oEPA
United States
Environmental Protection
Agency
EPA/600/R-18/227 | August 2018 | www.epa.gov/research
The Influence of Green Infrastructure
Practices on Groundwater Quality:
The State of the Science
Office of Research and Development
National Risk Management Research Laboratory - Groundwater, Watershed, and Ecosystem Restoration Division
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EPA/600/R-18/227
August 2018
The Influence of Green Infrastructure
Practices on Groundwater Quality:
The State of the Science
by
Jessica Brumley1", Christopher Marks*, Alexis Chau*,
Richard Lowrance*, Junqi Huang*, Cassie Richardson#,
Steven Acree*, Randall Ross*, and Douglas Beak*
+ NRC Post Doctoral Research Associate
Ada, OK 74820
*U.S. Environmental Protection Agency
Office of Research and Development
National Risk Management Research Laboratory
Ada, OK 74820
*ORAU Student Contractor
Ada, OK 74820
Environmental Research Apprenticeship Program
Ada, OK 74820
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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.
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Abstract
Green infrastructure (GI) technologies applied to stormwater are developed to mimic natural infiltration
and hydrologic processes. GI is a design strategy that enhances runoff storage volume, infiltrates runoff,
and contributes to groundwater recharge. Urban development often leads to the removal of vegetation
and soil, and replacing them with large stretches of impervious surfaces. This disturbance of the natural
hydrologic cycle due to urbanization is closely connected to deteriorating urban water quality and
enhanced flood risks.
When GI is used for urban runoff, there are concerns as to how the soils and subsurface
geology/sediments interact with the stormwater runoff constituents, thus providing possible risks of
groundwater quality impairment. Groundwater can be contaminated by many constituents: nutrients,
metals, dissolved minerals, pesticides, other organics, and pathogens. This review provides insight into
the current state of knowledge of the influence of GI on the subsurface environment and groundwater.
All types of GI were assessed, both surface and subsurface infiltration infrastructures from peer-
reviewed literature, published reports, and conference proceedings. Issues addressed include: 1)
pollutant risks that need further research, 2) new infrastructure that has not been researched in depth, and
3) determining local considerations when planning for green infrastructure.
When managing water resources, the tendency for contaminants to move between the ground and
surface water needs to be considered. This requires an understanding of the native soil characteristics in
the unsaturated zone and saturated zone as well as the hydrology. The primary geochemical processes
that need to be considered as stormwater infiltrates are dissolution and precipitation, redox, ion
exchange, adsorption/desorption, complexation/chelation, kinetics, mixing relationships, and colloid-
facilitated transport. Simulation models are a potentially affordable way to predict risk as well as
provide a decision-making tool for implementing GI. While many models are used to assess surface
water and groundwater transport, few integrate GI; those that do integrate GI do not address
groundwater contaminant transport.
The biology of the system can have various impacts. Microorganisms such as bacteria, viruses, and
parasites can be a contamination risk depending on the unsaturated and saturated zone conditions,
incubation time, and native microbial populations. Macrobiological organisms can enhance or cause
complications for green infrastructure, but research on these is limited. Riparian zones do not have any
studies specific to urban GI, but previous studies on riparian zone restoration show they could restore
denitrification to urban streams, induce recharge, and serve as a less manipulative approach for
enhancing infiltration into alluvial groundwater.
Overall, a better understanding of the risks associated with GI is needed to recognize the implications of
GI on a longer temporal scale and wider spatial scale. When implementing GI, the local geology,
climate, hydrology, biology, geochemistry, type of infrastructure, and contaminant loads need to be
carefully considered to reduce risks to groundwater.
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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 contaminants
affect our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory (NRMRL) within the Office of Research and
Development (ORD) is the Agency's center for investigation of technological and management
approaches for preventing and reducing risks from pollution that threaten human health and the
environment. The focus of the Laboratory's research program is on methods and their cost-effectiveness
for prevention and control of pollution to air, land, water, and subsurface resources; protection of water
quality in public water systems; remediation of contaminated sites, sediments and ground water;
prevention and control of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with
both public and private sector partners to foster technologies that reduce the cost of compliance and to
anticipate emerging problems. NRMRL's research provides solutions to environmental problems by:
developing and promoting technologies that protect and improve the environment; advancing scientific
and engineering information to support regulatory and policy decisions; and providing the technical
support and information transfer to ensure implementation of environmental regulations and strategies at
the national, state, and community levels.
As part of the Safe and Sustainable Water Resources Research Program (SSWR) a report on the current
understanding of the potential impacts to groundwater quality from the use of Green Infrastructure (GI)
for stormwater management was initiated as part of the SSWR 5.02.B research effort. The goal of this
report is to enhance scientific information on groundwater impacts and interactions from green
infrastructure. In addition, the effort will provide the basis for long-term research on the effectiveness of
GI as a suite of Best Management Practices for water resources. Potential impacts to groundwater
quality are of at least three general types: 1) direct contamination of groundwater by infiltrated dissolved
and suspended surface contaminants (e.g. microbials, 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. The results of this report will be used to inform current and future GI research efforts on
the potential pathways for changes in groundwater quality, as well as knowledge gaps requiring further
investigation.
Ann Keeley, Acting Director
ORD/NRMRL/GWERD
Ada, Oklahoma
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Table of Contents
Notice/Disclaimer Statement ii
Abstract iii
Foreword iv
Acronyms and Abbreviations x
Acknowledgments xiii
1.0 Introduction 1
1.1 Stormwater Definitions 1
1.2 Urban Stormwater 1
1.3 Green Infrastructure 3
1.3.1 Green Roofs 8
1.3.2 Bioretention or Swales 9
1.3.3 Permeable Pavement 11
1.3.4 Dry Wells 12
1.4 General Information on Groundwater 12
1.5 Contaminants 13
1.5.1 Contaminants in Stormwater 13
1.5.2 Contaminants in Groundwater Receiving Infiltrated Stormwater 14
1.6 Goal of Literature Review 15
2.0 What is Water Quality? 16
2.1 Human Consumption 16
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2.2 Industrial and Domestic Uses 17
2.3 Environmental Water Quality 17
2.4 Stormwater Composition and Potential Contaminants 17
2.5 Naturally Occurring Contaminants 17
3.0 Hydrology 18
3.1 Hydrology 18
3.2 Hydrologic Cycle 18
3.2.1 Water Movement in the Subsurface 18
3.3 Vadose Zone 21
3.3.1 Infiltration 21
3.3.2 Soil Texture 22
3.3.3 Soil Structure 23
3.3.4 Soil Organic Matter 23
3.4 Groundwater 24
3.4.1 Porosity and Permeability 24
3.4.2 Saturated Groundwater Flow 25
3.4.3 Surface Water - Groundwater Connection 25
3.5 Hydrology and Gl 26
4.0 Biology 27
4.1 Microbiology 27
4.1.1 Types of Microbial Contaminants 27
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4.1.2 Sources of Microbial Contaminants 28
4.1.3 Fate and Transport of Microbial Contaminants in Porous Media 29
4.1.4 Saturated Zones 29
4.1.5 Unsaturated Zones 30
4.1.6 Microbial Methods 31
4.2 Macrobiology 31
5.0 Geochemical Processes 33
5.1 Dissolution and Precipitation Reactions 33
5.2 Redox Processes 34
5.3 Ion Exchange Processes 34
5.4 Adsorption/Desorption Processes 34
5.5 Mixing Relationships 35
5.6 Colloids 37
6.0 Models 39
7.0 The Potential for Gl to Impact Water Quality 44
7.1 Sampling Methodology 44
7.2 Review of Potential Contamination from Green Infrastructure Infiltration Studies 45
7.2.1 Infiltration Basins/Recharge Basins 45
7.2.2 Roadside Stormwater Runoff Infiltration Systems 51
7.2.3 Permeable Pavement Systems 54
7.2.4 Roof Runoff Systems 57
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7.2.5 Bioretention Systems 58
7.2.6 Swale Systems 60
7.2.7 Aquifer Storage and Recovery Systems 61
7.2.8 Dry Wells/Diffusion Wells 63
7.2.9 Rain Gardens/Vegetative Strips 64
7.2.10 Riparian Zones 66
7.2.11 Results from Reviews 67
8.0 Conclusions/ Future Research Needs 69
8.1 Conclusions 69
8.2 Future Research 71
9.0 References 73
10.0 Quality Assurance/ Quality Control 99
Appendix 1 100
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Figures
Figure 1. A potential green roof system for the capture of precipitation 9
Figure 2. Diagram demonstrating the features and water movement within a typical
bioretention system and a typical grass swale system 10
Figure 3. Model diagram of a permeable pavement system using interlocking pavers 11
Figure 4. Diagram of a dry well showing the use of a sedimentation tank to collect sediments
and debris; and the use of direct runoff collection 12
Figure 5. The hydrologic cycle for natural, urban, and green infrastructure systems 19
Figure 6. Subsurface water profile 20
Figure 7. Soil textural classes showing the twelve USDA soil textural classes 22
Figure 8. Geochemical Process that are important in determining water quality 33
Figure 9. A chloride mixing curve for the mixing of two waters one containing 15,000 mg/L
chloride and one containing 50 mg/L Chloride 36
Figure 10. A Piper Diagram demonstrating the mixing of ambient groundwater with infiltrated
water that was impacted by road salt application 37
Tables
Table 1. Brief overview of the different terms used for sustainable stormwater management.. 4
Table 2. Some organisms of public health concern in groundwater used for drinking and
associated incubation periods for infection after exposure 27
Table A1. Stormwater organic contaminants 100
Table A2. Classification criteria used for solubility, volatility, and mobility in Table A1 117
Table A3. Possible Inorganic contaminants in stormwater 118
Table A4. Select properties of organic contaminants potentially found in stormwater 121
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Acronyms and Abbreviations
1,1,1-TCA
1,1,1 -Trichloroethane
AEC
Anion exchange capacity
AGI
Acute gastrointestinal illness
A1
Aluminum
AOX
Absorbed organically-bound halogens
As
Arsenic
ASR
Aquifer storage and recovery
AT
Alternative techniques
B
Boron
BMP
Best management practices
BOD
Biochemical Oxygen Demand
Ca
Calcium
CAFOs
Concentrated animal feeding operations
Cd
Cadmium
CEC
Cation exchange capacity
CFU
Colony-forming unit
cr
Chloride ion
CN"
Cyanide ion
Co
Cobalt
C032"
Carbonate ion
COD
Chemical oxygen demand
Cr
Chromium
Cs
Cesium
Cu
Copper
DDT
Di chl orodiphenyltri chl oroethane
DO
Dissolved oxygen
DOC
Dissolved organic carbon
DR3M-QUAL
Distributed routing rainfall runoff model
Eh
Oxidation/reduction potential
F"
Fluoride ion
Fe2+
Iron (II) ion/ferrous ion
FEFLOW
Finite element subsurface FLOW system
Foe
Organic carbon content
GI/GSI
Green infrastructure/green stormwater in:
HC03"
Bicarbonate ion
Hg
Mercury
hr
Hour
HS"
Bisulfide ion
HSPF
Hydrologic stimulation program-Fortran
I"
Iodide ion
IS
Ionic strength
IUWM
Integrated urban water management
K
Potassium
Koc
Organic carbon/water partitioning coeffu
Kow
Octanol/water partitioning coefficient
LID
Low impact development
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LIUDD
Low impact urban design and development
MAR
Managed aquifer recharge
MF
Membrane filtration
Mg
Magnesium
Mn
Manganese
Mo
Molybdenum
MODFLOW
Modular Flow Model
MOTH
Mineral oil type hydrocarbons
MOUSE
Hydroworks and Model of Urban Sewers
MPN
Most probable number
MTBE
Methyl tert-butyl ether
N
Nitrogen
Na
Sodium
nh3
Ammonia
nh4+
Ammonium ion
Ni
Nickel
N02"
Nitrite ion
NO3"
Nitrate ion
NOM
Natural organic matter
NOx
Nitric oxide
NPDES
National Pollutant Discharge Elimination System
ON
Organic nitrogen
ORP
Oxidation/reduction potential
P
Phosphorus
PAHs
Polycyclic aromatic hydrocarbon
Pb
Lead
Pb2+
Lead (II) ion
PCB
Polychlorinated biphenyl
PCE
P er chl oroethy 1 en e/tetrachl or oethy 1 ene
PFAs
Perfluoroalkyl and polyfluoroalkyl substances
pH
measure of acidity or basicity; negative log of hydronium ion concentration
PO43-
Phosphate ion
POC
Particulate organic carbon
PVC
Polyvinyl chloride
Rn
Radon
RZGI
Riparian zone green infrastructure
Sb
Antimony
sc
Source control
SCM
Stormwater control measures
Se
Selenium
SESOIL
Seasonal Soil Compartment Model
S042"
Sulfate ion
SOM
Soil organic matter
SPC
Specific conductivity
SQIDs/ SQUIDS
Stormwater quality improvement devices
STORM
Stormwater Treatment Overflow Runoff Model
SUDS/SuDS
Sustainable urban drainage systems/Sustainable drainage systems
SUTRA
Saturated-unsaturated transport
SUWM
Sustainable urban water management
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SVOCs
Semi-volatile organic compounds
SWAP
Soil Water Atmosphere and Plant
SWMM
Stormwater Management Model
SWS
Stormwater sediment
TDS
Total dissolved solids
THC
Total hydrocarbons
TKN
Total kjeldahl nitrogen
TMDLs
Total maximum daily loads
TN
Total N
TOC
Total organic carbon
TOUGH
Transport of Unsaturated Groundwater and Heat
TP
Total P
TPH
Total petroleum hydrocarbons
TSS
Total suspended solids
TVS
Total volatile solids
U
Uranium
U.S.
United States
USD A
United States Department of Agriculture
US EPA
United States Environmental Protection Agency
USGS
United States Geological Survey
V
Vanadium
voc
Volatile organic compounds
WBDOSS
Waterborne disease outbreak surveillance system
WQ-COSM
Water Quality Capture Optimization and Statistics Model
WSUD
Water sensitive urban design
yr
Year
Zn
Zinc
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Acknowledgments
The authors would like to acknowledge the Safe and Sustainable Water Resources National Program
Office for the funding this research effort and acknowledge Dr. Matthew Hopton, Dr. Hale Thurston,
and Dr. Christopher Impellitteri for their oversight of the Green Infrastructure projects. The authors
would also like to thank Ms. Rebecca Foster, Ms. Susan Mravik, and Dr. Ann Keeley for their
management of the ORAU Student Contractors, ERAP Student Interns, and NRC Post Doctoral
Research contracts. An additional thanks to Ms. Rebecca Foster for her management of the STICS
clearance process. A thanks to Mr. Steve Vandegrift for his QA support for this research effort.
We would also like to thank Ms. Diana Redmond and Ms. Renae Cochran for their support related to
tracking of funding and allocation of the funding for this research. The authors would like to thank
Ms. Kathy Tynsky for her graphic support and the final formatting of this report. We would also like to
thank the GWERD management team, Dr. Ann Keeley, Dr. David Jewett, Dr. Richard Wilkin, and
Dr. Mary Gonsoulin for their help and oversight of this project. We would also like to thank
Mr. Hisham Rafi and Mr. Brian Ramsey for their computer support. In addition, the authors would like
to thank Dr. Michael Borst for his helpful suggestions and insights. Finally, the authors would like to
thank the two external technical reviewers, and the three internal technical reviewers, who provided
constructive comments which improved this report.
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1.0 Introduction
The disturbance of the natural hydrologic cycle due to urbanization is closely connected to deteriorating
urban water quality, often from nonpoint source pollution. This creates an increased risk to groundwater
quality because of new pathways for contaminant introduction into groundwater, chemicals associated
with the nonpoint source pollution from anthropogenic activities, and wastewater exposure (Schirmer et
al., 2013). Due to risks to groundwater quality, it has been suggested that green infrastructure (GI) not
be used in areas with potentially high contaminant loading, i.e., recycling centers, gas stations, and
brownfields (Dietz, 2007). When infiltrating devices are installed and used for urban runoff, there are
concerns as to how the soils interact with the stormwater runoff pollution while infiltrating into the
subsurface, thus providing possible risks of groundwater quality impairment from areas with potentially
high contaminant concentrations (Tedoldi et al., 2016). Few studies address whether green infrastructure
technology can be a source or sink for stormwater contaminants, or whether they pose the risk of
groundwater contamination.
The US EPA (2010) produced a report that discussed various case studies of green infrastructure
implemented throughout the United States. Policies, goals, and incentives for using green infrastructure
in flood mitigation scenarios were discussed. Although infiltration technology was promoted, the
effects on groundwater quantity or quality were not addressed. Various reviews of green infrastructure
have been done over the years and most focused on the hydrology, or surface and underdrain water
contamination (Bedan and Clausen, 2009; Eckart et al., 2017). Also, most studies just focus on one type
of GI, such as permeable pavements (Brattebo and Booth, 2003) or vegetative swales to name a few.
There are few studies on GI designs and most of them rely on assumptions from surveys from local
governments and models of GIs, and many do not incorporate monitoring GI after installation for
groundwater quality (Bedan and Clausen, 2009). The purpose of this report is to give a review of the
literature that covers green infrastructure and its impacts on groundwater quality. This includes an
overview of the uses of GI for stormwater infiltration, the hydrology and geochemical aspects that
should be considered, biological concerns, and models that have been used.
1.1 Stormwater Definitions
Stormwater runoff is defined as runoff from rainfall or snowmelt events over pervious or impervious
surfaces (Minnesota Pollution Control Agency, 2018). As part of the Clean Water Act (33 U.S.C. 1251
et seq., 1972) and the 1987 Water Quality Act (33 U.S.C. 1251 et seq., 1987), the U.S. Environmental
Protection Agency (US EPA) developed a stormwater permitting program (33 U.S.C. 1251 - 1376,
2002). Through the National Pollutant Discharge Elimination System (NPDES), permits are required for
municipal, industrial, and construction site stormwater runoff. Stormwater is defined in the CFR
122.26(13) (NPDES) as "stormwater runoff, snow melt runoff, and surface runoff and drainage."
Industrial stormwater runoff is defined as "discharge from any conveyance that is used for collecting and
conveying storm water and that is directly related to manufacturing, processing or raw materials storage
areas at an industrial plant" (CFR 122.26(14) (NPDES) (Protection of Environment, 1990). Stormwater
runoff quantity and quality can reflect local geology and anthropogenic activities (Galfi et al., 2017).
1.2 Urban Stormwater
Urban stormwater runoff has been a documented issue for society dating back to 3000 B.C. and an
engineering concern since the late 1800s in populous areas (Burian and Edwards, 2002; Dietz, 2007;
Fletcher et al., 2015). Historically, the primary objective has been to move the stormwater quickly away
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from urban areas, and is now the leading cause of impairments in our nations waterways (Dietz, 2007;
US EPA, 2002; Fletcher et al., 2015). Today, many municipalities are seeing stormwater as an
opportunity for additional water supply, increasing biodiversity, and improving microclimates, thus
providing an opportunity for development of holistic approaches to urban stormwater management
(Ashley et al., 2013; Fletcher et al., 2015). Stormwater reuse is triggering a paradigm shift from
stormwater being considered a contaminant and a flood risk to a resource that can solve the pollutant
and flood risk issue (Walsh et al., 2012; Marlow et al., 2013; Jiang et al., 2015).
Urban development often leads to the removal of vegetation and soil, replacing them with large stretches
of impervious surfaces such as roads, buildings, parking lots, and driveways (Konrad, 2003; US EPA,
2010). A consequence of imperviousness is an increase in peak discharge and flood frequency on
streams; these effects are more pronounced for moderate storms after dry periods. Urban development
thus increases the chances of flooding (Konrad, 2003). Peak discharge and flooding are influenced by
the intensity of the storm, storm duration, snowmelt, topography and geology of the stream basin,
vegetation, and hydrologic conditions before the storm event. Human activities can influence peak
discharge by modifying these factors (Konrad, 2003). Runoff from urban development leads to
unnaturally high volumes of stormwater that erode stream banks; thus, large amounts of sediment can
enter downstream water bodies (US EPA, 2010).
Traditional municipal stormwater management, referred to as gray infrastructure, includes pipes, sewers,
and drainage networks developed alongside these impervious surfaces to either convey water to
treatment facilities or rapidly move the water from the urban area and downstream into receiving waters
(Berland et al., 2017; Konrad, 2003). This moves water out of the watershed, upsetting the hydrological
balance by preventing streams and groundwater from being recharged (US EPA, 2010). For
undeveloped areas, if storage capacity is reached in a natural area (forest/grassland) after a precipitation
event, the stormwater runoff flows through the soil and becomes a subsurface flow; this would recharge
aquifers or discharge into stream networks. The impermeable surfaces from urban areas create less
storage capacity and accelerate the runoff where it flows faster than either overland and subsurface flow
(Konrad, 2003).
Urban areas often use separate stormwater or combined stormwater management systems (Berland et al.,
2017). Separate systems use separate pipes for stormwater and wastewater where stormwater is
generally untreated and sent to surface waters. These are often in suburban areas or renovated urban
areas. In combined systems stormwater and wastewater are carried through the same conveyance
structure to a treatment facility. There is often limited storage capacity at the treatment facility and this
increases the risk of overflowing and contaminating surface water bodies (Berland et al., 2017).
Sometimes both these systems will lead to excessive soil moisture or raise shallow groundwater tables
that flow into sewers and lead to overflows (Berland et al., 2017).
Not including major natural disasters such as hurricanes, flood damage in the United States averages $6
billion annually (US EPA, 2010). More local governments are anticipating future flood risks, driving the
development of additional green infrastructure systems to protect floodplains and prevent flood damage
(US EPA, 2010). Various methods can be used for reducing flood hazards in urban areas including
making flood prone areas into parks and playgrounds; making buildings and bridges elevated; use of
floodwalls and levees; constructing buildings to withstand temporary inundation; use of drainage
systems with increased capacity; using rooftops and parking lots to store water; and promoting
infiltration and storage of water into the soil column through infiltration trenches; permeable pavements;
soil amendments; and reducing the impermeable surface area (Konrad, 2003).
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1.3 Green Infrastructure
Green infrastructure (GI) has hydraulic and hydrologic benefits that are developed to more closely
mimic natural infiltration and hydrologic processes. GI design strategy can retain storage volume,
infiltrate runoff, and contribute to the groundwater recharge. GI can reduce the stress on the wastewater
system, reduce combined sewer overflows to receiving water, reduce peak flow, restore impaired waters,
and improve watershed health (Bedan and Clausen, 2009; US EPA, 2010; Tedoldi et al., 2016). The
terminology used for green stormwater infrastructure varies from country, agency, and municipality. A
summary of these terms can be seen in Table 1. While various terminology is used to describe these
infrastructure designs, they all share two core principles: restore the natural hydrology, and reduce
flooding risks from storm events.
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Table 1. Brief overview of the different terms used for sustainable stormwater management.
Abbreviated Name
Full Name
History of Term
Reference
BMP
Best management
practices
• Used in North America
• Term coined as part of the Clean Water Act
• Used since 1949 for managing agricultural land
• Now a universal term for pollution prevention (Pollution
Prevention Act)
• Must satisfy wastewater permit applications (NPDES)
• Encompasses non-structural and structural practices
Fletcher et al., 2015
SUDS/SuDS
Sustainable urban drainage
systems/Sustainable
drainage systems
• Commonly used in the UK and Scotland
• First used in the late 1980s-1992
• Sustainable drainage triangle: quantity, quality, and
habitat
• SuDS are usually a train of technologies that work
together
Fletcher et al., 2015
AT
Alternative techniques
• Also known as: Compensatory techniques or Techniques
Alternative
• French term since early 1980s
• Solves drainage and pollution problems
• Also used to improve quality of life
• Aimed to counter the effect of urban expansion
Fletcher et al., 2015
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Table 1. Brief overview of the different terms used for sustainable stormwater management.
Abbreviated Name
Full Name
History of Term
Reference
SCM
Stormwater control
measures
• A term the National Research review suggested should
replace the term "best management practices"
• BMP is vague and practices were not always the best.
• Refers to structural and non-structural control.
• Now used by the U.S. Federal Highway Administration
and many state departments of transportation
Fletcher et al., 2015
LID
Low impact development
• Common in North America and New Zealand
• Minimizes cost of stormwater management
• "Design with nature approach"
Fletcher et al., 2015
LIUDD
Low impact urban design
and development
• Term primarily used in New Zealand
• Less about managing flow regimes and
• More focused on pollution prevention
• Focus on ecosystem health
• Merges with indigenous Maori perspectives
Fletcher et al., 2015
WSUD
Water sensitive urban
design
• Started in Australia in the 1990s
• Manage the water balance
• Maintain and/or enhance water quality
• Encourage water conservation
• Maintain water related environmental and recreational
opportunities
• Stormwater management is a subset of WSUD
• Encompasses all aspects of urban water cycle
management
Fletcher et al., 2015
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Table 1. Brief overview of the different terms used for sustainable stormwater management.
Abbreviated Name
Full Name
History of Term
Reference
IUWM
Integrated urban water
management
• First used in the 1990s
• Used as a broad term
• Relates to managing all parts of the water cycle in a
catchment
• Combines management of water supply, groundwater,
wastewater and stormwater
Fletcher et al., 2015
SC
Source control
• Initially term for on-site stormwater systems.
• 1980s, defined as a subset of onsite detention techniques
• Term used in "Urban Drainage Design Guidelines" in
Ontario and Vancouver
• As LID term became more common, SC was known as
small scale practices used throughout a watershed.
• Reproduce or maintain pre-development hydrological
conditions
MetroVancouver, 2012;
Fletcher et al., 2015
GI/GSI
Green infrastructure/Green
stormwater infrastructure
• 1990s, United States
• Concept and process that goes beyond stormwater
• Originated in landscape architecture and ecology
• Realized by the USEPA to use for stormwater
management
• Used interchangeably with BMPs and LIDs
• Network of decentralized stormwater management
practices that reduces runoff and improves health of
surrounding waterways through capturing and infiltrating
stormwater
Fletcher et al., 2015
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Table 1. Brief overview of the different terms used for sustainable stormwater management.
Abbreviated Name
Full Name
History of Term
Reference
SQIDs/SQUIDs
Stormwater quality
improvement devices
• Significant local use in Australia
• Coined by the Brisbane City Council.
• Used primarily in conference communications and in
relation to Australian studies.
• Use has diminished in recent years
• "Quality" implies one goal is being achieved (water
quality)
Fletcher et al., 2015
SUWM
Sustainable urban water
management
• An internationally used term, in conjunction with the UN's
push to address sustainable development for the future
• Followed the World Commission on Environment and
Development.
• Goal is to manage the urban water cycle
• Produce more benefit than more traditional, centralized
approaches.
• A more integrated approach for water supply, sewerage,
and stormwater management.
Larsen and Gujer 1997;
Marlow et al., 2013
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Eckart et al. (2017) defined LID as practices that retain water onsite and promote infiltration to protect
surface water quality. Thus, predevelopment volume runoff is maintained through ground storage,
infiltration, evapotranspiration, and reducing the peak flows (Eckart et al., 2017). There are two
categories of stormwater management technologies that can reduce the impervious area of the watershed
or area that is connected to the stormwater systems: retention and infiltration based technologies
(Fletcher et al., 2013; Eckart et al., 2017).
Historically, GI was considered the green spaces in an urban area, now the terminology is used to refer
to BMPs for wastewater/stormwater management (Berland et al., 2017). Green infrastructure can be
used over a wide variety of watersheds including residential, commercial, industrial, car parks, roads and
highways (Tedoldi et al., 2016). Green infrastructure generally has a more spatially distributed approach
to stormwater treatment that treats the concentrations of contaminants in smaller hydrologic quantities
before reaching end-of-pipe distribution, such as a stream, river, or reservoir. (Dietz, 2007) Traditional
end-of-pipe stormwater often accumulates large masses of contaminants which can be prevented with
more distributed green infrastructure.
Green stormwater infrastructure that encourages infiltration falls into two categories: surface infiltration
and subsurface infiltration. The purpose of either category is to redirect any stormwater runoff from the
surface into subsurface environments (Pitt et al., 1999). In surface infiltration, water is infiltrated from
the surface thus mimicking natural ecosystem processes. Surface infiltration structures include
bioretention, grass filters, constructed wetlands, and bioswales. Subsurface infiltration is when water is
directly infiltrated into the vadose zone (subsurface), with or without pretreatment. Examples of
subsurface infiltration are permeable pavement, french drains, soak-a-ways, and dry wells. Green roofs
are not considered infiltration technologies, but their use for stormwater management can be used in
conjunction with infiltration devices. The efficiency of these infiltration technologies is dependent on
the local hydrogeological conditions and brief descriptions of some of the most common GI
technologies are discussed below.
1.3.1 Green Roofs
Green roofs are designed to collect the rainwater and retain it, thus slowing down the runoff from roofs
(Figure 1). While they are designed to retain water and pollution, sometimes they can contribute to
stormwater pollution through leaching from the materials and substrates used. Although green roofs are
not designed to encourage infiltration into groundwater, the runoff from the roofs can become a part of
the surface stormwater, thus contributing to the surface stormwater pollution. If groundwater infiltration
systems are used alongside the green roofs, this could be a source of contamination. The contaminants of
concern are nutrients and metals. Nutrient retention and leaching can come from the flushing of the
substrates used on the roofs, from fertilizers, and from atmospheric deposition. Metals can come from
leaching of roof materials and atmospheric deposition (Wang et al., 2017).
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Green Roof
Green roof plants
Growing media
Filter layer
Drainage layer
Protection and —
moisture storage
Roof deck, insulation
and waterproofing
Drains to irrigate, or to
infiltration/GI device
Figure 1. A potential green roof system for the capture of precipitation.
1.3.2 Bioretention or Swales
Bioretention or swales are an urban stormwater practice where native ecosystems and landscape
processes are used to enhance stormwater quality by capturing stormwater runoff from impervious
surfaces (Figure 2). The processes are a combination of microbial processes, infiltration,
evapotranspiration, and plants (Schueler and Holland 2000). The runoff is directed to a bioretention area
from the impervious surface or occasionally a grass filter strip. These grass filter strips act as buffers that
reduce velocities and filter particulates. After the grass filter strip, runoff is directed to a sand trench.
This separates the planting bed from the impervious surface, thus augmenting the infiltration capacity of
the planting bed, slowing the velocity, evenly distributing the incoming runoff, and facilitating flushing
of the contaminants from the surrounding soil (Schueler and Holland 2000). The ponded water will
eventually infiltrate through the organic top soil and to the groundwater or evaporate. This organic
topsoil provides a medium for microorganisms and facilitates plant growth. Plant growth helps infiltrate
runoff and absorb heavy metals, nutrients and hydrocarbons. Considerations when designing a
bioretention system include the size of the drainage area, location, sizing guidelines, water budgets,
nutrient removal capabilities, grading, elevations, soil amendments, organic layers and mulch
amendments, planting concept used, design, number and size of plant material, and species, the plant
growth and fertility, the surrounding land use and cover, local hydrogeology, and system maintenance
(Schueler and Holland, 2000).
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Bioretention
Infiltration
Planting soil
Grass buffer
Runoff
Curb/Pavement
Grass Swale
Maximum water level Temporary ponding
Road/pavement
Limestone/gravel
Infiltration
Native soil
Figure 2. Diagram demonstrating the features and water movement within a typical bioretention system
and a typical grass swale system.
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1.3.3 Permeable Pavement
Permeable pavement can come in the form of pervious concrete, porous asphalt, or interlocking concrete
pavers which can allow stormwater runoff to infiltrate into a temporary storage gallery (Figure 3). These
systems can be used as parking lots, driveways, sidewalks, and low speed roads. The amount of rainfall
that infiltrates through is dependent on the volume of storage below the system and how quickly the
water infiltrates into the soil and groundwater below. These systems have the added benefit of
converting typically impervious urban surfaces to permeable surfaces to better mimic natural hydrology.
When designed properly, these systems can reduce runoff quantity, reduce total suspended solids (TSS)
and total phosphorus (TP) from surface water runoff. Contaminant risks with permeable pavements can
be from metals and oils from vehicular traffic.
Permeable Pavement
Curb
Interlocking concrete pavers
Runoff
Bedding layer
Base layer
Infi tration
Gallery
Native soil
Geotextile
Limestone
Figure 3. Model diagram of a permeable pavement system using interlocking pavers.
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1.3.4 Dry Wells
Dry wells allow stormwater to be discharged to deep soil horizons or even directly into the groundwater.
(Figure 4). Similar to subsurface devices such as infiltration trenches, but are narrow and take up less
surface area space. This allows dry wells to be used to retrofit other infrastructure and/or use in
conjunction with other infrastructure such as green roofs. These systems can be susceptible to clogging
due to sediment and debris, so a sediment settling tank is sometimes used alongside these wells. Due to
the direct infiltration deep into the soil, there is the risk of bypassing geochemical and infiltration
mechanisms that can remove contaminants, putting the groundwater at greater risk for contamination.
Grating
Gravel
Perforated
pipe
Dry Well
Stormwater
Runoff
Sediment tank
Native soil
Infi Itration
Water tab e
Sediment
Figure 4. Diagram of a dry well showing the use of a sedimentation tank to collect sediments and debris, and the use of
direct runoff collection.
1.4 General Information on Groundwater
Groundwater quality is of particular concern because ninety percent of the freshwater supply in the
United States is groundwater, and it accounts for 20% of the total water usage in the United States (U.S.)
(Michigan DEQ, 2015). Groundwater is used throughout the U.S. for public supply, individual wells,
irrigation, livestock, aquaculture, industrial purposes, mining, and thermoelectric production (Maupin et
al., 2014). Daily groundwater withdrawals in the U.S. amount to approximately 80 billion gallons of
water (Michigan DEQ, 2015). Fifty-three percent of Americans drink groundwater, and 37% of the
public water systems are dependent on more than 283,000 wells (Michigan DEQ, 2015). Fifteen million
households are on private wells in the United States (U.S. Census Bureau, 2008).
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Surface water and groundwater are hydrologically connected due to the geologic and hydraulic
conditions that contribute to the flow between them, yet this connectivity is difficult to observe and
measure (Winter et al., 1998; Sophocleous, 2002). When developing water management policies, these
connections are often ignored, which can be significant because anthropogenic processes can affect
these interactions (Winter et al., 1998). Groundwater naturally recharges through the infiltration of
precipitation though pervious surfaces such as grasslands or forests (Pitt et al., 1999). In the past,
infiltrating water was generally considered uncontaminated; urbanization has led to increased pollution
in infiltrating stormwater as the amount of permeable soil surface is reduced. (Pitt et al., 1999) These
hardscapes reduce the hydrologic sinks (infiltration, transpiration, etc.) that would occur in the non-
urban pervious surfaces, leading to large volumes of runoff and flooding, sewer system malfunction, and
impairment of surface and subsurface water resources (Berland et al., 2017). Sophocleous (2002) said
water and chemical fluxes between groundwater and surface water are heterogeneous and of various
scales, making them a challenge to quantify. The magnitude of the infiltration of surface water into
groundwater (specifically channels) depends upon vadose zone hydraulic properties, storage volume in
the vadose zone, channel geometry, wetted perimeter, flow duration and depth, antecedent soil moisture,
clogging layers and water temperature. The extent of impervious surface, especially in urbanized areas,
and permeability of recharge zones also contributes extensively to the magnitude of surface water
infiltrating to groundwater.
The chemical interactions between surface water and groundwater are controlled by the type of geologic
materials present and the time the water is in contact with these materials. The various chemical
reactions that affect the biological and geochemical characteristics of the basin are acid-base reactions,
precipitation and dissolution of minerals, sorption, ion exchange, oxidation-reduction reactions,
biodegradation, and dissolution and exsolution of gases (Winter et al., 1998). When it comes to
managing water resources, the tendency for contaminants to move between the ground and surface water
needs to be considered (Winter et al., 1998). For example, evapotranspiration by plants removes water,
but not the dissolved salts increasing the soil salinity, and if irrigation follows, the salts can be moved
down gradient into the hydrologic systems (Winter et al., 1998). If ground and surface water can be
treated as one resource, the fate and transport of contaminants can be addressed more effectively (Winter
et al., 1998). It is suggested that more data collection is needed through the coordination between
watershed management municipalities.
1.5 Contaminants
1.5.1 Contaminants in Stormwater
Nonpoint source pollution is a major part of the water quality problems in the U.S. waters. Urban
stormwater runoff and runoff from construction sites is one source that can contribute to excess
nutrients, bacteria, and toxic metals. (US EPA, 2002; Bedan and Clausen, 2009). Different watersheds
can produce different pollutant loads generated by runoff (Tedoldi et al., 2016). Groundwater can be
contaminated by many constituents: nutrients, metals, dissolved minerals, pesticides, other organics, and
pathogens (Pitt et al., 1999). Sources of these contaminants include residues from automobiles, lawn
treatments though fertilizers and pesticides, sewer overflows, and road deicing salts (Pitt et al., 1999).
Although only 3% of the U.S. groundwater has been contaminated, contamination has been found in all
50 states (US EPA, 2002; Michigan DEQ, 2015). The potential for groundwater contamination from
stormwater is based on several factors: contaminant abundance in stormwater; contaminant mobility in
the vadose zone; treatability of the contaminants; and the infiltration process used (Pitt et al., 1999).
While groundwater can be polluted by natural sources, many anthropogenic activities such as agriculture
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application (fertilizers, pesticides, animal manure), septic systems, leaking underground storage tanks,
storm-water drains, improper disposal or storage of waste, and chemical spills at industrial sites can
contribute to contamination (US EPA, 2002). Microbial contamination can come from human and
animal waste, septic tanks, sanitary landfills, and garbage dumps being the major sources. Septic tanks
and large concentrations of animals can also contribute to nitrate pollution. Concentrated animal feeding
operations (CAFOs) where thousands of animals are raised in small spaces produce large amounts of
animal wastes and manures that can contribute to pathogen and nutrient problems in the ground water.
Manures can also contribute to high levels of salts.
1.5.2 Contaminants in Groundwater Receiving Infiltrated Stormwater
Groundwater pollution can naturally come from a variety of sources: microorganisms, radionuclides,
nitrates, nitrites, heavy metals, and fluoride (US EPA, 2002). Bacteria, viruses, parasites and other
microorganisms can be found in groundwater, with shallow groundwater being at the greatest risk, due
to runoff picking up these organisms from wildlife and soils (US EPA, 2002). Radionuclides can be
naturally occurring radioactive elements such as radium (Ra) and uranium (U) that occur in the
underlying rock (US EPA, 2002). Nitrates (NO3") are commonly from human activities, but are
occasionally found naturally. They come from nitrogen compounds breaking down in the soil, and
where the flowing groundwater picks them up (USEPA, 2002). Heavy metals can occur naturally in
rocks and soils containing arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and selenium (Se)
(US EPA, 2002). Fluoride, although it is considered useful for dental health, can occur naturally in
excessive amounts in some areas.
Jiang et al. (2015) assessed the validity of using harvested stormwater for local applications and risks
from microbial contaminants. While this study did not look at groundwater infiltration, it did look at the
possible health hazards from using stormwater that could be transferred to groundwater while using the
various green stormwater infrastructure infiltration practices discussed in this review. Health hazards
include pathogens which introduce risks due to a low dose of microorganisms needed to cause an
infection, acute illnesses, risk of secondary transmission, and the potential for large scale outbreaks.
Heavy metals can come from mining and construction activities. Fertilizers and pesticides are used in
agriculture, golf courses, and suburban lawns and gardens. These chemicals may end up in the
groundwater depending on the types and amount of chemicals used, application process, and local
environmental conditions. The fertilizers can be a source of nitrate pollution, breaking down in the soils
(US EPA, 2002).
A series of four papers in 2017 assessed contaminants of emerging concern from 25 sources and
drinking water treatment plants across the United States (Benson et al., 2017; Conley et al., 2017;
Furlong et al., 2017; Glassmeyer et al., 2017). Three of those drinking water treatment plants had
groundwater as their source serving between 50,000 and 500,000 people and watersheds of 800 km2.
Their land usage was a mix of developed, agriculture, and non-developed with developed urban areas
taking up most the landscape and agriculture the least. The contaminants assessed included
pharmaceuticals, perfluoroalkyl and polyfluoroalkyl substances (PFASs), anthropogenic waste
indicators, inorganic constituents, and microorganisms. Inorganics were the only analytes that showed
significant reduction from the source for the drinking water treatment plant 24, which served the largest
populations. Drinking water treatment plant 5 showed carbamazepine, a medication to treat epilepsy and
neuropathic pain, in both the source and treated water (Glassmeyer et al., 2017).
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1.6 Goal of Literature Review
The goal of this literature review is to determine what research has been done on GI practices with
respect to groundwater quality, and the risks and impacts to the subsurface environment and
groundwater quality. All types of GI are assessed, including both surface and subsurface infiltration
infrastructures. The literature assessed includes peer-reviewed literature, published reports, and
conference proceedings, and provides insight into the current state of knowledge of the influence of GI
on the subsurface environment and groundwater. This report points out research gaps to determine
future needs for GI, including 1) contaminant risks that need further research 2) new infrastructure that
has not been researched in depth and 3) determining local conditions when planning for green
infrastructure.
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2.0 What is Water Quality?
A simple definition of water quality is the chemical, physical, radiological, and biological characteristics
of a water body or groundwater. This simplified definition does not consider any potential uses or
standards that human activities may place on it, but it does provide a framework for determining
standards for potential uses.
Most of the water on Earth is not toxic, but is also not potable. For example, 96% of the total surface
water is seawater in the oceans (Eby, 2004). Since seawater is too salty to safely drink, it is not potable.
Even if seawater is excluded, the remaining water is generally not toxic, but not potable because of other
characteristics.
Surface and ground water quality is a complex topic and is fundamentally coupled to the geology and
ecology of the earth and the waters intended use. For this reason, surface and ground water quality is
often categorized based on its intended uses, such as water for human consumption; water for industrial
and domestic uses; and environmental water quality. These water use categories in turn often have
specific water quality standards or regulations associated with them.
2.1 Human Consumption
Water used for human consumption needs to be potable—that is, safe to drink or prepare food. Water
quality in this case deals with the physical, chemical, biological, and radiologic characteristics that
reduce the likelihood of injury or disease in humans. Characteristics that would cause injury or disease
in humans are often called contaminants, or generically referred to as pollution. Many countries have
established regulations or water quality standards to protect humans from harm due to water
consumption. In the U.S., the U.S. Congress established the Safe Drinking Water Act that authorizes the
U.S. Environmental Protection Agency to limit the amounts of certain contaminants in public water
systems. Under the Safe Drinking Water Act, two basic standards were established: the National
Primary Drinking Water Standards and the National Secondary Drinking Water Standards. The primary
drinking water standards are regulated contaminants that affect human health. The primary drinking
water standards are legally enforceable standards and the U.S. EPA enforces public water systems to
comply with these standards. Secondary drinking water standards concern aesthetic characteristics of
water that affect the odor, taste, and or appearance of the water. These secondary drinking water
standards are not enforceable standards, and the U.S. EPA can only advocate their adoption. There is
also the USEPA Unregulated Contaminant Monitoring Rule which collects data on contaminants that
are present in drinking water, but set under the Safe Drinking Water Act (USEPA, 2017). Finally, it
should be noted that bottled water safety is regulated by the U.S. Food and Drug Administration
(21CFR129).
Some important water quality indicators for water that are to be used for human consumption are:
alkalinity and hardness; pH; taste and odor; major anions and cations (calcium (Ca), magnesium (Mg),
sodium (Na), potassium (K), bicarbonate (HCO3 ), carbonate (CO32"), chloride (CI"), and sulfate (SO42"
)); trace metals (As, Pb, Cd, mercury (Hg), etc.) and trace anions (fluoride (F~), cyanide (CN~), iodide (I~
), phosphate (PO43"), NO3", etc.); dissolved organic carbon (DOC); pesticides; volatile organic
compounds (VOC); semi-volatile organic compounds (SVOC); radiologic elements (radon (Rn), U,
etc.); microorganisms (fecal coliform bacteria, Cryptosporidium, Legionella, etc.); pharmaceuticals; and
personal care products to name a few. Some of these water quality indicators have regulatory limits
associated with them, but it is important to note that many are not regulated. Even indicators that do not
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have regulatory limits can potentially cause harm or are aesthetically unpleasing and need to be
considered in any discussion of water quality for human consumption.
2.2 Industrial and Domestic Uses
Water destined for industrial or domestic uses other than direct human consumption does not necessarily
need to be potable water. In these cases, water quality deals with the physical, chemical, biological, and
radiologic characteristics that reduce the probability of damage to equipment and infrastructure. Important
water quality indicators for industrial and domestic use include: total dissolved solids, hardness, pH, etc., and
depend on the industrial process the water will be used for.
2.3 Environmental Water Quality
Environmental water quality relates to water that may or may not be used for human activities, but is the
water quality that is needed to sustain the ecosystem. Environmental water quality varies geographically and
is related to the regional geology and ecology. Environmental water quality is the physical, chemical,
biological, and radiologic characteristics that will ensure the protection of ecosystems. Like human
consumption, environmental water quality is often regulated for non-drinking water purposes, protection of
fisheries, protection of wildlife, and maintaining healthy ecosystems. For instance, in the U.S., one regulation
that protects environmental water quality is the Clean Water Act.
Important water quality indicators for environmental water quality uses include: physical indicators
(temperature, specific conductance (SPC), total suspended solids (TSS), turbidity, total dissolved solids
(TDS), etc.), chemical indicators (pH, biochemical oxygen demand (BOD), chemical oxygen demand
(COD), dissolved oxygen (DO), hardness, metals, nutrients, inorganic anions, organic compounds), and
biological indicators (bacteria, fish, aquatic plants, insects, etc.). These water quality indicators in some cases
can be considered contaminants, but in other cases these indicators will vary based on the ecosystem, local
geology, and geography.
2.4 Stormwater Composition and Potential Contaminants
There is a considerable body of literature describing the potential chemical components and potential
contaminants in stormwater runoff. These potential chemical components and potential contaminants along
with mobility factors are listed in Appendix 1, Table Al. The components or contaminants are both inorganic
or organic substances. Inorganic substances include major anions, major cations, trace elements, and trace
anions, etc. Organic substances include VOCs, SVOCs, pesticides, NOM, pharmaceuticals, etc. Tables A3
and A4, in Appendix 1, list some chemicals found in stormwater; provide chemical properties that could be
used for determining the fate and transport of the chemicals in various environmental media; and, if
available, regulatory concentrations for the chemical.
2.5 Naturally Occurring Contaminants
When discussing potential contaminants to groundwater, it is important to recognize there are some
contaminants that are part of the natural environment. These naturally occurring contaminants are mainly
elements (As, Pb, zinc (Zn), etc.), inorganic compounds (NO3", ammonia (NH3), sulfate (SO42"), etc.) or
radionuclides (Rn, U, etc.). It is possible through mainly biological processes to produce organic substances
(humic acids, fulvic acids, organic acids, etc.) that could be classified as naturally occurring contaminants if
their concentrations are high enough to cause problems. Although naturally occurring contaminants may not
pose a problem in ambient water, if the water characteristics change, the contaminants could be released and
thus induce problems. The processes that could lead to the release of naturally occurring contaminants will
be discussed in Section 6.
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3.0 Hydrology
3.1 Hydrology
Hydrology is a very important aspect of GI. Since hydrology is not the focus of this review, a
comprehensive review of the relationship of hydrology to GI will not be discussed. However, a basic
discussion of hydrology will be presented below. Comprehensive reviews of hydrology can be found in
Freeze and Cherry (1979), Domenico and Schwartz (1990), and Fetter (2001).
3.2 Hydrologic Cycle
The hydrologic cycle includes evaporation and precipitation at the surface; the ground surface will have
overland flow (surface runoff) once reaching infiltration capacity, infiltration; the vadose zone with
interflow, recharge, capillary rise, and evapotranspiration; and groundwater with evapotranspiration and
base flow (Domenico and Schwartz, 1990; Freeze and Cherry, 1979; Fetter, 2001; Schirmer et al.,
2013). Figure 5 is a diagram of the hydrologic cycle for natural environments, urban impact, and GI
impact. The overland flow, interflow, and base flow feed surface water, and surface water can also
recharge the groundwater. Both surface water and evapotranspiration can return water to the
atmosphere, and the atmosphere returns water back to the ground and surface water via precipitation.
In an urban setting, the hydrologic cycle is interrupted. Before urbanization, groundwater was often
recharged through rainfall, runoff, and snowmelt infiltrating through grasslands and wooded areas
because of their permeable soils (Pitt et al., 1996). As urbanization reduces the pervious surface area
available for infiltration, there is less groundwater recharge and more surface runoff (Pitt et al., 1996).
This loss of soil water storage capacity reduces the base flows: the sum of the deep subsurface flow and
delayed shallow subsurface flow (Walsh et al., 2012). The increased surface runoff can lead to higher
peak discharge and volume, causing an increased frequency in nearby stream flooding (Konrad, 2003).
Once an urban environment is developed, there can be increases in evapotranspiration, surface runoff,
artificial interflow, artificial recharge, and base flow and groundwater withdrawals; there can be
decreases in infiltration, transpiration, and natural recharge (Schirmer et al., 2013). Urban stormwater
runoff brings in environmental flow problems such as excess water that leads to stream degradation,
economic loss, and public health concerns (Walsh et al., 2012; Cizek et al., 2017).
3.2.1 Water Movement in the Subsurface
Groundwater can be vulnerable due to the range of hydrogeologic environments. This makes the
subsurface highly complex and variable from the various geographic locations. It is outside the scope of
this report to discuss these complex groundwater and subsurface interactions, but the reader is referred
to the USEPA report (1985) that describes a method to evaluate the pollution potential of groundwater in
different hydrogeologic settings across the United States.
Once precipitation strikes the earth's surface it can move downward into the soil column through
infiltration, or after the upper soil zone is saturated and met its infiltration capacity, flow across the soil
surface as runoff. The objective of green infrastructure is to reduce runoff and enhance infiltration.
Infiltration will be the focus of this discussion.
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Natural Environment
Natural Cycle Urban Environment Urban Impact Green Infrastructure Gl Impact
Evaporation
M
Precipitation
Surface runoff
Infiltration
Transpiration
Interflow
N^Hal A
recharge
pillar
Capillar/ rise
Surface,
y c
/
~~~~
~~~~
~ ~~~
~~~~
nnnn
n
Upper soil layer
O
Evaporation
H
Precipitation
Green roofs
"g
~~~~
~~~~
i
~~~~
~~~~
r-1
Evapotranspiration
ft
Precipitation
Surface runoff
Infilttation A
TransjjBr
Infiltration basin
or rain garden
Porous/gravel layer
Surface runoff
Infiltration
Transpiratior
Subsurface infrastructure
(unsaturated)
Interflow
Artificial
recharge Capillary ris
recharge
Subsurface infrastructure
i unsaturated)
Interflow
Artificial
recharge ;
Capillary rise
Natural
recharge
Groundwater
Groundwater
Base flow
Base flow
Base flow
(saturated)
W
Groifflffivater
withdrawal
(saturated)
Groundwater
withdrawal
Figure 5. The hydrologic cycle for natural, urban, and green infrastructure systems. Arrows increasing or decreasing in size indicate change from previous hydrologic cycle.
Color of arrow indicates whether it is part of the natural hydrologic cycle (blue), urban hydrology (red), or green infrastructure hydrology (green).
(Modified from Schirmer et al., 2013).
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Water that moves below the earth's surface is called interstitial water (Freeze and Cherry, 1979;
Domenico and Schwartz, 1990; Fetter, 2001). Interstitial water is defined as water in the pore space of
soil, sediment, or rock (Freeze and Cherry, 1979; Domenico and Schwartz, 1990; Fetter, 2001).
Interstitial water can be further divided as shown in Figure 6. These different zones have different
functi ons and characteristics that are important in investigations of groundwater quality in GI systems.
The region of the soil/sediments (henceforth termed soil) which is partially filled with water, partially
aerated, and above the water saturated soil (aquifer) is termed the vadose zone (Figure 6) (Freeze and
Cherry, 1979; Domenico and Schwartz, 1990; Fetter, 2001; Brady and Weil, 2002; Eby, 2004). The
vadose zone water can be further divided into soil water (water in the soil), intermediate vadose water
(water between soil and the capillary fringe), and capillary water (water just above the water table)
(Domenico and Schwartz, 1990).
Vadose Water
Soil Water
adose Zone
Intermediate Vadose Water
Interstitial Water
- W/.J. _ - *'
>
Capillary Water
D
T}
>
J
Water Table
Phreatic Water
(groundwater)
'JP
dj
k_
J
c
Z
L
Water in unconnected pores
Figure 6. Subsurface water profile (modified from Domenico and Schwartz, 1990).
As shown in Figure 6, the zone below the vadose zone, phreatic water, is where all the pores are
saturated with water. Phreatic water can have two parts: phreatic water (groundwater) or water that is
contained in connected pores. The water can move through the connected pores, but water in
unconnected pores is not free to move (Freeze and Cherry, 1979; Domenico and Schwartz, 1990; Fetter,
2001). Phreatic water will be referred to as groundwater in this document and will be discussed in more
detail below
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3.3 Vadose Zone
The transport of water through the vadose zone, also known as the unsaturated zone, is a critical
component of the hydrologic cycle, agriculture, natural resource protection, subsurface weathering, fate
and transport of contaminants, fate and transport of other chemicals and nutrients, and ecological
function (Domenico and Schwartz, 1990; Williams et al., 1998; Brady and Weil, 2002; Eby, 2004). The
movement of water through the vadose zone is challenging under natural conditions because it is a
dynamic process that changes markedly over time and space (Williams et al., 1998). The use of GI or
other enhanced infiltration techniques could further confound the natural dynamic nature of water
movement through the vadose zone.
The vadose zone is not only involved in the transport of inorganic, organic compounds, and microbes
through the soil, but also acts as a sink for them (Brady and Weil, 2002; Eby, 2004). The biological and
geochemical processes that occur in the vadose zone can degrade, transform, sequester, and/or retard the
movement of chemicals (Brady and Weil, 2002; Eby, 2004). Therefore, it is essential to understand how
chemicals behave and are transported in the vadose zone to understand groundwater quality changes that
may occur as the result of infiltration.
3.3.1 Infiltration
Infiltration is the process of water entering the soil, which generally entails a downward movement of
water into the vadose zone through all or part of the soil surface (Hillel, 1998; Williams et al., 1998;
Brady and Weil, 2002). Knowledge of the infiltration process is prerequisite for managing soil water
flux and the transport of contaminants in the vadose zone (Hillel, 1998; Williams et al., 1998). An
abbreviated discussion on the infiltration process will be discussed below.
The infiltration rate is the volume flux of water that moves into the soil as a function of soil surface area
(Hillel, 1998). However, soils do have a maximum infiltration rate, termed the infiltration capacity, and
water supplied will either pond or runoff when the rate exceeds capacity (Hillel, 1998). Hillel (1998)
states that when the infiltration rate is less than the infiltration capacity, water can penetrate the soil as
fast as it is applied; in this case, the infiltration process is termed supply-controlled (or flux controlled).
The infiltration rate is initially high, gradually decreases, and eventually comes to a steady state (Hillel,
1998). Another way to think about this is the infiltration rate decreases until the soil reaches its
infiltration capacity. Once the infiltration capacity is reached, the rate of infiltration tends to approach
zero. The initial water content of the soil affects the suction gradient of the soil, so when comparing a
dry soil to the same soil when wetter, the wetter soil will have a lower suction gradient (Hillel, 1998).
This means that the wetter soil initially is, the faster it reaches a final infiltration rate. Soils with higher
hydraulic conductivity will in general have higher infiltration capacity (Hillel, 1998). If the surface of
the soil is porous (related to the soil texture) the infiltration will be greater, but is still limited by the
infiltration capacity. Physical properties of the soil, such as compaction or dense surface zones can act as
a barrier to infiltration (Hillel, 1998). Under dry soil conditions, hydrophobic organic substances coating
soil particles can severely delay water infiltration (soil water repellency) and enhance runoff (Miiller et
al., 2018). If the soil surface or the soil layers are composed of fine-textured materials, this can impede
the infiltration of water into the surface of the soil and slow the rate of water movement through the soil
(Hillel, 1998). Coarse grained materials can, in some situations, also impede the flow of water through
the soil layers.
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Once the process of infiltration ends, the vertical movement of water does not cease (Hillel, 1998; Brady
and Weil, 2002). Water will continue to penetrate further into the subsurface under the influence of
gravity and suction gradients. This process will distribute water into different depths in the soil. The rate
and time of this redistribution of water depends on the water storage capacity of the soil. Further
discussions on the process of water movement in the vadose zone is beyond the scope of this effort.
3.3.2 Soil Texture
The size distribution of mineral particles found in the soil is referred to as soil texture (Brady and Weil,
2002). A common approach for describing soil texture is the United States Department of Agriculture
(USDA) classification (Brady and Weil, 2002).
0.0
Sand
Figure 7. Soil textural classes showing the twelve USDA soil textural classes.
(Figure modified from Brady and Weil [2002]).
The pure end members of the particle size classes rarely exist in the natural environment. For this
reason, the USDA developed a soil textural class system, which contains 12 different textural classes
(Brady and Weil, 2002). Figure 7 shows the major textural classes as defined by the percentages of sand,
silt, and clay (Brady and Weil, 2002). Soils dominated by sand tend to have low water holding capacity,
a high drainage rate, low decomposition of organic matter rates, low compactability, very low shrink
swell capacity, and high leaching potential. Soils that are dominated by silts tend to have medium to
high water holding capacity, slow to medium drainage rates, medium decomposition of organic matter
rates, low compactability, low shrink swell capacity, and medium leaching potential. Finally, soils
dominated by clays have high water holding capacity, slow to very slow drainage rates, slow organic
matter decomposition rates, high compactability, moderate to very high shrink swell potential, and low
leaching potential (unless cracks are present) (Brady and Weil, 2002).
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3.3.3 Soil Structure
Soil structure is the arrangement and organization of the primary particles in the soil (Brady and Weil,
2002; Hillel, 1998). Soil particles differ in size, shape, orientation, and the associations and interlinking
between other soil particles. Soil particles typically form irregular patterns which can be impossible to
characterize in geometric terms. Soil structure is unstable in nature and is not consistent in time and
space (Hillel, 1998). Finally, soil structure is affected by biological activity, management practices,
changes in climate, and is vulnerable to mechanical and chemical processes (Brady and Weil, 2002;
Hillel, 1998).
The important aspect of soil structure in this discussion is soil pores. Soil pores are basically void spaces
in the soil structure between particles that make up the soil structure (Hillel, 1998; Brady and Weil,
2002). The pore spaces can vary in size and shape which largely determine the role the soil pore plays
(Hillel, 1998; Brady and Weil, 2002). Soil pores facilitate the transport of water, chemicals, and colloids
through the soil profile. The size of soil pores can be classified into three basic categories: micropores,
capillary pores (mesopores), and macropores (Hillel, 1998; Brady and Weil, 2002).
Micropores are one class of soil pores that are generally less than 0.001 mm in diameter (Hillel, 1998;
Brady and Weil, 2002). Micropores are typically found in clayey soils. Water in these narrow pores is
subject to adsorptive forces, and is often different than the water in wider pores (Hillel, 1998).
Micropores, according to Brady and Weil (2002), retain water that plants can use, and can accommodate
most bacteria.
Capillary pores are typically pores that range in size from a few micrometers to a few millimeters
(Hillel, 1998). The water moving through capillary pores obeys the laws of capillarity and Darcy (Hillel,
1998). Brady and Weil (2002) refer to capillary pores as mesopores. These pores retain water after
drainage (Hillel, 1998; Brady and Weil, 2002).
Macropores range in size from a few millimeters to centimeters, and are visible to the naked eye (Hillel,
1998; Brady and Weil, 2002). Examples of macropores are cracks and fissures in soil, decayed root
channels, and earthworm burrows. Water travels through macropores under the influence of gravity, and
the flow through macropores is rapid (Hillel, 1998; Brady and Weil, 2002). Macropores act as barriers to
capillary flow when empty and can act as preferential flow pathways (Hillel, 1998).
Soil structure and soil pore size control water movement through the vadose zone (Brady and Weil,
2002; Hillel, 1998). In a sandy soil, the pore size is generally larger, allowing water to move more
rapidly, whereas soil with a high clay content has a smaller pore size and slows the movement of water.
Also, a well aggregated soil promotes high infiltration rates as there are more pores for water to flow
through.
3.3.4 Soil Organic Matter
Soil organic matter (SOM) is a term used to describe all the organic components of the soil (Sposito,
1989; Brady and Weil, 2002). Therefore, SOM includes the living biomass (living organisms), detritus
(dead identifiable tissue), and humus (nonliving, nontissue) (Sposito, 1989; Brady and Weil, 2002).
Brady and Weil (2002) further subdivide the humus portion of SOM into humic substances (humin,
humic acids, and fulvic acids).
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Soil organic matter (SOM) influences soil physical properties, soil chemical properties, and affects the
biology and ecology of the system (Sposito, 1989; Brady and Weil, 2002). SOM influences many
properties and a complete discussion of this topic is beyond the scope of this report. It is found that
SOM helps to reduce the plasticity, cohesion, and stickiness of clayey soils. More importantly, SOM
increases infiltration rates and water-holding capacity in soils. The increase allows water to more easily
move through the vadose zone.
Soil organic matter in the vadose zone is important for many geochemical processes that occur such as
contributing to ion exchange, sorption of metals, sorption of organic compounds, and soluble organic
compounds are involved in chelation and complexation reactions in the soil. Other important chemical
properties that have not been discussed are that SOM also provides a significant contribution to the pH
buffering capacity in the vadose zone, and humic substances contribute to the weathering process
occurring in the vadose zone (Brady and Weil, 2002). A final contribution of SOM is the formation of
organic colloids, which will be discussed in Section 5.6.
3.4 Groundwater
The transport of water through the saturated zone (phreatic zone) is an essential component of the
hydrologic cycle, agriculture, natural resource protection, subsurface weathering, fate and transport of
contaminants, fate and transport of other chemicals and nutrients, and ecological function (Freeze and
Cherry, 1979; Domenico and Schwartz, 1990; Fetter, 2001; Brady and Weil, 2002; Eby, 2004;). The
movement of water through the saturated zone is an active process that changes over time and space
(Freeze and Cherry, 1979; Fetter, 2001; Domenico and Schwartz, 1990;). The use of GI or other
enhanced infiltration techniques could cause changes to the movement of water through the aquifer. The
movement of groundwater is important for understanding potential impacts to groundwater quality that
could result from GI practices. Therefore, a brief introduction to groundwater hydrology will be
provided, and more detailed discussions on ground water hydrology can be found in Freeze and Cherry
(1979), Domenico and Schwartz (1990) and Fetter (2001).
3.4.1 Porosity and Permeability
Porosity is defined as the void spaces in a rock or unconsolidated material (Freeze and Cherry, 1979;
Domenico and Schwartz, 1990; Fetter, 2001). Equation 1 defines the relationship between total porosity
(n), the void volume (Vv), and the total volume (Vt) as a percentage.
Vv
n = — Equation 1
Vf
It is important to understand that total porosity considers both the connected pores and the unconnected
pores. Since the unconnected pores do not contribute to the movement of water through the saturated
zone, total porosity is not necessarily the best parameter to use in the discussion of water movement
through the saturated zone. The effective porosity, which only considers connected pores, is a more
appropriate parameter to use when discussing the flow of water in the saturated zone.
Permeability can be defined as the ease by which water can move through a porous media, and is
quantified by a rate of appropriated units (Freeze and Cherry, 1979; Domenico and Schwartz, 1990;
Fetter, 2001). The primary importance of permeability is understanding the zones within a porous media
in which water can flow freely, and zones that impede the movement of water through them. Low
permeable media are sometimes referred to as aquitards (Domenico and Schwartz, 1990). Aquitards are
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sometimes referred to as confining layers, as they do not allow free movement between different zones
in the aquifer.
3.4.2 Saturated Groundwater Flow
In saturated sediments or rock, to get water to move requires energy (Freeze and Cherry, 1979; Price,
1985; Domenico and Schwartz, 1990; Fetter, 2001). In a groundwater system, this energy is typically
supplied through hydraulic head or head (Freeze and Cherry, 1979; Price, 1985; Domenico and
Schwartz, 1990; Fetter, 2001). Head is the distance water rises in a column in relationship to a fixed
point (Freeze and Cherry, 1979; Price, 1985; Domenico and Schwartz, 1990; Fetter, 2001). The
difference in heads between two measurement points separated by a fixed distance (also called the
hydraulic gradient) provides the energy that drives groundwater flow. The hydraulic gradient (i) is
shown mathematically in equation 2.
dh hi-h2 „ ,
i = — = Equation 2
dL L
Where hi is the head at point 1, h2 is the head at point 2, and L is the distance between point 1 and
point 2.
As was discussed earlier, the flow through a saturated porous system is related to permeability, i.e., how
easy or hard it is to move water through pore spaces in the media. The permeability measure in
groundwater flow systems is called the hydraulic conductivity (K) (Freeze and Cherry, 1979; Price,
1985; Domenico and Schwartz, 1990; Fetter, 2001). The more permeable the aquifer is, the higher
the K.
In a saturated system, the groundwater flow rate (Q) through a cross sectional area (A) in the aquifer is
described by Darcy's Law (Freeze and Cherry, 1979; Price, 1985; Domenico and Schwartz, 1990;
Fetter, 2001). Equations 3 and 4 demonstrate two different expressions of Darcy's Law.
Q = KiA Equation 3
Q = KAhl Equation 4
3.4.3 Surface Water- Groundwater Connection
Surface water and groundwater are usually connected as a localized process at the sediment/water
interface (Freeze and Cherry, 1979; Price, 1985; Domenico and Schwartz, 1990; Fetter, 2001; Hayashi
and Rosenberry, 2002; Parsons et al., 2004;). It is important to understand this connectivity as it relates
to the hydrology of GI systems and its potential to influence groundwater quality. As was shown in
Figure 5, water that enters the subsurface can be returned to surface water by interflow and base flow.
With respect to water in the vadose zone, it is possible that not all the water that enters is transported to
the aquifer. If the water reaches a layer that is impermeable, or if there are layers in the vadose zone of
contrasting permeabilities, the water may flow laterally and enter a surface water body. This type of
flow is known as interflow (Freeze and Cherry, 1979; Price, 1985; Domenico and Schwartz, 1990),
whereas groundwater that flows from the saturated zone into a surface water body is termed base flow
(Freeze and Cherry, 1979; Price, 1985; Domenico and Schwartz, 1990).
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3.5 Hydrology and Gl
Urban water quantity problems are strongly linked to urban development (Schirmer et al., 2013). Urban
areas have historically relied on groundwater from springs and shallow wells for potable water, but
accelerated urbanization has increasing demands and groundwater supplies are declining (Schirmer et
al., 2013). The over-exploitation of groundwater under urban areas has led to declining water levels,
possible land subsidence, and salt water intrusion in coastal cities (Schirmer et al., 2013).
The goal of GI is to return urban hydrology back to a more natural hydrologic cycle by using retention
and infiltration techniques (Eckart et al., 2017). There are two types of infiltration used in GI: surface
infiltration and subsurface infiltration. Surface infiltration allows the passage of stormwater directly
through the soil surface into the vadose zone. Subsurface infiltration directs the stormwater directly into
the deeper horizons and sometimes directly into the aquifer. The infiltration based techniques can
recharge and restore the groundwater baseflows, although these can be dependent on the site conditions
that influence the performance of the infrastructure (Eckart et al., 2017). Retention based technologies,
such as wetlands and ponds, can reduce the peak flows by storing the stormwater and reducing volume
through evapotranspiration and percolation into the vadose zone, but this does increase the duration of
the flows (Eckart et al., 2017).
Concerns with GI include: fluctuations in groundwater levels, limitations with large precipitation events,
clogging, and soil limitations. Increased groundwater levels due to mounding have been observed in
previous stream restoration projects, bioretention cells, and regenerative stormwater conveyance systems
(Hammersmark et al., 2008; Endreny and Collins, 2009; Cizek et al., 2017); though, mounding usually
only occurs for short periods of time after precipitation events (Cizek et al., 2017). For example,
retention basins can recharge and potentially raise the water table. Additional research is needed to
address the biological and geochemical processes that uniquely occur in the vadose zone when
mounding is present. This can adversely impact subsurface infrastructure, and thus negate the benefit
from using green infrastructure to naturalize the urban hydrology (Endreny and Collins, 2009). Various
types of green infrastructure function on different hydrologies. Simulations have shown that physical
properties of the soils and soil depths are important for the infiltration and surface runoff processes
(Xiao et al., 2007; Eckart et al., 2017;). Rainfall volume, duration, and time to peak ratio can impact the
performance of green infrastructure such as grassed swales, green roofs, permeable pavement and
bioretention cells such that the larger the precipitation event, the less effective this infrastructure can be
(Hunt et al., 2008; Qin et al., 2013; Eckart et al., 2017). Hydrology of infiltration is dependent on the
clogging rate of the infrastructure. Line et al. (2012) found there was eventual clogging in bioretention
cells that prevented infiltration, and Bergman et al. (2011) saw decreases in infiltration rates due to
clogging by fine particles in two infiltration trenches over 15 years.
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4.0 Biology
4.1 Microbiology
4.1.1 Types of Microbial Contaminants
Microbial contaminants within stormwater include a range of organisms spanning Eukaryota, Bacteria,
and viruses originating from various sources discussed below. Waterborne disease, especially acute
gastrointestinal illness (AGI), arising from the consumption of contaminated groundwater as drinking
water, is an important potential public health risk that may be influenced by stormwater infiltration
practices. Epidemiological studies have shown that untreated groundwater was the source of-30% of
waterborne disease outbreaks in the U.S. and The Freely Associated States (reported in the Waterborne
Disease Outbreak Surveillance System (WBDOSS)) between 1971 and 2006 (Craun et al., 2010) and
numerous more cases worldwide (Murphy et al., 2017). Within the WBDOSS reported groundwater-
associated outbreaks the primary etiological agents were Bacteria (-20%), viruses (-13%), and parasites
(-8%) (Wallender et al., 2014) and many reports of pathogen presence in groundwater systems under
outbreak and non-outbreak conditions globally (Murphy et al., 2017).
Table 2. Some organisms of public health concern in groundwater used for drinking and
associated incubation periods for infection after exposure. Organisms of interest and
incubation periods reproduced from (Moe, 2007).
Agent
Incubation Period (days)
Bacteria
Campylobacter spp.
3-5
Escherichia coli (pathogenic strains)
<1 -6
Salmonella spp.
<1 -28
Shigella spp.
1 -7
Vibrio cholerae
<1 -5
Yersinia spp.
2-7
Protozoa
Cryptosporidium parvum
7 -14
Entamoeba histolytica
14-28
Giardia lamblia (intestinalis)
5-25
Viruses
Astrovirus
1 -4
Enterovirus
3-14
Hepatitis A virus
15-50
Hepatitis E virus
15-65
Norovirus
1 -3
Rotavirus
1 -3
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Reliable recovery and enumeration of some of the pathogens listed in the Table 2 is difficult using
traditional cultivation-based methodologies such as Most Probable Number (MPN) or Membrane
Filtration (MF) with reasonable sample sizes. For this reason, public health officials have been assaying
much more easily cultivated "indicator organisms" for over a century to quantify the potential of public
health-related microbial contamination of water sources. Ideal attributes of indicator organisms have
been reviewed in detail by the National Research Council as well as comparisons of enumeration
methods targeting indicator organisms or the specific pathogens of interest (National Research Council,
2004). Briefly, indicator agents should be present in higher populations than the relevant pathogen
within the original source material and correlated with a health risk. Additionally, the indicator should
behave (die-off, filtration, sedimentation, biological survival mechanisms, etc.) similarly to the pathogen
under environmental selection pressures. Fecal indicators such as fecal coliforms, Enterobacteriaceae
spp., and E. coli for enteric bacteria and F+RNA coliphage for enteric viruses are routinely quantified
from water samples for public-health-risk testing. The presence of these indicator agents is indicative of
fecal contamination and correlated with the presence of enteric pathogens and disease risk. With the
development and adoption of molecular biology methods, more sensitive assays for the direct
enumeration of enteric pathogens and host-specific microflora have allowed for better water quality
monitoring and identification of pollution source through microbial source tracking (National Research
Council, 2004). Despite these advances, the cultivation-based MPN and MF methods remain the "gold
standard" accepted procedure for public health monitoring of microbial water quality.
4.1.2 Sources of Microbial Contaminants
Previous studies have demonstrated the high microbial loads of stormwater from a variety of settings
including agricultural, residential, and industrial areas (Geldreich et al., 1968; Mallard, 1980; Page et al.,
2012; Sidhu et al., 2013). Stormwater runoff microbial populations vary between any given storm event
or location based on numerous factors such as: storm intensity, land use practices within the stormflow
area, seasonal/environmental conditions, etc.; however, unified principles in water quality considerations
and human health risks have been obtained through years of investigation (Mallard, 1980; Selvakumar
and Borst, 2006; McCarthy et al., 2008; Page et al., 2016).
A previous study of stormwater collected from various land use settings (urban streets, suburban
business district, and rural) identified contact with soils and impervious surfaces as the primary source
of biological pollutant densities in stormwater runoff (Geldreich et al., 1968). Rainwater was shown to
generally harbor very few pollution indicator organisms (<1/100 mL) with the uncommon detection of
fecal coliforms/Enterococci (<2/100 mL) found to be associated with particulates from soils, insects, or
vegetation (Geldreich et al., 1968).
The impact of livestock on fecal contamination of agricultural runoff is well known, but warm-blooded
animal fecal material is a key source of stormwater runoff pollution within urban and suburban settings
as well. Microbial pollutants have been quantitatively and taxonomically linked to human fecal material
from combined sewage system overflow (Sauer et al., 2011; Sidhu et al. 2012; Sidhu et al., 2013), as
well as the presence of rodents, dogs, cats, and birds within city stormwater runoff events (Geldreich et
al., 1968; Mallard, 1980). Fecal coliforms have been observed at concentrations on the order of 103 -
105 CFUs/mL in urban stormwater with values varying across seasons and land use types (e.g.,
commercial/industrial, low-density residential, high-density residential) (Geldreich et al., 1968; Maestre
and Pitt, 2005; Selvakumar and Borst, 2006; Page et al., 2016). In each study, the highest fecal coliform
loads were found in high-density residential areas.
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Upon entering stormwater flow, microbial contaminants have been shown to be primarily adsorbed to
particulate material within the stormwater rather than existing as planktonic cells and communities
(Characklis et al., 2005). There are very few studies on the dynamics of indicator organisms or
pathogens within stormwater, but the main factors positively influencing removal have been identified
as duration within the surface aquatic environment (time), temperature, and sunlight exposure
(Geldreich et al., 1968; Mallard, 1980; Selvakumar et al., 2007). More detailed survival studies by
Selvakumar and Borst (2006) illustrated that exposure to sunlight resulted in significantly greater,
though organism-specific, reduction rates of indicator organisms and pathogens in stormwater. Based
upon these findings, the authors recommended that BMP for urban stormwater handling emphasize
engineering solutions to extend the lag time between surface runoff and stormwater introduction to
receiving waters while ensuring greater sunlight exposure times to maximize reduction of microbial
pollutants within stormwater (Selvakumar et al., 2007).
4.1.3 Fate and Transport of Microbial Contaminants in Porous Media
Fecal-associated microorganisms are adapted for optimal growth in a habitat characterized by stable
moderate temperature (host body temperature, ~37°C) and high concentrations of labile organic matter
(e.g. ingested food boluses, intestinal mucosa). Groundwater as a habitat is very different than these
conditions with temperatures typically ranging from 5 - 25°C and limited labile organic matter pools.
While gut-associated microbial contaminants are not expected to grow and thrive within the
groundwater environment, their rates of removal are affected by several, often interdependent,
environmental factors.
Overall, research has shown there is a general trend of differential survival for the various contaminant
organism types. Viruses tend to have the longest persistence times within any groundwater environment,
with some observed residual infectivity times of months to years (Charles et al., 2009; Regnery et al.,
2017). Enteric eukaryotes (Cryptosporidium spp. and Giardia spp.) and enteric bacteria typically have
die-off rates of five to ten times, and over one hundred times greater than enteric viruses, respectively
(John and Rose, 2005; Regnery et al., 2017). Within this general trend, physiological differences
between species and even strains of each type of organism result in variations in observed pathogen
removal rates. Pathogen removal or die-off rates are typically reported based upon first order decay
models; however, field and laboratory experiments have shown that biphasic models better approximate
the removal behavior of fecal eukaryotes and viruses within groundwater systems (Charles et al., 2009;
Toze et al., 2010). These studies have shown there is an initial rapid removal phase for the first few days
after introduction, followed by a slower phase two to hundreds of times less than the initial phase that
can lead to months or years of persistence. This biphasic behavior is likely due to a smaller
subpopulation of the contaminant organism that is more resistant to the unfavorable environmental
conditions allowing for greater persistence.
4.1.4 Saturated Zones
Temperature is the most studied, and one of the most important environmental factors influencing
pathogen survival in groundwater, since all chemical and biological processes are influenced by
temperature. Reviews compiling the data of numerous studies evaluating the relationship between
temperature and pathogen survival have shown that across system type, pathogen survival time
decreases with increasing groundwater temperature. (John and Rose, 2005; Foppen and Schijven, 2006;
Regnery et al., 2017).
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Aquifer hydrogeology can affect the physical removal of microbial contaminants from groundwater
through colloid adsorption and size-exclusion processes (Foppen and Schijven, 2006). Filtration is a
physicochemical process wherein the contaminant organisms adsorb to the surfaces of the aquifer
matrix. This process is dependent upon several chemical properties, namely groundwater chemistry
(ionic strength (IS) and pH) as well as cell surface and viral capsid properties such as charge, isoelectric
point, and hydrophobicity (Harvey and Ryan, 2004). Adsorption to solid media surfaces can extend the
persistence of enteric bacteria and viruses within the subsurface 10 to 100 times longer relative to
survival times suspended in groundwater (Hurst et al., 1980; Pachepsky and Shelton, 2011). This
removal is reversible depending upon groundwater chemistry (pH, IS, and salt types) and flow. Several
previous studies have documented increased colloid retention within porous media with increasing IS
and/or increases in cation valence number (e.g. Ca vs. Na) (Fontes et al., 1991; Grolimund et al., 1998;
Redman et al., 1999; Knappett et al., 2008; Zhuang and Jin, 2003). Similarly, the rapid infiltration of
precipitation has been shown to promote the release of adsorbed organisms back into the groundwater
due to increased hydrodynamic shear and decreased IS (Johnson et al., Assemi, 2007; Shen et al., 2007).
In contrast, physical straining is an irreversible process by which contaminant organisms become
entrapped in pore spaces too narrow to allow transport, removing them from the downstream flow
(McDowell-Boyer et al., 1986). Unlike filtration, straining is dependent only on pore space
geomorphology and thus cells are not released by changes in groundwater chemistry or flow. Both
filtration and straining affect the retention and removal of microbial contaminants within saturated
porous media systems. Groundwater chemistry and the ratio of colloid (microbial contaminant) size to
grain size are important factors for determining the contributions of each process to the overall retention
and removal.
The indigenous microorganisms within groundwater habitats play a significant role in the removal of
introduced enteric pathogens (Banning et al., 2002; Gordon and Toze, 2003). This antagonism by native
microflora is from a combination of competition for nutrients and predation. Introduced viruses are
removed by grazing protozoa (Pinheiro et al., 2007) and proteolytic exoenzymes that degrade the
capsids of enteroviruses secreted by common resident heterotrophic bacteria (e.g. Pseudomonas spp.)
(Cliver and Herrmann, 1972). Inhibition of these heterotrophic bacteria by sterilization or by chemical
inhibition of the aerobic respiratory chain dramatically reduce virus decay rates (Elliott et al., 201 la;
Gordon and Toze, 2003). As mentioned earlier, increasing temperature significantly increases the rate of
pathogen removal from groundwater systems. Microbial metabolic activity increases with increasing
temperature up to maximum viable temperature of a specific organism. Thus, temperature plays a direct
and an indirect role in the removal of pathogenic organisms and viruses within these systems. Gordon
and Toze (2003) documented maximum virus inactivation rates under conditions of available dissolved
oxygen at 28°C and low exogenous nutrient input (Gordon and Toze, 2003), conditions that would
stimulate maximum exoenzyme activity for metabolically active resident heterotrophic microorganisms.
4.1.5 Unsaturated Zones
The removal processes described for saturated conditions (mechanical filtration, straining, wedging, and
adsorption) apply to unsaturated zones as well. The presence of an air phase within unsaturated zones
creates two additional interfaces (air-water and air-sediment) not encountered under saturated conditions
that affect microbial contaminant attachment and removal. Specifically, microorganisms can become
adsorbed to grain surfaces at the air-water interface due to capillary action, and entrapment at the air-
sediment phase when the wetted grain surface layer becomes thinner than the organism (free-living or
particle-associated). As the moisture content decreases within the unsaturated medium, microorganisms
are subject to die-off and/or inactivation through desiccation. As flow is established through transient
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flushing events, these attached microorganisms and those entrained in small disconnected water bodies
can be released back into the bulk flow through the system (Rippy, 2015; Peng et al., 2016).
Unsaturated porous media can be subdivided into the rooting zone and the vadose zone below the
rooting zone. The rooting zone of a soil profile contains the highest native microbial populations,
greatest community diversity (mesofauna, protozoa, fungi, bacteria, archaea, viruses, etc.) and overall
metabolic activity (Fierer et al., 2003). As stormwater infiltrates through vegetated soil layers, the
microbial pollutants are subjected to competition for space and nutrients by the resident organisms that
are specifically adapted to living within this habitat. In addition, they face selection pressure from plant-
derived root exudates that select for beneficial resident microflora.
Below the rooting zone, resident microbial populations and activity decrease rapidly with increasing
depth. In this area of the vadose zone, biological antagonism is greatly reduced as a selection pressure,
and the physicochemical processes discussed above for desiccation and biocolloid transport exert the
greatest influence on microbial contaminant removal.
4.1.6 Microbial Methods
During the past few decades there has been rapid advancement of molecular microbiology methods for
identification and enumeration of microorganisms within environmental samples independent of
cultivation. These methods have been used to study the gut microflora of humans and other warm-
blooded animals to identify host-specific taxa for use in contamination source identification in Microbial
Source Tracking (Santo-Domingo et al., 2010; Harwood et al., 2014; McLellan and Eren, 2014; Tan et
al., 2015). Recently, studies employing these methods have evaluated the detection and cooccurrence of
traditional fecal indicator bacteria and their associated pathogens within contaminated environmental
samples. Some studies have found low correlation values for single organism indicator - pathogen
relationships indicating that currently accepted public health monitoring strategies may not be sufficient
(Harwood et al., 2005; Payment and Locas, 2011; McLellan and Eren, 2014).
4.2 Macrobiology
Macrobiological organisms can be used to enhance green infrastructure, but can also cause
complications. Vegetation is commonly used in green infrastructure for various reasons: nutrient and
metal retention, enhance ecosystem services, increase infiltration, and mimic natural hydrology.
Trees are frequently studied for use in green infrastructure systems, providing stormwater control by
canopy interception loss, evapotranspiration and infiltration (Berland et al., 2017). Canopy interception
is the water that is stored in the tree canopies, and the volume of water stored varies depending on the
tree's size, health, species, age, leaf area, canopy shape, and leaf/branch angles. Eventually this water
evaporates from the canopy (Berland et al., 2017). Canopy interception in various forests can reduce 18
to 45% of the precipitation that reaches the ground, consequently reducing the volume of stormwater
runoff, soil erosion and contaminant washout (Berland et al., 2017). Evapotranspiration is the combined
loss of water from the soil through evaporation, and water taken up by plants and transpired to the
atmosphere through plant leaf surfaces (Berland et al., 2017). Macrophytes assimilate contaminants into
their tissues and sequester or transform the contaminants (Maine et al., 2007). By increasing the
rhizosphere diversity in the subsurface environment, various chemical and biochemical reactions can
occur (Jenssen et al., 1993; Maine et al., 2007). Eichhornia crassipes (Mart.) Solms. is a fast-growing
species known to take up nutrients and contaminants (Tchobanoglous et al., 1989; Vesk and Allaway,
1997; Maine et al., 2007). Typha domingensis (Pers.) is also fast-growing and can obtain large biomass,
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thus allowing the efficient accumulation of nutrients and contaminants (Gersberg et al., 1986; Ellis et al.,
1994; Manios et al., 2003; Maine et al., 2007). Tanner (1996) discussed the nutrient uptake of different
macrophytes and concluded that as the total macrophyte biomass increased, the total nitrogen removal
increased as well. Maine et al. (2007) found that E. Crassipes sequestered metals in the biomass. During
senescence, the metals were retained in the sediments. For T. domingensis, metals were mostly retained
in the sediment, but chromium (Cr) and nickel (Ni) were retained in the biomass. Nitrate and SO42" were
removed by all vegetation. The authors recommended the use of Typha for wastewater treatment due to
their tolerance of high pH and conductivity as well as their efficient nutrient and metals removal
capabilities.
The types of plants commonly used in GI systems such as constructed wetlands, rain gardens, and
bioswales include common reeds (Phragmites australis (Cav.) Trin.), cattails (Typha spp.), bulrushes
(Scirpus spp.) and reed canarygrass (Phalaris arundinacea L.). These types of plants have also been
used in domestic and industrial wastewater treatment wetlands (Shepherd et al., 2001; Mbuligwe, 2005;
Vymazal, 2005; Vymazal and Kropfelova, 2005; Maine et al., 2009;). Plant selection is important
because they need to survive potentially toxic contaminants and the variability of the systems (Maine et
al., 2009).
The sediments in many infiltration basins used to manage stormwater runoff can be high in
contaminants such as heavy metals and hydrocarbons. Very few aquatic species can live in such
environments, one being the oligochaetes Limnodrilus hoffmeisteri (McCall and Tevesz, 1982;
Mermillod-Blondin et al., 2001; Shang et al., 2014). This species is known to be a bioturbator in
freshwater sediments and a sediment feeder that creates biogenic structures in the sediments and egests
fecal pellets at the water-sediment boundary, thus possibly contributing to the nutrient contamination
(Matisoff et al., 1999; Shang et al., 2014).
There are few studies into how various macroorganisms may influence green infrastructure. Pigeneret et
al. (2016) studied the impact of tubificid worms on the biological and geochemical processes in
infiltration basins. The authors showed that as contamination increased, the burrowing activity of the
worms also increased, thus increasing the macropores in the sediment. It was also observed that in the
presence of tubificid worms, the mean concentrations of ammonium (NH4+), nitrite (NO2") and NO3"
increased, most likely because of the mineralization of organic matter caused by the worms (Mermillod-
Blondin et al., 2008). While their study primarily focused on the contaminants effects on the worms in
the sediments, they demonstrated that increased burrowing activities could possibly affect infiltration
and nutrient concentrations.
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5.0 Geochemical Processes
5.1 Dissolution and Precipitation Reactions
Dissolution and precipitation reactions are two examples of a geochemical process that can modify
water quality in aqueous environments (Sposito, 1989; Hounslow, 1995; Stumm and Morgan, 1996;
Eby, 2004). An example of a dissolution or precipitation reaction is the dissolution of calcite (CaCC>3 fv>)
(Figure 8A).
Clay Mineral
Precip
Cal
Diss<
itation
:ite
Glutton
•/r-
Mineral image modified from
https://geolosv.com/minerals/calcite.shtml
Fe2+ e"
V
Reduction
Reductive Dissolution
Fe2+ pb2+
Mineral image modified from
https://www.mindat.ore/min-1856.html
Mineral image modified from
https://www.mindat.org/min-2821.html
Figure 8. Geochemical Process that are important in determining water quality. A. Dissolution/ precipitation, B.
Redox processes/ reductive dissolution, and C. Ion exchange processes. Calcite image in A is modified from
htti)s://geolog\.com/minerals/calcite.shtml. hematite image is modified from htt»s://www.mindat.org/min-1856.html.
and montmorillonite (clay) image is modified from htti)s://www.mindat.org/min-2821.html.
In the soil and unsaturated sediments (vadose zone), there is a continual cycle of dissolution and
precipitation based on wetting and drying cycles (Sposito, 1989). One can apply the wetting and drying
cycles to the calcite example. As water enters the dry sediments, the water hydrates the calcite surfaces
and begins to dissolve the calcite, forming Ca2+ and HCOr ions in solution (Sposito, 1989; Hounslow,
1995; Stumm and Morgan, 1996; Eby, 2004). The water containing the dissolved constituents is
transported through the vadose zone under the influence of gravity. During transport, the water becomes
attached to surfaces through hydration of dried surfaces or reacting with the sediments, and the total
volume of water decreases. The reduction in the volume of water increases the concentration of
dissolved constituents, in this case ("a2 and FICO3", and favors the precipitation of calcite (as was
shown in Figure 8A). If the volume of water is sufficient to move the dissolved constituents to the
saturated zone without precipitation of calcite, or if given enough time for wetting and drying cycles to
move the dissolved constituents to the groundwater, changes to groundwater quality may occur.
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5.2 Redox Processes
Another geochemical process that can lead to changes in water quality is redox reactions (Sposito, 1989;
Hounslow, 1995; Stumm and Morgan, 1996; Eby, 2004). Redox reactions involve the transfer of
electrons from one chemical species to another, as is shown in Figure 8B. Figure 8B shows that the Fe3+
in the hematite is being reduced to Fe2+, and Fe2+ is being released into solution.
Hematite and other minerals have been shown to have sorbed contaminants such as metals and organic
compounds (Sposito, 1989; Hounslow, 1995; Stumm and Morgan, 1996; Eby, 2004). Figure 8B shows
that as the hematite is dissolved under reducing conditions, it also liberates the sorbed Pb2+. The
liberated Pb2+ changes the water quality, since there was no soluble Pb2+ when the solution was
oxidized. The oxidation state of the Pb did not change. Conversely, if you have water containing
dissolved Fe2+ and Pb2+, it will cause Fe2+to precipitate out of the solution and form a solid Fe phase as
the system becomes oxidized. This solid phase is then available to sorb the Pb2+, and thus alter the water
quality.
5.3 Ion Exchange Processes
The ion exchange is a process by which an ion on the solid surface (mineral or natural organic matter
(NOM)) is exchanged with another ion in solution Figure 8C (Sposito, 1989; Hounslow, 1995; Stumm
and Morgan, 1996; Eby, 2004). If the surface has a net positive charge then it is capable of exchanging
anions and is called anion exchange capacity (AEC), whereas if the surface has a negative charge it
exchanges cations and is called cation exchange capacity (CEC) (Sposito, 1989; Hounslow, 1995;
Stumm and Morgan, 1996; Eby, 2004). Generally, in natural environments, most surfaces are negatively
charged except at low pH (Sposito, 1989; Stumm and Morgan, 1996), so CEC will be used to
demonstrate ion exchange processes in this discussion.
Ion exchange processes can affect water quality. For example, if a permeable pavement parking lot has
accumulated metals associated with brake pads in subsurface sediments and road salt is applied, then
those metals could be mobilized because of the influx of sodium replacing these metals on the exchange
sites.
5.4 Adsorption/Desorption Processes
Adsorption (sorption) processes are collective processes where there is an accumulation of matter on a
solid surface and the solid-water interface (Sposito, 1989; Hounslow, 1995; Stumm and Morgan, 1996;
Eby, 2004). Sorption processes influence the distribution of substances between solution and surface,
which affects their transport, the electrostatic properties of the solids, and the reactivity of surfaces
(Sposito, 1989; Stumm and Morgan, 1996). Sorption reactions are generally thought of in terms of the
intermolecular interactions between solute and solid phases (Stumm and Morgan, 1996). These
intermolecular interactions include: surface complexation, hydrophobic interactions, adsorption of
surfactants, and adsorption of polymers (Stumm and Morgan, 1996).
Organic molecules can interact with the surface via hydrophobic interactions. Organic molecules that are
hydrophobic, such as hydrocarbons, chlorinated compounds, PAHs, etc., are generally incompatible
with polar environments and will seek a nonpolar or less polar environment. These organic molecules
will seek to partition to a less polar or nonpolar parts of the mineral surface or organic surfaces. The
interaction with the surface will affect the mobility of the organic substance. Partitioning coefficients
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such as Kow and Koc can be used to predict the mobility of the organic compound (Stumm and Morgan,
1996; Eby, 2004).
Many surfactants are characterized as having both hydrophobic regions and hydrophilic regions in the
same molecule (Stumm and Morgan, 1996). Surfactants lower the interfacial tension and will be sorbed
at the surface (Stumm and Morgan, 1996). Because of their properties, surfactants can flocculate or
disperse naturally occurring colloids, increasing turbidity and changing water quality.
Desorption is the opposite of sorption, where a molecule goes from a surface back into solution. In
general, desorption occurs when there is a chemical change on the surface (dissolution of mineral,
changes in surface charge, etc.) or changes in solution chemistry (pH, increased DOC, etc.). Desorption
can affect water quality by mobilizing contaminants.
Sorption and desorption processes are important reactions with respect to water quality in GI systems.
These processes could have both positive and negative consequences with respect to GI systems.
Positive effects would be the removal of potentially harmful constituents in the infiltrating water through
sorption onto surfaces and the potential immobilization of these constituents. On the other hand,
negative consequences could be the desorption of harmful, naturally occurring constituents found in the
soils and sediments of the vadose zone or aquifer due to the changes caused by the infiltrating
stormwater.
5.5 Mixing Relationships
Mixing relationships can be useful when studying water infiltration into groundwater, estimating
concentrations of mixtures of source waters, or estimating the percent of source water mixing with
ambient groundwater (Eby, 2004; Hounslow, 1995). The other important use for mixing models is to
ascertain if the mixing of two waters is possible given the concentration of a mixed water. The more
complex the mixing, the more difficult the calculations become and graphical methods or software are
needed (Hounslow, 1995).
A simple binary mixing model is shown in Equation 5, where Cm is the concentration of the mixture, Ci
is the concentration of the more concentration solution, C2 is the concentration of the more dilute
solution and %Mix is the percentage of the mixture between solution 1 and solution 2.
%Mix = (SezIz) x 100 Equation 5
V C^— C2 J
An example of mixing could be the mixing of infiltration water from a parking lot treated with road salt
(NaCl). The infiltrating water has a CI" concentration of 12,000 mg/L, the ambient groundwater has a CI"
concentration of 50 mg/L, and a well down gradient of the infiltration gallery has a concentration of Ci
of 1500 mg/L. This would mean that the percentage of infiltrating water in the down gradient well
would be 12%.
If the end member concentrations are known, it is possible to plot the %Mix as a linear function of end
member concentration as is shown in Equation 6 and Figure 9.
cm = (Cx - C2)] + C2 Equation 6
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Using Equation 6, it is possible to calculate the approximate CI" concentration of a mixture at 10% of the
solutions previously described, which would be 1545 mg/L or 50 % which would be 7525 mg/L. A
graphical representation of this mixing is shown in Figure 9. If one wants to know the mixing at any
percent mixing, one could find that percentage on the x-axis and use the mixing curve to find the
corresponding CI" concentration of the y-axis. Conversely, if one knows the CI" concentration, one can
find the corresponding percent mixing of the two waters using this plot.
14000
12000
d 10000
CD
E
8000
-------
Figure 10. A Piper Diagram demonstrating the mixing of ambient groundwater with infiltrated water
that was impacted by road salt application.
Ambient Groundwater
Mix of two source waters
Infiltration Water
5.6 Colloids
Colloids are small particles ranging in size between true solutions and suspensions (Eby, 2004; Bradford
and Torkzaban, 2008). Colloids can be inorganic, organic, or biological, and have variable size ranges in
literature (Sposito, 1989; McGechan and Lewis, 2002; de Jonge et al., 2004; Eby, 2004; Shein and
Devin, 2007; Bradford and Torkzaban, 2008). A soluble species in water is defined as any substance that
passes through a 0.45 (im filter (Eby, 2004). However, Eby (2004) also points out there are many
molecular sized particles that can pass through a 0.45jim filter that are still considered to be colloidal in
nature. Eby (2004) defines colloids as particles that range from 10 nm to 10|im in size, which is
supported by others (Sposito 1989, Bradford and Torkzaban, 2008, Drake et al., 2014). de Jonge et al.
(2004) provide a range of lnm to lOjirn for the size of colloids. The range proposed by Shein and Devin
(2007) is lnm to 100 nm. McGechan and Lewis (2002) state the size range of colloids for nonliving
colloids is > 2)im in non-karst situations and > 4{im in karst situations. However, these authors discuss
biological colloids in different size ranges: viruses range from 20 - 200nm, bacteria range from 500nm -
3|im, and protozoa from 4jam - 14jam (McGechan and Lewis, 2002). Because of their unique nature,
colloids are important to chemical and biological transport in the vadose zone and in groundwater
(McGechan and Lewis, 2002).
Colloidal transport is an important mechanism by which contaminants can move through the soil
(Sposito, 1989; McGechan and Lewis, 2002; de Jonge et al., 2004; Eby, 2004; Shein and Devin, 2007;
Bradford and Torkzaban, 2008; Zhang et al., 2010). It is important to note the colloid can itself be the
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contaminant, or the contaminant is sorbed to the otherwise benign colloid (McGechan and Lewis, 2002).
In most cases the colloid itself is harmless, and the contaminants sorbed or associated with the colloid is
the concern (Sposito, 1989; McGechan and Lewis, 2002; de Jonge et al., 2004; Eby, 2004; Shein and
Devin, 2007; Zhang et al., 2010).
Unlike soluble components, which can freely move in solution, colloids have restricted movement in
some cases (McGechan and Lewis, 2002). Colloids can be restricted by capture, sorption and by
electrostatic interactions (Sposito, 1989; McGechan and Lewis, 2002; de Jonge et al., 2004; Shein and
Devin, 2007). Capture can be by straining where the colloid is larger than the pore and movement is
stopped by the pore (McGechan and Lewis, 2002). Straining is also implicated as an important capture
mechanism for biological colloids (Shein and Devin, 2007). Another capture mechanism is filtration
where the colloids can travel into the pore and are deposited inside the pore (clogging of the pore)
(McGechan and Lewis, 2002). Colloids are subject to the same geochemical process as solution species
due to their chemical composition, i.e., they can be sorbed to surfaces and can interact with surfaces
electrostatically, which will impede their transport (Sposito, 1989; McGechan and Lewis, 2002;
Bradford and Torkzaban, 2008). Colloids in macropores can move relatively unrestricted and quickly
(McGechan and Lewis, 2002; Shein and Devin, 2007). Other researchers discuss the chemical and
physical properties that are important for the transport of colloids (de Jonge et al., 2004; Shein and
Devin, 2007; Bradford and Torkzaban, 2008). Sposito (1989) and de Jonge et al. (2004) discuss in broad
terms the role of dispersion and aggregation; ionic strength; and pH as important factors that control
colloid transport. Shein and Devin (2007) state that as pH increases, the concentration of microbes in
groundwater increases, and water with low IS favors transport of microbe. Natural organic matter
(NOM) has also been implicated in influencing colloid transport (Kretzschmar et al., 1995). In the
presence of NOM, there is a decrease in attachment efficiency to the soil and sediments which increases
mobility. Also, when NOM is sorbed onto the colloid, there is more stability which leads to a decrease
in filter efficiency of the media, and thus facilitates transport (Kretzschmar et al., 1995). Zhang et al.
(2010) discuss colloids in unsaturated conditions. These authors found because colloids are retained
more under high IS, the greatest transport will be right after rainfall (lower IS), and colloids are less well
retained in the lower portions of the vadose zone due to concentration effects. Additionally, chemotaxis
needs to be considered in the transport of biological colloids (Shein and Devin, 2007). Some microbes
are capable of movement of their own making which allows them to move towards nutrients and away
from harmful environments.
As discussed previously, colloid-facilitated transport is an important mechanism for the movement of
contaminants into groundwater (de Jonge et al., 2004). This phenomenon has been demonstrated both in
laboratory experiments and in field based research. Colloid-facilitated transport has been shown for
inorganic and organic substances. Examples of organic substances includes atrazine, prochloraz, DDT,
and glyphosate. Inorganic examples include chromium (Cr), cesium (Cs), copper (Cu), Ni, phosphorous
(P), Pb, and zinc (Zn).
Colloids play an important role in the potential transport of contaminants in GI systems. In the
environment, dust is commonly deposited on the ground surface and can come from many sources such
as industrial sites, urban landscape, automobile exhaust, brake pads, etc. Dust particles from sources
such as these have contaminants associated with them. Precipitation can wash the deposited dust into the
GI system and could potentially allow the transport of the contaminant through the vadose zone into the
groundwater on the colloid. Many GI systems are designed to capture the colloids and minimize the
likelihood of transport into the subsurface. However, this could allow the buildup of contamination in
the upper portions of the vadose zone.
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6.0 Models
There have been many reviews of models and tools used for modeling stormwater management, and for
modeling green infrastructure implementation (Zoppou, 2001; Vaze and Chiew, 2003; Elliott and
Trowsdale, 2007; Obropta and Kardos, 2007; Jayasooriya and Ng, 2014). Zoppou (2001) reviewed the
mathematical processes in various stormwater models that focused on quality and quantity, but did not
discuss any incorporation of GI into those models. Vaze and Chiew (2003) compared models to estimate
event diffuse contaminant loads from urban stormwater, but did not address GI. Elliott and Trowsdale
(2007) studied modeling tools with GI in urban stormwater drainage modeling, but their discussion was
focused on water quantity. Ahiablame et al. (2012) compared three different models to assess benefit for
simulating GI effects on quality and quantity. Obropta and Kardos (2007) compared various urban
stormwater quality models, comparing the deterministic, stochastic, and hybrid approaches to modeling,
but did not evaluate their potential for modeling GI for stormwater management. Jayasooriya and Ng
(2014) present a comprehensive review of models for green infrastructure practices, but also evaluated
the model for the economics of GI.
Generally, a model is a description of an often complex system (Haefner, 2005) into a simplified form
(Imboden and Pfenninger, 2012). Since the 1960s, software tools have been used for water resource
management such as the design and optimization of sewer systems and wastewater treatment
technologies (Zoppou, 2001; Jayasooriya and Ng, 2014; Eckart et al., 2017). Modeling tools to simulate
the stormwater runoff quantity and quality emerged in the 1970s (Zoppou, 2001; Jayasooriya and Ng,
2014; Eckart et al., 2017). Simulation modeling is a valuable method to obtain spatial and temporal
information on a wide range of scales. There is a wide range of monitoring practices over various
periods and conditions, but the high cost can make modeling a more efficient method (Elliott and
Trowsdale, 2007; Eckart et al., 2017). The growing impact of urban stormwater on surface water quality
has driven the need for more accurate models for stormwater pollution and management (Beck, 2005;
Obropta and Kardos, 2007). Many urban stormwater management models often focus solely on the
surface flows and not subsurface flows (Barnett et al., 1995; Blanc et al., 1995; Palla and Gnecco, 2015;
Her et al., 2017; Herrera et al., 2017; Kong et al., 2017; Paule-Mercado et al., 2017). It is challenging to
develop stormwater and groundwater models alone, but there are additional difficulties to linking them
to portray groundwater quality effects.
Stormwater models can support many needs for management such as development of total maximum
daily loads (TMDLs), CSO management, BMP selection and placement, land use change impact
assessments, and an analysis of water quality changes and opportunities with stormwater management
systems (Obropta and Kardos, 2007). It is useful to include GI practices in stormwater management
tools so researchers and managers can better understand the behavior of the various GI practices
(Jayasooriya and Ng, 2014). Models on green infrastructure management have been used to compare
various scenarios for policy decisions on improving large scale water distribution and stormwater
treatment (Sharma et al., 2008).
Green stormwater infrastructure is part of integrated urban water management (IUWM) that achieves
greater sustainability for drinking, waste, storm, and receiving water management, which drives the need
for more accurate stormwater quality models (Beck, 2005; Obropta and Kardos, 2007). It is needed for
stormwater management to evaluate the efficiency of different control strategies and optimal solutions
(Beck, 2005; Rousseau et al., 2005; Obropta and Kardos, 2007). IUWM is dependent on integrated
models to provide decision support of alternative options, allow for long term planning, inform for
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design upgrades, and provide real time system control actions (Rauch et al., 2002; Beck, 2005; Rousseau
et al., 2005; Olsson, 2006; Obropta and Kardos, 2007).
Elliot and Trowsdale (2007) did a review of models for LID and found most current models do not
incorporate enough contaminants that are a concern to stormwater quality. It was also found that it can
be difficult to link the hydrologic models to other processes such as toxicity, habitat models, land use,
and cover maps (Thomas and Tellam, 2006; Elliott and Trowsdale, 2007). Hydrologic models are more
common than water quality models as the water quality data needed to calibrate these models is less
available and quality is more difficult to model (Elliott and Trowsdale, 2007; Eckart et al., 2017;). There
are many commonly used models for stormwater management: Stormwater Management Model
(SWMM), Storage Treatment Overflow Runoff Model (STORM), Hydrologic Simulation Program-
Fortran (HSPF), Distributed Routing Rainfall Runoff Model - Quality (DR3M-QUAL), Hydroworks,
and Model of Urban Sewers (MOUSE) (Obropta and Kardos, 2007). Commonly used groundwater and
soil moisture flow models include Modular Flow Model (MODFLOW), Hydrus, Saturated-Unsaturated
Transport (SUTRA), Soil Water Atmosphere and Plant (SWAP), Finite Element Subsurface FLOW
System (FEFLOW), and Transport of Unsaturated Groundwater and Heat (TOUGH). SWMM, Model
for Urban Stormwater Improvement Conceptualization (MUSIC), MOUSE, and Hydrologic Modeling
System (HEC-HMS) are popular and widely used software for urban hydrology and stormwater
drainage management, but there needs to be more research in modelling rainfall-runoff (Fletcher et al.,
2013; Eckart et al., 2017). Models such as SWAP (Kroes and Van Dam, 2003) and Hydrus (Simunek et
al., 1999) are rarely used for stormwater infiltration due to their complexity, and very few models take
clogging into account (Browne et al., 2008). While there are a variety of modelling techniques to
evaluate the effectiveness of urban GI practices, the primary issues needed to determine the model's
accuracy and certainty of the result are calibration, sensitivity analysis, and uncertainty analysis (Eckart
et al., 2017). SWMM is an EPA runoff model, and seems to be one of the more comprehensive
modeling programs that already incorporates permeable pavements, rain gardens, green roofs, street
planters, rain barrels, infiltration trenches, and vegetated swales (Jayasooriya and Ng, 2014). Many
vadose zone models were developed initially to predict organic or inorganic plume migration beneath
landfills (Clark and Pitt, 2007).
Often a large model is an integration of multiple submodels, and reliability of these integrated models is
dependent on the quality of the smaller models. Combinations can create integrated models such as the
water supply, wastewater treatment, stormwater, and receiving water models. The stormwater models
are considered the least developed, making them a hindrance to integrated model performance; for this
to improve, the stormwater model predictions need to improve (Rauch et al., 2002; Obropta and Kardos,
2007).
Models can be classified by two different approaches: deterministic and stochastic. Deterministic
models attempt to perfectly simulate the physical world through cause and effect relationships, while the
stochastic models use statistical patterns to simulate the phenomenon. Differences between deterministic
and stochastic models are not absolute. Deterministic models can have some sort of randomness and
uncertainty, but stochastic models do not ignore the causal relationships. The best way to differentiate
between the two classifications of models is that deterministic models will always have the same
response from the same model input, but stochastic models will have varying responses from the same
input but with consistent statistical properties (Nix, 1994; Zoppou, 2001; Obropta and Kardos, 2007).
There are various limitations to each classification of models. The deterministic models do not consider
variable uncertainty. The complex deterministic models can contain multiple optimum parameter sets
where no single parameter set can be identified as a unique solution set (Beven and Freer, 2001; Obropta
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and Kardos, 2007). For example, parameter non-uniqueness can be seen in rainfall/runoff and
stormwater quality models (Doherty and Johnston, 2003; Kanso et al., 2003; Obropta and Kardos,
2007). The stochastic model's limitations are that they are often only developed from available data
without detailed knowledge of the underlying processes, and when this data is sparse or noisy, it is often
inadequate to make reliable predictions (Vojinovic et al., 2003; Obropta and Kardos, 2007;). Stochastic
models cannot be extended to analyze alternate scenarios such as increased urbanization effects on
stormwater contaminant loads. These models are only applicable to specific conditions and data for what
the model is based on, but the advantage of these models is that uncertainty for the variables is built into
the model (Scholz, 1997; Zoppou, 2001; Obropta and Kardos, 2007).
It has been shown that stochastic modeling techniques are not just a substitute for deterministic models,
they can be combined with deterministic models to create hybrid deterministic-stochastic models that
enhance stormwater quality modeling (Warwick and Wilson, 1990; Gong et al., 1996; Scholz, 1997;
Obropta and Kardos, 2007). Hybrid models integrate stochastic and deterministic models which can take
the best of both types of model method, thus reducing stormwater quality model prediction error and
uncertainty (Obropta and Kardos, 2007).
Each model requires different sets of parameters and inputs depending on the purpose of the model.
Inputs can include catchment size, scale, human activities, climate, and natural characteristics such as
soil type and porosity. Outputs depend on the question the model is supposed to answer, but can include
runoff volume, runoff rates, and contaminant loading (Jayasooriya and Ng, 2014). An important part of
planning and implementing GI stormwater management practices is the spatial multi criteria analysis
(Eckart et al., 2017). Criteria that need to be considered when planning GI installation are site
characteristics, site suitability, performance of runoff controls, economic feasibility, slope, water table
depth, soil types, rainfall patterns, and catchment size (Jia et al., 2013; Eckart et al., 2017; Johnson and
Sample, 2017; Joyce et al., 2017). There is extensive literature that discusses different models and tools
for evaluating the best GI or treatment train for stormwater management (Jia et al., 2013; Charlesworth
et al., 2016; Eckart et al., 2017; Johnson and Sample, 2017; Joyce et al., 2017).
It is often the assumption that a simulation model of a system must include all the variables and
pathways, but more model complexity does not always improve the accuracy of the simulation (Doherty
and Johnston, 2003; Obropta and Kardos, 2007). The more complex the model, the greater the number
of non-unique parameter solution sets and model uncertainty, thus the fine detail of a system can only be
replicated by the amount of necessary complexity needed. Even the most complex model that represents
a system's true behavior will have uncertainty limits of its predictions and all models will need to have
uncertainty analyses done to understand the limits to the stormwater quality predictions (Obropta and
Kardos, 2007). Complex models come with the costs of more parameters to calibrate, more data to
collect, and more time and resources to implement the model. As the number of parameters in the model
increases, the harder it is to manually calibrate the model. The innate complexity of stormwater quantity
and quality processes drives these models to be more complex, requiring more automatic calibration
methods. These automatic methods need to locate global optima, have numerical stability, and
computational burden. The optimization criteria are important to perform automatic calibrations (Skahill
and Doherty, 2006; Obropta and Kardos, 2007). There are many examples of methods used to calibrate
water quantity and quality modes such as linear, nonlinear, heuristic, and probabilistic methods (Gauss-
Marquardt-Levenberg method, genetic algorithms, and Monte Carlo Markov chains), but few examples
of automatic calibrations with stormwater quality models. This could be due to automatic calibrations
needing more data, and this data is often lacking (Obropta and Kardos, 2007).
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Due to the expense of monitoring and sampling, the data needed to calibrate stormwater models is
lacking and oftentimes collected inconsistently, which is a great hindrance to the development of
suitable algorithms (Zoppou, 2001; Rauch et al., 2002; Obropta and Kardos, 2007). There are various
examples of simple models simulating comparably to complex models from Liner-Lundsford and Ellis
(1987), Vaze and Chiew (2003), and Ahyerre et al. (2005). Ultimately the intended use of the model is
what guides the selection of model complexity, but the least complex model that reliably answers the
questions is ideal (Rauch et al., 2002; Obropta and Kardos, 2007;). As there is an increased need for
accurate predictions, there will be a need for more complex models.
Water quality is not modeled as often as hydrology, more often being studied through experimentations
(Eckart et al., 2017). One of the difficulties of this is more field research is needed to characterize the
runoff water quality from different land use types (Ahiablame et al., 2012; Eckart et al., 2017). A Water
Quality Capture Optimization and Statistics Model (WQ-COSM) was developed to determine the water
quality capture volume for GI design (Guo et al., 2014; Eckart et al., 2017;). The SUSTAIN model has
been used to study water quality performance for a watershed in Taiwan (Chen et al., 2014). Chen et
al.'s (2014) model compared pervious pavements, bioretention cells, and grass swales for total
phosphorus (TP), total nitrogen (TN), suspended solids (SS), and biological oxygen demand (BOD)
removal; finding that pervious pavements had the largest reduction. Seo et al. (2017) modelled GI
impacts on water quantity and quality using the Soil and Water Assessment Tool (SWAT) and showed
that GI practices reduced contaminant loading for various land use types.
Modeling stormwater quality is generally dependent on the model of stormwater quantity, because if the
flow is not modeled correctly, the quality predictions will not be correct. Stormwater deposits, erodes,
and transports many different contaminants at uncertain rates and concentrations, and the contaminants
can vary between being soluble or particulate. The contaminants can also interact with and/or alter the
microbial degradation processes in the system. Most current stormwater quality models are less accurate
than the quantity models due to these many uncertain parameters. Most stormwater models are based on
modeling sediment loads and concentrations where the contaminant levels are then estimated as a
fraction of the sediment load. This makes the sediment grade and particle size important because smaller
particles will absorb a greater proportion of contaminants. The surface sediment processes are often
done separately from the subsurface processes due to the flow being different, and the contaminants
deposit differently than on the surface (Obropta and Kardos, 2007). Buildup and wash-off models are
the most common methods used to simulate stormwater runoff quality, which applies to most overland
processes, but can be very uncertain and unreliable (Robien et al., 1997; Zoppou, 2001; Kanso et al.,
2003; Obropta and Kardos, 2007;). Dechesne et al., (2004a) modeled a stormwater infiltration basin to
simulate hydraulic performance and pollution retention. The authors satisfactorily modeled the
hydraulics and clogging, but pollution flow was not as reliable. This study was not intended to model
contamination of the groundwater, but to determine the clogging and life-time of these basins.
Models have been developed to simulate contamination of groundwater, but not in green infrastructure
conditions. The modeling of groundwater recharge has been done on various scales, but is often limited
to the quantity and flows of the groundwater recharge and not the quality of the groundwater after
recharge (Abbott and Stanley, 1999; Bogena et al., 2005). Lee et al. (2006) modeled nitrogen pollution
in groundwater from cattle feed lots addressing the fate and transport of nitrogen species, dissolved
organic carbon, dissolved oxygen (DO), and biomass in the groundwater. They developed the code for
this using MODFLOW. While not a green infrastructure model, the processes can be transferred to
simulating green infrastructure practices. Thomas and Tellam (2006) developed a model in GIS to
estimate spatially distributed recharges and recharged water quality in unconfined aquifers. Although
they did not incorporate green infrastructure into this model, the model has the attributes that could be
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used for green infrastructure assessment. The processes included in their model included runoff and
interception using the curve number methods; Penman-Grindley evapotranspiration; the empirical index
approach to interflow; volatilization using Henry's law; distribution coefficient for sorption; and first
order decay for degradation. Data needed for this model includes meteorological, land use/cover,
hydraulic and geochemical attributes, and topographic and water table elevation. The model predicted
the distributions of surface runoff, infiltration, potential recharge, ground level slope, interflow, actual
recharge, surface runoff contaminant fluxes, travel time for contaminants through the vadose zone, and
the contaminant fluxes at the water table. This model showed promise for exploring the influences on
urban groundwater quantity and quality from land use changes. The results of the model included spatial
detail that could be incorporated with green infrastructure. Lee et al. (2003) developed a stochastic
model using GIS to evaluate groundwater quality in urban areas. Their study focused on concentrations
of nitrate based on land use, rainfall, and well depth. Although their model did not address green
infrastructure, their model found that land uses correlated well with nitrate concentrations.
Browne et al. (2008) developed a one-dimensional stormwater infiltration model to simulate the
hydraulic processes of inflow from a stormwater model for stormwater infiltration systems. Bioretention
and rain garden models were developed using the Green-Ampt equation (Dussaillant et al., 2003) and
Richard's equations (Dussaillant et al., 2004). The Green-Ampt model is the simpler of the two
methods, and shown to be effective when compared to Richard's equation, and thus is the most
commonly used equation for rain gardens/bioretention, although it would be challenging to apply the
Green-Ampt model to two-dimensional systems (Atchison et al., 2006). Their one-dimensional model
contained four blocks: the storage, clogging layer, saturated soil zone, and unsaturated soil zone. They
focused on the problem of simulating flow-through and ponding within the storage zone. One of the
difficulties the authors addressed was the transition between the boundary zones of storage and saturated
soil. They concluded their numerical model could obtain reliable results for both the unsaturated and
saturated flows with ponding, and thus be useful for stormwater infiltration systems' various conditions.
This model did not address the contaminants of the stormwater entering the soil and groundwater, but
addressing the difficulties of combining stormwater and groundwater models for infiltration needs to
first be done hydrologically before contaminant models can be reliable. The authors summarized
approaches to modelling flow through infiltration systems: 1) Estimate the time it takes for the system to
empty based on design events using empirical equations. 2) Assume a constant saturated hydraulic
conductivity and infiltration rate. 3) Assume clogged conditions control infiltration. 4) Adopt a general
unsaturated soil flow model. (Browne et al., 2008).
Clark and Pitt (2007) presented a methodology to predict the potential for contamination from
infiltration into the groundwater. The steps include determining the contaminants of concern, identifying
soil characteristics, and then using this information to predict the potential for groundwater
contamination. The model suggested for predicting groundwater contamination is SESOIL (Seasonal
Soil Compartment model). SESOIL can use actual or estimated field data to predict the depth of
migration, and model both organic and inorganic contaminants migrating through the vadose zone. It is
a theoretical fate and transport model that results in mass balances and assumes equilibrium partitioning
between phases. It incorporates three submodels: hydrologic cycle, sediment wash-load cycle, and
contaminant fate cycle.
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7.0 The Potential for Gl to Impact Water
Quality
The potential for impacts to groundwater quality is the primary focus of this review, only a limited
literature could be found that discusses the potential for groundwater impacts or impacts to groundwater.
The following section will present this literature.
7.1 Sampling Methodology
The literature demonstrated many different sampling design methods used for monitoring contamination
and potential changes to groundwater quality from stormwater infiltration. These methods all consider
several common questions in their soil and groundwater sampling design: 1) How many sampling points
are needed to adequately assess if groundwater changes occurred? 2) Where should the sampling points
be located? 3) At what depths should the groundwater samples be collected? and 4) How do you get a
background or control groundwater sample that will reflect the initial condition (Tedoldi et al., 2016)?
Tedoldi and colleagues concluded that there was no systematic method for determining the number of
sampling points; however, based on statistical consideration, a minimal number of sampling points
could be approximated to estimate a true mean contaminant concentration (Gilbert, 1987; Pennock et al.,
2007). Others, such as Mason et al. (1999), have suggested that professional judgement can play a role
in sampling design based on the researcher's knowledge of the study location. One common sampling
point is usually near where the water enters the infiltration structure (Mikkelsen et al., 1996; Dechesne et
al., 2004b; Dechesne et al., 2005; Winiarski et al., 2006; DiBlasi et al., 2009; Jones and Davis, 2013;
Paus et al., 2013; Tedoldi et al., 2016).
A 550 m2 infiltration basin was studied by Dechesne et al. (2004b) with what the authors termed a high
spatial resolution sampling design. They collected two sediment samples per 100 m2 at the following
depths: surface, 30 to 40 cm, 60 to 70 cm, and 100 to 110 cm. They concluded only three points were
needed to estimate the total mass of trapped contaminants within approximately a 25% error. The three
points were a point near the inflow, the lowest point in the basin, and a point that is representative of
most of the basin. This researcher then applied this method to other basins and found similar results
(Dechesne et al., 2005). Le Coustumer and Barraud (2007) in studying metals in a 7,400 m2 infiltration
basin found spatial heterogeneity of surface contamination. They found using a regular sampling grid of
approximately 15 points was not significantly different than using a grid of 100 points, and the standard
deviation was roughly constant for 30 samples or 100 samples. However, fewer samples spaced out over
a grid could lead to missing potential hot spots and erroneous conclusions. Other types of infiltration
structures, such as filter strips that receive runoff sampling points, should run in sampling transects
perpendicular to the filter strip (Boivin et al., 2008; Kluge and Wessolek, 2012; Kluge et al., 2014).
The vertical distribution of contaminants in the soil or below the infrastructure shows there is no
consensus on the sampling depth (Barth et al., 1989; Barraud et al., 2005; Ingvertsen et al., 2012; Jones
and Davis, 2013; Tedoldi et al., 2016). Two phase sampling was found to be the most effective strategy
for vadose zone sampling (Barth et al., 1989). The first phase sampling should give an understanding to
how sampling should be carried out in the second phase, which is the recommended sampling strategy
by the U.S. EPA (Barth et al., 1989) and by ISO 10381-5 (ISO, 2005), though it is rarely adopted
(Tedoldi et al., 2016). Instead, the depths sampled range from a few centimeters to several meters
(Mikkelsen et al., 1996; Mikkelsen et al., 1997; Norrstrom and Jacks, 1998; Mason et al., 1999;
Dechesne et al., 2004b; Barraud et al., 2005; Dechesne et al., 2005; Winiarski et al., 2006; Aryal et al.,
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2007; Stromvall et al., 2007; Camponelli et al., 2010; Ingvertsen et al., 2012; Kluge and Wessolek,
2012; Jones and Davis, 2013; Paus et al., 2013; Tedoldi et al., 2016;). Several researchers used depth
information from previous studies and tried to apply this information to set a maximum depth to be
sampled at in their current studies (Barraud et al., 2005; Ingvertsen et al., 2012; Jones and Davis, 2013).
This method led those researchers to very different depth values; 110 cm, 25 cm, and 90 cm,
respectively. Sampling only 20 - 30 cm assumes the contaminants are sequestered in the upper layers of
the vadose zone and will not account for anomalies, or see if contamination extends below the upper
layers (Tedoldi et al., 2016). Based on the literature discussed, the concentration of a contaminant is
generally the greatest near the surface and decreases with depth.
To assess if there is contamination or a change in water quality, a control concentration or background
concentration needs to be established (Tedoldi et al., 2016). Tedoldi et al. (2016) also point out
"uncontaminated" or "background" can lead to some confusion since in urban and industrial areas, the
concentrations could have already been elevated prior to the installation of the infiltration structure.
Desaules (2012) provided four possible methods for determining a reference value for metals in soil
systems or in the vadose zone. One method is a statistically derived value, such as percentiles from
measurements at wider scales. Another method is what Desaules (2012) termed "usual" content, which
is similar to the uncontaminated context. A third approach was deriving reference values by looking at
the concentrations in the deeper layers. This assumes deeper layers are not contaminated to any extent.
The final approach was to use the residual content in a sequential extraction method. Desaules (2012)
does point out difficulties can arise when using the first two methods for a site with low levels of
contamination and may not be appropriate. Except for Dechesne et al. (2004b; 2005), none of the review
literature were verified to see if their control sample was comparable to the background or
uncontaminated values. This can be problematic and cause significant bias on the site specific initial
conditions. Bowen et al. (2015) caution that use of national data sets could be problematic for
determining background concentrations due to potential issues with sample size limitations and
problems with the spatiotemporal distributions.
7.2 Review of Potential Contamination from Green Infrastructure Infiltration Studies
The potential for groundwater contamination resulting from the use of several different types of GI
practices has been studied to a limited extent, and results and information on study methods will be
presented below.
7.2.1 Infiltration Basins/Recharge Basins
Infiltration basins or recharge basins are a commonly used form of enhanced infiltration or GI.
Essentially, stormwater runs off mainly impervious surfaces and is collected in a basin where the water
is stored until infiltration occurs or it evaporates. The impacts of these structures on vadose zone and
groundwater will be discussed in this section.
Nightingale and Bianchi (1977) studied the effects of a recharge basin on groundwater. The facility rests
on a compound alluvial fan comprised of heterogenous layers of sands, silts, and clays. Groundwater
sampling was accomplished using a ten well monitoring network, and each well was screened below 20
m. An additional 25 to 30 wells were sampled semi-monthly. The sampled water was analyzed for SPC,
NO3", and CI". After five years of use, an estimated 65,815,000 m3 of water infiltrated into the
subsurface. Researchers found that in the beginning of the annual recharge period the SPC would
increase, but would then decrease by the end of the recharge year. They also found NO3" leached from
the soil during the first recharge event. Chloride concentrations followed the same pattern as NO3". The
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researchers concluded that use of this recharge basin decreased the groundwater salinity downgradient
for 1.6 km.
At a different site, Nightingale (1987a, 1987b), examined the impact of urban stormwater runoff on
alluvium soils comprising the vadose zone and groundwater at five retention/recharge basins using
ceramic/Teflon vacuum water extractors up to 26m deep for two years. The silt plus clay percentages
ranged from 3-74% for Basin F, 14-68% for Basin G, 5-46% for Basin M, 24-92% for Basin EE, and 2-
8%> for Basin MM with extensive stratification. Basin F was 2.4 ha in size with a depth of 2.7 m, had a
50,085 m3 storage capacity, and drained 132 ha. Basin G was 3.4 ha with a depth of 2.4 m, a storage
capacity of 43,793 m3, and drained 163 ha. Basin M drained 251 ha, was 3.6 ha in size, had a depth of
3.0 m, and a storage capacity of 122,067 m3. Next was Basin EE, which had a storage capacity of
246,847 m3, was 8.8 m deep, and was 4.0 ha in size. The final basin, Basin MM was 3.4 ha in size, had a
depth of 6.4 m, stored 145,344 m3 of water, and drained 310 ha. To assess groundwater contamination or
the potential for future ground water contamination, both the soil in the vadose zone and soil porewater
were sampled (Nightingale, 1987a, 1987b). The soil samples were taken at a point near the outfall and
midway between topographic highs and lows of the basin floor. The depth intervals the soil samples
were taken from were 0 - 2, 2 - 5, 5 - 8, 8 - 14, 14 - 20, and 24 - 30 cm, and they were analyzed for
As, Ni, Cu, and Pb. Soil porewater samples were obtained using a vacuum ceramic-teflon water
extractor. The porewater samplers were placed on top of a soil layer with low hydraulic conductivity.
The soil porewater samples were analyzed for SPC, major cations and anions, trace metals, and organic
compounds. Soil sampling showed metals accumulated in the top few centimeters of the soil, and there
was no significant leaching of the metals into lower portions of the vadose zone. The results of the soil
porewater sampling showed there was no trace metal contamination or contamination from most organic
compounds; however, diazinon was detected in three samples. The salinity, major cations, and anions
were reflective of the levels in the stormwater runoff.
Groundwater contamination by organic compounds from a rapid infiltration site was investigated in a
study authored by Tomson et al. (1981). The authors used soil column effluents, made from PVC and
filled with pea gravel and sandy loam soil, to collect samples. The soils types and geology under this
basin were not reported in this study. Although this examined the infiltration of secondary treated
wastewater, the results from this study would be a reasonable proxy to stormwater infiltration. In this
study, there were four parallel infiltration basins; each basin was 40,000 m2 with a 300,000 m2 holding
pond. The results showed the infiltration basin could not remove 100% of the organic compounds, that
there was little daily variation in organic compound concentration in the groundwater, and that different
classes of compounds had different removal efficiencies.
The results of a study exploring the impacts of three infiltration basins on groundwater in Perth,
Australia were presented by Appleyard (1993). They were located over predominantly sand sediments
with clay and limestone with these superficial formations forming an extensive aquifer under Perth.
Infiltration basin 1 received stormwater runoff from a light industrial area and a residential area. This
infiltration basin was about 1,200 m2 and drained at least 67,000 m2 catchment. The second infiltration
basin was in a residential area, was 380 m2, and drained 74,000 m2 The final infiltration basin received
water mainly from a major highway and a residential area. This infiltration basin's catchment was
approximately 58,000 m2 and was about 600 m2 in size. Three to four boreholes were sunk next to each
site, located to measure groundwater quality upgradient and downgradient of each basin and samples
were collected twice to observe seasonal variations. The results showed a major change in groundwater
quality down gradient of the infiltration basins because of a significant reduction in TDS due to
stormwater infiltration. These changes were, however, short-lived. Dissolved oxygen (DO) appeared to
control the concentration of Fe in the shallow groundwater. Infiltration increased DO concentration
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downgradient which in turn decreased the concentration of Fe in the shallow groundwater. Water
withdrawals near the infiltration basin could be affected by clogging and encrustation because of the
potential precipitation of Fe. The concentrations of pesticides, phenolic compounds, metals, and
nutrients near the infiltration basins were low. The concentrations of Pb at the third infiltration basin
were more than 3,500 mg/kg, which the author attributed to vehicular traffic.
Barbosa and Hvitved-Jacob sen (1999) conducted a study that considered the sequestration of heavy
metals contained in highway runoff by an infiltration pond. Three Portuguese soils were tested: Vila
Real and Costa da Caparica soil which were sandy with carbonates, and Estremoz soils which had high
silt, clay, organic matter and CEC. The first 25 cm of soil samples were collected from areas not too
close to the roads and used for sorption and desorption experiments. They monitored highway runoff
using a rain gauge, flowmeter, and an automated sampler. The total area of the catchment was 5,970 m2
with 2,500 m2 being road pavement. The heavy metals analyzed were Cd, Cr, Cu, Pb, and Zn. There was
no detectable Cd or Cr in the samples so they were excluded from further study. Samples were collected
prior to the runoff entering the pond, and then at different time intervals during ten rainfall events.
Results showed higher Zn concentrations than expected which authors attributed to galvanized
guardrails found on mountain roads. In addition, evidence of a first flush effect was observed. Because
of the effect, the first half of the runoff volume transported between 61 - 69% of the contaminants.
Authors also noted soil pH was important for controlling metal uptake by soils. Based on the results,
Barbosa and Hvitved-Jacob sen (1999) concluded the best soil type for removing contaminants from
infiltration ponds is one with a high sorption capacity and a high resistance to desorption at low pH.
A stormwater infiltration pond at the University Claude Bernard was the subject of a study by Datry et
al. (2003). The infiltration pond at that time had been in use for more than 30 yrs with a 2 m layer of
cobbles laid over fluvial sand and gravel sediments. It drained 2.5 ha, and received water from buildings,
parking lots, roads, and lawns. The infiltration pond had a surface area of 750 m2, and a storage capacity
of 4,000 m3. A series of pipes drained the catchment with one pipe responsible for 75% of the drainage.
The water table under the infiltration bed was 1.5 m below ground surface during dry periods, and the
regional water table was between 2.5 -3.5m below ground surface. Groundwater was monitored
through a network of 30 piezometers at the main inlet set at a 1,3m depth. The water and sediment were
analyzed for particulate organic carbon (POC), total N (TN), total P (TP), Cd, Cr, Ni, Pb, total
hydrocarbons (THC), As, boron (B), cobalt (Co), manganese (Mn), molybdenum (Mo), selenium (Se),
monocyclic aromatic hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), halogenate aliphatic
compounds, halogenated aromatic compounds, polychlorinated biphenyls (PCBs), phenolic compounds,
and pesticides. Investigators found the sediment stored in the infiltration pond had high concentrations
of POC, particulate N, TP, and TN, and were contaminated with hydrocarbons and heavy metals. Zinc
accounted for 60%, Pb for 24%, and Cu for 11% of the total metal concentrations in the sediments.
There were 33 organic compounds found in the sediments, 10 PCBs, 15 PAHs, and diuron. No
concentration gradient was found moving further from the inlet in the monitoring piezometer network.
The water in the infiltration sediment bed was enhanced in chemical composition compared to the
stormwater composition, but hydrocarbons and heavy metals were not detected in the water during dry
periods. During and shortly after rainfall events, the infiltrated water lowered the concentrations of most
contaminants. Dissolved oxygen and redox potential (ORP) rose during and shortly after the rainfall
event. The oxic conditions lasted only 1.2 days. Nitrate concentrations were much higher following the
rain event, and metals and hydrocarbons were detected at low concentrations in some of the
piezometers.
Datry et al. (2004) followed up Datry et al. (2003) in the same infiltration basin. A new nest of
monitoring wells was installed 1.6 m from the main stormwater inlet. The wells were evenly spaced in a
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1 m circle, and were installed at depths of 2, 3, 4, 5, 6, and 7 m below the surface of the infiltration bed.
Each well, except the 7 m well, had a 0.5 m screen while the 7 m well had a 2.5 m screen. Well 1 was
used to sample the stormwater circulating in the infiltration bed. Wells 3, 4, 5, and 6 collected
groundwater at depths of 1, 2, 3, and 4 m below the surface of the infiltration bed. Well 2 collected
water at the intersection of the infiltration bed and the fluvial sediments, and was dry throughout the
study period. An additional set of wells was installed at a reference site. Wells 7, 8, and 9 collected
groundwater at 1.5, 3, and 4 m below the groundwater table. The results for the sediment samples were
similar to Datry et al. 2003. During dry periods, the water in the infiltration bed was enriched when
compared to the water composition at the pipe inlet, which was similar to what was reported in the
previous study (Datry et al., 2003). At the reference site, there were no differences in solute
concentrations among depths. The SPC and the concentrations of HCO3" and Ca in the groundwater
below the infiltration basin did not differ from the reference site during dry weather, but were
significantly lower during rainfall events. The reference site concentrations of SO42", CI", silica, Mn, and
Na were always lower in the groundwater at the infiltration site with greater difference during rainfall
events. Hydrocarbons were detectable at the reference site during dry weather, but never in the
groundwater below the infiltration basin. Groundwater below the infiltration site was enriched with
PO43" and DOC compared to the reference site. Vertical concentration gradients developed in the
groundwater below the infiltration basin during rainfall events. For HCO3", NO3", and DOC the vertical
gradients were largest 12 hrs. after an event, dissipated gradually with time, but were still detectable
after 108 hrs. Phosphate had the highest differences between depths at the end of the recharge event.
Sixteen stormwater detention basins were examined to determine if there were impacts to groundwater
quality in an area of New Jersey with a shallow water table and sandy, unconsolidated soils (Fischer et
al., 2003). The basins examined shared common depths, surface areas, and number and sizes of inlets.
They were all similar in ground coverings, which consisted of grass and mixed weeds, and the basins
were in urban watersheds that drained commercial, public, and residential areas. In each basin, a well
was installed near the center by hand auguering to a depth of 1 m, and the wells were 1.9 cm in diameter
and stainless-steel construction. The water was sampled for metolachlor, prometon, atrazine, desethyl-
atrazine, simazine, dieldrin, carbaryl, chloroform, MTBE, toluene, benzene, PCE, 1,1,1-TCA, carbon
disulfide, DO, NH3 + organic N, and NO2" + NO3". The results of this study revealed DO concentrations
in the study wells were lower than background groundwater, and the study wells had a higher detection
frequency for benzene and toluene. Metolachlor, prometon, and carbaryl detection frequency was larger
in the basin wells than in the background groundwater in the winter and summer. Atrazine was detected
more frequently in the study wells in the summer than in the background wells, whereas desethyl-
atrazine, simazine, and dieldrin were detected more frequently in the background wells than in the basin
wells. NH3 + organic N was detected more frequently in the basin wells than it was in the background
wells. Conversely NO2" + NO3" was detected more frequently in the background wells than in the basin
wells.
Dechesne et al. (2004a) examined an infiltration basin that drained water from a 7 ha truck parking lot
with the capacity to hold 2,616 m3 of stormwater. The basin had permable calcareous sand, pebbles, and
rocks. Research focused on soil sampling at 10 points and at several depths in an xy-grid to determine
the concentrations of various contaminants including THC, six PAHs, Cd, Cr, Cu, Ni, Pb, Zn, and As.
The results showed after 14 years of operation, THC and PAH concentrations were highest in the upper
portion of the vadose zone and quickly decreased with depth. They were not detectable at a 1 m depth.
Zinc however, was not well retained the upper 1 m of the vadose zone and was determined to be very
mobile. The other metals studied were retained in the upper portion of the vadose zone and rapidly
decreased in concentration with depth.
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Dechesne et al. (2005) built upon the Dechesne et al. (2004a) study using the same basin with a reported
depth of groundwater at 4.0 m and three additional basins with fluvioglacial soils that were highly
calcareous with low organic matter. The first new infiltration basin was in an urban use area. This basin
drained 33.7 ha with a water holding capacity of 3,800 m3. The depth to the water table was 2.8 m. The
second new infiltration basin was also in a mixed urban use area, drained 74 ha, and had a water holding
capacity of 20,700 m3. The depth to the water table was 10 m. The final infiltration basin was in a mixed
urban use area. This basin drained 50 ha, and had a water holding capacity of 7,960 m3. The depth to the
water table was 10 m. Ten soiling sampling points were made in each basin and collected at four
different depths. Researchers only looked at THC for organic analysis. There were significant
concentrations of THC in the upper portions of the vadose zone, and THC was still detectable to a depth
of 0.03 - 0.40 m. As was the case in the earlier study, zinc was very mobile, but even after 21 yrs. zinc
did not go more than 0.50 m into the vadose zone. The other metals were concentrated in the upper
layers of the vadose zone, and rapidly decreased in concentration with depth and did not extend below
0.30 - 0.40 m in depth.
Birch et al. (2005) examined the ability of a stormwater infiltration basin to remove contaminants from
urban stormwater. The basin was layered with filtration media of zeolite and coure quartzitic sand, but
there was no information given on the deep geological layers. The site was in an urban suburb in eastern
Sydney, Australia and was monitored over nine rainfall events from the outflow drain. The total area of
the basin was 450 m2, the catchment area was 2.668 ha, and it drained an urban area with terrace houses,
streetscapes, and parklands. Authors measured the concentrations of TSS, TP, total kjeldahl nitrogen
(TKN), Nitric oxide (NOx), TN, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn, organochlorine pesticides, and fecal
coliforms. Results showed decreases in the concentrations of TSS, Cu, Pb, Zn, fecal coliforms, TP, and
TKN. The basin was ineffective at reducing TN, but authors did note a conversion of TKN to NOx was
possible. Concentrations of Cr, Fe, and Ni did increase, and authors believe this may be due to leaching
of clay minerals from the filter bed.
Winiarski et al. (2006) studied an infiltration basin in France with a fluvio-glacial complex in the
underlying aquifer. This infiltration basin was approximately 5.5 m deep with a surface area of 7,406
m2, and held up to 30,856 m3 of stormwater. The depth to the water table was approximately 13 m. The
infiltration basin primarily drained an industrial area, and was in operation for about 20 years at the time
of publication. Soil samples were taken near the inlet pipe, the infiltration basin, in the middle of the
infiltration basin, and point a distance away from the stormwater inlet pipe. The soil samples were taken
every 0.05 m to 0.10 m, every 0.1 m to 0.5 m, and every 0.2 m to a depth of 4 m. The metals of interest
were Pb, Cd, and Cu as well as bacterial counts. The samples nearest the inflow were found to have the
highest concentrations of Pb and Cd near the surface which then decreased to background concentrations
at 1.5 m below the surface. The pH of the soil was lowest at the surface and then increased in a pattern
similar to the decrease in Pb and Cd concentrations to 1.5 m below surface. Total organic carbon (TOC)
and silt content followed a pattern similar to Pb and Cd concentrations. At 2.4 - 3 m the sample in the
middle of the infiltration basin showed an increase in metal concentrations with decreasing pH while
TOC and silt content increased. The point a distance away from the inlet showed little influence by the
infiltration water and the profile was similar to the background sample profile. The bacterial populations
were 104 — 107 CFU/g at the three sampling locations. The bacterial populations were found to decrease
with depth with approximately a two-log concentration difference from the surface to the deepest depth.
The authors concluded metals were concentrated in the upper portion of the vadose zone after 20 years
of infiltration due to the presence of carbonates that can either precipitate metal carbonates or exchange
metals and retain the metals. Since the metal concentrations were correlated with silt particles and
organic matter, they theorized the metals were possibly transported in the colloidal fractions and that
filtration could be a mechanism for the retention of metals in the upper portion of the vadose zone.
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Another stormwater detention pond was studied by Mayer et al. (2008). The capture zone of this pond
was 130 ha and 80% of the water it received was road runoff. The pond was approximately 300 m long
and 25 - 40 m wide. It was made up of two parts: a sediment forebay and settling pond, but the
underlying geology was not discussed. The authors investigated the sediment in the pond and the
porewater at the deepest point in the settling pond, approximately 4 m depth. To examine porewater,
dialysis membrane samplers were used with cells placed 1-cm apart. Soils were collected that were 15-
20 cm long using gravity corers. Results indicated CI" from the road salt increased, metal chloride
complexes increased, and that certain metal chloride complexes would be stabilized in solution. Metal
chloride complexes were especially of concern for Cd.
Mermillod-Blondin et al. (2008) looked at potential effects of bioturbation of tubificid worms and
stormwater sediment (SWS) thickness on the release of nutrients and contaminants in stormwater
retention systems. The authors collected SWS and tubificid worms and transferred them into 18
columns. Six had a SWS layer of 2-cm, six had a 5-cm thick SWS layer, and six had an 8 cm thick SWS
layer. Experiments were conducted for 24 days (10 days before worms, and 14 days after worms were
introduced). Results showed nutrient release increased with SWS thickness and bioturbation, and the
release of two PAHs (acenaphthene and naphthalene) was influenced solely by SWS thickness. There
was no evidence to suggest that bioturbation of the tubificid worms influenced contaminant mobility.
Based off these findings, authors concluded stormwater pond management should include a control for
SWS accumulation and biological activity to limit the release and mobility of contaminants and nutrients
to prevent potential disruption of nearby ecosystems.
A research study conducted by Camponelli et al. (2010) investigated Cu and Zn in sediments below a
stormwater retention pond. This pond drained a six-lane highway and had an approximate surface area
of 446 m2, but the authors did not report on the local geology under the retention pond. Runoff water
samples were collected from twelve storm events at the culvert that enters the pond. Soil samples were
collected along three transects, with seven samples per a transect. Retention pond sediment cores were
collected over six transects with two samples per a transect. Results showed stormwater draining into
the retention pond sometimes exceeded US EPA water quality criteria. In the vadose zone the top 10 cm
had the highest concentrations of Cu and Zn, and Cu and Zn concentrations dropped with increasing
depth. The variability in the decrease of Cu and Zn concentrations in the vadose zone was due to
heterogeneity of the sediments.
Denitrification rates were studied in a managed groundwater recharge system (Schmidt et al., 2011). It
sat over a perched aquifer within an eolian, fluvial, and alluvial sediments. The system consisted of a 3
ha infiltration pond that was created in a natural depression and had a maximum depth of 6 m.
Infiltration caused a wetting front and an inverted water table downward into the vadose zone forming a
1 - 2 m saturated zone. Infiltrating water then passed 20 - 30 m of vadose zone before reaching an
aquifer. The water from the infiltration pond was collected from under the infiltration pond by twelve
piezometer nests that were screened between 30, 50, 60, 90, and 100 cm. The study found the dominant
form of nitrogen was NO3" in the pond water. The NO3" concentrations in the saturated zone below the
pond vadose zone interface was less than the pond concentrations for all depths, indicating that
denitrification is a significant process for N inputs from a managed aquifer recharge system.
Mermillod-Blondin et al. (2015) examined the ability of three managed aquifer recharge (MAR) systems
to process dissolved organic matter (DOM) moving from infiltration basins to urban aquifers. These
underlying aquifers consisted of fluvio-glacial carbonated sediments, consisting of quartz, carbonates
and trace clays chlorite and anorthite. Fluorescent spectroscopic properties, biodegradable DOC and
refractory fraction of DOC, and DOC consumption by microorganisms were monitored during a
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stormwater event. Samples were collected for 24 hrs. after the stormwater event at 50 cm below the
basin surface from recharge and reference wells. Results revealed a large decrease in DOC
concentrations at all three MAR systems. Retention was higher for biodegradable DOC versus refractory
DOC which led authors to conclude that microbial processes were dominant in DOC removal during
infiltration. However, even with the high retention rates of the DOC, results showed DOC levels in the
aquifer increased after stormwater runoff infiltration.
Al-Rubaei et al. (2017) investigated the performance efficiency of a 19-year-old combined pond-
wetland system for surface water quality and quantity treatment and no information was given on the
geology of the site. They monitored the removal of TSS, nitrogen compounds, phosphorus, and heavy
metals over the course of four seasons through water and sediment sampling. Water was sampled for
stormwater inflow, after pre-treatment in the pond, and the discharge from the wetland. Sediment
samples were taken at nine locations using a Van Veen grab or plastic scoop. They identified the main
factors affecting system performance: season, air temperature, antecedent dry period, rainfall
depth/intensity, and duration of storm events. The wetland system studied was built to reduce pollution
flowing to a lake. The catchment area of the wetland had 130 ha of residential area, 190 ha of
industrial/commercial area, and major roads. A total of 53 storms were monitored for flow, and 13 were
sampled and analyzed for water quality. Results showed the mean volume reductions were 40% for the
pond, 28% for the wetland, and the pond-wetland system decreased peak flows by 41 - 95%. The
system also removed an average of 91% of the TSS, 80% of the phosphorus, 94% of the particulate Cd,
91% of the particulate Cu, 83% of the particulate Pb, and 92% of the particulate Zn. Removal of Ni and
nitrogen compounds was highly variable. Results also showed removal of dissolved heavy metals were
less efficient and more variable.
Stormwater infiltration basins were the most commonly assessed GI for groundwater contamination
found in the literature, but much of the literature focuses on surface water analysis or basin soil
assessment. There were a wide variety of monitoring methods and variables assessed for effects on
groundwater. Metals were found to be retained in the soils or vadose zone of the infiltration pond, but
often metal transport did not go deeper into the groundwater (Nightingale, 1987a, 1987b; Datry et al.,
2003; Dechesne et al., 2005; Winiarski et al., 2006; Camponelli, et al., 2010). Other studies solely
assessed the reduction of contaminants in the surface water (Barbosa and Hvitved-Jacob sen, 1999; Al-
Rubaei et al., 2017). If reduction in contaminants are found, it can be deduced that the contaminants
remain in the infiltration basin and could act as a potential groundwater contaminant over time. This
issue has yet to be addressed in the literature. It was also found that denitrification could be a significant
process in managing nitrogen in stormwater infiltration basins (Schmidt et al., 2011), but there is a risk
of nitrogen leaching out of the sediments during recharge events (Nightingale and Bianchi, 1977). Other
studies observed the risk of groundwater contamination from organic compounds and found that 100%
of organic contaminants cannot be removed in infiltration basins leading to increased groundwater
detection in some cases, but their detectability decreased with depth in the vadose zone (Tomson et al.,
1981; Datry et al., 2003; Fischer et al., 2003; Dechesne et al., 2004a and 2005). It seems that stormwater
infiltration basins can be successful at removing several contaminants from stormwater, but 100% of
these contaminants will not be removed and will concentrate in the vadose zone. Long term implications
for this have not been explored, causing concern for future changes to groundwater.
7.2.2 Roadside Stormwater Runoff Infiltration Systems
Another type of infiltration system used to decrease stormwater runoff are roadside stormwater runoff
infiltration systems. In this system, runoff from roadways is collected and infiltrated into the subsurface.
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The following discussion will examine the potential for groundwater impacts from the use of these
systems.
Two old infiltration systems were investigated for potential contamination from Cr, Co, Ni, Cu, Zn, Cd,
Pb, absorbed organically bound halogens (AOX), and PAHs (Mikkelsen et al., 1996). The first site was
in an area used for light industrial activities, mixed residential, and agricultural land use. The infiltration
system was a series of depressions in the road shoulder which had been in operation since 1959. The
depth to groundwater was 15 - 20 m, and the vadose zone was comprised of gravel. The second site was
in a heavily trafficked area with a waste incineration plant 1.2 km from the site. At this site, road runoff
was drained into infiltration shafts which were built in 1949 and 1982. The groundwater depth at this
location was 20 m, and the vadose zone was also comprised of gravel. At the first location, researchers
reported high concentrations of total and dissolved metals in the upper 30 cm which decreased to
background concentrations further down in the vadose zone. PAH and AOX concentrations were highest
in the upper layers but were near background concentration by 1.5 m. In both shafts at the second
location, the runoff sludge had infiltrated to a depth of 70 cm in shaft one and 30 cm in shaft two. The
researcher found the sludge in shaft one was anaerobic and had a sulfide and petroleum smell. Shaft two,
was half full of water because it was clogged and anaerobic. In both shafts, the total concentrations of
metals, PAHs, and AOX correlated with the extent of the sludge layer. The researchers concluded that
runoff from a large area was concentrated into a small area which caused a significant buildup of metals,
PAHs, and AOX in upper soil layers and sludge layers. Researchers also point out the results of this
study may not be applicable to other sites with different soil characteristics and geology, so there is a
need for more field research investigating stormwater infiltration.
Mikkelsen et al. (1997) is a continuation of the research at the study sites used in 1996. At the first site,
results indicated the top layer in the vadose zone was formed by runoff born particles. The
contamination was retained in this layer by filtration of particles in the runoff, and by sorption of
contaminants to the particles in this layer. Any contaminants that passed through this layer also passed
through the sub-base gravel and were retained in upper sediments of the vadose zone. The second site
patterns were more difficult to ascertain according to the researchers; they found pH increased with
depth, which slowed the downward migration of metals through sorption and precipitation. However,
precipitation of metal sulfides and reductive dissolution of Fe and Mn oxides could possibly account for
the variable depth profiles observed. This research concluded, based on the depth to groundwater,
groundwater contamination is unlikely in a reasonable time frame. However, the authors noted that
soluble inorganic and organic contaminants were not studied and could potentially reach groundwater.
Norrstrom and Jacks (1998) examined the effects of stormwater runoff and the use of de-icing salts on
groundwater under an infiltration pond near a roadside built in 1964. The study site was in the recharge
area for the reserve water supply of a nearby town. Prior to the road being built in 1964, the groundwater
CI" concentrations were 15 mg/L. In 1998, the CI" concentrations were 120 mg/L, and were shown to be
the result of road salt application. In thirty-four years, the CI" concentrations increased eight-fold.
Stephenson et al. (1999) designed a stormwater runoff system to convey stormwater from a highway and
drain it into a sinkhole, thereby directly into a karst aquifer. In this system, they drained three different
portions of the highway into one sinkhole. Most of the drainage volume went through a 107-cm concrete
culvert. The second drainage route was a 1.8 m concrete ditch, and the final drainage route was through
a 46-cm corrugated metal culvert. The groundwater quality was monitored at a nearby spring for Zn, Pb,
TPH, PAH, TDS, and total volatile solids (TVS). They found detectable Zn, TPH, TDS and TVS in the
only stormwater runoff sampling event monitored. The authors concluded the contaminant load was
closely related to the runoff volume rather than the concentration of the contaminants. The authors also
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stated that contamination of groundwater is enhanced in karst areas because there is a more direct path
to the groundwater.
A roadside infiltration trench used to infiltrate stormwater into the subsurface was studied by Norrstrom
(2005). Three piezometers were installed at 2.5 and 4.5 m below land surface near the infiltration trench.
Both whole samples and dissolved samples (filtered to < 0.45 |im) were taken. The researcher looked at
the effects of road salt on the infiltration of metals to groundwater. Results indicated that Cd and Pb
content was moderately high in the groundwater and was primarily associated with the particulate
fraction. Zinc on the other hand was, to a lesser extent, bound to the particulate fraction. The results
indicate the greatest risk to groundwater in areas subjected to road salt was the colloidal transport of Pb.
Cadmium and Zn were likely transported as chloride complexes and free ions. The factors that primarily
modify the transport of these metals are soil properties, accumulation of metals in the vadose zone, and
the hydrogeology.
The leaching behavior of organic contaminants from ditches that received stormwater runoff from
roadways was investigated by Stromvall et al. (2007). This study looked at PAH content and SVOCs in
soil cores taken from the ditches. Research indicated near surface concentrations were the highest for
both PAHs and SVOCs, and concentrations decreased with increased depth. Semi-volatile hydrocarbons
were detected to a depth of 1 m, and PAHs were detected to a depth of 1.5 m. The researchers
hypothesized that these organic compounds were likely transported into deeper zones through colloidal
transport with the compounds sorbed to colloids.
Kluge et al. (2014) also investigated metal concentrations (Cd, Cr, Cu, Ni, Pb, and Zn) in embankments
along a highway. In this study, wick lysimeters were used to sample roadway runoff, and soil cores were
used to examine metal concentrations with depth. The runoff samples were subsequently split into two
samples: one not filtered and one filtered with a 0.2 |im filter for dissolved metals. The >0.2 |im fraction
was considered the particulate bond metals. For metals Cr, Cu, Ni, Pb, and Zn, the particulate bound
fraction was the dominant fraction. However, for Cd the dissolved fraction was the dominate fraction. In
the vadose zone there was a horizontal gradient except for Cd. Metal concentrations were the largest
near the highway and decreased in concentration as distance from the highway increased. In the case of
Cd, samples at 2 m had significantly lower Cd concentrations than at 5 m. From a 5 m distance, there
was a decrease in Cd concentrations with distance from the highway. The vertical gradient showed
concentrations for all metals were highest in the upper portion of the vadose zone and decreased with
depth.
Aryal et al. (2006) studied the Experimental Sewer System constructed in 1986 in a highly urbanized
residential area. The drainage area was 8.25 ha with 244 infiltration trenches and inlets. The researcher
sampled all the infiltration inlets, but only data from 17 of the infiltration inlets were used in this
investigation. They also collected roadside dust from all locations. The heavy metal content of the dust
and sediments were similar in concentration, but were higher than soil metal concentrations in the area.
They concluded that sediments at a 3-cm depth were different than sediments at the other depths, that
road dust was the source of metals in the infiltration inlets, and the decreasing metal content with depth
could be due to metal leaching to underlying sediments. The positive correlation of Cu and Zn indicates
a common source, but the other metals were location specific.
The research presented in this study (Aryal et al., 2007) was an extension of the research conducted
earlier (Aryal et al., 2006). In this study, they present more detailed data on two of the 17 cores. The
cores were chosen because of the thick sediment above the mouth of the trench. The continuation of this
research was in part because of the unreliability of using total metals as a surrogate for metal mobility
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and contamination. Researchers used a sequential extraction method to parse out the metal
concentrations in various fractions on the sediment layers. The authors concluded metal concentrations
decreased with increasing depth, and speciation of the metals in the sediments showed different orders
of mobility. The first conclusion suggested metals were possibly released to lower layers of the vadose
zone or to groundwater. The second conclusion applied to the long-term fate and transport of the metals
in the vadose zone for several reasons. If the pH of the sediments became more acidic then Zn, Mn, and
Co would be more mobile. If the sediments become more reduced, then Pb, Mn, and Co become more
mobile, but if the sediments become more oxidized, Cu, Pb, and Cr become more mobile.
Azah et al. (2017) examined potential risks of roadway and stormwater residuals for 16 PAHs in
samples collected from Florida. Samples consisted of street sweepings, catch basin sediments,
stormwater pond sediments, and ditch cleaning sediments from 14 municipalities. The results showed
that of the 16 PAHs, benzo [a] pyrene was the only one detected above appropriate risk-based regulatory
values for soil and groundwater. The least detected PAH was acenaphthylene. Based on the results,
authors concluded there may be beneficial use for ditch cleanings, stormwater pond sediments, and catch
basin sediments in industrial and residential settings, but street sweeping debris may pose a threat to
residential areas. They also found the residuals used in the study were unlikely to leach PAHs into the
groundwater. As was the case with infiltration/retention ponds, roadside stormwater runoff infiltration
systems did not show impacts to groundwater quality in most cases, and the contamination was
generally confined to the upper portion of the vadose zone. Norrstrom and Jacks (1998), Stephenson et
al. (1999), and Norrstrom (2005) did show roadside stormwater runoff infiltration systems could cause
changes to groundwater quality from significant increases in CI" concentrations, and metal and organic
contamination. It is important to note that in most cases contamination did not occur. Although the
contaminants appeared to be concentrated in the upper portion of the vadose zone and decreased with
depth, this could indicate that leaching of the contaminants was occurring and there could be a future
impact to groundwater quality.
For roadside runoff infiltration systems, results could vary based on differing soil conditions and
geology. This suggests a need for more studies across various conditions. Most of these studies showed
that contaminants tend to concentrate into small areas in the upper layers of the soils and most did not
assess contamination of the groundwater below these systems. With insoluble contaminants, it is
unlikely that groundwater will be contaminated, but there is a concern with colloidal transport from road
salt could occur if the contaminants sorb to the colloids. Areas with karst aquifers are at risk because the
direct path to the groundwater could mean biogeochemical processes are bypassed and not removing
contaminants.
7.2.3 Permeable Pavement Systems
Permeable pavements are made from construction materials that have large enough pores to allow water
to flow through, or use paver systems that allow water to pass in the space between pavers. The
permeable pavement systems allow stormwater to flow from the surface, and in some cases, enter a
reservoir (infiltration gallery) that stores the stormwater for a short period of time while infiltration
occurs. Groundwater quality could potentially be impacted by permeable pavement systems, which will
be discussed in this section.
Permeable pavement systems act as in situ bio-reactors that contain diverse communities of bacteria,
fungi, protozoa, and other invertebrates. The native microbial communities have been shown to remove
more than 98% of hydrocarbons (e.g. from motor oil and fuels) from infiltrated stormwater runoff
(Coupe et al., 2003; Scholz and Grabowiecki, 2007). Engineers have promoted this beneficial
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bioremediation process through the incorporation of nutrient-releasing geotextiles within permeable
pavement systems (Spicer et al., 2006). Despite these studies, very little information is available on the
fate and transport of microbial contaminants through porous pavement systems. As unsaturated systems,
stormwater microorganisms would likely be subjected to the removal mechanisms discussed above for
saturated and unsaturated systems.
The characterization of particles that could potentially clog porous pavements or be transported to
groundwater was conducted by Colandini et al. (1995). Particle size distribution, heavy metal content,
and interactions between metals and particles were investigated in this study. The authors took samples
from pervious asphalt pavements used for highway and motorway constructions and porous pavements
with a reservoir structure. Samples were collected by spraying high-pressure water on the pavement and
then collecting the sludge through depression-suction. Results showed most of the clogging materials
were sand with a variable silt content and low clay content. The researchers also found the materials
were often contaminated with heavy metals (Pb, Cu, Cd, Zn). Porous pavements with reservoir
structures also showed lower heavy metal content in clogging particles, however, all particle sizes are
contaminated with heavy metals, with fine particles showing the highest metal concentrations.
Brattebo and Booth (2003) studied the long-term effectiveness of four commercial permeable pavement
systems for structural durability, ability to infiltrate precipitation, and water quality of the infiltrated
water compared to traditional asphalt. The four different systems used were Grasspave®, Gravelpave®,
Turfstone®, and UNI Eco-Stone®, and construction occurred in 1996. After six years, the authors found
no major signs of wear and tear, and virtually all rainwater infiltrated into the permeable pavements with
very little surface runoff. Results showed infiltrated water had lower levels of Cu and Zn than direct
runoff from the asphalt, however, concentrations of Zn increased during the five years for infiltrated
water and direct runoff. Authors also found 89% of the samples from the asphalt had motor oil, but none
of the samples from the infiltrated water had any. Neither lead or diesel fuel were detected in any
sample, and conductivity and hardness stayed constant.
Gilbert and Clausen (2006) compared replicated asphalt, permeable paver, and crushed-stone driveways
for water quality and quantity from stormwater runoff. Runoff samples were taken weekly, and rainfall
was measured for 22 months. Contaminants analyzed were TSS, TKN, NO3-N, NH4-N, TP, Zn, Pb, and
Cu. The sites chosen were driveways in residential areas. Results showed runoff from asphalt driveways
was much higher, while crushed stone driveways had the least amount of runoff. Permeable paver
driveways had the lowest concentration of contaminants, and crushed-stone driveways and asphalt
driveways had similar levels of contaminants. The authors also noticed infiltration rates for the
permeable paver and crushed-stone driveways decreased over time, but stated they believed the decrease
was due to particle clogging. They concluded permeable pavers and crushed-stone driveways are
preferable to asphalt for pollution control even with the decreased infiltration rates.
Boving et al. (2007) conducted a study to investigate the performance of permeable pavement, and to
determine the fate and transport of metals, nutrients, PAHs (naphthalene, acenaphthylene, acenaphthene,
fluorene, phenanthrene, anthracene, fluoroanthene, pyrene, chrysene, and benzo (a) pyrene), and bacteria
in a functioning parking lot. The site was a porous asphalt parking lot over a regional aquifer. It was
built in 2002, had 800 parking spaces, and an area of 24,200 m2 Results showed the pavement clogged
with sand in heavy traffic areas, and areas that remained porous were effective at removing organic and
metal contaminants, but not nutrients and anions. They also found contaminant concentrations varied
with the seasons, i.e., higher metal and chloride concentrations in the winter and early spring due to road
salting. PAH flux was lower than typical reported concentrations in Rhode Island suggesting the
structure was effective in removing PAHs.
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A study was conducted during 2007 to look at water quality differences between four different types of
permeable pavement and standard asphalt (Collins et al., 2010a). The permeable sections studied were
pervious concrete, permeable interlocking concrete pavement (one with an open surface area of 12.9%,
and the other 8.5%), and concrete grid pavers filled with sand. Composite samples of atmospheric
deposition and asphalt runoff were compared to subsurface samples from the permeable pavement
sections. The authors focused on different nitrogen species (TN, TKN, organic N (ON), NO2-N, NO3-N
and NH4-N) and pH. Samples were collected from 20 different storm events at seven different locations
(atmospheric deposition, two asphalt runoff areas, and the four permeable pavement areas). The results
showed permeable pavement drainage had higher pH than the asphalt runoff, and the pervious concrete
had the highest pH of all the permeable pavement sections. Permeable pavements had more buffering
capacity, however all pavements were able to buffer acidic rainfall. Permeable pavement drainage also
had lower NH4-N and TKN concentrations and higher NO2-N and NO3-N concentrations.
Borst and Brown (2014), examined three permeable pavement systems in relation to potential road salt
effects on groundwater quality. This study was conducted at a 0.4 ha parking lot. The three permeable
pavement systems used were interlocking concrete pavers, pervious concrete, and porous asphalts
installed into three separate rows. The driving lanes were paved with traditional impervious hot mix
asphalt. Each pavement system received direct rainfall and runoff from the driving lanes immediately
upgradient. Under each row was an infiltration gallery constructed of recycled crushed concrete
aggregate. The water collected in the infiltration gallery was routed to collection tanks with a capacity of
3.4-4.1 m3. Calcium chloride (CaCh) was applied to the parking lot surface for de-icing. Although no
vadose zone samples or groundwater samples were taken, the collected runoff samples were useful for
demonstrating the potential environmental effects to the vadose zone and groundwater. The results
indicated that CI" concentration from the winter salt application was attenuated as it passed through the
permeable pavement system, but the CI" concentration was still significant. After April, the CI"
concentrations were detectable, but did not exceed 230 mg/L.
Drake et al. (2014) conducted a study to compare three different partial infiltration permeable pavement
systems and a standard asphalt system on their ability to trap pollutants and improve water quality. The
three permeable pavement systems used were AquaPave®, Eco-Optiloc®, and Hydromedia®. The
contaminants they focused on were TSS, TPH, Cu, Fe, Mn, Zn, TN, and TP. The pavement cells were
230 - 233 m2 in size, and had a parking capacity for 8-10 vehicles. The cells were also separated by
concrete to prevent cross-flow of stormwater. The authors found the different permeable pavement
systems performed similarly and reduced TSS by >80%>. Results also showed the effluent from the
permeable pavement systems contained less heavy metals (65-93% removal), and reduced the
concentration for most nitrogen species (decreased concentration of NH3, NO2", and ON, but increased
levels of NO3") and phosphorus in comparison to the standard asphalt.
Brown and Borst (2015) studied three permeable pavement types: interlocking concrete pavement,
pervious concrete, and porous asphalt, located at a 0.4 ha parking lot in Edison, NJ. They investigated
how these three different pavement types would influence nutrient concentrations in stormwater runoff.
Thirteen rainfall events were sampled over a year with infiltrate samples collected directly from the
tanks below the pavement systems. It was found that permeable pavement systems were unlikely to
reduce the TN in stormwater due to lacking anaerobic conditions. Different pavement types did affect
the amount of bioavailable phosphorous with the most reduction seen in porous asphalt. The authors
suggest a need to research the different permeable pavement construction materials to assess the risk of
leaching from the materials. There is also a concern of nitrogen contamination from organic material
when runoff is from vegetated areas. This organic bound nitrogen could clog the pavement surfaces and
be a contaminant risk to groundwater if it infiltrates through to non-anaerobic conditions.
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Most cases of the permeable pavement systems research looked at infiltrated water rather than the water
traveling from the system through the vadose zone. Researchers believed for the most part that
contaminants would not impact groundwater because contaminants were particle bound or retained in
the infrastructure. However, in two studies there was a potential risk to groundwater quality from CI"
(Borst and Brown, 2014) and N03" and phosphorous (Drake et al., 2014).
7.2.4 Roof Runoff Systems
Another type of GI is the roof runoff system which captures stormwater running off roofs and infiltrates
the runoff into the subsurface. The potential for groundwater contamination is discussed next.
Roof runoff infiltration was investigated by Mason et al. (1999) at an industrial site using large building
roofs in which the roof runoff was channeled into a single PVC pipe that emptied into an infiltration pit
with an area of 106 m2, and was 3 - 4 m in depth. The approximate volume of the infiltration pit was
150 m3. Researchers sampled the infiltration pit and lysimeters at depths of 1 m and 1.6 m and analyzed
for nutrients (NH3, ortho-P, NO3", TN, and DOC), heavy metals (Cd, Cr, Cu, Pb, and Zn), major anions
(CI" and SO42"), and major cations (Ca, Mg, Na, and K). The results showed that for NO3" and ortho-P in
the lysimeters, samples followed the concentration patterns in the roof runoff, and the higher initial
concentrations were later diluted by the soil water or the large flush of runoff later in the event. This
indicated there was not an effective retention mechanism for ortho-P or nitrate in the vadose zone. For
DOC there was slight retention in the vadose zone. NH3 showed significant decreases in concentration
further down in the vadose zone which was attributed to possible denitrification processes and sorption
to negatively charged surfaces. The dissolved metal fraction (<0.45 |im) was the focus for the transport
of metals in the vadose zone because the researchers assumed that particulate bound metals would be
trapped in the upper layers of the vadose zone. Cadmium, Cr, Cu, and Zn were primarily associated with
the dissolved phase in study, whereas, Pb was found to be associated with the particulate phase.
Researchers considered the metal accumulation in soil water in the upper portion of the vadose zone,
metals in the soil water in the unsaturated zone, and metal accumulation in the vadose zone. In the soil
water in the upper portion of the vadose zone, the concentrations were much lower compared to metal
concentrations in the soil water at 1 m and 1.6 m. This indicated a significant portion of the metals were
transported downward through the vadose zone. They concluded this was likely caused by dilution of
the infiltrating water, and uneven water distribution in the infiltration pit causing inadequate contact of
inflow water in the upper layers of the vadose zone. In the unsaturated zone, researchers found there
were two different behaviors for the metals. Initially concentrations for Cu, Cd, and Cr were high, and
then their concentrations decreased which followed the trend found in inflow water. They were only
found to be partly retained in the vadose zone, and a significant fraction was transported into lower
portions of the vadose zone. Zn and Pb did not follow the inflow water trend. Instead, Zn and Pb
concentrations decreased quickly even when their concentrations were high in the inflow water. This
behavior showed retention of Zn and Pb in the vadose zone took place, at least in the short term. Finally,
researchers looked at the accumulation of these metals in the vadose zone during a two-year period at
depths of 0 -0.05, 1, and 1.6 m. They found that, except for Cr, all metal concentrations increased in the
upper depth during the first four months, and Zn and Cd increased more than Cu and Pb. This showed
there was some retention of the metals except Cr initially. In 1994, the researchers found metal
concentrations were unevenly distributed in the various depths in the vadose zone, with the largest
concentrations in the deepest portion (1.6 m) of the vadose zone. However, by 1996 the metals were
evenly distributed between the 1 m and 1.6 m zones. There appeared to be a temporary accumulation of
some metals in the upper portion of the vadose zone to a depth of approximately 1 m. As time passed
there was a downward migration of metals deeper into the vadose zone. Chloride and SO42" behaved as
conservative tracers and did not react with the components of the vadose zone. In fact, the authors used
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these species as internal tracers for the study. The major cation species on the other hand were found to
interact considerably with the matrix in the vadose zone, and Ca concentrations in the lysimeters were
found to be in equilibrium with calcite. The authors concluded major cations approached the typical
composition of water in contact with carbonate rocks.
Farreny et al. (2011) investigated four different types of roofs (clay tiles, metal sheet, polycarbonate
plastic, and a flat gravel roof) for their ability to harvest stormwater runoff. They compared slope and
roughness of the roofs, and analyzed the physicochemical contamination of the roof runoff. Results
showed smooth sloping roofs could collect up to 50% more rainwater than flat rough roofs. They also
showed flat roofs had higher concentrations of contaminants except for NH3 due to weathering, particle
deposition, and plants.
There is a potential for groundwater quality changes with the use of roof runoff systems. The research
presented by Mason et al. (1999) indicated that metals are transported to the vadose zone, and with time,
metals move downward in the vadose zone. However, CI" and SO42" behaved as conservative species
and were not retained in the vadose zone.
7.2.5 Bioretention Systems
Biofilters are engineered infiltration systems typically consisting of a water retention or ponding zone,
vegetated surfaces, subsurface filtration media, and a drainage layer. Rain gardens, bioswales, and
bioretention ponds are all examples of biofilters. These systems are typically subdivided into two groups
based upon whether the infiltration design incorporates a submerged (also known as a saturated) zone
within the vertical profile (Rippy, 2015; Peng et al., 2016). Submerged zones maintain moisture within
the infiltration profile between precipitation events. Designs that incorporate a submerged zone typically
include labile organic carbon additions to the saturated section to promote microbial denitrification for
the remediation of influent nitrate. This anaerobiosis in the submerged zone contrasts the aerobic
unsaturated conditions at the surface of the biofiltration system.
Vegetated infiltration systems enhance removal of introduced microbial contaminants by maximizing
the antagonistic pressure from the native communities. Rain gardens and bioswales can be managed to
enhance populations of mesofauna and protozoan grazers within the infiltration systems to promote
greater removal of introduced stormwater microbial contaminants (Mehring et al., 2015; Chandrasena et
al., 2017; Pavao-Zuckerman and Sookhdeo, 2017). The antagonistic biological processes together with
the physicochemical discussed earlier result in greater stormwater microbial contaminant removal in
vegetated infiltration systems (Mehring et al., 2015; Rippy, 2015; Petterson et al., 2016; Richkus et al.,
2016; Chandrasena et al., 2017).
Hares and Ward (1999) conducted a study to compare wet biofiltration and dry pond treatment facilities
and their removal efficiencies for selected stormwater contaminants. They monitored the concentrations
of vanadium (V), Cr, Mn, Co, Ni, Cu, Zn, Mo, Cd, antimony (Sb), and Pb. The sites chosen were along
a motorway which had a high daily traffic density. Samples were taken during dry weather conditions
and during initial stages of storm events. Results showed the two ponds had similar discharge volume,
but the biofiltration pond had higher heavy metal removal efficiencies than the dry pond treatment
facility. The performance and efficiency of bioretention systems for removing heavy metals and
nutrients were studied by Davis et al. (2001) using synthetic stormwater runoff. The synthetic
stormwater was prepared so that it mimicked actual urban stormwater runoff concentrations, and results
indicated that adsorption of heavy metals to soil increased with increasing pH. They found very little
adsorption at pH=~4, and the release of Cu and Zn at very low pHs. The authors also found Pb had the
strongest adsorption to the soil whereas Zn was the weakest. Results showed high reductions in Pb, Cu,
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and Zn (>92%), and moderate reduction for phosphorus (80%), TKN (65-75%), and NH3 (60-80%).
However, results for NO3" were varied with little removal.
Davis (2007) conducted a study of two bioretention systems with different designs (one system was a
standard design while the other had an anoxic sump) to compare water quality from parking lot
stormwater runoff. The contaminants analyzed were TSS, TP, NO3-N, total Cu, Pb, and Zn. Twelve
events were analyzed for TSS, PO43", and Zn, nine events were analyzed for Cu and Pb, and three events
were analyzed for NO3-N. Authors found both bioretention systems showed large improvements in
parking lot runoff without any significant differences in efficiency between the two. Median percent
removals were 47% for TSS, 76% for PO43", 57% for Cu, 83% for Pb, 62% for Zn, and 83% for NO3-N.
Hunt et al. (2008) conducted a study to look at the effectiveness of a bioretention cell in reducing
contaminant concentrations in an urban setting. The bioretention cell treated runoff from a nearby
municipal building parking lot, and had a drainage area of 0.37 ha. Composite and grab samples were
collected and monitored for TKN, TN, NO2-N, NO3-N, NH3-N, BOD, fecal coliform, E. Coli, TSS, Cu,
Zn, Fe, and Pb. Results showed a decrease in the concentrations of TN, TKN, NH3-N, BOD, fecal
coliform, E. Coli, TSS, Zn, Cu, and Pb, however the concentration ofN02-N andN03-N remained
almost unchanged, and the concentration of Fe increased. Peak flows were also shown to be reduced by
at least 96% which authors believe may support the use for bioretention systems to help protect from
local flooding.
Li and Davis (2008) studied bioretention to reduce metal concentrations in infiltrating stormwater
runoff. Each cell was in a parking lot and the cells dimensions were 2.9 x 5.4 x 6.3 m with a median
depth of approximately 1.1m. The cells were comprised of mulch (20 % by volume), sand (50 % by
volume) and top soil (30 % by volume). To evaluate the effectiveness of the media in retaining metals,
soil cores were taken from the surface to 85 - 90 cm. The metals of interest were Zn, Pb, and Cu.
Results indicated that most of the metals captured, based on sequential extractions, were of
anthropogenic origin. The metals were mainly in the upper layers of the media and decreased in
concentration further down the media profile.
A monitored bioretention cell was studied to understand the behavior of PAHs in stormwater runoff
(DiBlasi et al., 2009). This bioretention cell received stormwater runoff from a catchment that was 90%
impervious surfaces (parking lots, roads, and sidewalks), trapezoidal in shape with dimensions 50.3 m x
2.4 - 4.8 m, and area of 181 m2 The incoming stormwater entered the bioretention cell through a flume
and incoming water was sampled. There was an underdrain that directed the infiltrating water to a
discharge manhole where it was sampled again. The results showed PAH content was reduced from 31-
99% in the bioretention cell, and indicated the dominant PAHs were fluoranthene and pyrene.
This study was conducted at three different locations to look at the effect of bioretention systems on
water quality (Elliott et al., 201 lb). Data was collected over a period of six years, and examined the
water quality of inflow, effluent, the vadose zone, and groundwater. The contaminants analyzed were
CI", TSS, NH3, ON, NO3", TKN, NO2", TP and PO43". The three locations were chosen to see if
surrounding land uses had any effect on resulting water quality after treatment. Samples were collected
from lysimeters and observation wells installed both in and outside the rain gardens. Results showed the
rain gardens were effective in reducing concentrations of some nutrients like nitrogen and total
phosphorus, however authors found CI" was continually leached into the vadose zone, posing a potential
problem. The authors also concluded the rain gardens could remove nutrients before recharge water
reached groundwater, and the rain gardens did not have a negative impact on groundwater.
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David et al. (2015) studied the efficiency of a bioretention system in a semiarid region. The bioretention
system was 427 m2 and consisted of four rain gardens and one bioswale with a drainage area of about
16,200 m2 The contaminants monitored were total Hg, PCBs, PAHs, dioxins, TSS, Cd, Cu, Ni, Pb, and
Zn. Samples were collected from various storm events (different seasons and different intensity storms)
before and after installation of the bioretention system. The results showed the system was very effective
at removing metals and organic contaminants. Authors also found contaminants originating from cars
and pavements had higher concentrations in the runoff and were effectively treated, whereas
contaminants originating from atmospheric sources had lower levels in runoff and were reduced less
after treatment.
Valtanen et al. (2017) conducted a study using large scale lysimeters to look at how biofiltration systems
retain nutrients and metals, and infiltrate water in different seasons. They used eight open-air lysimeters
that were either vegetated or non-vegetated. Six of the lysimeters contained three plant species tolerant
to drought and excessive soil moisture conditions. Lysimeters were irrigated six times during the study
using artificial stormwater designed to mimic typical rainfall and snowmelt events, and irrigation
volume was calculated to represent the runoff depth of an impervious surface of 27 m2, so the surface
area of a lysimeter was 3% of its catchment area. The contaminants studied were Zn, Cu, aluminum
(Al), PO43", NO3", and NaCl. These contaminants and their concentrations in the artificial stormwater
were chosen based off measurements taken from the surrounding area. Inflow and outflow rate were
measured using pressure sensors, and temperature was continuously monitored at different depths:
surface, 35 cm below surface, and 85 cm below surface. One-liter samples were taken during irrigation
events at a frequency of one sample per 30 L of drainage water that passed through the soil. Their results
suggested that roots in the vegetated soils increased infiltration rates since the vegetated lysimeters had
higher outflow rates than the non-vegetated ones. Both the vegetated and non-vegetated systems retained
almost 100% of the PO43" regardless of the season which led authors to conclude PO43" was retained by
the soil and not the vegetation. The root system also had a significant effect on nitrate retention which
suggested that inactive and active vegetation increase the bioretention of NO3". Cu and Zn were well
retained by both vegetated and non-vegetated systems which suggested temperature and vegetation do
not impact the retention of these metals. Al behaved differently than the other two metals in summer and
spring because the amounts of Al in the outflow were much larger than Al levels in the inflow, and
salting increased the leaching of Al.
The reviewed literature for bioretention systems found that most contaminants were retained in the
media of the bioretention system. Nonetheless, there were exceptions that could pose a risk to
groundwater quality over time (Davis, 2007; Hunt et al., 2008; Elliott et al., 201 lb; Valtanen et al.,
2017). In these exceptions, there was little attenuation of NO3", CI" and was found to be unretained; the
salt inputs would also increase the leaching of Al in some cases.
7.2.6 Swale Systems
Backstrom (2003) reviewed previous studies of grassed swales and their ability to retain TSS, particles,
and heavy metals after rainfall and snowmelt events. Results showed a removal of 79 - 98% of TSS
during simulated events, and swales with thin vegetation had the least amount of TSS removal. There
was no significant removal of TSS when the influent had concentrations of TSS below 40 mg/L. Swales
were more efficient in trapping larger particles than smaller particles. The author concluded that grassed
swales should be used as primary treatment devices even with their inability to produce consistent
removal rates. Backstrom (2003) also states that contaminants are not permanently bound to the grassed
swale and during runoff events with low contaminant concentrations, the grassed swales release
contaminants.
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A study investigating swale-trench system designed for the capture of PO43", Cd, Cr, Cu, Pb, and Zn
contaminated runoff from roadways was conducted by Ingvertsen et al. (2012). Seven sites were
investigated, but details of any of the swale-trenches were not provided. In general, this study showed
PO43" soil concentrations were the highest in the 0 - 5 cm depth, decreased in concentration in the 5 - 15
cm depth, and were lowest in the 15 - 25 cm depth. For most sites, there were no systematic trends in
concentration in any of the depths looked at for Cd, Cr, Cu, and Pb. Zinc, however, showed a trend of
decreasing concentration with increasing depth.
The water quality purification potential of non-vegetated bioswales using simulated stormwater runoff
was investigated by Li et al. (2016). A group of 10 bioswales were studied. Five of the bioswales were
permeable, and five had anti-seepage properties. The authors focused on the effects of various media on
water purification, time intervals, effects of contaminant concentration on purification, and the effects of
the ditch widths on purification. Results showed different inflow concentrations led to poor removal
rates for nitrogen and phosphorus while other bioswales with a constant inflow concentration had
removal rates of 35 - 50% for nitrogen and 95% for phosphorus. Long running time intervals showed
decreases in removal rates for both COD and Zn. The authors concluded that adsorption ability for the
bioswale with respect to COD and Zn could not be restored in a short period. Larger widths were
associated with higher removal rates for COD and NH3-N (up to 90%).
The swale systems reviewed indicated that removal of contaminants was similar to what was reported
for other GI systems. The one exception was the work of Backstrom (2003) who suggested that grassed
swales may release contaminants into the surrounding media. However, this research did not indicate if
the contaminants released would travel through the vadose zone and reach groundwater.
7.2.7 Aquifer Storage and Recovery Systems
Aquifer storage and recovery (ASR) stores water in the aquifer by injecting the water into the aquifer
and then using the water when it is needed. Surface water is injected from various sources: from treated
potable water, treated stormwater, reclaimed water, or river water. The water recovered can be used for
municipal, industrial or agricultural uses, but there is concern that mixing of this water into the existing
aquifer water can change the water quality for future use.
A study of an aquifer storage and recovery (ASR) project in South Australia was conducted by Herczeg
et al. (2004). Stormwater runoff was collected and held in three holding lakes used for aesthetics and as
a first order cleanup step. Water from the lakes was injected into a confined aquifer composed of
Tertiary limestone and was 50 to 70 m thick. The aquifer had solution cavities and highly variable
porosity because the cementation was heterogenous in nature. Samples were taken from the injection
well and three monitoring wells down gradient at 25, 65, and 325 m. The researchers found injection of
high organic matter and oxygenated stormwater in the suboxic limestone aquifer caused several
geochemical reactions to occur: carbonate mineral dissolution, sulfide oxidation, and aerobic CO2
production. They found that once dissolved oxygen was consumed, further degradation of the organic
matter occurred via anaerobic oxidation using Fe3+ and sulfide reduction. They also found the initial
injection of the stormwater into the aquifer would change the groundwater chemistry by mixing the two
different water types and the biogeochemical processes. This caused the release of Ca, Mg, HCO3", and
SO42". They also found there were no new geochemical changes when water was withdrawn from the
aquifer.
An ASR system in Florida was studied by Jones and Pichler (2007). The storage zone of the aquifer was
the Suwannee Limestone. The water enters in the eastern part of the aquifer and eventually discharges
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into the Gulf of Mexico. The results showed that except for Eh, DO, Fe, and As most of the parameters
did not vary more than 7%. This showed conditions in the storage zone did not cause large seasonal
variations in the water chemistry. The researchers hypothesized mechanisms by which As became more
mobile in this aquifer: oxidation of pyrite, release from hydrous ferric oxides, and microbial activity.
Pyrite in the aquifer near the injection zone could become more oxidized from the injection water. This
caused the dissolution of pyrite and the release of As associated with the pyrite into solution. The
hydrous ferric oxides could also be oxidized and cause the ferric oxides to dissolve which would release
any As sorbed by the hydrous ferric oxides and/to be released into solution. The researcher indicated the
role of microbes would not be great in the immediate area at the injection site. The water down gradient
and the presence of nutrients could cause the microbes to oxidize the pyrite and release the As into the
groundwater.
Another ASR study was conducted be Page et al. (2010). This study investigated urban stormwater from
a mixed residential and industrial catchment that was passed through two settling ponds and a
constructed wetland prior to injection into the aquifer. The aquifer was a confined sandy limestone that
was 60 m thick and was 160 to 220 m below ground surface. There were six wells that consisted of two
recovery wells and four injection wells. The wells were screened over 17 m at depths ranging from 165
- 182 m. This research indicated there were no pathogens in the groundwater prior to injection, there
were pathogens in the wetland treated stormwater prior to injection, and there were low levels of
pathogens in the recovered water. The researchers also found lower Fe concentrations in the wetland
treated water than in the recovered water. The ambient As concentrations in the aquifer and aquifer
materials suggested the aquifer could be a source of As in the recovered water. However, the authors
point out that although the iron oxides in the aquifer sequester the As, changes in groundwater chemistry
could alter this equilibrium and in some cases release As into the groundwater. The authors monitored
over 300 organic chemicals and most were not detected. Simazine (a broadleaf herbicide) was the most
frequently detected organic compound in the wetland but not in the recovered water.
Vanderzalm et al. (2010) further studied the ASR system that was described in Page et al. (2010). The
results of the study indicated the principal changes to water quality in the recovered water was the
dissolution of calcite. This caused increases in the concentrations of HCO3", Ca, Mg, and Na. The excess
concentrations of Ca and HCO3" can be explained by the dissolution of calcite, but Ca cannot balance
the increase in concentration of HCO3". Therefore, authors suggested that cation exchange was possibly
occurring as well to balance excess HCO3". The aquifer was providing a passive treatment for nutrients
and DOC during the storage phase. The injection of oxygenated water induces the oxidation of DOC and
pyrite in the aquifer. An important aspect of this study was that ion exchange and DOC sorption will
reduce the sorptive capacity of the aquifer over time.
Another ASR site was studied to understand the As mobilization in the storage aquifer (Vanderzalm et
al., 2011). The aquifer storage zone was the lower portion of a carbonate aquifer approximately 60 m in
thickness. The storage zone was roughly 100 - 160 m below ground surface. The results of this study
indicated the injection of reclaimed water into this aquifer mobilized As. The mobilized concentrations
of As did not decline during the study period. The researchers stated the initial injection of oxygenated
water into the aquifer caused reduced minerals in the aquifer to dissolve and release As in the zone near
the injection. Further away from the injection site As was released because of desorption of As from the
Fe oxyhydroxides in the aquifer. During the storage phase of the ASR cycle microbial action caused by
the injected DOC resulted in an increase of As because of reductive dissolution of Fe oxyhydroxides and
the loss of sorption sites. Microbial action also played a role in the conversion of As5+ to the more
mobile As3+ species.
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A groundwater replenishment system was studied by Fakhreddine et al. (2015). This system was
designed for potable water reuse and in 2015 had a production capacity of 70 million gallons per day
and it was anticipated that the production capacity will be expanded to 100 million gallons per day. In
this system, secondary treated waste water was collected and further treated using microfiltration,
reverse osmosis, ultraviolet light, and a hydrogen peroxide addition to produce water for recharge into
the aquifer. Prior to infiltration, the ambient groundwater and recharge water did not have detectable As.
The As was found to be associated with the sediments at low concentrations under the basin. The higher
clay content layers had the highest As concentrations and redox sensitive elements Fe, sulfur (S), and
Mn. The researchers argued that As release was a function of ionic composition and not surface changes
caused by low ionic strength. They stated that when the concentrations of Ca and Mg decreased, the
release of As into the surrounding water was comparable with NaCl inputs. Therefore, the lack of
divalent cations in the low ionic strength infiltration water caused the desorption of As by stripping Ca
and Mg from the surface of the clays. The release of As associated with ASR could be mitigated by
treating the infiltration water with lime and dolomitic lime prior to infiltration.
Aquifer and storage systems are different than other GI infrastructure systems in that water is directly
injected into the aquifer. Because the stormwater is directly injected into the aquifer, changes in
groundwater quality occur. Changes in water quality can be as simple as mixing two waters from
different sources, which can perturb equilibrium conditions and change the groundwater chemistry. In
other cases, unintended consequences can happen, such as the mobilization of As.
7.2.8 Dry Wells/Diffusion Wells
Emmanuel et al. (2009) studied the effects of hospital wastewater effluent discharge into a karstic
formation where the groundwater was used for human consumption. The contaminants monitored were
COD, chloroform, dichloromethane, dibromochloromethane, dichlorobromomethane, bromoform, Cr,
Ni, Pb, and fecal coliforms. The site was at an emergency hospital that used septic tanks to collect
effluent which was then treated and discharged into a diffusion well located in a saturated and non-
saturated area. Samples were collected from one septic tank, the discharge line of the drinking water
supply well, and the effluent of the septic tank. Results showed concentrations for most metals as below
threshold values, however concentrations for Cr, Ni, and Pb were high and posed a potential for major
health risks. COD concentrations were also high. These authors concluded that hospital wastewater
effluent was creating a health risk for consumers who drink the groundwater.
Edwards et al. (2016) evaluated the potential for stormwater to contaminate groundwater using dry
wells. They concluded that although dry wells are an effective means to infiltrate stormwater and
recharge aquifers, the success of the use of dry wells depends on the local subsurface conditions, the
maintenance of the dry well, and the quantity and quantity of the influent water. They found in a few of
the studies reviewed that the infiltrated stormwater was the possible source of groundwater
contamination. They also reported groundwater monitoring showed in many cases there was a decrease
in the concentration of contaminants between the land surface and groundwater. This suggested the
contaminants were attenuated in the vadose zone. The authors of the review did note that this apparent
reduction in groundwater concentration could be due to the study not persisting long enough for the true
effects of the contamination to be observed.
The use of dry wells or diffusion wells can in some cases reduce contamination of groundwater from the
infiltrating stormwater. However, dry wells or diffusion wells can also be the source of groundwater
contamination. If this type of GI is used to infiltrate wastewater there will likely be groundwater quality
changes and possible contamination.
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7.2.9 Rain Gardens/Vegetative Strips
The effects of rain gardens on the water quality was investigated by Tornes (2005). In this study, five
rain gardens were selected representing a range of land uses and hydrologic conditions. Land use ranged
from impervious surfaces to grassy areas. This study found that some of the effects of rain gardens were
enhanced infiltration and the reduction of dissolved ions.
A study, authored by Deletic and Fletcher (2006), focused on the performance of grass filters and grass
strips. The sites chosen were a grass filter strip and a grass swale. For the grass filter strip, the focus was
on the filtering efficiency with respect to differing particle sizes; in the grass swale, the focus was on
removal efficiency for TN and TP. Total suspended solids were recorded at both locations, and both had
uneven contaminant inflow. Results showed that grass strips and grass swales were effective in
removing sediment from stormwater runoff. Authors did note that TSS removal is mainly a physical
process that relies on flow rate, grass density, and particle size and density. Results also suggested
sediment deposition may cause soil clogging leading to less infiltration and more over flow. TN and TP
removal were also present in the grass swale. With respect to TP, high surface area to volume ratio
allowed rapid soil sorption. For TN, rapid removal was also seen, which suggested potential chemical or
biochemical processes were responsible for removing nitrogen.
A study was conducted examining the efficiency of a vegetative filter strip in conjunction with a
subsurface drainage system installed 1.2 m below the soil surface (Bhattarai et al., 2009). Concentrations
for N03"N, PO43", and TP were measured from surface water samples entering and leaving the filter
strip, and from subsurface outflow. Soil samples were also taken and analyzed for concentrations of
plant-available PO43" and NO3-N. The vegetative filter strip was built in 1994, reconstructed in 2001,
and can treat runoff from a cattle feed lot. Results showed decreases in PO43", NO3-N, and TP, with as
much as 75% for PO43" and 70% for TP for surface flow. Subsurface outflow results also showed a
decrease in PO43" and TP, but an increase in NO3-N concentrations. These results led the authors to
conclude that while a vegetative filter strip can be effective at removing nutrients from surface flow and
reducing runoff, the subsurface drain may increase NO3-N transport and therefore be detrimental to the
environment.
Duchemin and Hogue (2009) conducted a study to evaluate three types of filter strip systems and their
effect on water quality within the first year of their implementation. The first system was a vegetative
buffer strip comprised of grass, the second strip was comprised of a combination of grass and poplar
trees (two years old), and the third was a control strip with no vegetation. The water quality parameters
monitored were TSS, P, N, and E. coli. The site was an area that covered 2,328 m2, and was divided into
four sections each containing three plots (one for each type of strip system). Runoff and drainage water
volume were measured for each plot during rainfall events. The results showed that while the vegetated
strips were effective in reducing contaminant loads in stormwater runoff (reduced water volumes by
15%, TSS by 85%, TP by 75%, dissolved P by 30%, NH3 by 50%, NO3" by 60%, and E. coli by 25%),
there was not a significant difference in performance between the two vegetated strips.
Yang et al. (2010) conducted a study of 42 monophasic and biphasic rain gardens using simulated runoff
to compare hydraulic performance and removal efficiencies of dissolved nutrients and atrazine. The
design for the biphasic system was two PVC columns with saturated water conditions in the first
column, and unsaturated conditions in the second column. The monophasic design used the same setup
as the biphasic design except both columns were unsaturated and constructed with a traditional
underdrainage approach. Experiments consisted of five simulated runoff events every five days at a
constant temperature of 20°C. Temperature, pH, NO3", PO43", and glucose (C source) were measured
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from the effluent of each column. Their results showed higher removal of NO3" from the biphasic
system (60% removal for low concentration runoff, and 34% removal for high concentration). Most
NO3" removal was observed in column one with some additional removal in column two. Overall
removal efficiency of NO3" in both systems decreased with the high concentration contaminant runoff.
The authors concluded that with the high NO3" loadings, there was not enough available C substrates for
complete denitrification. At low concentration loadings, both systems had 100% retention of atrazine
and PO43". With the higher concentration loadings, both systems were effective at removing atrazine (84
- 89%>) and PO43" (89 - 94%>). Higher removal of the PO43" was observed in the biphasic system which
suggested increased saturated retention time may increase PO43" removal. Results also indicated the
soil's capacity to sorb or degrade atrazine became less effective over time. The authors concluded that C
substrate increased overall removal efficiency, and the biphasic system was better at reducing peak flow,
runoff volume, and contaminant loads.
LeFevre et al. (2012) looked at biodegradation of total petroleum hydrocarbons (TPH) in 58 rain gardens
representing different ages, catchment land uses, and four upland (do not receive runoff from impervious
surfaces) sites. Specifically, the authors wanted to look at the potential for biodegradation of petroleum
hydrocarbons and the factors involved with petroleum hydrocarbon biodegradation, given that rain
gardens frequently experience variable contaminant loading and different soil moisture conditions. The
authors analyzed concentrations of TPH, 16S rRNA genes for bacteria, and two functional genes that
encode for enzymes used in petroleum hydrocarbon degradation. 75 soil samples were collected from
just below the ground surface. Results showed the rain gardens all had similar levels of TPH
concentrations, and 39% of samples from the rain gardens had concentrations below detection limits. All
the upland site samples had concentrations below detection limit. The authors also noted the functional
genes and 16S rRNA genes were higher in rain gardens that had deeply rooted plants versus those that
had shallow rooting depth (grass) or zero vegetation. They also found the soils were all able to
mineralize naphthalene. Based off these results, the authors suggested that concentrations of petroleum
hydrocarbons were decreasing instead of accumulating, and that rain gardens may be more efficient than
retention ponds for treating petroleum hydrocarbons.
Komlos and Traver (2012) conducted a nine-year study to look at the ability of a biofiltration rain
garden to trap PO43" from stormwater runoff. The site was in a parking lot that had a drainage area of
0.52 ha with about 35% being impervious surface. Data were collected from rainfall events. Results
showed the median concentration for PO43" decreased from 0.21 - 0.25 mg/L to 0.03 mg/L, and there
was no decrease in efficiency of removal. Authors examined how much of the PO43" sorbed onto the soil
to determine the potential longevity of the rain garden. They found the first 10 cm were saturated with
PO43", but calculations based off their data and assumptions indicated that saturation at greater depths
would not occur for another 20+ yrs. They concluded that infrequent maintenance would be required for
long term operation of rain gardens for removal of PO43".
Yang et al. (2013) conducted a two-year study to look at hydraulic performance and removal efficiencies
of biphasic rain gardens. They used natural and simulated runoff events, and monitored the influent and
effluent. Three agricultural events had high concentrations of NO3", PO43", and atrazine, and five urban
events had spiked concentrations of NO3", PO43", glyphosate, dicamba, and 2,4-D. The biphasic rain
gardens were effective in removing NO3" (91%), PO43" (99%), atrazine (90%), dicamba (92%),
glyphosate (99%), and 2,4-D (90%) with high levels of contaminant loading in simulated events. The
authors also found increased retention time of runoff and water saturated conditions significantly
affected contaminant removal.
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Most of the contaminants were found sequestered in the upper portions of the soil profile for rain
gardens and swale systems. It should be noted that none of the studies looked at compounds of interest
in the deeper layers of the vadose zone or groundwater. In one case, it was suggested that there was a
potential for NO3" to be transported into deeper layers of the vadose zone (Bhattarai et al., 2009).
7.2.10 Riparian Zones
Urban riparian zones have the potential to function as green infrastructure. They have been studied
extensively in the past 20 years, but few of these studies are relevant to an understanding of how riparian
zones and riparian buffers affect groundwater quality while providing GI functions. Although the
studies that address hydrology and water quality in urban riparian systems have some relevance to
effects of riparian zone GI (RZGI) on groundwater, there are no specific studies where RZGI has been
applied as a technique for stormwater infiltration. Thus, the understanding of RZGI on stormwater and
groundwater will draw on relevant studies of riparian zones in urban settings.
Groffman et al. (2003) in a seminal study on urban riparian ecology suggest that riparian "hydrologic
drought", caused by lowered water tables, is a general effect of urbanization. These urban riparian zones
should provide an ideal place to infiltrate stormwater because of this hydrologic drought. As a
conceptual model, there are at least three potential ways to achieve this infiltration. One is to direct
stormwater across riparian zones to promote direct infiltration. The second is to allow overbank flows
into floodplains where flow retention and infiltration can occur. The third is to enhance bank infiltration
and hyporheic extraction (Nutzmann et al., 2011). As Correll (2005) proposes, smaller streams can be
more actively managed for chemical and sediment retention, while for larger streams it is more a matter
of floodplain protection. For urban stormwater, more active management of the floodplain is probably
required, especially because current practices typically shunt stormwater to larger streams where
possible.
Restoration of stream health should be the goal of stormwater controls driven by the Clean Water Act.
A flow regime similar to pre-development flows is necessary to restore stream health. (Poff et al., 1997).
Biotic integrity can be maintained in streams with intact riparian zones or significant amounts of
groundwater flow (or both), despite a high degree of urban land use (greater than 30%) (Miltner et al.,
2004). Source control management techniques can affect baseflow and localized groundwater, but there
is poor understanding of how source control stormwater management can make riparian buffer functions
possible and allow further contributions to stream health. Studies from agricultural watersheds
demonstrate that healthy riparian buffers need watershed management to function (Lowrance et al.,
1985). Restoring baseflow is key to restoring riparian buffers and having functioning riparian buffers in
alluvial systems. Although, simply infiltrating water in the upper watershed will not assure restoration of
baseflow, and water harvesting systems that retain water in the watershed may not help to restore
baseflow (Walsh et al., 2012, Hamel et al., 2013). Without a good understanding of local hydrogeology
and the interactions with enhanced infiltration systems, the effects on streamflow are highly speculative
unless enhanced recharge takes place in nearby stream areas.
Riparian zones in urban settings are often affected by lowered localized water tables. These lowered
water tables are generally due to less water infiltrating locally, primarily due to impervious and less
pervious surfaces. This is the same condition that leads to lower baseflow in small headwater urbanized
streams. Groffman et al. (2003) speculated on the effects of "urban hydrologic drought" on urban
riparian systems. When riparian buffers are installed as GI, they may be affected by this urban
hydrologic drought which can lead to 1) minimal effects on water quality, 2) low survival for riparian
vegetation, and 3) invasive upland species, including exotics, in the riparian zone.
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Riparian buffer restoration may have little effect on instream flows unless connections are reestablished
between filtered or spread flows and the riparian system (Walsh et al., 2005). Restoration of riparian
buffers in urban landscapes will either require active management to control terrestrial invasives, or
restoration of alluvial aquifers, high water tables, and wet soils to encourage the survival of native
species and the exclusion of exotics that can thrive in less wet environments (Auble et al., 1997).
Increasing infiltration through restoration or preservation of riparian buffers along smaller streams such
as ephemeral waterways may have the ability to enhance local water resources (Gallo et al., 2013. Urban
landscapes deliver more water to ephemeral channels due to decreased infiltration in the watershed. This
provides an opportunity for focused infiltration within those ephemeral channels.
The interaction of nitrogen removal through denitrification and the restoration of infiltration to soil and
groundwater in urban riparian zones has been better studied than other potential stormwater
contaminants. Because biological denitrification occurs in organic rich soil, subsoil, or aquifer material
which becomes saturated, nitrate removal in riparian buffers is dependent on the re-wetting of the soil
and aquifer through enhanced infiltration. Groffman and Crawford (2003) reported studies on restoring
denitrification potential in urban riparian zones and found that denitrification was enhanced in areas of
high organic matter and higher soil moisture. They propose routing high NO3" runoff through these areas
to enhance the denitrification capacity of urban riparian buffers. Palta et al. (2016) point out that in
urban riparian areas, high denitrification may be found in areas of groundwater seeps or standing water.
This supports the general idea that re-wetting urban riparian zones will be necessary to restore
denitrification.
Unlike riparian buffers on smaller streams, larger rivers that were often the focus for the development of
urban areas require different management approaches. For larger rivers, studies have shown that
increasing infiltration of flood waters on floodplains for detention and infiltration is likely to have the
largest effect on groundwater, rather than connecting the riparian buffer to direct stormwater runoff
(Jacobson et al. 2015; McMillan and Noe, 2017). McMillan and Noe (2017) note that low floodplains
(riparian zones) frequently inundated by upstream sources will provide greater water quality
improvement than more extensive but higher floodplains. This is partly due to infiltration of the ponded
water in the floodplain and recharge of alluvial groundwater. This is further justification for expanding
stream corridors wherever possible to "incorporate the protection of natural systems" as green
infrastructure (Collins et al., 2010b). A less manipulative approach to stream restoration with feasible
steps that expand the stream corridor and limited channel manipulation (Palmer et al., 2014) will create
opportunities for infiltration into alluvial groundwater.
Bank infiltration is a form of artificial groundwater recharge that has been applied in urban
environments, and is a longstanding practice for pre-treatment of river water that is used for drinking
water (Nutzmann et al., 2011; Schmidt, et al. 2003). The practice depends on the infiltration of surface
water into streambanks and riverbeds driven by groundwater pumping from alluvial aquifers. By serving
as "natural filtration," the practice may have beneficial effects on surface water if the water is
discharged back to surface sources. The induced recharge can also be used as either drinking water
supply or to re-water floodplains.
7.2.11 Results from Reviews
A review of stormwater infiltration practices on groundwater contamination was conducted by Pitt et al.
(1999). This review found the most concerning contaminants in groundwater through stormwater
infiltration were C1-, pesticides, PAHs, pathogens, and heavy metals. They found it was rare to have
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groundwater contamination, except for CI", in residential areas because infiltration occurred through
surface soils which retains most contaminants, and that contamination of the groundwater was more
likely in commercial and industrial areas where subsurface infiltration was used.
In a review article by Kabir et al. (2014), the authors reviewed the literature concerning ion and metal
pollution in urban GI systems. They concluded the current understanding of GI systems in relation to
metals and nutrients rely on empirical principles. They state the need to do more comprehensive studies
that use pathways, thermodynamics, kinetics, and microbial dynamics along with understanding the role
of plants in the system. They also found there are high pollution loads and high ecological risk
associated with the use of GI in urban areas. The authors also stated the effects of GI on the watershed
scale require further research.
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8.0 Conclusions/ Future Research Needs
8.1 Conclusions
Stormwater reclamation for eventual reuse is triggering a paradigm shift from stormwater seen as a
contaminant and a flood risk to a resource that can solve these risks. GI design strategy retains storage,
infiltrates runoff, and contributes to the renewed groundwater recharge to more closely resemble the
hydrology before urban development. The disturbance of the natural hydrologic cycle due to
urbanization is closely connected to deteriorating urban water quality. This creates an increased risk to
groundwater quality because of new pathways for contaminant introduction into groundwater, chemicals
associated from anthropogenic activities, and wastewater exposure. This literature review determined
what research that has been done on GI practices with respect to groundwater quality and the risks and
impacts to the subsurface environment. The issues addressed include: 1) contaminant risks that need
further research, 2) new infrastructure that has not been researched in depth, and 3) determination of
local considerations when planning for green infrastructure.
Any pollutant found in stormwater could be a potential groundwater contaminant when used with GI
infiltration technology. GI can return the urban hydrology to a more natural hydrologic cycle through
retention and infiltration methods. Surface and subsurface infiltration can influence the impact the
infiltrating stormwater has on the groundwater chemistry. Retention techniques can influence the water
table depth through mounding, which have been seen in restoration projects, bioretention cells, and
regenerative stormwater conveyance systems. Concern with GI for stormwater infiltration include
fluctuations in groundwater levels, limitations with large precipitation events, clogging, and soil
limitations. The infiltration is dependent on the clogging rate of the infrastructure.
Depending on the water's chemical, biological, and physical conditions, there is the risk of potential
contaminants leaching from native soils and geology. When it comes to managing water resources, the
tendency for contaminants to move between the ground and surface water needs to be considered.
Urbanization can introduce contaminants that are otherwise not an issue in natural stormwater
hydrology. Groundwater can be contaminated by many constituents: nutrients, metals, dissolved
minerals, pesticides, other organics, and pathogens; the sources of which include residues from
automobiles, lawn treatments though fertilizers and pesticides, sewer overflows, and road deicing salts.
Due to risks affecting groundwater quality, it is suggested that infiltrating GI not be implemented in
areas with potentially high contaminant loading, i.e. recycling centers, gas stations, and brownfields.
When infiltrating devices are installed and used for urban runoff, there are concerns as to how the soils
interact with the stormwater runoff pollution while infiltrating into the subsurface, thus providing
possible risks of groundwater quality impairment from areas with potentially high contaminant
concentrations.
The chemical interactions between surface water and groundwater are controlled by the type of geologic
materials present and the amount of time the water is in contact with these materials. The various
chemical reactions that affect the biological and geochemical characteristics of the basin are acid-base
reactions, precipitation and dissolution of minerals, sorption, ion exchange, oxidation-reduction
reactions, biodegradation, and dissolution and exsolution of gases. It is concluded that when
implementing green stormwater infrastructure for infiltration, the properties of the unsaturated and
saturated zones interacting with the infiltrating water need to be considered. These considerations
encompass the understanding of the native soil texture, structure, and organic matter content of the
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unsaturated zone, as well as considering the porosity and permeability of the saturated zone and the flow
of the groundwater. Kinetics and mixing relationships also require examination. Colloidal transport also
needs to be considered as a mechanism that can transport contaminants through the soil, by either being
a contaminant itself or having a contaminant sorb to a benign colloid. Colloids can be restricted by
capture, sorption and static interaction. As discussed previously, colloid-facilitated transport could be an
important mechanism for the movement of contaminants into groundwater (de Jonge et al., 2004).
The potential and actual impacts to groundwater quality as the results of GI practices were reviewed.
The results presented were mixed; in some cases, there were impacts or potential impacts, and in other
cases there were no impacts found. Many of the studies' results were problematic for several reasons. In
most cases, the results—reflecting only what occurred in the vadose zone or the infrastructure—were
extrapolated to predict what may occur to the groundwater. This extrapolation ignores other processes
that could facilitate the transport of contaminants to the groundwater, such as preferential flow. Since
there was no attempt made to measure concentrations of contaminants in aquifers or deeper in the
vadose zone, there is no definitive evidence of changes in groundwater quality.
In studies that did include groundwater monitoring, it is unknown in some cases if the sampling strategy
would detect changes in groundwater quality. Information on groundwater flow direction was not
included, therefore the relationship of monitoring points to the potential transport of contaminants could
not be ascertained. Another potential problem was that the studies did not account for lag between the
time of water infiltration and the time it takes to transport the infiltrated water to the aquifer. In most
studies, that sampling occurred at or very close to the precipitation event. Because lag time was not
considered, transient changes to groundwater quality were not accounted for, even in systems that were
monitored for decades.
The only system that consistently showed impacts to groundwater quality was ASR. The ASR impacts
fell into one of two categories: unintended consequences, or the mixing of two waters with different
composition and characteristics.
Simulation models can be an affordable way for predicting quality and quantity changes, as well as a
decision-making tool for implementing green infrastructure. While there are many models in use for
surface water and groundwater transport, there are few that integrate green infrastructure, and those that
have do not address groundwater contaminant transport. Green infrastructure models have been
implemented in various formats, but none specifically addressed groundwater contamination from this
infrastructure. Problems associated with implementing models for assessing green infrastructure
technologies and influence on groundwater include the amount of data available for calibration and
validating these models, indicating a need for more field research to obtain this data.
Microbiological organisms such as bacteria, viruses, and parasites can be a contamination risk
depending on the unsaturated and saturated zone conditions, incubation time, and native microbial
population behavior. Microbial contaminants are a concern primarily if they present a public health
threat from consuming contaminated groundwater, with the most common waterborne disease being
acute gastrointestinal illness. While gut-associated microbial contaminants are not expected to grow and
thrive within the groundwater environment, their rates of removal are affected by several, often
interdependent, environmental factors. Research has shown there is a general trend of differential
survival for the various contaminant organism types. Viruses tend to have the longest persistence times
within any groundwater environment; enteric eukaryotes (iCryptosporidium spp. and Giardia spp.) and
enteric bacteria typically have die-off rates of five to ten times, and over one hundred times larger than
enteric viruses, respectively. Pathogen removal or die-off rates are typically reported based upon first
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order decay models; however, field and laboratory experiments have shown that biphasic models better
approximate the removal behavior of fecal eukaryotes and viruses within groundwater systems. Hence,
these studies have shown that there is an initial rapid removal phase for the first few days after
introduction, followed by a slower phase two to hundreds of times less than the initial phase that can
lead to months or years of persistence.
In saturated zones, factors influencing pathogen survivals in groundwater are temperature, water
chemistry, and biological processes. Aquifer hydrogeology can influence the mechanical filtration,
adsorption, wedging, and straining processes that can remove pathogens. There is also the potential of
competition for nutrients and predation by indigenous microorganisms can play a significant role in the
removal of introduced enteric pathogens. In unsaturated zones, the same processes from the saturated
zone can apply but the air phase within the unsaturated zones can create two new interfaces, air-water
and air-sediment that do not exist in saturated conditions which can both adsorb and entrap organisms.
The decreased moisture can subject microorganisms to die-off or inactivation through desiccation. The
highest native microbial populations are going to be in the rooting zone of the soil profile. Below the
rooting zone, microbial populations and activity decrease with depth.
Macrobiological organisms can enhance or cause complications with green infrastructure. Vegetation is
often used to retain nutrients and metals, enhance ecosystem service, increase filtration, and mimic the
natural hydrology. The selection of the plants is important because they need to survive potentially toxic
contaminants and the perturbations of the GI systems. There are few studies on how various
macroorganisms can influence the green infrastructure. Bioturbator species that live in the sediment can
increase the possible risk of nutrient contamination, and burrowing activity of worms can increase the
macropores in the sediment and influence the infiltration. Macrobiological organisms can enhance or
cause complications for green infrastructure, but research on these effects is limited.
Urban riparian zones can function as green infrastructure, but few studies have been done on their
influence on groundwater. Previous studies on riparian zone restoration show that they could be useful
to restore denitrification to urban streams. By serving as "natural filtration," the practice may have
beneficial effects on surface water if the water is discharged back to surface sources. This induced
recharge can also be used for either drinking water supply or to re-water floodplains. This is also a less
manipulative, more feasible way to create opportunities for filtration into alluvial groundwater.
8.2 Future Research
Analogous to what the Pitt et al. (1999) and the recent Kabir et al. (2014) reviews concluded, we concur
that more research is required to understand the potential groundwater quality impacts that can result
from the implementation of GI. Apart from conservative chemical species such as chloride, a more
complete understanding of what conditions are likely to cause groundwater quality impairment is
necessary to mitigate or prevent these potential impacts. This review also indicates there is an apparent
risk to the vadose zone "quality." Stormwater infiltration is causing the soil and vadose zone sediments
to degrade, and the potential future impacts and risks to groundwater quality because of this are
unknown—making long-term GI studies crucial.
Since land use and environmental conditions are likely to change, future groundwater risks are possible
at many current GI sites if the infrastructure is not properly maintained. Further research is needed to
determine the best monitoring methods for groundwater at these sites throughout their lifetime.
Changing conditions will likely change the chemical and physical properties which can alter the
retention properties in the soil/vadose zone. These potential land use changes and maintenance problems
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need to be addressed in future research. Another issue encountered is that, once the GI system is no
longer functional or is "decommissioned," what practices should then be implemented to mitigate the
potential environmental issues created by trapping the contaminants in the vadose zone. This emphasizes
the need for long term monitoring methods that addresses placement of sampling points and timing of
sampling to determine the long-term impacts to the subsurface. Currently GI performance standards are
not included into the National Pollutant Discharge Elimination System (NPDES) permits, including
impacts on groundwater. Including this into the NPDES system may be benefitial to protection
groundwater quality.
Additional research is needed to understand the impacts and benefits that various macrobiological
organisms have on GI, and how these affect the hydrology, fate, and transport of contaminants in GI
systems. Vegetation is the most common addition to GI, but there is an inadequate understanding as to
how this vegetation influences groundwater quality over time. Addressing whether preferential flow
increases over time or if nutrient and metal concentrations change over time is a necessity. Previous
studies on riparian buffer zones have shown various benefits to restoring these in non-GI situations, but
further studies are needed to determine the benefits and potential issues with implementing them as part
of urban GI.
Simulation modeling of GI systems needs to be addressed to help users understand the potential
groundwater impacts. Further research of simulation models is needed to address the location and
spacing of GI stormwater practices to determine if there are diminishing returns on the quantity of
stormwater controls. Simulation models are necessary to determine how large GI projects can be
designed to effectively reduce runoff and have the least environmental impact (Brown et al., 2012;
Eckart et al., 2017). Research on the use of models to demonstrate how GI performs under different
temporal scales, spatial scales, and climatic conditions is needed since there is a lack of data on the
performance of these technologies. Simulation research and improvements in modeling techniques are
also needed so that they can assist in understanding the role of GI in restoring the water balance,
reducing contaminants over the long term, evaluating various GI performance, as well as acting as
decision support tools (Dietz, 2007; Ahiablame et al., 2012; Fletcher et al., 2013; Eckart et al., 2017).
Overall, there are several research areas necessary for a better understanding of the risks of a GI
infiltration technology that have been proposed as the result of this effort. There needs to be more
investigations looking at the GI interactions on a longer temporal scale and wider spatial range. When
implementing GI, the local geology, climate, hydrology, biology, geochemistry, type of infrastructure,
and contaminant loads need to be carefully considered to reduce the risk to groundwater quality.
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9.0 References
Abbott, M.D.; and Stanley, R.S. 1999. Modeling groundwater recharge and flow in an upland fractured
bedrock aquifer. System Dynamics Review. 15. 163.
Ahiablame, L.M.; Engel, B.A.; and Chaubey, I. 2012. Effectiveness of Low Impact Development
Practices: Literature Review and Suggestions for Future Research. Water Air and Soil Pollution. 223.
4253-4273. doi: 10.1007/sl 1270-012-1189-2.
Ahyerre, M.; Henry, F.O.; Gogien, F.; Chabanel, M.; Zug, M.; and Renaudet, D. 2005. Test of the
efficiency of three storm water quality models with a rich set of data. Water Science and Technology.
51. 171-177.
Al-Rubaei, A.M.; Engstrom, M.; Viklander, M.; and Blecken, G.-T. 2017. Effectiveness of a 19-Year
Old Combined Pond-Wetland System in Removing Particulate and Dissolved Pollutants. Wetlands. 37.
485-496. DOI 10.1007/sl3157-017-0884-6
Appleyard, S.J. 1993. Impact of Stormwater Infiltration Basins on Groundwater Quality, Perth
Metropolitan Region, Western-Australia. Environmental Geology. 21. 227-236.
Aryal, R.K.; Furumai, H.; Nakajima, F.; and Hossain, M.A. 2007. Vertical distribution and speciation of
heavy metals in stormwater infiltration facilities: possible heavy metals release to groundwater. Water
Practice and Technology. 2. doi: 10.2166/wpt.2007.052.
Aryal, R.K.; Murakami, M.; Furumai, H.; Nakajima, F.; and Jinadasa, H.K.P.K. 2006. Prolonged
deposition of heavy metals in infiltration facilities and its possible threat to groundwater contamination.
Water Science and Technology. 54. 205-212. doi: 10.2166/wst.2006.584.
Ashley, R.; Lundy, L.; Ward, S.; Shaffer, P.; Walker, L.; Morgan, C.; Saul, C.; Wong, T.; and Moore, S.
2013. Water-sensitive urban design: opportunities for the UK. Proceedings of the Institution of Civil
Engineers - Municipal Engineer. 166. 65-76. doi: 10.1680/muen. 12.00046.
Atchison, D.; Potter, K.; and Severson, L. 2006. Design Guidelines for Stormwater Bioretention
Facilities. University of Wisconsin Water Resources Institute. Publication No: WIS-WRI-06-01.
https://publications.aqua.wisc.edu/product/design-guidelines-for-stormwater-bioretention-facilities/.
Auble, G.T.; Scott, M.L.; Friedman, J.M.; Back, J.; and Lee, V.J. 1997. Constraints on establishment of
plains cottonwood in an urban riparian preserve. Wetlands. 17. 138-148.
Azah, E.; Kim, H.; and Townsend, T. 2017. Assessment of direct exposure and leaching risk from PAHs
in roadway and stormwater system residuals. Science of The Total Environment. 609. 58-67. doi:
10.1016/j.scitotenv.2017.07.136.
Backstrom, M. 2003. Grassed swales for stormwater pollution control during rain and snowmelt. Water
Science and Technology. 48. 123-132.
Bannerman, R. T., Legg, A. D., and Greb, S. R. 1996. Quality of Wisconsin stormwater, 1989-94. Rep.
No. 2331-1258. US Geological Survey.
73
-------
Banning, N.; Toze, S.; and Mee, B.J. 2002. Escherichia coli survival in groundwater and effluent
measured using a combination of propidium iodide and the green fluorescent protein. Journal of Applied
Microbiology. 93. 69-76. doi: 10.1046/j.l365-2672.2002.01670.x.
Barbosa, A.E.; and Hvitved-Jacob sen, T. 1999. Highway runoff and potential for removal of heavy
metals in an infiltration pond in Portugal. Science of The Total Environment. 235. 151-159. doi:
10.1016/S0048-9697(99)00208-9.
Barcelo, D. 2012. Emerging organic contaminants and human health. Vol. 20. Springer. Heidelberg. 466
p. doi: 10.1007/978-3-642-28132-7
Barnett, A.G.; MacMurray, H.L.; Wallace, P.L.; and Lester, R.T. 1995. Modelling of inundation
management during extreme storms. Water Science and Technology. 32. 201-207. doi: 10.1016/0273-
1223(95)00556-3.
Barraud, S.; Dechesne, M.; Bardin, J.-P.; and Varnier, J.-C. 2005. Statistical analysis of pollution in
stormwater infiltration basins. Water Science and Technology. 51. 1-9.
Barth, D.S.; Mason, B.J.; Starks, T.H.; Brown, K.W. 1989. Soil Sampling Quality Assurance User's
Guide, 2nd ed. U.S. Environmental Protection Agency. Office of Research and Development.
Environmental Monitoring Systems Laboratory. Las Vegas. EPA/600/8-69/046.
Beck, M.B. 2005. Vulnerability of water quality in intensively developing urban watersheds.
Environmental Modelling and Software. 20. 381-400. doi: 10.1016/j.envsoft.2004.02.002.
Bedan, E.S.; and Clausen, J.C. 2009. Stormwater Runoff Quality and Quantity from Traditional and
Low Impact Development Watersheds. Journal of the American Water Resources Association. 45. 998-
1008. doi: 10.1111/j.l752-1688.2009.00342.x.
Bell, W., Stokes, L., Gavan, L. J., and Nguyen, T. 1995. Assessment of the pollutant removal
efficiencies of Delaware sand filter BMPs. City of Alexandria. Department of Transportation and
Environmental Services. Alexandria, VA.
Benson, R.; Conerly, O.D.; Sander, W.; Batt, A.L.; Boone, J.S.; Furlong, E.T.; Glassmeyer, S.T.;
Kolpin, D.W.; Mash, H.E.; Schenck, K.M.; and Simmons, J.E. 2017. Human health screening and public
health significance of contaminants of emerging concern detected in public water supplies. Science of
The Total Environment. 579. 1643-1648. doi: 10.1016/j.scitotenv.2016.03.146.
Bergman, M.; Hedegaard, M.R.; Petersen, M.F.; Binning, P.; Mark, O.; and Mikkelsen, P.S. 2011.
Evaluation of two stormwater infiltration trenches in central Copenhagen after 15 years of operation.
Water Science and Technology. 63. 2279-2286.
Berland, A.; Shiflett, S.A.; Shuster, W.D.; Garmestani, A.S.; Goddard, H.C.; Herrmann, D.L.; and
Hopton, M.E. 2017. The role of trees in urban stormwater management. Landscape and Urban Planning.
162. 167-177. doi: 10.1016/j.landurbplan.2017.02.017.
Beven, K.; and Freer, J. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic
modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology. 249.
11-29. doi: 10.1016/S0022-1694(01)00421-8.
74
-------
Bhattarai, R.; Kalita, P.K.; and Patel, M.K. 2009. Nutrient transport through a Vegetative Filter Strip
with subsurface drainage. Journal of Environmental Management. 90., 1868-1876. doi:
10.1016/j.jenvman.2008.12.010.
Birch, G.F.; Fazeli, M.S.; and Matthai, C. 2005. Efficiency of an infiltration basin in removing
contaminants from urban stormwater. Environmental Monitoring Assessment, 101. 23-38. doi:
10.1007/sl0661-005-9126-0.
Blanc, D.; Kellagher, R.; Phan, L.; and Price, R. 1995. Flupol-mosqito, models, simulations, critical
analysis and development. Water Science and Technology. 32. 185-192. doi:
https://doi.org/10.1016/0273-1223(95X)0554-Z.
Bogena, H.; Kunkel, R.; Schobel, T.; Schrey, H.P.; and Wendland, E. 2005. Distributed modeling of
groundwater recharge at the macroscale. Ecological Modelling. 187. 15-26. doi:
10.1016/j.ecolmodel.2005.01.023.
Boivin, P.; Saade, M.; Pfeiffer, H.R.; Hammecker, C.; and Degoumois, Y. 2008. Depuration of highway
runoff water into grass-covered embankments. Environmental Technology. 29. 709-720. doi:
10.1080/09593330801986972.
Borst, M.; and Brown, R.A. 2014. Chloride Released from Three Permeable Pavement Surfaces after
Winter Salt Application. Journal of the American Water Resources Association. 50. 29-41. doi:
10.1111/jawr. 12132.
Boving, T.B.; Stolt, M.H.; Augenstern, J.; and Brosnan, B. (2007). Potential for localized groundwater
contamination in a porous pavement parking lot setting in Rhode Island. Environmental Geology. 55.
571-582. doi: 10.1007/s00254-007-1008-z.
Bowen, Z.H.; Oelsner, G.P.; Cade, B.S.; Gallegos, T. J.; Farag, A.M.; Mott, D.N.; Potter, C.J.; Cinoto,
P.J. Clark, M.L.; Kappel, W.M.; Kresse, T.M.; Melcher, C.P.; Paschke, S.S.; Susong, D.D.; and Varela,
B.A. 2015. Assessment of surface water chloride and conductivity trends in areas of unconventional oil
and gas development-Why existing national data sets cannot tell us what we would like to know. Water
Resources Research. 51. 704-715. doi: 10.1002/2014WR016382.
Bradford, S.A.; and Torkzaban, S. 2008. Colloid transport and retention in unsaturated porous media: A
review of interface-, collector-, and pore-scale processes and models. Vadose Zone Journal. 7. 667-681.
Brady, N.; and Weil, R. 2002. The nature and properties of soils. 13th ed. Prentice Hall. Upper Saddle
River, NJ. 960 p.
Brattebo, B.O.; and Booth, D.B. 2003. Long-term stormwater quantity and quality performance of
permeable pavement systems. Water Research. 37. 4369-4376. doi: 10.1016/S0043-1354(03)00410-X.
Breault, R. F., and Granato, G. E. 2003. A Synopsis of Technical Issues of Concern for Monitoring
Trace Elements in Highway and Urban Runoff. In: The National Highway Runoff Data and
Methodology Synthesis. U.S. Department of Transportation. Federal Highway Administration. United
States Geological Survey. Publication No: FHWA-EP-03-054. 165-234.
75
-------
Brown, R.A. and Borst, M. 2015. Nutrient infiltrate concentrations from three permeable pavement
types. Jounral of Enviromental Management. 164. 74-85. doi: 10.1016/j.jenvman.2015.08.038
Brown, R.A.; Line, D.E.; and Hunt, W.F. 2012. LID treatment train: Pervious concrete with subsurface
storage in series with bioretention and care with seasonal high water tables. Journal of Environmental
Engineering. 138. 689-697. doi: 10.1061/(ASCE)EE. 1943-7870.0000506.
Browne, D.; Deletic, A.; Mudd, G. M.; and Fletcher, T. D. 2008. A new saturated/unsaturated model for
stormwater infiltration systems. Hydrological Processes. 22., 4838-4849. doi: 10.1002/hyp.7100.
Buchelli, T.D.; Miiller, S.R.; Heberle, S.; Schwarzenbach, R.P. 1998. Occurrence and behavior of
pesticides in rainwater, roof runoff, and artificial stormwater infiltration. Environmental Science and
Technology. 32. 3457-3464.
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, doi: 10.1061/40644(2002)284.
Camponelli, K.M.; Lev, S. M.; Snodgrass, J. W.; Landa, E. R.; and Casey, R. E. 2010. Chemical
fractionation of Cu and Zn in stormwater, roadway dust and stormwater pond sediments. Environmental
Pollution. 158. 2143-2149. doi: 10.1016/j.envpol.2010.02.024.
Chandrasena, G.I.; Shirdashtzadeh, M.; Li, Y.L.; Deletic, A.; Hathaway, J. M.; and McCarthy, D. T.
2017. Retention and survival of E. coli in stormwater biofilters: Role of vegetation, rhizosphere
microorganisms and antimicrobial filter media. Ecological Engineering. 102. 166-177. doi:
10.1016/j.ecoleng.2017.02.009.
Chang, G. C., Parrish, J. H., and Soeur, C. 1990. The first flush of runoff and its effects on control
structure design. Environmental and Conservation Services Department. Environmental Resource
Management Division. City of Austin, Texas.
Characklis, G.W., Dilts, M.J.; Simmons, O.D., 3rd; Likirdopulos, C.A., Krometis, L.A.; and Sobsey,
M.D. 2005. Microbial partitioning to settleable particles in stormwater. Water Research. 39. 1773-1782.
doi: 10.1016/j.watres.2005.03.004.
Charles, K.J.; Shore, J.; Sellwood, J.; Laverick, M.; Hart, A.; and Pedley, S. 2009. Assessment of the
stability of human viruses and coliphage in groundwater by PCR and infectivity methods. Journal of
Applied Microbiology. 106. 1827-1837. doi: 10.1111/j.l365-2672.2009.04150.x.
Charlesworth, S.; Warwick, F.; and Lashford, C. 2016. Decision-Making and Sustainable Drainage:
Design and Scale. Sustainability. 8. 782-793.
Chen, C.-F.; Sheng, M.-Y.; Chang, C.-L.; Kang, S.-F.; and Lin, J.-Y. 2014. Application of the
SUSTAIN Model to a Watershed-Scale Case for Water Quality Management. Water. 6. 3575-3589.
Cizek, A.R.; Hunt, W.F.; Winston, R.J.; and Lauffer, M.S. 2017. Hydrologic Performance of
Regenerative Stormwater Conveyance in the North Carolina Coastal Plain. Journal of Environmental
Engineering. 143. 05017003-1 -05017003-8. doi: 10.1061/(ASCE)EE. 1943-7870.0001198.
76
-------
Clark, S.E., and Pitt, R. 2007. Influencing factors and a proposed evaluation methodology for predicting
groundwater contamination potential from stormwater infiltration activities. Water Environment
Research. 79. 29-36. doi: 10.2175/106143006x143173.
Cliver, D.O., and Herrmann, J.E. 1972. Proteolytic and microbial inactivation of enteroviruses. Water
Research. 6. 797-805. doi: 10.1016/0043-1354(72)90032-2.
Colandini, V.; Legret, M.; Brosseaud, Y.; and Balades, J.-D. 1995. Metallic pollution in clogging
materials of urban porous pavements. Water Science and Technology. 32. 57-62. doi: 10.1016/0273-
1223(95)00538-X.
Collins, K.A., Hunt, W.F., and Hathaway, J.M. 2010a. Side-by-Side Comparison of Nitrogen Species
Removal for Four Types of Permeable Pavement and Standard Asphalt in Eastern North Carolina.
Journal of Hydrologic Engineering. 15. 512-521. doi: 10.1061/(Asce)He.1943-5584.0000139.
Collins, K.A.; Lawrence, T.J.; Stander, E.K.; Jontos, R.J.; Kaushal, S.S.; Newcomer, T.A.; Grimm,
N.B.; Cole Ekberg, M.L. 2010b. Opportunities and challenges for managing nitrogen in urban
stormwater: A review and synthesis. Ecological Engineering. 36. 1507-1519.
Conley, J.M.; Evans, N.; Mash, H.; Rosenblum, L.; Schenck, K.; Glassmeyer, S.; Furlong, E.T.; Kolpin,
D.W.; and Wilson, V.S. 2017. Comparison of in vitro estrogenic activity and estrogen concentrations in
source and treated waters from 25 US drinking water treatment plants. Science of The Total
Environment. 579. 1610-1617. doi: 10.1016/j.scitotenv.2016.02.093.
Correll, D.L. 2005. Principles of planning and establishment of buffer zones. Ecological Engineering.
24. 433-439.
Coupe, S.J.; Smith, H.G.; Newman, A.P.; and Puehmeier, T. 2003. Biodegradation and microbial
diversity within permeable pavements. European Journal of Protistology. 39. 495-498. doi:
10.1078/0932-4739-00027.
Craun, G.F.; Brunkard, J.M.; Yoder, J.S.; Roberts, V.A.; Carpenter, J.; Wade, T.; Calderon, R.L.;
Roberts, J.M.; Beach, M.J.; and Roy, S. L. (2010). Causes of outbreaks associated with drinking water in
the United States from 1971 to 2006. Clinical Microbiology Reviews. 23. 507-528. doi:
10.1128/cmr.00077-09.
Datry, T., Malard, F., and Gibert, J. 2004. Dynamics of solutes and dissolved oxygen in shallow urban
groundwater below a stormwater infiltration basin. Science of the Total Environment. 329. 215-229. doi:
10.1016/j. scitotenv.2004.02.022.
Datry, T.; Malard, F.; Vitry, L.; Hervant, F.; and Gibert, J. 2003. Solute dynamics in the bed sediments
of a stormwater infiltration basin. Journal of Hydrology. 273. 217-233. doi: 10.1016/S0022-
1694(02)00388-8.
David, N.; Leatherbarrow, J.E.; Yee, D.; and McKee, L.J. 2015. Removal Efficiencies of a Bioretention
System for Trace Metals, PCBs, PAHs, and Dioxins in a Semiarid Environment. Journal of
Environmental Engineering. 141. 04014092-1 -04014092-8. doi: 10.1061/(ASCE)EE.1943-
7870.0000921.
77
-------
Davis, A. P. (2007). Field performance of bioretention: Water quality. Environmental Engineering
Science. 24. 1048-1064. doi: 10.1089/ees.2006.0190.
Davis, A.P.; Shokouhian, M.; Sharma, H.; and Minami, C. 2001. Laboratory study of biological
retention for urban stormwater management. Water Environment Research. 73. 5-14.
de Jonge, L.W.; Kjasrgaard, C.; and Moldrup, P. 2004. Colloids and colloid-facilitated transport of
contaminants in soils. Vadose Zone Journal. 3. 321-325.
Dechesne, M.; Barraud, S.; and Bardin, J.P. 2004a. Indicators for hydraulic and pollution retention
assessment of stormwater infiltration basins. J Environ Manage. 71. 371-380. doi:
10.1016/j.jenvman.2004.04.005.
Dechesne, M.; Barraud, S.; and Bardin, J.P. 2004b. Spatial distribution of pollution in an urban
stormwater infiltration basin. Journal of Contaminant Hydrology. 72. 189-205. doi:
10.1016/j.jconhyd.2003.10.011.
Dechesne, M.; Barraud, S.; and Bardin, J.-P. 2005. Experimental Assessment of Stormwater Infiltration
Basin Evolution. Journal of Environmental Engineering. 131. 1090-1098. doi: 10.1061/(ASCE)0733-
9372(2005)131:7(1090).
Deletic, A.; and Fletcher, T.D. 2006. Performance of grass filters used for stormwater treatment - a field
and modelling study. Journal of Hydrology. 317. 261-275. doi: 10.1016/j.jhydrol.2005.05.021.
Desaules, A. 2012. Critical evaluation of soil contamination assessment methods for trace metals.
Science of the Total Environment. 426. 120-131. doi: 10.1016/j.scitotenv.2012.03.035.
DiBlasi, C.J.; Li, H.; Davis, A.P.; and Ghosh, U. 2009. Removal and Fate of Polycyclic Aromatic
Hydrocarbon Pollutants in an Urban Stormwater Bioretention Facility. Environmental Science and
Technology. 43. 494-502. doi: 10.1021/es802090g.
Dierkes, C.; and Geiger, W.F. 1999. Pollution retention capabilities of roadside soils. Water Science and
Technology. 39. 201-208. doi: 10.1016/S0273-1223(99)00024-4.
Dietz, M.E. 2007. Low Impact Development Practices: A Review of Current Research and
Recommendations for Future Directions. Water, Air, and Soil Pollution. 186. 351-363. doi:
10.1007/sl 1270-007-9484-z.
Dodder, N.G.; Maruya, K.A.; Ferguson, P.L.; Grace, R.; Klosterhaus, S.; La Guardia, M.J.; Lauenstein,
G.G. Ramirez, J. 2014. Occurrence of contaminants of emerging concern in mussels (Mytilus spp.) along
the California coast and the influence of land use, storm water discharge, and treated wastewater
effluent. Marine Pollution Bulletin. 81. 340-346.
Doherty, J.; and Johnston, J.M. 2003. Methodologies for calibration and predictive analysis of a
watershed model. Journal of the American Water Resources Association. 39. 251-265. doi:
10.1111/j. 1752-1688.2003.tb043 81.x.
Domenico, P.; and Schwartz, F. 1990. Physical and chemical hydrogeology. John Wiley and Sons. New
York. 824 p.
78
-------
Drake, J.; Bradford, A.; and Van Seters, T. 2014. Stormwater quality of spring-summer-fall effluent
from three partial-infiltration permeable pavement systems and conventional asphalt pavement. Journal
of Environmental Management. 139. 69-79. doi: 10.1016/j.jenvman.2013.11.056.
Duchemin, M.; and Hogue, R. 2009. Reduction in agricultural non-point source pollution in the first
year following establishment of an integrated grass/tree filter strip system in southern Quebec (Canada).
Agriculture, Ecosystems and Environment. 131. 85-97. doi: 10.1016/j.agee.2008.10.005.
Dussaillant, A.; Cozzetto, K.; Brander, K.; and Potter, K. 2003. Green-Ampt model of a rain garden and
comparison to Richards equation model. WIT Transactions on Ecology and the Environment. 67. 891-
900.
Dussaillant, A.R.; Wu, C.H.; and Potter, K.W. 2004. Richards Equation Model of a Rain Garden.
Journal of Hydrologic Engineering. 9. 219-225. doi: 10.1061/(ASCE)1084-0699(2004)9:3(219).
Eby, G. 2004. Principles of Environmental Geochemistry. Brooks/Cole, Cengage Learning. Belmont,
CA. 514 p.
Eckart, K.; McPhee, Z.; and Bolisetti, T. 2017. Performance and implementation of low impact
development - A review. Science of The Total Environment. 607. 413-432. doi:
10.1016/j.scitotenv.2017.06.254.
Edwards, E.C.; Harter, T.; Fogg, G.E.; Washburn, B.; and Hamad, H. 2016. Assessing the effectiveness
of drywells as tools for stormwater management and aquifer recharge and their groundwater
contamination potential. Journal of Hydrology. 539. 539-553. doi: 10.1016/j.jhydrol.2016.05.059.
Ehrenfeld, J. G., and Schneider, J. P. 1991. Chamaecyparis thyoides wetlands and suburbanization:
effects on hydrology, water quality and plant community composition. Journal of Applied Ecology. 28:
467-490.
Elliott, A.H.; and Trowsdale, S.A. 2007. A review of models for low impact urban stormwater drainage.
Environmental Modelling and Software. 22. 394-405. doi: 10.1016/j.envsoft.2005.12.005.
Elliott, M.A.; DiGiano, F.A.; and Sobsey, M.D. 201 la. Virus attenuation by microbial mechanisms
during the idle time of a household slow sand filter. Water Research. 45. 4092-4102. doi:
https://doi.Org/10.1016/i.watres.2011.05.008.
Elliott, S.; Meyer, M.H.; Sands, G.R.; and Horgan, B. 201 lb. Water Quality Characteristics of Three
Rain Gardens Located Within the Twin Cities Metropolitan Area. Minnesota Cities and the
Environment. 4. 1-15.
Ellis, J.B. 2000. Infiltration systems: A sustainable source-control option for urban stormwater quality
Management. J. CIWEM. 27-34.
Ellis, J.B.; Shutes, R.B.; Revitt, D.M.; and Zhang, T.T. 1994. Use of macrophytes for pollution
treatment in urban wetlands. Resources, Conservation and Recycling. 11. 1-12. doi: 10.1016/0921 -
3449(94)90074-4.
79
-------
Emmanuel, E.; Pierre, M. G.; and Perrodin, Y. 2009. Groundwater contamination by microbiological
and chemical substances released from hospital wastewater: health risk assessment for drinking water
consumers. Environmental International. 35. 718-726. doi: 10.1016/j.envint.2009.01.011.
Endreny, T.; and Collins, V. 2009. Implications of bioretention basin spatial arrangements on
stormwater recharge and groundwater mounding. Ecological Engineering. 35. 670-677. doi:
10.1016/j.ecoleng.2008.10.017.
Eriksson, E., Baun, A., Scholes, L., Ledin, A., Ahlman, S., Revitt, M., Noutsopoulos, C., and Mikkelsen,
P. S. 2007. Selected stormwater priority pollutants—a European perspective. Science of the Total
Environment. 383:41-51.
Fakhreddine, S.; Dittmar, J.; Phipps, D.; Dadakis, J.; andFendorf, S. 2015. Geochemical Triggers of
Arsenic Mobilization during Managed Aquifer Recharge. Environmental Science and Technology. 49.
7802-7809. doi: 10.1021/acs.est.5b01140.
FAO. 2000. Assessing Soil Contamination A Reference Manual. Food and Agriculture Organization of
the United Nations. URL: www.FAO.org/docrep/003/x2570E/x2570E066.htm. Last accessed on March
14, 2018.
Farreny, R.; Morales-Pinzon, T.; Guisasola, A.; Taya, C.; Rieradevall, J.; and Gabarrell, X. 2011. Roof
selection for rainwater harvesting: quantity and quality assessments in Spain. Water Research. 45. 3245-
3254. doi: 10.1016/j.watres.2011.03.036.
Fetter, C.W. 2001. Applied Hydrogeology. Prentice Hall, Inc. Upper Saddle River, NJ. 598 p.
Fierer, N.; Schimel, J.P.; and Holden, P.A. 2003. Variations in microbial community composition
through two soil depth profiles. Soil Biology and Biochemistry. 35. 167-176. doi: 10.1016/S0038-
0717(02)00251-1.
Fischer, D.; Charles, E.G.; and Baehr, A.L. 2003. Effects of stormwater infiltration on quality of
groundwater beneath retention and detention basins. Journal of Environmental Engineering. 129. 464-
471. doi: 10.1061/(Asce)0733-9372(2003)129:5(464).
Fletcher, T.D.; Andrieu, H.; and Hamel, P. 2013. Understanding, management and modelling of urban
hydrology and its consequences for receiving waters: A state of the art. Advances in Water Resources.
51. 261-279. doi: 10.1016/j.advwatres.2012.09.001.
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. 2015. SUDS, LID, BMPs, WSUD and more - The evolution and application of
terminology surrounding urban drainage. Urban Water Journal. 12. 525-542. doi:
10.1080/1573062X.2014.916314.
Fontes, D.E.; Mills, A.L.; Hornberger, G.M.; and Herman, J.S. 1991. Physical and chemical factors
influencing transport of microorganisms through porous media. Applied and Environmental
Microbiology. 57. 2473-2481.
80
-------
Foppen, J.W.; and Schijven, J.F. 2006. Evaluation of data from the literature on the transport and
survival of Escherichia coli and thermotolerant coliforms in aquifers under saturated conditions. Water
Research. 40. 401-426. doi: 10.1016/j.watres.2005.11.018.
Freeze, R.A.; and Cherry, J.A. 1979. Groundwater. Prentice-Hall. Englewood Cliffs, NJ. 604 p.
Furlong, E.T.; Batt, A.L.; Glassmeyer, S.T.; Noriega, M.C.; Kolpin, D.W.; Mash, H.; and Schenck,
K.M. 2017. Nationwide reconnaissance of contaminants of emerging concern in source and treated
drinking waters of the United States: Pharmaceuticals. Science of The Total Environment. 579. 1629-
1642. doi: 10.1016/j.scitotenv.2016.03.128.
Galfi, H.; Osterlund, H.; Marsalek, J.; and Viklander, M. 2017. Mineral and Anthropogenic Indicator
Inorganics in Urban Stormwater and Snowmelt Runoff: Sources and Mobility Patterns. Water Air and
Soil Pollution. 228. 263-281. doi: 10.1007/sl 1270-017-3438-x.
Gallo, E.L.; Brooks, P.D.; Lohse K.A.; and McLain, J.E.T. 2013. Temporal patterns and controls on
runoff magnitude and solution chemistry of urban catchments in the semiarid southwestern United
States. Hydrologic Processes. 27. 995-1010. doi: 10.1002/hyp.9199.
Gasperi, J.; Zgheib, S.; Cladieri, M.; Rocher, V.; Moilleron, R.; Chebbo, G. 2012. Priority pollutants in
urban stormwater: Part 2 - Case of combined sewers. Water Research. 46. 6693-6703.
Geldreich, E.E.; Best, L.C.; Kenner, B.A.; and Van Donsel, D.J. 1968. The bacteriological aspects of
stormwater pollution. Journal of the Water Pollution Control Federation. 40. 1861-1872.
Gersberg, R.M.; Elkins, B.V.; Lyon, S.R.; and Goldman, C.R. 1986. Role of aquatic plants in
wastewater treatment by artificial wetlands. Water Research. 20. 363-368. doi: 10.1016/0043-
1354(86)90085-0.
Gilbert, J.K.; and Clausen, J.C. 2006. Stormwater runoff quality and quantity from asphalt, paver, and
crushed stone driveways in Connecticut. Water Research. 40. 826-832. doi:
10.1016/j.watres.2005.12.006.
Gilbert, R.O. 1987. Statistical methods for environmental pollution monitoring: Van Nostrand Reinhold
Company. New York, NY. 320 p.
Glassmeyer, S.T.; Furlong, E.T.; Kolpin, D.W.; Batt, A.L.; Benson, R.; Boone, J.S.; Conerly, O.;
Donohue, M.J.; King, D.N.; Kostich, M.S.; Mash, H.E.; Pfaller, S.L.; Schenck, K.M.; Simmons, J.E.;
Varughese, E.A.; Vesper, S.J. Villegas, E.N.; and Wilson, V.S. 2017. Nationwide reconnaissance of
contaminants of emerging concern in source and treated drinking waters of the United States. Science of
The Total Environment. 581. 909-922. doi: 10.1016/j.scitotenV2016.12.004.
Gong, N.; Denoeux, T.; and Bertrand-Krajewski, J.-L. 1996. Neural networks for solid transport
modelling in sewer systems during storm events. Water Science and Technology. 33. 85-92. doi:
10.1016/0273 -1223 (96)003 73 -3.
Gordon, C.; and Toze, S. 2003. Influence of groundwater characteristics on the survival of enteric
viruses. Journal of Applied Microbiology. 95. 536-544. doi: 10.1046/j.1365-2672.2003.02010.x
81
-------
Granato, G.E.; Zenone, C.; Carenas, P.A. 2003. National highway runoff water-quality data and
methodology synthesis, Volume I - Technical issues for monitoring highway runoff and urban
stormwater. U.S. Geological Survey. FHWA-EP-03-054.
Groffman, P.M.; and Crawford, M.K. 2003. Denitrification potential in urban riparian zones. Journal of
Environmental Quality. 32. 1144-1149.
Groffman, P.M.; Bain, D.J.; Band, L.E.; Belt, K.T.; Brush, G.S.; Grove, J.M.; Pouyat, R.V.; Yesilonis,
I.C.; and Zipperer, W.C. 2003. Down by the Riverside: Urban Riparian Ecology. Frontiers in Ecology
and the Environment. 1. 315-321. http://www.istor.org/stable/3868092.
Grolimund, D.; Elimelech, M.; Borkovec, M.; Barmettler, K.; Kretzschmar, R.; and Sticher, H. 1998.
Transport of in Situ Mobilized Colloidal Particles in Packed Soil Columns. Environmental Science and
Technology. 32. 3562-3569. doi: 10.1021/es980356z.
Guo, J.C.Y.; Urbonas, B.; and MacKenzie, K. 2014. Water Quality Capture Volume for Storm Water
BMP and LID Designs. Journal of Hydrologic Engineering. 19. 682-686. doi: 10.1061/(ASCE)HE. 1943-
5584.0000847.
Haefner, J.W. 2005. Modeling Biological Systems: Principles and Applications. 2nd ed. Springer
Publishing Company, Incorporated. New York, NY. 475 p.
Hamel, P.; Daly, E.; and Fletcher, T.D. 2013. Source-control stormwater management for mitigating the
impacts of urbanisation on baseflow: A review. Journal of Hydrology. 485. 201-211.
Hammersmark, C.T.; Rains, M.C.; and Mount, J.F. 2008. Quantifying the hydrological effects of stream
restoration in a montane meadow, northern California, USA. River Research and Applications. 24. 735-
753. doi: 10.1002/rra.l077.
Hares, R.J.; and Ward, N.I. 1999. Comparison of the heavy metal content of motorway stormwater
following discharge into wet biofiltration and dry detention ponds along the London Orbital (M25)
motorway. Science of The Total Environment. 235. 169-178. doi: 10.1016/S0048-9697(99)00210-7.
Hartmann, P. C., Quinn, J. G., Cairns, R. W., and King, J. W. 2005. Depositional history of organic
contaminants in Narragansett Bay, Rhode Island, USA. Marine Pollution Bulletin. 50. 388-395.
Harvey, R.W.; and Ryan, J.N. 2004. Use of PRD1 bacteriophage in groundwater viral transport,
inactivation, and attachment studies. FEMS Microbiology Ecology. 49. 3-16. doi:
10.1016/j.femsec.2003.09.015.
Harwood, V. J.; Staley, C.; Badgley, B. D.; Borges, K.; and Korajkic, A. 2014. Microbial source
tracking markers for detection of fecal contamination in environmental waters: relationships between
pathogens and human health outcomes. FEMS Microbiology Reviews. 38. 1-40. doi: 10.1111/1574-
6976.12031.
Harwood, V.J.; Levine, A.D.; Scott, T.M.; Chivukula, V.; Lukasik, J.; Farrah, S.R.; and Rose, J.B. 2005.
Validity of the Indicator Organism Paradigm for Pathogen Reduction in Reclaimed Water and Public
Health Protection. Applied and Environmental Microbiology. 71. 3163-3170. doi:
10.1128/aem.71.6.3163-3170.2005.
82
-------
Haugland, R.A.; Siefring, S.; Varma, M.; Oshima, K.H.; Sivaganesan, M.; Cao, Y.; Raith, M.; Griffith,
J.; Weisberg, S.B.; Noble, R.T.; Blackwood, A.D.; Kinzelman, J.; Anan'eva, T.; Bushon, R.N.; Stelzer,
E.A.; Harwood, V.J.; Gordon, K.V.; and Sinigalliano, C. 2016. Multi-laboratory survey of qPCR
enterococci analysis method performance in U.S. coastal and inland surface waters. Journal of
Microbiological Methods. 123. 114-125. doi: 10.1016/j.mimet.2016.01.017.
Hayashi, M.; and Rosenberry, D.O. 2002. Effects of ground water exchange on the hydrology and
ecology of surface water. Ground Water. 40. 309-316.
Her, Y.; Jeong, J.; Arnold, J.; Gosselink, L.; Glick, R.; and Jaber, F. 2017. A new framework for
modeling decentralized low impact developments using Soil and Water Assessment Tool.
Environmental Modelling and Software. 96. 305-322. doi: 10.1016/j.envsoft.2017.06.005.
Herczeg, A.L.; Rattray, K.J.; Dillon, P.J.; Pavelic, P.; and Barry, K.E. 2004. Geochemical Processes
During Five Years of Aquifer Storage Recovery. Groundwater. 42. 438-445. doi: doi: 10.1111/j .1745-
6584.2004.tb02691.x.
Herrera, J.; Bonilla, C.A.; Castro, L.; Vera, S.; Reyes, R.; and Gironas, J. 2017. A model for simulating
the performance and irrigation of green stormwater facilities at residential scales in semiarid and
Mediterranean regions. Environmental Modelling and Software. 95. 246-257. doi:
10.1016/j.envsoft.2017.06.020.
Hillel, D. 1998. Environmental soil physics: Fundamentals, applications, and environmental
considerations. Academic Press. San Diego, CA. 771 p.
Horsley, S.W. 2000. Article 96: The StormTreat System: A new technology for treating stormwater
runoff. In: Schueler, T.R. and Holland, H.K. (Eds.)The Practice of Watershed Protection. Center for
Watershed Protection. 495-497.
Hounslow, A. 1995. Water quality data: analysis and interpretation. Lewis Publishers. Boca Raton. 397
P-
Hunt, W.F.; Smith, J.T.; Jadlocki, S.J.; Hathaway, J.M.; and Eubanks, P.R. 2008. Pollutant removal and
peak flow mitigation by a bioretention cell in urban Charlotte, NC. Journal of Environmental
Engineering. 134. 403-408. doi: 10.1061/(Asce)0733-9372(2008)134:5(403).
Hurst, C.J.; Gerba, C.P.; and Cech, I. 1980. Effects of environmental variables and soil characteristics on
virus survival in soil. Applied and Environmental Microbiology. 40. 1067-1079.
Imboden, D.M., and Pfenninger, S. 2012. Introduction to systems analysis: mathematically modeling
natural systems. Springer Science and Business Media. New York, NY. pp. 252.
Ingvertsen, S. T., Cederkvist, K., Regent, Y., Sommer, H., Magid, J., and Jensen, M. B. (2012).
Assessment of Existing Roadside Swales with Engineered Filter Soil: I. Characterization and Lifetime
Expectancy. JEnviron Qual, 41(6), 1960-1969. doi: 10.2134/jeq2011.0318.
ISO. 2005. Soil quality — Sampling — Part 5: Guidance on the procedure for the investigation of urban
and industrial sites with regard to soil contamination. International Organization for Standardization.
Geneva, Switzerland. Vol. 10381-5. pp. 35.
83
-------
Jacobson, R.B.; Lindner, G.; and Bitner, C. 2015. The role of floodplain restoration in mitigating flood
risk, Lower Missouri River, USA. In: P.F. Hudson and H. Middelkoop (eds.). Geomorphic approaches
to Integrated Floodplain Management of Lowland Fluvial Systems in North America and Europe. P.
203-243. doi: 10.1007/978-l-4939-2380-9_9.
Jayasooriya, V.M.; and Ng, A.W.M. 2014. Tools for Modeling of Stormwater Management and
Economics of Green Infrastructure Practices: A Review. Water, Air, and Soil Pollution. 225. 2055. doi:
10.1007/sl 1270-014-2055-1.
Jenssen, P.; Maehlum, T.; and Krogstad, T. 1993. Potential use of constructed wetlands for wastewater
treatment in northern environments. Water Science and Technology. 28. 149-157.
Jia, H.; Yao, H.; Tang, Y.; Yu, S.L.; Zhen, J.X.; and Lu, Y. 2013. Development of a multi-criteria index
ranking system for urban runoff best management practices (BMPs) selection. Environmental
Monitoring Assessment, 185. 7915-7933. doi: 10.1007/sl0661-013-3144-0.
Jiang, Y.; Yuan, Y.; and Piza, H. 2015. A Review of Applicability and Effectiveness of Low Impact
Development/Green Infrastructure Practices in Arid/Semi-Arid United States. Environments. 2. 221.
John, D.E.; and Rose, J.B. 2005. Review of factors affecting microbial survival in groundwater.
Environmental Science Technology. 39. 7345-7356. doi: 10.1021/es047995w.
Johnson, R.D.; and Sample, D.J. 2017. A semi-distributed model for locating stormwater best
management practices in coastal environments. Environmental Modelling and Software. 91. 70-86. doi:
10.1016/j.envsoft.2017.01.015.
Johnson, W.P.; Li, X.; and Assemi, S. 2007. Deposition and re-entrainment dynamics of microbes and
non-biological colloids during non-perturbed transport in porous media in the presence of an energy
barrier to deposition. Advances in Water Resources. 30. 1432-1454. doi:
10.1016/j.advwatres.2006.05.020.
Jones, G.W. and Pichler, T. 2007. Relationship between Pyrite Stability and Arsenic Mobility During
Aquifer Storage and Recovery in Southwest Central Florida. Environment Science and Technology 41.
723-730. doi: 10.1021/es061901w.
Jones, P.S.; and Davis, A.P. 2013. Spatial Accumulation and Strength of Affiliation of Heavy Metals in
Bioretention Media. Journal of Environmental Engineering. 139. 479-487. doi:
10.1061/(ASCE)EE. 1943-7870.0000624.
Joyce, J.; Chang, N.-B.; Haiji, R.; Ruppert, T.; and Imen, S. 2017. Developing a multi-scale modeling
system for resilience assessment of green-grey drainage infrastructures under climate change and sea
level rise impact. Environmental Modelling and Software. 90. 1-26. doi: 10.1016/j.envsoft.2016.11.026.
Kabir, M.I.; Daly, E.; and Maggi, F. 2014. A review of ion and metal pollutants in urban green water
infrastructures. Science of The Total Environment. 470-471. 695-706. doi:
10.1016/j.scitotenv.2013.10.010.
84
-------
Kanso, A.; Gromaire, M.-C.; Gaume, E.; Tassin, B.; and Chebbo, G. 2003. Bayesian approach for the
calibration of models: application to an urban stormwater pollution model. Water Science and
Technology. 47. 77-84.
Kasprzyk-Hordern, B., Dinsdale, R. M., and Guwy, A. J. 2008. The occurrence of pharmaceuticals,
personal care products, endocrine disruptors and illicit drugs in surface water in South Wales, UK.
Water Research. 42. 3498-3518.
Kluge, B.; and Wessolek, G. 2012. Heavy metal pattern and solute concentration in soils along the
oldest highway of the world—the AVUS Autobahn. Environmental Monitoring Assessment. 184. 6469-
6481. doi: 10.1007/sl0661-011-2433-8.
Kluge, B.; Werkenthin, M.; and Wessolek, G. 2014. Metal leaching in a highway embankment on field
and laboratory scale. Science of The Total Environment. 493. 495-504. doi:
10.1016/j.scitotenv.2014.05.120.
Knappett, P.S.K.; Emelko, M.B.; Zhuang, J.; and McKay, L.D. 2008. Transport and retention of a
bacteriophage and microspheres in saturated, angular porous media: Effects of ionic strength and grain
size. Water Research. 42. 4368-4378. doi: 10.1016/j.watres.2008.07.041.
Komlos, J.; and Traver, R.G. 2012. Long-Term Orthophosphate Removal in a Field-Scale Storm-Water
Bioinfiltration Rain Garden. Journal of Environmental Engineering. 138. 991-998. doi:
10.1061/(ASCE)EE. 1943-7870.0000566.
Kong, F.; Ban, Y.; Yin, H.; James, P.; and Dronova, I. 2017. Modeling stormwater management at the
city district level in response to changes in land use and low impact development. Environmental
Modelling and Software. 95. 132-142. doi: 10.1016/j.envsoft.2017.06.021.
Konrad, C.P. 2003. Effects of Urban Development on Floods Fact Sheet. U.S. Geological Survey.
USGS Fact Sheet FS-076-03.
Krein, A.; Kebler, S.; Meyer, B.; Pailler, J.-Y.; Guignard, C.; Hoffmann, L. 2013. Concentrations and
loads of dissolved xenobiotics and hormones in two small river catchments of different land use in
Luxembourg. Hydrological Processes. 27. 284-296.
Kretzschmar, R.; Robarge, W.P.; and Amoozegar, A. 1995. Influence of natural organic matter on
colloid transport through saprolite. Water Resources Research. 31. 435-445.
Kroes, J.; and Van Dam, J. 2003. Reference Manual SWAP; version 3.0.3: Alterra-rapport 773. Alterra,
Green World Research, Project 230427-report 773. Wageningen, Netherlands.
Larsen, T.A. and Gujer, W. 1997. The concept of sustainable urban water management. Water Science
and Technology, 35. 3-10.
Launay, M.A.; Dittmer, U.; Steinmetz, H. 2016. Organic micropollutants by combined sewer overflows
- Characterisation of pollutant sources and storm water-related processes. Water Research. 104. 82-92.
Le Coustumer, S. and Barraud, S. 2007. Long-term hydraulic and pollution retention performance of
infiltration systems. Water Science Technology. 55. 235-243.
85
-------
Lee, M.S.; Lee, K.K.; Hyun, Y.J.; Clement, T.P.; and Hamilton, D. 2006. Nitrogen transformation and
transport modeling in groundwater aquifers. Ecological Modelling. 192. 143-159. doi:
10.1016/j.ecolmodel.2005.07.013.
Lee, S.M.; Min, K.D.; Woo, N.C.; Kim, Y.J.; and Ahn, C.H. 2003. Statistical models for the assessment
of nitrate contamination in urban groundwater using GIS. Environmental Geology. 44. 210-221. doi:
10.1007/s00254-002-0747-0.
Lefevre, G.H.; Hozalski, R.M.; and Novak, P.J. 2012. The role of biodegradation in limiting the
accumulation of petroleum hydrocarbons in raingarden soils. Water Research. 46. 6753-6762. doi:
10.1016/j.watres.2011.12.040.
Leisenring, M., Clary, J., and Hobson, P. 2014. International stormwater best management practices
(BMP) database pollutant category statistical summary report: solids, bacteria, nutrients, and metals.
Geosyntec Consultants, Inc. and Wright Water Engineers, Inc.
Li, H.; and Davis, A.P. 2008. Heavy Metal Capture and Accumulation in Bioretention Media.
Environmental Science and Technology. 42. 5247-5253. doi: 10.1021/es702681j.
Li, J.; Jiang, C.; Lei, T.; Li, Y. 2016. Experimental study and simulation of water quality purification of
urban surface runoff using non-vegetated bioswales. Ecological Engineering. 95. 706-713.
dx.doi.org/10.1016/j.ecoleng.2016.06.060.
Lin, Y.-C.; Lia, W.W.-P.; Tung, H.-H.; Lin, A.Y.-C. 2015. Occurrence of pharmaceuticals, hormones,
perfluorinated compounds in groundwater in Taiwan. Environmental Monitoring and Assessment. 187.
256.
Linder-Lunsford, J.B. and Ellis, S.R. 1987. Comparison of conceptually based and regression rainfall-
runoff models, Denver Metropolitan Area, Colorado, and potential applications in urban areas. U.S.
Geological Survey. Water-Resources Investigations Report 87-4104.
Line, D.E.; Brown, R.A.; Hunt, W.F.; and Lord, W.G. 2012. Effectiveness of LID for Commercial
Development in North Carolina. Journal of Environmental Engineering. 138. 680-688. doi:
10.1061/(ASCE)EE. 1943-7870.0000515.
Lopez, B.; Ollivier, P.; Togola, A.; Baran, N.; Ghestem, J.-P. 2015. Screening French groundwater for
regulated emerging contaminants. Science of the Total Environment. 518-519. 562-573.
Lowrance, R.; Leonard, R.; and Sheridan, J. 1985. Managing riparian ecosystems to control nonpoint
pollution. Journal of Soil and Water Conservation. 40. 87-91.
Maestre, A.; and Pitt, R. 2005. The National Stormwater Quality Databases, Version 1.1 - a Compilation
and Analysis ofNPDES Stormwater Monitoring Information. U.S. Environmental Protection Agency,
Office of Water. Washington, D.C.
Mahler, B.J.; Van Metre, P.C.; Crane, J.L.; Watts, A.W.; Scoggins, M.; Williams, E.S. 2012. Coal-tar-
based pavement sealcoat and PAHs: Implications for the environment, human health, and stormwater
management. Environmental Science and Technology. 46. 3039-3045.
86
-------
Maine, M. A.; Sune, N.; Hadad, H.; Sanchez, G.; and Bonetto, C. 2007. Removal efficiency of a
constructed wetland for wastewater treatment according to vegetation dominance. Chemosphere. 68.
1105-1113. doi: 10.1016/j.chemosphere.2007.01.064.
Maine, M.A.; Sune, N.; Hadad, H.; Sanchez, G.; and Bonetto, C. 2009. Influence of vegetation on the
removal of heavy metals and nutrients in a constructed wetland. Journal of Environmental Management.
90. 355-363. doi: 10.1016/j.jenvman.2007.10.004.
Mallard, G. E. 1980. Microorganisms in Stormwater - a summary of recent investigations. U.S.
Geological Survey. Open-File Report 80-1198.
Manios, T.; Stentiford, E.I.; and Millner, P.A. 2003. The effect of heavy metals accumulation on the
chlorophyll concentration of Typha latifolia plants, growing in a substrate containing sewage sludge
compost and watered with metaliferus water. Ecological Engineering. 20. 65-74. doi: 10.1016/S0925-
8574(03)00004-1.
Maryland Department of Environment. 2000. Maryland Stormwater Manual, Vol. 1. Baltimore, D. 212
P-
Marlow, D.R.; Moglia, M.; Cook, S.; and Beale, D.J. 2013. Towards sustainable urban water
management: A critical reassessment. Water Research. 47. 7150-7161. doi:
10.1016/j.watres.2013.07.046.
Mason, Y.; Ammann, A. A.; Ulrich, A., and Sigg, L. 1999. Behavior of Heavy Metals, Nutrients, and
Major Components during Roof Runoff Infiltration. Environmental Science Technology. 33. 1588-1597.
doi: 10.1021/es980922q.
Matisoff, G.; Wang, X.; and McCall, P.L. 1999. Biological Redistribution of Lake Sediments by
Tubificid Oligochaetes: Branchiura sowerbyi and Limnodrilus hoffmeisteri/Tubifex tubifex. Journal of
Great Lakes Research. 25. 205-219. doi: 10.1016/S0380-1330(99)70729-X.
Maupin, M.A., Kenny, J.F., Hutson, S.S., Lovelace, J.K., Barber, N.L. and Linsey, K.S., 2014.
Estimated use of water in the United States in 2010 (No. 1405). US Geological Survey.
Mayer, T.; Rochfort, Q.; Borgmann, U.; and Snodgrass, W. 2008. Geochemistry and toxicity of
sediment porewater in a salt-impacted urban stormwater detention pond. Environmental Pollution. 156.
143-151. doi: 10.1016/j.envpol.2007.12.018.
Mbuligwe, S. E. 2005. Comparative treatment of dye-rich wastewater in engineered wetland systems
(EWSs) vegetated with different plants. Water Research. 39. 271-280. doi:
10.1016/j.watres.2004.09.022.
McCall, P.L.; and Tevesz, M.J. 1982. The effects of benthos on physical properties of freshwater
sediments Animal-sediment relations, pp. 105-176. Springer. New York.
McCarthy, D.T.; Deletic, A.; Mitchell, V.G.; Fletcher, T.D.; and Diaper, C. 2008. Uncertainties in
stormwater E. coli levels. Water Research. 42. 1812-1824. doi: 10.1016/j.watres.2007.11.009.
87
-------
McDowell-Boyer, L.M.; Hunt, J.R.; and Sitar, N. 1986. Particle transport through porous media. Water
Resources Research. 22. 1901-1921. doi: 10.1029/WR022i013p01901.
McEachran, A.D.; Shea, D.; Nichols, E.G. 2017. Pharmaceuticals in a temperate forest-water reuse
system. Science of the Total Environment. 581-582. 705-714.
McGechan, M.B.; and Lewis, D.R. 2002. SW—Soil and Water: Transport of Particulate and Colloid-
sorbed Contaminants through Soil, Part 1: General Principles. Biosystems Engineering. 83. 255-273.
doi: 10.1006/bioe.2002.0125.
McLellan, S.L.; and Eren, A.M. 2014. Discovering new indicators of fecal pollution. Trends in
Microbiology. 22. 697-706. doi: 10.1016/j.tim.2014.08.002.
McMillan, S. K., & Noe, G. B. 2017. Increasing floodplain connectivity through urban stream
restoration increases nutrient and sediment retention. Ecological Engineering. 108. 284-295. doi:
10.1016/j.ecoleng.2017.08.006.
Mehring, A.S.; Levin, L.A.; and Li, J.-T. 2015. Potential roles of soil fauna in improving the efficiency
of rain gardens used as natural stormwater treatment systems. Journal of Applied Ecology. 52. 1445-
1454. doi: 10.1111/1365-2664.12525.
Mermillod-Blondin, F.; Gerino, M.; Degrange, V.; Lensi, R.; Chasse, J.-L.; Rard, M.; and des
Chatelliers, M.C. 2001. Testing the functional redundancy of Limnodrilus and Tubifex (Oligochaeta,
Tubificidae) in hyporheic sediments: an experimental study in microcosms. Canadian Journal of
Fisheries and Aquatic Sciences. 58. 1747-1759. doi: 10.1139/f01-l 19.
Mermillod-Blondin, F.; Nogaro, G.; Vallier, F.; and Gibert, J. 2008. Laboratory study highlights the key
influences of stormwater sediment thickness and bioturbation by tubificid worms on dynamics of
nutrients and pollutants in stormwater retention systems. Chemosphere. 72. 213-223. doi:
10.1016/j.chemosphere.2008.01.052.
Mermillod-Blondin, F.; Simon, L.; Maazouzi, C.; Foulquier, A.; Delolme, C.; and Marmonier, P. 2015.
Dynamics of dissolved organic carbon (DOC) through stormwater basins designed for groundwater
recharge in urban area: Assessment of retention efficiency. Water Research. 81. 27-37. doi:
10.1016/j.watres.2015.05.031.
Metcalfe, C. D., Chu, S., Judt, C., Li, H., Oakes, K. D., Servos, M. R., and Andrews, D. M. 2010.
Antidepressants and their metabolites in municipal wastewater, and downstream exposure in an urban
watershed. Environmental Toxicology and Chemistry. 29. 79-89.
Metro Vancouver, 2012. Stormwater source control design guidelines 2012. Prepared by Lanarc
Consultants, Kerr Wood Leidal Associates and Goya Ngan for Metro Vancouver. Vancouver, Canada.
May 2012.
Michigan DEQ. 2015. Groundwater Statistics. Office of Drinking Water and Municipal Assistance.
Environmental Assistance Center, www.michigan.gov/deq.
88
-------
Mikkelsen, P. S.; Hafliger, M.; Ochs, M.; Jacobsen, P.; Tjell, J. C.; and Boiler, M. 1997. Pollution of
soil and groundwater from infiltration of highly contaminated stormwater - A case study. Water Science
and Technology. 36. 325-330. doi: 10.1016/S0273-1223(97)00578-7.
Mikkelsen, P. S.; Hafliger, M.; Ochs, M.; Tjell, J.C.; Jacobsen, P.; and Boiler, M. 1996. Experimental
assessment of soil and groundwater contamination from two old infiltration systems for road run-off in
Switzerland. Science of The Total Environment. 189. 341-347. doi: 10.1016/0048-9697(96)05229-1.
Miltner, R.J.; White, D.; and Yoder, C. 2004. The biotic integrity of streams in urban and
suburbanizing landscapes. Landscape and Urban Planning. 69. 87-100.
Mineart, P., and Singh, S. 1994. Storm inlet pilot study. Woodward Clyde Consultants. Alameda County
Urban Runoff Clean Water Program.
Minnesota Pollution Control Agency (MPCA). 2018. Minnesota Stormwater Manual.
https://stormwater.pca.state.mn.us/index.php/Main_Page Accessed June 7, 2018.
Moe, C. 2007. Waterborne Transmission of Infectious Agents. In: Hurst, C.; Crawford, R.; Garland, J.;
Lipson, D.; Mills, A.; and Stetzenbach, L. (Eds.). Manual of Environmental Microbiology, 3rd ed. pp.
222-248. AMS Press. Washington, D.C.
Miiller, K.; Mason, K.; Strozzi, A.G.; Simpson, R.; Komatsu, T.; Kawamoto, K.; and Clothier, B. 2018.
Runoff and nutrient loss from a water-repellent soil. Geoderma. 322. 28-37. doi:
10.1016/j.geoderma.2018.02.019.
Murphy, H.M.; Prioleau, M.D.; Borchardt, M.A.; and Hynds, P.D. 2017. Review: Epidemiological
evidence of groundwater contribution to global enteric disease, 1948 - 2015. Hydrogeology Journal. 25.
981-1001. doi: 10.1007/sl0040-017-1543-y.
National Research Council. 2004. Indicators for Waterborne Pathogens. Committee on Indicators for
Watherborne Pathogens, Division on Earth and Life Studies. The National Academies Press.
Washington, DC. www.nap.edu.
National Stormwater Quality Database, Version 3.0. http://www.bmpdatabase.org/nsqd.html.
Nguyen, H.V-M.; Lee, M-H.; Hur, J.; Schlautman, M.A. 2013. Variations in spectroscopic
characteristics and disinfection byproduct formation potentials of dissolved organic matter for two
contrasting storm events. Journal of Hydrology. 481. 132-142.
Nightingale, H. I. 1987a. Accumulation of as, Ni, Cu, and Pb in Retention and Recharge Basins Soils
from Urban Runoff. Water Resources Bulletin. 23. 663-672.
Nightingale, H. I. 1987b. Water-Quality beneath Urban Runoff Water Management Basins. Water
Resources Bulletin. 23. 197-205.
Nightingale, H. I.; and Bianchi, W. C. 1977. Ground-Water Chemical Quality Management by Artificial
Recharge. Groundwater. 15. 15-22.
89
-------
Nilsen, E.; Rosenbauer, R.; Furlong, E.; Burkhardt, M.; Werner, S.; Greaser, L.; Noriega, M. 2007.
Pharmaceuticals, personal care products and anthropogenic waste indicators detected in streambed
sediments of the lower Columbia River and selected tributaries. U.S. Geological Survey.
https://or.water.usgs. gov/proi/Emerging.../NilsenNGWApaper4483 l-4.pdf.
Nix, S. J. 1994. Urban stormwater modeling and simulation. Lewis Publishers. Boca Raton.
Norrstrom, A.C. 2005. Metal mobility by de-icing salt from an infiltration trench for highway runoff.
Applied Geochemistry. 20. 1907-1919. doi: https://doi.org/ 10.1016/i.apgeochem.2005.06.002.
Norrstrom, A.C.; and Jacks, G. 1998. Concentration and fractionation of heavy metals in roadside soils
receiving de-icing salts. Science of The Total Environment. 218. 161-174. doi: 10.1016/S0048-
9697(98)00203-4.
Nutzmann, G.; Wiegand, C.; Contardo-Jara, V.; Hamann, E.; Burmester, V.; and Gerstenberg, K. 2011.
Contamination of Urban Surface and Ground Water Resources and Impact on Aquatic Species, pp. 43-
88, In: Endlicher, W. (ed.), Perspectives in Urban Ecology: Ecosystems and Interactions between
Humans and Nature in the Metropolis of Berlin. Springer. New York. DOI 10.1007/978-3-642-17731-6.
Obropta, C.C.; and Kardos, J.S. 2007. Review of Urban Stormwater Quality Models: Deterministic,
Stochastic, and Hybrid Approaches. Journal of the American Water Resources Association. 43. 1508-
1523. doi: 10.1111/j.l752-1688.2007.00124.x.
Olsson, G. 2006. Instrumentation, control and automation in the water industry - state-of-the-art and
new challenges. Water Science and Technology. 53. 1-16. doi: 10.2166/wst.2006.097.
Pachepsky, Y.A.; and Shelton, D.R. 2011. Escherichia coli and Fecal Coliforms in Freshwater and
Estuarine Sediments. Critical Reviews in Environmental Science and Technology. 41. 1067-1110. doi:
10.1080/10643380903392718.
Page, D. W.; Barry, K.; Gonzalez, D.; Keegan, A.; and Dillon, P. 2016. Reference pathogen numbers in
urban stormwater for drinking water risk assessment. Journal of Water Reuse and Desalination. 6. 30-39.
doi: 10.2166/wrd.2015.024.
Page, D.; Dillon, P.; Vanderzalm, J.; Toze, S.; Sidhu, J.; Barry, K.; Levett, K.; Kremer, S.; and Regel, R.
2010. Risk Assessment of Aquifer Storage Transfer and Recovery with Urban Stormwater for Producing
Water of a Potable Quality. Journal of Environmental Quality. 39. 2029-2039. doi:
10.2134/jeq2010.0078.
Page, D.; Gonzalez, D.; and Dillon, P. 2012. Microbiological risks of recycling urban stormwater via
aquifers. Water Science and Technology. 65. 1692-1695. doi: 10.2166/wst.2012.069.
Palla, A.; and Gnecco, I. 2015. Hydrologic modeling of Low Impact Development systems at the urban
catchment scale. Journal of Hydrology. 528. 361-368. doi: 10.1016/j.jhydrol.2015.06.050.
Palmer, M.A.; Filoso, S.; and Fanelli, R. M. 2014. From ecosystems to ecosystem services: Stream
restoration as ecological engineering. Ecological Engineering. 65. 62-70. doi:
10.1016/j.ecoleng.2013.07.059.
90
-------
Palta, M. M., Ehrenfeld, J. G., Gimenez, D., Groffman, P. M., & Subroy, V. 2016. Soil texture and water
retention as spatial predictors of denitrification in urban wetlands. Soil Biology and Biochemistry. 101.
237-250. doi: 10.1016/j soilbio.2016.06.011.
Parsons, D.F.; Hayashi, M.; and van der Kamp, G. 2004. Infiltration and solute transport under a
seasonal wetland: bromide tracer experiments in Saskatoon, Canada. Hydrological Processes. 18. 2011-
2027. doi: 10.1002/hyp.l345.
Paule-Mercado, M. A.; Lee, B. Y.; Memon, S. A.; Umer, S. R.; Salim, I.; and Lee, C. H. 2017. Influence
of land development on stormwater runoff from a mixed land use and land cover catchment. Science of
The Total Environment. 599-600. 2142-2155. doi: 10.1016/j.scitotenv.2017.05.081.
Paus, K.H.; Morgan, J.; Gulliver, J.S.; Leiknes, T.; and Hozalski, R.M. 2013. Assessment of the
Hydraulic and Toxic Metal Removal Capacities of Bioretention Cells After 2 to 8 Years of Service.
Water, Air, and Soil Pollution. 225. 1803. doi: 10.1007/sl 1270-013-1803-y.
Pavao-Zuckerman, M. A.; and Sookhdeo, C. 2017. Nematode Community Response to Green
Infrastructure Design in a Semiarid City. Journal of Environmental Quality. 46. 687-694. doi:
10.2134/jeq2016.11.0461.
Payment, P.; and Locas, A. 2011. Pathogens in Water: Value and Limits of Correlation with Microbial
Indicators. Groundwater. 49. 4-11. doi: 10.1111/j.l745-6584.2010.00710.x.
Peng, J.; Cao, Y.; Rippy, M.; Afrooz, A.; and Grant, S. 2016. Indicator and Pathogen Removal by Low
Impact Development Best Management Practices. Water. 8. 600.
Pennock, D.; Yates, T.; and Braidek, J. 2007. Soil sampling designs. Soil sampling and methods of
analysis. CRC Press Boca Raton. 198pp.
Petterson, S.R.; Mitchell, V.G.; Davies, C.M.; O'Connor, J.; Kaucner, C.; Roser, D.; and Ashbolt, N.
2016. Evaluation of three full-scale stormwater treatment systems with respect to water yield, pathogen
removal efficacy and human health risk from faecal pathogens. Science of the Total Environment, 543.
691-702. doi: 10.1016/j.scitotenv.2015.11.056.
Phillips, P.J.; Chalmers, A.T.; Gray, J.L.; Kolpin, D.W.; Foreman, W.T.; Wall, G.R. 2012. Combined
sewer overflows: An environmental source of hormones and wastewater micropollutants. Environmental
Science and Technology. 46. 5336-5343.
Pigneret, M.; Mermillod-Blondin, F.; Volatier, L.; Romestaing, C.; Maire, E.; Adrien, J.; Guillard, L.;
Roussel, D.; and Hervant, F. 2016. Urban pollution of sediments: Impact on the physiology and
burrowing activity of tubificid worms and consequences on biogeochemical processes. Science of the
Total Environment. 568. 196-207. doi: 10.1016/j.scitotenv.2016.05.174.
Pinheiro, M.D.O.; Power, M.E.; Butler, B.J.; Dayeh, V.R.; Slawson, R.; Lee, L.E.J.; Lynn, D.H.; and
Bols, N. C. 2007. Use of Tetrahymena thermophila To Study the Role of Protozoa in Inactivation of
Viruses in Water. Applied and Environmental Microbiology. 73. 643-649. doi: 10.1128/aem.02363-06.
Pitt, R.; Clark, S.; and Field, R. 1999. Groundwater contamination potential from stormwater infiltration
practices. Urban Water. 217-236.
91
-------
Pitt, R.; Clark, S.; Keith, P. 1994. Potential groundwater contamination from intentional and
nonintentional stormwater infiltration. U.S. Environmental Protection Agency, Office of Research and
Development, Risk Reduction Engineering Laboratory, Cincinnati, OH. Cooperative Agreement No.
CR 819573.
Pitt, R.; Clark, S.; Parmer, K.; Field, R.; and O'Connor, T.P. 1996. Groundwater contamination from
stormwater infiltration. CRC Press. Boca Raton, pp. 222-226.
Poff, N.L.; Allan, J.D.; Bain, M.B.; Karr, J.R.; Prestegaard, K.L.; Richter, B.D.; Sparks, R.E.; and
Stromberg, J.C. 1997. The natural flow regime. Bioscience. 47. 769-784.
Price, M. 1985. Introducing groundwater (1st ed.). George Allen and Unwin. London, England. 195 p.
Protection of Environment. 1990. Stormwater discharges. 40 C.F.R. §122.26. https://www.ecfr.gov/.
Qin, H.; Li, Z.; and Fu, G. 2013. The effects of low impact development on urban flooding under
different rainfall characteristics. Journal of Environmental Management. 129. 577-585. doi:
10.1016/j.jenvman.2013.08.026.
Radjenovic, J., Petrovic, M., and Barcelo, D. 2009. Fate and distribution of pharmaceuticals in
wastewater and sewage sludge of the conventional activated sludge (CAS) and advanced membrane
bioreactor (MBR) treatment. Water Research. 43. 831-841.
Rauch, W.; Bertrand-Krajewski, J.-L.; Krebs, P.; Mark, O.; Schilling, W.; Schiitze, M.; and
Vanrolleghem, P. A. 2002. Deterministic modelling of integrated urban drainage systems. Water Science
and Technology. 45. 81-94.
Redman, J.A.; Grant S.; Olson, T.M.; Adkins, J.M.; Jackson, J.L.; Castillo, M.S.; and Yanko, W.A.
1999. Physicochemical mechanisms responsible for the filtration and mobilization of a filamentous
bacteriophage in quartz sand. Water Research. 33. 43-52. doi: 10.1016/S0043-1354(98)00194-8.
Regnery, J.; Gerba, C.P.; Dickenson, E.R.V.; and Drewes, J.E. 2017. The importance of key attenuation
factors for microbial and chemical contaminants during managed aquifer recharge: A review. Critical
Reviews in Environmental Science and Technology. 47., 1409-1452. doi:
10.1080/10643389.2017.1369234.
Richkus, J.; Wainger, L.A.; and Barber, M.C. 2016. Pathogen reduction co-benefits of nutrient best
management practices. PeerJ. 4. e2713. doi: 10.7717/peerj.2713.
Rippy, M.A. 2015. Meeting the criteria: linking biofilter design to fecal indicator bacteria removal.
Wiley Interdisciplinary Reviews: Water. 2. 577-592. doi: 10.1002/wat2.1096.
Robien, A.; Striebel, T.; and Herrmann, R. 1997. Modeling of dissolved and particle-bound pollutants in
urban street runoff. Water Science and Technology., 36. 77-82. doi: 10.1016/S0273-1223(97)00616-1.
Rousseau, A. N.; Mailhot, A.; Quilbe, R.; and Villeneuve, J.-P. 2005. Information technologies in a
wider perspective: integrating management functions across the urban-rural interface. Environmental
Modelling and Software. 20. 443-455. doi: 10.1016/j.envsoft.2004.02.008.
92
-------
Santo-Domingo, J.W.; Lamendella, R.; and Ashbolt, N. 2010. Microbial Source Tracking: Current and
Future Molecular Tools in Microbial Water Quality Forensics. In: Sen, K.; and Ashbolt, N. (Eds.).
Environmental Microbiology: Current Technology and Water Applications. Caister Academic Press.
Norwich, UK.
Sauer, E.P.; VandeWalle, J.L.; Bootsma, M.J.; and McLellan, S.L. 2011. Detection of the human
specific Bacteroides genetic marker provides evidence of widespread sewage contamination of
stormwater in the urban environment. Water Research. 45. 4081-4091. doi:
10.1016/j.watres.2011.04.049.
Schirmer, M.; Leschik, S.; and Musolff, A. 2013. Current research in urban hydrogeology - A review.
Advances in Water Resources. 51. 280-291. doi: 10.1016/j.advwatres.2012.06.015.
Schmidt, C.; Lange, F.T.; Brauch, H.-J.; and Kiihn, W. 2003. Experiences with riverbank filtration and
infiltration in Germany. In: Proceedings of International Symposium of Artificial Recharge of
Groundwater. Korea Water Research Institute, Daejon, Korea, pp. 117-131.
Schmidt, C.M.; Fisher, A.T.; Racz, A.J.; Lockwood, B.S.; and Huertos, M.L. 2011. Linking
denitrification and infiltration rates during managed groundwater recharge. Environmental Science and
Technology. 45. 9634-9640. doi: 10.1021/es2023626.
Scholz, K. 1997. Stochastic simulation of urban hydrological processes. Water Science and Technology.
36. 25-31. doi: 10.1016/S0273-1223(97)00622-7.
Scholz, M.; and Grabowiecki, P. 2007. Review of permeable pavement systems. Building and
Environment. 42. 3830-3836. doi: 10.1016/j.buildenv.2006.11.016.
Schueler, T.R. and Holland, H.K. 2000. The Practice of Watershed Protection. Center for Watershed
Protection.742 p.
Selvakumar, A.; and Borst, M. 2006. Variation of microorganism concentrations in urban stormwater
runoff with land use and seasons. Journal of Water Health. 4. 109-124.
Selvakumar, A.; Borst, M.; and Struck, S. 2007. Microorganisms Die-Off Rates in Urban Stormwater
Runoff. Proceedings of the Water Environment Federation. 2007. 214-230. doi:
10.2175/193864707786619125.
Seo, M.; Jaber, F.; Srinivasan, R.; and Jeong, J. 2017. Evaluating the Impact of Low Impact
Development (LID) Practices on Water Quantity and Quality under Different Development Designs
Using SWAT. Water. 9. 193.
Shang, J.; Liao, Q.; Zhang, L.; and Fan, C. 2014. The influence of different benthic fauna on inorganic
nitrogen flux and denitrification in a large shallow hyper-eutrophic lake. Fundamental and Applied
Limnology. 184. 101-108.
Sharma, A.K.; Gray, S.; Diaper, C.; Liston, P.; and Howe, C. 2008. Assessing integrated water
management options for urban developments - Canberra case study. Urban Water Journal. 5. 147-159.
doi: 10.1080/15730620701736829.
93
-------
Shein, E.; and Devin, B. 2007. Current problems in the study of colloidal transport in soil. Eurasian Soil
Science. 40. 399-408.
Shen, C.; Li, B.; Huang, Y.; and Jin, Y. 2007. Kinetics of Coupled Primary- and Secondary-Minimum
Deposition of Colloids under Unfavorable Chemical Conditions. Environmental Science and
Technology. 41. 6976-6982. doi: 10.1021/es070210c.
Shepherd, H.L.; Grismer, M.E.; and Tchobanoglous, G. 2001. Treatment of High-Strength Winery
Wastewater Using a Subsurface-Flow Constructed Wetland. Water Environment Research. 73. 394-403.
Sidhu, J.P.S.; Ahmed, W.; Gernjak, W.; Aryal, R.; McCarthy, D.; Palmer, A.; Kolotelo, P.; andToze, S.
2013. Sewage pollution in urban stormwater runoff as evident from the widespread presence of multiple
microbial and chemical source tracking markers. Science of The Total Environment. 463. 488-496. doi:
10.1016/j.scitotenv.2013.06.020.
Sidhu, J.P.S.; Hodgers, L.; Ahmed, W.; Chong, M.N.; and Toze, S. 2012. Prevalence of human
pathogens and indicators in stormwater runoff in Brisbane, Australia. Water Research. 46. 6652-6660.
doi: 10.1016/j.watres.2012.03.012.
Simunek, J.; van Genuchten, M.T.; and Sejna, M. 1999. The HYDRUS-2D Software Package for
Simulating the Two-dimensional Movement of Water, Heat, and Multiple Solutes in Variably-saturated
Media: Version 2.0: Colorado School of Mines.
Skahill, B.E.; and Doherty, J. 2006. Efficient accommodation of local minima in watershed model
calibration. Journal of Hydrology. 329. 122-139. doi: 10.1016/j.jhydrol.2006.02.005.
Sophocleous, M. 2002. Interactions between groundwater and surface water: the state of the science.
Hydrogeology Journal. 10. 52-67. doi: 10.1007/sl0040-001-0170-8.
Spicer, G.E.; Lynch, D.E.; Newman, A.P.; and Coupe, S.J. 2006. The development of geotextiles
incorporating slow- release phosphate beads for the maintenance of oil degrading bacteria in permeable
pavements. Water Science and Technology. 54. 273-280. doi: 10.2166/wst.2006.580.
Sposito, G. (1989). The chemistry of soils. Oxford University Press. New York. 278 p.
Sprague, A. 2012. Mitigating Impacts of stormwater, wastewater and pharmaceutical in the environment
- Final report. Ecology Action Centre and Bay of Fundy Workshops. Environment Canada.
Stephenson, J.B.; Zhou, W.F.; Beck, B.F.; and Green, T.S. 1999. Highway stormwater runoff in karst
areas — preliminary results of baseline monitoring and design of a treatment system for a sinkhole in
Knoxville, Tennessee. Engineering Geology. 52. 51-59. doi: 10.1016/S0013-7952(98)00054-4.
Stromvall, A.-M.; Norin, M.; and Pettersson, T. 2007. Organic contaminants in urban sediments and
vertical leaching in road ditches. 235-247. In: Morrison, G.; and Rauch, S. (ed.). Highway and Urban
Environment. Springer. Dordrecht, The Netherlands.
Stumm, W.; and Morgan, J.J. 1996. Aquatic Chemistry: Chemical Equilibria and Rates in Natural
Waters, 3rd ed. John Wiley and Sons, Inc. New York. 1022 p.
94
-------
Sui, Q., Huang, J., Deng, S., Yu, G., and Fan, Q. 2010. Occurrence and removal of pharmaceuticals,
caffeine and DEET in wastewater treatment plants of Beijing, China. Water Research. 44. 417-426.
Tan, B.; Ng, C.; Nshimyimana, J.; Loh, L.-L.; Gin, K.; and Thompson, J. 2015. Next-generation
sequencing (NGS) for assessment of microbial water quality: current progress, challenges, and future
opportunities. Frontiers in Microbiology. 6. doi: 10.3389/fmicb.2015.01027.
Tanner, C.C. 1996. Plants for constructed wetland treatment systems — A comparison of the growth and
nutrient uptake of eight emergent species. Ecological Engineering. 7. 59-83. doi: 10.1016/0925-
8574(95)00066-6.
Tchobanoglous, G.; Maitski, F.; Thompson, K.; and Chadwick, T.H. 1989. Evolution and performance
of city of San Diego pilot-scale aquatic wastewater treatment system using water hyacinths. Research
Journal of the Water Pollution Control Federation. 1625-1635.
Tedoldi, D.; Chebbo, G.; Pierlot, D.; Kovacs, Y.; and Gromaire, M.C. 2016. 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. 569-570. 904-926. doi:
10.1016/j.scitotenv.2016.04.215.
Thomas, A.; and Tellam, J. 2006. Modelling of recharge and pollutant fluxes to urban groundwaters.
Science of the Total Environment. 360. 158-179. doi: 10.1016/j.scitotenv.2005.08.050.
Tomson, M.B.; Dauchy, J.; Hutchins, S.; Curran, C.; Cook, C.J.; and Ward, C.H. 1981. Groundwater
contamination by trace level organics from a rapid infiltration site. Water Research. 15. 1109-1116. doi:
10.1016/0043-1354(81)90080-4.
Tornes, L. H. 2005. Effects of rain gardens on the quality of water in the Minneapolis-St. Paul
metropolitan area of Minnesota, 2002-04: US Geological Survey. Scientific Investigations Report 2005-
5189.
Toze, S.; Bekele, E.; Page, D.; Sidhu, J.; and Shackleton, M. 2010. Use of static Quantitative Microbial
Risk Assessment to determine pathogen risks in an unconfined carbonate aquifer used for Managed
Aquifer Recharge. Water Research. 44. 1038-1049. doi: 10.1016/j.watres.2009.08.028.
U.S. Census Bureau. 2008. American Housing Survey for the United States: 2007. Current Housing
Reports. Series H150/07. U.S. Government Printing Office. Washington, D.C.
US EPA. 1983. Results of the Nationwide Urban Runoff Program. Volume I - Final Report. Water
Planning Division, US Environmental Protection Agency. Washington, D.C. Rep. 5927A.
US EPA. 1985. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential
Using Hydrogeologic Settings. U.S. Environmental Protection Agency, Office of Research and
Development, Ada, OK. EPA 600-2-85-018.
US EPA. 2002. Drinking Water from Household Wells. U.S. Environmental Protection Agency, Office
of Ground Water and Drinking Water. Washington, D.C. EPA 816-K-02-003.
95
-------
US EPA. 2010. Green Infrastructure Case Studies: Municipal Policies for Managing Stormwater with
Green Infrastructure. U.S. Environmental Protection Agency, Office of Wetlands, Oceans and
Watersheds. Washington, D.C. EPA-841-F-10-005.
US EPA. 2012. Sustainable Futures/ P2 Framework Manual. U.S. Environmental Protection Agency,
Office of Chemical Safety and Pollution Prevention. EPA-748-B12-001.
US EPA. 2017. The Third Unregulated Contaminant Monitoring Rule (UCMR 3): Data Summary,
January 2017. Office of Water, US Environmental Protection Agency. Washington, D.C. EPA-815-S-
17-001.
Valtanen, M.; Sillanpaa, N.; and Setala, H. 2017. A large-scale lysimeter study of stormwater
biofiltration under cold climatic conditions. Ecological Engineering. 100. 89-98. doi:
10.1016/j.ecoleng.2016.12.018.
Vanderzalm, J.L.; Dillon, P.J.; Barry, K.E.; Miotlinski, K.; Kirby, J.K.; and Le Gal La Salle, C. 2011.
Arsenic mobility and impact on recovered water quality during aquifer storage and recovery using
reclaimed water in a carbonate aquifer. Applied Geochemistry. 26. 1946-1955. doi:
10.1016/j.apgeochem.2011.06.025.
Vanderzalm, J.L.; Page, D.W.; Barry, K.E.; and Dillon, P.J. 2010. A comparison of the geochemical
response to different managed aquifer recharge operations for injection of urban stormwater in a
carbonate aquifer. Applied Geochemistry. 25. 1350-1360. doi: 10.1016/j.apgeochem.2010.06.005.
Vaze, J.; and Chiew, F.H.S. 2003. Comparative evaluation of urban storm water quality models. Water
Resources Research. 39. 5-1-5-10. doi: 10.1029/2002WR001788.
Vesk, P. A.; and Allaway, W.G. 1997. Spatial variation of copper and lead concentrations of water
hyacinth plants in a wetland receiving urban run-off Aquatic Botany. 59. 33-44. doi: 10.1016/S0304-
3770(97)00032-6.
Vojinovic, Z.; Kecman, V.; and Babovic, V. 2003. Hybrid Approach for Modeling Wet Weather
Response in Wastewater Systems. Journal of Water Resources Planning and Management. 129. 511-
521. doi: 10.1061/(ASCE)0733-9496(2003)129:6(511).
Vymazal, J. 2005. Horizontal sub-surface flow and hybrid constructed wetlands systems for wastewater
treatment. Ecological Engineering. 25. 478-490. doi: 10.1016/j.ecoleng.2005.07.010.
Vymazal, J.; and Kropfelova, L. 2005. Growth of Phragmites australis and Phalaris arundinacea in
constructed wetlands for wastewater treatment in the Czech Republic. Ecological Engineering. 25. 606-
621. doi: 10.1016/j.ecoleng.2005.07.005.
Wallender, E.K.; Ailes, E.C.; Yoder, J.S.; Roberts, V.A.; and Brunkard, J.M. 2014. Contributing factors
to disease outbreaks associated with untreated groundwater. Ground Water. 52. 886-897. doi:
10.1111/gwat. 12121.
Walsh, C.J.; Fletcher, T.D.; and Burns, M.J. 2012. Urban stormwater runoff: a new class of
environmental flow problem. PLoS One. 7. e45814. doi: 10.1371/journal.pone.0045814.
96
-------
Walsh, C.J.; Roy, A.H.; Feminella, J.W.; Cottingham, P.D.; Groffman, P.M.; and Morgan, R. 2005.
The urban stream syndrome: current knowledge and the search for a cure. Journal of the North
American Benthological Society. 24. 706-723.
Wang, H.; Qin, J.; Hu, Y. 2017. Are green roofs a source or sink of runoff pollutants? Ecological
Engineering. 107. 65-70. dx.doi.org/10.1016/j.ecoleng.2017.06.035.
Warwick, J.J.; and Wilson, J.S. 1990. Estimating Uncertainty of Stormwater Runoff Computations.
Journal of Water Resources Planning and Management. 116. 187-204. doi: 10.1061/(ASCE)0733-
9496(1990)116:2(187).
Watts, A.W.; Ballestero, T.P.; Foseen, R.M.; Houle, J.P. 2010. Polycyclic aromatic hydrocarbons in
stormwater runoff form sealcoated pavements. Environmental Science and Technology. 44. 8849-8854.
Whittemore, D. 2008. Water-quality effects of stormwater runoff into sand pits on ground water in
Sedgwick County, Kansas—Phase II: Kansas Geological Survey, Open-file Report. 2008-4.
Wilkinson, J.L.; Hooda, P.S.; Barker, J.; Barton, S.; Swinden, J. 2016. Ecotoxic pharmaceuticals,
personal care products, and emerging contaminants: A review of environmental, receptor-mediated,
developmental, and epigenetic toxicity with discussion of proposed toxicity to humans. Critical Reviews
in Environmental Science and Technology. 46. 336-381.
Williams, J.R.; Ouyang, Y.; Chen, J.-S.; Ravi, V. 1998. Estimation of Infiltration Rate in the Vadose
Zone: Application of Selected Mathematical Models, Volume II. US EPA, Office of Research and
Development, National Risk Management Research Laboratory. EPA/600/R-97/128b.
Winiarski, T.; Bedell, J.P.; Delolme, C.; and Perrodin, Y. 2006. The impact of stormwater on a soil
profile in an infiltration basin. Hydrogeology Journal. 14. 1244-1251. doi: 10.1007/sl0040-006-0073-9.
Winter, T.C.; Harvey, J.W.; Franke, O.L.; and Alley, W.M. 1998. Ground water and surface water; a
single resource: US Geological Survey Circular 1139.
Writer, J.H.; Barber, L.B.; Ryan, J.N.; Bradley, P.M. 2011. Biodegradation and attenuation of steroidal
hormones and alkylphenols by stream biofilms and sediments. Environmental Science and Technology.
45. 4370-4376.
Xiao, Q.; McPherson, E.G.; Simpson, J.R.; and Ustin, S.L. 2007. Hydrologic processes at the urban
residential scale. Hydrological Processes. 21. 2174-2188. doi: doi:10.1002/hyp.6482.
Yang, H.; McCoy, E.L.; Grewal, P.S.; and Dick, W.A. 2010. Dissolved nutrients and atrazine removal
by column-scale monophasic and biphasic rain garden model systems. Chemosphere. 80. 929-934. doi:
10.1016/j.chemosphere.2010.05.021.
Yang, H.B.; Dick, W.A.; McCoy, E.L.; Phelan, P.L.; and Grewal, P.S. 2013. Field evaluation of a new
biphasic rain garden for stormwater flow management and pollutant removal. Ecological Engineering.
54. 22-31. doi: 10.1016/j.ecoleng.2013.01.005.
Zgheib, S., Moilleron, R., and Chebbo, G. 2012. Priority pollutants in urban stormwater: Part 1-Case of
separate storm sewers. Water Research. 46. 6683-6692.
97
-------
Zhang, S.; Pang, S.; Wang, P.; Wang, C.; Han, N.; Liu, B.; Han, B.; Li, Y.; Anim-Larbi, K. 2016.
Antibiotic concentration and antibiotic-resistant bacteria in two shallow urban lakes after stormwater
event. Environmental Science Pollution Research. 23. 9984-9992.
Zhang, W.; Morales, V.L.; Cakmak, M.E.; Salvucci, A.E.; Geohring, L.D.; Hay, A.G.; Parlange, J.-Y.;
and Steenhuis, T.S. 2010. Colloid transport and retention in unsaturated porous media: Effect of colloid
input concentration. Environmental Science and Technology. 44. 4965-4972.
Zhuang, J.; and Jin, Y. 2003. Virus retention and transport through Al-oxide coated sand columns:
effects of ionic strength and composition. Journal of Contaminant Hydrology. 60. 193-209. doi:
10.1016/SO169-7722(02)00087-6.
Zoppou, C. 2001. Review of urban storm water models. Environmental Modelling and Software. 16.
195-231. doi: 10.1016/S1364-8152(00)00084-0.
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10.0 Quality Assurance/ Quality Control
This research was conducted using an approved Quality Assurance Project Plan (QAPP), G-GWERD-
0030990 vO, March 28, 2017. Unless otherwise indicated below, the data used to generate this report
followed the QAPP.
• Peer reviewed data values from Toxnet or EPI Suite were preferentially used, if peer reviewed
data was not available, then (est) was indicated after the data value in Table A4 (Appendix 1) to
signify that the data quality was not known.
99
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Appendix 1
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
1,1,1,2-tetrachloroethane
630-20-6
MM
S
V
0.22
29
1,1,1-trichoroethane
71-55-6
MM to M
S
V
MCL= 200
280
29
1,1,2,2-tetrachloroethane
79-34-5
MM to M
S
MV
0.03
29
1,1,2-trichloroethane
79-00-5
MM to M
S
MV
MCL= 500
0.013
1
29
1,1-dichloroethane
75-34-3
M to HM
S
V
0.78
29
1,2,3-trichloropropane
96-18-4
M
S
MV
0.00032
0.04
29
1,2,3-trimethylbenzene
526-73-8
SM to MM
SS
V
1.5
14
1,2,4-trichlorobenzene
120-82-1
BM to SM
SS
V
MCL= 70
1.2
14
1,2,4-trimethylbenzene
95-63-6
MM
SS
V
2.1
14
1,2-dichloro benzene
95-50-1
BM to MM
MS
V
MCL= 600
30
3
14
1,2-dichloroethane
107-06-2
M
s
V
MCL= 5
0.048
0.6
29
1,2-dichloropropane
78-87-5
M
s
V
MCL= 5
0.15
1
29
1,3-dichloro benzene
541-73-1
BM to MM
MS
V
3
5,15
1,3-dichloropropane
142-28-9
MM
S
MV
13
29
1,4-dichloro benzene
106-46-7
BM to MM
SS
V
MCL= 75
0.46
3
14,17
11-ketotestosterone
53187-98-7
MM to M
MS
NV
31
17a-estradiol
57-91-0
BM to MM
SS
NV
28
17a-ethynylestradiol
57-63-6
BM to MM
MS
NV
28
17p-estradiol
50-28-2
BM to MM
SS
NV
28
100
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
19-norethisterone
68-22-4
MM
SS
NV
40
1-acetyl-1-methyl-2-
dimethyl-oxamoyl-
2-phenylhydrazide
(AMDOPH)
519-65-3
M to HM
s
NV
1,28
1-chloro-2-methylpropane
513-36-0
MM to M
MS
MV
1
1-chlorobutane
109-69-3
MM to M
S
V
26
1
1-chloropentane
543-59-9
MM
MS
V
29
1 H-benzotrazole
95-14-7
MM
VS
SV
39
1 -methylnaphthalene
90-12-0
IM to MM
SS
MV
5.8
25, 30
2, 4-D
94-75-9
MM to M
MS
NV
MCL= 70
5,8,15
2,2',3,4,4',5,5'-
heptachlorobiphenyl (PCB
180)
35065-29-3
IM
NS
NV
MCL= 0.5
26
2,2',3,4,4',5-
hexachlorobiphenyl (PCB
138)
35065-28-2
IM
NS
NV
MCL= 0.5
26
2,2',4,4',5,5'-
hexachlorobiphenyl (PCB
153)
35065-27-1
IM
NS
NV
MCL= 0.5
26
2,2',5,5'-tetrachorobiphenyl
(PCB 52)
35693-99-3
BM
NS
SV
MCL= 0.5
26
2,3',4,4',5-
pentachlorobiphenyl (PCB
118)
31508-00-6
IM
NS
SV
MCL= 0.5
26
2,4,4'-trichlorobiphenyl
(PCB 28)
7012-37-5
BM
SS
SV
MCL= 0.5
26
101
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
2,4,5,2',5'-
pentachlorobiphenyl (PCB
101)
37680-73-2
IM to BM
NS
NV
MCL= 0.5
26
2-chloro-2-methylpropane
507-20-0
MM to M
Sto MS
MV
1
2-chlorobutane
78-86-4
MM to M
S
MV
1
2-Chloromethylphenol
40053-98-3
MM
S
NV
29
2-methyl-4-
chlorophenoxyacetic Acid
94-74-6
M
MS
NV
MCL= 60
0.2
0.44
39
2-methylnaphthalene
91-57-6
IM to MM
SS
MV
19
25,30
2-methylthiobenzothiazole
615-22-5
SM to MM
MS
NV
39
3-Chloromethylphenol
60760-06-7
MM
S
NV
29
3-p-coprostanol
360-68-9
IM
NS
NV to SV
31
4-Chloromethylphenol
35421-08-0
MM
S
NV
29
4'-hydroxy-diclofenac
64118-84-9
MM
SS
NV
33
4-methyl-1 H-benzotriazole
29878-31-7
M
S
NV
39
4-Nitrophenol
100-02-7
MM to M
VS
NV
1
4-nonylphenol
104-40-5
BM
SS
MV
27,39,40
4-nonylphenoldiethoxylate
20427-84-9
SM
SS
NV
27
4-
nonylphenolmonoethoxylate
104-35-8
SM
SS
NV
27
4-tert-Butylphenol
98-54-4
SM to MM
MS
NV
26
5-methyl-1 H-benzotriazole
136-85-6
M
S
NV
550
39
Acebutolol
37517-30-9
M
MS
NV
37
Acenaphthene
83-32-9
IM to MM
SS
MV
550
26
102
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Acenaphthylene
208-96-8
IM to SM
SS
MV
26
Aceslfame
33665-90-6
HM
vs
NV
39
Acetaldehyde
75-07-0
HM
vs
MV
0.38
8
38
Acetaminophen
103-90-2
M
vs
NV
37
Acetophenone
98-86-2
MM to M
s
MV
58
17
Acrylamide
79-06-1
M
vs
NV
0.011
38
Alachlor
15972-60-8
SM to MM
MS
NV
MCL= 2
0.86
0.5
8
Aldrin
309-00-2
BM to MM
MS
MV
0.75
Not
Detectable
26
Alkylphenol ethoxylates
68412-54-4
HM
SS
VV
28
Aminotriazole
61-82-5
M
VS
NV
26
Amitriptyline
50-48-6
SM
SS
NV
40
Amoxicillin
26787-78-0
MM to M
s
NV
40
AM PA
74341-63-2
M to HM
vs
NV
26
Androstenedione
63-05-8
SM
SS
NV
31
Anthracene
120-12-7
SM
NS
V
5800
5
Anthraquinone
84-65-1
BM to SM
SS
NV
14
17
Atenolol
29122-68-7
M to HM
VS
NV
37,40
Atrazine
1912-24-9
SM to M
SS
NV
MCL= 3
0.19
7.5
5,15
Atrazine amide-l
142179-76-8
M
s
NV
1,28
Azithromycin
83905-01-5
SM
SS
NV
17
Bentazone
25057-89-0
MM to M
MS
NV
12
1,28
Benz(a)anthracene
56-55-3
IM
NS
V
12
5,15
103
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Benzene
71-43-2
M
S
V
MCL= 5
0.23
1
29
Benzo(a)pyrene
50-32-8
IM
NS
NV
MCL= 0.2
4
Not
Detectable
5
Benzo(e)pyrene
192-97-2
IM
NS
V
5
Benzo(a)fluoranthene
203-33-8
IM
NS
SV
5
Benzo(b)fluoranthene
205-99-2
IM
NS
NV
41
5
Benzo(b)naphtho(2,3-
d)thiophene
243-46-9
IM
NS
NV
5
Benzo(ghi)perylene
191-24-2
IM
NS
SV
6
Benzo(j)fluoranthene
205-82-3
BM
NS
NV
78
5
Benzo(k)fluoranthene
207-08-9
IM to BM
NS
SV
400
5
Benzophenone
119-61-9
MM
MS
SV
17,31
Benzothiazole
95-16-9
MM
S
SV
39
Benzyl acetate
140-11-4
MM
S
MV
28
Beta-sitosterol
83-46-5
IM
NS
SV
31
Bezafibrate
41859-67-0
MM
SS
NV
1
Bis(2-chloroethyl) ether
111-44-4
MM
VS
MV
0.0036
1
5
Bis(2-chloroisopropyl) ether
39638-32-9
MM to M
SS
SV
5
Bis(2-ethlyhexyl)phthalate
(DEHP)
117-81-7
IM to BM
NS
SV
MCL= 6
1300
5
1,5,15
Bis(tributyltin) oxide
56-35-9
IM to SM
SS
SV
29000
26
Bisphenol A
80-05-7
SM to MM
MS
NV
5800
31, 39,40
Bromochloroacetic acid
5589-96-8
HM
VS
NV
MCL= 60
34
Bromodichloroacetic acid
71133-14-7
M to HM
S
NV
MCL= 60
34
104
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Bromodichloromethane
75-27-4
MM to M
S
V
MCL= 80
0.036
28
Butylbenzylphthalate (BBP)
85-68-7
SM
ss
sv
230
5
Butylated hydroxyanisole
25013-16-5
MM
MS
sv
450
17
Caffeine
58-08-2
SM to M
VS
NV
24,28
CA-ibuprofen
SM to M
Sto MS
NV
28
Carbamazepine
298-46-4
MM
SS
NV
1
Carbamazepine-10,11-
epoxid
36507-30-9
MM to M
MS
NV
33
Carbendazim
10605-21-7
SM to MM
SS
NV
39
Carbon tetrachloride
56-23-5
M
Sto MS
V
MCL= 5
0.18
5
29
Carbozole
86-74-8
SM to MM
SS
SV
17
Cefazolin
25953-19-9
M
MS
NV
42
Cefriaxone
73384-59-5
MM to HM
MS
NV
42
Ceftazidime
72558-82-8
BM to HM
MM
NV
28
Chlordane
57-74-9
BM
NS
MV
MCL= 2
0.05
1,5,15
Chlorfenvinphos
470-90-6
MM
MS
NV
3.1
26
Chloroacetic acid
79-11-8
M
VS
NV
MCL= 60
0.81
38
Chlorodifluoromethane
75-45-6
HM
S
V
MCL= 80
4300
28
Chloromethane
74-87-3
M
S
V
4.9
29
Chlorpyrifos
2921-88-2
BM to MM
SS
MV
12
12
Chlortetracycline
57-62-5
M to HM
MS
NV
33
Cholesterol
57-88-5
BM
NS
MV
31
Choline Chloride
67-48-1
HM
VS
NV
38
105
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Chrysene
218-01-9
IM
NS
SV
1200
1
Cimetidine
51481-61-9
M
VS
NV
17
Ciprofloxacin
85721-33-1
M to HM
VS
NV
28
Cis-androsterone
53-41-8
SM to MM
SS
NV
31
Citalopram
59729-33-8
BM to SM
SS
NV
17
Clarithromycin
81103-11-9
MM
SS
NV
37
Clofibric Acid
882-09-7
M
MS
NV
1
Cocaine
50-36-2
MM
S
NV
35
Codeine
76-57-3
MM
VS
NV
17,40
Coronene
191-07-1
IM
NS
NV
25
Cotinine
486-56-6
MM
VS
NV
17
Cyanazine
21725-46-2
MM
MS
NV
0.041
1
Cyanazine amide
36576-42-8
M
S
NV
1,28
Cyclophosphamide
50-18-0
M
VS
NV
40
DDT
50-29-3
IM
NS
SV
77
0.2
6
Debromodiphenyl ether
1163-19-5
IM
NS
NV
7800
29
Deethylcyanazine
36556-77-1
M to HM
S
NV
1,28
Dehyronifedipine
67035-22-7
SM to MM
SS
NV
17
Deisopropylatrazine
1007-28-9
M
MS
NV
1,28
Desethylatrazine
6190-65-4
SM to M
S
NV
1,28
Desethylsimazine
1007-28-9
M
MS
NV
26
Diatriozate
117-96-4
M to HM
VS
NV
39
Diazinon
333-41-5
SM to M
SS
NV
6.5
0.7
5,12,15
106
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Dibenz([a,h)anthracene
53-70-3
IM
NS
SV
13
14,20,25,30
Dibenzofuran
132-64-9
SM
SS
MV
15
25
Dibenzothiophene
132-65-0
BM to SM
SS
MV
12
29
Dibromoacetic acid
631-64-1
HM
VS
NV
MCL= 60
34
Dibromochloroacetic acid
5278-95-5
M to HM
S
NV
MCL= 60
34
Dibromochloromethane
124-48-1
M
S
MV
MCL= 80
0.045
28
Dibutyltin
1002-53-5
MM to M
S
MV
26
Dichloroacetic acid
79-43-6
M
VS
NV
MCL= 60
0.31
34
Dichloromethane
75-09-2
M to HM
VS
W
MCL= 5
2.7
5
1
Dichlorprop
120-36-5
MM to M
MS
NV
39
Diclofenac
15307-86-5
MM
SS
NV
1
Dieldrin
60-57-1
BM to SM
NS
V
0.069
0.004
26
Dienochlor
2227-17-0
IM
NS
MV
29
Diethyl phthalate (DEP)
84-66-2
SM to M
S
SV
610
28
D i h yd rotestoste ro n e
521-18-6
SM to MM
SS
NV
31
Diisobutyl phthalate (DiBP)
84-69-5
IM to SM
SS
SV
28
Diisodecyl phthalate (DiDP)
26761-40-0
IM
SS
SV
28
Diisononyl phthalate (DiNP)
28553-12-0
IM
SS
SV
28
Diltiazem
42399-41-7
SM to MM
MS
NV
17
Dimethenamid
87674-68-8
MM to M
MS
NV
8
Dimethyl phthalate (DMP)
131-11-3
MM to M
S
SV
28
Dimethyltin
23120-99-2
M to HM
VS
V
26
Dimetridazole
551-92-8
M
VS
NV
37
107
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Di-n-butyl phthalate (DBP)
84-74-2
SM
SS
sv
230
50
28
Di-n-octyl phthalate (DnOP)
117-84-0
IM to BM
NS
sv
5700
28
Dioctyltin
94410-05-6
BM to SM
SS to NS
V
26
Diphenhydramine
58-73-1
MM
S
NV
17,18
Dipropyl phthalate (DPP)
131-16-8
MM
SS
NV
28
Diuron
330-54-1
MM to M
SS
NV
1.5
26
D-limonene
5989-27-5
SM
SS
V
17
Endrin
72-20-8
BM
NS
SV
MCL= 2
9.2
Not
Detectable
26
Epitestosterone
481-30-1
SM to MM
SS
NV
31
Erythromycin
114-07-8
MM
SS
NV
37,40
Estriol
50-27-1
SM
SS
NV
28
Estrone
53-16-7
BM to MM
SS
NV
28
Ethylbenzene
100-41-4
MM
MS
V
MCL= 700
1.7
14,29
Ethylene thiourea
96-45-7
M
VS
NV
0.036
Not
Detectable
38
Flumequine
42835-25-6
BM to SM
S
NV
37
Fluoranthrene
206-44-0
IM to BM
SS
V
8900
1,5,15
Fluorene
86-73-7
IM to SM
SS
V
540
5
Fluoxetine
54910-89-3
BM to SM
SS
NV
17,21,23,32,40
Formaldehyde
50-00-0
HM
VS
SV
80
8
38
Galaxolide
1222-05-5
BM
SS
MV
28
Gemfibrozil
25812-30-0
MM
SS
NV
28
Gestodene
60282-87-3
SM to MM
SS
NV
38
108
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Glyphosate
1071-83-6
SM
VS
NV
MCL= 700
880
26
Hydroxyatrazine
67-68-5
MM to HM
vs
NV
1,28
Hydroxydeethylatrazine
19988-24-0
MM to HM
VS
NV
1,28
Hydroxydeisopropylatrazine
7313-54-4
MM to HM
vs
NV
1,28
Ibuprofen
15687-27-1
SM
ss
SV
28
Ifosfamide
3778-73-2
M
s
NV
40
Indeno pyrene
193-39-5
IM
NS
SV
240
6
Indole
120-72-9
MM
s
SV
17
Indomethacin
53-86-1
SM to MM
ss
NV
1
lohexol
66108-95-0
M to HM
MS
NV
39
lomeprol
78649-41-9
M to HM
MS
NV
39
lopamidol
60166-93-0
M to HM
VS
NV
39
lopromide
73334-07-3
M to HM
SS
NV
1
Isophorone
78-59-1
MM
VS
SV
26
17
Isopropyl chloride
75-29-6
M
S
V
29
Isoproturon
34123-59-6
MM
MS
NV
26
Isoquinoline
119-65-3
SM to M
S
NV
17
Ketoprofen
22071-15-4
MM
MS
NV
28
Lincomycin
154-21-2
M
MS
NV
37
Lindane
58-89-9
BM to HM
SS
V
MCL= 0.2
0.24
0.05
1,5,15
Lomefloxacin
98079-51-7
M to HM
VS
NV
35
Malathion
121-75-5
BM to MM
MS
NV
10
7
5
Mecoprop
93-65-2
M to HM
MS
NV
0.46
1,28
109
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Metalaxyl
57837-19-1
MM to M
S
NV
33
8
Metaldehyde
108-62-3
MM
MS
MV
26
Metazachlor
67129-08-2
SM to MM
MS
NV
8
Metformin
1115-70-4
M
VS
NV
38
Methyl dihydrojasmonate
24851-98-7
MM
SS
NV
28
Methyl salicylate
119-36-8
MM to M
S
SV
17
Methyl tert-butyl ether
(MTBE)
1634-04-4
M to HM
VS
MV
3.2
1
Methyl paraben
99-76-3
M
S
NV
38
Methyltriclosan
4640-01-1
BM
SS
NV to SV
39
Metolachlor
51218-45-2
SM to M
MS
NV
320
10
8
Metoprolol
37350-58-6
M
VS
NV
37,40
Metronidazole
443-48-1
M
VS
NV
37,38
Miconazole
22916-47-8
IM to BM
NS
NV
17
Monobromoacetic acid
79-08-3
M
VS
NV
MCL= 60
34
Monobutyltin
78763-54-9
MM to M
SS
MV
26
Monochloroacetic acid
79-11-8
M
VS
NV
MCL= 60
0.81
34
Musk ambrette
83-66-9
SM
SS
SV
38
Musk ketone
81-14-1
SM
SS
NV
38
Musk xylene
81-15-2
BM to SM
SS
NV
38
m-xylene
108-38-3
MM
MS
V
MCL=
10000
19
14,29
N,N-Diethyl-3-
methylbenzamide (DEET)
134-62-3
MM
MS
NV
39
110
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
N4-acetyl-Sulfamethazine
100-90-3
MM to
HM
VS
NV
33
Nalidixic Acid
389-08-2
M
ss
NV
37
Naphthalene
91-20-3
IM to MM
ss
VV
0.54
5,15
Naproxen
22204-53-1
MM
ss
NV
28
Nitrilotriacetic acid
139-13-9
HM to MM
VS
38
Nonylphenol
25154-52-3
BM
ss
SV
28
Nonylphenol ethoxylate
9016-45-9
SM
ss
NV
28
Norfloxacin
70458-96-7
BM
MS
NV
37,42
N-propyl chloride
540-54-5
M
s
V
29
Octylethoxylate
9002-93-1
SM to MM
ss
NV
28
Octylphenol
949-13-3
BM
ss
NV
28
Ofloxacin
82419-36-1
BM
VS
NV
37
OH-ibuprofen
51146-55-5
M
s
NV
28
op' DDA
3424-82-6
SM to MM
ss
NV
1,28
Oxazepam
604-75-1
MM
ss
NV
38
o-xylene
95-47-6
MM to M
MS
V
MCL=
10000
19
14,29
Oxytetracycline
79-57-2
BM to MM
MS
NV
33,42
Para-cresol
106-44-5
MM to M
VS
SV
150
17
Para-tert-octylphenol
140-66-9
BM
ss
SV
26,39,40
Pentachlorophenol
87-86-5
IM to SM
ss
NV
MCL= 1
0.4
1,5
Pentoxifylline
6493-05-6
M
MS
NV
37
Ill
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Perfluorobutanesulfonic
Acid (PFBS)
375-73-5
MM to M
MS
MV
21
37,40
Perfluoroctanionic Acid
(PFOA)
335-67-1
MM to M
S
V
37,40
Perfluorodecanoic Acid
(PFDA)
335-76-2
IM to SM
NS
V
37
Perfluorododecanoic Acid
(PFDoA)
307-55-1
IM to BM
NS
V
37
Perfluoroheptanoic Acid
(PFHpA)
375-85-9
BM to M
SS
V
37
Perfluorohexanesulfonic
Acid(PFHxS)
355-46-4
HM
SS
MV
37
Perfluorohexanoic Acid
(PFHxA)
307-24-4
BM to M
VS
SV
37
Perfluorononanoic Acid
(PFNA)
375-95-1
IM to MM
NS
MV
37,40
Perfluorooctanesulfonic
Acid (PFOS)
1763-23-1
BM to MM
NS
NV
37,40
Perfluoroundecanoic Acid
(PFUnA)
4234-23-5
SM to HM
NS
NV
37
Perylene
198-55-0
IM
NS
SV
25
Phenanthrene
85-01-8
IM to SM
SS
V
1,5,15
Phenol
108-95-2
SM
VS
MV
330
1
Pipemidic Acid
51940-44-4
M to HM
VS
NV
37
pp' DDA
83-05-6
SM to MM
SS
NV
1,28
Primidone
125-33-7
MM to M
MS
NV
1
Progesterone
57-83-0
SM
SS
NV
38
112
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Propazine
139-40-2
MM to M
SS
NV
30
1,28
Propranolol
525-66-6
MM
MS
NV
31,39,40
Propylparaben
94-13-3
MM
MS
NV
38
Propyphenazone
479-92-5
MM to M
MS
NV
1
p-xylene
106-42-3
MM
MS
V
MCL=
10000
19
14,29
Pyrene
129-00-0
IM to SM
SS
V
1300
5,15
Roxithromycin
80214-83-1
SM to HM
NS
NV
37
Salicylic acid
69-72-7
MM
S
NV
28
Sertaline
79617-96-2
IM to SM
S
NV
17
Simazine
122-34-9
SM to M
SS
NV
MCL= 4
0.3
0.5
1,28
Skatole
83-34-1
MM
MS
SV
17
Sodium alkylbenzene
sulfonate
68411-30-3
16,22
Sodium
decylbenzenesulfonate
1322-98-1
SM to MM
MS
NV
16,22
Sodium
dodecycbenzenesulfonate
25155-30-0
BM to MM
MS
NV
16,22
Sodium N-
tridecylbenzenesulfonate
26248-24-8
BM to MM
SS
NV
16,22
Sodium
Undecylbenzenesulfonate
27636-75-5
SM to MM
SS
NV
16,22
Sotalol
3930-20-9
M to HM
MS
NV
37
Sucralose
56038-13-2
M
VS
NV
39
Sulfadiazine
68-35-9
M
SS
NV
37,42
113
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Sulfadimethoxine
122-11-2
MM to M
MS
NV
33
Sulfamethazine
57-68-1
MM to M
S
NV
33,37
Sulfamethoxazole
723-46-6
M
MS
NV
1
Sulfamonomethoxine
1220-83-3
M
S
NV
37
Sulfaniamide
63-74-1
M
S
NV
32
Sulfathiazole
72-14-0
MM to M
MS
NV
33,37
Terbuthylazine
5915-41-3
MM
SS
NV
8
Terbutryn
886-50-0
BM to MM
SS
NV
1.9
39
Tert-Amyl chloride
594-36-5
MM to M
MS
MV
17
Tertrabutyltin
1461-25-2
IM to BM
NS
V
26
Testosterone
58-22-0
SM
SS
NV
31
Tetrachloroethylene
127-18-4
MM
MS
V
MCL= 5
1.8
26
Tetracycline
60-54-8
M to HM
MS
NV
33,42
Thiabendazole
148-79-8
SM
SS
NV
17
Toluene
108-88-3
MM to M
MS
V
MCL=
1000
76
26
Tolylfluanid
731-27-1
SM
SS
SV
29
Tonalide
21145-77-7
BM to SM
SS
MV
28
Tramadol
27203-92-5
MM
S
NV
38
Tribromoacetic acid
75-96-7
HM
VS
NV
34
Tribromomethane
75-25-2
MM
S
MV
MCL= 80
2.4
28
Tributyl phosphate
126-73-8
SM
MS
SV
25
39
Tributyltin
688-73-3
SM
SS
V
8.2
26,29
114
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Tr
chloroacetic acid
76-03-9
MM
VS
NV
MCL= 60
0.22
34
Tr
chloroethylene
79-01-6
SM to M
s
V
MCL= 5
0.1
5
29
Tr
chloromethane
67-66-3
MM to M
s
V
MCL= 80
0.061
7
28
Tr
closan
3380-34-5
BM to SM
ss
NV
28
Tr
cyclohexyltin chloride
3091-32-5
IM
SS to NS
SV
26
Tr
cyclohexyltin Hydroxide
13121-70-5
IM to BM
SS to NS
NV
26
Tr
ethylphosphate
78-40-0
M
VS
NV
39
Tr
fluoromethane
75-46-7
M
S
V
MCL= 80
28
Tr
iodomethane
75-47-8
M
MS to SS
MV
MCL= 80
28
Tr
isobutyl phosphate
126-71-6
SM
MS to SS
NV
39
Tr
methoprim
738-70-5
M
MS
NV
17,37
Tr
methyltin Chloride
1066-45-1
M to HM
VS
MV
26
Tr
-n-Butyltin Hydride
688-73-3
SM
SS
V
8.2
26
Tr
octyltin chloride
2587-76-0
IM
NS
MV
26
Tr
phenyl phosphate
115-86-6
SM
SS
SV
39
Tr
phenylphosphine oxide
791-28-6
SM to MM
MS to SS
NV
150
39
Tr
phenyltin Acetate
900-95-8
BM to M
SS
NV
26
Tr
phenyltin chloride
639-58-7
IM to SM
SS
NV
26
Tr
phenyltin hydride
892-20-6
IM to SM
SS
NV
26
Tr
phenyltin hydroxide
76-87-9
SM
SM
NV
26
Tr
die
s(1,3-
;hloroisopropyl)phosphate
13674-87-8
SM
SS
NV
800
39
Tris(2-
butoxyethyl) phosphate
78-51-3
SM
S
NV
31,39
115
-------
Table Al. Possible stormwater organic contaminants. Criteria for the classification of mobility, solubility, and volatility can be found in Table A2.
Compound
CAS#
Mobility*
Solubility6
Volatility0
Drinking
Water
Standard0
Superfund
Regional
Screening
Levels for
Groundwater
Protection5
NY State
Groundwater
Effluent
LimitsF
Reference
Hg/L
Hg/kg
Hg/L
Tris(2-
chloroethyl)phosphate
115-96-8
MM
S
SV
3.8
39
Tylosin
1401-69-0
BM to MM
S
NV
37
Valsartan
137862-53-
4
BM to MM
SS
NV
18,41
Venlafaxine
93413-69-5
MM
MS
NV
17,32,40
Xylene
1330-20-7
MM
MS
V
MCL=
10000
19
26
a-BHC
319-84-6
SM
SS
SV
0.01
1
a-Endosulfan
959-98-8
SM
SS
NV
1
a-Hexachloro-cyclohexane
319-84-6
SM
SS
SV
0.041
0.01
1
AMobility Classes: HM= Highly Mobile, M= Mobile, MM= Moderately Mobile, SM= Slightly Mobile, BM= Hardly Mobile, IM= Immobile;
BSolubility Classes: NS= Negligible Soluble, SS= Slightly Soluble, MS= Moderately Soluble, S= Soluble, VS= Very Soluble;
"Volatility Classes: W= Very Volatile, V= Volatile, MV= Moderately Volatile, SV= Slightly Volatile, NV= Nonvolatile;
DSafe Drinking Water Act, 42 U.S.C. §300f et seq. (1974): https://www.epa.gov/laws-reaulations/summarv-safe-drinking-water-act:
ERegional Screening Levels for Chemical Contaminants at Superfund Sites as of May 2018, Risk-based SSL for Protection of Groundwater; https://www.epa.gov/risk/regional-screening-
levels-rsls-generic-tables: and
FRules and Regulations of the State of New York: 6CRR-NY 703.6 Groundwater effluent limitations for discharges to class GA waters.
References:
1. US EPA, 1983; 2. Chang et al., 1990; 3. Ehrenfeld et al., 1991; 4. Mineart et al., 1994; 5. Pitt et al., 1994; 6. Bannerman et al., 1996; 7. Bell et al., 1995; 8. Buchelli et al., 1998; 9. Ellis,
J.B., 2000; 10. Horsley, 2000; 11. Maryland Department of Environment, 2000; 12. Schueler and Holland, 2000; 13. Breault and Granato, 2003; 14. Granato et al., 2003; 15. Clark and Pitt,
2007; 16. Eriksson et al., 2007; 17. Nilsen et al., 2007; 18. Kasprzyk-Hordern et al., 2008; 19. Whittemore, 2008; 20. Diblasi et al., 2009; 21. Radjenovic et al., 2009; 22. Hartmann et al.,
2005; 23. Metcalfe et al., 2010; 24. Sui et al., 2010; 25. Watts et al., 2010; 26. Zgheib et al., 2012; 27. Writer et al., 2011; 28. Barcelo, 2012; 29. Gasperi et al., 2012; 30. Mahler et al., 2012;
31. Philips et al., 2012; 32. Sprague, 2012; 33. Krein et al., 2013; 34. Nguyen et al., 2013; 35. Dodder et al., 2014; 36. Leisenring et al., 2014; 37. Lin et al., 2015; 38. Lopez et al., 2015; 39.
Launay et al., 2016 40. Wilkinson et al., 2016; 41. McEachran et al., 2017; 42. Zhang et al., 2016; 43. National Stormwater Quality Database, Version 3.0
116
-------
Table A2. Classification criteria used for solubility, volatility, and mobility in Table Al.
Solubility Classifications1
Volatility from Water to the
gas phase Classifications1
Mobility Classifications2
Solubility,
log Cw3
(mg/L)
Classification
Log Kh 4
(atm-m3/mole)
Classification
Log Koc (L/kg)
Classification
<-1
Negligible
Soluble
>-1
Very Volatile
<1
Highly Mobile
-1-2
Slightly
Soluble
-1 --3
Volatile
1 -2
Mobile
2-3
Moderately
Soluble
-3--5
Moderately
Volatile
2-3
Moderately
Mobile
3-4
Soluble
-5--7
Slightly
Volatile
3-4
Slightly Mobile
>4
Very Soluble
<-7
Nonvolatile
4-5
Hardly Mobile
>5
Immobile
1 U.S. EPA. 2012.
2 FAO. 2000.
3 Cw= water solubility.
4 Kh= Henry's Law Constant.
117
-------
Table A3. Possible Inorganic contaminants in stormwater.
Compound
CAS#
Drinking Water
Standard*
Superfund Regional
Sreening Levels for
Groundwater
Protection6
NY State
Groundwater
Effluent Limits0
Reference
Hg/L
mg/kg
Hg/L
Aluminum (Al)
7429-90-5
sMCL=50 to 200
3000
2000
9
Ammonia (NH3)
7664-41-7
2,12
Antimony (Sb)
7440-36-0
0.035
6
1,13
Arsenic (As)
7440-38-2
MCL= 10
0.0015
50
1,15,36
Barium (Ba)
7440-39-3
MCL= 2000
16
2000
19
Beryllium (Be)
7440-41-7
MCL= 4
1.9
1
Biological Oxygen Demand (BOD)
2,12
Boron (B)
7440-42-8
1.3
13
Bromide (Br)
24959-67-9
13
Cadmium (Cd)
7440-43-9
MCL= 5
0.069
10
1,5,11,12,15
Calcium (Ca)
7440-70-2
13
Cerium (Ce)
7440-45-1
13
Chloride (CI )
16887-00-6
sMCL=250000
500000
3,5,11,15
Chromium (Cr)
7440-47-3
MCL= 100
4000000
1,5,10,15
Cobalt (Co)
7440-48-4
0.027
13
Chemical Oxygen Demand (COD)
2,5,12
Copper (Cu)
7440-50-8
TT action level= 1300;
sMCL=1000
2.8
400
1,2,11,12
Cyanide (CN )
57-12-5
MCL= 200
0.0015
400
1
Fluoride (F )
16984-48-8
MCL=4000;
sMCL=2000
12
3000
19
Iodide (I )
20461-54-5
13
Iron (Fe)
7439-89-6
sMCL=300
35
600
2,19
Lead (Pb)
7439-92-1
TT action level= 15
14
50
1,2,5,11,13,15
Lithium (Li)
7439-93-2
1.2
13
Magnesium (Mg)
7439-95-4
13
118
-------
Table A3. Possible Inorganic contaminants in stormwater.
Compound
CAS#
Drinking Water
Standard*
Superfund Regional
Sreening Levels for
Groundwater
Protection6
NY State
Groundwater
Effluent Limits0
Reference
Hg/L
mg/kg
Hg/L
Manganese (Mn)
7439-96-5
sMCL=50
2.8
600
9
Mercury (Hg)
7439-97-6
MCL= 2
0.0033
1.4
19
Molybdenum (Mo)
7439-98-7
0.2
13
Nickel (Ni)
7440-02-0
2.6
200
1,15
Nitrate (NO3 )
14797-55-88
MCL= 10000 (as N)
20000
2,5,12,15
Nitrite (NO2 )
14797-65-0
MCL= 1000 (as N)
2000
2,5,12,15
Palladium (Pd)
7440-05-03
13
Phosphate (PO43 )
14265-44-2
2,43
Platinum (Pt)
7440-06-4
9
Potassium (K)
7440-09-7
13
Rhodium (Rh)
7440-16-6
9
Selenium (Se)
7782-49-2
MCL= 50
0.052
20
1,13
Silicon (Si)
7440-21-3
13
Silver (Ag)
7440-22-4
sMCL=100
0.08
100
19
Sodium (Na)
7440-23-5
13
Strontium (Sr)
7440-24-6
42
13
Sulfate (SO42 )
14808-79-8
sMCL=250000
500000
19
Tin (Sn)
7440-31-5
13
Titanium (Ti)
7440-32-6
13
Total Dissolved Solids (TDS)
sMCL=500000
36
Total Kjeldahl N
2,12,43
Total Nitrogen
11,12
Total Organic Carbon
2,11
Total Phosphorus
11,12,15
Total Suspended Solids
2,7,11,12,15
Tungsten (W)
7440-33-7
13
119
-------
Table A3. Possible Inorganic contaminants in stormwater.
Compound
CAS#
Drinking Water
Standard*
Superfund Regional
Sreening Levels for
Groundwater
Protection6
NY State
Groundwater
Effluent Limits0
Reference
Hg/L
mg/kg
Hg/L
Turbidity
36
Vanadium (V)
7440-62-2
8.6
13
Zinc (Zn)
7440-66-6
sMCL=5000
37
5000
1,2,5,11,12,15
ASafe Drinking Water Act, 42 U.S.C. §300f et seq. (1974): https://www.epa.gov/laws-reaulations/summarv-safe-drinking-water-act;
BRegional Screening Levels for Chemical Contaminants at Superfund Sites as of May 2018, Risk-based SSL for Protection of Groundwater: https://www.epa.gov/risk/regional-screening-
levels-rsls-generic-tables: and
cRules and Regulations of the State of New York: 6CRR-NY 703.6 Groundwater effluent limitations for discharges to class GA waters.
References:
1. US EPA, 1983; 2. Chang et al., 1990; 3. Ehrenfeld et al., 1991; 4. Mineart et al., 1994; 5. Pitt et al., 1994; 6. Bannerman et al., 1996; 7. Bell et al., 1995; 8. Buchelli et al., 1998; 9. Ellis,
J.B., 2000; 10. Horsley, 2000; 11. Maryland Department of Environment, 2000; 12. Schueler and Holland, 2000; 13. Breault and Granato, 2003; 14. Granato et al., 2003; 15. Clark and Pitt,
2007; 16. Eriksson et al., 2007; 17. Nilsen et al., 2007; 18. Kasprzyk-Hordern et al., 2008; 19. Whittemore, 2008; 20. Diblasi et al., 2009; 21. Radjenovic et al., 2009; 22. Hartmann et al.,
2005; 23. Metcalfe et al., 2010; 24. Sui et al., 2010; 25. Watts et al., 2010; 26. Zgheib et al., 2012; 27. Writer et al., 2011; 28. Barcelo, 2012; 29. Gasperi et al., 2012; 30. Mahler et al., 2012;
31. Philips et al., 2012; 32. Sprague, 2012; 33. Krein et al., 2013; 34. Nguyen et al., 2013; 35. Dodder et al., 2014; 36. Leisenring et al., 2014; 37. Lin et al., 2015; 38. Lopez et al., 2015; 39.
Launay et al., 2016 40. Wilkinson et al., 2016; 41. McEachran et al., 2017; 42. Zhang et al., 2016; 43. National Stormwater Quality Database, Version 3.0
120
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
1,1,1,2-tetrachloroethane
630-20-6
C2H2CI4
167.85
-70.2A
130.2A
3.03A
1,1,1-trichoroethane
71-55-6
C2H3CI3
133.40
-30A
74 A
3.11A
1,1,2,2-tetrachloroethane
79-34-5
C2H2CI4
167.85
-42.3A
146A
3.45A
1,1,2-trichloroethane
79-00-5
C2H3CI3
133.40
-35A
113 - 114A
3.66A
1,1-dichloroethane
75-34-3
C2H4CI2
98.96
-96.93A
57.4A
3.7A
1,2,3-trichloropropane
96-18-4
C3H5CI3
147.43
-13.9A
158A
3.24A
1,2,3-trimethylbenzene
526-73-8
C9H12
120.19
-25.4A
176.12A
1.88A
1,2,4-trichlorobenzene
120-82-1
C6H3CI3
181.45
16.9A
213.5A
1.69A
1,2,4-trimethylbenzene
95-63-6
C9H12
120.19
-43.77A
168.89A
1.76A
1,2-dichloro benzene
95-50-1
C6H4CI2
147.01
-16.7A
180.1A
2.19A
1,2-dichloroethane
107-06-2
C2H4CI2
98.96
-35.3A
83.5A
3.93A
1,2-dichloropropane
78-87-5
CsHeCb
112.98
-100.4A
96.4A
3.45A
1,3-dichloro benzene
541-73-1
C6H4CI2
147.00
-24.8A
173A
2.1A
1,3-dichloropropane
142-28-9
CsHeCb
112.98
-99.5A
120.4A
3.44A
1,4-dichloro benzene
106-46-7
C6H4CI2
147.01
53.09A
174A
1.88A
11-keto testosterone
53187-98-7
C19H26O3
302.41
173.50 (est)c
422.75 (est)°
2.94 (est)°
17a-estradiol
57-91-0
C18H24O2
272.39
221.5°
395.47 (est)°
1.91°
17a-ethynyl estradiol
57-63-6
C20H24O2
296.40
183°
411.21 (est)°
2.07°
17p-estradiol
50-28-2
C18H24O2
272.39
221.5°
395.47 (est)°
1.91°
19-norethisterone
68-22-4
CM
O
CD
CM
X
O
CM
O
298.42
204A
400.27 (est)°
0.85A
1 -acetyl-1 -methyl-2-dimethyl-
oxamoyl-
2-phenylhydrazide
519-65-3
C13H17N3O3
263.29
178.74 (est)C
434.89 (est)°
3.64 (est)°
1 -chloro-2-methylpropane
513-36-0
C4H9CI
92.57
-130.3°
68.5°
2.97 (est)°
1-chlorobutane
109-69-3
C4H9CI
92.57
-123.1A
78.5A
3.04A
121
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°C
°C
mg/L
1-chloropentane
543-59-9
C5H11CI
106.59
-99A
107.8A
2.29A
1 H-benzotrazole
95-14-7
CeHsNs
119.13
98.5A
204A
4.3A
1 -methylnaphthalene
90-12-0
O
X
6
142.20
-30.43A
244A
1.41A
2, 4-D
94-75-7
C8H6CI2O3
221.03
138A
160°
2.73-2.83A
2,2',3,4,4',5,5'-heptachlorobiphenyl
(PCB 180)
35065-29-3
C12H3CI7
395.32
163.56 (est)c
415.60 (est)c
-3.55 (est)c
2,2',3,4,4',5-hexachlorobiphenyl
(PCB 138)
35065-28-2
C12H4CI6
360.88
146.34 (est)c
396.90 (est)c
-2.63°
2,2',4,4',5,5'-hexachlorobiphenyl
(PCB 153)
35065-27-1
C12H4CI6
360.88
146.34 (est)c
396.90 (est)c
-2.89°
2,2',5,5'-tetrachorobiphenyl (PCB
52)
35693-99-3
C12H6CI4
291.99
122.32 (est)c
359.51 (est)c
-1.07°
2,3',4,4',5-pentachlorobiphenyl
(PCB 118)
31508-00-6
C12H5CI5
326.44
134.60 (est)c
378.21 (est)c
-2.15c
2,4,4'-trichlorobiphenyl (PCB 28)
7012-37-5
C12H7CI3
257.54
100.85 (est)c
340.70 (est)c
-0.47°
2,4,5,2',5'-pentachlorobiphenyl
(PCB 101)
37680-73-2
C12H5CI5
326.44
134.60 (est)c
378.21 (est)c
-1.87°
2-chloro-2-methylpropane
507-20-0
C4H9CI
92.57
-26c
50c
3.00°
2-chlorobutane
78-86-4
C4H9CI
92.57
-140°
68c
3.10c
2-Chloromethylphenol
40053-98-3
C7H7CIO
142.58
45.6 (est)c
242 (est)c
3.52 (est)c
2-methyl-4-chlorophenoxyacetic
Acid
94-74-6
C9H9CIO3
200.62
118 - 119A
286.74°
2.8A
2-methylnaphthalene
91-57-6
0
X
6
142.2
34.6A
241.1A
1.39A
2-methylthiobenzothiazole
615-22-5
c8h7ns2
181.28
44c
310.03 (est)c
2.04c
3-Chloromethylphenol
60760-06-7
C7H7CIO
142.58
45.6 (est)c
242 (est)c
3.52 (est)c
3-p-coprostanol
360-68-9
C27H48O
388.68
185.5°
428.87 (est)c
-3.47 (est)c
4-Chloromethylphenol
35421-08-0
C7H7CIO
142.58
45.6 (est)c
242 (est)c
3.52 (est)c
4'-hydroxy-diclofenac
64118-84-9
C14H9CI2NO3
310.13
193.26 (est)c
458.61 (est)c
1.25°
122
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
4-methyl-1 H-benzotriazole
29878-31-7
C7H7N3
133.15
76 - 87c
160°
3.49 (est)c
4-Nitrophenol
100-02-7
CeHsNOs
139.11
113 - 114A
279A
4.19A
4-nonylphenol
104-40-5
O
"vT
CM
X
in
6
220.36
42c
293 - 297°
0.85A
4-nonylphenoldiethoxylate
20427-84-3
C19H32O3
308.46
140.16 (est)c
404.90 (est)c
0.02 (est)c
4-nonylphenolmonoethoxylate
104-35-8
C17H28O2
264.40
116.18 (est)c
369.64 (est)c
0.04 (est)c
4-tert-Butylphenol
98-54-4
0
"vT
X
O
6
150.22
98c
237°
2.63°
5-methyl-1 H-benzotriazole
136-85-6
C7H7N3
133.15
00
0
1
00
ro
0
210-212
(est)c
3.49 (est)c
Acebutolol
37517-30-9
C18H28N2O4
336.43
119-123°
504.10 (est)c
2.41°
Acenaphthene
83-32-9
C12H10
154.21
93A
277.5A
0.59A
Acenaphthylene
208-96-8
C12H8
152.21
89.4A
280A
0.59A
Acesulfame
33665-90-6
C4H4KNO4S
201.24
123.2A
357.88 (est)c
5.77A
Acetaldehyde
75-07-0
C2H4O
44.05
-123.4A
20.8A
6A
Acetaminophen
103-90-2
C8H9NO2
151.16
168A
340.65 (est)c
4.15A
Acetophenone
98-86-2
CsHsO
120.15
20.5A
202A
3.79A
Acrylamide
79-06-1
C3H5NO
71.08
84.5A
192.6A
5.57A
Alachlor
15972-60-8
C14H20CINO2
269.77
<
1
O
100c
2.38A
Aldrin
309-00-2
C12H8CI6
364.90
104A
329.77 (est)c
2.23A
Alkylphenol ethoxylate
68412-54-4
Ci5H240(C2H40)g
617.60
140.16 (est)c
404.90 (est)c
0.04 - 0.02 (est)c
Aminotriazole
61-82-5
C2H4N4
84.08
159A
258.30 (est)c
5.45A
Amitriptyline
50-48-6
C20H23N
277.40
196.5C
381.64 (est)c
0.99A
Amoxicillin
26787-78-0
C16H19N3O5S
365.40
329.94 (est)c
657.45 (est)c
3.54A
AM PA
74341-63-2
C7H10N2O4
186.17
437.63 (est)c
297.85 (est)c
4.33 (est)c
Androstenedione
63-05-8
C19H26O2
286.40
173-174A
200A
1.76A
Anthracene
120-12-7
O
X
6
178.23
216A
341,3A
-1.36A
123
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Anthraquinone
84-65-1
C14H8O2
208.22
286A
377A
0.13A
Atenolol
29122-68-7
C14H22N2O3
266.33
146- 148A
438.63 (est)°
4.12A
Atrazine
1912-24-9
CsHmCINs
215.68
173A
205A
1.54A
Atrazine amide-l
142179-76-
8
C7H10CIN5O
215.64
159.27 (est)c
385.84 (est)°
3.43 - 3.72 (est)°
Azithromycin
83905-01-5
C38H72N2O12
748.98
113-115A
846.60 (est)°
0.37A
Bentazone
25057-89-0
C10H12N2O3S
240.28
139.4 -141A
415.31 (est)°
2.70A
Benz(a)anthracene
56-55-3
C18H12
228.29
155- 157A
437.6A
-2.03A
Benzene
71-43-2
CeHe
78.11
5.6A
80.08A
3.25A
Benzo(a)fluoranthene
203-33-8
C20H12
252.31
169.41°
442.75°
-1.69°
Benzo(a)pyrene
50-32-8
CM
X
O
CM
O
252.32
179A
495°
-2.79A
Benzo(b)fluoranthene
205-99-2
CM
X
O
CM
O
252.32
168.4A
442.75 (est)°
-2.82A
Benzo(e)pyrene
192-97-2
C20H12
252.32
177.5°
310-312°
-2.25°
Benzo(j)fluoranthene
205-82-3
CM
X
O
CM
O
252.31
166°
442.75 (est)°
-1.97°
Benzo(k)fluoranthene
207-08-9
CM
X
O
CM
O
252.32
217A
480A
-2.12A
Benzo(b)naphtho(2,3-d)thiophene
243-46-9
C16H10S
234.32
148.75 (est)°
404.91 (est)°
-1.21°
Benzo(ghi)perylene
191-24-2
C22H12
276.34
278.3A
550A
-3.59A
Benzophenone
119-61-9
C13H10O
182.22
48.5A
305.9A
2.14A
Benzothiazole
95-16-9
C7H5NS
135.19
2A
227 - 228A
3.23°
Benzyl acetate
140-11-4
C9H10O2
150.18
-51A
213A
3.21°
Beta-sitosterol
83-46-5
C29H50O
414.72
147°
448.98 (est)°
-4.33 (est)°
Bezafibrate
41859-67-0
C19H20CINO4
361.82
186°
538.10 (est)°
0.09 (est)°
Bis(2-chloroethyl) ether
111-44-4
C4H8CI2O
143.01
-51.9A
178.5A
4.01A
Bis(2-chloroisopropyl) ether
39638-32-9
C6H12CI2O
171.06
-96.8- 101.8A
187.3A
1.60°
Bis(2-ethlyhexyl) phthalate
117-81-7
C24H38O4
390.56
-55A
384A
-0.57A
124
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Bis(tributyltin) oxide
56-35-9
C24H540Sn2
596.11
45A
180°
0.6A
Bisphenol A
80-05-7
CM
o
CD
X
in
6
228.29
153A
360.5A
2.48A
Bromochloroacetic acid
5589-96-8
C2H2BrCI02
173.39
31.5°
215A
5.40A
Bromodichloroacetic acid
71133-14-7
C2HBrCI202
207.83
47.94 (est)c
234.61 (est)c
3.69A
Bromodichloromethane
75-27-4
CHBrCh
163.80
-57A
90A
3.6A
Butyl benzyl phthalate
85-68-7
C19H20O4
312.35
-35A
370A
0.43A
Butylated hydroxyanisole
25013-16-5
C11H16O2
180.25
51A
268A
2.32A
Caffeine
58-08-2
C8H10N4O2
194.19
236.2A
178A
4.33A
CA-ibuprofen
15935-54-3
C13H16O4
236.26
152.42 (est)c
395.40 (est)c
2.32-3.16 (est)c
Carbamazepine
298-46-4
C15H12N2O
236.27
190.2A
410.02 (est)c
1.26A
Carbamazepine-10,11-epoxid
36507-30-9
C15H12N2O
252.27
175.10 (est)c
419.72 (est)c
2.44 (est)c
Carbendazim
10605-21-7
C9H9N3O2
191.19
300A
404.73 (est)c
0.9A
Carbon tetrachloride
56-23-5
CCU
153.81
-23A
76.8A
2.9 - 3.06A
Carbozole
86-74-8
C12H9N
167.21
245A
354.6A
0.26A
Cefazolin
25953-19-9
C14H14N8O4S3
454.51
198 - 200A
757.72 (est)c
2.32A
Cefriaxone
73384-59-5
C18H18N8O7S3
554.58
349.84 (est)c
915.45 (est)c
2.36 (est)c
Ceftazidime
72558-82-8
C22H22N6O7S2
546.57
349.84 (est)c
852.33 (est)c
2.60°
Chlordane
57-74-9
C10H6CI8
409.78
104- 107A
175°
-1.25A
Chlorfenvinphos
470-90-6
C12H14CI3O4P
359.56
-23 - -19A
167-170°
2.09A
Chloroacetic acid
79-11-8
C2H3CIO2
94.50
50 - 63A
189.3A
5.93A
Chlorodifluoromethane
75-45-6
CHCIF2
86.47
-157.42A
-40.8A
3.44A
Chloromethane
74-87-3
CH3CI
50.49
-97.6A
-23.7A
3.7A
Chlorpyrifos
2921-88-2
C9H11CI3NO3PS
350.59
41 - 42A
160 (decomp)A
-0.39 - 0.14A
Chlortetracycline
57-62-5
C22H23CIN2O8
478.88
168.5°
764.02 (est)c
2.79°
125
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°C
°C
mg/L
Cholesterol
57-88-5
C27H46O
386.65
148.5A
360A
-1.02A
Choline Chloride
67-48-1
C5H14NOCI
139.63
305A
380.89 (est)°
6 (est)°
Chrysene
218-01-9
C18H12
228.29
255A
448A
-2.7A
Cimetidine
51481-61-9
C10H16N6S
252.34
141 - 143A
480.81 (est)°
4.02°
Ciprofloxacin
85721-33-1
C17H18FN3O3
331.35
225 - 257A
566.55 (est)°
4.48A
Cis-androsterone
53-41-8
C19H30O2
290.45
178°
386.13 (est)°
1.50°
Citalopram
59729-33-8
C20H21FN2O
324.39
164.03 (est)c
178°
1.49 (est)°
Clarithromycin
81103-11-9
C38H69NO13
747.95
217 - 225A
842.47 (est)°
0.23A
Clofibric Acid
882-09-7
C10H11CIO3
214.65
118-119°
321.21 (est)°
2.77°
Cocaine
50-36-2
C17H21NO4
303.36
98A
362.63 (est)°
3.26A
Codeine
76-57-3
C18H21NO3
299.36
157.5A
250A
4.08°
Coronene
191-07-1
C24H12
300.36
437.3°
525C
-3.55°
Cotinine
486-56-6
C10H12N2O
176.22
41°
250°
6A
Cyanazine
21725-46-2
C9H13CIN6
240.69
167.5- 169A
369.47 (est)°
2.23A
Cyanazine amide
36576-42-8
C9H15CIN6O
258.71
176.52 (est)°
422.76 (est)°
3.31°
Cyclophosphamide
50-18-0
C7H15CI2N2O2P
261.09
49.5 - 53A
359.82 (est)°
4.6A
Debromodiphenyl ether
1163-19-5
Ci2BrioO
959.17
305A
425A
-4A
Deethylcyanazine
36556-77-1
C7H11CIN6O
230.66
175.43 (est)°
420.44 (est)°
3.04°
Dehyronifedipine
67035-22-7
C17H16N2O6
344.32
178.45 (est)°
451.04 (est)°
0.75 (est)°
Deisopropylatrazine
1007-28-9
CsHsCINs
173.60
112.32 (est)°
304.59 (est)°
2.83°
Desethylatrazine
6190-65-4
CeHioCINs
187.63
136A
310.23 (est)°
3.51A
Diatrizoate
117-96-4
C11H9I3N2O4
613.92
> 300A
654.65 (est)°
5.7A
Diazinon
333-41-5
C12H21N2O3PS
304.35
87.58°
125A
1.60- 1.78A
Dibenz(a,h)anthracene
53-70-3
C22H14
278.35
269A
524A
-2.6A
126
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Dibenzofuran
132-64-9
C12H8O
168.19
86.5A
287A
0.49A
Dibenzothiophene
132-65-0
Ci2H8S
184.26
99.5A
332.5A
0.17A
Dibromoacetic acid
631-64-1
C2H2BP202
217.84
48A
232 - 234A
6.32A
Dibromochloroacetic acid
5278-95-5
C2HBr2CI02
252.29
68.10 (est)c
263.59 (est)c
3.38A
Dibromochloromethane
124-48-1
CHBr2CI
208.28
-20A
121.3- 125A
3.43A
Dibutyltin
1002-53-5
C8Hi8Sn
232.94
-7.44 (est)c
174.74 (est)c
3.20°
Dichloroacetic acid
79-43-6
C2H2CI2O2
128.94
-4 - 9.7A
194A
4.58°
Dichlorodiphenyltrichloroethane
(DDT)
50-29-3
C14H9CI5
354.49
108.5A
260A
-2.26A
Dichloromethane
75-09-2
CH2CI2
84.93
-95A
39.75A
4.12A
Dichloroprop
120-36-5
C9H6CI2O3
235.06
117.5A
334.17 (est)c
2.54A
Diclofenac
15307-86-5
C14H11CI2NO2
296.15
156 - 158A
423.77 (est)c
0.37A
Dieldrin
60-57-1
C12H8CI6O
380.91
175.5A
330°
-0.71A
Dienochlor
2227-17-0
O
O
O
6
474.61
122- 123A
250A
-1.6A
Diethyl phthalate
84-66-2
"vT
O
"vT
X
CM
6
222.24
-40.5A
295A
3.03A
D i h yd rotestoste ro n e
521-18-6
C19H30O2
290.44
181°
386.13 (est)c
1.62°
Diisobutyl phthalate
84-69-5
C16H22O4
278.34
-64A
296.5A
0.79A
Diisodecyl phthalate
26761-40-0
C28H46O4
446.66
-50A
463.36 (est)c
-0.55A
Diisononyl phthalate
28553-12-0
C26H42O4
418.61
-48A
440.16 (est)c
-0.7A
Diltiazem
42399-41-7
C22H26N2O4S
414.52
212A
540.45 (est)c
2.67A
Dimethenamid
87674-68-8
C12H18CINO2S
275.79
138.56 (est)c
127°
2.39°
Dimethyl phthalate
131-11-3
C10H10O4
194.18
5.5A
283.7A
3.6A
Dimethyltin
23120-99-2
C2H6Sn
148.78
-82.83 (est)c
32.31 (est)c
4.78 (est)c
Dimetridazole
551-92-8
C5H7N3O2
141.13
138.5°
296.07 (est)c
4.26°
Di-n-butyl phthalate
84-74-2
C16H22O4
278.35
-35A
340A
1.05A
127
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Di-n-octyl phthalate
117-84-0
C24H38O4
390.56
25A
220 - 248°
-1,66A
Dioctyltin
94410-05-6
Ci6H34Sn
345.15
78.31 (est)c
314.03 (est)c
-2.37 - -0.02 (est)°
Diphenhydramine
58-73-1
C17H21NO
255.36
168°
150- 165°
3.49A
Dipropyl phthalate
131-16-8
C14H18O4
250.29
18.19 (est)c
317.5°
1.58°
Diuron
330-54-1
C9H10CI2N2O
233.09
158- 159A
180-190
(decomp)A
1.62A
D-limonene
5989-27-5
C10H16
136.24
-74A
177.6A
1.14A
Endrin
72-20-8
C12H8CI6O
380.91
~200A
330°
-0.60A
Epitestosterone
481-30-1
C19H28O2
288.43
155°
390.02 (est)°
1.83°
Erythromycin
114-07-8
C37H67NO13
733.94
191A
853.10 (est)°
0.62A
Estriol
50-27-1
C18H24O3
288.39
288A
431.81 (est)°
1.44A
Estrone
53-16-7
C18H22O2
270.37
260.2A
154°
1.48A
Ethyl benzene
100-41-4
CsHio
106.17
-94.95A
136.2A
2.23A
Ethylene thiourea
96-45-7
C3H6N2S
102.16
203A
347.18 (est)°
4.3A
Flumequine
42835-25-6
C14H12FO3
261.25
253 - 255A
402.68 (est)°
3.34A
Fluoranthene
206-44-0
C16H10
202.26
110.2A
384A
-0.6A
Fluorene
86-73-7
C13H10
166.22
114.76A
294A
0.23A
Fluoxetine
54910-89-3
C17H18F3NO
309.33
105.27 (est)c
347.23 (est)°
1.78°
Formaldehyde
50-00-0
CH2O
30.03
-92A
-19.5A
5.6A
Galaxolide
1222-05-5
C18H26O
258.41
-5C
325°
0.24A
Gemfibrozil
25812-30-0
CO
0
CM
CM
X
in
6
250.34
62A
159A
1.04A
Gestodene
60282-87-3
C21H26O2
310.43
197.9A
413.40 (est)°
0.91A
Glyphosate
1071-83-6
CsHsNOsP
169.07
230A
417.49 (est)°
4.02A
Hydroxyatrazine
67-68-5
CsHisNsO
197.24
164.13 (est)c
409.52 (est)°
6.00 (est)°
Hydroxydeethylatrazine
19988-24-0
C6H11N5O
169.19
173.57 (est)c
416.44 (est)°
5.42°
128
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Hydroxydeisopropylatrazine
7313-54-4
C5H9N5O
155.16
171.42 (est)c
411.84 (est)c
5.84°
Ibuprofen
15687-27-1
C13H18O2
206.29
cn
>
323.11 (est)c
1.32A
Ifosfamide
3778-73-2
C7H15CI2N2O2P
261.10
<
1
CD
CO
353.49 (est)c
3.58°
Indeno pyrene
193-39-5
C22H12
276.33
164A
536A
-3.16A
Indole
120-72-9
CsHyN
117.15
52A
253A
3.55A
Indomethacin
53-86-1
C19H16CINO4
357.79
155- 162A
514.50 (est)c
-0.03A
lohexol
66108-95-0
C19H26I3N3O9
821.14
174- 180°
960.86 (est)c
2.03°
lomeprol
78649-41-9
C17H22I3N3O8
777.09
349.84 (est)c
904.94 (est)c
2.10°
lopamidol
60166-93-0
C17H22I3N3O8
777.08
300A
942.21 (est)c
5.08A
lopromide
73334-07-3
C18H24I3N3O8
791.12
349.84 (est)c
885.14 (est)c
1.38°
Isophorone
78-59-1
C9H14O
138.21
-8.1A
215.32A
4.08A
Isopropyl chloride
75-29-6
C3H7CI
78.54
-117.2A
35.7A
3.48A
Isoproturon
34123-59-6
C12H18N2O
206.30
158°
344.22 (est)c
2.16c
Isoquinoline
119-65-3
C9H7N
129.16
26.47°
243.2°
3.19°
Ketoprofen
22071-15-4
C16H14O3
254.29
94c
403.57 (est)c
2.08°
Lincomycin
154-21-2
C18H34N2O6S
406.54
135- 148A
606.28 (est)c
2.97A
Lindane
58-89-9
C6H6CI6
290.83
112.5A
311A
0.86A
Lomefloxacin
98079-51-7
C17H19F2N3O3
351.36
239 - 240.5°
558.54 (est)c
4.07 - 4.44 (est)c
Malathion
121-75-5
C10H19O6PS2
330.36
3.85A
156- 157°
2.15A
Mecoprop
93-65-2
C10H11 CIO3
214.60
93 - 95A
298°
2.94A
Metalaxyl
57837-19-1
C15H2I NO4
279.33
71 - 72A
295.9A
3.92A
Metaldehyde
108-62-3
C8H16O4
176.21
246.2A
115A
2.35A
Metazachlor
67129-08-2
C14H16CIN3O
277.75
85c
411.60 (est)c
2.40°
Metformin
1115-70-4
C4H11N5
165.63
218 - 232A
280.57 (est)c
6.03A
129
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Methyl dihydrojasmonate
24851-98-7
C13H22O3
226.32
-10c
309.32 (est)c
1.96 (est)c
Methyl salicylate
119-36-8
CsHsOs
152.15
-8.6A
220 - 224A
3.87A
Methyl tert-butyl ether (MTBE)
1634-04-4
C5H12O
88.15
-108.6A
55A
4.71A
Methyl para ben
99-76-3
CsHsOs
152.15
125.2A
270.5A
3.4A
Methyltriclosan
4640-01-1
C13H9CI3O2
303.56
123.54 (est)c
362.44 (est)c
-0.39 (est)c
Metolachlor
51218-45-2
C15H22CINO2
283.80
-62.1A
282°
2.72A
Metoprolol
37350-58-6
C15H25NO3
267.37
116.15 (est)c
362.44 (est)c
6A
Metronidazole
443-48-1
C6H9N3O3
171.15
158- 160A
357.30 (est)c
4.04A
Miconazole
22916-47-8
C18H14CI4N2O
416.13
215.55 (est)c
506.31 (est)c
-1.95 (est)c
Monobromoacetic acid
79-08-3
C2H3Br02
138.95
49A
208A
6.24A
Monobutyltin
78763-54-9
C4HioSn
176.83
-47.79 (est)c
85.67 (est)c
1.93°
Monochloroacetic acid
79-11-8
C2H3CIO2
94.49
50 - 63A
189.3A
5.93A
Musk ambrette
83-66-9
C12H16N2O5
268.27
85A
185A
0.38A
Musk ketone
81-14-1
C14H18N2O5
294.30
135.5A
401.75 (est)c
-0.41A
Musk xylene
81-15-2
C12H15N3O6
297.26
110A
411.56 (est)c
-0.33A
m-xylene
108-38-3
CsHio
106.17
-47.85A
139.1A
2.21A
N,N-Diethyl-3-methylbenzamide
(DEBT)
134-62-3
C12H17NO
191.27
-45c
290°
2.96A
N4-acetyl-Sulfamethazine
100-90-3
C14H16N4O3S
320.37
225.71 (est)c
528.06 (est)c
3.06°
Nalidixic Acid
389-08-2
C12H12N2O3
232.24
229 - 230A
397.21 (est)c
2A
Naphthalene
91-20-3
C10H8
128.17
80.2A
217.9A
1.49A
Naproxen
22204-53-1
C14H14O3
230.26
155A
379.70 (est)c
1.2A
Nitrilotriacetic acid
139-13-9
C6H9NO6
191.14
242A
429.28 (est)c
4.77A
Nonylphenol
25154-52-3
O
"vT
CM
X
in
6
220.35
42c
293 - 297A
0.7A
Nonylphenol ethoxylates
9016-45-9
C19H32O3
308.46
140.16 (est)c
404.90 (est)c
0.02 (est)c
130
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Norfloxacin
70458-96-7
C16H18FN3O3
319.33
220-221A
552.97 (est)c
2.45A
N-propyl chloride
540-54-5
C3H7CI
78.54
-122.8A
46.6A
3.43A
Octylethoxylate
9002-93-1
O
CM
CM
X
"3"
6
250.38
_4C
120°
0.73 (est)c
Octylphenol
949-13-3
C13H22O
206.33
82.77 (est)c
310.93 (est)c
0.49 (est)c
Ofloxacin
82419-36-1
C18H20FN3O4
361.37
250 - 257A
576.13 (est)c
4.03A
OH-ibuprofen
51146-55-5
C13H18O3
222.29
122.61 (est)c
354.25 (est)c
3.47°
op' DDA
34113-46-7
C14H10CI2O2
281.13
145.16 (est)c
395.09 (est)c
0.53 - 0.88 (est)c
Oxazepam
604-75-1
C15H11CIN2O2
286.72
205 - 206A
491.71 (est)c
1.3A
o-xylene
95-47-6
C8H10
106.17
-25.16A
144.5A
2.25A
Oxytetracycline
79-57-2
C22H24N2O9
460.43
184.5A
781.66 (est)c
2.5A
Para-cresol
106-44-5
CyHsO
108.13
34.77A
201,9A
4.33A
Para-tert-octylphenol
140-66-9
O
CM
CM
X
"vT
6
206.36
00
1
00
cn
>
158°
0.68°
Pentachlorophenol
87-86-5
CeHCIsO
266.32
174A
309 - 310A
1.15A
Pentoxifylline
6493-05-6
C13H18N4O3
278.31
217.99 (est)c
511.53 (est)c
2.66°
Perfluorobutanesulfonic Acid
(PFBS)
375-73-5
C4HF9O3S
300.10
36.86 (est)c
210 - 212A
2.54A
Perfluoroctanionic Acid (PFOA)
335-67-1
C8HF15O2
414.07
54.3A
192A
3.52A
Perfluorodecanoic Acid (PFDA)
335-76-2
C10HF19O2
514.09
50.26 (est)c
239.28 (est)c
-2.09 (est)c
Perfluorododecanoic Acid (PFDoA)
307-55-1
C12HF23O2
614.10
108°
249°
-3.89 (est)c
Perfluoroheptanoic Acid (PFHpA)
375-85-9
C7HF13O2
364.06
30A
175A
0.56A
Perfluorohexanesulfonic
Acid(PFHxS)
355-46-4
C6HF13O3S
400.11
41.25 (est)c
238 - 239A
0.79A
Perfluorohexanoic Acid (PFHxA)
307-24-4
C6HF11O2
314.05
23.07 (est)c
157A
ro
>
Perfluorononanoic Acid (PFNA)
375-95-1
C9HF17O2
464.08
38.94 (est)c
221.92 (est)c
-1.20A
Perfluorooctanesulfonic Acid
(PFOS)
1763-23-1
C8HF17O3S
500.13
51.90 (est)c
249A
-2.49A
131
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Perfluoroundecanoic Acid (PFUnA)
4234-23-5
C11HF21O2
564.09
349.84 (est)c
917.72 (est)c
-1.72 (est)c
Perylene
198-55-0
CM
X
O
CM
O
2252.32
273 - 274A
350 - 400A
-3.4A
Phenanthrene
85-01-8
O
X
"3"
6
178.23
99A
338.4A
0.04A
Phenol
108-95-2
CeHsOH
94.11
40.91A
181.75A
4.92A
Pipemidic Acid
51940-44-4
C14H17N5O3
303.32
253 - 255°
566.52 (est)c
6C
pp' DDA
83-05-6
C14H10CI2O2
281.13
167- 169°
395.09 (est)c
0.53 - 0.76 (est)c
Primidone
125-33-7
C12H14N2O2
218.26
281 - 282A
463.54 (est)c
2.7A
Progesterone
57-83-0
C21H30O2
314.46
129A
396.19 (est)c
0.94A
Propazine
139-40-2
CgHieCINs
229.71
229.7A
318.46 (est)c
0.93A
Propranolol
525-66-6
C16H21NO2
259.34
96c
386.48 (est)c
2.36°
Propylparaben
94-13-3
C10H12O3
180.20
96.1A
285.14 (est)c
2.7A
Propyphenazone
479-92-5
C14H18N2O
230.31
103°
364.43 (est)c
2.82°
p-xylene
106-42-3
CsHio
106.17
13.3A
138.3A
2.21A
Pyrene
129-00-0
C16H10
202.25
150.62A
394A
-0.87A
Roxithromycin
80214-83-1
C41H75N2O15
837.05
349.84 (est)c
917.72 (est)c
-1.72 (est)c
Salicylic acid
69-72-7
CyHeOs
138.12
159A
211A
3.35A
Sertraline
79617-96-2
C17H17CI2N
306.23
243 - 245A
387.42 (est)c
3.58A
Simazine
122-34-9
C7H12CIN5
201.66
225 - 227A
307.45 (est)c
0.79A
Skatole
83-34-1
C9H9N
131.18
95A
265 - 266A
2.7A
Sodium decylbenzenesulfonate
1322-98-1
Ci6H25Na03S
321.44
276.79 (est)c
637.41 (est)c
2.25 (est)c
Sodium dodecycbenzenesulfonate
25155-30-0
Ci8H29Na03S
348.48
287.63 (est)c
660.62 (est)c
2.90A
Sodium N-tridecylbenzenesulfonate
26248-24-8
C19H31 NaOsS
362.50
293.05 (est)c
672.22 (est)c
0.74°
Sodium Undecylbenzenesulfonate
27636-75-5
Ci7H27Na03S
334.45
282.21 (est)c
649.01 (est)c
1.74 (est)c
Sotalol
3930-20-9
C12H20N2
272.36
206.5 - 207°
407.42 (est)c
2.74 (est)c
132
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Sucralose
56038-13-2
C12H19CI3O8
397.64
130A
551.89 (est)c
4.36A
Sulfadiazine
68-35-9
C10H10N4O2S
250.78
255.5C
428 (est)c
1.89°
Sulfadimethoxine
122-11-2
C12H14N4O4S
310.33
203.5C
475.30 (est)c
2.54°
Sulfamethazine
57-68-1
C12H14N4O2S
278.33
176A
451.19 (est)c
3.18A
Sulfamethoxazole
723-46-6
C10H11N3O3S
253.28
167A
414.01 (est)c
2.79A
Sulfamonomethoxine
1220-83-3
C11H12N4O3S
280.30
190.01 (est)c
451.65 (est)c
3.60 - 3.88 (est)c
Sulfanilamide
63-74-1
C6H8N2O2S
172.20
165.5A
341.95 (est)c
3.92A
Sulfathiazole
72-14-0
C9H9N3O2S2
255.32
175 (form a);
202 (form b)A
428.28 (est)c
2.57A
Terbuthylazine
5915-41-3
CgHieCINs
229.71
175.5A
321.23 (est)c
0.95A
Terbutryne
886-50-0
C10H19N5S
241.36
104A
154- 160A
1.4A
Tert-Amyl chloride
594-36-5
C5H11CI
106.59
-73.5°
85.5°
2.90°
Tertrabutyltin
1461-25-2
Ci6H36Sn
347.17
-97A
145A
-4.19--1.94 (est)c
Testosterone
58-22-0
C19H28O2
288.42
151A
390.02 (est)c
1.37A
Tetrachloroethylene
127-18-4
C2CI4
165.83
-22.3A
121 3A
2.31A
Tetracycline
60-54-8
C22H24N2O8
444.44
170- 175A
745.32 (est)c
2.36A
Thiabendazole
148-79-8
C10H7N3S
201.25
30 4- 305A
443.05 (est)c
1.7A
Toluene
108-88-3
C7H8
92.14
-94.9A
110.6A
2.72A
Tolylfluanid
731-27-1
C10H13CI2FN2O2S2
347.24
93A
DecompA
-0.05A
Tonalide
21145-77-7
C18H26O
258.41
54.5A
331.88 (est)c
0.1A
Tramadol
27203-92-5
C16H25NO2
263.38
115.18 (est)c
355.82 (est)c
3.06A
Tribromoacetic acid
75-96-7
C2HBr302
296.74
129- 135A
245A
5.3A
Tribromomethane
75-25-2
CBr3
251.72
8.69A
149.1A
3.49A
Tributyl phosphate
126-73-8
C12H27O4P
266.32
<-80A
289A
2.45A
Tributyltin
688-73-3
Ci2H28Sn
291.07
28.89 (est)c
80c
0.64°
133
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Tr
chloroacetic acid
76-03-9
C2HCI3O2
163.38
57.5A
195.5A
4.73A
Tr
chloroethylene
79-01-6
C2HCI3
131.40
-84.7A
87.2A
3.11A
Tr
chloromethane
67-66-3
CHCb
119.38
-63.41A
61.17A
3.9A
Tr
closan
3380-34-5
C12H7CI3O2
289.54
54 - 57.3A
280 - 290A
1A
Tr
cyclohexyltin chloride
3091-32-5
Ci8H33CISn
403.62
129.5C
370.70 (est)c
-2.59 - -0.98 (est)c
Tr
cyclohexyltin Hydroxide
13121-70-5
CisHssOSn
385.17
195A
390.72 (est)c
-2.92 - 0.69 (est)c
Tr
ethylphosphate
78-40-0
C6H15O4P
182.15
-56.4A
215.5A
5.7A
Tr
fluoromethane
75-46-7
CFs
69.01
-155.18A
-82A
3.61A
Tr
iodomethane
75-47-8
CHb
393.73
119A
218A
2A
Tr
isobutyl phosphate
126-71-6
C12H27O4P
266.32
16.43 (est)c
264°
1.21 - 2.68 (est)c
Tr
methoprim
738-70-5
C14H18N4O3
290.32
199- 203°
449.23 (est)c
2.6A
Tr
methyltin Chloride
1066-45-1
CsHgSnCI
199.27
37.5A
154- 156A
4.30 - 4.40 (est)c
Tr
-n-Butyltin Hydride
688-73-3
Ci2H28Sn
291.06
28.89 (est)c
112.5- 113.5A
0.64°
Tr
octyltin chloride
2587-76-0
C24H5iCISn
493.82
424 (est)c
154.33 (est)c
-6.15--6.31 (est)c
Tr
phenyl phosphate
115-86-6
C18H15O4P
326.29
50.5A
245A
0.28A
Tr
phenylphosphine oxide
791-28-6
C18H15OP
278.29
156.5°
>360°
1.80 - 2.31 (est)c
Tr
phenyltin Acetate
900-95-8
C2oHi802Sn
409.07
122- 123A
406.83 (est)c
0.95A
Tr
phenyltin chloride
639-58-7
Ci8Hi5CISn
385.47
103.5A
240A
1.60A
Triphenyltin hydride
892-20-6
CisHieSn
351.03
94.01 (est)c
358.52 (est)c
-0.86 - 0.079
(est)c
Triphenyltin hydroxide
76-87-9
CisHieSn
367.03
119A
409.44 (est)c
0.79A
Tris(1,3-
dichloroisopropyl)phosphate
13674-87-8
C9H15CI6O4P
430.89
27c
236 - 237A
0.85A
T ris(2-butoxyethyl)phosphate
78-51-3
C18H39O7P
395.48
-70A
215 - 228A
3.04A
Tris(2-chloroethyl)phosphate
115-96-8
C6H12CI3O4P
285.48
-55A
330A
3.89A
134
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Chemical Formula
Molecular
Weight
T 1
1 m
Tb2
Log Cw3
Units
g/mol
°c
°C
mg/L
Tylosin
1401-69-0
C46H77NO17
916.10
128- 132A
-47.4A
3.7A
Valsartan
137862-53-
4
C24H29N5O3
435.50
116 - 117A
674.38 (est)c
0.147A
Venlafaxine
93413-69-5
C17H27NO2
277.40
118.80 (est)c
363.80 (est)c
2.43A
Xylene
1330-20-7
C8H10
106.17
-47.4A
137.2- 160A
2.03A
a-BHC
319-84-6
C6H6CI6
296.77
157.4A
287.78A
0.3A
a-Endosulfan
959-98-8
C9H6CI6O3S
406.90
106°
401.28 (est)c
0.17°
a-Hexachloro-cyclohexane
319-84-6
C6H6CI6
290.83
157.4A
287.78A
0.3A
1Melting Point
2Boiling Point
3CW is the solubility in water
4Kh is the Henry's Law constant
5K0c is the organic carbon - water partitioning coefficient
6K0W is the octanol - water partitioning coefficient
7P° is the vapor pressure
8pKa is the acid dissociation constant
AToxnet: https://toxnet.nlm.nih.aov/newtoxnet/hsdb.htm - Only values that were peer reviewed were used unless denoted by est for estimated value
CEPI Suite version 4.1: U.S. EPA. Office of Pollution Prevention and Toxics and Syracuse Research Corporation. Copyright 2000 - 2012. Peer reviewed values were used
when available, otherwise the values obtained are delineated using est
Decomp= decomposes
est= estimated value
135
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
1,1,1,2-tetrachloroethane
630-20-6
-2.6A
2.6A
2.93A
-1.80A
1,1,1-trichoroethane
71-55-6
-1.79A
1.82-2.18A
2.49A
-0.79A
1,1,2,2-tetrachloroethane
79-34-5
-3.38A
1.9 - 2.37A
2.39A
-2.12A
1,1,2-trichloroethane
79-00-5
-3.08A
1.92-2.32A
1.89A
-1.52A
1,1-dichloroethane
75-34-3
-2.25A
0.96- 1.48A
1.79A
-0.53A
1,2,3-trichloropropane
96-18-4
-3.46A
1.89- 1.98A
2.27A
-2.32A
1,2,3-trimethylbenzene
526-73-8
-2.36A
2.8 - 3.04A
3.66A
-2.65A
1,2,4-trichlorobenzene
120-82-1
-2.85A
3.1 - 4.03A
4.02A
-3.22A
1,2,4-trimethylbenzene
95-63-6
-2.21A
2.73A
3.78A
-2.56A
1,2-dichloro benzene
95-50-1
-2.82A
2.45-4.3A
3.43A
-2.74A
1,2-dichloroethane
107-06-2
-2.93A
1.52A
1.48A
-0.99A
1,2-dichloropropane
78-87-5
-2.55A
1.67A
1.98A
-1.16A
1,3-dichloro benzene
541-73-1
-2.55A
2.47 - 4.7A
3.53A
-2.55A
1,3-dichloropropane
142-28-9
-3.01A
2.46A
2A
-1,62A
1,4-dichloro benzene
106-46-7
-2.57A
2.44 - 4.8A
3.44A
-2.64A
11-keto testosterone
53187-98-7
-14.87 (est)c
1.97 - 2.42 (est)c
1.92 (est)c
-11.83 (est)c
17a-estradiol
57-91-0
-14.85--13.44
(est)c
2.90-4.19 (est)c
4.01°
-11.58 (est)c
17a-ethynyl estradiol
57-63-6
-14.10 (est)c
2.71 - 4.65 (est)c
3.67°
-11.59 (est)c
17p-estradiol
50-28-2
-14.85--13.44
(est)c
2.90-4.19 (est)c
4.01c
-11.58 (est)c
19-norethisterone
68-22-4
-9.24A
2.34A
2.97A
-9.39A
1 -acetyl-1 -methyl-2-dimethyl-
oxamoyl-
2-phenylhydrazide
519-65-3
-16.93 (est)c
-0.05 - 1.00 (est)c
-0.76 (est)c
-11.42 (est)c
1 -chloro-2-methylpropane
513-36-0
-4.71c
1.78-2.16 (est)c
2.49 (est)c
-0.71°
1-chlorobutane
109-69-3
-1.78A
1.97-2.01A
2.39A
-0.88A
136
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
1-chloropentane
543-59-9
-1.55A
2.38A
2.73A
-1.39A
1H-benzotrazole
95-14-7
-6.5A
2.16A
1.44A
-4.28A
8.37A
1 -methylnaphthalene
90-12-0
-3.29A
2.76 - 5.78A
3.87A
-4.06A
2, 4-D
94-75-7
-7.01A
1.30-2.13A
2.81A
-9.71A
2.73A
2,2',3,4,4',5,5'-heptachlorobiphenyl
(PCB 180)
35065-29-3
-8C
5.54 - 5.64 (est)c
8.27 (est)c
-8.89°
2,2',3,4,4',5-hexachlorobiphenyl
(PCB 138)
35065-28-2
-7.68°
5.19-5.33 (est)c
7.44°
-8.30°
2,2',4,4',5,5'-hexachlorobiphenyl
(PCB 153)
35065-27-1
-7.64°
5.32 - 5.36 (est)c
6.34°
-8.35°
2,2',5,5'-tetrachorobiphenyl (PCB
52)
35693-99-3
-6.70°
4.43 - 4.89 (est)c
6.09c
-7.96 (est)c
2,3',4,4',5-pentachlorobiphenyl
(PCB 118)
31508-00-6
-6.54°
5.01 - 5.11 (est)c
7.12c
-7.93°
2,4,4'-trichlorobiphenyl (PCB 28)
7012-37-5
-6.70°
4.18-4.68 (est)c
5.62°
-6.28°
2,4,5,2',5'-pentachlorobiphenyl
(PCB 101)
37680-73-2
-7.05c
4.83-5.11 (est)c
5.68°
-7.48°
2-chloro-2-methylpropane
507-20-0
-4.72 (est)c
1.64-2.13 (est)c
2.45 (est)c
-0.40°
2-chlorobutane
78-86-4
-4.62°
1.78-2.02 (est)c
2.33°
-0.69°
2-Chloromethylphenol
40053-98-3
-10.31 --9.66
(est)c
2.37 - 2.77 (est)c
2.31 (est)c
-4.83 (est)c
2-methyl-4-chlorophenoxyacetic
Acid
94-74-6
-9.32A
1.7- 1.79A
3.25A
-8.11A
3.13A
2-methylnaphthalene
91-57-6
-3.29A
3.00 - 5.96A
3.86A
-4.14A
2-methylthiobenzothiazole
615-22-5
-10.96 (est)c
2.73 - 3.41 (est)c
3.15c
-6.00 (est)c
3-Chloromethylphenol
60760-06-7
-10.31 --9.66
(est)c
2.37 - 2.77 (est)c
2.31 (est)c
-4.83 (est)c
3-p-coprostanol
360-68-9
-7.38 - -6.79
(est)c
5.39-6.19 (est)c
8.82 (est)c
-12.14 (est)c
4-Chloromethylphenol
35421-08-0
-10.31 --9.66
2.37 - 2.76 (est)c
2.31 (est)c
-4.83 (est)c
137
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
(est)c
4'-hydroxy-diclofenac
64118-84-9
-18.31 (est)c
2.33 - 2.77 (est)c
3.70°
-11.97 (est)c
4-methyl-1 H-benzotriazole
29878-31-7
-9.79 (est)c
1.94 (est)c
1.71 (est)c
-3.01 (est)c
4-Nitrophenol
100-02-7
-7.89A
1.2-2.7A
1.91A
-6.89A
7.15A
4-nonylphenol
104-40-5
-4.47A
4.51A
5.76A
-5.97A
4-nonylphenoldiethoxylate
20427-84-3
-11.58 (est)c
3.39 - 3.41 (est)c
5.30 (est)c
-5.16 (est)c
4-nonylphenolmonoethoxylate
104-35-8
-9.78 - -8.90
(est)c
3.48 - 3.66 (est)c
5.58 (est)c
-9.63 (est)c
4-tert-Butylphenol
98-54-4
-8.92°
2.92-3.11 (est)c
3.31°
-4.30°
5-methyl-1 H-benzotriazole
136-85-6
-9.79 (est)c
1.93- 1.94 (est)c
1.71 (est)c
-6.39 (est)c
Acebutolol
37517-30-9
-22.52 (est)c
1.40 - 1.64 (est)c
1.71°
-13.67 (est)c
Acenaphthene
83-32-9
-3.74A
2.97, 3.59, 5.33,
5.87A
3.92A
-5.54A
Acenaphthylene
208-96-8
-3.9A
3.75 - 6.21A
3.93A
-5.20A
Acesulfame
33665-90-6
-8.02A
0.6A
-1.33A
-7.93A
5.67A
Acetaldehyde
75-07-0
-4.18A
0A
-0.34A
0.07A
13.57A
Acetaminophen
103-90-2
-9.06A
1.32A
0.46A
-7.08A
9.38A
Acetophenone
98-86-2
-4.98A
1 - 2.43A
1.58A
-3.28A
Acrylamide
79-06-1
-8.77A
1.7A
-0.67A
-5.05A
Alachlor
15972-60-8
-8.08A
2.08 - 3.33A
3.52A
-7.54A
Aldrin
309-00-2
-4.36A
2.60 - 4.45A
6.50A
-6.80A
Alkylphenol ethoxylate
68412-54-4
3.39 - 3.41 (est)c
-11.59 (est)c
5.30 (est)c
-13.92 (est)c
Aminotriazole
61-82-5
-12.66A
1.06- 1.71A
-0.97A
-9.24A
4.2, 10.7A
Amitriptyline
50-48-6
-7.16A
3.55A
4.92A
-9.32A
9.76A
Amoxicillin
26787-78-0
-20.6A
2A
0.87A
-19.21A
3.2, 11.7A
AM PA
74341-63-2
-18.30 (est)c
-1.82- 1.00 (est)c
-3.52 (est)c
-11.93 (est)c
138
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Androstenedione
63-05-8
-7.43A
3.79A
2.75A
-11.02A
Anthracene
120-12-7
-1,64A
3.41 - 3.93A
4.45A
-8.07A
Anthraquinone
84-65-1
-7.63A
3.44 - 4.24A
3.39A
-9.82A
Atenolol
29122-68-7
-20.86 (est)c
0.61 - 1.83 (est)c
0.16A
-12.00 (est)c
9.6A
Atrazine
1912-24-9
-8.59A
1.73 - 3.07A
2.61A
-9.42A
1.60A
Atrazine amide-l
142179-76-
8
-10.09--11.42
(est)c
1.46- 1.54 (est)c
1.32 (est)c
-8.99 (est)c
Azithromycin
83905-01-5
-28.28A
3.49A
4.02A
-26.47A
8.74A
Bentazone
25057-89-0
-8.66A
1.12- 2.25A
2.80A
-8.34A
3.30A
Benz(a)anthracene
56-55-3
-2.24A
5.74 - 6.27A
5.76A
-9.56A
Benzene
71-43-2
-2.25A
1.93A
2.13A
-0.91A
Benzo(a)fluoranthene
203-33-8
-9.18c
5.02, 5.78 (est)c
5.78°
-10.49°
Benzo(a)pyrene
50-32-8
-2.92A
6.0 - 8.17A
6.13A
-11.14A
Benzo(b)fluoranthene
205-99-2
-6.18A
6.60 - 7.96A
5.78, 6.6A
-9.18A
Benzo(e)pyrene
192-97-2
-9.52°
5.59 - 5.78 (est)c
6.44°
11.12°
Benzo(j)fluoranthene
205-82-3
-9.58 - -9.09
(est)c
5.30 - 5.78 (est)c
6.11 (est)c
-10.46 (est)°
Benzo(k)fluoranthene
207-08-9
-6.23A
5.61 - 8.44A
6.11A
-11.9A
Benzo(b)naphtho(2,3-d)thiophene
243-46-9
-8.57 (est)c
4.63 - 4.99 (est)c
5.34 (est)c
-9.34 (est)°
Benzo(ghi)perylene
191-24-2
-6.48A
4.61 - 7.08A
6.63A
-12.88A
Benzophenone
119-61-9
-5.72A
2.63 - 2.71A
3.18A
-5.6A
Benzothiazole
95-16-9
-6.43A
2.47A
2.01A
-4.74A
Benzyl acetate
140-11-4
-4.96A
2.45A
1.96A
-3.63A
Beta-sitosterol
83-46-5
-6.53 (est)c
5.85 - 6.69 (est)c
9.65 (est)c
-12.31 (est)°
Bezafibrate
41859-67-0
-17.67 (est)c
2.31 - 2.62 (est)c
4.25 (est)c
-13.10 (est)°
Bis(2-chloroethyl) ether
111-44-4
-4.54A
2.08A
1.29A
-2.69A
139
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Bis(2-chloroisopropyl) ether
39638-32-9
-6.48 (est)c
1.66-2.90 (est)c
3.73 (est)c
-3.03 - -2.95A
Bis(2-ethlyhexyl) phthalate
117-81-7
-6.57A
4.94 - 6.23A
7.6A
-9.73A
Bis(tributyltin) oxide
56-35-9
-6.89A
3.33 - 7.41 (est)c
3.84A
-7.99°
Bisphenol A
80-05-7
-10.4A
2.06 - 3.59A
3.32A
-10.28A
9.6A
Bromochloroacetic acid
5589-96-8
-7.66A
0.28A
0.61A
-3.74A
1.40A
Bromodichloroacetic acid
71133-14-7
-8.1A
0.51 - 1.00 (est)c
1.53A
-4.33A
0.03A
Bromodichloromethane
75-27-4
-2.67A
1.72-2.4A
2A
-1.18A
Butyl benzyl phthalate
85-68-7
-5.89A
3.21 - 4.00A
4.73A
-7.97A
Butylated hydroxyanisole
25013-16-5
-5.92A
2.92A
3.5A
-5.51A
Caffeine
58-08-2
-10.96A
1.85 - 3.89A
-0.07A
-8.93A
0.7, 14.0A
CA-ibuprofen
15935-54-3
-15.07 (est)c
1.25-3.10 (est)c
1.97 (est)c
-9.15 (est)c
Carbamazepine
298-46-4
-9.96A
2.71A
2.45A
-9.63A
13.9A
Carbamazepine-10,11-epoxid
36507-30-9
-15.16 (est)c
1.31 -2.51 (est)c
0.95 (est)c
-10.00 (est)c
Carbendazim
10605-21-7
-11.82A
2.09 - 3.45A
1.52A
-12.01A
4.2, 4.29A
Carbon tetrachloride
56-23-5
-1.56A
1.85A
2.83A
-0.82A
Carbozole
86-74-8
-6.94A
2.06- 3.7A
3.72A
-8.70A
-6A
Cefazolin
25953-19-9
-22.7A
1.08A
-0.58A
-20.71A
3.6A
Cefriaxone
73384-59-5
-33.07 (est)c
-1.15-2.94 (est)c
-1.37 (est)c
-29.75 (est)c
Ceftazidime
72558-82-8
-36.61 (est)c
-0.65 - 4.57 (est)c
-1.60°
-23.52 (est)c
Chlordane
57-74-9
-4.31
4.30-4.88A
6.16A
-7.89A
Chlorfenvinphos
470-90-6
-7.54A
2.47A
3.81A
-8.01A
Chloroacetic acid
79-11-8
-8.03A
1.49A
0.22A
-4.07A
2.87A
Chlorodifluoromethane
75-45-6
-1.39A
0.93A
1.08A
0.98A
Chloromethane
74-87-3
-2.05A
1.11A
0.91A
0.75A
Chlorpyrifos
2921-88-2
-4.45A
2.99 - 4.49A
4.96A
-7.58A
Chlortetracycline
57-62-5
-26.46 (est)c
0.25- 1.86 (est)c
-0.62°
-24.12 (est)c
140
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Cholesterol
57-88-5
-3.78A
4.2A
8.74 (est)c
-11.99A
Choline Chloride
67-48-1
-18.69 (est)c
-2.43-0.16 (est)c
-5.16A
-12.19 (est)c
Chrysene
218-01-9
-6.03A
5.12 - 7.79A
5.73, 5.81A
-11.09A
Cimetidine
51481-61-9
-15.02A
1.59A
0.4A
-11.14A
6.8A
Ciprofloxacin
85721-33-1
-16.97°
-0.004- 1.00
(est)c
0.28A
-15.44A
6.09, 8.74A
Cis-androsterone
53-41-8
-11.20 (est)c
2.75 - 3.34 (est)c
3.69°
-10.80 (est)c
Citalopram
59729-33-8
-13.57 (est)c
3.23 - 4.44 (est)c
3.74 (est)c
-9.83 (est)c
Clarithromycin
81103-11-9
-31.76 (est)c
2.18A
3.16A
-27.52A
8.99A
Clofibric Acid
882-09-7
-10.66 (est)c
1.63 - 1.64 (est)c
2.57°
-7.01 (est)c
Cocaine
50-36-2
-10.38A
2.78A
2.3A
-9.60A
8.61A
Codeine
76-57-3
-13.12A
2.4 - 2.85A
1.19A
-11.14A
8.21, 10.6A
Coronene
191-07-1
-11.57--10.67
(est)c
6.63 - 6.80 (est)c
7.64°
-14.55°
Cotinine
486-56-6
-11.48A
2.11A
0.07A
'-6.30A
Cyanazine
21725-46-2
-9.59A
2.26-2.57A
2.22A
-9.74A
0.87A
Cyanazine amide
36576-42-8
-16.87 (est)C
1.37- 1.97 (est)c
1.19 (est)c
-10.09 (est)c
Cyclophosphamide
50-18-0
-10.85A
1.72A
0.63A
-7.23A
Debromodiphenyl ether
1163-19-5
-7.92A
5.45A
9.97A
-13.04A
Deethylcyanazine
36556-77-1
-17.33 (est)c
0.82 - 1.64 (est)c
0.15 (est)c
-10.02 (est)c
Dehyronifedipine
67035-22-7
-16.06--15.97
(est)c
2.93 - 3.38 (est)c
3.15 (est)c
-10.81 (est)c
Deisopropylatrazine
1007-28-9
-8.10--8.93
(est)c
1.37- 1.84 (est)c
1.15°
-6.56 (est)c
Desethylatrazine
6190-65-4
-8.82A
1.58 - 3.48A
1.51A
-6.91A
Diatrizoate
117-96-4
-17.74A
1A
1.37A
-17.33A
1.13, 7.95A
Diazinon
333-41-5
-7.20A
1.60 - 3.16A
3.81A
-6.93A
2.6A
141
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Dibenz(a,h)anthracene
53-70-3
-6.85A
5.76 - 7.68A
6.5A
-11.90A
Dibenzofuran
132-64-9
-3.68A
3.62A
4.12A
-5.49A
Dibenzothiophene
132-65-0
-4.47A
3.72 - 4.31A
4.38A
-6.57A
Dibromoacetic acid
631-64-1
-8.35A
0.18A
0.7A
-4.52A
1.48A
Dibromochloroacetic acid
5278-95-5
-8.59A
0.51 - 1.05 (est)c
1.62A
-5.17A
0.03A
Dibromochloromethane
124-48-1
-3.11A
1.92A
2.16A
-2.14A
Dibutyltin
1002-53-5
-3.42 (est)c
1.29-2.90 (est)c
1.49°
-2.66 (est)c
Dichloroacetic acid
79-43-6
-8.08A
1.88A
0.92A
-3.63A
1.26A
Dichlorodiphenyltrichloroethane
(DDT)
50-29-3
-5.08A
5.05 - 5.54A
6.91A
-9.68A
Dichloromethane
75-09-2
0.41A
0.9- 1.68A
1.25A
-0.24A
Dichloroprop
120-36-5
-10.06A
1.53 - 2.11A
3.43A
-10.01A
3.1A
Diclofenac
15307-86-5
-11.33A
2.39A
4.51A
-10.1A
4.15A
Dieldrin
60-57-1
-1.95A
3.29 - 4.37A
5.40A
-8.11A
Dienochlor
2227-17-0
-4.26A
5.30 - 7.28 (est)c
3.23A
-8.54A
Diethyl phthalate
84-66-2
-6.21A
1.84 - 3.24A
2.47A
-5.56A
D i h yd rotestoste ro n e
521-18-6
-13.00--10.20
(est)c
2.67 - 3.34 (est)c
3.55°
-10.95 (est)c
Diisobutyl phthalate
84-69-5
-11.03A
3.14, 5.9A
4.11A
-7.21A
Diisodecyl phthalate
26761-40-0
-5.96A
5.46, 5.78, 6.04A
10.36A
-9.16A
Diisononyl phthalate
28553-12-0
-5.82A
5.51A
9.37A
-9.15A
Diltiazem
42399-41-7
-19.06 (est)c
2.30 - 3.47 (est)c
2.7A
-13.53 (est)c
8.06A
Dimethenamid
87674-68-8
-12.22 (est)c
1.98-2.15 (est)c
2.15°
-6.44°
Dimethyl phthalate
131-11-3
-6.71A
1.74 - 2.58A
1.6A
-5.39A
Dimethyltin
23120-99-2
-1.15 (est)c
-2.69 - 1.34 (est)c
-3.10°
-0.11 (est)c
Dimetridazole
551-92-8
-9.46 (est)c
1.71 - 1.93 (est)c
0.31°
-6.65 (est)c
142
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Di-n-butyl phthalate
84-74-2
-5.74A
3.05A
4.5A
-7.58A
Di-n-octyl phthalate
117-84-0
-5.59A
6.3, 4.38, 5.22A
8.1A
-9.88A
Dioctyltin
94410-05-6
-2.50 (est)c
3.91 -4.99
4.50 (est)c
-6.11 (est)c
Diphenhydramine
58-73-1
-11.43 (est)c
2.58A
3.27A
-8.12A
8.98A
Dipropyl phthalate
131-16-8
-9.39°
2.54 - 2.60 (est)c
3.27°
-6.76°
Diuron
330-54-1
-9.30A
2.35 - 2.94A
2.68A
-10.04A
D-limonene
5989-27-5
-1.55A
3.05A
4.57A
-2.59A
Endrin
72-20-8
-5.19A
4.06A
5.20A
-8.40A
Epitestosterone
481-30-1
-11.45 (est)c
2.55 - 3.34 (est)c
3.32°
-10.65 (est)c
Erythromycin
114-07-8
-31.27 (est)c
2.76A
3.06A
-27.56A
8.9A
Estriol
50-27-1
-11.89A
3.08A
2.45A
-13.89A
10.54A
Estrone
53-16-7
-9.42A
2.66 - 4.26A
3.13A
-12.48A
Ethylbenzene
100-41-4
-2.1A
2.35 - 2.41A
3.15A
-1.90A
Ethylene thiourea
96-45-7
-10.85A
1.11A
-0.66A
-8.58A
Flumequine
42835-25-6
-12.57A
3.44 - 4.39A
1.6A
-9.49A
6.5A
Fluoranthene
206-44-0
-1.98A
4.47 - 5.47A
5.16A
-7.92A
Fluorene
86-73-7
-1.14A
3.7-4.21,6.45,
6.52A
4.18A
-6.10A
Fluoxetine
54910-89-3
-10.05 (est)c
3.05 - 4.97 (est)c
4.05c
-7.48 (est)c
Formaldehyde
50-00-0
-6.47A
0.9A
0.35A
0.71A
13.27A
Galaxolide
1222-05-5
-3.89A
4.59, 4.86A
5.9A
-6.15A
Gemfibrozil
25812-30-0
-7.92A
2.63A
4.77A
-7.39A
4.5A
Gestodene
60282-87-3
-9.17A
2.51 - 3.91 (est)c
3.26A
-11.83A
Glyphosate
1071-83-6
-21.39 (est)c
3.41 - 3.69A
-3.40A
-9.89A
2.34, 5.73, 10.2A
Hydroxyatrazine
67-68-5
-13.33--14.87
(est)c
-0.17-2.77 (est)c
-1.74 (est)c
-9.62 (est)c
143
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Hydroxydeethylatrazine
19988-24-0
-18.34 (est)c
-0.42 - 2.66 (est)c
-2.27°
-9.90 (est)c
Hydroxydeisopropylatrazine
7313-54-4
-18.46 (est)c
-0.65 - 2.47 (est)c
-2.69 (est)c
-9.77 (est)c
Ibuprofen
15687-27-1
-6.82A
3.53A
3.97A
-7.21A
4.91, 5.2A
Ifosfamide
3778-73-2
-10.85A
1.85A
0.86A
-7.41A
Indeno pyrene
193-39-5
-6.46A
5.78 - 8.82A
6.7A
-12.77A
Indole
120-72-9
-6.28A
2.27 - 2.54A
2.14A
-4.80A
-2.4A
Indomethacin
53-86-1
-13.5A
2.95 - 3.15A
0.91A
-12.89A
4.5A
lohexol
66108-95-0
-31.58 (est)c
-2.13-1.00 (est)c
-3.05°
-31.27 (est)c
lomeprol
78649-41-9
-26.75 (est)c
-1.99- 1.00 (est)c
-2.79°
-31.40 (est)c
lopamidol
60166-93-0
-24.96A
1A
-2.42A
-Z2.11k
10.7A
lopromide
73334-07-3
-31.00 (est)c
-1.67- 1.00 (est)c
-2.05c
-30.68 (est)c
Isophorone
78-59-1
-5.18A
2.3A
1.7A
-3.24A
Isopropyl chloride
75-29-6
-1.74A
1.72A
1.9A
-0.17A
Isoproturon
34123-59-6
-11.72 (est)c
2.30 - 2.44 (est)c
2.87°
-10.49°
Isoquinoline
119-65-3
-9.38--9.16
(est)c
1.81-3.19 (est)c
2.08c
-4.32°
Ketoprofen
22071-15-4
-13.67 (est)c
2.08 - 2.59 (est)c
3.12°
-8.72 (est)c
Lincomycin
154-21-2
-22.52A
1.84A
o
ro
>
-19.77A
7.6, 7.8A
Lindane
58-89-9
-5.29A
2.60 - 3.30A
3.72A
-7.26A
Lomefloxacin
98079-51-7
-20.87 (est)c
0.01 -1.70 (est)c
-0.30°
-14.25 (est)c
Malathion
121-75-5
-8.31A
2.96 - 4.25A
2.36A
-7.28A
Mecoprop
93-65-2
-8.42A
0.7- 1.6A
3.13A
-7.80A
3.10, 3.21, 3.78A
Metalaxyl
57837-19-1
-8.52A
1.48 - 2.45A
1.65A
-8.13A
Metaldehyde
108-62-3
-4.28A
2.38A
0.12A
-4.18A
Metazachlor
67129-08-2
-13.24 (est)c
2.46 - 3.00 (est)c
2.13c
-8.82 (est)c
Metformin
1115-70-4
-15.12A
1.08- 1.28A
-2.64A
-7.00A
144
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Methyl dihydrojasmonate
24851-98-7
-10.28--9.30
(est)c
2.18-2.70 (est)c
2.98 (est)c
-5.81 (est)c
Methyl salicylate
119-36-8
-6.03A
1.52 - 2.11A
2.55A
-4.35A
9.8A
Methyl tert-butyl ether (MTBE)
1634-04-4
-3.23A
1.04- 1.08A
0.94A
-0.48A
-3.7A
Methyl paraben
99-76-3
-8.64A
1.94A
1.96A
-6.51A
8.4, 8.5A
Methyltriclosan
4640-01-1
-8.55 - -6.85
(est)c
4.07 (est)c
5.22 (est)c
-8.04 (est)c
Metolachlor
51218-45-2
-8.05A
1.34-3.37A
3.13A
-7.39A
Metoprolol
37350-58-6
-10.68A
1.79A
1.88A
-9.44A
9.6A
Metronidazole
443-48-1
-10.77A
1.36A
-0.02A
-9.39A
2.38A
Miconazole
22916-47-8
-11.61 (est)c
4.83 - 5.74 (est)c
6.25 (est)c
-12.63 (est)c
Monobromoacetic acid
79-08-3
-8.19A
1.6A
0.41A
-3.81A
2.89A
Monobutyltin
78763-54-9
-4.01 (est)c
1.86-2.87 (est)c
3.31 (est)c
-1.00 (est)c
Monochloroacetic acid
79-11-8
-8.03A
1.49A
0.22A
-4.07A
2.87A
Musk ambrette
83-66-9
-6.15A
3.72A
4A
-7.77A
Musk ketone
81-14-1
-8.72A
3.94A
4.3A
-9.12A
Musk xylene
81-15-2
-8.11A
3.83 - 4.53 (est)c
4.4A
-9.08A
m-xylene
108-38-3
-2.14A
2.22 - 2.44A
<
C\J
CO
-1.96A
N,N-Diethyl-3-methylbenzamide
(DEET)
134-62-3
-7.68A
2.06A
2.02A
-5.58A
N4-acetyl-Sulfamethazine
100-90-3
-12.20 (est)c
1.98-2.26 (est)c
1.48 (est)c
-13.43 (est)c
Nalidixic Acid
389-08-2
-18.29 (est)c
1.00- 1.34 (est)c
1.41A
-10.09 (est)c
8.6A
Naphthalene
91-20-3
-0.37A
2.05 - 5.59A
3.3A
-3.95A
Naproxen
22204-53-1
-9.47A
2.52A
3.18A
-8.60A
4.15A
Nitrilotriacetic acid
139-13-9
-18.92 (est)c
<2.46A
-3.81 (est)c
-11.04A
3.03, 3.07, 10.7A
Nonylphenol
25154-52-3
-5.25A
4 - 4.7A
5.71A
-6.91A
10.25A
Nonylphenol ethoxylates
9016-45-9
-11.59--11.57
3.39 - 3.41 (est)c
5.30 (est)c
-10.92 (est)c
145
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
(est)c
Norfloxacin
70458-96-7
-21.06 (est)c
4.79A
0.46A
-15.05A
6.32, 8.75A
N-propyl chloride
540-54-5
-1.89A
1.6A
2.04A
-0.35A
Octylethoxylate
9002-93-1
-9.91 - -9.15
(est)c
2.90 - 3.26 (est)c
4.86 (est)c
6.99
Octylphenol
949-13-3
-8.35 - -8.06
(est)c
4.13-4.33 (est)c
5.50 (est)c
-6.89 (est)c
Ofloxacin
82419-36-1
-22.30 (est)c
4.64A
-0.39A
-14.89A
5.97, 9.28A
OH-ibuprofen
51146-55-5
-14.26 (est)c
1.01 - 1.46 (est)c
2.29 (est)c
-9.26 (est)c
op' DDA
34113-46-7
-7.49 - -8.71
(est)c
2.56 - 3.45 (est)c
4.35 (est)c
-9.06 (est)c
Oxazepam
604-75-1
-9.26A
2.59A
2.24A
-14.26A
1.55, 10.9A
o-xylene
95-47-6
-2.29A
1.38-2.4A
3.12A
-2.06A
Oxytetracycline
79-57-2
-24.77A
2.29 - 4.97A
-0.9A
-26.90A
9.5A
Para-cresol
106-44-5
-6A
1.43 - 2.81A
1.94A
-3.84A
10.26A
Para-tert-octylphenol
140-66-9
-5.16A
4.00 - 4.01 (est)c
5.28A
-6.20°
Pentachlorophenol
87-86-5
-7.61A
3.1- 5.09A
5.12A
-6.84A
4.7A
Pentoxifylline
6493-05-6
-16.25 (est)c
1.00- 1.38 (est)c
0.29°
-12.80 (est)c
Perfluorobutanesulfonic Acid
(PFBS)
375-73-5
-4.84A
1.42 - 2.26A
1.82A
-4.45A
-3.31A
Perfluoroctanionic Acid (PFOA)
335-67-1
-1.04A
1.92 - 2.59A
4.81A
-4.38A
-0.5 - 4.2A
Perfluorodecanoic Acid (PFDA)
335-76-2
-2.60 (est)c
3.56 - 5.72 (est)c
6.15 (est)c
-4.45 (est)c
Perfluorododecanoic Acid (PFDoA)
307-55-1
-1.16 (est)c
4.30 - 7.03 (est)c
7.49 (est)c
-5.25 (est)c
Perfluoroheptanoic Acid (PFHpA)
375-85-9
-1.77A
1.52-4A
4.15A
-3.76A
-2.29A
Perfluorohexanesulfonic
Acid(PFHxS)
355-46-4
-3.4A
0.97A
3.16A
-5.22A
0.14A
Perfluorohexanoic Acid (PFHxA)
307-24-4
-5.48 (est)c
1.63-4.7A
3.48A
-2.59A
-0.16A
Perfluorononanoic Acid (PFNA)
375-95-1
-3.32 (est)c
2.4 - 5.08A
5.48A
-3.96A
-0.21A
146
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Perfluorooctanesulfonic Acid
(PFOS)
1763-23-1
-3.39A
2.1 - 4.7A
4.49A
-5.58A
<1.0A
Perfluoroundecanoic Acid (PFUnA)
4234-23-5
-33.30 (est)c
0.86 - 3.98 (est)c
2.75 (est)c
-31.87 (est)c
Perylene
198-55-0
-6.38A
6.84A
6.3A
-12.18A
Phenanthrene
85-01-8
-1.59A
3.96 - 7A
4.46A
-6.8A
Phenol
108-95-2
-3.39A
3.46 - 3.49A
1.46A
-3.34A
9.99A
Pipemidic Acid
51940-44-4
-20.84 (est)c
-0.61 - 1.00 (est)c
-2.15c
-14.63 (est)c
pp' DDA
83-05-6
-7.62 - 8.77
(est)c
2.63 - 3.45 (est)c
4.48 - 4.64°
-9.31 (est)c
Primidone
125-33-7
-12.71 (est)c
1.38-2.14 (est)c
0.91A
-12.36 (est)c
Progesterone
57-83-0
-7.19A
3.45A
3.87A
-6.33A
Propazine
139-40-2
-8.34A
1.92-2.7A
2.93A
-9.77A
1.7A
Propranolol
525-66-6
-15.10 (est)c
2.45 - 2.96 (est)c
3.48°
-9.91 (est)c
Propylparaben
94-13-3
-8.37A
2.46A
3.04A
-6.40A
8.5A
Propyphenazone
479-92-5
-11.74 (est)c
1.93-2.81 (est)c
1.94°
-7.87 (est)c
p-xylene
106-42-3
-2.16A
2.39-2.73A
3.15A
-1.94A
Pyrene
129-00-0
-2.05A
3.54 - 6.8A
4.88A
-8.23A
Roxithromycin
80214-83-1
-33.30 (est)c
0.86 - 3.98 (est)c
2.75 (est)c
-31.87 (est)c
Salicylic acid
69-72-7
-8.14A
2.6A
2.26A
-6.97A
2.98A
Sertraline
79617-96-2
-10.29 (est)c
3.81 - 5.23 (est)c
5.29 (est)c
-8.81 (est)c
Simazine
122-34-9
-9.03A
1.89 - 3.55A
2.18A
-10.54A
1.62A
Skatole
83-34-1
-5.68A
2.78A
2.6A
-5.14A
Sodium decylbenzenesulfonate
1322-98-1
-10.45 (est)c
2.04 - 3.57 (est)c
2.02 (est)c
-16.77 (est)c
Sodium dodecycbenzenesulfonate
25155-30-0
-10.20°
2.58-4.10 (est)c
0.045A
-17.52°
Sodium N-tridecylbenzenesulfonate
26248-24-8
-10.08 (est)c
2.86 - 4.35 (est)c
2.52A
-17.90 (est)c
Sodium Undecylbenzenesulfonate
27636-75-5
-10.32 (est)c
2.13-3.83 (est)c
2.51 (est)c
-17.15 (est)c
147
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0'7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Sotalol
3930-20-9
-16.60 (est)c
0.74- 1.35 (est)c
0.24°
-14.75 (est)c
Sucralose
56038-13-2
-18.4A
1A
-1A
-16.37A
Sulfadiazine
68-35-9
-12.80 (est)c
1.39- 1.87 (est)c
-0.09°
-11.16 (est)c
Sulfadimethoxine
122-11-2
-16.89 (est)c
1.48 - 2.45 (est)c
1.63°
-14.71 (est)c
Sulfamethazine
57-68-1
-9.71A
1.65- 2.39A
0.14A
-11.05A
2.07, 2.65, 7.49,
7.65A
Sulfamethoxazole
723-46-6
-12.19A
1.86A
0.89A
-10.04A
1.6, 5.7A
Sulfamonomethoxine
1220-83-3
-16.62 (est)c
1.68 - 1.89 (est)c
0.70°
-10.96 (est)c
Sulfanilamide
63-74-1
-9.82A
1.04A
-0.62A
-8.02A
10.43, 10.58,
11,63A
Sulfathiazole
72-14-0
-13.24A
1.99-2.3A
0.05A
-10.26A
2.2, 7.24A
Terbuthylazine
5915-41-3
-7.64A
2.18 - 2.71A
3.4A
-9.05A
2A
Terbutryne
886-50-0
-7.68A
2.56 - 4.62A
3.74A
-8.65A
4.3A
Tert-Amyl chloride
594-36-5
-4.59 (est)c
1.94-2.19 (est)c
2.52°
-1.04 (est)c
Tertrabutyltin
1461-25-2
-2.20 (est)c
4.90-8.13 (est)c
9.37 (est)c
-5.20A
Testosterone
58-22-0
-8.46A
3.25 - 3.52A
3.32A
-10.65A
Tetrachloroethylene
127-18-4
-1.75A
2.33A
3.4A
-1,62A
Tetracycline
60-54-8
-26.33 (est)c
-0.12-1.64 (est)c
-1.37A
-23.56 (est)c
3.3A
Thiabendazole
148-79-8
-10.68A
3.4 - 3.67A
2.47A
11,28A
4.64A
Toluene
108-88-3
-2.18A
1.57 - 2.25A
2.73A
-1.43A
Tolylfluanid
731-27-1
-6.12A
3.51A
3.9A
-8.71A
Tonalide
21145-77-7
-3.85A
3.8 - 4.8A
5.7A
-6.17A
Tramadol
27203-92-5
-10.81A
2.79A
3.01A
-9.22A
9.23, 13.08A
Tribromoacetic acid
75-96-7
-8.48A
0.72A
1.71A
-6.44A
0.72A
Tribromomethane
75-25-2
-3.27A
2.06-2.1A
2.4A
-2.15A
Tributyl phosphate
126-73-8
-5.85A
3.38A
4A
-5.83A
148
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Tr
butyltin
688-73-3
-2.81 (est)c
3.56 - 3.91 (est)c
4.10c
-4.28 (est)c
Tr
chloroacetic acid
76-03-9
-7.87A
2.11A
1.33A
-4.10A
0.51A
Tr
chloroethylene
79-01-6
-2.04A
1.69 - 3.43A
2.61A
-1.04A
Tr
chloromethane
67-66-3
-2.44A
1.53 - 2.29A
1.97A
-0.59A
Tr
closan
3380-34-5
-7.68A
3.38 - 4.20A
4.76A
-8.22A
7.9A
Tr
cyclohexyltin chloride
3091-32-5
-5.76 (est)c
5.72-6.15 (est)c
7.09 (est)c
-6.91 - -7.95 (est)c
Tr
cyclohexyltin Hydroxide
13121-70-5
-7.58 (est)c
4.42 - 5.05 (est)c
8.20 (est)c
-9.31 (est)c
Tr
ethylphosphate
78-40-0
-7.44A
1.81A
0.8A
-3.29A
Tr
fluoromethane
75-46-7
-1.02A
1.51A
0.64A
1.67A
Tr
iodomethane
75-47-8
-4.51A
1.54A
3.03 (est)c
-4.28A
Tr
isobutyl phosphate
126-71-6
-8.50 (est)c
3.02-3.15 (est)c
3.60 (est)c
-4.77 (est)c
Tr
methoprim
738-70-5
-13.62A
1.88A
0.91A
-10.89A
7.12A
Trimethyltin Chloride
1066-45-1
-4.40 (est)c
0.243 - 1.64 (est)c
0.24 - 1.64
(est)c
-2.91 (est)c
Tr
-n-Butyltin Hydride
688-73-3
-2.82 (est)c
3.56 - 3.91 (est)c
4.10°
-4.28A
Tr
octyltin chloride
2587-76-0
-3.63 (est)c
7.21 -9.19 (est)c
10.59 (est)c
-7.48 (est)c
Tr
phenyl phosphate
115-86-6
-5.48A
3.4-3.55A
4.59A
-8.08A
Tr
phenylphosphine oxide
791-28-6
-12.28 (est)c
2.26 - 3.29 (est)c
2.83°
-11.47°
Tr
phenyltin Acetate
900-95-8
-8.53 (est)c
1.32-4.84 (est)c
3.43A
-7.73A
Tr
iphenyltin chloride
639-58-7
-8.53 (est)c
3.64 - 5.72 (est)c
4.19A
-2.12A
Tr
iphenyltin hydride
892-20-6
-7.11 (est)c
3.04 - 5.53 (est)c
3.50 (est)c
-6.99 (est)c
Tr
iphenyltin hydroxide
76-87-9
-11.93 (est)c
3.30A
3.53A
-9.33A
5.20A
Tr
die
is(1,3-
;hloroisopropyl)phosphate
13674-87-8
-8.59A
3.04A
3.65A
-9.43A
Tris(2-butoxyethyl)phosphate
78-51-3
-10.92A
3.1A
3.75A
-8.80A
Tris(2-chloroethyl)phosphate
115-96-8
-5.48A
2.59A
1.43, 1.78A
-4.10A
149
-------
Table A4. Select properties of organic contaminants potentially found in stormwater.
Compound
CAS#
Log Kh4
Log Koc5
Log Kow6
Log P0 7
pKa8
Units
L m3/mol
L/kg
L/kg
atm
Tylosin
1401-69-0
-37.24A
2.74 - 4.98A
1.63A
-36.59A
7.73A
Valsartan
137862-53-
4
-17.51A
2.4 - 4.36A
4A
-17.97A
3.6, 4, 4.61A
Venlafaxine
93413-69-5
-10.69A
2.28A
CO
ro
>
-9.48A
10.09A
Xylene
1330-20-7
-2.29--2.14A
2.11 - 2.52A
-2.06 - -1.94A
a-BHC
319-84-6
-5.18A
3.25 - 3.30A
3.8A
-7.23A
a-Endosulfan
959-98-8
-10.04 (est)c
3.21 - 3.83 (est)c
3.83°
-9.10c
a-Hexachloro-cyclohexane
319-84-6
-5.18A
3.25 - 3.30A
3.8A
-7.23A
1Melting Point
2Boiling Point
3CW is the solubility in water
4Kh is the Henry's Law constant
5K0c is the organic carbon - water partitioning coefficient
6K0W is the octanol - water partitioning coefficient
7P° is the vapor pressure
8pKa is the acid dissociation constant
AToxnet: httDs://toxnet.nlm.nih.aov/newtoxnet/hsdb.htm - Onlv values that were Deer reviewed were used unless denoted bv est for estimated value
CEPI Suite version 4.1: U.S. EPA. Office of Pollution Prevention and Toxics and Syracuse Research Corporation. Copyright 2000 - 2012. Peer reviewed values were used when
available, otherwise the values obtained are delineated using est
Decomp= decomposes
est= estimated value
150
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