts to water quality have
occurred. This method is somewhat tedious and provides a yes or no answer to whether impacts occurred
or not and does not account for ail standards simultaneously. In addition, it does not account for values for
contaminants that do not exceed the MCL or other regulatory standards, but in combination with other data,
could potentially degrade water quality. The WQI, introduced in Section 5.4, attempts to bridge the gap and
provide an index of water quality. The WQI score and its assessment are provided in Table 5-1.
For the Louisville study there are two WQI indices that were calculated using the National Primary Drinking
Water Standards and the included National Secondary Drinking Water Standards (EPA, 2017) and using the
Commonwealth of Kentucky Drinking Water Standards (401 KAR 10:031). Figure 6-41 shows plots of the WQI
versus time for the data collected in this study.
0 L » jr * i p * i • r *t | i- • r i « ¦ p » p » i j t * rr * * p i r * p j 1
11/7/2015 11/7/2016 11/7/2017 11/7/201 &
Date
240 -
220 -
200 -
180-
£ 160
~o
c
~ 140
A3
- 120
-------
The use of Federal drinking water standards (MCLs and sMCLs) is shown in Figure 6-41A. Using these standards,
most of the samples indicate excellent to good water quality. Only four data points would be considered of poor
water quality, and all of these are for one well MW-03 in April 2017, October 2017, May 2018, and August 2018.
On the other hand, using the State water quality limits (Figure 6-41B), the only data that would be considered
excellent water quality would be MW-08 in July 2017. Most of the samples collected would be considered good
to poor water quality. Eleven samples would be considered very poor water quality, and these occurred mainly
in February 2016 and August 2018. Finally, five samples would be considered unsuited for drinking water. These
samples were MW-01 in February 2016, MW-06 in September 2016, and MW-03 in April 2017, October 2017,
May 2018, and August 2018.
The differences in the WQIs are due to the differences in standards used and the differences in some to the
regulatory limits. Because there are slightly different standards, the weighting factor becomes different in the
two WQI calculations. The differences in concentrations of the regulatory limits affects the quality ratings for
individual contaminants and this also affects the weighting factors.
What is apparent in both WQI plots is that in general, initially, the WQIs decrease with time; however, in 2018
the WQIs appeared to be increasing. One possible explanation for this is that chromium concentrations are
increasing in wells near the infiltration galleries, but this explanation does not explain the trend in wells that the
chromium concentrations are not increasing. Another possible explanation is this is part of the natural cycle.
Additional samplings are needed to ascertain if there is a cyclic nature to the water quality at this research site.
At this time, an adequate explanation for this trend cannot be determined; however, it is apparent that there are
changes in water quality during the study, but the question of whether Gl is the cause of the changes seen will
take more monitoring based on this method.
6.7.4.3 Dilution Effects
At the Louisville study site, dilution of the major anions and cations and several trace components appears to
be playing a major role in changing water quality. Although these would appear to be benign and not causing
human health risk, this could potentially cause a water quality problem. Changes in water quality can negatively
impact drinking water treatment and distribution systems (Santana et al., 2014) as was observed recently in
Flint, Michigan (Pieper et al., 2018; Roy and Edwards, 2019). It is important to stress that the groundwater at
the Louisville Gl study site is not being used as a source of drinking water, but it could serve as a model for other
locations.
If changes in water quality affect the major anions and cations this can potentially cause problems for water
treatment. In this example, dilution of the major anions and cations will change the ionic strength of the water
and this would affect the flocculation step in water treatment for example. The water's buffer capacity would
likely change, which is another problem that dilution could cause. This means that any pH control used could
change the pH of the water, and this could lead to corrosion problems in the water distribution network and
potentially increase the levels of metals such as copper and lead at the tap. Changes in pH could also affect water
disinfection or cause potential disinfection by-products (Suslow, 2001; Ge et al. 2008; Hu et al., 2010; Hansen et
al., 2012).
71
-------
7.0
Yakima Study
7.1 Yakima Study Summary
Analysis of the major anions and cations, geochemical processes, and other constituents leads to the conclusion
that mixing of the upgradient groundwater with the infiltrated treated wastewater from the outfall would
explain the changes in water quality seen in the wells near the outfall. Mixing is likely the major process changing
the water quality in the Yakima study. For many parameters the treated wastewater in the outfall had larger
concentrations than the upgradient groundwater and the wells near the outfall had concentrations between
these two sources.
The analysis of the water quality in relationship to drinking water regulatory standards suggested that the
contaminants of concern in the groundwater and treated wastewater were nitrate + nitrite and arsenic. Nitrate
+ nitrite would be of the biggest concern in the groundwater if the State of Washington arsenic regulatory limits
were not considered. If you consider the State of Washington arsenic regulatory limits, then all the samples
collected (which had arsenic analysis), would be of concern. The WQI analysis determined that the groundwater
and treated wastewater had excellent to good water quality during the study. The New York effluent limits
showed that the treated wastewater was good.
Reverse ion exchange did not seem to be an important geochemical process in the wells near the outfall. This
suggests that sodium loading is not an issue in this study. However, there was some evidence that phosphate
concentrations in the groundwater were increasing and this could potentially affect the subsurface sediments.
Because of the hydrology, the phosphate increases could be potentially problematic to surface water at some
point. Therefore, continued monitoring would be important in this system.
A final point about the Yakima study is that the infiltration of treated wastewater is potentially an important
and useful management practice for the disposal or conveyance of treated wastewater. The initial results of this
study suggest that the concentrations in the groundwater near the outfalls would be no greater than the treated
wastewater when mixing upgradient water and infiltrated treated wastewater. If the treated wastewater is of
acceptable water quality, then the infiltration of treated wastewater into groundwater could potentially be a way
to dispose of treated wastewater based on the results of this study.
7.2 Overview
The Yakima River is a major tributary of the Columbia River in Washington State with a drainage area of 15701
km2 and an average discharge of 99 m3/s. Over the past 100 years, several reaches of the Yakima River have
undergone some form of levee construction and channel-migration restriction in response to flooding. Levee
construction has modified natural fluvial processes by reducing the river's total sediment supply, eliminating off-
channel habitats, and disconnecting the river from its historical floodplain. However, these levees historically fail
to protect completely in large flood events (YCFCZD, 2007). The levees are also thought to impair nutrient and
thermal retention, affect Chinook salmon rearing and migration seasons, and a myriad of other river floodplain
functions (Tockner and Stanford 2002, Stanford et al. 2002). This has led to the development of a comprehensive
flood management plan that integrates the consideration of floodplain restoration with environmental concerns.
A local consortium (that includes federal, state, and municipal agencies) determined to set back levees and
re-establish floodplains between Union and Selah Gap. This location was identified as the highest restoration
potential within the Yakima River because of the channel's high level of constriction and constraint caused by
levees and other infrastructure that prevent connection between the river and floodplain (Stanford et al., 2002).
72
-------
The benefits of setting back levees and restoring reclaimed floodplains can enhance a variety of ecosystem
services like flood control, excess nutrient attenuation, temperature pollution mitigation, and salmonid habitat.
Indeed, recent findings by EPA researchers has demonstrated that levee setback and restoration can enhance
important hydrological characteristics, and particularly hyporheic flow (Singh et ai. 2018). However, this levee
setback effort poses a challenge to existing municipal infrastructure, including a wastewater treatment plant. In
this case, the rearrangement of levee systems would allow the river to migrate and potentially expose an existing
discharge point within the river channel which would violate the terms of an NPDES discharge permit. To prevent
this from occurring, the City of Yakima decided to develop a floodplain restoration approach that incorporated
indirect discharge of effluent across the surface of the floodplain to manage the system proactively. As a result,
the wastewater treatment plant's effluent outfall was moved from directly discharging within the river channel
to discharging over the floodplain in two conveyance channels. This restoration approach created floodplain
habitat and surface water features that would effectively be more resilient to alterations of river channel
morphology change and provide ecosystem service benefits.
7.3 Climate
Depending on the reference, the climate of Yakima, Washington is a cold semi-arid climate (Plantmap.com,
2019). The cold semi-arid climate is described by warm to hot dry summers and tend to have cold winters
with large diurnal temperature variations. Precipitation patterns are typically dry summers, with relatively wet
winters. Figure 7-1 shows the temperature and precipitation patterns for the Yakima study site (NOAA, 2019d).
The hot season typically runs from mid-June to mid-September (Figure 7-1A), in which the mean temperature
extremes range from 4 °C to 31 °C (NOAA, 2019d) The coldest part of the year typically runs from mid-
November to mid-February with mean temperature extremes ranging from -6 °C to 10 °C (NOAA, 2019d). The
annual precipitation in Yakima is 209.6 mm. The wettest part of the year is mid-October to the end of March
(Figure 7-1B) and the most snowfall occurs during the wettest part of the year. The mean snowfall in Yakima is
548.6 mm. The windiest part of the year is from mid-February to mid-August with an average wind speed of 2.9
m/s (Weatherspark, 2019). Typically, for most of the year, the wind is out of the west and only out of the North
from mid-December to mid-January (Weatherspark, 2019).
I—•— Mnamum Dahr
-• - M«mh
- Minmum
o
-------
7.4 Gl Design
As part of this project, the City of Yakima and the U.S. EPA worked to evaluate the effects of effluent on shallow
groundwater and floodplain nutrient changes caused by the indirect discharge of the treated wastewater onto
the floodplain. This type of Gl is a simple alternative surface conveyance structure for treated wastewater
composed of surface ditches that have been implemented within a larger river floodplain restoration project.
These ditches include localized groundwater infiltration which differs from the other cases in this report (e.g.,
Fort Riley and Louisville) that focus on intentional stormwater infiltration. In the Yakima study, infiltration
occurs along two discharge channels creating a water feature and conveyance design that is more sustainable
and resilient to natural river channel processes, such as channel migration, when compared to mid channel
piped conveyance that could be left exposed and out of compliance for common discharge permits. This
more sustainable and resilient alternative does, however, expose shallow floodplain groundwater to potential
communication with treated effluent. In this study, direction of flow and changes to water quality composition
provide insight to the effects of this Gl practice on groundwater quality.
In 2015, effluent began moving across the floodplain in shallow (1 m deep and 2 m wide) ditches (Figure 7-2).
These ditches convey the flow of effluent for ~650 meters where it meets the Yakima River. As the treated
wastewater moves across the floodplain surface, some of the effluent migrates into the shallow groundwater
immediately below and downstream of the channels, but ultimately enters the river system. This is depicted in
Figure 7-3.
Before
After
Figure 7-2. Yakima study site aerials before outfall construction and after outfalls were constructed, The
red oval shows the locations of the outfalls.
74
-------
Figure 7-3. Pictures of the outfall at the Yakima study site. A. shows the outfall origin, and B. shows the
outfall channel downstream of the outfall origin.
7.5 Monitoring Network Yakima, Washington
Groundwater and samples collected along the outfall are all part of the sample collection activities at the Yakima
study site. The monitoring network is depicted in Figure 7-4.
Figure 7-4, The monitoring network at the Yakima study site. Red circles show the locations of the
monitoring wells, the yellow circles represent the outfall sampling locations, and the blue circles show the
approximate location of surface water sampling.
75
-------
7.5.1 Subsurface Monitoring Network - Yakima, Washington
The subsurface monitoring system was designed to monitor the chemical and hydrological interaction of the
surface discharged wastewater infiltrating to groundwater. The network consisted of a series of wells installed
at the study site. The location of the wells is depicted in Figure 7-4. This network consisted of nine monitoring
wells. The groundwater monitoring wells were constructed with screens 1 m in length. The monitoring well
locations are given in Table C-l, Appendix C.
7.5.2 Outfall Monitoring Network - Yakima, Washington
The outfall samples are taken along the western outfall of the treated wastewater flowing out of the water
treatment plant. The locations of the outfall samples are shown in Figure 7-3 and the locations are listed in
Table C-2, Appendix C. Two of the monitoring wells are close to this outfall, BCF836 and MW 06, and are 23 m
and 27 m respectively west of the outfall. Outfall sampling location Outfall 01 and Outfall 03 are the nearest
corresponding samples to these wells.
7.6 Yakima Hydrology
The hydrology at the Yakima study site is described in Narr et al., 2019 and Singh et al., 2018. Singh et al. (2018)
described the Yakima study site on a basin scale following floodplain restoration efforts. Narr et al. (2019)
describes the hydrology on a local scale at the Yakima infiltration site. Based on those published studies the
groundwater flows generally toward the river following the surface river flow patterns (Figure 7-5).
~ Wells
High Flow
Yakima tributary
high_ftow
— Yakima River
BCF836 4
MW-05
MW-06
BCF837
BCF838
BCF839
A Wells
a) Low Flow
Yakima tributaries
Pathlines
Yakima River
BCF836
MW-05
BCF837 % MW-06
BCF838
BCF839
Figure 7-5. Groundwater flow directions at the Yakima Study site, (a.) Groundwater flow under river low flow
conditions, (b.) Groundwater flow under river high flow conditions. Figure modified from Narr et al., 2019.
76
-------
At the basin scale, after levees were setback as part of the larger flood plain restoration, there is likely greater
infiltration and flux of water between the surface and groundwater which is described as greater hyporheic flow
(Singh et al., 2018). There was also a corresponding shift in flow direction in two transects discussed that suggest
infiltration and flow direction change in the floodplain river system when large scale restoration (i.e., extensive
large river levee setback) takes place in a basin. Specific examples of the basin wide pattern were represented
in two transects. In transect A (north part of the basin), the flow shifted from south and southeast to the east
after flood plain restoration. In the southern part of the basin (transect B), the flow shifted from southeast
to south and southwest. These changes were likely driven by greater infiltration during flood events and river
flow patterns. This is relevant because the direction of shallow groundwater is critical for understanding how
groundwater and surface water will move, but in the case of Gl that incorporates surface infiltration, these
patterns help determine potential for mixing of surface water into groundwater. For a more detailed description
of the basin hydrology, refer to Singh et al. (2018).
A localized version of the groundwater model in Singh et al. (2018) was developed as the basis for the more
site-specific hydrology presented in Narr et al. (2019). Figure 7-5 shows the flow directions (black arrows)
under river low flow conditions and high flow conditions. One important point is that based on the analysis of
hydrology present in Narr et al. (2019) the upgradient wells, wells not influenced by the outflow channels would
be MW-05, BCF837, BCF838, and BCF839. MW-04 is also considered an upgradient well and is not depicted in
Figure 7-5 since it north and west of MW-05 (see Figure 7-4). During river low flow conditions (modeled using
river discharge of 67.39 m3/s), the groundwater moves southeast across the site from all upgradient wells
(Figure 7-5a); however, under river high flow conditions (modeled using river discharge of 453.07 m3/s), the
upgradient groundwater moves in a more easterly direction (Figure 7-5b). Regardless of the river flow conditions,
the upgradient water flows towards BCF836 and MW-06 - which are the wells nearest the outflow channels.
These wells next to the outflow are the first observable locations of infiltrated treated wastewater that enter
the shallow groundwater on the floodplain. Although there is always potential variability in flow direction,
particularly during flood, drought, or aberrant inland groundwater elevation, the surface water that infiltrates
into the shallow floodplain groundwater generally tends to move into the river system. The river system is the
intended receiving water for the surface water outfall. The well placement also allows us to observe the water
quality patterns of infiltrated water as it infiltrates into shallow groundwater. For a more detailed understanding
of the site-specific hydrology, see Narr et al. (2019).
7.7 Yakima Study Results
All of the water quality data for groundwater and the outfalls are in Appendix E, Tables E-5 and E-6.
7.7.1 Background Groundwater Quality
To assess changes in groundwater quality it is important to understand what the groundwater quality was
before the implementation of the Gl infrastructure. Background sampling events were undertaken in November
2014 and March 2015. A literature and database search was conducted to gather more groundwater quality
information for this study site.
The USGS NWIS (USGS, 2016) was the only data source located that provided background water quality data for
this study. Only groundwater data from outwash aquifers and only wells within a 10 km radius of the study site
south of the Yakima were used. The total number of sampling points was 331. The data from the monitoring
wells were compared against the NWIS groundwater quality data from the NWIS database. The results of the
statistical analysis indicate that 56% of the parameters in the NWIS database that could be compared with
the study data were significantly different. Because of this, the NWIS data would not be suitable for use in
understanding the background water quality in this study. For detailed statistics see Table C-3 in Appendix C.
77
-------
As was the case in the Louisville study, other methods can be used to help define background in geochemical
data (Matschullat et al., 2000; Reimann et al., 2008). Matschullat et al. (2000) proposed several other means of
determining background concentrations. The background ranges discussed in this study are given in Table 7-1.
The lower and upper critical values define the background concentration ranges in Table 7-1. The percentage of
samples included in the background range is also shown. A complete background concentration table can be
found in Appendix C, Table C-4.
Table 7-1. Study-specific background ranges determined using the 2-sigma method for the Yakima Study.
Parameter
Units
N
n
Mean
Std Dev
Median
Min
Max
Lower
Critical
Value
Upper
Critical
Value
Percent
Used
Specific
Conductance
iaS/cm
252
244
333
122
345
76
628
88
577
96.8
PH
252
224
6.47
0.28
6.48
5.83
7.09
5.91
7.03
88.9
Dissolved
Organic Carbon
mg/L
252
234
1.02
0.61
0.86
0.33
3.18
MDL1
2.24
92.9
Bicarbonate
mg HC03 /L
250
228
85.9
31.1
88.5
23.6
148
23.6
148
91.2
Nitrate + Nitrite
mg N/L
247
226
2.09
2.16
2.09
0.005
7.78
MDL
6.41
91.5
Chloride
mg/L
254
236
25.6
13.8
27.0
1.61
55.7
MDL
53.2
92.9
Sulfate
mg/L
254
232
16.2
7.62
16.4
2.23
36.9
0.98
31.5
91.3
Fluoride
mg/L
224
206
0.09
0.05
0.09
0.01
0.22
MDL
0.19
92.0
Phosphate
mg P/L
28
25
0.589
0.690
0.250
0.113
2.490
MDL
1.969
89.3
Calcium
mg/L
254
238
29.2
9.49
31.0
9.16
51.1
10.3
48.2
93.7
Potassium
mg/L
254
242
3.81
1.55
4.13
0.73
8.74
0.71
6.91
95.3
Magnesium
Hg/L
254
238
10.9
3.79
11.3
3.29
19.5
3.36
18.5
93.7
Sodium
mg/L
254
228
14.7
5.38
14.8
3.97
28.8
3.94
25.5
89.8
xMethod Detection Limit.
7.7.2 Major Anions and Cations, pH, Specific Conductivity
Detailed analysis of time series and trend analysis for major anions and cations, pH and specific conductivity can
be found in Appendix C, Sections 3.1 - 3.9.
7.7.3 Major Anion and Cation Geochemical Processes
Based on the compositional analysis (Appendix C, Sections 3.1 - 3.9) several parameters, SPC, chloride, sulfate,
potassium and sodium suggested that the groundwater near the outfalls was being influenced by the infiltrated
wastewater; however, there are other geochemical processes that can modify the composition of groundwater.
Geochemical processes that may participate in the altering of groundwater quality for major anions and cations
are oxidation/reduction reactions, precipitation/dissolution reactions, and ion exchange reactions. These
processes will be discussed below with respect to the wells near the outfall (BCF836 and MW-06).
Since sodium concentration data suggest that wells near the outfall were potentially a mixture of upgradient
water and wastewater from the outfalls, a plot of sodium and chloride was made and is shown in Figure 7-6A.
The gray shaded areas in Figure 7-6A show where the outfall water samples plotted, and the yellow shaded areas
show where the upgradient groundwater plotted. The outfall samples (treated wastewater) plot above the halite
dissolution line in Figure 7-6A. This indicates that the treated wastewater had sodium in excess of chloride. The
upgradient groundwater lies on or slightly below the halite dissolution line, which would indicate that neither
sodium nor chloride are in excess or there is a slight excess of chloride (Figure 7-6A). This suggests that sodium
in equilibrium with a solid phase or reverse ion exchange are important geochemical processes in the upgradient
wells. The wells near the outfall, BCF836 and MW-06, before the wastewater outflows had more upgradient
water characteristics than outfall characteristics (Figure 7-6A). As wastewater infiltrated into the groundwater
from the outflow, both BCF836 and MW-06 groundwater shifted to be more like the treated wastewater (as
78
-------
indicated by the blue arrow in Figure 7-6A). Regardless of the geochemical processes happening, this data
further supports the mixing of the infiltrated wastewater with the upgradient groundwater in the wells near the
outflow.
The relationship between sodium + potassium versus total cations is shown in Figure 7-6B. Most of the treated
wastewater in the outflows plotted between the y = x and y = 0.5x trend lines (the cyan shaded area in Figure
7-6B). The upgradient groundwater in all samples collected plot below the y = 0.5x trend line (magenta shaded
areas in Figure 7-6B). BCF836 is the closest well to the start of the outflow channel (Figure 7-3). With BCF836,
all the data plots below the y = 0.5X trend line, but with time was shifting to greater sodium + potassium and
greater total cations as indicated by the black arrow in Figure 7-6B. MW-06 is located about halfway down the
outflow channel (Figure 7-4). The initial groundwater data before the infiltration of treated wastewater is plotted
below the y = 0.5x trend line (Figure 7-6B). After the initiation of infiltration of treated wastewater, the data
shifted to greater sodium + potassium and greater total cations and data that plotted above the y = 0.5x trend
line in the outflow data field as indicated by the magenta dashed arrow in Figure 7-6B. In both wells near the
outflow, the groundwater gradually shifts away from water similar to upgradient water. It is likely that in BCF836,
the water over time is being influenced by the infiltrated wastewater, but this groundwater is being influenced
to a greater extent by the upgradient water than in MW-06. MW-06 is likely being influenced to a greater extent
by the infiltrated wastewater than in BCF836. This is further evidence that the groundwater at BCF836 and MW-
06 is the result of the mixing of the infiltrated wastewater with upgradient water flowing to the wells near the
outfall.
CT
_l
CT
-------
Although calcium, magnesium, and bicarbonate concentrations did not indicate mixing (Appendix C Sections 3.4,
3.6, 3.7), geochemical processes related to these constituents might give some information about the potential
for mixing of the upgradient water with the infiltrated wastewater from the outflows. Figure 7-7A is a plot of
calcium versus magnesium. The gray shaded areas represent the data collected in the outflow samples, and
the yellow shaded areas represent the upgradient groundwater in Figure 7-7A. The upgradient data indicates
excess calcium to magnesium more so than in the outflow samples. Most of the data for BCF836 is more like the
upgradient water, and there was no temporal pattern to this data (Figure 7-7A). In the case of MW-06, most the
data was similar to the outflow data with respect to calcium and magnesium and again there was no temporal
pattern to the data (Figure 7-7A). The majority of the groundwater data would indicate that calcite would be an
important geochemical phase controlling calcium solubility.
A plot of bicarbonate + sulfate versus calcium + magnesium is shown in Figure 7-7B. The sulfate concentrations
did indicate mixing of the upgradient groundwater (Appendix C, Section 3.5). The infiltrated wastewater
influenced the groundwater near the outflows. The upgradient groundwater (yellow shaded area) in Figure
7-7B is mainly above the 1:1 trend line, suggesting that both precipitation/dissolution reactions and reverse ion
exchange reactions could be dominant processes in the upgradient water. The treated wastewater in the outflow
(gray shaded areas) is mainly below the 1:1 trend line (Figure 7-7B), which would indicate that ion exchange
reactions are the dominant process in the outflow samples. BCF836 is plotted like the upgradient groundwater,
but the groundwater shifted to greater calcium + magnesium and bicarbonate + sulfate with time as shown by
the black arrow in Figure 7-7B. In most samples calcium and magnesium were in excess compared to bicarbonate
and sulfate. MW-06 before the infiltration of treated wastewater was similar to the upgradient water, but with
time MW-06 plotted more like the outfall data as indicated by the magenta arrow in Figure 7-7B. Like BCF836
with time MW-06 shifted to greater calcium + magnesium and greater bicarbonate + sulfate. Unlike BCF836,
MW-06 shifted to bicarbonate and sulfate being in excess to calcium and magnesium. This analysis suggests that
the mixing of the infiltrated treated wastewater with upgradient groundwater has a greater influence in MW-06.
Calcite SI versus dolomite SI for the study data is shown in Figure 7-7C. The importance of this analysis is that
most samples, regardless of the source, are undersaturated with respect to calcite and dolomite. Only a few
samples showed equilibrium with these phases. All samples, regardless of the source, were undersaturated with
respect to gypsum (Figure 7-7D).
-------
Calcium (meq/L)
6
4
uy 2
3
1 0
o
D ~2
-4
-6
c
i
Calcite yrvder$6iurated !
Dotomite oversaturalsd J
9
i
ii
i
Cafcrte oversatufated
Dolomite oversaturated
Calcite undersaturated 'Jr
Dolomite undersaturated j£
/
i fl
Catefte oversaturated
Dolomite undersaturated
-6
-2
0
Calcite Si
cr
a>
?
'to
d)
c
CJ>
ra
+
E
_o
to
O
CO
ZJ
N
0
0 2 4 6 8 10
Bicarbonate + Sulfate (meq/L)
Figure 7-7. Plots of A. Magnesium versus calcium. B. Calcium + magnesium versus bicarbonate + sulfate.
C. Dolomite SI versus calcite SI. D. Gypsum SI. In A black circles are BCF836, black arrow shows the trend
in BCF836, magenta triangles are MW-06, magenta arrow shows the trend in MW-06, gray shaded areas
represent the outflow data, and yellow shaded areas represent the upgradient groundwater data. In B black
circles are BCF836, magenta triangles are MW-06, gray shaded areas represent the outflow data, and yellow
shaded areas represent the upgradient groundwater data. In C and D the black circles are BCF836, red circles
are BCF837, green circles are BCF838, blue circles are BCF839, blue triangles are MW-04, cyan triangles
are MW-05, magenta triangles are MW-06, black stars are Outfall 1, red stars are Outfall 2, green stars are
Outfall 3, blue stars are Outfall 4, and gray shaded areas indicate equilibrium with solid phase. In C yellow
shaded area represents data that is undersaturated with respect to calcite and oversaturated with respect
to dolomite; green shaded area represents data that is oversaturated with respect to calcite and dolomite;
cyan shaded area represents data the is undersaturated with respect to calcite and dolomite; and purple
shaded area represents data that is oversaturated with respect to calcite and undersaturated with respect
to dolomite.
81
-------
Plots of the CAI indices are shown in Figure 7-8A. The upgradient groundwater data - yellow shaded area -
indicates that reverse ion exchange is the dominant process in the majority of the samples. Contrast this with
the treated wastewater (gray shaded area in Figure 7-7A). This indicates ion exchange is the dominant process.
Only three of fifteen groundwater samples in BCF836 plot in the reverse ion exchange field, and seven of fifteen
groundwater samples plot in the upgradient groundwater field (Figure 7-8A). Two BCF836 samples plotted in the
outfall data field, and the remainder of the samples fell between these two fields. It should be noted that there
was no temporal pattern to the BCF836 data (Figure 7-8A). Other than one data point, the MW-06 data indicated
that ion exchange was the dominant process. Most of the data plotted in the outfall data field (Figure 7-8A). As
was the case with BCF836, there was no temporal pattern to MW-06 data. The CAI indicate that the data from
the wells near the outfall were different than the upgradient groundwater in most cases. This could suggest that
the processes taking place in the outfall samples would be similar to the majority of the groundwater samples
taken near the outfall.
The groundwater data collected near the outfall, with the exception of two samples, indicates that Ca+Mg-HC03-
S04 is in excess compared with Na+K-CI (Figure 7-8B). There was no temporal pattern to the BCF836 data. More
than half of the time, the BCF836 data plotted in or near the upgradient groundwater data (yellow shaded area
in Figure 7-8B). This is in contrast to the groundwater data for MW-06, where there was a temporal pattern to
the data (Figure 7-8B). The magenta arrow in this plot shows the direction of the shift in MW-06 data. Overtime,
the data from MW-06 moved from upgradient, like groundwater, to outfall, like water (gray shaded area in Figure
7-8B). This again supports the idea that mixing of upgradient water and treated wastewater in the outfalls are
mixing near the outfalls.
1.0
0.5-
0.0
<
o
-0,5-
-1.0-
-1.5-
-2.0-
-2,5
A
Reverse Ion
Exchange
/
;
i
/
A
*
A
w
A
A
Ion Exchange
i i i ¦ i ¦ i i i i « i > i i i i ¦ i i * i i ¦ i ¦ I i i i ¦ i • 11 i i ¦ i ¦ i i
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0
CAI2
-5 -4 -3 -2 -1 0 1 2 3 4 5 6
Ca+Mg-HC03-S04 (meq/L)
Figure 7-8. Plots of the A. Chloro-alkaiine index 1 versus chloro-alkaline index 2 and B. Na+K-CI versus
Ca+Mg-HC03-S04. The black circles are BCF836, magenta triangles are MW-06, gray shaded areas
represent treated wastewater in the outfalls, and the yellow shaded areas represent the upgradient
water samples. In B the magenta arrow shows the direction of the shift in data with time in MW-06.
-------
Piper diagrams can be used to show mixing of different source waters (Hounslow, 1995). Piper diagrams showing
the upgradient groundwater and treated wastewater in the outfalls for BCF836 and MW-06 are shown in Figures
7-9 and 7-10. Initially, the groundwater data in BCF836 plotted in the upgradient groundwater field in the mixing
diamond of the Piper diagram (Figure 7-9). With time, the groundwater data in BCF836 shifted towards the
infiltrated treated wastewater data in the outfalls in the mixing diamond of the Piper plot (black arrow in Figure
7-9 mixing diamond). This would indicate that there is mixing of upgradient groundwater with the infiltrated
treated wastewater. Further evidence of mixing is the shift that can be seen in the metals trilinear plot in Figure
7-9. For BCF836, the water shifts with time from a Ca-type/mixed-type water towards a Na-type water. Sodium
concentrations increase in the wells near the outfall, and the sodium concentrations in the outfall are greater
than the upgradient groundwater.
Mixed
• November 2014
• March 2015
• June 2015
• September 2015
January 2016
• April 2016
• June 2016
• November 2016
• February 2017
• June 2017
• September 2017
• December 2017
• March 2018
• June 2018
• September 2018
Figure 7-9. Piper diagram for BCF836 groundwater during the Yakima Study. Black arrows in the mixing
diamond and metals trilinear plots show the direction of change in this well with time. The gray shaded
areas in all plots represent the treated wastewater in the outfall and the yellow shaded areas represent the
upgradient groundwater data.
83
-------
The Piper diagram for MW-06 is shown in Figure 7-10. The trends in MW-06 are similar to what was reported
previously in BCF836. The difference is that shift occurred more quickly and the mixing is greater in MW-06 than
BCF836 (Figure 7-10).
Mixed
• November 2014
• March 2015
• June 2015
• September 2015
January 2016
• April 2016
• June 2016
• November 2016
• February 2017
• June 2017
• September 2017
• December 2017
• March 2018
• June 2018
• September 2018
Figure 7-10. Piper diagram for MW-06 groundwater during the Yakima Study. Black arrows in the mixing
diamond and metals trilinear plots show the direction of change in this well with time. The gray shaded
areas in all plots represent the treated wastewater in the outfall and the yellow shaded areas represent the
upgradient groundwater data.
84
-------
7.7.4 Summary of Major Anion and Cation Analysis
The evidence from the time series analysis showed that even though the calcium, magnesium and bicarbonate
were not significantly different than the two source waters (upgradient groundwater and the infiltrated treated
wastewater), there were differences in chloride, sulfate, potassium and sodium in the source waters. The mixing
of the source waters explains the concentrations in the groundwater in the wells near the outfalls.
Mixing of the source waters to explain the groundwater near the outfalls was further demonstrated by the
geochemical analysis in the previous section. Before the wastewater released to the outflows, the makeup of
the groundwater in the wells near the outfall was more similar to upgradient water characteristics than outfall
characteristics. Once the wastewater infiltrated into the groundwater from the outflow, the groundwater in the
wells near the outfall became more like the treated wastewater. This is further evidence that the groundwater
near the outflow was the result of mixing of the infiltrated wastewater with upgradient water flowing to the
wells near the outfall.
Mixing of the two source waters, infiltrated wastewater and the upgradient groundwater was clearly
demonstrated using Piper diagrams. The Piper diagrams also indicated that the two wells near the outflow
had different amounts of mixing. The well closest to the outlet of the treated wastewater into the outflow
channel showed less influence of the treated wastewater when the infiltrated water mixed with the upgradient
groundwater. The well near the outflow further down the outflow channel had the greatest influence of the
infiltrated wastewater when mixed with the upgradient groundwater.
7.7.5 Other Chemical Constituents
7.7.5.1 Dissolved Organic Carbon
The site-specific background DOC concentrations in the groundwater at the study site ranged from 0.33 - 3.13
mg/L and in the outfall, concentrations ranged from 1.98 - 11.4 mg/L (Figure 7-11). There were significant
differences in DOC concentrations between the near outfall groundwater samples and the outfall samples
(p < 0.001), between the upgradient groundwater samples and the outfall samples (p < 0.001), and between
upgradient groundwater samples and near outfall groundwater samples (p < 0.001). The DOC concentrations
were the largest in the outfall samples, followed by the wells near the outfall, and finally, upgradient
groundwater.
O
O) 5:
A
B
¦§ 1(H
5-
0
9/1/2014 9/1/2015 9/1/2016 9K1/2017 9/1/2018
Date
Figure 7-11. Time series plots
for dissolved organic carbon
concentrations in A. Upgradient
wells, B. Wells near the outfall, and
C. Outfall samples. Gray shaded areas
indicate the range of the site-specific
background for dissolved organic
carbon in groundwater (Table 7-4).
Black circles and lines are BCF836 data,
red circles and lines are BCF837 data,
green circles and lines are BCF838 data,
blue circles and lines are BCF839 data,
green triangles and lines are MW-04
data, blue triangles and lines are MW-
05 data, cyan triangles and lines are
MW-06 data, black stars and lines are
Outfall 1 data, red stars and lines are
Outfall 2 data, green stars and lines are
Outfall 3 data, and blue stars and lines
are Outfall 4 data. Gray shaded areas
represent the site-specific background
ranges in the Yakima Study.
85
-------
In the upgradient wells (Figure 7-11C), there were trends in DOC concentrations in three of the five wells.
In BCF837 and MW-04, the DOC concentrations were increasing with time. These increasing trends were
significant in BCF837 (p = 0.017) and not a significant trend in MW-04 (p = 0.108). The DOC concentrations were
significantly decreasing in BCF839 (p = 0.041). In the wells near the outfall (Figure 7-11B), only MW-06 had
increasing DOC concentrations and this was a significant trend (p = 0.003). In the outfall samples, Outfall 3 and
Outfall 4 had increasing DOC concentrations during the study, in Outfall 3, the increasing trend was significant
(p = 0.050), and in Outfall 4 the increasing trend was significant (p = 0.064). These two outfall samples were the
furthest two sampling points from the outlet of the treated wastewater into the outfall channels.
7.7.5.2 Nitrate + Nitrite
Site-specific nitrate + nitrite concentrations in the groundwater ranged from 0.005 - 7.78 mg N/L and ranged
from 1.16 - 17.9 mg N/L in the outfall samples (Figure 7-12). There was significant difference in nitrate + nitrite
between the outfall samples and the upgradient groundwater (p < 0.001) and between the outfall samples and
the groundwater in the wells near the outfall (p < 0.001). There were no differences in nitrate + nitrite between
upgradient groundwater and the groundwater near the outfalls.
There were increasing nitrate + nitrite concentrations in all but one well, MW-04, in the upgradient groundwater
(Figure 7-12C). The increasing trend in nitrate + nitrite was significant in BCF838 (p < 0.001), BCF 839 (p = 0.018),
BCF837 (p = 0.075), and MW-05 (p = 0.099). In the wells near the outfall (Figure 7-12B), one of the two wells,
MW-06, showed increasing nitrate + nitrite concentrations with time. This increasing trend was significant (p =
0.049). There were no trends in the wastewater in the outfall samples (Figure 7-12A).
:—4—
rr>» rg
* - r:» :
9/1/2014
9/1/2015
9/1/2016
Date
¦ P-
9/1/2017
9/1/2018
Figure 7-12. Time series plots for
nitrate + nitrite concentrations in
A. Upgradient wells, B. Wells near the
outfall, and C. Outfall samples. Gray
shaded areas indicate the range of the
site-specific background for nitrate
+ nitrite in groundwater (Table 7-4).
Black circles and lines are BCF836 data,
red circles and lines are BCF837 data,
green circles and lines are BCF83S data,
blue circles and lines are BCF839 data,
green triangles and lines are MW-04
data, blue triangles and lines are MW-
05 data, cyan triangles and lines are
MW-06 data, black stars and lines are
Outfall 1 data, red stars and lines are
Outfall 2 data, green stars and lines are
Outfall 3 data, and blue stars and lines
are Outfall 4 data. Gray shaded areas
represent the site-specific background
ranges in the Yakima Study.
86
-------
7.7.5.3 Fluoride
Groundwater site-specific fluoride concentrations ranged from 0.01 - 0.22 mg/L. The treated wastewater from
the outfalls ranged from 0.07 - 0.83 mg/L (Figure 7-13). The fluoride concentrations were significantly different
between the upgradient groundwater and the infiltrated wastewater in the outfall (p < 0.001) and between
the wells near the outfall and the wastewater in the outfall (p < 0.001), There was also a significant difference
between the upgradient groundwater and the groundwater in the wells near the outfall (p < 0.001). Overall,
the fluoride concentrations in the wastewater in the outfall were greater than the concentrations of fluoride in
the two groundwater sources. The concentration of fluoride in the wells near the outfall were larger than the
concentration of fluoride in the upgradient wells.
There were increasing fluoride concentrations in one of the five upgradient wells (Figure 7-13C). The well,
BCF837 had a significantly increasing fluoride trend (p = 0.002). In the wells near the outfall (Figure 7-13B), only
MW-06 had a significantly increasing fluoride concentration trend (p < 0.001). There were no trends in fluoride
concentration in the outfall samples (Figure 7-13A).
0.9
0.6 -j
0.3-
00
0.9
0.6
0.3
CD
E
4)
"D
'C
o
_D
LL
B
o.o
0.9-
0.6-
0.3-
0.0
9/1/2014
/
9/1/2015 9/1/2016 9/1/2017
Date
9/1/2018
Figure 7-13. Time series plots for
fluoride concentrations in
A. Upgradient wells, B. Wells near
the outfall, and C. Outfall samples.
Gray shaded areas indicate the range
of the site-specific background for
fluoride in groundwater (Table 7-4).
Black circles and lines are BCF836
data, red circles and lines are BCF837
data, green circles and lines are
BCFS38 data, blue circles and lines are
BCF839 data, green triangles and lines
are MW-04 data, blue triangles and
lines are MW-05 data, cyan triangles
and lines are MW-06 data, black stars
and lines are Outfall 1 data, red stars
and lines are Outfall 2 data, green
stars and lines are Outfall 3 data,
and blue stars and lines are Outfall 4
data. Gray shaded areas represent the
site-specific background ranges in the
Yakima Study.
7.7.5.4 Phosphate
Because of high laboratory background phosphate concentrations, any phosphate concentration less than 0.100
mg P/L was not used. Therefore, most (92%) of the phosphate data in the upgradient wells was not used in this
analysis.
The site-specific phosphate concentration range in the groundwater samples were from 0.113 - 2.490 mg P/L
and the phosphate range in the outfall samples were from 0.510 - 7.780 mg P/L (Figure 7-14). There were
significant differences in phosphate concentration between the upgradient groundwater and the infiltrated
groundwater in the outfalls (p < 0.001) and between the groundwater in the wells near the outfalls and
the infiltrated groundwater in the outfall (p < 0.001). There were also significant differences in phosphate
concentrations in the upgradient wells and the wells near the outfall (p < 0.001). The phosphate concentrations
in the outfall samples were larger than the concentrations in the wells near the outfall and the upgradient wells.
In addition, the phosphate concentrations in the wells near the outfall were larger than the upgradient wells.
This suggests that the phosphate concentrations in the wells near the outfall likely result from the mixing of
upgradient groundwater and infiltrated wastewater from the outfall.
-------
Phosphate concentration increased in all the upgradient wells (Figure 7-14C). These increasing trends were
significant or nearly significant in BCF837 (p = 0.055) and BCF838 (p = 0.013). These trends were not significant in
three of the wells BCF839 (p = 0.116), MW-04 (p = 0.148), and MW-05 (p = 0.107). In the wells near the outfall,
only MW-06 had increasing phosphate concentrations (Figure 7-14C). In MW-06, increasing trend was significant
(p = 0.020). There were no trends in phosphate concentrations in the outfall samples (Figure 7-14A). Because
of the proximity of the site to the Yakima River and a pond at the study site, the increasing trends in phosphate
concentrations could be potentially problematic given the close connectivity of the groundwater and surface
water (Narr et al., 2019).
jjj
"5
_c
Q.
O
SI
1 A
i
'A
V
~
¦ B
i c
- ¦ ~ i i ¦ » i ¦ ¦ k—i—,
H-" "¦»¦¦¦«¦
9/1/2014
9/1/2015
9/1/2016
Date
9/1/2017
9/1/201S
Figure 7-14. Time series plots for
phosphate concentrations in
A. Upgradient wells, B. Wells near
the outfall, and C. Outfall samples.
Gray shaded areas indicate the range
of the site-specific background for
phosphate in groundwater (Table
7-4). Black circles and lines are
BCFS36 data, red circles and lines are
BCF837 data, green circles and lines
are BCF838 data, blue circles and lines
are BCF839 data, green triangles and
lines are MW-04 data, blue triangles
and lines are MW-05 data, cyan
triangles and lines are MW-06 data,
black stars and lines are Outfall 1
data, red stars and lines are Outfall 2
data, green stars and lines are Outfall
3 data, and blue stars and lines are
Outfall 4 data. Gray shaded areas
represent the site-specific background
ranges in the Yakima Study.
7.7.5.5 Summary of Other Chemical Constituents
The concentrations of DOC, nitrate + nitrite, fluoride, and phosphate in the treated wastewater in the outfall
were greater than the corresponding concentrations in the near outfall wells, and the upgradient wells. The
concentrations of DOC, fluoride, and phosphate in the wells near the outfall were greater than the upgradient
wells. Nitrate + Nitrite concentrations in the wells near the outfall and the upgradient wells were not
significantly different. Because in most cases the concentrations in the wells near the outfall were between the
concentrations in the outfall and the upgradient wells, mixing of the infiltrated treated wastewater with the
upgradient water would be a probable mechanism occurring in the Yakima Study.
It is likely that the infiltrated treated wastewater represents the maximum concentrations in the shallow near
river groundwater at this site - assuming the concentration trends do not change drastically. This means that the
concentrations in the treated wastewater could be used as a worst-case scenario for most constituents analyzed
for in the study. The regulatory framework for this site is primarily based on point source discharge and the
hydrology of the site and the relatively rapid movement of shallow groundwater to the river suggests that the
infiltrated water enters the river and will dilute as the water moves downstream. Further study related to the
mixing zone and dynamics of groundwater of this site is relevant for continued investigation.
Finally, the phosphate observations do suggest that phosphorous may be building in the sediments in the
long-term. At this site, phosphorus removal technology is likely to be implemented as part of the wastewater
treatment process (personal communication, M. Price, Yakima City Wastewater Manager) and monitoring
continues at the site. Phosphorus build up in the subsurface and increasing phosphate concentrations are
88
-------
potential concerns. The study site discharges to the Yakima River and at high water may discharge to a nearby
restored pond. The phosphorus may enrich sediments in the most nearby subsurface sediments and erosion of
these sediments could be a concern for surface water quality. There is, however, a close hydrologic connection
and relatively quick transit times from the shallow aquifer to Yakima River (Singh et al., 2018, Narr et al.,
2019). Increasing phosphate concentration in the groundwater may act as a source of P in the surface water
leading to eutrophication, but further observations are needed before any relationship should be inferred. As
enhancements to wastewater treatment are implemented, continued monitoring and the likely decrease in P
concentrations would address these potential concerns.
7.8 Impacts to Groundwater Quality
7.8.1 Regulatory Standards
Please note that the current understanding of the Yakima site is that the outfall channels are designed to
transport treated wastewater to the river. Infiltration of treated wastewater to the shallow hyporheic zone
groundwater suggests that the system functions well as conveyance to the river with minimal movement of
water outside of the river floodplain system (Narr et al. 2019). In this report, the application of drinking water
standards to the shallow near river groundwater should not be construed as impacts to drinking water. It is
possible that similar conveyance methods are under consideration in areas where the groundwater is used as
a drinking water source. Therefore, an improved understanding of how wastewater and Gl practices influence
water quality in a drinking water aquifer is needed. This report compares the data collected at the Yakima site to
drinking water standards as an informative exercise. Based on the current published study (Narr et al. 2019) and
the findings of this report the effect on shallow groundwater quality is minimal.
One measure of water quality are federal and state drinking water standards such as the National Primary
Drinking Water Standards (MCL; EPA, 2017), National Secondary Drinking Water Standards (sMCL; EPA, 2017)
and State of Washington's, Washington State Water Quality Standards (State of Washington, 2016). Figure
7-15 gives the number of exceedances and percent of groundwater samples and Figure 7-16 gives the number
of exceedances and percent of outfall samples that exceed these drinking water standards for each regulated
contaminant analyzed.
Two contaminants, arsenic (1.6 % of samples) and nitrate + nitrite (12.1 % of samples), exceeded MCLs in the
groundwater in this study (Figure 7-15). All other contaminants were less than the MCL. There were several
exceedances in the sMCL for the aluminum (1.6 % of samples), iron (29.6 % of samples), and manganese (61.2
% of samples) in groundwater samples (Figure 7-15). In the wastewater in the outfall, there were again two
contaminants that exceeded MCLs in this study. Beryllium exceeded the MCL in 3.4 % of samples and 71.2 % of
samples for nitrate + nitrite (Figure 7-16). The wastewater in the outfall exceeded the sMCL for aluminum (3.6 %
of samples), manganese (3.8 % of samples), and total dissolved solids (7.7 % of samples) (Figure 7-16).
The State of Washington's water quality standards are different than the Federal standards (Section 5.4, Table
5-2). The MCL for arsenic in the state of Washington is 200x lower (0.05 ng/L) than the Federal standards. For
this reason, any detectable arsenic in a sample will be larger than the Washington State standard. In this study
100 % of the arsenic samples were greater than the Washington State regulatory standard. Aluminum, iron,
manganese and nitrate + nitrite exceedances in groundwater were the same as with the Federal standards
(Figure 7-15). The infiltrated wastewater in the outfalls compared to the Washington State water quality is shown
in Figure 7-16. With the exception of arsenic, the exceedances for the infiltrated wastewater in the outfalls are
the same as in the Federal Standards. As was the case in groundwater, all the arsenic samples collected exceeded
the Washington State standard (Figure 7-16).
89
-------
100 -
E 90-
o
_ro
u
CD
0
a:
CD
C
T3
(D
0
O
X
LU
-*—*
C
0
o
L_
0
CL
80-
70 ¦;
60-;
50-
40-
30 -j
20 -j
10-
0
T"
CO
l—r
c
E
o c
c C - £
o o
T"
£
13 ZD
t—r
c c
o
-T~
0)
3 :s
i—i—r
i—r
i—r
1
p £ £ ro
|^
_ c U ~
fO iz 2 G)W o
05^ + a3 CO
(D W
4—>
to
£ £
13 13 03
CO -2T
i—i—i—r
S |g g
= H N
c 3 m
p CO x:
*3 I—
CO
Figure 7-15. Percentage of groundwater samples exceeding regulatory standards for analytes which
have regulatory standards. Black bars show data for National Primary Drinking Water Standards and
National Secondary Drinking Water Standards, red bars show data for State of Washington Drinking Water
Standards, green bars show data for One-Day Health Advisories, blue bars show data for Ten-Day Health
Advisories, cyan bars show data for Drinking Water Equivalent Level, magenta bars show data for Lifetime
Health Advisory, and dark yellow bars show data for Taste Threshold.
90
-------
•S 70 ^
60 A
100
1 90
_i
so
o
«*—»
_CD
13
CD
CD
DC
U)
c 50
"O
0
0 40
0
X
W 30
1 20
L—
0
0- 10
0
-
__
-
¦
.
1 1 *1 1 1 1 1 1 1 I 1 'l 1 I
1 I 1
1 1 1 ^1 1
C 03 >
33 C Q
cog
111
zy E c
< < <
E £ E
S'g| 6
£2 w ^co
< 00
CD
§ £
Eco^
P ® ^ 3JS-C 3 > 3 3ffl 3Q.t
C - c ^ = H N
-------
Another set of criteria that can be applied to the groundwater and infiltrated wastewater in the outfalls in
this study are those of the Drinking Water Standards and Health Advisories (DWSHA; EPA, 2018). Figure 7-15
(groundwater) and Figure 7-16 (infiltrated wastewater in the outfall) show the five different advisory levels that
are part of the DWSHA.
There were two contaminants that exceeded the One-Day Health Advisory during the study in the groundwater
(Figure 7-15): nitrate + nitrite exceeded in 12.1 % of the samples and manganese exceeded in 12.7 % of the
samples. The 10-Day Health Advisory was exceeded by the same contaminants and the percentages were used
as found in the One-Day Health Advisory (Figure 7-15). The only contaminant that exceeded the DWEL in the
groundwater was arsenic and this standard was exceeded in 1.6 % of the samples taken (Figure 7-15). In the
groundwater samples, only manganese had exceedances of the Lifetime Health Advisory and this occurred in
48.5 % of the groundwater samples collected in this study (Figure 7-15). The only contaminant that exceeded the
taste threshold in this study for groundwater samples was sodium (17.8 % of the samples).
The One-Day Health Advisory was also applied to the treated wastewater in the outfalls (Figure 7-16). The
only contaminant that exceeded this standard was nitrate + nitrite and it was exceeded in 71.2% of the treated
wastewater samples. The 10-Day Health Advisory was exceeded by the same contaminants and the percentages
were used as found in the One-Day Health Advisory (Figure 7-16). There were no exceedances in any
contaminant when comparing the treated wastewater with the DWEL standards (Figure 7-16). Comparing the
Lifetime Health Advisory to the treated wastewater in the outfalls (Figure 7-16), it was found that none of these
samples exceeded any of the Lifetime Health Advisory limits. The only contaminant that exceeded the taste
threshold in this study for treated wastewater samples was sodium (96.2 % of the samples).
7.8.2 Water Quality Index
Comparing water quality data to regulatory standards to determine impacts to water quality is somewhat
tedious and provides a yes or no answer to whether impacts occurred or not. It does not account for all
standards simultaneously. In addition, this method does not account for values for contaminants that do not
exceed the MCL or other regulatory standards. The WQI, introduced in Section 5.4, attempts to bridge the gap
and provide an overall index of water quality. The WQI score and its assessment are provided in Table 5-1.
Unlike the Louisville study, where two WQI indices were calculated, the Yakima study will only use the Federal
Standards because the State of Washington's arsenic standard limit indicates that the water is unsuitable
for drinking. Figure 7-17 shows plots of the WQI versus time for groundwater and the treated wastewater in
the outfall collected during this study. The WQI data for groundwater indicated that the groundwater was of
excellent or good water quality. The treated wastewater in the infiltration galleries for most samples was of
excellent water quality.
The treated wastewater in the outfalls can also be compared with the New York Effluent Limitations for
Discharges to Groundwater (6 CRR-NY §703.6) using the WQI approach. The effluent standards used in this
analysis are shown in Section 5.4, Table 5-2. Using the New York limits, the WQI analysis indicates that the
treated wastewater in the outfalls would have good water quality (Figure 7-18).
92
-------
240-
^22°
w 200
03
^ 160
O
160
x 140
60
40
20
1/1/2017
1/1/2018
Date
Figure 7-17. Water Quality indices
plotted over time for the Yakima
Study using the Federal Drinking
Water Standards. The gray shaded
areas show samples that are of
excellent water quality, olive shaded
areas show samples that are of
good water quality, blue shaded
areas show samples of poor water
quality, teal shaded areas show data
of very poor water quality, light
yellow shaded areas show water
that is unsuited for drinking without
adequate treatment. BCF836 data
are the black lines and circles,
BCF837 data are the red lines and
circles, BCF838 data are the green
lines and circles, BCFS39 data are the
blue lines and circles, MW-Q4 data
are the green lines and triangles,
MW-05 data are the blue lines and
triangles, MW-06 data are the cyan
lines and triangles, Outfall 1 data
are the black lines and stars, Outfall
2 data are the red colored lines and
stars, Outfall 3 data are the green
lines and stars, and Outfall 4 data
are the blue lines and stars.
1/1/2017
1/1/2018
Date
1/1/2019
Figure 7-18. Water Quality indices
plotted over time for the treated
wastewater in the outfall in the
Yakima Study using the New York
Effluent Limitations for Discharges
to Groundwater. The gray shaded
areas show samples that are of
excellent water quality, olive shaded
areas show samples that are of
good water quality, blue shaded
areas show samples of poor water
quality, teal shaded areas show data
of very poor water quality, light
yellow shaded areas show water
that is unsuited for drinking without
adequate treatment. Outfall 1 data
are the black lines and stars, Outfall
2 data are the red colored lines and
stars, Outfall 3 data are the green
lines and stars, and Outfall 4 data
are the blue lines and stars.
93
-------
8.0
Fort Riley Study
8.1 Fort Riley Study Summary
The Fort Riley study was the most comprehensive Gl study in this report. The vadose zone and aquifer materials
were characterized for chemical and physical properties, the hydrogeology was characterized, and water quality
samples were taken and analyzed.
The characterization of the vadose zone and aquifer showed that there was a decrease in finer grained materials
in the upper portion of the sediment profile. This was confirmed by the ERI analysis and from texture analysis
of core materials. There was also heterogeneity of texture at the study site, and in general, the clay content
decreased as one moves south across the study site.
The decreases in fine-grained materials were also related to the chemical properties of the vadose zone and
aquifer sediments. The CEC, total calcium, Na, Al, Mn, Fe, Si, total organic carbon, and total C concentrations
decrease with depth. This means that the sorptive and reactive properties of the vadose zone materials
would decrease with increasing depth. The ability of the vadose zone to remove contaminants and modify the
infiltrated groundwater would be less with depth.
The hydrologic assessments indicated that changes in water table elevation were not related to the infiltrated
stormwater. The water table elevations were a function of the natural infiltration and changes in water level in
the stormwater retention basin south of the Gl site. There was little evidence for mounding under the infiltration
gallery likely due to the high hydraulic conductivity of the aquifer materials and the small volume of groundwater
infiltrated through the gallery relative to the volume infiltrated through the storm water retention basin. The
predominant groundwater flow direction was to the southeast and fluctuated from the east to south during the
study, with minor deviations to the north.
From the analysis of the temperature data, it was shown that if there was a sufficient temperature contrast
between the ambient temperature and the temperature of the infiltrated water, then temperature could be used
to monitor water movement in the vadose zone and groundwater. This was also confirmed by the tensiometer
data.
In addition, soil water tension could also be used to monitor wetting fronts and water movement through the
vadose zone. Based on the tensiometer data, it was shown that water movement through the vadose zone
following an infiltration event was within hours to approximately a day. The other important point that the
tensiometers showed is that there was an effective radius in which the water moves from the bottom and
sides of the infiltration gallery. The infiltrated water only moved from the infiltration gallery to a radius of less
than 3.1m. The final point that the tensiometers showed is that using water level elevations in the infiltration
gallery was not the best method for predicting an infiltration event. The tensiometers showed water movement
(infiltration) in the vadose zone following precipitation events where there was no rise in measured water levels
in the infiltration gallery. The rise in water levels in the infiltration gallery is analogous to a related rates problem.
That is, if the rate of water entering the infiltration gallery is greater than the rate of exfiltration out of the
gallery, then one will get a rise in the water level in the infiltration gallery. The converse is also true. The rate of
water entering the infiltration gallery is related to rainfall intensity and volume.
The analysis of water in the vadose zone (SPW) indicated that major anions and cations were transported
through the vadose zone. The SPWs were nested between 327 m-msl to 322 m-msl, which also corresponds
to the zone where CEC and other sorptive properties were largest. The predominant geochemical process that
could be examined with the data collected, indicated that ion exchange and precipitation/dissolution reactions
were the most important. In the later dates, there was indication that reverse ion exchange may be occurring
94
-------
which could be problematic since sodium could build up in the vadose zone. The buildup of sodium in the vadose
zone could potentially cause dispersion of the fine-grained materials in the vadose zone and cause reduced
infiltration rates through the vadose zone to the aquifer.
In the vadose zone, it was demonstrated that chloride (conservative species) moved through the vadose zone
faster than other analytes and could be used as a tracer for the movement of chemical species. This also
shows that the interactions of nonconservative species are retarded by interactions with the vadose zone solid
phases. Another important point the chloride showed was that as chloride concentrations increased, barium
concentrations also increased and would be more mobile. Although this barium behavior is not likely a problem
at this site, it does suggest that other contaminants that have similar behavior with chloride could be mobilized if
de-icing agents are used and be a risk to groundwater quality.
The groundwater analysis showed that the upgradient concentrations of some chemical species were larger than
the background groundwater and the groundwater downgradient. The upgradient source for these chemical
species is not known.
It was also demonstrated in this study that the stable isotopes of water can also be used to track the infiltrated
water in the groundwater. As was the case with temperature there are limitations to the use of the stable
isotopes of water. If the isotopic composition of the infiltrated water is not sufficiently different than the isotopic
composition of the groundwater, then the mixing of the infiltrated water with the groundwater would not yield
isotopic compositions in the mixed water different enough to detect. There is also an effective radius where the
stable isotopes of the infiltrated water can be distinguished from the ambient groundwater isotopic composition.
As the infiltrated water moves through the groundwater, the more mixing occurs and the more the isotopic
composition becomes like the ambient isotopic composition. At some distance, the infiltrated water will not be
distinguishable from the native groundwater.
Sulfate concentration in general in the downgradient wells was decreasing in concentration as was observed
in the SPW samples. Background wells and upgradient wells showed no trends in sulfate concentrations or
increasing sulfate concentrations. This suggested dilution from the infiltrated stormwater. There was also
evidence of dilution in barium, strontium, arsenic, and uranium.
There was also evidence for mixing causing changes to groundwater quality downgradient of the infiltration
gallery. There was evidence of a chloride spike in the well next to the infiltration gallery. In the analysis of
the trilinear plot of the major anions, the chloride was related to the mixing of the infiltrated water with the
background groundwater. However, the upgradient water is difficult to rule out as being the source of the spike.
The increasing phosphate concentrations in the wells immediately downgradient of the infiltration gallery were
not related to infiltrated groundwater. The increasing phosphate concentrations appeared to be from upgradient
groundwater moving under the infiltration gallery to these wells.
As was the case in the SPW samples, changes in barium concentration could be related to chloride
concentrations. In some of the wells, as the chloride concentrations increased, so did the barium concentrations,
and as chloride concentration decreased, so did the barium concentrations.
The geochemical processes that dominate the groundwater in this study were mixed. In some cases, such as
sodium and potassium the dominant process appeared to be ion exchange or reverse ion exchange. In other
cases, such as with calcium and magnesium the dominant process was likely precipitation/dissolution reactions.
There was not one dominant mechanism affecting the groundwater quality downgradient. It is possible that the
infiltration of stormwater has not enough time to measurably affect downgradient groundwater quality. It is also
possible that there will be no long-term changes to groundwater. More monitoring of this system is needed to
fully address the question of whether there will be long-term changes to water quality.
95
-------
The analysis of the organic contaminants in the SPW indicated that the most analyzed contaminants were
not detectable. The contaminant with the most detects in both the SPWs and in the groundwater was DEET,
which the likely source was from the sampling and not likely present in the SPW or groundwater. There was no
detected organic compound that had concentration of regulatory concern.
The groundwater results of the water quality in reference to regulatory standards or advisory limits suggested
that the main drivers to risk could be the MCL exceedance of uranium and arsenic. The WQI analysis indicated
that the background well water quality and downgradient water quality was excellent - good. The upgradient
wells groundwater quality was rated as good - poor based on the WQI analysis. The WQI analysis suggests that
there were not major differences in water quality in the background wells and downgradient wells.
8.2 Introduction
The Fort Riley project is part of the Gl research and development effort of the U.S. EPA Office of Research and
Development (ORD) Safe and Sustainable Water Resources (SSWR) research program, EPA Region 7's Regional
Applied Research Effort (RARE) program, and the joint EPA - U.S. Army's Net Zero Initiative. The project is being
conducted under a Memorandum of Understanding (MOU) between the Army and ORD signed in 2011. This Net
Zero water project was completed at Fort Riley, KS.
The project was conducted at the Seitz Elementary School (39.0700278° latitude and -96.8421944° longitude) in
the Camp Forsyth area of Fort Riley, Kansas. The school is part of the Geary County school district, Kansas school
system (USD 475). Although the school is surrounded by the Post, the property is owned and managed by the
school district. The K-5 school opened in the fall of 2012.
Researchers with EPA ORD collaborated with the US Army Corps of Engineers (USAGE) to design an 80-space
parking lot to collect stormwater runoff and infiltrate the runoff into the subsurface. The parking lot was
built behind the school and includes a permeable interlocking concrete pavement (PICP) section along the
downgradient edge (Figure 8-1). Under the PICP, a storage gallery was constructed, and this gallery was designed
to capture 100% of the runoff volume from a 5-year 24-hr rain event (108 mm) on the parking lot. During the
design phase, the estimated time required for water in the gallery to drain was based on geotechnical studies of
the soil at the original estimated depth of the bottom of the storage gallery (Kaw Valley, 2014), previous studies
completed during school construction (Kaw Valley, 2011), and additional studies undertaken by U.S. EPA.
ifirllilS
96
Figure 8-1. Location of
parking lot (outlined in
black) and the storage
gallery (red area) at the
Fort Riley Study site.
(Source: Google Earth)
-------
8.3 Climate
Fort Riley is in the Flint Hills Uplands Region of Kansas (Goodin et al., 1995) and has a humid subtropical
climate (Plantmaps.com, 2019). This climate type is described in the Louisville Study (Section 6.3). Figure 8-2
shows monthly temperature and precipitation data obtained from the NOAA (NOAA, 2019b). As is shown on
Figure 8-2A, the hot season ranges from the beginning of June and lasts until September, in which the mean
temperature extremes are approximately 15 °C to 33 °C (NOAA, 2019b). The cold season typically runs from
December through February, in which the mean temperature extremes range from -8 °C to 8 °C (NOAA, 2019b).
The mean yearly precipitation at Fort Riley is 905 mm (NOAA, 2019b). Between April and September is typically
the part of the year which has the greatest precipitation, and the most snowfall occurs during December and
January (Figure 8-2B). Fort Riley typically receives 399 mm of snow each year. The average wind speeds at Fort
Riley show only slight differences ranging from 4.1 to 5.9 m/s (Weatherspark, 2019a). The windier part of the
year typically is from February to mid-May where the average wind speeds are greater than 5.0 m/s. The wind
direction from mid-March to December is most often out of the south and from January to mid-March is most
often out of the North (Weatherspark, 2019a).
140
E 120
40
30-
25 -
20-
O
v
t5-
$
10-
0-
5-
-10-
15-
-20
£2
0)
i-
>¦-
E
Figure 8-2. Summary
of temperature and
precipitation data
obtained from NOAA
(2019b) for the Fort
Riley study. Panel A is
the Temperature Data
and Panel B is the
Precipitation data.
8.4 Regional Geology/Hydrogeology
Fort Riley is in the Flint Hills Upland physiographic region (Shoewe, 1949), characterized by rolling hills and deep
stream valleys with steep valley walls (USGS, 1996). The upland geology is dominated by Permian shales and
limestones. The area was strongly impacted by Kansan and Nebraskan glaciation (Frye and Leonard, 1952). The
current hydrogeologic setting of Fort Riley is the direct result of Pleistocene glacial processes and subsequent
erosion of the shales and limestones that comprise the hills surrounding the site.
97
-------
8.5 Hydrogeologic Setting
The study area is located on alluvial deposits of the Republican River. The sediments consist of approximately
20 m of course to fine sand, silt and interbedded clay layers. The coarser sediments commonly found above the
weathered bedrock surface are glaciofluvial, deposited near the terminus of glaciers during the end of Kansan
glaciation. The upper portion of the alluvium is generally characterized by an increase in fine-grained sediments
with relatively low permeability, consistent with flood plain deposits. Seitz Elementary School is situated
between the Republican River to the south and a weathered shale and limestone ridge to the north, which
contains the Fort Riley Limestone unit (Figure 8-3). The Republican River joins the Smoky Hill River approximately
3.6 km southeast of the site to form the Kansas River. Two stormwater retention basins are located within about
60 m and 0.5 km from the infiltration gallery, respectively. Possible impacts of the nearest basin are discussed
later.
Elementary?
School fj
Kansas
River
Storrri Water t
i ( v*
Retention Basins
Republican
p River
Smoky Hill River
Figure 8-3. Location of Seitz Elementary School in relation to surface water features in the area.
(Source: Google Earth)
98
-------
8.6 Parking Lot and Storage Gallery Design
The parking lot was constructed using traditional hot-mix asphalt except for the PICP at the southern end of
the parking lot. The parking lot was sloped from north to south at approximately 1.2% to direct stormwater to
the PICP section. The outer perimeter of the parking lot has swales graded to direct off site runoff around the
parking lot away from the PICP section. The parking lot drainage area is 1833.7 m2 and the PICP is 175.7 m2. The
completed parking lot is shown in Figure 8-4.
Figure 8-4. A picture of the Fort Riley parking lot showing the PGR section.
Razzaghmanesh and Borst (2019) reported that the contributing drainage area to PICP area was 10.4 and that
this is approximately double the recommended ratio. This design feature should increase the frequency of water
accumulation in the storage gallery and aid in the monitoring efforts of water moving into and out of the storage
gallery. However, it should be noted that the doubling of this ratio will likely mean more frequent maintenance
of the PICP because of more rapid accumulation of sediment between the pavers leading to surface plugging.
The storage gallery design was heavily influenced by the low infiltration rates (less than 0.15 cm/h) of the surface
soil at the site. The low infiltration rate necessitated the need for a deep storage gallery. The final storage gallery
design dimensions were 3.2 m x 54.9 m x 3.3 m (LxWxD), which would capture the 5-year, 24 h rainfall event
(107.8 mm; Razzaghmanesh and Borst, 2019). The design of the storage gallery is shown in Figure 8-5 and a
picture showing this installation of the storage gallery is shown in Figure 8-6. The walls and bottom of the storage
gallery were lined with nonwoven, double-needle punched geotextile. The storage gallery has three layers
under the PICP surface. The layer immediately under the pavers is a 5 cm bedding layer consisting of American
Association of State Highway and Transportation Officials (AASHTO) No. 8 aggregate (Figure 8-5). The second
layer beneath the pavers is the choker layer, which consists of 15.2 cm of AASHTO No. 57 aggregate (Figure 8-5).
The final layer is the storage gallery. The storage gallery is 3.1 m of AASHTO No. 2 aggregate (Figures 8-5 and 8-6).
99
-------
Liner
Storage Gallery—
Geotextile Fabric
10.2 cm Monitoring Well
Concrete Reverse Gutter
5 cm Bedding Laver
15.2 cm Choker/Base
Permeable Interlocking
Concrete Pavement (PICP)
Undisturbed Native Soil
Figure 8-5. Storage gallery design at the Fort Riley study site. (Source: USACOE, Zieson)
Figure 8-6. Photo showing the installation of the storage gallery at the Fort Riley study site,
100
-------
8.7 Subsurface Monitoring Network
The subsurface monitoring system was designed to focus on the subsurface movement of water and associated
water quality changes arising from the implementation of the stormwater recharge gallery. Specifically, the
network was designed to:
1) Monitor changes in water chemistry as the exfiltrate migrates through the vadose zone.
2) Monitor changes in groundwater chemistry attributable to stormwater infiltration.
3) Monitor aquifer recharge and possible mounding resulting from the stormwater infiltration.
4) Allow determination of changes in groundwater movement that might be attributable to stormwater
infiltration.
The primary monitoring network consists of a series of tensiometers, soil porewater samplers, piezometers,
wells, and temperature sensors installed within and near the infiltration gallery. The locations of the monitoring
points are depicted in Figure 8-7. The initial network, installed in 2015, consisted of twelve tensiometers, twelve
soil porewater samplers, twelve piezometers, and nine monitoring wells. Four additional wells and two vadose
zone temperature profilers were added to the network in 2017.
¦F-Rpwua
FRPW09 ~
Figure 8-7. Aerial view of the soil porewater samplers, piezometers, and monitoring wells at the Fort Riley
study site. The soil porewater samplers are the blue round targets, the piezometers are the yellow square
targets, monitoring wells in the storage gallery are the purple circular targets, the temperature profilers are
the cyan square targets, and the monitoring wells are the red circular targets. The locations of FRPW11 and
FRPW12 are not shown.
101
-------
8.7.1 Groundwater Monitoring Wells and Piezometers
The initial groundwater monitoring wells and piezometers were installed in boreholes drilled using hollow-
stem auger techniques. They were constructed using 5.08-cm diameter, schedule 40 PVC casing and slotted
PVC screens. Wells were constructed with screens 3.1 m in length and piezometers with screens 0.6 m in
length. All monitoring wells were screened (Table D-l, Appendix D) at the water table. The piezometers were
installed within the alluvial aquifer at selected depths between the bottom of the well screens and the top of
bedrock. The annular space between the borehole wall and the well screen relied on natural collapse of aquifer
materials with a well sock placed around the screen in the saturated zone. A silica sand filter pack was placed
using a tremie pipe adjacent to the portion of the screen within the vadose zone in each monitoring well. The
piezometers were constructed using a well sock covering the screen and natural collapse of aquifer materials.
For both wells and piezometers, the annular space above the natural collapse or sand pack was sealed with
bentonite to the surface and were completed at the surface with a concrete well pad and a lockable metal
protective cover. For purposes of determining groundwater elevations, the designated measuring point on the
top of the well casing was surveyed to 0.30 cm relative to the NAVD88 vertical datum.
The water level and temperature in all wells and piezometers were monitored using pressure transducers. Wells/
piezometers within approximately 75 m of a central location were equipped with Campbell Scientific, Inc., model
CS451 differential pressure transducers and hard wired to above-ground data loggers. The water level with
temperature and time-date stamp was routinely recorded at a 10 min intervals. Distant piezometers (FRPW01,
FRPW02, FRPW07, FRPW08, FRPW10, FRPW11, and FRPW12) and wells (FRGW01, FRGW08, FRGW10, FRGW11,
FRGW12, and FRGW13) were monitored using self-contained temperature/absolute pressure loggers (model
3001 produced by Solinst Canada Ltd.) recording at 1-hour intervals.
Based on preliminary data, four additional wells (FRGW10, FRGW11, FRGW12, and FRGW13) (Figure 8-7)
were installed in February 2017. These wells were also installed in boreholes drilled using hollow-stem auger
techniques and constructed using 5.08-cm diameter, schedule 40 PVC casing and slotted PVC screens 3.1 m in
length. The wells were constructed with screens 3.1 m in length and screened (Table D-l, Appendix D) at the
water table within the alluvial aquifer. A silica sand filter pack was placed adjacent to the screen in each well
using a tremie pipe. A seal consisting of bentonite pellets was tremied in place above the filter pack, and the
borehole annular space was grouted to the surface. Wells FRGW10, FRGW11, and FRGW13 were finished with
a concrete well pad and lockable protective steel casing. Well FRGW12 was installed in a flush-mounted vault
within the parking lot.
8.7.2 Tensiometers and Porewater Samplers
Twelve tensiometers and twelve porewater samplers were installed in a three-dimensional array within the
vadose zone near the infiltration gallery (Figure 8-7). Each tensiometer and porewater sampler was installed
in an individual borehole angled at about 30 degrees to the horizontal plane. The tensiometers chosen for this
application were model TS1 produced by UMS. The tensiometers were also hardwired to the data loggers and
recorded at a frequency of 10 min (with a 1 min frequency used during rainfall events). The porewater samplers
were also manufactured by UMS (model SIC20 for shallower locations and SICS20 for deeper locations). One
porewater sampler (T1A) was installed using the core barrel method. The remainder of the porewater samplers
and the tensiometers were installed in a borehole drilled with 10.2 cm outside diameter continuous flight
augers. The soils at the site had enough cohesion to allow the borehole to remain open after the augers were
removed from the hole. Based on the cohesiveness of the soils, the remaining eleven porewater samplers and
all the tensiometers were installed using the open hole method also described below. Once installed, bentonite
chips were used to seal the upper 30 cm to 60 cm of each borehole to mitigate excess infiltration of water along
the tensiometer cables and porewater sampler tubes. The porewater samplers were finished at the surface with
a concrete pad and lockable steel protective casing.
102
-------
8.7.2.1 Core Barrel Method
Installation was accomplished using a CME-45C drill rig equipped for directional drilling. The borehole was
advanced with an NQ core barrel (approximately 5.08-cm inside diameter) equipped with a shale bit and using
potable water as drilling fluid. Cuttings from the borehole were segregated and reused in a soil slurry to backfill
the hole as the core barrel was withdrawn. When the borehole reached the required depth, the water swivel was
removed for insertion of the porewater sampler. The porewater sampler was equipped with an approximately
0.6-m long socket constructed of PVC pipe, friction fitted to the upper body of the sampler. Sections (each 3.1
m long) of 1.27-cm PVC pipe were bolted together and used as a ramrod to push the porewater sampler to the
design installation depth by hand. The lead section of the 1.27-cm PVC ramrod had a 0.30-m steel tube affixed
to the pipe to facilitate easy removal of the ramrod from the socket. The PVC socket and ramrod assembly
permitted the porewater sampler to be held in place while the NQ core barrel was withdrawn, and the soil slurry
pumped to the bottom of the borehole. Once it was determined that the soil slurry had firmly anchored the
instrument in place, the ramrod was withdrawn from the borehole. The remaining portion of the borehole was
backfilled by flushing the soil cuttings down into the borehole with potable water.
8.7.2.2 Open Hole Method
Each borehole was advanced using 10.2-cm outside diameter continuous flight augers and a CME-45C drill
rig equipped for directional drilling. Once the planned depth was reached, the augers were removed and
the cuttings from the borehole were segregated and reused to backfill the borehole. The tensiometers and
porewater samplers were equipped with an approximately 0.6-m long socket constructed of PVC pipe which
was friction fitted to the upper body of each instrument. Sections of 1.27-cm PVC pipe were bolted together
and used as a ramrod to push the tensiometer or porewater sampler down into the open borehole by hand, to
the designed installation depth. The lead section of the 1.27-cm PVC ramrod had a 0.30-m steel tube affixed
to the pipe to facilitate easy removal of the ramrod from the socket. Once the instrument had been pushed to
the design installation depth, soil cuttings from the borehole were mixed with potable water in a steel trough
and pumped to the bottom of the hole to surround the instrument. The PVC ramrod held the instrument in
place while the soil slurry was emplaced. Once it was determined that the soil slurry had firmly anchored the
instrument in place, the ramrod was withdrawn from the borehole. The borehole was then backfilled by flushing
the soil cuttings into the hole with potable water.
8.7.3 Infiltration Gallery Water Level
Water level and temperature within the infiltration gallery were monitored using Campbell Scientific, Inc., model
CS451 differential pressure transducers installed in two wells built within the infiltration gallery using 3.81-cm
diameter schedule 80 PVC casing and screen (Figure 8-7). The screen of each well was 1.52 m long and placed
at the bottom of the gallery. The pressure transducers were positioned at the bottom of each well within a
small sump created by the well end cap. This results in a slightly elevated baseline pressure recorded by each
transducer between rainfall events.
8.7.4 Infiltration Gallery Monitoring Wells
Four wells constructed of 10.2-cm diameter schedule 80 PVC casing and screen (Figure 8-7) were installed in the
northwestern end of the infiltration gallery to allow for collection of water samples from the gallery.
8.7.5 Vadose Zone Temperature Profilers
In February 2017, two cased borings (Figure 8-7) were installed about 1 m south of the southern edge of the
parking lot. The borings were constructed of 2.4-m long, 15.2-cm diameter schedule 40 PVC pipe installed in a
20-cm diameter boring. The annular space between the casing and the borehole wall was filled with bentonite
clay grout.
103
-------
A SensorRod manufactured by Alpha Mach, Inc. (Naranjo and Turcotte, 2015) was installed in the bottom of each
cased borehole using procedure G-LMMD-SOP-1222-O adapted for installation of sensors through a borehole.
Each SensorRod contains four iButton temperature loggers. The bottom iButton was positioned at the tip of the
rod. The second, third and fourth iButtons were located 0.30 m, 0.601 m and 0.91 m, respectively, above the
tip of the rod. Each SensorRod was driven into the base of the open borehole such that the bottom button was
at an elevation equivalent to 0.15 m above the bottom of the gallery, and the top iButton was at an elevation
equivalent to approximately 1.1 m above the bottom of the gallery. The temperature loggers were programmed
to record the temperature at 30 min intervals.
8.7.6 Weather Station
To facilitate data interpretation, the site was instrumented with a weather station on the southwest corner of
the school roof. The weather station monitored and logged wind speed, wind direction, air temperature, relative
humidity, barometric pressure, rainfall, and solar radiation. The logger program recorded readings at 1-minute
intervals during rain events and 10-minute intervals otherwise.
8.7.7 Fort Riley Subsurface Monitoring Network Summary
This design will not only allow for the collection of data for chemistry, but it will also allow for water level
measurements and data to be collected for wetting fronts in the vadose zone. This design will provide a means
to understand the exfiltration of water from the storage gallery through the vadose zone into the groundwater
and groundwater movement in the aquifer. This design will also provide data that can be used to understand the
changes in groundwater movement that may occur as the result of this Gl practice.
8.8 Fort Riley Study Results
8.8.1 Subsurface Characterization
Subsurface materials of the unsaturated and saturated zones were characterized using geophysical methods
including borehole logging, electrical resistivity imaging surveys, and direct push electrical conductivity (EC)
survey.
8.8.1.1 Electrical Resistivity Imaging Survey
Electrical resistivity imaging (ERI) surveys were performed along five transects (Figure 8-8). The ERI surveys were
conducted using AGI SuperSting with 1 meter spacing between 64 electrodes. All surveys were conducted using
dipole-dipole arrays. The ERI surveys were conducted along the red lines in the figure below. The green and red
markers indicate the locations of electrode 1 (El) and electrode 64 (E64), respectively. Results of the ERI surveys
are illustrated on Figure 8-8 and in Section 2.1 in Appendix D.
The ERI results in Figure 8-8 are plotted on the same color scale, where cool colors (e.g., purple and blue)
indicate low electrical resistivity (e.g., high electrical conductivity) materials such as silts and clays, and hot colors
(e.g., yellow and red) indicate high electrical resistivity (e.g., low electrical conductivity) materials, such as sands
and gravels.
These results indicate fine grained materials in the upper portion of the profile and coarse grained materials
with increasing depth. The results for the ERI survey approximately 1-meter south of the infiltration gallery are
presented in Figure 8-9, where a relatively continuous unit with resistivity values greater than approximately 220
Ohm-m extends from east to west, punctuated by zones with higher resistivity values, which may represent sand
zones in the unsaturated zone.
Results for other ERI lines are presented in Section 2.1 in Appendix D.
104
-------
Legend
.% ERI Profiles
l. •' Parking lot
cf permeable pavement
Figure 8-8. Approximate locations and results of ERI surveys. Coo! colors (purple and blue) indicate low
resistivity materials (e.g., silt and clay) and hot colors (yellow and red) indicate high resistivity materials
(e.g., sands).
Resistivity (Ohm-m)
OQU10U»OU»QU»O^OUlQinOV>OU»Q
oooooooooooooocjoooo
Q
Figure 8-9. Results
for transect TIL08-1
adjacent to infiltration
gallery. Y-axis is depth
in m.
105
-------
8.8.1.2 Borehole Geophysical Methods
Geophysical logging results for piezometer FRPW04 (Figure 8-10) are typical of responses observed across the
site. Two natural gamma signatures are shown in Figure 8-10. The data represented by the red line is from the
dedicated natural gamma tool (9012), which has a gamma detector located approximately one meter from the
bottom of the tool. This configuration allows a larger portion of a shallow well to be logged. Data represented
by the black line are from the gamma detector on the induction tool (9510), located 2.5 m from the bottom of
the tool. These results suggest the presence of less clay in sediments below about 321 m and an increase in clay
upwards from 321 m, as indicated by the natural gamma response. The induction conductivity log for FRPW04
indicates relatively clean sands below 321 m and a zone with relatively high clay content around 325 m. The
induction log for the interval between 321 - 324 m suggests interbedded sand, silt, and clay sediments, which is
also reflected by the natural gamma log.
Natural gamma logs for boreholes along west to east and south to north transects (see Section 2.2 in Appendix
D) indicate lower clay content in sediments below about 321 m. The transition between the coarse-grained
sediments in the lower portion of the profile upwards into the fine-grained sediments varies from abrupt to
gradual.
Depth FRPW04 GAM (NAT) FRPW04 COND FRPW04 RES
1 inn 1 •— ¦ 1
Im.lUUm o CPS 250 0 MMHO/M 50 0 OHM-M 100
FRPW04 GAM(NAT)9012
I 1
0 CPS 250
325.0
320.0
Figure 8-10. Responses from natural gamma and induction conductivity logging tools for FRPW04.
106
-------
8.8.1.3 Direct-Push Soil Electrical Conductivity Profiling Survey
Soil EC profiles were measured at 13 locations, primarily on the western portion of the site (Figure 8-11).
TNSf-4
;
TNS1
CW02
tNsSsb
Legend
0 Parking lot
Permeable Pavement
0 Temporary Wels & EC Profiles
® Welte and Piezometers
Figure 8-11. Locations of soil EC profiles, monitoring wells and piezometers in relation to the permeable
pavement.
107
-------
Results of the EC profiles along a north-south transect are presented in Figure 8-12. It is noted that the highest
EC values for most of the locations occur above 326 msl, except for temporary well, TNS11 that has a sharp
increase in EC just below 322 msl. Since the water level in the aquifer was approximately 321m rnsl, most of the
EC profiles depict stratigraphy within the unsaturated zone. These results suggest a decrease in clay content in
the upper portion of the vadose zone towards the south. Several of the units observed in the EC logs appear to
be laterally persistent. For example, the EC spike observed in TNS1-1 (just above 327 msl) may be traced laterally
at least 77 m (TNS1-6) and possibly 93 m (TNS1-7).
Depth TNS1-1EC TNS1-2EC TNS1-3EC TNS1-4EC TNS1-5EC TNS1-6EC TNS1-7EC TNS1-8EC
I 1 1 1 1 1 1 1 1
1m:75m
0 mSfrn 250 0 m9'm 250 0 mS/m 250 0 mS/m250 0 mS/m 250 0 mS/m 250 0 mS'm 250 0 mSm 250
325.0
320.0
Figure 8-12. Soil EC profile results for north (TNS1-1) to south (TNS1-8) transect. Note that ail profiles are
plotted on the same scale.
108
-------
The EC profile for TNS1-7 is compared to natural gamma and induction log results for piezometer FRPW10,
located approximately 5 meters away (Figure 8-13). The upper portion of the natural gamma log suggests finer
grained material between about 323 msl and 328 msl, with a decrease in fines below that interval. The induction
log indicates the presence of fine-grained materials above 326 msl with sand units at 325 msl, just below 324 msl
and between 321 msl and 320 msl. The logs also indicate a uniform sand with some fine-grained material from
321 msl to 318 msl. The TNS1-7 EC profile generally agrees with the induction log but appears to differentiate
more of the fine-grained units. This is expected, given the close sensor spacing of the EC tool, relative to the
induction tool.
Depth FRPW10 GAMfNAT) TNSI-7EC FftPWtOCOWD FRPWIOftES
2SO 0
Figure 8-13. Soil electrical conductivity profile (TNS1-7) compared to natural gamma and
induction log from adjacent well (FRPVV10). Black line is Natural Gamma log FRPW10, red
line is conductivity TNS1-7, green line is conductivity FRPW10, and blue line is resistivity
FRPW10.
109
-------
The natural gamma log for FRGW11 was compared to EC profiles from a north-south transect (Figure 8-14).
FRGW11 exhibits the general natural gamma signature observed in many of the wells on site, indicating a higher
percentage of fine-grained material from 328.5 msl to about 324 msl, with a decrease in fines toward the bottom
of the well The EC log for TEW2-3 located 4 m from FRGW11 generally agrees with the natural gamma log from
FRGW11 but suggests greater variability in stratigraphy throughout the well.
Ueplh 1LW1-3 LC TLW2-3tC FRGW11 GflM(NAT) FLW-3LC ILYV4LC
1m 10flm
300 50
CPS
200 0
200 0
mS'm
200 0
200
Figure 8-14, Soil EC profiles (redlines) from north (TEW1-3) to south (TEW4) transect, and natural
gamma for FRGVV11 (black line).
no
-------
The EC profile for TNS1-7 was compared to ERI survey results from RIL09-1, located approximately 2 m from
profile (Figure 8-15). The EC results correlate well with the ERI results. Specifically, the highly resistive feature
denoted by the red area of the underlying ERI figure corresponds favorably with the low conductivity EC results
between 5 and 7 m below land surface.
TMStfEC
Figure 8-15. EC profile results for TNS1-7 agree favorably with the RIL09-1 ERI survey results. The vertical
blue line represents the location of TNS1-7. Y-axis is depth.
in
-------
8.8.1.4 Site Specific Geologic Impacts on Gl Design
The original design for the permeable pavement called for a shallow (e.g., 1 m) infiltration gallery; however, early
site characterization results suggested the permeability of the shallow soils was too low to ensure adequate
drainage. The subsequent design extended the depth of the trench below the shallow, low permeability soils.
The current design appears more than adequate to store and transmit high intensity storm water runoff events.
The sediments underlying the infiltration gallery appear to be transmissive enough to allow the relatively rapid
infiltration of water from the gallery. Further, the high hydraulic conductivity aquifer material appears to transmit
water quickly, preventing the formation of a long-lasting mound under the gallery.
Preliminary assessment of ERI survey results conducted on October 8, 2020, before and after a high intensity,
precipitation event is presented as percent difference in resistivity values in Figure 8-16. The orientation of
the surveys is identical to RIL8-01 depicted iri Figures 8-8 and 8-9, with electrodes El and E64 located east and
west of the infiltration gallery, respectively. These results suggest preferential infiltration of lower electrical
conductivity water on the eastern end of the gallery. This may be related to the heterogeneity and anisotropy
of the subsurface sediments. However, further investigation/site characterization is required to make this
determination.
West
Change In Resistivity (%) Positive = More Conductive
Figure 8-16. Percent change in resistivity profile from ERI surveys conducted before and after high intensity
precipitation event on October 8, 2018. Y-axis = depth (m), x-axis = distance (m).
112
-------
8.8.2 Aquifer Hydraulic Conductivity Structure
Table 8-1. Saturated Hydraulic Conductivity of Aquifer Materials Estimated
using Pneumatic Slug Testing Techniques in Wells and Piezometers.
Pneumatic slug tests were performed
in wells and piezometers at the Fort
Riley site to estimate the range and
distribution of hydraulic conductivity
of the aquifer materials. Results (Table
8-1) of the tests performed in wells,
which were constructed at the water
table using 3.0 m long screens, ranged
from approximately 5.8 m/d to 26 m/d
with an average of approximately 16
m/d. Results of pneumatic slug tests
performed in the piezometers, which
were constructed using 0.6 m long
screens, ranged from approximately
3.4 m/d to 70 m/d. These results
indicate that the hydraulic conductivity
of aquifer materials at the study site
is relatively high with a significant
degree of aquifer heterogeneity.
The borehole flowmeter methodology was used to characterize the hydraulic conductivity distribution of aquifer
materials adjacent to five wells (FRGW01, FRGW04, FRGW10, FRGW11, and FRGW13) (Figure 8-7). Each of these
wells, except well FRGW10, was screened from approximately the water table to the midpoint of the alluvial
aquifer. At wells FRGW04, FRGW10, and FRGW13, the bulk hydraulic conductivity appears to be moderately high
and increases with depth (Figure 8-17) within the portion of the aquifer screened by these wells. Well FRGW11
exhibits a similar pattern with depth but appears to be screened in materials with a lower bulk hydraulic
conductivity. The data from well FRGW01 indicate materials with a more uniform hydraulic conductivity may
exist in this area apart from a relatively thin zone of lower hydraulic conductivity material near the middle of
the screen. All the profiles indicate a significant, but not extreme, degree of heterogeneity may exist within the
upper half of the aquifer at the Fort Riley site.
322 -
Well
Estimated Saturated
Hydraulic Conductivity
(m/d)
Piezometer
Estimated Saturated
Hydraulic Conductivity
(m/d)
FRGW01
22
FRPW01
46
FRGW04
18
FRPW02
29
FRGW05
6.1
FRPW03
8.8
FRGW07
20
FRPW04
9.4
FRGW08
5.8
FRPW05
11
FRGW09
19.8
FRPW08
3.4
FRGW11
5.8
FRPW09
20
FRGW12
23
FRPW10
70
FRGW13
26
FRPW11
55
FRPW12
10
,W1
W4
W10 W11 W13
321
CO
on
E 320
HI
319
- Weler Table
31& H 1 1 i 1 1 i 1 1 r
1 i 1 1
0 19 20 0 ID 20 0 10 20 0 10 20 0 10 20
Hydraulic Conductivity (m/d)
Figure 8-17. Saturated hydraulic
conductivity distribution
estimated for materials adjacent
to the screened intervals of wells
FRGW01, FRGW04, FRGW10,
FRGW11, and FRGW13 based on
characterization using borehole
flowmeter techniques. Well
names are abbreviated for
simplicity.
-------
8.8.3 Groundwater Flow Field Characterization
The elevation of the water table at
the study site varied within a range
of approximately 1.5 m (Figure 8-18)
between July 2015 and October 2018.
Groundwater elevations were manually
measured in each well and piezometer
during each sampling event and other
site characterization studies using an
electronic water level indicator. These
data were used to create potentiometric
surface maps representing observed
water-table elevations between August
2015 and October 2018 (Section
3.1 in Appendix D). The average
direction of groundwater flow near
the infiltration gallery as inferred from
these potentiometric surfaces was
approximately east-southeast; however,
estimated groundwater flow directions
for all measurement events ranged from
southeast (Figure 8-19) to approximately
northeast (Figure 8-20).
322.0 n
120
2019
Figure 8-18. Groundwater elevation recorded using a pressure
transducer/data logger at well FRGW01 between July 2015
and September 2018. Daily rainfall recorded at National
Oceanic and Atmospheric Administration weather station
GHCND:US1KSGE0001 located in Junction City, Kansas, during
the same period is plotted for comparison.
Water
h filtration
0 Distance (m) 100
Table Elevation
March 29, 2018
GaBmy
320.41
Figure 8-19. Shallow potentiometric surface interpreted from groundwater elevation measurements obtained
using an electronic water level indicator on March 29, 2018. All posted groundwater elevations are provided in
m NAVD88. The contour interval between equipotential lines is 0.05 m. The groundwater flow direction near
the infiltration gallery is interpreted to be approximately southeast on this date.
114
-------
0 Distance (m) 100
321.Q4
A
Water Table Elevation
October 9, 2018
. •.«¦
Infiltrator!
Gallery
Figure 8-20. Shallow potentiometric surface interpreted from groundwater elevation measurements obtained
using an electronic water level indicator on October 9, 2018, immediately following a significant rain event. All
posted groundwater elevations are provided in m NAVD88. The contour interval between equipotentiai lines is
0.05 m. Groundwater flow direction near the infiltration gallery is interpreted to be approximately northeast
on this date.
To better evaluate the temporal variation in hydraulic gradients near the infiltration gallery, data obtained from
pressure transducers/data loggers installed in wells FRGW02, FRGW05, and FRGW07 (Figure 8-21) were used to
estimate the direction and magnitude of the hydraulic gradient from groundwater elevations measured every ten
minutes between February 2016 and September 2017. Automated estimation of the magnitude and direction of
the hydraulic gradient using a three-point methodology was performed using the 3PE spreadsheet application
(Beljin et al., 2014). Wells FRGW02, FRGW05, and FRGW07 were chosen for this analysis based on their locations
in areas south and east of the gallery their relative positions within the flow field, and the length of the data
record available for each well. The magnitude of the hydraulic gradient calculated from this expanded data set
ranged from approximately 0.0008 m/m to 0.0020 m/m (Figure 8-22) with a mean of 0.0011 m/m and a median
value of 0.0012 m/m. Assuming the hydraulic conductivity of aquifer materials is relatively isotropic in the
horizontal plane (i.e.,, does not vary significantly as a function of horizontal direction), the calculated hydraulic
gradient direction should reflect the horizontal direction of groundwater flow. Therefore, the predominant
direction of groundwater flow was inferred from these data to be east-southeast to southeast with a median
direction of approximately 135 degrees from north. Overall, calculated groundwater flow directions ranged from
north to south. However, estimated directions ranging from north to east were observed in less than 6% of the
data (Figure 8-23). Based on this assessment, it appears that groundwater originating from the infiltration gallery
would move through the southeastern portion of the monitoring network (Figure 8-24).
115
-------
Distance (m)
Infiltration
Gallery
Figure 8-21. Location of wells FRGW02, FRGW05, and FRGW07 used for automated calculation of temporal
variations in the magnitude and direction of the hydraulic gradient between February 2016 and September
2017. Well names on the map are abbreviated for simplicity. (Source: USACOE, Zieson)
f
270 -
180 -
90
0
W
S
E
N
V
I
2016
-1 1-
2017
2018
Figure 8-22. Temporal
variation in the magnitude
(upper plot) and direction
of the hydraulic gradient
(lower plot) calculated
using groundwater
elevation measurements
made by pressure
transducers in wells
FRGW02, FRGW05, and
FRGW07 between February
2016 and September 2017.
Gaps in the record reflect
lack of data for one or more
of the wells used in the
three-point analysis.
116
-------
West
North
'orccntagc of Observations
Hydraulic Gradient Magnitude
¦ <=0,0001
South
Figure 8-23. Rose
diagram depicting the
azimuthal distribution
of the hydraulic gradient
vectors (i.e., inferred
groundwater flow
directions) calculated
using groundwater
elevation measurements
made by pressure
transducers in wells
FRGW02, FRGW05,
and FRGW07 between
February 2016 and
September 2017.
Figure 8-24. Distribution
of variation in inferred
groundwater flow
direction between
February 2016 and
September 2017
superimposed on
monitoring network.
Well names are
abbreviated for
simplicity.
117
Infiltration
GalSery
-------
From these data, it was possible to estimate preliminary values of the rate of groundwater flow using Darcy's
Law (Freeze and Cherry, 1979) which relates groundwater velocity, hydraulic gradient magnitude, hydraulic
conductivity, and effective porosity (i.e., the fraction of pore space within saturated aquifer materials through
which water moves). Using the mean hydraulic gradient magnitude calculated from these data, the mean
hydraulic conductivity estimated from pneumatic slug tests performed in the monitoring wells at the site, and a
range of effective porosity representative of materials with a moderately high hydraulic conductivity (i.e., 0.20 -
0.40), preliminary estimates of groundwater flow velocity range from approximately 17 m/yr to 33 m/yr.
It was also noted that hydraulic gradient directions and, therefore, inferred groundwater flow directions changed
from approximately southeast to northeast abruptly on several occasions between March and October of 2016
(Figure 8-25). These changes in flow direction occurred rapidly and were sustained for as long as three weeks
before returning to the dominant southeast flow direction. Comparison of these changes in the direction of
the hydraulic gradient with precipitation measured by the onsite weather station indicate a likely correlation
with periods of increased rainfall. Given the proximity of the stormwater retention basin - which is located
immediately south of the gallery and receives storm water from the surrounding neighborhoods - it is possible
that these abrupt northward changes in hydraulic gradient are the result of stormwater infiltration from the
retention basin.
270 -i West
c
O
-~—»
If
O o
180 -
E
o
c
03
T3
TO —
en
CD .
X
South
0 J North
Mar 1
Jun 1
Sep 1
2016
Figure 8-25. Temporal variability of hydraulic gradient and inferred groundwater flow direction between March
and October of 2016 as estimated using groundwater elevations measured in wells FRGW02, FRGW05, and
FRGW07. Daily precipitation was recorded by the onsite weather station. Periods where the groundwater flow
direction ranges from northeast to east appear to be associated with periods of significant rainfall. Gaps in the
record reflect lack of data for one or more of the wells used in the three-point analysis.
118
-------
8.8.4 Water-Table Mounding due to Gallery Infiltration
Precipitation events of sufficient intensity and duration to result in a significant rise in water elevation within the
infiltration gallery were identified using data from pressure transducers installed within the gallery. Precipitation
events that resulted in an increase of 0.5 m or greater peak water elevation in the gallery were identified, and
data from all available pressure transducers for these events were examined to determine in which wells the
response to precipitation was most rapid and greatest. In general, wells closest to the gallery responded no
quicker to precipitation than did the wells farther from the gallery. It is also noted that the increases in water-
table elevation began within minutes of the onset of increased water elevation in the gallery. In contrast,
responses observed at the tensiometers installed directly beneath the gallery were delayed by several hours
from the onset of increased water elevation in the gallery. The largest increases in water-table elevation were
observed in wells closest to the storm water retention basin located south of the infiltration gallery. Based on
these data, the degree of water-table mounding due solely to infiltration of precipitation through the gallery
could not be discerned from the rise in the water table associated with aquifer recharge from the retention
basin and other areas surrounding the site. This indicates that the rise in groundwater elevation at the site due
to precipitation is mainly due to infiltration from the storm water retention basin and, possibly, other nearby
locations rather than the infiltration gallery.
8.8.5 Infiltration Gallery Side Wall Temperature Monitoring
Vadose zone temperature profiles were monitored at locations TP1 and TP2 (Figure 8-7) adjacent to the gallery
for indications of exfiltration through the sidewall of the gallery. The monitoring design included installation of
vertical temperature profilers at depth adjacent to the southern sidewall of the gallery. Temperature can provide
an indirect method to detect water flow through the vadose zone for situations in which there is a temperature
contrast between infiltrating water and the soil in contact with the gallery. During stormwater infiltration, heat
transfer through the vadose zone occurs due to a combination of processes, including conductive and convective
processes (Rutten et al., 2010). Conductive heat transport is driven by a temperature gradient and is dependent
on the thermal properties of the vadose zone material, including the soil particles, porosity, and the pre-existing
soil moisture content. Convective heat transport is due to movement of water through the pore space within the
soil column. The rate and magnitude of soil temperature change in response to convective heat transport will be
governed by the velocity of water flow and the temperature contrast between the vadose zone and water that
flows out of the infiltration gallery. It is anticipated that the rapid influx of infiltrating stormwater would result in
a rapid, step change in vadose zone temperature adjacent to the infiltration gallery.
Temperature profile sensors installed adjacent to the lower portion of the infiltration gallery were programmed
for continuous temperature monitoring for extended time periods during 2017 and 2018. This data was
evaluated within the context of monitored stormwater flow into the infiltration gallery. Concurrent data was
recorded by shallow water content reflectometers (Campbell Scientific model CS650) embedded in the gallery
aggregate and transducers installed within gallery piezometers were also evaluated to assess the connection
between vadose zone temperature response to stormwater flow through the infiltration gallery. An example
of the system response to a rainfall event during July 2018 was provided in Figure 8-26. Shallow temperature
within the infiltration gallery (dashed, black line) was regulated by diel temperature fluctuations in the above-
ground atmosphere. This daily fluctuation was perturbed in response to inflow of stormwater generated by
three rainfall events that occurred during the evening of July 13 and the early morning of July 14. Stormwater
flow down through the infiltration gallery caused concurrent increases of water temperature within the gallery
(gray line). There was an overall increase in temperature at depth, since the temperature of infiltrating water was
higher than the temperature at the bottom of the infiltration gallery. Given the vertical position of the gallery
piezometer screen, an increase in water height within the gallery was only observed after the third rainfall event
(blue line). For the expanded temperature scale used in this figure, a slight upward inflection in soil temperature
was observed at the deepest vadose zone temperature sensor at location TP1 (brown line).
119
-------
40
150
35 -
- 120
CD
fU
i_
0J
Q.
£
£
30
25
•TDR6Ternp
¦PTITemp
•TP 1-0 Temp
¦PT1 Height
Hourly Precip
20 -
15
7/11
7/12
7/13 7/14
CalendarDate
V,/
7/15
- 90
- 60
30
0
7/16
HZ
O
c
•<
"D
-i
o
~a^
<—t-
cu
r—t*
o
CD
Si
a>
-<
ID
rt
a>
—i
I
era'
3 o
3 3
Figure 8-26. Trends in monitored precipitation, temperature, and gallery water height for a five-day period
in July.
The link between both water accumulation and temperature variations in the infiltration gallery to concurrent
variations in vadose zone temperature was used as a metric to assess water flow through the gallery sidewall.
The temporal trend in soil temperature over the depth monitored at location TP1 is shown in Figure 8-27 for a
period extended across July through early September 2018. During this time, the vadose zone at the monitored
depth was gradually warming in response to sustained, warmer atmospheric temperatures during summer. This
gradual increase was perturbed in response to stormwater infiltration events, which are highlighted by spikes
in the gallery water height. The response in vadose zone temperature to the focused stormwater infiltration
events was characterized as an approximate step increase added to the longer-term temperature trend. Cursory
inspection of the temperature trend at each monitored depth indicated a general correspondence between the
magnitudes of the gallery water height increase and the corresponding vadose zone temperature increase. The
degree of correspondence between vadose zone temperature increase and potential stormwater exfiltration
across the gallery sidewall at the monitored depth was evaluated through comparison of the magnitude of
stormwater flow into the gallery and the rate of temperature increase at each depth for the vadose zone
temperature profiles. The magnitude of gallery stormwater flow was approximated by integrating the area
under the peak of monitored gallery water height versus time, expressed in units of centimeter-day (cm d).
In two instances, there were two infiltration events that occurred within a two-day period (29-30 June 2017
and 29-30 July 2018). For these periods, only a single vadose zone temperature step increase was discernable,
so the contributions from both infiltration events were summed to estimate the overall magnitude for the
infiltration event. The rate of vadose zone temperature increase was evaluated by isolating the temperature
step increase from the longer-term warming trend. This was accomplished by fitting a linear trend to the vadose
120
-------
zone temperature approximately two days prior to the step increase and subtracting this contribution from the
temperature trend for several days following the infiltration event. The temperature step increase isolated from
the overall trend revealed the characteristic of an increasing asymptotic curve of the form T, = T + A*(l - ekt),
where T is the detrended temperature (°C) at each time, T is the starting temperature, A is the amplitude of the
asymptote temperature (°C), k is the rate of temperature increase (°C/d), and t is elapsed time (d). Optimized
values for parameters A and k were derived through best-fit regression to the temperature step change.
Subsequently, the fitted trend was numerically differentiated to determine the maximum rate of temperature
increase at each depth for each infiltration event.
U
o
CD
=3
4-»
ro
!—
O)
Cl
E
at
h—
i—
CD
¦4—'
03
-a
c
ro
"o
(-0
26 i
25 -
24 -
23 -
22
21
20
19 -
18
17
16
TP1-3
TP 1-2
TP1-1
TP1-0
IL
- 250
L 200
CD
m
(b
150 •<
CD
r-t-
fD
X
100 2.
Qrq
ZT
r+
o
3
- 50
0
7/1 7/11 7/21 7/31 8/10 8/20 8/30 9/9
CalendarDate
Figure 8-27, Trends in monitored vadose zone temperature and gallery water height during July-September
2018.
-------
Comparison of the maximum rate of vadose zone temperature change versus the magnitude of each infiltration
event is shown in Figure 8-28. As previously stated, heat transfer by conduction and convection are the two
primary factors contributing to a change in vadose zone temperature at the monitored depth. When there is a
temperature contrast between infiltrating water and the vadose zone, heat conduction will always be active.
When gallery water actively exfiltrates through the sidewall into the monitored vadose zone, it is anticipated that
the combined influence of heat transfer by conduction and convection will increase the rate of the temperature
change. Examination of the discontinuous trend in response to increased infiltration magnitude (Figure 8-28)
provides insight into the processes that are likely driving vadose zone temperature changes during the monitored
period. First, there appears to be a minimum threshold of approximately 20 cm d for an infiltration to cause a
substantial change to the rate of temperature increase. A linear fit to the derived rates of temperature change
at the four depths monitored at the TP1 location produced a range of threshold values from 16-22 cm d. Heat
transfer still occurs for infiltration events of smaller magnitude, but the ability to discern changes is limited
by the accuracy of the analysis approach. Above the minimum threshold for infiltration magnitude, there is a
general linear correspondence between increasing infiltration magnitude and rate of vadose zone temperature
change. For monitored depths of 0.3-1.1 m above the bottom of the infiltration gallery, the magnitude of this
correspondence was essentially identical. In contrast, the rate of vadose zone temperature change at a depth of
0.15 m above the bottom of the infiltration gallery shows proportionately larger increases for a given infiltration
magnitude. A likely cause for this increased rate of temperature change was the influence of convective heat
transfer from gallery water exfiltration across the sidewall and contacting the temperature sensor at the
monitored depth. This assessment depends on the assumption that factors controlling heat conduction at the
four depths were consistent across the monitored depth interval.
3.0 -i
U
o
a
+->
TO
i_
a)
a.
E
£
o>
c
o
INI
0>
in
O
T3
ra
>
H-
o
O)
4->
ra
cc
-a
c
QJ
f-
0)
s_
rj
ro
oi
CL
E
O)
2.5
2.0
H 1.5
4—
O
CD
>
ro
>
V
01
a
l.o -
0.5
0.0
~
TP1-0 Data
•
TP1-1 Data
•
TP1-2 Data
•
TP1-3 Data
—
TP1-0 Regression
TP1-1 Regression
TP1-2 Regression
—
TP1-3 Regression
~
CP
a
® 9 • >%¦•
¦ ' r i" '—' ' I r '
• ' ~
xi-'
1 1 1—I I—I 1 |—I » T f | 1 1—I 1—| 1—I 1 I—I l—l—I 1
10 20 30 40 50 60 70 80
Infiltration Magnitude, cm d
Figure 8-28. Trends in the rate of vadose zone temperature change as a function of infiltration magnitude for
seven events monitored during June 2017 and July-September 2018.
-------
8.8.6 Vadose Zone Properties
Infiltration and water/ solute movement through the vadose zone is determined by the physical and chemical
properties of the vadose zone (Hou et al, 2019). Physical and chemical characterization of the vadose zone
soils and sediments were investigated as part of the Fort Riley study. These properties will help understand the
transport behavior of water and solutes through the vadose zone to groundwater and aid in the evaluation of
potential risks to groundwater quality.
Before construction of the infiltration gallery at the Fort Riley study site, three vadose zone sediment samples
were collected from three different surficial and vertical points below the ground surface (bgs), sample A (0.91-
1.52 m bgs), sample B (1.52-2.13 m bgs), and sample C (2.13-2.74 m bgs). Approximately 2.5 kg of sample were
collected at each point. These were undisturbed soils excavated for the construction of the filtration gallery. The
characteristics of these soil/sediment samples are summarized in Table E-9 in Appendix E.
The measured total organic carbon (TOC) for the three samples were 0.725, 0.183 and 0.069 % for soil A, B, and
C, respectively. The soil texture of sample A was loam, and sample B and C were silt-loam. The specific surface
area (BET) for this soil was determined to be 28.06, 15.93, and 15.39 m2/g for samples A, B, and C, respectively.
In addition, chemical analysis of soil extracts (1:1 soikwater) showed that these soil samples contain background
concentrations of chloride, fluoride, sulfate, and phosphorus.
Two additional soil/ sediment cores were collected in February 2017 during installation of monitoring wells
FRGW11 and FRGW13. These cores were collected over the entire length of the borehole and comprised not
only the vadose zone, but also the saturated interval of the aquifer to the bottom of the well screen. These core
samples were analyzed for physical and chemical properties (Table E-10 in Appendix E).
In general, the vadose zone and the screened portion of the aquifer have coarse textures that are identified as
sand, loamy sand, sandy loam, silt loam, and loam (Table E-10 in Appendix E). As can be seen in Figure 8-29,
sand is present at the top of the vadose zone which may correspond to fill material, decreases in the middle of
the vadose zone, and increases in the lower part of the vadose zone and into the aquifer for both FRGW11 and
FRGW13. In the middle of the vadose zone, the silt tends to dominate and rapidly decreases in the lower part of
the vadose zone and into the aquifer. The percentage of clay is the highest in the middle of the vadose zone, but
it was never a dominant component of the texture in both FRGW11 and FRGW13. The clay content was low in
the lower portion of the vadose zone and into the aquifer.
330
FRGW11
FRGW13
328
a> 322
318
70
O
ID
2D
30
50
60
70
60
00 10D0
10
20
30
40
50
60
B0
90 1DO
Percentage Percentage
Figure 8-29. Comparison of sand, silt and clay content from the continuous cores taken during the installation
of FRGW11 and FRGW13.
123
-------
The dominance of sand in the lower portion of the cores suggests that water will be more easily infiltrated and
more rapidly transported through the lower vadose zone because of greater hydraulic conductivity (Domenico
and Schwartz, 1990; Hiliel, 1998). However, caution should be exercised when generalizing water movement
based on texture. The initial cores collected at the site suggest much higher content of fine-grained materials
in the upper portion of the vadose zone. This suggests that spatial heterogeneity at the study site needs to be
accounted for, and there may be areas where infiltration is slower.
Soil texture can also affect chemical properties. Figure 8-30 compares the composition of sand, silt and clay to
select chemical properties for the cores collected during the installation of FRGW11 and FRGW13. From Figure
8-30, the chemical properties CEC, total carbon, and total calcium for the most part follow the trends in the silt
and clay fractions. This effect is muted in the total sodium plot, but there appears to be an anomaly in the lower
depths, which is likely due to the influence of groundwater concentrations of sodium.
It is well known that CEC is a function of mineralogy, organic matter content and surface area (Sposito, 1989;
Hounslow, 1995; Sparks, 1995). Smaller particles have greater surface area and the greater surface area in
general makes the smaller particles more reactive. Similarly, clay minerals typically fall into the silt and clay size
range and they are known to have more charged sites and high surface area for CEC to occur (Sposito, 1989;
Hounslow, 1995; Sparks, 1995). Other minerals such as carbonates often are found in association with the silt
and clay fractions. These minerals along with organic carbon would be represented by the total carbon fraction
in Figure 8-30. They not only follow the clay and silt fraction data, but also trend with the CEC data since they
also contribute to the CEC. Total calcium also trends with the silt and clay fractions of the two core samples.
In the case of total calcium, the calcium was attracted to the CEC exchange site and was associated with the
carbonate minerals in the vadose zone.
FBGW13
FRGW11
CEC
5
*
200
Percentage Percentage CEC •jcrnolJcrn) TcAai Carbon Total Cartaum (rag/kg) Total Sodium (mgiHj
Figure 8-30. The relationship of vadose zone textural properties to chemical properties in the vadose zone and
upper aquifer at FRGW11 and FRGW13. The magenta diamonds and lines represent the clay fraction, the cyan
triangles and lines represent the silt fraction, and the blue squares and lines represents the sand fraction. For CEC,
total carbon, total calcium, and total sodium, the black circles and lines are the data from FRGW11 and the red
circles and lines are the data from FRGW13.
124
-------
Figure 8-31 shows total aluminum, calcium, iron, manganese, silicon, and TOC concentrations decreasing with
increasing depth in the vadose zone and screened portion of the aquifer at the wells FRGW11 and FRGW13. The
importance of this was that these parameters can serve as a rough proxy for aluminum, iron, and manganese
oxyhydroxide phases; calcium for carbonate phases; aluminum and silicon for aluminosilicate phases; and TOC
for natural organic matter. These phases are important phases for not only CEC type reactions, but important in
specific sorption of inorganic and organic contaminants.
The decreasing CEC and sorptive capacity of the vadose zone and screened portion of the aquifer has important
implications to the transport of contaminants through the vadose zone and screened portion of the aquifer. First,
the ability to capture and retain infiltrated contaminants is likely the greatest in the upper portions of the vadose
zone and the ability of the vadose zone to capture contaminants decreases steadily with depth. Secondly, over
time, it is possible that the upper portion of the vadose zone ability to capture contaminants could decrease
as the sorption sites become filled. Because the lower portion of the vadose zone has less ability to capture
contaminants, contaminants could migrate to the aquifer and change the water quality.
TQtai Organic Carbon
Total AJuiwnum
• #
C WOO i«00 1M00 JOOOC©
axra o
iOOM 4«tt «0C$«>» iMOO SHOOO 0 1 01 ft1 0* 0.5 M 0 7 0 A
Total Alunrwum Ongilq) To*al Csteiwi (mfl'tog) Total Iron (ngflcg} Total Manganese ling/Jig) Total (rogtqj) Total C*g»nic Carbon <%}
Figure 8-31. Concentration profiles versus depth for total aluminum, total calcium, total iron, total manganese,
total silicon, and total organic carbon in the cores collected during the installation of FRGW11 and FRGW13.
The black lines and circles represent the data for FRGW11 and the red lines and circles represent the data for
FRGW13.
125
-------
8.8.7 Precipitation Patterns During the Study
Infiltration of stormwater depends on precipitation and therefore, it is important to understand the precipitation
patterns that occur. The daily precipitation patterns at the Fort Riley site during the entire study are shown in
Figure 8-32. The daily precipitation patterns indicate a similar pattern to what NOAA reported (2019b), that most
of the precipitation comes between April and September.
140 -
t 100
o so
0
1/1/2015
1/1/2016
1/1/2017
Date
1/1/2018
1/1/2019
Figure 8-32. Daily precipitation
during the Fort Riley study.
Figure 8-33 shows the annual precipitation and monthly precipitation that occurred during the study. The mean
annual precipitation at Fort Riley is 905 mm (NOAA, 2019b). For three of the study years (2015, 2016, and 2018),
the precipitation was similar to the mean annual precipitation as is shown in Figure 8-33A.
Although the precipitation for three years of the study was typical, there are different patterns in the monthly
distribution of precipitation among the years, Figure 8-33B and Table D-2 Appendix D. For example, in June of
2016 there was considerably less precipitation (29.2 mm) than in June of 2015 (136 mm), 2017 (111 mm), and
2018 (50.3 mm) (Figure 8-33B). Another example would be in November. In 2015, there was significantly more
precipitation (128 mm) than in any other year during the study, 2016 (0.76 mm), 2017 (3.56 mm), and 2018
(21.1 mm).
E 1000
E 200-
2 100
Month
Figure 8-33. Annual and mean
monthly precipitation for
the Fort Riley Study. A. Total
annual precipitation, the value
in the text box in each bar is
the total annual precipitation
and the green line represents
the mean annual precipitation
from NOAA (2019b).
B. Monthly mean precipitation
for each year of the study.
126
-------
8.8.8 Movement of Water Through the Vadose Zone
Razzaghmariesh and Borst (2019) previously reported on the use of the network of tensiometers to monitor
water movement through the vadose zone at the Fort Riley site. These authors provide several key points which
are important to understanding of water movement for this study. The first point is that beyond 3.1m from
the gallery, the tensiometers showed very little change after a rainfall event. Therefore, the effective recharge
radius of the storage gallery was less than 3.1m. This point verifies the original study data collection design,
in which tensiometers T5A and T5B and SPW samplers FRLW11 and FRLW12 can be considered outside the
influence of the gallery. These sampling points can be used as natural or background conditions at the study
site. Razzaghmanesh and Borst (2019) also demonstrated that the water accumulating in the storage gallery was
positively correlated with soil tension changes. This means that following a precipitation event, water moves
out of the gallery into the vadose zone and this water movement from the gallery to the vadose zone can be
detected and tracked. They also point out that there was a positive correlation between total rainfall and rises
in groundwater elevation (Razzaghmanesh and Borst, 2019). This point suggests that the water table is being
recharged directly as the result of precipitation or the precipitation is filling the pond just to the south of the
study site. This would result in the water level in the pond rising causing a rise in the groundwater elevation. The
final point is that there is effective exfiltration for the side gallery walls supporting the hypotheses developed
by Lee et al. (2015). This means that with time, as the bottom of the gallery clogs with fine materials, water will
move out the sidewalls of the gallery into the vadose zone.
The remaining discussion in sections below will discuss water movement from the gallery to the vadose zone
and through the vadose zone by using the tensiometer and temperature data from the tensiometers to monitor
changes in soil temperature resulting from the heat exchange during water movement. See Table D-l, Appendix
D for positions of the sampling point. Figure 8-34 shows the depths and location of the soil porewater samplers.
Figure 8-34. Depths and locations of the soil pore samplers. (Modified from: USACOE and Zieson)
FRLW03
3.05 m bgs
0.61 m from
FRLW06
3.05 rn bgs
3.05 m from gallery edge
V
FRLW07
3.96 m bgs
3.05 m from gallery edge
FRLW11
4.88 m bgs
10.67 m from gallery edge
FRLW12
6.40 m bgs
10.67 rn from gallery edge
FRLW08
4.88 m bgs
3.05 m from gallery edge
Gallery
Edge
FRLW01 & FRLW09
m bgs
FRLW04
3.96 m bgs
0.61 m from gallery edge
FRLW05
4.88 m bgs
0.61 m from gallery edge
FRLW02 & FRLW10
6.40 m bgs
M— Existing / Finish Grade
Gallery
Edge
127
-------
8.8.8.1 Daily Precipitation and Water Level Inside the Infiltration Gallery
The Fort Riley study was designed to capture stormwater runoff from the parking lot and infiltrate the water
into the subsurface. Piezometers PT01 and PT02 were installed in the infiltration gallery to monitor changes in
water level using two pressure transducers located in sumps positioned at the bottom of the gallery. The sumps
were never completely empty, and retained approximately 12-13 cm of water, which served as the "zero" for
the pressure transducers. Figure 8-35 shows the fluctuations in water level in the infiltration gallery and the
corresponding precipitation during the entire study period.
0)
E
o
£
£
c
o
+-*
eg
Q.
O
£
0_
A
300
250
200
150
100
50
0
140
120
100
80
60
40
20
0
9/1/2015
J.
il
B
-lU-*
Jill
k
L
-i . >,
m
U
9/1/2016
9/1/2017
9/1/2018
Date
Figure 8-35. Changes in water level elevation inside the infiltration gallery and the daily precipitation during
the study at the Fort Riley study side. A. Water levels measured inside the infiltration gallery measured by
PT1. B. Daily precipitation.
The water level inside the infiltration gallery (Figure 8-35A) in some cases corresponded to the daily precipitation
pattern (Figure 8-35B). However not all precipitation events caused a rise in the water levels in the infiltration
gallery (Figure 8-35). There are a few potential reasons why precipitation can happen with no rise in water level
in the gallery.
To have a rise in water level in the infiltration gallery the rate at which stormwater runoff enters the gallery must
exceed the rate of water exfiltrating through the bottom and sides of the gallery (relative rates). The rate at
which the stormwater enters the infiltration gallery is related to the rainfall intensity and duration once surface
"wetting" is overcome (Jungerius and ten Harkes, 1994; Hillel, 1998; IJDFCD, 2016a; UDFCD, 2016b; Gunaratne
et al., 2017). An example of the stormwater infiltration rate exceeding the exfiltration rate of the gallery was on
April 24, 2016 and is shown in Figure 8-36. As can be seen in this example, the intensity of the rainfall averaged
47.5 mm/hr and the rain lasted for 1 hr (Figure 8-36B). This caused the water level in the gallery to rise to 121
cm (Figure 8-36A). An example of a stormwater event where the stormwater infiltration rate did not exceed the
exfiltration rate of the gallery was on July 25, 2016 and is shown in Figure 8-37. During this event, there was
no discernable rise in the water level in the infiltration gallery (Figure 8-37A). In this case, because the gallery
was new, the surface clogging was minimal and likely not playing a significant role in preventing the stormwater
128
-------
from entering the gallery. The initial rainfall intensity during the first hour of the event was 58.2 mm/hr (Figure
8-37B). The asphalt surface likely needed "wetting" (abstraction volume) before runoff could occur and may
be a possible explanation for why the water level did not rise. The total duration of the precipitation event on
July 25, 2016 was 6.67 hr with an average rainfall intensity of 11.5 mm/hr for this event. Even though the total
precipitation on July 25, 2016 was 77.0 mm and was 29.5 mm larger than the April 24, 2016 rainfall event, there
was no rise in the water level in the infiltration gallery. The likely causes were the wetting of the parking lot
surface and that the precipitation fell over a longer period of time which did not cause an increase in the water
level in the infiltration gallery.
300
5-
A
B
i ii
&
&
A
fy
&
<£>
4s
/
.GO
&
&
«5 _l
o
E
E
c
,2
5,
'5
01
300
200
100
0
15
10
5
0
A
- B
M - ¦ ¦
¦
. . .
. .
,4'
#
ff
0
•#
4V
Date Time
,4
#
r
Figure 8-37. A single
precipitation event in July
2016 showing the
A. Changes in water level in
the infiltration gallery and
B. The rainfall intensity and
duration.
129
-------
Another explanation for why there would be no rise in the water levels in the infiltration gallery would be
because of surface clogging of the pavers with fine grain materials (Figure 8-38). As clogging increases the
stormwater infiltration will decrease. Under some clogging conditions, the runoff may bypass the paver section.
Like the previous explanation, there will be no rise in water level in the gallery once the rate of stormwater
entering the infiltration gallery is equal to or less than the rate of exfiltration out of the gallery.
The important point in this discussion is that even when water levels inside the infiltration gallery do not rise
following a precipitation event, one cannot assume that water is not being conveyed into the subsurface.
It possibly just means that the rate of exfiltration is greater than the rate at which the stormwater runoff is
entering the infiltration structure. Another means other than relying on water level rise needs to be incorporated
into the system to know if exfiltration is occurring.
Figure 8-38. Surface clogging of a permeable paver infiltration gallery. The gravel spaces between the pavers
allows the stormwater runoff to enter the infiltration gallery. A. An unclogged surface, and B. Clogged
surface with fine grained materials in the gravel spaces.
130
-------
8.8.8.2 Tensiometer Data Collection
To track the movement of water through the vadose zone, tensiometers were used to measure soil moisture
tension. Tensiometers were installed in five clusters: two clusters were directly under the infiltration gallery
(Clusters 1 and 2), one cluster 0.6 m from the side of the infiltration gallery (Cluster 3), a cluster 3.1m from the
side of the infiltration gallery (Cluster 4), and Cluster 5 was 10.67 m from the side of the gallery. Tensiometers in
each cluster were placed at different depths, and each cluster and depth has an associated SPW sampler. Figure
8-34 and Table D-l, in Appendix D give the location and depth of each tensiometer. Tensiometers were also
equipped to measure temperature.
The tensiometers recorded soil moisture tension and temperature data every 10 minutes. An example of the
tensiometer data output is shown in Figure 8-39. Although the tensiometers are designed to provide continuous
data, issues with the loggers and batteries in general caused gaps in the data. Consequently, only the best data
was used.
00
Q_
c
o
-------
8.8.8.3 Soil Moisture Tension and Water Movement Through the Vadose Zone
Two events were chosen to show how soil moisture tension can be used to understand the water movement
through the vadose zone. The events chosen were ones when there were water level rises in the infiltration
gallery and when there were complete or nearly complete data sets in all the clusters at all the depths and
similar total precipitation.
The first event chosen was on November 17, 2015 (Figure 8-40). This event was chosen because it was a single
water level rise in the infiltration gallery over a short period (about 7 hr). The total precipitation was 56.4 mm.
Figure 8-40 shows that except for tensiometer cluster 5, there was a time lag between the water level rise in the
infiltration gallery and the onset of a major change in soil moisture tension. For tensiometer clusters 1, 2, and 3
the lag time varied with the depth of the tensiometer, the wetting front arrived first in the shallowest depth and
the lag time was longer with each progressive depth. For tensiometer cluster 4, the shallowest depth had soil
moisture tensions twice that in the deeper two depths and the wetting front arrival was longer than the middle
depth. The higher soil moisture tensions in the shallowest depth most likely is the result of the vadose zone at
that depth being drier than the deeper depths or that the shallower tensiometer was installed in a part of the
vadose zone composed of finer grained materials. The slow downward trend in the soil moisture tension data
in cluster 5 is similar to the soil moisture before and after the event. This indicates that the precipitation event
had no influence on the tensiorneters, which was expected since cluster 5 tensiometers were installed to be the
control for natural water movement in the vadose zone.
1 I
ii;
Pi
OhiSttr 1
^—
¦- ' =
s
/
r*
/
{
i
li -
J? :
CtutfirS
"^~r*
rv
Ouster 3
Cluster 5
Figure 8-40. Soil moisture tension data during the November 17, 2015 precipitation event. For Clusters 1, 2, and 5 the black line
shows the soil moisture tension for T1A, T2A, and T5A; the red line shows the soil moisture tension for TIB, T2B, and T5B. For
Clusters 3 and 4 the black line represents the soil moisture tension data for T3A and T4A; the red line shows the soil moisture
tension data for T3B and T4B; and the green line shows the soil moisture tension data for T3C and T4C. The groundwater
elevation data in the lowest right corner of the figure shows the groundwater elevation changes in FRGW04 (black line) and
FRGW05 (red line).
132
-------
Groundwater elevation data are important in understanding the depth of the vadose zone. During this event
the groundwater elevation changed 0.09 m in FRGW04 and 0.15 m in FRGW05. The slightly larger change in
the groundwater elevation in FRGW05 than in FRGW04 is believed to be because FRGW05 is closer to the
stormwater retention pond south of these wells. The water level in the pond is thought to be an influence on
groundwater elevations at this study site. The groundwater elevation in FRGW05 and FRGW04 suggest that the
tensiometers TIB, T2B, and T5B are likely in the saturated zone or in the capillary fringe above the aquifer.
Figure 8-41 shows the data for the water level changes in the infiltration gallery along with the changes in soil
moisture tension during the late May 2016 precipitation events. The data for the infiltration gallery PT1 shows
multiple rises in the water level in the infiltration gallery from May 25- May 28, 2016 (Figure 8-41); however,
our analysis will concentrate on the 4 water level rises beginning on May 26th and ending in the early morning
of May 27th. The duration of this event was 15.67 hr and the total precipitation was 51.6 mm. Unlike the earlier
precipitation event discussion, only tensiometer clusters 1, 2, and 3 showed any changes in soil moisture tension.
In tensiometer clusters 4 and 5, the soil moisture tension remained constant or was already in a gradual decline
prior to the May 25th precipitation. As was the case previously, the lack of changes in soil moisture tension likely
indicates no influence of the infiltration gallery on the tensiometers in clusters 4 and 5. Similar to the previous
example, tensiometer clusters 1, 2 and 3 showed the soil moisture tension changing first in the shallowest
tensiometer and the soil moisture tension changing progressively as the tensiometer depth increased.
Cluster 1
Gmi TV,, .u, v*.
Cluster J
Cluster 4
Figure 8-41. Soil moisture tension data during the May 26, 2016 precipitation event. For Clusters 1, 2, and 5 the black line
shows the soil moisture tension for T1A, T2A, and T5A; the red line shows the soil moisture tension for TIB, T2B, and T5B. For
Clusters 3 and 4 the black line represents the soil moisture tension data for T3A and T4A; the red line shows the soil moisture
tension data for T3B and T4B; and the green line shows the soil moisture tension data for T3C and T4C. The groundwater
elevation data in the lowest right corner of the figure shows the groundwater elevation changes in FRGW04 (black line) and
FRGW05 (red line).
133
-------
The groundwater elevation data indicated that the depth of the vadose zone was initially 320.87 m and changed
0.38 m and 0.73 m for FRGW04 and FRGW05, respectively. The groundwater elevation in FRGW05 and FRGW04
suggests that the tensiometers TIB, T2B, and T5B were likely in the saturated zone or in the capillary fringe
above the aquifer by the end of the event.
Lag times were estimated in both events based on the start of the rise in water levels in the infiltration gallery
and the start of the change in soil moisture tension. Table 8-2 gives the estimated lag times for these two
events. Both events had similar precipitation depths. Tensiometer clusters 1 and 2 are directly below the
infiltration gallery and would represent the most direct path for stormwater to reach groundwater. Because
the deepest tensiometers in clusters 1, 2, and 5 were close to or in the saturated zone, it can be estimated
that the stormwater runoff in both the November 11, 2015 and May 26, 2016 precipitation events reached
the groundwater rapidly in 6 hr. For both dates, the stormwater reached the groundwater fastest in cluster 2.
In cluster 1 the stormwater reached the groundwater in 11 hr to 7 hr for the November 17, 2015 and May 26,
2016 events, respectively. The difference is likely due to differences in the physical properties of the vadose zone
between the water table and the bottom of the infiltration gallery as well as initial soil moisture content.
At 0.6 m from the gallery wall T3A had a response to the infiltrated stormwater in 0.33 to 0.17 hr in November
17, 2015 and May 26, 2016, respectively. It is unknown if this water was coming from water exfiltrating from
the sidewall of the gallery or if the water is from the lateral movement of water from the bottom of the gallery.
The infiltrated stormwater in both events showed a response in the T3B in approximately 3 hr. The tensiometer
T3C was the deepest tensiometer in this cluster and was at an equivalent depth as the shallowest tensiometers
in clusters 1 and 2. In November 2015, the T3C response to the infiltrating stormwater was approximately two
times that in cluster 1 and 2. However, in May 2016, the response to the stormwater was similar to the response
of the shallow tensiometer in cluster 2 -about 3 hr and was twice as fast as the shallow tensiometer in cluster
1. Although the amount of precipitation was similar in both events, the November 17, 2015 event was short
lived, and on May 26, 2016, the precipitation fell over a longer duration and there was rainfall the day before this
event. The more recent precipitation before the May 26, 2016 event may have "primed" the system and allowed
for quicker transport to cluster 3.
Tensiometer Cluster 4 is 3.1 m from the side of the infiltration gallery. The data from T4B and T4C was not
recorded for the May 26, 2016 precipitation event, and there was no discernible response in T4A for this event.
Therefore, only the November 17, 2015 event will be discussed. Because of the distance from the infiltration
gallery, T4B had the fastest response to the infiltrated stormwater in this event, 3.17 hr. Razzaghmanesh and
Borst (2019) reported that the captured stormwater exfiltrates from the gallery but moving outward in a
parabolic pattern which is supported by the work of Bouwer (2002). So, water reaching T4B first is reasonable.
The infiltrated stormwater then moved downward to T4C in approximately 14 hr and could have moved laterally
or upwards through capillary action to T4A in the 7.17 hr.
The importance of the data presented in Table 8-2 is that the transport of water from the infiltration gallery was
rapid, on the order of hours to a day, and limited to less than 3.1 m from the gallery wall.
134
-------
Table 8-2. Estimated lag times of water exiting the infiltration gallery to reaching tensiometers for the
November 17, 2015 and May 26, 2016 precipitation events.
Id
Start Date/ Time
(MM/DD/YYYY/ hr:min)
Lag Time
(hr)
November 17, 2015
PT1
11/17/2015/ 03:00
Cluster 1
T1A
11/17/2015/ 10:20
3.67
TIB
11/17/2015/ 14:00
11.00
Cluster 2
T2A
11/17/2015/ 06:10
3.17
T2B
11/17/2015/ 09:20
6.33
Cluster 3
T3A
11/17/2015/ 03:20
0.33
T3B
11/17/2015/ 06:00
3.00
T3C
11/17/2015/ 09:20
6.33
Cluster 4
T4A
11/17/2015/ 10:10
7.17
T4B
11/17/2015/ 06:10
3.17
T4C
11/17/2015/ 17:00
14.00
Cluster 5
T5A
No Response
T5C
No Response
May 26, 2016
PT1
5/26/2016/ 14:00
Cluster 1
T1A
5/26/2016/ 19:40
5.67
TIB
5/26/2016/ 21:20
7.33
Cluster 2
T2A
5/26/2016/ 17:20
3.3
T2B
5/26/2016/ 20:10
6.17
Cluster 3
T3A
5/26/2016/ 14:10
0.17
T3B
5/26/2016/ 17:10
3.17
T3C
5/26/2017/ 17:00
3.00
Cluster 4
T4A
No Response
T4B
No Data Recorded
T4C
No Data Recorded
Cluster 5
T5A
No Response
T5B
No Response
135
-------
8.8.8.4 Temperature and Water Movement Through the Vadose Zone
In addition to soil moisture tension the tensiometers also measured temperature. Temperature can also be
potentially used to monitor water movement through the vadose zone. In this section the temperature data
from the tensiometer will be used to understand the movement of water in the vadose zone
Figures 8-42 and 8-43 show the temperature data obtained from the tensiometers from the November 17, 2015
and May 26, 2016 precipitation events. What is apparent from these figures is that temperature differences
could only be measured in the tensiometer Clusters 1, 2, and 3. These correspond to the tensiometer cluster
located directly below the infiltration gallery and 0.6 m from the side of the infiltration gallery. Tensiometer
Clusters 4 and 5 show either no changes in temperature data or were the continuation of gradual changes in
temperature before and after the precipitation events (Figures 8-42 and 8-43).
The temperature change in PT1 on November 17, 2016 was a negative change with the temperature initially at
20.72 °C dropping to 15.21 °C and rising again to approximately 18 °C. The net temperature change was
-2 °C (Figure 8-42). Although there appears to be changes in temperature based on the tensiometer data, the
maximum temperature change in any tensiometer was 0.36 °C. The precision and accuracy of the temperature
measurement of the tensiometer is 0.1 °C and 0.4 °C (UMS, 2007). The small temperature changes observed are
not reliable because the temperature change was within the precision and accuracy of the tensiometer.
ii
CfUElH* 1
r<
/
-jf
/
if ll
A i!
r<
s s J
3 / J
ii
P
1—
Ouster 3
i
~™ —
A<
f # #
/ / s
A
Jp
4
li
i'
Cluster s
/ ^
/
I!
»! 11
f|
Oyster 4
r<
/
' /
9 J> J
Jr J? J?
J
* d
!i
Figure 8-42. Temperature data during the November 17, 2015 precipitation event. For Clusters 1, 2, and 5 in the upper panel
the black line shows the temperature for T1A, T2A, and T5A; the red line shows the temperature for TIB, T2B, and T5B. For
Clusters 3 and 4 in the upper panel the black line represents the temperature data for T3A and T4A; the red line shows the
temperature data for T3B and T4B; and the green line shows the temperature data for T3C and T4C. For all clusters the black
line in lower panel shows gallery water level and the red line shows the water temperature.
136
-------
During the May 26, 2016 precipitation event, the temperature change in PT1 was +2 °C, The initial temperature
in the infiltration gallery was 19.16 °C, and the final temperature was 21.16 °C (Figure 8-43). There were no
observable temperature changes in tensiometer Clusters 4 and 5. There were observable temperature changes
in tensiometer clusters 1, 2, and 3, but the apparent changes in temperature below 4.88 m depth (TIB and T2B)
are not reliable as discussed previously in the November 17, 2015 event. In depths 4.88 m or shallower, the
tensiometer temperature could be used to calculate lag times (Figure 8-43). The lag times in the tensiometers
directly below the infiltration gallery (T1A and T2A) showed water reaching these tensiometers rapidly with
lag times of less than 0.01 hr and 0.67 hr, respectively. The cluster 3 tensiometers (0.6 m from gallery) showed
water moving to T3A (depth 3.1 m) in 1.33 hr, T3B (depth 3.96 m) in 1.67 hr and to the deepest tensiometer, T3C
(depth 4.88 m), in 5.38 hr.
Clustet 1
C-ius'er 2
Cluster 3
Cluster 4
Cluster S
Figure 8-43. Temperature data during the May 26, 2016 precipitation event. For Clusters 1, 2, and 5 in the upper panel the
black line shows the temperature for T1A, T2A, and T5A; the red line shows the temperature for TIB, T2B, and T5B. For
Clusters 3 and 4 in the upper panel the black line represents the temperature data for T3A and T4A; the red line shows the
temperature data for T3B and T4B; and the green line shows the temperature data for T3C and T4C. For all clusters the black
line in lower panel shows gallery water level and the red line shows the water temperature.
137
-------
8.8.8.5 Summary of the Movement of Water in the Vadose Zone
The analysis of data water movement in vadose zone of this report agreed with what Razzaghmanesh and
Borst (2019) concluded. The analysis of tensiometer data indicated that the effective recharge radius of the
infiltration gallery is less than 3.1 m, and that the SPW samplers FRLW11 and FRLW12 should reflect the vadose
zone background conditions. The temperature and tension data also support Razzaghmanesh and Borst (2019)
assertion that the water moved out of the infiltration gallery both through the bottom and the sides of the
gallery. The tensiometers also showed that water movement through the vadose zone to the groundwater is
rapid occurring within hours to a day of the stormwater runoff event. This also verified that tensiometers, if
made more robust, can be used to track water movement from the gallery and through the vadose zone.
Although temperature could be used to monitor water movement through the vadose zone, temperature data
has important limitations. In November 2015 the temperature contrast in the tensiometers was so small that
the changes in temperature could not be distinguished from the noise in the tensiometer temperature sensor.
In contrast, in May 2016, the temperature contrast was sufficient in the tensiometers that water movement
could be tracked at depths 4.88 m or shallower. Because of the limitations in the use of temperature data, the
tensiometer clusters beyond 0.6 m from the infiltration gallery could not detect water movement which could
be detected using tension data. This indicates, at least for this study, that tension is the better way to track water
movement in the vadose zone.
8.8.9 Fate and Transport of Constituents through the Vadose Zone
Monitoring the fate and transport of chemical constituents through the vadose zone was accomplished using
SPW samplers. The locations and placement of the SPW samplers are given in Table D-l in Appendix D and
shown in Figure 8-7. The SPW samplers were paired with tensiometer clusters previously discussed; FRLW01
and FRLW02 paired with tensiometer cluster 1; FRLW09 and FRLW10 paired with tensiometer cluster 2; FRLW03,
FRLW04, and FRLW05 paired with tensiometer cluster 3; FRLW06, FRLW07, and FRLW08 paired with tensiometer
cluster 4; and FRLW11 and FRLW12 paired with tensiometer cluster 5.
FRLW11 and FRLW12 were designed to provide information on the natural conditions and processes occurring
in the vadose zone. Based on the tensiometer data collected from tensiometer cluster 5 in Sections 8.8.8.3 and
8.8.8.4, these SPW samplers were useful in determining natural conditions and processes; however, FRLW11
was not used in this discussion because only small water volumes could be obtained. Based on the very high soil
moisture tensions reported, FRLW11 appears to be in a tight material that limited water movement through this
zone. In most cases, even obtaining 20 mL of sample to conduct the pH and conductivity measurements was not
possible. Another potential problem with FRLW11 is that it was in the root zone of a mature cottonwood tree,
and the water uptake by the cottonwood could potentially be significant during the growing season. It is also
important to note that FRLW12 was at times in the saturated zone and would be more reflective of groundwater
conditions rather than the vadose zone. Although FRLW12 is not the ideal control, it will be used as a control
SPW.
All the SPW data are reported in Appendix E, Tables E-12 and E-13.
8.8.9.1 Alkalinity, Major Anions and Cations, pH, Specific Conductivity
Detailed analysis of time series and trend analysis for major anions and cations, pH and specific conductivity in
the vadose zone can be found in Appendix D, section 4.2.
8.8.9.1.1 Comparison of Major Anion and Cation Spikes in 2018
There were several sampling points where spikes can be seen in the major anion and cation data as well as in
SPC. The following analysis focused on the March 2018 data set because spikes in the data were seen for most of
the parameters in most of the clusters. Figure 8-44 shows the chloride vs SPC and sodium vs SPC for porewater
clusters 1 and 2. The SPC spike that occurred in FRLW01 and FRLW09 (Figure 8-44A and 8-44B) showed an
identical chloride spike pattern as the SPC (Figure 8-44A). Sodium had a spike in FRLW01 in March of 2018, but
138
-------
the spike in FRLW09 did riot occur until June 2018 (Figure 8-44B). The sodium in FRLW01 had not returned yet
to baseline in June 2018 (blue arrow in Figure 8-44B). This suggested that movement of sodium was retarded in
FRLW09 with respect to SPC and chloride, and sodium was, to a lesser extent, in FRLW01, FRLW02 and FRLW10
did not have required sample volume for chloride and metal analysis, but, based on SPC FRLW02, would not
show a concentration spike in 2018, One possible explanation is that in March 2018 and June 2018 FRLW02 was
near to or below the water table (elevation ~ 321 m), and the SPC was indicative of the groundwater rather than
the soil porewater.
1/10017 1/1/2018
Date
18000
160QQ
14O00 ~
12000 =
10000 55
8000 i
6000 ^
4000 -<§
2000 '¦§
0 3
18000 "g
16000 O
14000 u
12000 |c
10000 u
8000 q.
6000 «
4000
2000
0
~5
E,
«
"O
o
£.1
O
ro
E,
E
3
"O
o
cn
7000
6000'
5000 ¦
4000 ¦
3000
2000 H
1000
0
1400
12Q0
1000
800 H
600
400 H
200
0
1/1/2015
1/1/2016
Figure 8-44. Chloride,
Sodium, arid SPC with
respect to time for soil
porewater clusters 1 and 2.
A. Chloride and SPC.
B. Sodium and SPC. FRLW01
are the black circles, FRLW02
are the black triangles,
FRLW09 are the red circles
and FRLW10 are the red
triangles. The solid lines
represent the data for
chloride or sodium in A and
B, respectively. The dashed
lines represent the SPC data
in both A and B. Blue arrows
highlight the June 2018 data
points. Gray shaded areas
show spikes.
Only SPC and chloride data were available in porewater cluster 3 (0.6 m from the gallery wall) because of small
sample volumes and the data is shown in Figure 8-45. In FRLW03 (the shallowest SPW in this cluster), the SPC
and chloride both had a spike in March 2018 and had returned to baseline in September 2018. The deepest SPW
sample in this cluster, FRLW05, showed both SPC and chloride increasing in March 2018, but did not peak until
later in June 2018, and returned to baseline by September 2018. This would indicate that movement of both
chloride and SPC were retarded in FRLW05.
•
'
r \
' /
in
k
~ ' I'
u ^
\V
1V
,,,,,, .till.. . i . »i i S, d, i .
1/1/2015
1/1/2016
1/1/2017
Date
1/1/2018
Figure 8-45. Chloride and
SPC data with time in soil
porewater cluster 3. Green
circles represent the FRLW03
data for both chloride and
SPC, the green diamonds
represent the FRLW05 data
for chloride and SPC. The
solid lines represent the
chloride data and the dashed
lines represent the SPC data.
139
-------
In soil porewater cluster 4, SPC, chloride, and sodium had spikes but these spikes did not occur in March 2018
(Figure 8-46A and 8-46B). Instead, the spikes occurred in June 2018. In the middle and deepest SPWs, FRLW07
and FRLW08, the SPC and chloride spike returned to baseline by September 2018 (Figure 8-46A). However, in
FRLW06, the shallowest SPW, the chloride had not completely returned to baseline in September 2018. Because
of limited SPW sample volume, there was no sodium data for FRLW06 and FRLW08 in March 2018, so no
evaluation can be made. FRLW07 did show a spike in sodium in June 2018 (Figure 8-46B) and had not returned to
baseline in September 2018 (black arrows in Figure 8-46B). FRLW08 showed an increase in sodium concentration
after June 2018 and potentially was still rising in September 2018. The increase in sodium concentration in
FRLW08 was delayed in comparison to SPC and chloride in all SPWs and sodium in FRLW07.
In SPW cluster 4 the delay was not easily explained. The delay could be because of the time it takes to move
through the 3 m of vadose zone from the wall to the SPWs. The delay could also be from the infiltration through
the vadose zone from the surface. There was no tensiometer data during 2018 to compare lag times to. Based on
earlier tensiometer data, the response of the wetting front depends on the intensity and duration of the rainfall
events. In some cases, the lag time was 3 to 14 hours and in other cases the water did not appear to reach this
SPW cluster.
1/1/2015
1/1/2016
1/1/2017
Date
1/1/2018
Figure 8-46. SPC, chloride
and sodium data with
time for soil porewater
cluster 4. A. Chloride and
SPC data. B. Sodium and
SPC data. Blue circles are
for FRLW06, blue triangles
are for FRLW07, and blue
diamonds are for FRLW08.
Solid lines represent the
chloride data in A and
sodium data in B. Dashed
lines are for SPC in both
A and B. Black arrows
highlight the sodium data
points in September 2018.
8.8.9.1.2 Geochemical Processes for Major Anions and Cations in the Vadose Zone
As precipitation or infiltrated water moves through the vadose zone, it is modified by interaction with the
surrounding media in the vadose zone. This monitoring shows that several geochemical processes are occurring
in the subsurface that affect the major anions and cations.
Specific conductivity was shown to be a good proxy for the movement of dissolved constituents through the
vadose zone. Chloride was the only major anion which correlated well with SPC. Chloride is a conservative
species, and as such, it is not expected to have major interactions with the vadose zone media, so, the high
correlation between SPC and chloride is expected. Bicarbonate and sulfate did not correlate well with SPC.
Both bicarbonate and sulfate can react with the media in the vadose zone. Except for potassium, the other
major cations, calcium, magnesium, and sodium correlated with SPC. Calcium, magnesium and sodium are
not conservative species and participate in other geochemical processes such as dissolution/precipitation, ion
exchange, sorption and interactions with biota in the vadose zone.
140
-------
Since it has been shown that there are spikes in SPC, calcium, and sodium concentration, and road salt (halite)
was used as a deicing agent, the dissolution of halite was likely the dominant geochemical process governing
the sodium concentrations in the vadose zone. Figure 8-47A is a plot of sodium verses chloride. Most of the data
are above the 1:1 line indicating that there was an excess of sodium compared to chloride in the soil porewater.
This suggests that ion exchange or aluminosilicate weathering controlled the sodium concentrations (Schoeller,
1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). Some
of the data, generally correlating to the samples with spikes, did lie below the 1:1 line, and chloride is enriched
with respect to sodium in the porewater. This suggests reverse ion exchange - that is, sodium in the porewater
replacing calcium and magnesium on the solids in the vadose zone (Schoeller, 1965, Schoeller, 1967, Kortatsi,
2007; Zaidi et al, 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). The gray shaded data highlights where
the spikes in concentrations of sodium and chloride were observed (Figure 8-47A). Most of the data when
spikes were occurring suggests that reverse ion exchange was occurring. This makes sense since chloride the
conservative species does not interact with the surrounding media and would have been in excess compared
with sodium that does interact with the surrounding media. In this case, some of the sodium was removed from
solution. Figure 8-47B is a plot of sodium + potassium verses total cations. Except for three porewater samples
all the data was below the y = 0.5X trendline, and this suggested that silicate weathering was not a dominant
geochemical process affecting sodium concentrations in the vadose zone (Figure 8-47B) and ion exchange
processes likely dominate (Batabyal and Gupta, 2017; Ndoye et al., 2018).
100
10
C
CD
E
E
TD
O
W
0.1
0.01 0.1 1 10 100
Chloride (meq/L)
0 25 50 75 100 125 150 175 200
Total Cations (meq/L)
Figure 8-47. A. Sodium vs. chloride. B. Sodium + potassium vs. total cations. In both plots the black circles are FRLW01
data, black triangles are FRLW02 data, green circles are FRLW03 data, green diamonds are FRLW05 data, blue circles
are FRLW06 data, blue triangles are FRLW07 data, blue diamonds are FRLW08 data, red circles are FRLWQ9 data, red
triangles are FRLW10 data, and cyan triangles are FRLW12 data. Gray shaded area in A shows the chloride and sodium
data during spike events.
141
Exchange
-------
Figure 8-48A is a plot of calcium + magnesium verses bicarbonate + sulfate. Data falling on the 1:1 trend line
indicated that calcite, dolomite, and gypsum are likely controlling the Ca and Mg concentrations in the soil
porewater (Schoeller, 1965, Schoeller, 1967, Kortatsi, 2007; Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et
al., 2018). Most of the soil porewater data fell on this line indicating the dominant geochemical process controlling
calcium and magnesium concentrations in the soil porewater was precipitation/dissolution. Several samples lie
above the 1:1 trend line which suggests that ion exchange or mineral weathering was responsible for the excess
calcium and magnesium in these porewater samples (Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et al.,
2018). In very few cases bicarbonate + sulfate was in excess and there was depletion of calcium + magnesium in
those porewater samples. This indicated that reverse ion exchange was occurring, i.e., calcium and magnesium
replacing sodium or potassium on the solid phases in the vadose zone (Schoeller, 1965, Schoeller, 1967, Kortatsi,
2007; Zaidi et al., 2015; Batabyal and Gupta, 2017; Ndoye et al., 2018). A plot of magnesium versus calcium
is shown in Figure 8-48B. Most of the study data plots below the 1:1 trend line, which suggests that calcite is
the dominant carbonate phase controlling the Ca and Mg concentrations in the porewater samples (Ndoye et
al., 2018). There are a few samples that indicate (points above the 1:1 trend line) dolomite was the dominant
carbonate phase. Figure 8-48C is a plot of the dolomite saturation index (SI) versus the calcite SI. Most of the soil
porewater samples were likely in equilibrium with either calcite or dolomite solid phases present in the vadose
zone (Figure 8-48C). There were a few samples that were oversaturated with respect to calcite and dolomite
(Figure 8-48C). The excess Ca or Mg in this case would be available to participate in other geochemical processes,
such as ion exchange, occurring in the vadose zone or potentially be transported to the groundwater (Sposito,
1989; Hounslow, 1995; Batabyal and Gupta, 2017). Calcium concentrations can also be controlled by gypsum if
gypsum was present in the vadose zone. Figure 8-48D shows a plot of the gypsum SI for the porewater data. The
soil porewater was undersaturated with respect to gypsum.
40 GO » t|» 120 140
OioirfcanHto + Sulfate (rnnqil |
20 25 W
Ciriciiam (mr-^1 )
c
L-jkt# Lrcknjlurjfrd ¦
Do4*m.1r awrrului ji.rd ¦
¦
¦
i
Li
*
<_ikiLr cwmt.rjlrd
Un'ofTiiir nvrruluuerd
P
rwut^m r
-4 -2 0 3 4
Caiete SI
Figure 8-48. A. Plot of calcium + magnesium vs. bicarbonate + sulfate. B. Plot of magnesium vs. calcium. C. Plot of dolomite SI vs.
calcite SI. D. Plot of Gypsum SI. In all plots the black circles are FRLW01, black triangles are FRLW02, green circles are FRLW03, green
diamonds are FRLW05, blue circles are FRLW06, blue triangles are FRLW07, blue diamonds are FRLW08, red circles are FRLW09, red
triangles are FRLW10, and cyan triangles are FRLW12. In plot C the gray shaded areas indicate equilibrium with a calcite or dolomite,
yellow shaded area indicates that calcite is undersaturated and dolomite is oversaturated, green shaded area indicates both calcite
and dolomite are oversaturated, blue shaded area indicates both calcite and dolomite are undersaturated, and purple shaded area is
oversaturated with respect to calcite and undersaturated with respect to dolomite. In D the gray shaded area indicates equilibrium
with gypsum.
142
-------
Figure 8-49A is a plot of CAI1 verses CAI2. As is shown in this figure, most of the CAIs are negative suggesting ion
exchange reactions were the dominant process overall. However, there were also limited positive CAIs, occurring
mostly in 2018 when the major spikes in major anions and cations occurred. The positive CAIs indicated that
reverse ion exchange was occurring late in the study, meaning sodium and potassium are moving to the vadose
zone solids. If this trend continues, this would suggest that sodium loading on the vadose zone solids could occur
and this could be problematic. Excess sodium could cause dispersion of the vadose zone solids, which could limit
infiltration of the stormwater into the subsurface.
A plot of sodium + potassium - chloride versus calcium + magnesium - bicarbonate - sulfate is shown in Figure
8-49B. Data that plots on or close to the trend line y = -x indicates that ion exchange reactions are the dominant
process (Zaidi et al., 2015, Batabyal and Gupta, 2017; Ndoye et al. 2018). Most of the soil porewater data plotted
on this line, which indicated that ion exchange reactions are a dominant geochemical process in the vadose zone.
O -60
10 12
O
I
+
-80-
-100-
-120
i-i—
60 30 100
Ca+Mg-HC03-S04
Figure 8-49. Plots indicating the significance of ion exchange reactions. A. Plot of CAI1 vs. CAI2. B. Plot of
Na+K-CI vs. Ca+Mg-HC03-S04. The dashed black lines in both plots represent the origin of the plots and
divides the negative and positive values. In all plots the black circles are FRLW01, black triangles are FRLW02,
green circles are FRLW03, green diamonds are FRLW05, blue circles are FRLW06, blue triangles are FRLW07,
blue diamonds are FRLW08, red circles are FRLW09, red triangles are FRLW10, and cyan triangles are FRLW12.
The major geochemical processes that were occurring that were modifying the infiltrated stormwater are
precipitation/dissolution reactions and ion exchange reactions. As the infiltrated stormwater was modified, the
modified stormwater (soil porewater) moved through the vadose zone.
143
-------
8.8.9.2 Other Soil Porewater Constituents
In addition to the major anions and cations discussed above, other parameters were also studied. Most of
these parameters did not show any significant changes or trends that warranted further discussion; however,
parameters such as barium, uranium, nitrate + nitrite, and fluoride will be discussed in this section.
8.8.9.2.1 Barium - Soil Porewater
Barium time series plots for each SPW cluster are shown in Figure 8-50. Barium in FRLW12 (Cluster 5) showed
little to no change in barium concentration. In cluster 1 (Figure 8-50A), FRLW01 showed data spikes in barium
concentrations similar to what was observed for calcium in FRLW01. The deeper SPW sampler, FRLW02, showed
little change in barium concentration. In SPW cluster 2, FRLW09 showed similar trends in barium concentration
changes as was seen in FRLW01 (Figure 8-50B). FRLW10 appeared to behave like FRLW02 for the limited data
obtained in this SPW sampler (Figure 8-50B). Because of the very limited data for SPW cluster 3 (Figure 8-50C),
no analysis was performed. The pattern in barium concentrations in cluster 4 was initially different than the
pattern seen for calcium in the same cluster initially (Figure 8-50D). A significant rise in barium concentration was
observed in the fall and winter of 2015 - 2016. FRLW07 and FRLW08 appeared to have a similar concentration
pattern to what was observed for calcium in these SPWs (Figure 8-50D). Because of the lack of data in FRLW06
from 2017 - 2018, no analysis was performed for barium in FRLW06 (Figure 8-50D). Between the winter of 2016
and spring of 2018, the concentrations of barium showed little change in FRLW07 (Figure 8-50D).
U1O017
M-Z01 B
£
B
i
/1
/ \
t 1
/ \
1
/ 1
/ \
/ 1
/ i
i
i
/ 1
0
V1S017
Date
1200-
c
1200-
10M-
1000-
i
600-
1
fiac-
E
600 -
E
Zf
600-
ID
DQ
T
CO
m
400-
+00 -
200 -
i ¦
200 «
0-
0-
in,-2013
Dale
i/iooi6
1S1J3Q17
Figure 8-50. Changes in barium concentration in relationship to time for the Fort Riley SPWs. In all graphs
the cyan triangles and lines represent FRLW12 the unimpacted control. A. SPW cluster 1, black circles and
lines are FRLW01, black triangles and lines are FRLW02; B. SPW cluster 2, red circles and lines are FRLW09,
red triangles and lines are FRLW10; C. SPC cluster 3, green circles and lines are FRLW03, green diamonds and
lines are FRLW05; and SPW cluster 4, blue circles and lines are FRLW06, blue triangles and lines are FRLW07,
blue diamonds and lines are FRLW08.
-------
Barium concentrations were generally largest when there were chloride concentration spikes. Plot 8-51 is a plot
of barium versus chloride for the SPW data. Most of the barium concentrations are clustered together when
chloride concentrations were in their normal range. But when chloride spikes occurred, as happened in the
spring of 2018, then the barium concentrations increased. The two largest concentrations of barium on this plot
correspond to the two highest chloride concentrations observed in this study.
Based on the analysis above, when excess chloride concentrations were not a factor, barium concentrations
were likely controlled by the solid phases through dissolution/precipitation, sorption, and ion exchange. It has
been reported that barium is more mobile in the presence of chloride and is more easily leached (Madejon,
2013). Therefore, barium movement downward in the vadose zone was dominantly caused by spikes in chloride.
At this study site, barium transport was mainly caused by the application of de-icing salts on the parking lot
and sidewalks. When excess chloride is not present, the transport of barium in the vadose zone would likely be
minimal and not a factor affecting groundwater quality.
0.020
0.015
? 0.010
m 0.005
0.000
Figure 8-51. A plot of barium vs.
chloride. Black circles are FRLW01,
black triangles are FRLW02,
green circles are FRLW03, green
diamonds are FRLW05, blue
circles are FRLW06, blue triangles
are FRLW07, blue diamonds are
FRLW08, red circles are FRLW09,
and red triangles are FRLW10.
200
Chloride (meq/L)
8.8.9.2.2 Uranium-Soil Porewater
Uranium was consistently detected in the groundwater with some samples exceeding regulatory limits. Because
of this, it was important to understand the role played by the Gl and vadose zone in uranium mobility at the
study site. Figure 8-52 is a plot of uranium concentrations with respect to time for the SPW samples at this site.
In SPW cluster 1 (Figure 8-52A), there was little change in uranium concentrations during the study. FRLW02, on
the other hand, initially had high concentrations of uranium, but by August 2016, the uranium concentrations
stabilized. There was little change in uranium concentration for the remainder of the study. FRLW09, in SPW
cluster 2, was similar to FRLW01 with little change in uranium concentrations with time (Figure 8-52B). FRLW10
had initial uranium concentration lower than FRLW02, and even though there are missing data points, it appears
that the uranium concentration stabilized by 2016 and through September 2017 did not appear to increase in
concentration. There was no uranium data for FRLW10 after September 2017. SPW clusters 1 and 2 are directly
below the infiltration gallery. Cluster 3 did not have enough data to predict uranium trends (Figure 8-52C).
FRLW06, in cluster 4, initially had large uranium concentrations, and the concentrations decreased until August
2016. Between August 2016 and September 2018, there were no metal samples collected in FRLW06. Uranium
concentrations in FRLW07 after September 2015 appeared to oscillate around a mean concentration, and overall
the changes in uranium concentration were minor (Figure 8-52C). The uranium concentration in FRLW08 was
high initially and dropped until August 2016 (Figure 8-52C). After August 2016 there were only a few data points,
145
-------
but in June and September 2018, the uranium concentrations were lower than in August 2016, which suggests
that the uranium concentrations were stable after August 2016. Finally, in SPW cluster 5, FRLW12, uranium
concentration increased and peaked in March 2016 and stabilized by August 2016 (Figure 8-52). After August
2016 the uranium concentrations leveled off and there was little change in uranium concentrations for the
remainder of the study.
600 -
500-
. J
Ch
=L
4Q0-
£
300 -
c
ra
3
200 -
100-
0-
1/1/2015
200
%> 150
£
3
C
CO
1/1/2016
1/1/2017
Date
1/1/2016
100-
1/1/2015
171/24516
1/1/2017
Date
1/1/2018
200 -
150-
I 100-
50-
1/1/2015
£
3
C
1/1/2016
1/1/2017
Date
i/i/20ia
200
100
1/1/2015
1/1/2016
1/1/2017
Date
1/1/2018
Figure 8-52. Changes in uranium concentration in relationship to time for the Fort Riley SPWs. In all graphs
the cyan triangles and lines represent FRLW12 the unimpacted control. A. SPW cluster 1, black circles and
lines are FRLW01, black triangles and lines are FRLW02; B. SPW cluster 2, red circles and lines are FRLW09,
red triangles and lines are FRLW10; C. SPC cluster 3, green circles and lines are FRLW03, green diamonds and
lines are FRLW05; and SPW cluster 4, blue circles and lines are FRLW06, blue triangles and lines are FRLW07,
blue diamonds and lines are FRLW08. The red dashed line indicates the MCL for uranium.
The initial high uranium concentrations in the SPW samples were likely caused by installing the SPW samplers.
The installation disturbed the vadose zone materials and used potable water to make the slurry. The installation
process likely disrupted the existing equilibrium and caused the large uranium concentration before August 2016.
After August 2016, it was not likely that the transport of uranium through the vadose zone to the groundwater
was a major process affecting the groundwater uranium concentrations.
146
-------
8.8.9.2.3 Fluoride - Soil Porewater
Figure 8-53 is a plot comparing the changes in fluoride concentration with the changes in chloride concentration
during the study in the shallowest SPW samplers under the gallery (FRLW01 and FRLW09). The spikes in fluoride
concentrations took longer to appear compared to the spikes in the chloride concentrations. This suggests that
the movement of fluoride was retarded compared to chloride. This was not surprising since fluoride is generally
not considered a conservative tracer. The other point was that the major dips in the fluoride concentrations
occur with the chloride spikes. The smaller fluoride concentrations were less than the quantification limit and
their concentrations are estimates. Because of the two reasons presented, the use of Cl/F ratios for tracking
deicing sources was limited and likely not useful.
-6C DO
-400
1/1/2015
1/1/2016
1/1/2017
Date
1/1/201S
Figure 8-53. Plot comparing
fluoride concentration and
chloride concentration vs
time for the SPW data in
the Fort Riley Study. Solid
lines represent the fluoride
concentrations, dashed
lines represent the chloride
concentrations, black circles
are FRLW01, red circles are
FRLW09, and cyan triangles
are FRLW12.
8.8.9.2.4 Nitrate + Nitrite-Soil Porewater
Figure 8-54 shows the nitrate + nitrite concentrations versus time for the SPW data. In cluster 5, FRLW12, nitrate
+ nitrite concentrations were small (less than 1 mgN/L) between September 2015 and August 2016 (Figure 8-54).
Between August 2016 and June 2017, there were no nitrate + nitrite data available, but in June 2017, the nitrate
+ nitrite concentrations were larger than 1.5 mg N/L (Figure 8-54). The nitrate + nitrite concentrations peaked
in March 2018 at 4.34 mg N/L and was still larger than 3 mg N/L in September 2018. In SPW cluster 1 (Figure
8-54A), after the initial sampling the nitrate + nitrite concentrations declined from 2.66 mg N/L to less than 1
mg N/L during the remainder of the study period. In FRLW02 (Figure 8-54A) the nitrate + nitrite concentrations
were between 1 and 2 mg N/L in 2015 but were less than 1 mg N/L for the remainder of the study. In Cluster 2,
FRLW10, with the exception of September 2015 nitrate + nitrite concentrations were less than 1 mg N/L (Figure
8-54B). In FRLW09, most of the nitrate + nitrite concentrations were less than 1 mg N/L except for August 2016,
September 2017, and March 2018 where the concentrations of nitrate + nitrite were > 1 mg N/L and this highest
concentration was 1.15 mg N/L in March 2018 (Figure 8-54B). In SPW cluster 3, there were very few data points
for nitrate + nitrite concentrations and they ranged from 0.49 - 2.09 mg N/L (Figure 8-54C). Initially, in FRLW06
(cluster 4), the nitrate + nitrite concentration was 7.82 mg N/L and decreased rapidly until April 2016 (Figure
8-54D). The nitrate + nitrite concentration appeared to spike in March 2017 (8.80 mg N/L) and decreased to >
1 mg N/L for the rest of the study (Figure 8-54D). The concentration of nitrate + nitrite in FRLW07 decreased
from 1.96 in September 2015 and remained less than 1 mg N/L until August 2017, when the nitrate + nitrite
concentration increased to 5.26 mg N/L (Figure 8-54D). The nitrate + nitrite concentrations then decreased until
the end of the study in FRLW07, and in September 2018, the nitrate + nitrite concentrations were 1.91 mg N/L.
Finally, in FRLW08, the nitrate + nitrite concentrations appeared to spike in September 2015, March 2016, March
2017 and March 2018 (Figure 8-54D). Except for September 2015, it appears that there was a flush of nitrate +
nitrite each spring in FRLW08 (Figure 8-54D).
-------
1/1/2015
1/1/2016
1/1/2017
Dale
1/1/2018
1/1/2015
1/1/2016
1/1/2017
Date
1/1/20 IS
en
E
Z
+
g
rn
1/1/2015 1/1/2016 1/1/2017 1/1/2018
Dale
CB
E
Z
t-
s
re
\
1/1/2018
1/1/2015
1/1/2016
1/1/2017
Date
Figure 8-54. Changes in nitrate + nitrite concentration in relationship to time for the Fort Riley SPWs. In all
graphs the cyan triangles and lines represent FRLW12 the unimpacted control. A. SPW cluster 1, black circles
and lines are FRLW01, black triangles and lines are FRLW02; B. SPW cluster 2, red circles and lines are FRLW09,
red triangles and lines are FRLW10; C. SPC cluster 3, green circles and lines are FRLW03, green diamonds and
lines are FRLW05; and SPW cluster 4, blue circles and lines are FRLW06, blue triangles and lines are FRLW07,
blue diamonds and lines are FRLW08. The red dashed line indicates the MCL for nitrate + nitrite.
The largest concentrations of nitrate + nitrite were in SPW clusters 4 and 5. This is likely because these SPW
clusters are in grassy areas that were possibly subject to fertilization. Also, cluster 4 was in a low spot
topographically where ponding was observed. Any fertilizer would runoff to this low point and infiltrate through
the vadose zone.
8.8.9.2.5 Stormwater Contaminants
The NURP and NSQD have identified several inorganic contaminants and organic contaminants that are
important to evaluate when infiltrating stormwater in a Gl system (U.S. EPA, 1983; NSQD, 2015). Summary of
NURP and NSQD inorganic compounds as well as other trace metals are shown in Table 8-3. Summaries of NURP
and NSQD organic contaminants are shown in Appendix D, Table D-3 (halogenated aliphatics and aromatics),
Appendix D, Table D-4 (PAHs), Appendix D, Table D-5 (pesticides), and Appendix D, Table D-6 (phenols, ethers,
and phthalates).
The geogenic background concentration ranges for the stormwater contaminants in the SPW were not known. It
is likely that for most of the inorganic compounds and trace metals the concentrations measured were within the
background ranges (Table 8-3). Nitrate + nitrite, barium and uranium were discussed previously and will not be
discussed here. Aluminum, initially high in most of the SPW samplers, was high in the December 2015 sampling.
148
-------
In FRLW06 the concentrations for arsenic were much greater than in the other SPW samplers throughout the study.
For chromium FRLW06 and FRLW08 had greater concentrations than the other SPW samplers. The September
2015 sampling had greater concentrations of cobalt (FRLW12), copper (FRLW03, FRLW05, FRLW06, and FRLW09),
manganese (FRLW01, FRLW07, and FRLW12) and were similar to concentrations in other SPWs for the rest of the
study. In the case of FRLW12 the nickel concentration was greater than other SPWs throughout the study. Zinc was
only detected in FRLW01 and the zinc concentration was the highest in September 2015 and the zinc concentration
decreased throughout the study.
For the halogenated aliphatics and monocyclic aromatics, only 1,2-dichlorbenzene was detected in 10 % of the
samples. The source of the 1,2-dichlorobenzene is not known but is commonly used for cleaning metal surfaces
and synthesis of pesticides. There was no detectable PAHs or pesticides in the SPW samples. In the case of
phenols, ethers, and phthalates in the SPW samples, 2-4-Dichlorophenol was detected in one sample, phthalates,
butylbenzylphthalate (15 % of samples), diethylphthalate (13 % of samples), and di-n-butylphthalate (5% of
samples). 2-4-dichlorophenol is a known degradation product of the herbicide 2,4-D and the antimicrobial triclosan
(Daughetry and Karel, 1994; Latch et al., 2005). Phthalates are ubiquitous in the environment, so it is not surprising
that these were detected (Griffiths et al., 1985; Serodio and Nogueira, 2006; Stiles et al., 2008; Teil et al., 2013).
Table 8-3. Summary of trace components from the Fort Riley soil porewater samples.
Ammonia
Total
Nitrogen
Phosphate
Ag
Al
As
Ba
Be
Cd
Co
Cr
Cu
Units
mg N/L
mg N/L
mg P/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
M-g/L
Hg/L
Hg/L
Hg/L
Total Number
of Analyses
94
47
121
83
83
83
83
83
83
83
83
83
Number of
Detects
9
47
16
4
82
83
83
5
2
11
67
74
Percent Detects
10
100
13
5
99
100
100
6
2
13
81
89
Mean
0.16
1.15
0.06
1.0
15
5.6
223
0.6
1.0
1.6
1.4
1.2
Standard
Deviation
0.19
0.91
0.06
0.0
28
5.0
201
0.0
0.6
2.8
0.7
0.7
Minimum
0.00
0.01
0.01
1.0
2
1.0
19
0.6
0.6
0.6
0.5
0.5
25th Percentile
0.04
0.50
0.03
1.0
6
3.1
95
0.6
0.8
0.7
0.9
0.7
Median
0.07
0.90
0.05
1.0
9
4.2
170
0.6
1.0
0.8
1.2
1.1
75th Percentile
0.15
1.55
0.07
1.0
15
6.4
295
0.6
1.3
0.9
1.7
1.5
Maximum
0.52
3.67
0.47
1.0
247
30
1198
0.6
1.5
10
3.6
5.0
Fe
Mn
Mo
Ni
Pb
Sb
Se
Th
Tl
U
V
Zn
Units
Mg/L
Hg/L
Mg/L
Mg/L
Hg/L
M-g/L
Hg/L
Hg/L
M-g/L
Hg/L
Hg/L
Hg/L
Total Number
of Analyses
83
83
83
83
83
83
83
83
83
83
83
83
Number of
Detects
2
37
71
80
3
66
64
28
6
82
82
10
Percent Detects
2
45
86
96
4
80
77
34
7
99
99
12
Mean
139
46
8.5
3.2
0.6
0.8
11.6
0.6
0.4
40
92
285
Standard
Deviation
115
108
4.9
2.5
0.1
0.2
17.0
0.1
0.2
70
111
287
Minimum
58
0.5
1.1
0.5
0.5
0.5
0.8
0.5
0.2
1.4
7.4
75
25th Percentile
99
0.7
4.9
1.6
0.6
0.7
1.9
0.5
0.2
7.3
25
102
Median
139
1.6
8.1
2.4
0.7
0.8
3.5
0.5
0.5
23
45
190
75th Percentile
180
11
12
4.7
0.7
0.9
11.9
0.6
0.6
48
121
350
Maximum
220
497
24
11.1
0.7
1.5
69
1.0
0.6
576
548
1030
149
-------
8.8.9.3 Soil Porewater Organic Constituents
Organic analysis performed on the soil porewater samplers was for semi-volatile compounds (SVOCs) since the
sampling method for soil porewater samples requires a vacuum and this would cause errors in VOC analysis. The
classes of compounds tested for in the SVOCs were chlorinated compounds, hormones, phthalates, surfactants,
PAHs, pesticides, solvents, antimicrobials, and plasticizers. Most of the compounds were not detected, and
detected compounds were not above their MCLs or other known regulatory limits. The detected compounds
are shown in Table 8-4. The compounds detected in this study fall into a few categories: pesticides, chlorinated
compounds and phthalates. The most commonly detected compound was diethyltoluamide (DEET). DEET was
detected in 70 % of the samples and the detection was likely caused by sample contamination during sampling.
Atrazine and butylbenzylphthalate were detected in 15 % of the samples. Ten percent of the samples collected
had 1,2-dichlorobenzene, diethylphthalate, and metolachlor detected. In 7 % of the samples, 2,4-dichlorophenol
was detected, and in 5 % of the samples, di-n-butylphthalate was detected.
Table 8-4. SVOC compounds detected in the soil porewater samples collected at the Fort Riley study site.
Analyte
Units
MCL
Screening
Level1
N
Detects
Detection
Frequency (%)
Mean
o
Median
Min
Max
1,2-Dichlorobenzene
Hg/L
20
2
10
0.031
0.030
0.031
2,4-Dichlorophenol
Hg/L
46
15
1
7
0.270
Atrazine
Hg/L
3
30
20
3
15
0.077
0.042
0.120
0.077
0.160
Butylbenzylphthalate
Hg/L
16
20
3
15
0.027
0.002
0.027
0.025
0.030
Diethylphthalate
Hg/L
15,000
20
2
10
0.755
Diethyltoluamide (DEET)
Hg/L
20
14
70
0.303
0.671
0.114
0.025
2.600
Di-n-butylphthalate
Hg/L
900
20
1
5
0.290
Metolachlor
Hg/L
2,700
20
2
10
0.135
'U.S. EPASuperfund screening levels for the protection of groundwater (U.S. Environmental Protection Agency, 2019).
8.8.10 Vadose Zone Summary
Water moved from the infiltration gallery through the vadose zone relatively quickly depending on the intensity
and duration of the precipitation event. It was also shown that monitoring water level rises in the gallery was
not the best method for gauging if infiltration events occurred. To have water levels rise in the gallery, the rate of
infiltration at the surface of the gallery had to be greater than the rate of exfiltration out the bottom and sides of
the infiltration gallery. If this condition was not met, then there would be no rise in water levels in the infiltration
gallery. Not meeting this condition likely happened on a regular basis.
The infiltrated water would reach the SPWs near the gallery typically in hours to a day following the precipitation
event. It was also shown that from the gallery walls there was a 3-m effective radius of influence of the infiltrated
water in the vadose zone. SPWs farther way did not show any influence of the infiltrated water in the vadose
zone.
The chemical and physical properties investigated in this study indicated that the clay and silt content in the
sediments were predominately from the surface to a depth of approximately 6 m. This correlated well with
other properties that depended on the clay and silt content of the sediments such as CEC, total aluminum,
total calcium, total iron, total manganese and TOC. The SPWs were all positioned in this depth interval with
the deepest at 322 m-msl. The importance of this was that geochemical processes that could modify the
infiltrated stormwater would be primarily occurring in this part of the vadose zone, so any contaminant reaching
the deepest SPWs would be more likely to move to the groundwater. Because CEC, total aluminum, total
calcium, total iron, total manganese and TOC were the largest in the vadose zone where the SPW samplers
were positioned, it was not surprising to find that ion exchange was an important mechanism for the fate and
transport of analytes through the vadose zone.
150
-------
Precipitation/dissolution was also indicated as a major geochemical process that occurred in the vadose zone.
This would be especially true in the cases of calcium, magnesium, and bicarbonate.
Chloride and sodium moved relatively quickly into the vadose zone following runoff after the application of
de-icing agents. Chloride quickly moved below the SPW samplers as was indicated by the changes in chloride
concentrations. Sodium concentrations did not return to background quickly, which suggests that sodium was
being retained in the vadose zone.
A potentially important ion exchange mechanism that could potentially affect the Gl was reverse ion exchange.
Reverse ion exchange is the process where sodium and potassium in solution replaces calcium and magnesium
on the solids liberating calcium and magnesium into solution. In 2018, the data analysis indicated that reverse
ion exchange was potentially occurring in the vadose zone. If this process continued, this could cause dispersion
of the fine grain materials such as clay in the vadose zone. This process could potentially contribute to plugging
of the pores in the vadose zone and negatively affect water exfiltration from the storage gallery.
Although barium is not likely to be a groundwater problem, the interaction of barium with chloride is worthy
to note as a mechanism that could mobilize other metals. Barium in the presence of chloride was more mobile.
This was because barium likely formed stable solution complexes with chloride which makes barium more
soluble. Although barium is not a problem in this case, in areas with higher barium concentrations in the soils
and sediment of the vadose zone, the application of de-icing agents (for example) could create water quality
problems with the increased soluble barium. An example of another metal that would be expected to behave
similarly in the presence of chloride is cadmium (not evidenced in this study). Cadmium also forms stable
solution complexes with chloride and these complexes enhance the mobility of cadmium (Smolder and Mertens,
2013). The use of Gl in areas with high cadmium concentrations or other contaminants that can form soluble
complexes with chloride at the surface or in the vadose zone should be discouraged, especially in areas where
de-icing agents are used.
8.8.11 Groundwater
All groundwater quality data can be found in Appendix E, Tables E-14 - E17.
8.8.11.1 Tracking Infiltrated Water in Groundwater - Stable Isotopes of Water
The stable isotopes (6180 and 62H) of water can potentially be used to track infiltrated water in groundwater.
Figure 8-55 shows a plot of 6180 versus 62H for the groundwater samples. The gray shaded region in this figure
indicates where the background well data plotted, the yellow shaded region where the upgradient well data
plotted, and the cyan shaded area indicated the region where the precipitation data plotted. The upgradient data
and the background data show considerable overlap, meaning that the isotopic composition of these waters
was very similar. A large portion of the downgradient data was clustered in the same region as the upgradient
and background data. This indicated that these data had similar isotopic composition; however, there were
downgradient data that did not plot the same as the background or upgradient data. FRGW04 (the well closest
to the infiltration gallery) had the most points that plotted outside the background and upgradient regions.
FRGW03 and FRGW06, farther from the storage gallery than FRGW04, had several points outside this cluster.
This suggested that the 6180 and 62H could be useful in tracking the infiltrated water in the groundwater.
151
-------
-30-
o
o
-40-
o
CM
to
-50-
-9 -8 -7 -6 -5 -4
5180 (%o)
Figure 8-55. Plot of 6lsO versus 62H for the groundwater collected at the Fort Riley Gl study site. Gray shaded
regions show where the background well samples plotted. The yellow shaded regions show where the
upgradient wells plotted. The cyan shaded regions show where the precipitation samples plotted. The black
line is the Global Meteoric Water Line (Craig, 1961a,b). The red line is the Kansas Meteoric Water Line (Kendal
and Coplen, 2001). FRGW03 are green circles, FRGW04 are blue circles, FRGW06 are magenta circles, FRGW07
are dark yellow circles, FRGW09 are wine-colored circles, and FRGW13 are pink circles.
152
-------
Not only were the changes in isotopic composition with time for the groundwater needed, but the isotopic
composition of the SPW was needed as well. Because of the limited SPW samples, isotopic analysis was not
done on many of the samples collected. The only SPW samplers that had mostly complete time series isotopic
compositions were FRLW01 and FRLW02. As was discussed previously, these SPW were the shallowest SPWs in
cluster 1 and cluster 2 directly beneath the infiltration gallery. Figure 8-56 shows the time series plots for 5180
and S2H in both the groundwater samples and in the SPW samples. The changes in 6180 and S2H groundwater
paralleled any SPW changes. This suggested that there was no preferential fractionation of 5lsO or 62H in
relationship to other isotopes and that 618G and 52H moved at the same rate through the vadose zone to the
aquifer.
Figure 8-56, Plots of the changes in 6lsO and 62H with time for the A. Downgradient samples and B. SPW
samples FRLW01 and FRLW09. Gray shaded region represents the background groundwater isotopic
composition. Green circles and lines are FRGW03, blue circles and lines are FRGW04, magenta circles and
lines are FRGW06, dark yellow circles and lines are FRGW07, wine-colored circles and lines are FRGW09, pink
circles and lines are FRGW13, black triangles and lines are FRLW01, and red circles and lines are FRLW09.
153
-------
Figures 8-57 and 8-58 show the time series data for 6180 and 62H, respectively. For both 6180 and 62H, the peaks
and valleys in the plots for both isotopes are different in time for the SPW and groundwater samples. The peaks
and valleys showed up earlier in the SPW samples than the groundwater. This finding is not unexpected since
the infiltrated stormwater would reach the SPW samplers first, then travel through the remainder of the vadose
zone to the groundwater, and then travel with the groundwater to the wells. For example, in Figure 8-57, there
was a peak in 6180 in September 2015 in SPW samplers FRLW01 and FRLW09. The first peak in the groundwater
was in FRGW04 in December of 2015 followed by a peak in FRGW06 in April 2016. This was also the same in
62H in Figure 8-58. An example of a valley in the time plot was FRLW01 in December 2015 (decreasing 6180
and 62H composition in FRLW09) and was still present in FRLW01 and FRLW09 in March and April 2016 (Figures
8-57 and 8-58). The valley in FRGW04 and FRGW03 occurred in May of 2016 and continued through August
2016. In FRGW06, the valley did not appear until February 2017. From these examples, the transport times in
groundwater were different and since there was no peak in FRGW03 in the first example this could represent
differences in groundwater flow velocity and groundwater flow directions.
During the infiltration events, Figures 8-57 and 8-58, the magnitude of the change was generally larger in the
SPW samples than in the groundwater. The probable reason for this was that as the infiltrated stormwater
reaches the groundwater it was diluted by the formation groundwater changing the isotopic composition of the
infiltrated groundwater - which is clearly seen in FRGW04. As the mixed water moves further downgradient,
it mixed with more formation water, further changing the isotopic composition of the infiltrated groundwater
as seen in FRGW03 and FRGW06. Dilution of the infiltrated stormwater by the formation groundwater was
likely the reason why no changes in the isotopic composition were observed in any of the samples in FRGW09
(background isotopic composition) during the study. That is, there was an effective distance through which the
infiltrating stormwater can be tracked.
Kendal and Coplen (2001) stated that the isotopic composition of precipitation was affected by seasonal
differences. In general, the 6180 and 62H values are more positive (higher) in the summer than in the winter. In
the winter, the 6180 and 62H values are more negative (lower). As is shown in Figures 8-57 and 8-58, in both SPW
the higher values were in September 2015, August 2016, September 2017, and June 2018, the warmer months
of the year. Conversely, the lower values in the SPWs were in December 2015, March - April 2016, March 2017,
and March 2018, the colder months of the year. However, since there was a lag time in the transport of the
infiltrated stormwater to and through the groundwater, the higher and lower trends were not seen until later in
the monitoring wells. This also suggests that when the infiltrated stormwater has isotopic composition similar
to the ambient isotopic composition of the groundwater the infiltrated stormwater cannot be tracked. This is a
potential limitation to the use of 6180 and 62H for tracking infiltrated stormwater.
-2
-3-
A
Figure 8-57. Changes in 6lsO with time for
the SPW and downgradient groundwater
in the Fort Riley study. Gray shaded region
represents the background groundwater
isotopic composition. Green circles and
lines are FRGW03, blue circles and lines
are FRGW04, magenta circles and lines
are FRGW06, dark yellow circles and lines
are FRGW07, wine-colored circles and
lines are FRGW09, pink circles and lines
are FRGW13, black triangles and lines are
FRLW01, and red triangles and lines are
FRLW09.
¦4-
-5-
-6-
al -7-
-9-
-10-
-11 -
-12-
-13 -h-r-r
1/1/2015
1/1/2016
1/1/2017
1/1/2016
Date
-------
-10-
Figure 8-58. Changes in S2H with time for
the SPW and downgradient groundwater
in the Fort Riley study. Gray shaded region
represents the background groundwater
isotopic composition. Green circles and
lines are FRGW03, blue circles and lines
are FRGW04, magenta circles and lines
are FRGW06, dark yellow circles and lines
are FRGW07, wine-colored circles and
lines are FRGW09, pink circles and lines
are FRGW13, black triangles and lines are
FRLW01, and red triangles and lines are
FRLW09.
-70-
i t I
i ' I
¦80 -
1/1/2015 1/1/2016 1/1/2017 1/1/2018
Date
8.8.11.2 Background Groundwater Quality
No background groundwater sampling was conducted before the construction of the parking lot. Therefore, a
literature and database search were conducted to gather more groundwater quality information for this study
site.
The only literature found for groundwater in the Fort Riley area were two USGS reports (Myers et al. 1996,
Myers et al., 1999), but these only dealt with the hydrology. The background groundwater quality sources were
the USGS NWIS (USGS, 2016) and the USGS National Uranium Resource Evaluation database (USGS, 2004). Only
groundwater data from outwash or alluvial aquifers within a 10 km radius of the study site were used. The total
number of data points was 112.
Both the monitoring wells (GW) and the piezometers (PW) wells were compared against the USGS groundwater
quality data. The results of statistical analysis comparing USGS groundwater quality data to GW and PW data
are given in Appendix D Table D-7. Most parameters in the USGS databases that could be compared with the
study data were significantly different. There were no statistical differences in the GW wells for the following
parameters: fluoride, barium, manganese, sodium, nickel, and vanadium. In the case of the PW samples, 70 %
of the parameters in the USGS databases were significantly different. The parameters that had no significant
differences with the PW wells were DOC, iron, potassium, lithium, molybdenum, nickel, selenium, strontium, and
vanadium. Because of the large number of parameters in USGS databases that were statistically different from
the study data, it was determined that the USGS databases would not be suitable for describing the background
water quality in this study.
Other methods have been proposed to delineate background in geochemical data (Matschullat et al.,
2000; Reimann et al., 2008). All these techniques were applied to the study data and based on this analysis
the Iterative 2a technique was selected to define the background ranges. This choice was based on being
appropriately conservative and yielding consistent results. The results of the statistical comparisons are given
in Appendix D Table D-8. The background ranges of all analytes that will be used in this discussion are given in
Table 8-5. The lower and upper critical values define the background concentration ranges in Table 8-5, and the
percentage of samples included in the background range is also shown.
-------
Table 8-5. Study specific background ranges determined for the Fort Riley Gl Study.
Parameter
Units
N
n
Mean
Std
Dev
Median
Min
Max
Lower
Critical
Value
Upper
Critical
Value
Percent of
samples
Included
Specific
Conductance
US/cm
162
154
1402
221
1408
960
1934
960
1844
95.1
PH
162
159
6.73
0.18
6.72
6.12
7.24
6.37
7.09
98.1
Bicarbonate
mg HC03/L
162
139
563
59
573
416
695
445.62
681.26
85.8
Nitrate +
Nitrite
mg N/L
160
143
0.24
0.34
0.13
0.01
1.58
BDL1
0.92
89.4
Chloride
mg/L
162
144
31.34
12.42
29.6
6.98
83.4
6.5
56.18
88.9
Sulfate
mg/L
162
156
231
101
216
48.0
440
28.45
433.33
96.3
Phosphate
mg P/L
162
152
0.053
0.034
0.055
0.001
0.168
BDL
0.121
93.8
Arsenic
Pg/L
162
144
4.2
1.7
4.4
0.3
9
0.8
7.6
88.9
Barium
Pg/L
162
141
98
21
100
60
164
56
140
87.0
Calcium
mg/L
162
156
216
40.5
218
139
309
135.3
297.3
96.3
Potassium
mg/L
162
158
15.8
5.48
15.2
4.77
27.0
4.85
26.77
97.5
Magnesium
mg/L
162
155
42.5
6.68
42.8
26.3
63.2
29.14
55.86
95.7
Sodium
mg/L
162
153
30.4
8.7
29.4
16.7
53.6
13
47.8
94.4
Nickel
Pg/L
162
148
2.7
1.5
2.7
0.3
6.3
BDL
5.7
91.4
Uranium
Pg/L
162
156
43
26
36
0.3
145
BDL
94.9
96.3
Vanadium
Pg/L
162
154
2.5
2.7
1.8
0.3
12.6
BDL
7.9
95.1
Q.
00
o
%0
162
141
-6.31
0.21
-6.31
-6.77
-5.74
-6.73
-5.89
87.0
d2H
%0
162
146
-39.33
1.48
-39.18
-43.73
-35.19
-42.29
-36.37
90.1
'BDL = below detection limit
8.8.11.3 Monitoring Wells and Piezometers
When the GW wells were compared with the PW wells, it was found that 59 % of the parameters were
significantly different (Appendix D Table D-9). In general, the GW wells were completed at shallower depths than
the PW wells. During three special sampling events, it was determined that there was stratification in the aquifer
in this study. Therefore, since it was expected that the top of the aquifer would be a better indicator of potential
changes to groundwater quality caused by the Gl only the GW wells will be used in this analysis.
156
-------
8.8.11.4 Upgradient Discussion
Preliminary analysis of the data suggested that wells believed to be upgradient of the Gl, FRGW01 and FRGW11,
had much larger concentrations of some parameters than other wells in this study. Because of this, a sampling
event was conducted to examine the upgradient concentration outside the monitoring well network in October
2018. Temporary wells were installed as described in Section 3.1.1.2, and the well locations are given in
Table D-l in Appendix D. Select parameter concentration maps for uranium, antimony, nickel, and arsenic are
shown in Figure 8-59. As can be seen in each concentration map, northwest and west of the Gl study site had
concentrations larger than the study site. This suggested that groundwater flowing towards the study site had
larger concentration than at the study site complicating the data analysis. Therefore, FRGW01 and FRGW11 will
be designated as upgradient wells but will be treated differently than other background wells designated as
background wells (FRGW02, FRGW05, FRGW08, FRGW10, and FRGW12). The wells designated as downgradient
wells (FRGW03, FRGW04, FRGW06, FRGW07, FRGW09, and FRGW13) were wells that are downgradient to the
Gl infiltration gallery based on the general flow direction. The downgradient wells will be compared with the
upgradient wells and the background wells.
"FRGW09
Antimom
Oct. 2018
0.0-0.5
0.5-1.0
1-5
5-10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50
50-55
55-60
60-65
65-70
70-75
75-80
80-90
90-100
100-200
200-300
300-350I
H?0<§
gags®. af®:
mm
Figure 8-59. Concentration maps for the October 2018 upgradient sampling at the Fort Riley Gl study site.
A. Uranium concentration map. B. Antimony concentration map. C. Nickel concentration map. D. Arsenic
concentration map. In all maps north is the upper most edge of the map.
157
-------
8.8.11.5 Major Anions and Cations, pH, Specific Conductivity
The major anion and cation, pH, and SPC will be discussed in this section. It is important to note that data
collection in FRGW01, FRGW02, FRGW03, FRGW04, FRGW05, FRGW06, FRGW07, FRGW08, and FRGW09 was
initiated in September 2015. In wells FRGW10, FRGW11, FRGW12, and FRGW13 the initiation of data collection
began in March 2017.
Detailed analysis of time series and trend analysis for major anions and cations, pH and specific conductivity in
the vadose zone can be found in Appendix D, section 5.2.
8.8.11.6 Major Anion and Cation Geochemical Process
As was discussed in the previous two studies, geochemical processes that can modify the composition of
groundwater quality need to be identified, and their contribution and importance need to be identified to fully
understand the changes to groundwater quality. Several geochemical processes that may participate in the
altering of groundwater quality for major anions and cations are oxidation/reduction reactions, precipitation/
dissolution reactions, and ion exchange reactions.
The relationship of sodium compared to chloride is plotted in Figure 8-60A. The gray shaded regions in this
plot show where the background well groundwater plotted, and the yellow shaded regions show where the
upgradient well groundwater concentrations are plotted. Most of the downgradient data did not plot on the
halite dissolution line suggesting that dissolution/precipitation was not a major process controlling sodium
and chloride concentrations. Much of the downgradient groundwater data plotted in the same region as the
background groundwater with only a few data points for FRGW04 plotted in the same region as the upgradient
groundwater. These correlate with when spikes in chloride concentrations occurred. Generally, the data in this
plot indicated that sodium was in excess compared with chloride suggesting that ion exchange or weathering
reactions were important in controlling sodium and chloride concentrations. Only the upgradient samples and
a few of the downgradient samples indicate that reverse ion exchange was an important process, and when
this occurred in the downgradient wells, the wells were the ones nearest to the infiltration gallery (FRGW04,
FRGW03, and FRGW13).
Figure 8-60B is a plot of sodium + potassium concentration versus the total cation concentration in the sample.
The tan shaded region is where the background well groundwater plotted, and the cyan shaded region is where
the upgradient well groundwater plotted. This figure indicates that silicate weathering is not the dominant
process influencing sodium and chloride concentrations in the groundwater in this study.
158
-------
10
IX
-------
The processes that can affect calcium and magnesium concentrations are shown in Figure 8-61. The plot of
calcium + magnesium verses bicarbonate + sulfate is shown in Figure 8-61A. Most samples in the background
wells and in the wells downgradient plot on or near the 1:1 trend line. This suggested that, in these samples,
the dominant process controlling calcium and magnesium concentrations would be solid phases such as calcite,
dolomite and gypsum. There are a few samples downgradient, and most of the upgradient samples above the
1:1 trend line would indicate that calcium and magnesium are in excess compared with bicarbonate and sulfate.
This indicated that reverse ion exchange would be important. A plot of magnesium versus calcium is shown in
Figure 8-61B. All the groundwater samples in this study plotted below or near the 1:1 trend line indicating that
dolomite was not an important phase controlling calcium or magnesium concentrations. Figure 8-61C is a plot
of the dolomite SI versus the calcite SI. Most of the data in the study plotted in the region that suggested that
calcium and magnesium concentrations were in equilibrium with calcite and dolomite. In Figure 8-61D, data
indicated that all the samples collected were undersaturated with respect to gypsum. This suggests that any
gypsum if present would dissolve into solution.
Calcium (meqil)
20
Bicarbonate + Sulfate (met}!.)
Cakitc bftdcruriuratcd
Dolomite ever saturated
Equilibrium
&
Catole urak-rvHur aletf
Dolomite' uftdcriatiaratcd
Cakitr cncf uturj'rd
Dolonvtc ovwsjcuraicd
l-M ----- -
£qu«fttwium
Calcite owrsartktrated
Dolomite unite fsatorjCed
Calcite Si
D
if}
E
0
-2-
-s-
, I I II ! IM , ! , I
Figure 8-61. Plots of A. Magnesium versus calcium, B, Calcium + magnesium versus bicarbonate + sulfate.
C. Dolomite SI versus calcite SI. D. Gypsum SI. Black circles are FRGW01, red circles are FRGW02, green
circles are FRGW03, blue circles are FRGW04, cyan circles are FRGW05, magenta circles are FRGW06, dark
yellow circles are FRGW07, purple circles are FRGW08, wine-colored circles are FRGW09, dark cyan circles
are FRGW10, orange circles are FRGW11, violet circles are FRGW12, and pink circles are FRGW13. In C the
gray shaded region indicates equilibrium between the sample and solids, the yellow shaded region indicates
the sample is undersaturated with respect to calcite and oversaturated with respect to dolomite, green
shaded region is where the sample is oversaturated with respect to calcite and dolomite, blue shaded
region is where the sample is undersaturated with respect to both calcite and dolomite, and the purple
shaded region is where the sample is oversaturated with respect to calcite and undersaturated with respect
to dolomite.
160
-------
Figure 8-62A is a plot of CAI1 vs CAI2. The gray shaded region indicated the background well samples, and
the yellow shaded region indicated the upgradient wells. This suggests that in most of the background
wells, ion exchange reactions would be important. As was suggested previously, most of the upgradient
well data suggested that reverse ion exchange was important. The majority of the downgradient well data
suggested that ion exchange reactions were important, but as indicated by the black arrow in FRGW04,
there was a shift towards reverse ion exchange reactions' importance in later samples. Figure 8-62B is a plot
of sodium+potassium-chloride versus calcium+magnesium-bicarbonate-sulfate. In this figure, ion exchange
reactions should plot near the y = -x trendline and have negative slope. None of the data in the study plotted
near this trend line. The slope is negative, but it is questionable as to what extent ion exchange plays based on
this analysis.
<
O
-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
0
Reverse Ion Exchange
10-i
o.s-
0.6-
0.4
0.2
0.0
-0.2
-0.4
-0
Exchange
CA12 Ca+Mg-HC03-S04
Figure 8-62. Plots of the A. Chloro-alkaline index 1 versus chloro-alkaline index 2 and B. Na+K-CI versus
Ca+Mg-HC03-S04. Green circles are FRGW03, blue circles are FRGW04, magenta circles are FRGW06, dark
yellow circles are FRGW07, wine-colored circles are FRGW09, and pink circles are FRGW13. In A the green
shaded areas represent the region where ion exchange reactions would be important, and the magenta
shaded areas represent the region where reverse ion exchange reactions would be important.
161
-------
8.8.11.7 Major Anion and Cation Mixing
The statistical analysis of the groundwater concentration in this study was shown to have differences in chloride,
sulfate, calcium, magnesium, potassium, and sodium concentrations depending on if the groundwater was
upgradient, downgradient or background. This suggests that mixing influences the concentrations of the major
anions and cations in the downgradient wells. The stable water isotope data (Section 8.8.11.1) supported mixing
of SPW and groundwater. The assumption in this analysis is that the background groundwater was mixed with
either upgradient groundwater or infiltrated stormwater in the infiltration gallery to produce the downgradient
groundwater at this study site.
The Piper diagram in Figure 8-63 shows where the different sources of groundwater and SPW plot. The gray
shaded regions are the background groundwater samples, the yellow shaded regions are the upgradient
groundwater samples, and the cyan shaded regions are the SPW samples. FRGW04 was the well most likely
to show mixing because of its proximity to the infiltration gallery and because only known sources that could
potentially influence the groundwater would be the upgradient water. The upgradient water would have to flow
under impermeable paved surfaces once it moved past the transect running from the upgradient wells FRGW01
and FRGW11 to reach FRGW04 (See Figure 8.7). In Figure 8-63, the lower left trilinear plot shows where the
major cations in the data collected for FRGW04 plotted in relationship to the different sources. This indicated
that Ca was the dominant cation and that the data plotted where all three sources overlapped.
The lower right trilinear plot in Figure 8-63 shows where the major anions in FRGW04 plotted. All the samples
in 2015 and 2016 were only in the background groundwater field. All the FRGW04 data in 2017 and June 2018
plotted in the overlap of the background groundwater and the upgradient groundwater in the anion trilinear
plot. In March 2018 and September 2018, the data in FRGW04 plotted in the overlap between the upgradient
groundwater and the SPW. In March 2018 and September 2018, dates were the same as the ones that showed
potential spikes in CI concentrations. All the data, except for the March 2018 and September 2018, would plot in
the background groundwater region, assuming the upgradient groundwater is not influencing the downgradient
concentrations, the results in March and September 2018 would suggest that these points were the result of
mixing of SPW with the background GW. The stable isotopes of water support this.
The mixing diamond in Figure 8-63 indicated that most of the samples (10) plot in the region where all three
sources overlap. Of the samples that do not plot in this region, two samples plot solely in the background
groundwater region, two samples plot in the overlap between the upgradient groundwater and the background
groundwater, and one sample in the overlap between the upgradient groundwater and the SPW. The water-
type (Ca-HC03) did not show a pattern of changing after the initial sampling. If mixing is occurring, there is no
evidence of changing water-type at this point.
162
-------
September 2015
December 2015
March 2016
April 2016
May 2016
August 2016
December 2016
February 2017
March 2017
June 2017
September 2017
November 2017
March 2018
June 2018
September 2018
Figure 8-63. Piper diagram examining potential mixing in the groundwater. The gray shaded regions
represent the background groundwater, the yellow shaded regions represent the upgradient groundwater,
and the cyan shaded regions represent the SPW.
163
-------
8.8.11.8 Summary of Isotopes, Major Anion and Cations
Upgradient concentrations for some parameters were much larger than any of the site wells. This complicated
the analysis since the use of the upgradient data was not at background concentrations for the site. Upgradient
groundwater data was treated as a distinct groundwater source.
The stable isotopes of water, 6180 and 62H, showed that with limitations, could be used to track the infiltrated
stormwater in the groundwater. A limitation is that if the isotopic composition was not significantly different
from the ambient isotopic composition, tracking would be limited as the mixing with groundwater would
not yield detectable change. Another limitation identified was that there was an effective distance from the
infiltration gallery the isotopes could be tracked. As the infiltrated stormwater moves through the aquifer, the
mixing with ambient groundwater will change the isotopic composition. At a certain distance from the infiltration
gallery, the infiltrated stormwater will be the same as the ambient groundwater.
Sulfate concentrations in four of the six downgradient wells showed decreasing sulfate concentrations with time.
These downgradient wells, FRGW03, FRGW04, FRGW06, and FRGW13, were the wells near SPW samplers. The
farther downgradient wells, FRGW07 and FRGW09, did not show trends in sulfate concentrations. All the SPW
samples showed decreasing sulfate concentrations that correlated with the decreasing trends in these down
gradient wells. The upgradient well FRGW01 which also showed decreasing sulfate concentrations would not
likely be the sources.
The chloride time series data showed that the upgradient groundwater chloride concentrations were larger
than the background groundwater and the downgradient groundwater. There were limited chloride spikes in
the time series data. The chloride spikes in wells FRGW02 and FRGW10 were likely from FRGW01 based on the
groundwater flow directions determined. In FRGW04, the chloride spike could not be tracked to a single source
based on the data discussed. Two possible sources would be groundwater moved into the well from upgradient
or from the infiltration of stormwater.
The geochemical analysis indicated that the dominant processes affecting groundwater composition were
precipitation/dissolution and ion exchange reactions. The sodium data suggested that ion exchange reactions
were the most important process for sodium, but for calcium and magnesium the data suggests that
precipitation/dissolution processes were more important for calcium and magnesium. In the upgradient data,
the calcium and magnesium data suggested reverse ion exchange would also be important. In FRGW04, the well
closest to the infiltration gallery, in the 2018 samplings reverse ion exchange was important. This corresponds to
the SPW sample analysis that found that reverse ion exchange was important. Reverse ion exchange reactions
suggested that sodium loading on the sediments could potentially be problematic, leading to dispersion of the
sediments and potential plugging issues.
The mixing analysis was mixed. In the cation trilinear plot, mixing was not occurring since all the study data
plotted in a cluster. Similarly, the mixing diamond suggested that mixing was not likely occurring, and that water-
types were not changing overall in the study. The anion trilinear plot did suggest that mixing of different source
water was occurring. This mixing was likely between the background groundwater and the infiltrated stormwater
(SPW data).
8.8.11.9 Other Chemical Constituents
8.8.11.9.1 Phosphate
The phosphate concentrations in the study ranged from 0.001 - 0.332 mg P/L (Figure 8-64). There were no
significant differences in phosphate concentrations between the upgradient wells and either the downgradient
wells or the background wells. There were significant differences between the background wells and
downgradient wells (p = 0.002). The phosphate concentrations in the background wells were larger than the
downgradient wells. There were no trends in the phosphate concentrations in the upgradient wells (Figure
164
-------
8-64A). There were trends in the phosphate concentrations in the downgradient wells (Figure 8-64B). In
the downgradient wells, there were significant increasing phosphate concentrations in FRGW04 (p = 0.037)
and in FRGW13 (p = 0,031). These are the two downgradient wells nearest the infiltration gallery. The other
downgradient well with a phosphate concentration trend was FRGW09 and this was a significant decreasing
phosphate trend (p = 0.019). This was the furthest downgradient well from the infiltration gallery. The
only background well that had a phosphate trend was FRGW12 (Figure 8-64C). In FRGW12 the phosphate
concentrations were significantly increasing trend (p = 0.013). FRGW12 is the well in the parking lot, and it is
near the infiltration gallery. This well would be upgradient of FRGW04 and FRGW13. This would suggest that
the increasing concentrations in phosphate in FRGW04 and FRGW13 were likely coming from groundwater
upgradient. The phosphate concentrations that were greater than the site-specific background range occurred
in 2018 and happened in some of the background wells (FRGW02, FRGW05, and FRGW08), upgradient wells
(FRGW01) and downgradient wells (FRGW07, FRGW09, and FRGW13).
Q_
O
E
Cl
CO
o
0.0
0.3
-------
8.8.11.9.2 Arsenic
Arsenic concentrations in the groundwater ranged from 0.5 - 15.4 [ig/L (Figure 8-65). There were significant
differences in arsenic concentrations between the background wells and the upgradient wells (p < 0.001), and
there were significant differences in arsenic concentrations between the background wells and downgradient
wells (p < 0.001). The arsenic concentrations in the upgradient wells were also significantly different than the
downgradient wells (p < 0.001). The upgradient wells had larger arsenic concentration than in the downgradient
wells and background wells. The background wells had larger arsenic concentrations than the downgradient
wells. In the upgradient wells, there were trends in arsenic concentrations in both wells (Figure 8-65A). The
arsenic concentrations in FRGW01 showed an increasing trend in arsenic and this trend was significant (p
= 0.082). In FRGW11, the arsenic concentrations were decreasing with time, The arsenic trend in FRGW11
was significant (p = 0.060). There were arsenic trends in two of the downgradient wells (Figure 8-65B). The
downgradient well FRGW04 had increasing arsenic concentrations with time, and this trend was significant (p
= 0.062). The arsenic trend in FRGW06 was decreasing arsenic concentrations with time and this again was a
significant trend (p = 0.056). The background well arsenic time series data is plotted in Figure 8-65C. The trend in
arsenic trend concentration in FRGW05 was a decreasing trend and this was a significant trend (p = 0.099). There
were increasing arsenic concentrations in FRGW12 during the study, and this was a significant trend (p = 0.060).
15
10
5
0
_1
15
O)
10
o
c
Q>
5
(f>
<
0
15
10
5
0
1/1/2015
1/1/2016
1/1/2017
Date
1/1/2018
Figure 8-65. Time series plots for arsenic concentrations in A. Upgradient wells, B. Downgradient wells, and
C. Background wells. The gray shaded regions represent the site-specific background range. Black circles
and lines are FRGW01, red circles and lines are FRGW02, green circles and lines are FRGW03, blue circles
and lines are FRGW04, cyan circles and lines are FRGW05, magenta circles and lines are FRGW06, dark
yellow circles and lines are FRGW07, purple circles and lines are FRGW08, wine-colored circles and lines are
FRGW09, dark cyan circles and lines are FRGW10, orange circles and lines are FRGW11, violet circles and
lines are FRGW12, and pink circles and lines are FRGVV13. The red dashed lines represent the As MCL of
10 ng/L.
166
-------
There were several arsenic MCL exceedances in the groundwater during this study. The arsenic MCL is 10 ng/L
Most arsenic MCL exceedances were in the upgradient wells. FRGW01 exceeded the arsenic MCL in 53% of
the samples and in 43% of the samples collected in FRGW11. There was only one arsenic MCL exceedance in
the downgradient well FRGW09 and one arsenic MCL exceedance in the background well FRGW10. This was
not unexpected since the data from this study showed that the upgradient groundwater had higher arsenic
concentrations than the study site wells.
8.8.11.9.3 Barium
Barium concentration ranged from 60 - 364 |ig/L (Figure 8-66). Like arsenic, barium concentrations were
significantly different between the background wells and the upgradient wells (p <0.001) and the barium
concentrations between the background wells and downgradient wells (p < 0.001). Again, the barium
concentrations in upgradient wells were larger than the background wells and downgradient wells. The
background wells had larger barium concentrations than the downgradient wells. In the upgradient wells,
there was a time trend in the barium concentrations in FRGW01 (Figure 8-66A). The barium concentrations
were increasing in FRGW01, and this trend was significant (p = 0.075). There were increasing trends in barium
concentrations in the downgradient wells (Figure 8-66B). The increasing barium concentrations in FRGW03
(p = 0.082), FRGW04 (p = 0.002), and FRGW07 (p = 0.007) were significant. There were no trends in barium
concentration in the background wells (Figure 8-66C).
300
200
100
0
IT 300
_ —'
£ 200
3
£ 100
0
300
200
100
0
1/1f2015 1/1*2016 1/1*2017 1/1/2013 1/1/2019
Date
Figure 8-66. Time series plots for barium concentrations in A. Upgradient wells, B. Downgradient wells, and
C. Background wells. The gray shaded regions represent the site-specific background range. Black circles
and lines are FRGW01, red circles and lines are FRGW02, green circles and lines are FRGW03, blue circles
and lines are FRGW04, cyan circles and lines are FRGW05, magenta circles and lines are FRGW06, dark
yellow circles and lines are FRGW07, purple circles and lines are FRGW08, wine-colored circles and lines are
FRGW09, dark cyan circles and lines are FRGW10, orange circles and lines are FRGW11, violet circles and
lines are FRGW12, and pink circles and lines are FRGW13.
167
A
\
1
)/
•
/
•
1
"*¦ -t
B
t—!_
^114
C
—) !
-------
8.8.11.9.4 Nickel
Nickel concentration ranged from 0.5 - 12 |ig/L in this study (Figure 8-67). There were significant differences
in nickel concentrations between the background wells and the upgradient wells (p < 0.001) and differences
in nickel concentrations between the background wells and downgradient wells (p < 0.001). There were also
significant differences between the upgradient wells and downgradient wells (p < 0.001). The upgradient wells
had the largest nickel concentrations, followed by the downgradient wells, and finally the background wells. The
upgradient well FRGW11 (Figure 8-67A) showed decreasing concentrations in nickel with time. This nickel trend
was significant (p = 0.088). In the downgradient wells, two wells showed trends in nickel concentrations (Figure
8-67B). These wells, FRGW07 and FRGW09, both had increasing nickel concentrations with time. This trend was
significant in FRGW07 (p = 0.075) and in FRGW09 (p = 0.004). The background wells, FRGW05 and FRGW10, also
had trends in nickel concentrations (Figure 8-67C). In FRGW05, the nickel concentrations were increasing with
time (p = 0.002). The nickel trend in FRGW10 was a significantly decreasing during the study (p = 0.031).
10 -
n>
—t
m-
1/1/2015
Date
Figure 8-67. Time series plots for nickel concentrations in A. Upgradient wells, B. Downgradient wells, and
C. Background wells. The gray shaded regions represent the site-specific background range. Black circles
and lines are FRGW01, red circles and lines are FRGW02, green circles and lines are FRGW03, blue circles
and lines are FRGW04, cyan circles and lines are FRGW05, magenta circles and lines are FRGW06, dark
yellow circles and lines are FRGW07, purple circles and lines are FRGW08, wine-colored circles and lines are
FRGW09, dark cyan circles and lines are FRGW10, orange circles and lines are FRGW11, violet circles and
lines are FRGW12, and pink circles and lines are FRGVV13.
There was a spike in the nickel concentrations in FRGW01 in April 2016 which had corresponding nickel
concentration spikes in the downgradient wells FRGW03 and FRGW04 (Figure 8-67). There were no spikes in any
of the other downgradient wells or background wells.
168
-------
8.8.11.9.5 Vanadium
Vanadium concentration ranged from 0.5 - 39 |ig/L (Figure 8-68). The vanadium concentration was significantly
different between the background wells and the upgradient wells (p < 0.001). There were also significant
differences in vanadium concentrations between the upgradient wells and the downgradient wells (p < 0.001).
There were no differences in vanadium concentrations between the background wells and the downgradient
wells. The vanadium concentrations in the upgradient wells were larger than the downgradient wells and
background wells. There were no trends in the vanadium concentrations in the upgradient wells (Figure 8-68A).
In the downgradient wells, there was a trend in FRGW07 (Figure 8-68B). There was a significantly increasing
trend in vanadium concentrations in FRGW07 (p = 0.002). FRGW08 in the downgradient wells (Figure 8-68C) had
a significantly increasing trend in vanadium concentrations (p = 0.003). Vanadium concentrations were much
larger in the upgradient wells than in any of the other wells. The reason for the higher vanadium concentrations
upgradient is not known at this time.
40-
30-
20-
10-
0-
,-.40-
J! 30-
E
.2 20-
"O
(0
ro 10-
>
0-
40-
30-
20-
10-
0-
1/1/2015 1/1/2016 1/1/2017 1/1/2018
Date
Figure 8-68. Time series plots for vanadium concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells. The gray shaded regions represent the site-specific background range. Black circles
and lines are FRGW01, red circles and lines are FRGW02, green circles and lines are FRGW03, blue circles
and lines are FRGW04, cyan circles and lines are FRGW05, magenta circles and lines are FRGW06, dark
yellow circles and lines are FRGW07, purple circles and lines are FRGW08, wine-colored circles and lines are
FRGW09, dark cyan circles and lines are FRGW10, orange circles and lines are FRGW11, violet circles and
lines are FRGW12, and pink circles and lines are FRGW13.
169
A
——1-
/\
• / \
.•/-—•—.
B
»-
—=1=1—
a 1
c
2 ?
—ac—j—
=1=1=1 . ,
-------
8.8.11.9.6 Uranium
Uranium concentration ranged from 0.5 - 349 |ig/L during this study (Figure 8-69). There were significant
differences in uranium concentration between the background wells and the upgradient wells (p < 0.001)
and between the upgradient wells and the downgradient wells (p < 0.001). There were no differences in
concentrations between the background and upgradient wells. The upgradient wells had larger uranium
concentrations than the background wells or downgradient wells. In the upgradient wells (Figure 8-69A), only
FRGW01 had a trend in uranium concentrations. This was a decreasing trend and was significant (p = 0.099).
Three of the downgradient wells (Figure 8-69B) had uranium concentration trends. FRGW04 and FRGW13 had
decreasing uranium concentrations, and these concentration trends were significant (p = 0.002 and p = 0.031,
respectively). FRGW07 showed increasing uranium concentrations during the study, and this was a significant
trend (p = 0.001). In the background wells (Figure 8-69C), FRGW08 and FRGW10 showed trends in uranium
concentrations. FRGW08 showed decreasing trend in uranium concentrations and was significant (p = 0.061).
The trend in FRGW10 indicated that the uranium concentrations were increasing but was not a significant trend
(p = 0.106).
300
200
100
? °
§>300
200
3
E 100
rc
D 0
300
200
100
0
1/1/2015 1/1/2016 1/1/2017 1/1/2018
Date
Figure 8-69. Time series plots for uranium concentrations in A. Upgradient wells, B. Downgradient wells,
and C. Background wells. The gray shaded regions represent the site-specific background range. Black circles
and lines are FRGW01, red circles and lines are FRGW02, green circles and lines are FRGW03, blue circles
and lines are FRGW04, cyan circles and lines are FRGW05, magenta circles and lines are FRGW06, dark
yellow circles and lines are FRGW07, purple circles and lines are FRGW08, wine-colored circles and lines are
FRGW09, dark cyan circles and lines are FRGW10, orange circles and lines are FRGW11, violet circles and
lines are FRGW12, and pink circles and lines are FRGW13. The red dashed line represents the uranium MCL.
The uranium MCL is 30 jig/L. There were a significant number of samples that exceeded this MCL The uranium
MCL was exceeded in the upgradient wells, downgradient wells and the background wells.
170
-------
8.8.11.9.7 Organic Compounds in Groundwater
Organic analysis performed on the groundwater was for SVOCs and for VOCs. The classes of compounds tested
for in the SVOCs were chlorinated compounds, hormones, phthalates, surfactants, PAHs, pesticides, solvents,
antimicrobials, and plasticizers. Most of the compounds were not detected and detected compounds were
not above their MCLs or other known regulatory limits. The compounds detected in this study fall into a few
categories: solvents, pesticides, polyaromatic hydrocarbons (PAH), flame retardants, and phthalates. The most
commonly detected VOC was acetone and was detected in 5.7% of the samples. Acetone is a common solvent
and could have been detected as the result of lab contamination. Benzene and carbon disulfide were detected
in 1.9% of the samples. The most commonly detected SVOC was diethyltoluamide (DEET). DEET was detected
in 43.4% of the samples and the detection was likely caused by sample contamination during sampling. Di-n-
butylphthalate was detected in 15.1% of the samples. The PAH, benzo(k)fluoranthene, was detected in 7.5%
of the samples collected. The flame retardants, Tris (2-chloroethyl) phosphate (TCEP) and Tris (2-butoxyethyl)
phosphate (TBEP) were detected in 3.8% and 2.3%, respectively. Pendimethalin was detected in 1.9% of the
samples.
8.8.11.9.8 Stormwater Contaminants and Other Trace Metals
The NURP and NSQD have identified several inorganic contaminants and organic contaminants that are
important to evaluate when infiltrating stormwater in a Gl system (U.S. EPA, 1983; NSQD, 2015). Summary
of NURP and NSQD inorganic compounds as well as other trace metals are shown in Table 8-6. Site-specific
background concentrations of these parameters are given in Table 8-5 and these will be used to compare data
to. Total nitrogen, total phosphorous, aluminum, arsenic, barium, copper, iron, manganese, molybdenum, nickel,
selenium, uranium, and vanadium all had data that was outside the site-specific background concentration
range. Phosphate, arsenic, barium, uranium, and vanadium were discussed previously and will not be discussed
here. In the case of total nitrogen and total phosphorous there was only one sample with a concentration
outside the site-specific background concentrations range. For selenium one sampling, in April 2016, had
concentrations outside the site-specific background concentration range. This event is likely an outlier for
selenium concentrations. Nickel concentrations were consistently greater than the site-specific background
range in FRGW01 starting in April 2016. In this well nickel concentrations are likely influenced by the upgradient
groundwater. Molybdenum had concentrations greater than the site-specific background in FRGW02 (December
2016) and FRGW11 (November 2017 and September 2018). Both wells are upgradient of the infiltration gallery,
so these exceedances were not caused by the Gl. Copper concentration exceeded the site-specific background
concentrations in three downgradient wells, FRGW04 (March 2017), FRGW06 (March 2017), and FRGW07
(March 2017, September 2017 and November 2017). Copper also exceeded the site-specific background in
upgradient wells FRGW11 (March 2017), and FRGW01 (September 2017). The exceedances in the upgradient
wells were not related to Gl. In the downgradient wells, Gl cannot be ruled out as the source of the copper
exceedances. Iron in wells FRGW06, FRGW09, and FRGW10 consistently had concentrations that were greater
than the site-specific background iron concentration range. The iron exceedances are not caused by the Gl,
rather these are likely due to the wells or natural conditions around the wells. In well FRGW06 the manganese
concentrations were consistently greater than the site-specific background concentrations. Like iron, this was not
caused by the Gl, it was likely caused by natural conditions around the wells or the wells themselves.
Summary of NURP and NSQD organic contaminants are found in Appendix D, Table D-10 (halogenated aliphatics
and aromatics), Table D-ll (PAHs), Table D-12 (pesticides), and Table D-13 (phenols, ethers, and phthalates).
In the groundwater at this site there were no detections of halogenated aliphatics or aromatics (monocyclic),
NURP and NSQD pesticides, and phenols or ethers in any of the samples collected. There were four samples
that detected benzo(k)fluoranthene in May 2016. The detections of benzo(k)fluoranthene were not related to
Gl since similar concentrations of benzo(k)fluoranthene were detected in all the blank samples. There were two
phthalates detected, di-n-butylphthalate and di-n-octylphthalate. The source of these phthalates is unknown,
and phthalates are ubiquitous in the environment (Griffiths et al., 1985; Serodio and Nogueira, 2006; Stiles et al.,
2008; Teil etal., 2013).
171
-------
Table 8-6. Summary of trace components for the Fort Riley groundwater samples.
nh3
Total N
P°4
Total P
Ag
Al
As
Ba
Be
Cd
Co
Cr
Cu
Units
mg N/L
mg N/L
mgP/L
mgP/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Mg/L
Mg/L
Hg/L
Total Number of
Analyses
162
162
162
162
162
162
162
162
162
162
162
162
162
Number of
Detects
73
161
154
147
8
53
160
162
9
12
109
7
98
Percent Detects
45
99
95
91
5
33
99
100
6
7
67
4
60
Mean
0.07
1.06
0.067
0.080
1.0
31
5.1
115
0.8
0.5
1.6
0.7
1.7
Standard
Deviation
0.06
2.31
0.056
0.034
0.0
67
2.7
56
0.4
0.0
1.2
0.1
3.1
Minimum
0.03
0.04
0.009
0.026
1.0
1
<1
60
0.5
0.5
0.5
0.5
0.5
25th Percentile
0.04
0.18
0.028
0.059
1.0
5
3.1
82
0.6
0.5
0.8
0.6
0.6
Median
0.05
0.31
0.056
0.075
1.0
11
4.4
100
0.6
0.5
1.1
0.7
0.8
75th Percentile
0.07
0.55
0.081
0.098
1.0
29
5.8
119
0.8
0.6
2.1
0.8
1.1
Maximum
0.44
15.3
0.332
0.308
1.0
426
15
364
1.8
0.6
7.8
0.8
17
Fe
Mn
Mo
Ni
Pb
Sb
Se
Th
Tl
U
V
Zn
Units
Hg/L
Hg/L
Hg/L
Hg/L
Hg/L
Mg/L
Hg/L
Hg/L
Hg/L
Hg/L
Mg/L
Mg/L
Total Number of
Analyses
162
162
162
162
162
162
162
162
162
162
162
162
Number of
Detects
122
162
162
160
11
67
133
17
11
161
151
6
Percent Detects
75
100
100
99
7
41
82
10
7
99
93
4
Mean
824
121
00
CO
3.2
0.8
0.7
6.2
0.6
0.5
51
3.6
134
Standard
Deviation
1344
106
4.1
2.1
0.5
0.2
15.0
0.2
0.2
49
5.3
101
Minimum
31
1.1
0.8
0.6
0.5
0.5
0.6
0.5
0.2
<1
0.5
51
25th Percentile
145
43
5.8
1.5
0.6
0.6
1.6
0.5
0.5
25
1.2
54
Median
352
103
7.7
2.7
0.6
0.7
2.4
0.6
0.5
37
2.0
96
75th Percentile
955
177
12
4.2
0.9
0.8
5.8
0.6
0.6
57
2.7
200
Maximum
11480
839
23
12
2.1
1.8
161
1.2
0.9
349
39
287
172
-------
8.8.11.9.9 Summary of Other Chemical Constituents and Organic Analysis
The higher concentrations of some parameters in the upgradient wells that were suspected and were shown
in the special sampling examining the upgradient water quality were shown to be occurring throughout the
study. The upgradient concentrations of arsenic, barium, vanadium, and uranium were in general consistently
larger than other study sample wells. This suggested that there was potentially an upgradient source for some
constituents.
The barium, arsenic, and uranium concentrations in the downgradient water was lower than the upgradient
groundwater and background groundwater. This suggests the possibility of dilution of the groundwater by the
infiltrated stormwater. Additional data is needed to confirm this.
The phosphate concentrations just downgradient of the infiltration gallery were increasing. Other wells
downgradient of the infiltration gallery showed no trends or decreasing phosphate concentrations. The well
just upgradient of the infiltration gallery also showed increasing phosphate concentrations. This suggested
the increasing phosphate concentrations just downgradient of the infiltration gallery were likely the result of
groundwater migrating under the infiltration gallery to these wells, and they were not the result of the Gl.
Barium concentrations were influenced by chloride concentrations. In the upgradient wells, when chloride
concentrations increased, so did the barium concentrations. Conversely, when the chloride concentration
decreased, so did the barium concentrations.
Finally, the results of the organic analysis indicated that VOCs and SVOCs would not be a problem in this study.
The two compounds with the most detections (DEET and phthalate) could be related to contamination from
the sampling or sampling equipment. The solvents detected could have also resulted from the sampling or
lab contamination. The other detections that could not be related to field or lab contamination did not have
concentrations of regulatory concern.
8.8.11.10 Impacts to Groundwater Quality
8.8.11.10.1 Regulatory Standards
One measure of water quality are federal and state drinking water standards such as the National Primary
Drinking Water Standards (MCL; EPA, 2017), National Secondary Drinking Water Standards (sMCL; EPA, 2017)
and State of Kansas Drinking Water Standards (KDHE, 2015). Except for the National Secondary Drinking Water
Standards, all contaminant limits are based on human health risk.
The National Primary Drinking Water Standards were exceeded for four contaminants during the study (Figure
8-70). Uranium exceeded the MCL in 63.0% of the samples, arsenic in 8.0% of the samples, nitrate + nitrite
in 1.3% of the samples, and selenium in 0.6% of samples. The National Secondary Drinking Water Standards
were exceeded for seven contaminants in this study (Figure 8-70). TDS that exceeded the sMCL in 99.4% of
the samples, manganese in 72.2% of the samples, iron in 43.2% of the samples, sulfate 42.0% of the samples
collected, pH in 8.6% of the samples, aluminum in 4.3% of the samples, and chloride 2.5% of the samples. The
State of Kansas water standards are the same as the Federal standards (Figure 8-70).
Another set of criteria that could be applied to the groundwater at this study site are those of the Drinking
Water Standards and Health Advisories (DWSHA; EPA, 2018). Figure 8-70 shows the five different advisory levels
that are part of the DWSHA. There were no exceedances of the One-Day Health Advisory during the study.
The risk posed based on the One-Day Health Advisory would be no risk. As was the case with the One-Day
Health Advisory, there were no exceedances of the 10-Day Health Advisory (Figure 8-70). The DWEL did have
exceedances in 8.0% of the samples for arsenic (Figure 8-70). There were two contaminants that exceeded the
Lifetime Health Advisory (Figure 8-70), manganese exceeded this standard in 3.7% of the samples and selenium
exceeded this standard in 0.6% of the samples. In this study, 47.5% of the samples exceeded the taste threshold
for sodium, and 42.0% of the samples for sulfate (Figure 8-70).
173
-------
100
o
O)
0
01
CD
_c
T3
0
0
a
X
LU
c
0
o
L_
0
CL
10 H
0
_
¦
¦
•
1 1
, .1..
E ¦§ ^EE = E«E
c
o
c
o
¦— — — ¦— m— i— I— ¦— KJ
E E ,i 2 ® >-m j= % £ O §
t ^ ¦£= > CD T- TD x: On-
3 E 03 o £= Li-
< < < CO O q
c: c .y
§<§:
[?S
nj
o
O
+
to
<3
-J ^ -J -J lU —r i—i —J - —;
c(3:° :c 511- c
^ o o f n 5 ro
O CO £ w £ Js
w eo K D
Figure 8-70. Percentage of groundwater samples exceeding regulatory standards for analytes which have
regulatory standards. Black bars show data for National Primary Drinking Water Standards and National
Secondary Drinking Water Standards, red bars show data for State of Kansas Drinking Water Standards,
green bars show data for One-Day Health Advisories, blue bars show data for Ten-Day Health Advisories,
cyan bars show data for Drinking Water Equivalent Level, magenta bars show data for Lifetime Health
Advisory, and dark yellow bars show data for Taste Threshold.
174
-------
8.8.11.10.2 Water Quality Index
Comparing water quality data to regulatory standards to determine if impacts to water quality have occurred
is somewhat tedious and provides a yes or no answer to whether impacts occurred (sample exceeded a
contaminant value) or not, and it does not account for all standards simultaneously. In addition, this method
does not account for values for contaminants that do not exceed the MCL or other regulatory standards. The
method in the previous section also does not consider water quality data that is not a human health risk data
that could potentially degrade water quality. The WQI, introduced in Section 5.4, will attempt to bridge the gap
and provide an index of water quality. The WQI score and its assessment are provided in Table 5-1.
For the Fort Riley study there are two WQI indices that were calculated using the National Primary Drinking
Water Standards and they included the use of the National Secondary Drinking Water Standards (EPA, 2017) and
the State of Kansas Drinking Water Standards (KDHE, 2015). Figure 8-71 shows plots of the WQI versus time for
the data collected in this study.
V
1/1/2015 1/1/2016 1/1/2017 1/1/2018
Date
Figure 8-71. Water Quality indices
plotted overtime for the Fort Riley
Study. FRGW01 data are the black
lines and circles, FRGW02 data are
the red lines and circles, FRGW03
data are the green lines and circles,
FRGW04 data are the blue lines
and circles, FRGW05 data are the
cyan lines and circles, FRGW06 data
are the magenta lines and circles,
FRGW07 data are the dark yellow
lines and circles, FRGW08 data are
the purple lines and circles, FRGW09
data are the wine-colored lines and
circles, FRGW10 data are the dark
cyan lines and circles, FRGW11 data
are the orange lines and circles,
FRGW12 data are the violet lines
and circles, and FRGW13 are the
pink lines and circles. The gray
shaded areas show samples that
are of excellent water quality, olive
shaded areas show samples that are
of good water quality, blue shaded
areas show samples of poor water
quality, teal shaded areas show data
of very poor water quality, light
yellow shaded areas show water
that is unsuited for drinking without
adequate treatment.
The use of Federal drinking water standards (MCLs and sMCLs) and the State of Kansas standards to calculate
the WQIs are shown in Figure 8-71. Since the standards have the same values, the WQIs are the same. The WQI
indicated that for the background groundwater, the water quality would be excellent - good. The upgradient
groundwater was good - poor. The poor water quality was in March 2017, and it was also almost poor in
November 2017. These correspond to the highest measured uranium concentrations. In the downgradient wells,
the WQI indicated that the groundwater quality would be excellent - good. The WQIs indicated that the use of Gl
did not degrade groundwater quality since the water quality was similar to the background water quality.
175
-------
9.0
Summary, Conclusions, and Future Research Needs
The three study locations were selected to represent different types of Gl and climates. The site diversity was
expected to correspond to a diversity of results. It is important to note that these three sites do not represent
every potential risk of Gl on groundwater quality. The aim of this study is to shed light on potential common
issues and show an array of possible groundwater quality outcomes.
9.1 Types of Green Infrastructure
The three study locations have different Gl types in their designs. In addition, different water types infiltrate into
each subsurface. The Louisville and the Fort Riley studies both have engineered infiltration systems whereas
the Yakima study used a "natural" system to infiltrate the water. The Louisville study site Gl consisted primarily
of subsurface infiltration galleries (Section 6) to capture and infiltrate stormwater runoff. The Yakima study site
used indirect discharge as the Gl and did not have a discrete infiltration infrastructure (Section 7). Here the
treated wastewater was released onto the floodplain where it infiltrated into the subsurface. The Fort Riley study
used a permeable pavement Gl system to capture stormwater runoff in an infiltration gallery (Section 8).
There were also differences in the types of water infiltrated into the subsurface. The Yakima study infiltrated
treated wastewater whereas the Fort Riley and Louisville studies infiltrated stormwater runoff. The infiltration
water quality varied among the sites. The Fort Riley and Louisville infiltrated stormwater runoff had discrete
water pulses that coincided with precipitation events and the vadose zone had times when the pore spaces
remained unsaturated. In the Yakima study, continuous infiltration was based on the outfall from the wastewater
treatment plant. This suggests that mounding may result in a fully saturated zone from the surface, except at
the margins of the mound. The saturation differences are important in how both the water and constituents in
the water interact with the vadose zone. For example, where unsaturated conditions occur, evapotranspiration
can remove the infiltrated water trapped in the vadose zone pore spaces that can cause dissolved components
to precipitate forming solid phases. These solid phases cannot move through the vadose zone until they are
redissolved and transported further into or through the vadose zone. In contrast, when the pore spaces in the
vadose zone are continuously saturated the constituents in the infiltrated water can more freely be transported
into the groundwater.
9.2 Vadose Zone
The Fort Riley Gl study had the most comprehensive examination of the vadose zone. The vadose zone
and aquifer materials were characterized for chemical and physical properties, and the hydrogeology was
characterized. In addition, water quality samples were taken.
The characterization of the vadose zone and aquifer at the Fort Riley site showed that there was a decrease
in finer grained materials in the upper portion of the sediment profile. This was confirmed by ERI analysis and
texture analysis of core materials. There was heterogeneity in texture and in general, the clay content decreased
as one moves south across the study site. The decreases in fine-grained materials were also related to the
chemical properties of the vadose zone and aquifer sediments. The CEC, total Ca, total Na, total Al, total Mn,
total Fe, total Si, total organic carbon, and total C concentrations decrease with depth. This indicated that the
sorptive and reactive properties of the vadose zone materials also decreased with depth. Due to this trend,
the ability of the vadose zone to remove contaminants and modify the infiltrated groundwater decreased with
depth.
The hydrology at the Fort Riley study site suggested that changes in water table elevation were not related to
the infiltrated stormwater. The water table elevations were a function of the natural infiltration and changes
in height of water in the stormwater retention basin located south of the Gl site. There was little evidence for
mounding.
-------
Analysis of the Fort Riley temperature data showed that if there was a large temperature contrast between the
ambient vadose zone temperature and the infiltrated water temperature, then temperature data could be used
to monitor water movement in the vadose zone and groundwater. This was also supported by the tensiometers
temperature data.
At Fort Riley, soil water tension monitored wetting fronts and water movement through the vadose zone. The
tensiometer data showed water movement through the vadose zone following an infiltration event happened
within hours to about a day. There was an effective radius in which the water moves out the sides of the
infiltration gallery. This is an important point because the infiltrated water only moved out from the infiltration
gallery to a radius of less than 3.1 m. While the radius is site specific, the method is transferable. Understanding
the limited radius is critical when considering Gl placement near existing infrastructure. In this application, the
measured radius is roughly the depth of the gallery.
The tensiometers showed that using infiltration gallery water elevations was not the best method for predicting
infiltration. The tensiometers showed water movement (infiltration) in the vadose zone following precipitation
events when there was no corresponding rise in infiltration gallery water levels. The rise in water levels in the
infiltration gallery is a related rates problem. That is, the water level in the gallery will only rise if the rate of
water entering the infiltration gallery exceeds the exfiltration rate from of the gallery. Tensiometers proved to
be a valuable monitoring tool, clearly demonstrating sidewall exfiltration. The instrument selected was designed
to operate autonomously. However, the instruments failed early in the monitoring period limiting the ability to
observe temporal changes in the patterns.
The geochemistry of the infiltrated stormwater at both the Fort Riley and the Louisville study sites could be
compared in the vadose zone. In the Louisville study, the predominant geochemical processes occurring in the
vadose zone were ion exchange and dissolution/precipitation reactions, which was also the case with the Fort
Riley study. The SPW samplers in the Fort Riley study were nested between 327 m-msl to 322 m-msl, which also
corresponds to the zone where CEC and other sorptive properties were prevalent. This data was not available
for the Louisville study, but it was expected that the CEC and other sorptive properties in the Louisville study
would be largest near the surface also, so it was expected that ion exchange would be an important process
in the vadose zone. In both studies, one process that could potentially be problematic is reverse ion exchange
reactions. Reverse ion exchange was suggested at both study locations in the later samplings. Reverse ion
exchange could lead to sodium loading on the fine-grained materials in the vadose zone and aquifer, and lead
to these particles dispersing. Dispersion of the fine-grained particles in the vadose zone over time could cause
plugging and lower the infiltration rates of stormwater.
In the Louisville and Fort Riley vadose zones, the chloride (conservative species) moved faster than other
analytes. This also showed that the movement of nonconservative species was inhibited by interactions with
the vadose zone solid phases. As chloride concentrations increased, barium concentrations also increased and
were more mobile. Although this barium behavior is not likely a problem at these sites, it does suggest that other
contaminants that have similar behavior with chloride could mobilize when de-icing agents are used and thus be
a risk to groundwater quality.
9.3 Stable Isotopes as Water Movement Tracers
In addition to temperature and soil water tension, the stable isotopes of water could potentially be used to
monitor infiltrated water movement in the vadose zone and groundwater. Although there were differences in the
usefulness, the Fort Riley study showed the potential utility of this approach. In the Fort Riley study, the runoff
isotopic composition was sufficiently different from the isotopic composition of the groundwater that it could
be effectively used as a tracer. If the infiltrated water isotopic composition is not sufficiently different than the
groundwater isotopic composition, then mixing infiltrated water with groundwater would not yield changes in
isotopic compositions in the mixed water large enough to detect.
177
-------
The Fort Riley study also indicated that there was an effective radius where the stable isotopes of the infiltrated
water can be distinguished from the ambient groundwater isotopic composition. As the infiltrated water
moves through the groundwater, more mixing occurs and the more the isotopic composition becomes like the
ambient isotopic composition. At some distance the infiltrated water will be indistinguishable from the native
groundwater.
9.4 Groundwater
During the study there were changes to groundwater quality at all three locations. In the Louisville and the
Yakima studies these changes in groundwater quality could be related to changes brought about using Gl. In
the Fort Riley study, the changes in groundwater quality were difficult to attribute to the use of Gl. Changes in
groundwater at Fort Riley could be because of the upgradient groundwater flowing into the site.
In the Yakima study, the changes in groundwater quality near the outfalls resulted from the mixing of the
upgradient water with the treated wastewater. This conclusion was based on the analysis of the major anions
and cations, geochemical processes, and other constituents. The concentration of many parameters in the
treated wastewater was larger than the upgradient groundwater. The groundwater wells near the outfall
had concentrations less than the outfall and more than the upgradient groundwater. The groundwater wells
near the outfall documented the likely mixing of these two waters. Contrast this with the infiltration from the
precipitation that occurs over much larger areas, whereas the infiltration from the wastewater is limited to the
infiltration structure. Therefore, temporal distribution of the two types of infiltration is also a major difference.
The chemistry of precipitation would be expected to be very dilute compared to the wastewater and it would
be expected that the changes to upgradient water small or only dilute the upgradient water compared with the
mixing of infiltrated treated wastewater with upgradient water.
Dilution was the likely reason for changes to groundwater quality in the Louisville study. Many of the measured
parameters showed decreasing groundwater concentrations with time. Even though the concentrations in the
stormwater runoff were not measured, it is expected that the major anion and cations concentrations would
be smaller than those in the groundwater. Therefore, infiltrating large quantities of stormwater would have the
effect of diluting the groundwater near the infiltration gallery. Because of the linear design of the groundwater
monitoring network it is not known how far away from the infiltration gallery these changes to groundwater
quality would be quantifiable.
In the Fort Riley study, there was not one dominant demonstrated mechanism affecting the groundwater
quality downgradient of the infiltration gallery. In some cases, such as with sulfate, the concentrations in the
downgradient wells generally decreased. The background and upgradient wells did not show trends in sulfate
concentrations. This suggests that constituent dilution from the infiltrated stormwater might be occurring. There
was also evidence of dilution in barium, strontium, arsenic, and uranium. There was also evidence that mixing
of infiltrated stormwater with background groundwater could have caused changes in groundwater quality
downgradient of the infiltration gallery. For example, there was evidence of a chloride spike in the well next to
the infiltration gallery. In the analysis of the trilinear plot of the major anions, the chloride was related to the
mixing of the infiltrated water with the background groundwater. However, it is difficult to rule out upgradient
water as the source of the spike. It is also possible that the stormwater infiltration did not have enough time to
significantly affect groundwater quality or that stormwater infiltration will not cause any long-term changes to
groundwater quality.
At all three study locations there was evidence of increasing groundwater phosphate concentrations. At the
Louisville and Yakima study sites the increasing phosphate concentrations were likely caused by the runoff in the
Gl. In the Fort Riley study, there was evidence that suggested the increasing phosphate concentrations were the
result of upgradient water and not the infiltrated groundwater. Increasing phosphate in the groundwater could
degrade water quality, especially if the groundwater is used as an irrigation source. Surficial sediment phosphate
loading could be potentially problematic if these sediments were allowed to erode into a surface water body.
178
-------
The groundwater at the Louisville study site is not used as a drinking water source. If it were used as a drinking
water source, it would be important to monitor the chromium concentrations to determine if they continue to
increase.
At Fort Riley, the groundwater analysis showed that, for some chemical species, the upgradient concentrations
were larger than both the background groundwater and the downgradient groundwater concentrations. There
is no obvious source for these chemical species and the long-term effects of the higher concentrations are
unknown.
Organic contaminant analysis was only done for the Fort Riley study. Most of the organic compounds analyzed
were not detectable. DEET had the most detects in both the SPW samples and in the groundwater samples. DEET
was most likely the result of contamination during sample collection and probably not present in the SPW or
groundwater. The compounds detected in this study fall into a few categories: solvents, pesticides, polyaromatic
hydrocarbons (PAH), flame retardants, chlorinated compounds and phthalates. None of the detected organic
compounds had concentrations of regulatory concern.
9.5 Conclusions
This is the first known monitoring effort to demonstrate potential changes to groundwater from Gl practice.
The project begins to fill the gaps identified by "The Influence of Green Infrastructure Practices on Groundwater
Quality: The State of the Science" (Brumley et al., 2018), and in some cases identify the necessary techniques to
obtain the data.
The climatic variable that affects the groundwater quality in the Fort Riley and Louisville sites is precipitation. In
areas that received regular infiltration from stormwater, dilution of the groundwater is potentially the biggest
concern. The increased infiltration from the Gl would dilute the groundwater near the Gl more than what would
occur naturally and with time the diluted groundwater could move away from the Gl structure. In the Louisville
study, which has a regular precipitation pattern, most parameters showed decreasing concentration trends.
Whereas in Fort Riley, which gets less precipitation and the wet season is more discrete, there were parameters
which showed dilution and others that did not. Data is needed from wet years and drought years to understand
the role of dilution. The climatic influence in Yakima could not be inferred in this study since it receives almost
continuous infiltration.
Both the Louisville and the Fort Riley studies indicated that there were changes to groundwater quality. However,
these short-term studies indicated that the stormwater infiltration overall did not have negative effects on
drinking water quality. It needs to be stressed that for both these studies, the long-term equilibrium was likely
not yet established. Louisville study projections in analyte concentrations can be made that suggest in the
future the groundwater near the infiltration gallery will become more diluted. The Louisville groundwater could
shift from a Ca-HC03 type water to a Na-HC03 type water. This water softening through dilution could present
negative consequences if the groundwater is being used as a drinking water source. Water treatment plants
may have to alter their treatment processes because of water quality changes if softening of the groundwater
occurred in the future after the implementation of Gl.
The Yakima study looked at treated wastewater infiltration. Based on this short-term study, it would appear that
treated wastewater infiltration could be an important management option for wastewater disposal. The results
of this study suggest that if both the upgradient groundwater concentrations and the infiltrated wastewater
composition stay the same, the downgradient groundwater concentrations will be no larger than those found in
the treated wastewater.
179
-------
9.6 Research Needs
The results presented in this report are short-term results. The long-term effects of stormwater and wastewater
infiltration needs to be understood in the context of the results presented here. Monitoring at these study sites
should continue. Each of the studies showed data gaps and these data gaps need to be addressed to have a
complete understanding of how groundwater quality is affected by Gl practices.
The research presented also shows the need for long-term studies of stormwater infiltration in dry areas similar
to the precipitation conditions at the Yakima site. Research should also include measurements or estimations of
evapotranspiration. This is a major question that was not addressed in this study.
In addition, the infiltration of treated wastewater in other climatic conditions would be important to study. This
study indicated that in a dry region this is likely a good practice, but in areas that receive more water it is not
understood how treated wastewater infiltration would affect groundwater quality or hydrology. Although these
effects could be modeled, there are still questions that cannot be addressed using a model and these questions
can only be adequately addressed with the collection of data.
The vadose zone needs to be addressed in more detail. Long-term use of Gl requires a better understanding of
the vadose zone capacity to sequester contaminants and conditions that could mobilize the contaminants for
release to the groundwater. Since the vadose zone is the apparent contaminant sink it is not clear what would
happen if the Gl use is discontinued or if the land use changes. This should include laboratory column studies
with samples of the vadose zone and the water that will infiltrate at the site. Column studies will give a general
understanding of the likely period where sorptive process will function. Cores should be available from the
Gl design investigation (e.g., determination of depth to groundwater, soil structure, and subsurface hydraulic
conductivity).
It is important to refine the vadose zone monitoring. A better understanding of the vadose zone is essential
to provide guidance on Gl placement. Municipalities and communities would need to understand the zone of
influence and how that changes with local soil structure to design and implement Gl.
We need to improve techniques to measure the hydrology in the vadose zone. Protocols for monitoring vadose
zone water movement that are cost effective, easily understood, autonomous, and reliable are needed to
document long-term Gl performance. This would allow users to document the whole life costs and benefits.
Further characterization of subsurface materials with electrical conductivity profiling, electrical resistivity
imaging and borehole geophysics is recommended. Most of the EC profiling occurred on the western portion of
the site. Additional EC profiling south and east of the infiltration gallery will provide a better understanding of
the heterogeneity and anisotropy of subsurface materials that control groundwater flow. Additional ERI profiling
immediately south of the infiltration gallery prior to and following natural or anthropogenic recharge events,
over time may provide insight related to the impact of permeability reductions on the base of the gallery. Any
additional wells installed on site should be logged with borehole geophysical tools including natural gamma and
induction conductivity.
Another research need is to understand the background water quality prior to the initiation of Gl. Background
groundwater quality should be monitored for an extended period of time (greater than one year) to understand
the temporal variations in groundwater quality. In addition, if SPW is to be monitored the background SPW
chemistry should also be established.
Although there has been research looking at stormwater quality, site specific stormwater quality is critical to
understanding impacts to groundwater. Each site has its own unique chemical signature that will likely not be
captured at other locations. The unique chemical signature will be the result of environment, land usage, and
human activities for example.
180
-------
References
7NT. 2015. Geotechnical Exploration CSO 190 Green Infrastructure Louisville, Jefferson County, Kentucky G094-Ky. 47.
6 CRR-NY §703.6. 2019. Compilation of Codes Rules and Regulations of the State of New York. Title 6. Department of Environmental
Conservation. Chapter X. Division of Water Resources. Subchapter A. General Article 2. Classification and Standards of Quality and
Purity. Part 703. Surface Water and Groundwater Quality Standards and Groundwater Effluent Limitations.
Batabyal, A.K. and Gupta, S. Fluoride-Contaminated Groundwater of Birbhum District, West Bengal, India: Interpretation of Drinking and
Irrigation Suitability and Major Geochemical Processes Using Principal Component Analysis. Environ. Monit. Assess. 2017.189. 369.
Battelle. Wise and Denton County Retrospective Case Study Characterization Report. Submitted to American Petroleum Institute and
America's Natural Gas Alliance. Battelle Contract No. CON00011206. 2013.124 pp.
Bedan, E.S. and J.C. Clausen. Stormwater Runoff Quality and Quantity from Traditional and Low Impact Development Watersheds (1).
J. Am. Water Resour. As. 45. 2009. 998-1008.
Berland, A.; Shiflett, S.A.; Shuster, W.D.; Garmestani, A.S.; Goddard, H.C.; Herrmann, D.L.; and Hopton, M.E. The Role of Trees in Urban
Stormwater Management. Landscape Urban Plan. 162. 2017.167-177.
Bethke, C.M.; Farrell, B.; Yeakel, S. GWB Essentials Guide. The Geochemist's Workbench Release 12. Aqueous Solutions, LLC.
Champaign, IL. 2018a. 198 pp.
Bethke, C.M.; Farrell, B.; Yeakel, S. GWB Reaction Modeling Guide. The Geochemist's Workbench Release 12. Aqueous Solutions, LLC.
Champaign, IL. 2018b. 208 pp.
Bora, M. and Goswami, D. Water Quality Assessment in Terms of Water Quality Index (WQI): Case Study of the Lolong River, Assam, India.
Appl. Water Sci. 2017. 7. 3125-3135.
Boudreault, J.-P.; Dube, J.-S.; Chouteau, M.; Winiarski, T.; and Hardy, E. Geophysical Characterization of Contaminated Urban Fills.
Eng. Geo!. 2010. 116. 196-206.
Bouwer, H. and Rice, R.C. A Slug Test Method for Determining Hydraulic Conductivity of Unconfined Aquifers with Completely or Partially
Penetrating Wells. Water Resour. Res. 1976.12. 423-428.
Bouwer, H. Artificial recharge of groundwater: Hydrogeology and Engineering. Hydrogeol J. 2002.10.121-142.
Brattebo, B.O. and Booth, D.B. Long-Term Stormwater Quantity and Quality Performance of Permeable Pavement Systems. Water Res.
2003. 37. 4369-76.
Brown, R.M.; McClellan, N.I.; Deininger, R.A.; and O'Conner, M.F. A Water Quality Index - Do We Dare? Water and Sewer Works. 1970.
117. 339-343.
Brumley, J.; Marks, C. Chau, A.; Lowrance, R.; Huang, J.; Richardson, C.; Acree, S. Ross, R.; and Beak. D. The influence of green
infrastructure practices on groundwater quality: The state of the science. EPA/600/R-18/227. U.S. Environmental Protection Agency.
Office of Research and Development, National Risk Management Research Laboratory. Ada. 2018.
Burian, J.B. and Edwards, F.G. 2002. Historical perspectives of urban drainage. In: Proceedings of 9th International Conference on Urban
Drainage (9ICUD), Portland, OR. American Society of Civil Engineers. 2002.
Butler, J. J., Jr. The Design, Performance, and Analysis of Slug Tests. Lewis Publishers. Boca Raton. 1997.
Commonwealth of Kentucky. 2016. Surface Water Standards. 401 KAR 10:031.
Craig, H. Standard for Reporting Concentrations of Deuterium and Oxygen-18 in Natural Waters. Science. 1961.133.1833-1834.
Craig, H. Isotopic Variations in Meteoric Waters. Science. 1961.133.1702-1703.
Cude, C.G. Oregon Water Quality Index: A Tool for Evaluating Water Quality Management Effectiveness. J. Am. Water Resourc. As. 2001.
37. 125-137.
181
-------
Daugherty, D.D.; and Karel, S.F. Degradation of 2,4-Dichlorophenoxyacetic Acid by Pseudomonas cepacian DBOl(pROlOl) in a Dual-
Substrate Chemostat. Appl. Environ. Microbiol. 1994. 60. 3261-3267.
de Jonge, L.W.; Kjaergaard, C.; and Moldrup, P. Colloids and Colloid-Facilitated Transport of Contaminants in Soils. Vadose Zone J. 2004. 3.
321-325.
Dietz, M.E. Low Impact Development Practices: A Review of Current Research and Recommendations for Future Directions. Water Air Soil
Pollut. 2007. 186. 351-363.
Domenico, P.A.; and Schwartz, F.W. Physical and chemical hydrogeology. John Wiley and Sons. New York, NY. 1990. 824 p.
Eckart, K.; McPhee, Z.; and Bolisetti, T. 2017. Performance and Implementation of Low Impact Development-A Review. Sci. Total Environ.
2017. 607. 413-432.
Fletcher, T.D.; Shuster, W.; Hunt, W.F.; Ashley, R.; Butler, D.; Arthur, S.; Trowsdale, S.; Barraud, S.; Semadeni-Davies, A.; Bertrand-Krajewski,
J.-L.; Mikkelsen P.S.; Rivard, G.; Uhl, M.; Dagenais, D.; and Viklander, M. SUDS, LID, BMPs, WSUD and More - The Evolution and
Application of Terminology Surrounding Urban Drainage. Urban Water J. 2015.12. 525-542.
Frye, J.C., and Leonard, A.B. Pleistocene geology of Kansas. Kansas Geological Survey Bulletin 99.1952. 230 p.
Ge, F.; Zhu, L.; and Wang, J. Distributions of Chlorination Products of Phenols Under Various pHs in Water Disinfection. Desalination. 2008.
225. 156-166.
Gebrehiwor, A.B.; Tadesse, N.; and Jigar, E. Application of Water Quality Index to Assess Suitability of Groundwater Quality for Drinking
Purposes in Hantebet Watershed, Tigray, Northern Ethiopia. J. Food Agric. Sci. 2011.1. 22-30.
Goodin, D.G.; Mitchell, J.E.; Knapp, M.C.; and Bivens, R.E. Climate and weather atlas of Kansas: An introduction. Educational Series 12.
Kansas Geological Survey. 1995. Reprinted 2004.
Griffiths, W.C.; Camara, P.; and Lerner, K.S. Bis-(2-ethylhexyl) phthalate a Ubiquitous Environmental Contaminant. Ann. Clin. Lab. Sci. 1985.
15.140-151.
Gunaratne, G.L.; Vogwill, R.I.J.; and Hipsey, M.R. Effect of Seasonal Flushing on Nutrient Export Characteristics of an Urbanizing, Remote,
Ungauged Coastal Catchment. Hydrolog. Sci. J. 2017. 62. 800-817.
Hansen, K.M.S.; Willach, S.; Antoniou, M.G.; Mosbael, H.; Albrechtsen, H.-J.; and Andersen, H.R. Effect of pH on the Formation of
Disinfection Byproducts in Swimming Pool Water- Is Less THM Better? Water Res. 2012. 46. 6399-6409.
Hillel, D. Environmental Soil Physics. Academic Press. San Diego. 1998. 771p.
Hou, X.; Li, T.; Vanapalli, S.K.; Xi, Y. Water Percolation in a Thick Unsaturated Loess Layer Considering the Ground-Atmosphere Interaction.
Hydrol. Process. 2019. 33. 794-802.
Hounslow, A.W. Water quality data: Analysis and interpretation. Lewis Publishers. Boca Raton. 1995. 397p.
Hu, J.; Song, H.; and Karanfil, T. Comparative Analysis of Halonitromethane and Trihalomethane Formation and Speciation in Drinking
Water: The Effects of Disinfectants, pH, Bromide, and Nitrite. Environ. Sci. Technol. 2010. 44. 794-799.
Jungerius, P.D.; and ten Harkel, M.J. The Effect of Rainfall Intensity on Surface Runoff and Sediment Yield in the Grey Dunes Along the
Dutch Coast Under Conditions of Limited Rainfall Acceptance. Catena. 1994. 23. 269-279.
K-GCRD-SOP-1092-1. Standard Operating Procedure for Estimation of Hydraulic Conductivity Distribution Using the Tisco/Quantum
Engineering (Tisco/QE) Electromagnetic Borehole Flowmeter. 2010.12p.
K-GCRD-SOP-1103-1. Standard Operating Procedure for Performance of Slug Tests in Saturated Porous Media Using Pneumatic Techniques.
2010. 9p.
K-GCRD-SOP-1132-0. Manual Measurement of Groundwater Levels for Hydrogeologic Characterization. 2010. 4 p.
K-GCRD-SOP-1134-O. Water Level Monitoring Using Automated Pressure Transducer/Data Loggers. 2010. 9 p.
K-GCRD-SOP-1153-0. SOP for Soil Pore Water Sampling Using UMS SIC20 and SICS20 Pore Water Samplers. 2016.16 p.
K-LRTD-SOP-1222-0. Sediment Temperature Profile Logging iUsing iButton Loggers. 16 p.
Kabata-Pendias, A. and Mukherjee, A.B. Chapter 11-2, Trace Elements of Group 2. In: Trace Elements from Soil to Human. Springer-Verlag,
Berlin. 2007. pp 112-123.
182
-------
Kansas Department of Health and Environment (KDHE). 2015. Kansas Surface Water Quality Standards. Tables of Numeric Criteria.
Bureau of Water.
Kaw Valley Engineering, Inc. Geotechnical Engineering Report Forsyth Elementary School Rifle Range Road and Potomac Drive Fort Riley,
Kansas. 2011. Prepared for Geary County Unified School District No. 475. Project No. A10G5339. 2011. 32p.
Kaw Valley Engineering, Inc. Geotechnical Engineering Report Seitz Elementary School Parking Lot 27500 Rifle Range Road Fort Riley,
Kansas. Prepared forZieson Construction Company, LLC. Project No. A14G7065. 2014. 22p.
Kendall, C.; and Coplen, T.B. Distribution of Oxygen-18 and Deuterium in River Waters Across the United States. Hydrol. Process. 2001.15.
1363-1393.
Keys, W.S. Borehole geophysics applied to ground-water investigations. National Water Well Association. 1989. 313 p.
Konrad, C.P. Effects of urban development on floods. USGS Fact Sheet FS-076-03. U.S. Geologic Survey. Tacoma. 2003.
Kortatsi, B.K. Hydrochemical Framework of Groundwater in the Ankobra Basin, Ghana. Aquat. Geochem. 2007.13. 41-74.
Latch, D.E.; Packer, J.L.; Stender, B.L.; VanOverbeke, J.; Arnold, W.A.; and McNeill, K. Aqueous Photochemistry of triclosan: Formation of
2,4-Dichloropheno, 2,8-Dichlorodienzo-p-Dioxin, and Oligomerization Products. Environ. Toxicol. Chem. 2005. 24. 517-525.
Lee, J.G.; Borst, M.; Brown, R.A.; Rossman, L.; and Simon, M.A. Modeling the Hydrologic Processes of a Permeable Pavement System.
J. Hydrol. Eng. 2015. 20.
Madejon, P. Chapter 19, Barium. In: Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability, 3rd ed.
Alloway, B.J., ed. Springer Science + Business Media Dordrecnt. Heidelberg. 2013. pp 507-513.
Matschullat, J.; Ottenstein, R.; and Reimann, C. Geochemical Background-Can We Calculate It? Environ. Geol. 2000. 71. 990-1000.
McGrane, S.J. Impacts of Urbanization on Hydrological and Water Quality Dynamics, and Urban Water Management: A review.
Hydrolog. Sci. J. 2016. 61. 2295-2311.
Milliken, G.A.; Johnson, D.E. Analysis of messy data, Volume 1: Designed experiments, 2nd ed. CRC Press. Boca Raton. 2009. p 45.
Minnesota Stormwater Manual, https://stormwater.pca.state.mn.us/index.php?title=Common_pollutants_of_concern_and_sources_in_
stormwater_runoff. 2019. (accessed July 7, 2019).
Molz, F.J.; Boman, G.K.; Young, S.C.; and W.R. Waldrop, W.R. Borehole Flowmeters: Field Application and Data Analysis. J. Hydrol. 1994.
163. 347-371.
Naranjo, R.C. and Turcotte, R. A New Temperature Profiling Probe for Investigating Groundwater-Surface Water Interaction.
Water Resour. Res. 2015. 51. 7790-7797.
Narr, C.F.; Singh, H.; Mayer, P.; Keeley, A.; Faulkner, B.; Beak, D.; and Forshay, K.J. Quantifying the Effects of Surface Conveyance of Treated
Wastewater Effluent on Groundwater, Surface Water, and Nutrient Dynamics in a Large River Floodplain. Ecol. Eng. 2019.129.
122-133.
National Stormwater Quality Database (NSQD). National Stormwater Quality Database, Version 4.02. March 17, 2015. [Online],
International Stormwater BMP Database, http://www.bmpdatabase.org/nsqd.html. (accessed August 17, 2019).
Ndoye, S.; Fontaine, C.; Gaye, C.B.; and Razack, M. Groundwater Quality and Suitability for Different Uses in the Saloum Area of Senegal.
Water. 2018.10.1837.
NOAA. Climate of Kansas. The National Climatic Data Center (NCDC). National Oceanic and Atmospheric Administration. 2019a.
https://www.ncdc.noaa.gov/climatenormals/clim60/states/Clim_KS_01.pdf. (accessed April 9, 2019).
NOAA. Data Tools: 1981-2010 Normals, Manhattan, KS US. National Centers for Environmental Information. National Oceanic and
Atmospheric Administration. 2019b. https://www.ncdc.noaa.gov/cdo-web/datatools/normals. (accessed April 9, 2019).
NOAA. Data tools: 1981-2010 Normals, Louisville International Airport, KY US. National Centers for Environmental Information.
National Oceanic and Atmospheric Administration. 2019c. https://www.ncdc.noaa.gov/cdo-web/datatools/normals.
(accessed April 9, 2019).
NOAA. Data tools: 1981-2010 Normals, Yakima Airport, WA US. National Centers for Environmental Information National Oceanic and
Atmospheric Administration. 2019d. https://www.ncdc.noaa.gov/cdo-web/datatools/normals. (accessed April 15, 2019).
183
-------
Panno, S.V.; Hackley, K.C.; Hwang, H.H.; Greenberg, S.; Krapac, I.G.; Landsberger, S.; and O'Kelly, D.J. Database for the characterization and
identification of the sources of sodium and chloride in natural waters of Illinois. Illinois State Geological Survey. Open File Series
2005-1. 2005. 51 pp.
Panno, S.V.; Hackley, K.C.; Hwang, H.H.; Greenberg, S.E.; Krapac, I.G. Landsberger, S.; and O'Kelly, D.J. Characterization and Identification of
Na-CI Sources in Ground Water. Groundwater. 2006. 44.176-187.
Pieper, K.J.; Martin, R.; Tang, M.; Walters, L.A.; Parks, J.; Roy, S.; Devine, C.; and Edwards, M.A. Evaluating Water Lead Levels During the
Flint Water Crisis. Environ. Sci. Techno!. 2018. 52. 8124-8132.
Pitt, R.; Clark, S.; and Field, R. Groundwater Contamination Potential from Stormwater Infiltration Practices. Urban Water. 1999.1.
217-236.
Plantmaps.com. Interactive United States Koppen Climate Map. 2019. https://www.plantmaps.com/koppen-climate-classification-map-
united-states.php. Last accessed April 15, 2019.
Price, M. Yakima City Wastewater Manager, Yakima, WA. Personal communication.
Razzaghmanesh, M. and Borst, M. Monitoring the Performance of Urban Green Infrastructure using a Tensiometer Approach. Sci. Total
Environ. 2019. 651. 2535-2545.
Reimann, C.; Filzmoser, P.; Garrett, R.; and Dutter, R. Statistical Data Analysis Explained. John Wiley and Sons, West Sussex, UK. 2008.
343 pp.
Reimann, C.; Fabian, K.; Birke, M.; Filzmoser, P.; Demetriades, A.; Negrel, P.; Oorts, K.; Matschullat, J.; de Caritat, P.; and The GMAS
Project Team. GEMAS: Establishing Geochemical Background and Threshold for 53 Chemical Elements in European Agricultural Soil.
Appl. Geochem. 2018. 99. 302-318.
Roy, S.; and Edwards M.A. Preventing Another Lead (Pb) in Drinking Water Crisis: Lessons from the Washington D.C. and Flint Ml
Contamination Events. Curr. Opin. Environ. Sci. Health. 2019. 7. 34-44.
Rutten, M.M.; Steele-Dunne, S.C.; and van de Giesen, N. Understanding Heat Transfer in the Shallow Subsurface using Temperature
Observations. Vadose Zone J. 2010. 9.1034-1045.
Saeedi, M.; Abessi, 0.; Sharifi, F.; and Meraji, H. Development of Groundwater Quality Index. Environ. Monit. Assess. 2010.163. 327 - 335.
Santana, M.V.E.; Zhang, Q.; and Micelcic, J.R. Influence of Water Quality on the Embodied Energy of Drinking Water Treatment.
Environ. Sci. Technol. 2014. 48. 3084 - 3091.
Schirmer, M.; Leschik, S.; and Musolff, A. Current Research in Urban Hydrogeology - A Review. Adv. Water Resour. 2013. 51. 280 - 291.
Schoeller, H. Qualitative evaluation of groundwater resources. In: Methods and Techniques of Groundwater Investigations and
Development. UNESCO. 1965. 54-83.
Schoeller, H. Geochemistry of groundwater. In: An International Guide for Research and Practice. UNESCO. 1967.1-18.
Schulmeister, M.K.; Butler, J.J.; Healey, M.M.; Zeng, L.; Wysocki, D.A.; and McCall, G.W. Direct-Push Electrical Conductivity Logging for
High-Resolution Hydrostratigraphic Characterization. Ground Water Monitoring & Remediation. 2003. 23. 52-62.
Serodio, P.; and Nogueira, J.M.F. Considerations on Ultra-Trace Analysis of Phthalates in Drinking Water. Water Res. 2006. 40. 2572-2582.
Shah, K.A. and Joshi, G.S. Evaluation of Water Quality Index for River Sabarmati, Gujarat, India. Applied Water Science. 2017. 7.1349-1358.
Shuster, W.D.; Dadio, S.; Drohan, P.; Losco, R.; and Shaffer, J. Residential Demolition and its Impact on Vacant Lot Hydrology: Implications
for the Management of Stormwater and Sewer Systems Overflow. Landscape and Urban Planning. 2014.125. 48-56.
Shuster, W.D.; Burkman, C.E.; Grosshans, J.; Dadio, S.; and Losco, R. Green Residential Demolitions: Case Study of Vacant Land Reuse in
Storm Water Management in Cleveland. Journal of Construction Engineering Management. 2015.141. 06014011.
Singh, H.V.; Faulkner, B.R.; Keeley, A.A.; Freudenthal, J.; and Forshay, K.J. Floodplain Restoration Increases Hyporheic Flow in the Yakima
River Watershed, Washington. Ecological Engineering. 2018.116.110-120.
Smolders, E.; and Mertens, J. Chapter 10, Cadmium. In '.Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability.
3rd ed. Alloway, B.J., ed. Springer Science + Business Media Dordrecnt. Heidelberg. 2013. pp. 283-312.
Sparks, D.L. Environmental Soil Chemistry. Academic Press, San Diego. 1995. 267p.
184
-------
Sposito, G. The Chemistry of Soils. Oxford University Press. New York. 1989. 277p.
Springer, R.K. and Gelhar, L.W. Characterization of large-scale aquifer heterogeneity in glacial outwash by analysis of slug tests with
oscillatory response, Cape Cod, Massachusetts. U.S. Geological Survey Water Resources Investigations Report 91-4034.1991.
Stanford, J.A.; Snyder, E.B.; Lorang, M.N.; Whited, D.C.; Matson, P.L.; and Chaffin, J.L. The reaches project: ecological and geomorphic
studies supporting normative flows in the Yakima River Basin. Washington. 2002.
State of Washington. Washington State Water Quality Standards: Human Health Criteria and Implementation Tools: Overview of key
decisions in rule amendment. Department of Ecology. Water Quality Program. Publication no. 16-10-025. 2016.
Stigter, T.Y.; Riberio, L.; and Dill, A.M.M.C. Application of a groundwater quality index as an assessment and communication tool in
agro-environmental policies-Two Portuguese case studies. J. Hydrol. 327. 2006. 578-591.
Stiles, R.; Yank, I.; Lippincott, R.L.; Murphy, E.; and Buckley, B. Measurement of Drinking Water Contaminants by Solid Phase
Microextraction Initially Quantified in Source Water Samples by the U.S. Geological Survey. Environ. Sci. Technol. 2008. 42.
2976-2981.
Suslow, T.V. Water disinfection: A practical approach to calculating dose values for preharvest and postharvest applications. UC Agriculture
and Natural Resources. California Digital Library, University of California. eScholarship.org. 2001.
Swanson, W.R.; and Lamie, P. Urban Fill Characterization and Risk-Based Management Decisions-A Practical Guide. Proceedings of the
Annual International Conference on Soils, Sediments, Water and Energy. 2010.12. 83-100.
Tedoldi, D.; Chebbo, G.; Pierlot, D.; Kovacs, Y.; and Gromaire, M.C. Impact of Runoff Infiltration on Contaminant Accumulation and
Transport in the Soil/Filter Media of Sustainable Urban Drainage Systems: A Literature Review. Science of the Total Environment.
2016. 569-570.
Teil, M.J.; Blanchard, M.; Moreau-Guigon, E.; Bargnat, C; Alliot, F.; Bourges, C.; and Chevreuil, M. Phthalate Fate in Hydrographic Network
of the River Seine Basin (France) Under Contrasted Hydrological Conditions. Water, Air and Soil Pollution. 2013. 224.1-14.
Terrado, M.; Borrell, E.; de Campos, S.; Barcelo, D.; and Tauler, R. Surface-Water-Quality Indicies for the Analysis of Data Generated by
Automated Sampling Networks. Trends in Analytical Chemistry. 2010. 29. 40-52.
Tockner K.; and Stanford J.A. Riverine Flood Plains: Present State and Future Trends. Environmental Conservation. 2002. 29. 308-330.
Tyagi, S.; Sharma, B.; Singh, P.; and Dobhai, R. Water Quality Assessment in Terms of Water Quality Index. American Journal of Water
Resources. 2013.1. 34-38.
UDFCD. Chapter 5 Rainfall. In: Urban Storm Drainage Criteria Manual: Volume 1. Management, Hydrology, and Hydraulics. Urban
Drainage and Flood Control District. Denver. 2016a. 5-1- 5-14.
UDFCD. Chapter 6 Runoff. In: Urban Storm Drainage Criteria Manual: Volume 1. Management, Hydrology, and Hydraulics. Urban Drainage
and Flood Control District. Denver. 2016a. 6-1- 6-24.
UMS. User Manual TS1 Tensiometer. Miinchen GmbH Miinchen. 2007. www.ums-muc.de.
U.S. EPA. Results of the Nationwide Urban Runoff Program, Volume I-Final Report. U.S. Environmental Protection Agency. Water Planning
Division. PB84-185552. Washington, DC. 1983. https://www3.epa.gov/npdes/pubs/sw_nurp_vol_l_finalreport.pdf.
U.S. EPA. Establishing Background Levels. EPA/540/F-94/030. U.S. Environmental Protection Agency. Office of Solid Waste and Emergency
Response. 9285.7-19FS. Washington, DC. 1995.
U.S. EPA. Assigning values to non-detected/non-quantified pesticide residues in human health food exposure assessments. U.S.
Environmental Protection Agency. Office of Pesticide Programs. 2000. 34 pp.
U.S. EPA. STOrage and RETrieval data available of the World Wide Web (EPA STORET), U.S. Environmental Protection Agency.
Washington D.C. 2019. https://www.epa.gov/waterdata/water-quality-data-wqx.
U.S. EPA. Green Infrastructure Case Studies: Municipal Policies for Managing Stormwater with Green Infrastructure. EPA-841-F-10-005.
U.S. Environmental Protection Agency. Office of Wetlands, Oceans and Watersheds. Washington, D.C. 2010.
U.S. EPA. National Primary Drinking Water Regulations Table. EPA 816-F-09-004. U.S. Environmental Protection Agency. 2017.
https://www.epa.gov/ground-water-and-drinking-water/national-primary-drinking-water-regulation-table. (accessed July 29, 2019).
185
-------
U.S. EPA. 2018 Drinking Water and Health Advisories Tables. EPA 822-F-18-001. Office of Water. U.S. Environmental Protection Agency.
2018. https://www.epa.gov/dwstandardsregulations/2018-drinking-water-standards-and-advisory-tables. (accessed July 29, 2019).
USGS. Effects of Pumping Municipal Wells at Junction City, Kansas, on Streamflow in the Republican River, Northeast Kansas, 1992-94.
U.S. Geological Survey. Water-Resources Investigations Report 96-4130.1996.
USGS. National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance Data. U.S. Geological
Survey. Denver. 2004. https://mrdata.usgs.gov/nure/water/. (accessed October 28, 2016).
USGS. National Water Information System Data (NWIS). U.S. Geological Survey. 2016. https://nwis.waterdata.usgs.gov/usa/nwis/qwdata.
(accessed April 16, 2016).
Varol, S. and Davraz, A. Evaluation of the Groundwater Quality with WQI (Water Quality Index) and Multivariate Analysis: A Case Study of
theTefenni plain (Burdur/Turkey). Environmental Earth Science. 2015. 73.1725-1744.
Vasanthavigar, M.; Srinivasamoorthy, K.; Vijayaragavan, K.; Ganthi, R.R.; Chidabaram, S.; Anandhan, P.; Manivannan, R.; and Vasudevan, S.
Application of Water Quality Index for Groundwater Quality Assessment: Thirumanimuttar Sub-Basin, Tamilnadu, India.
Environ. Monit. Assess. 2010.171. 595-609.
Vengosh, A.; Pankratov, I. Chloride/Bromide and Chloride/Fluoride Ratios of Domestic Sewage Effluents and Associated Contaminated
Ground Water. Ground Water. 1998. 36. 815-824.
Weatherbase. Louisville, Kentucky. 2019. https://www.weatherbase.com/weather/weather-summary.php?s=32427&cityname-Louisvill,+
Kentucky,+United+States+of+America. (accessed on April 9, 2019).
Weatherspark. Average weather in Fort Riley North Kansas, United States. 2019a. https://weatherspark.com/y/8988/Average-Weather-in-
Fort-Riley-North-Kansas-United-States-Year-Round. (accessed April 9, 2019).
Weatherspark. Average weather in Louisville Kentucky, United States. 2019b. https://weatherspark.com/y/15227/Average-Weather-in-
Louisville-Kentucky-United-States-Year-Round. (accessed April 9, 2019).
Willis, M. and Irvine, K.N. Application of the National Sanitation Foundation Water Quality Index in the Cazenovia Creek, NY, Pilot
Watershed Management Project. Middle States Geographer. 1996. 95-104.
YCFCZD. Upper Yakima Comprehensive Flood Hazard Management Plan. Yakima County Flood Control Zone District. 2007.
Young, S. C.; Julian, H.E.; Pearson, H.S.; Molz, F.J.; and Boman, G.K. Application of the electromagnetic borehole flowmeter.
EPA/600/R-98/058. U.S. Environmental Protection Agency. Office of Research and Development. Ada. 1998.
Zaidi, F.K.; Nazzal, Y.; Jafri, M.K.; Naeem, M.; and Ahmed, I. Reverse Ion Exchange as a Major Process Controlling the Groundwater
Chemistry in an Arid Environment: A Case Study from Northwestern Saudi Arabia. Environ. Monit. Assess. 2015.187. 607.
186
-------
&EPA
United States
Environmental Protection
Agency
Office of Research and Development
Washington, DC 20460
------- |