United States
Environmental Protection
Agency
EPA/600/R-14/083 | April 2014 | WWW.epa.gov/epa-research
Technical Resource Document
on Monitored Natural Recovery
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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EPA/600/R-14/083
April 2014
TECHNICAL RESOURCE DOCUMENT
ON
MONITORED NATURAL RECOVERY
by
Battelle
Columbus, OH 43201
for
U.S. Environmental Protection Agency
National Risk Management Research Laboratory
Contract No. EP-W-09-024
Work Assignment 4-07
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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NOTICE
The U.S. Environmental Protection Agency (EPA), through its office of Research and
Development (ORD), funded and managed the preparation of this document. This document is a
technical resource primarily intended for federal project managers considering the use of monitored
natural recovery (MNR) for remediating contaminated sediment sites under the Comprehensive
Environmental Response, Compensation, and Liability Act (CERCLA), more commonly known as
'Superfund'. Many aspects of this document may also be useful to state and local government
organizations, consultants, and other members of the environmental community and their technical
advisors.
This document does not substitute for EPA statutory provisions, regulations, guidance or
policies, nor is it a regulation or guidance itself. It cannot establish or affect legally binding requirements
or obligations on EPA, states, or the regulated community. This document is intended to provide factual
information and should not be interpreted as providing advice regarding a specific site, situation, or set of
circumstances. Any decisions regarding a particular site will be made based on relevant statutes,
regulations, guidance, and policies. EPA decision-makers retain the discretion to adopt approaches on a
site-by-site basis that differ from this document, where appropriate. This document would not be
controlling and cannot be relied upon to contradict or argue against any EPA position taken
administratively or in court.
Any opinions expressed in this report are those of the authors and do not necessarily reflect
the views of EPA. Any mention of trade names or commercial products does not constitute endorsement
or recommendation for use and shall not be used for advertising or product endorsement purposes.
Citations to reference material (e.g., studies, papers, analyses) do not necessarily represent endorsement
of, or agreement with, that material by EPA.
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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 pollutants affect
our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory (NRMRL) 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 groundwater; 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.
Contaminated sediment sites pose significant risks throughout the United States to human
health and ecological receptors/resources. Several approaches are available for remediating such sites and
contributing to the recovery of the associated ecosystems. These approaches range from engineered
solutions such as dredging and in-situ capping to reliance on natural physical, chemical, and biological
processes to contain, destroy, and/or reduce the bioavailability and toxicity of contaminants in sediments.
Reliance on natural recovery processes combined with a comprehensive monitoring program over time
has become known as monitored natural recovery (MNR). This report was prepared as a technical
resource document (TRD) on MNR to complement Agency guidance on MNR provided in a previous
publication, Contaminated Sediment Remedial Guidance for Hazardous Waste Sites, Chapter 4,
Monitored Natural Recovery (EPA, 2005a). EPA's Office of Research and Development is making this
report available to the user community as supplemental material to the above referenced guidance
document.
Cynthia Sonich-Mullin, Director
National Risk Management Research Laboratory
in
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CONTENTS
NOTICE ii
FOREWORD iii
FIGURES vii
TABLES viii
HIGHLIGHTS ix
ACRONYMS AND ABBREVIATIONS x
ACKNOWLEDGMENTS xiii
ABSTRACT xiv
1.0 INTRODUCTION 1
1.1 Purpose and Objectives 1
1.2 MNR Processes and Mechanisms 2
1.2.1 Natural Sediment, Physical, Chemical, and Biological Processes 2
1.2.1.1 Physical Processes 3
1.2.1.2 Chemical Processes 3
1.2.1.3 Biological Processes 3
1.2.2 Risk Reduction Processes 3
1.2.2.1 Contaminant Isolation/Burial 3
1.2.2.2 Contaminant Dispersion 4
1.2.2.3 Contaminant Sequestration 4
1.2.2.4 Contaminant Transformation 4
1.3 Assessing MNR 4
1.3.1 Source History and Control 4
1.3.2 Lines-of-Evidence 6
1.3.3 Conceptual Site Model Development 7
1.3.4 CSM Structure and Relevant Processes 7
1.3.5 Management of Risk 10
1.3.5.1 Risk of Remedy 10
1.3.5.2 Adaptive Management 10
2.0 SEDIMENTATION AND CONTAMINANT ISOLATION 11
2.1 Contaminant Burial and Isolation 11
2.2 Potential for Contaminant Mobilization 12
2.3 Contaminant Transport via Sediment Transport Processes 13
2.3.1 Sediment Transport Processes 13
2.3.1.1 Sediment Erosion 13
2.3.1.2 Sediment Transport 14
2.3.1.3 Sediment Deposition 15
2.3.2 Physical Properties Related to Sediment Transport 16
2.3.2.1 Sediment Grain Size 16
2.3.2.2 Sediment Bulk Density 17
2.3.2.3 Sediment Cohesiveness 17
2.3.2.4 Bioturbation 18
2.4 Measures of Surface Sediment Contaminant Concentrations 18
2.4.1 Measuring Changes in Surface Sediment Contamination 18
2.4.1.1 Core Analysis Methods 19
2.4.1.2 Surface Sediment Analysis 23
2.4.1.3 Bathymetry 29
2.4.1.4 Watershed Mass Balance 29
IV
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2.4.2 Uncertainties in Determining Reductions in Surface Sediment Contamination 29
2.4.3 Predicting Sediment Erosion and Transport: A Tiered Approach 31
2.4.3.1 Tier 1 Estimates of Sediment Transport Processes 31
2.4.3. la Using the Conceptual Site Model 35
2.4.3.1b Mass Balance Estimates 35
2.4.3.1c Estimating Bottom Shear Stress 36
2.4.3.Id Estimating Critical Shear Stress 39
2.4.3.1e Identifying Erosional Events 41
2.4.3.If Propeller-Induced Scour 41
2.4.3.1g Wind-Induced Wave Erosion 42
2.4.3.2 Moving from Tier 1 to Tier 2 Sediment Erosion Characterization 42
2.4.3.3 Tier 2 Estimates of Sediment Erosion and Transport 43
2.4.3.3a Site-Specific Sediment Erosion and Critical Shear Stress
Measurements 44
2.4.3.3b Water Column Hydrodynamic Studies 48
2.5 Summary 48
3.0 FATE OF COMMON ORGANIC CONTAMINANTS IN SEDIMENTS 49
3.1 Introduction 49
3.1.1 Monitored Natural Recovery 49
3.1.2 Non-polar Organic Compounds of Concern 49
3.1.2.1 Polycyclic Aromatic Hydrocarbons 51
3.1.2.2 Polychlorinated Biphenyls 56
3.1.2.3 Polychlorinated Dibenzo-p-Dioxins and Polychlorinated
Dibenzofurans 62
3.2 Measuring Fate Processes and Natural Recovery 63
3.2.1 Physical and Chemical Processes Affecting HOC Fates 64
3.2.2 Application of HOC Partitioning in Sediment to MNR 65
3.2.3 Influence of Sediment-Water Partitioning on HOC Bioavailability 66
3.2.4 Sorptionto Organic Colloids 68
3.2.5 Non-Linear and Non-Equilibrium Sorption 70
3.2.6 Intraparticle and Intraorganic Matter Diffusion 72
3.2.7 Enhanced Sorptionto Black Carbon Sorbents 72
3.2.8 Sorption of PAHs, Coplanar PCBs, and PCDDs/PCDFs to Black Carbon
Sorbents 73
3.2.9 Partitioning of PAHs from Sediment NAPL 76
3.2.10 Physical/Chemical Transformation 77
3.2.10.1 HOC Dissolution and Evaporation 77
3.2.10.2 Electrophilic Substitution 79
3.2.10.3 Oxidation/Reduction 80
3.2.10.4 Photooxidation 80
3.2.10.5 Advective and Diffusive Transport Affecting Dispersal of
Sediment-Associated HOCs 81
3.3 Biological Transformation of HOCs in Sediments 83
3.3.1 PAH Biodegradation 83
3.3.2 PCB Biotransformation 88
3.3.3 PCDD and PCDF Biotransformation 91
3.4 Assessing Contaminant Transformation 92
3.4.1 Analytical Methods Used in Support of Fingerprinting 93
3.4.2 Hydrocarbon Fingerprinting and Weathering Processes 95
3.4.2.1 Hydrocarbon Source Fingerprinting 96
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3.4.2.2 Differentiating Petrogenic from Pyrogenic PAH Signatures 97
3.4.2.3 Understanding Urban Background Sediments 99
3.4.2.4 Characterization of Hydrocarbon Weathering 100
3.5.2.5 Summary of Hydrocarbon Fingerprinting and Weathering
Processes 102
3.4.3 Chemical Fingerprinting of PCBs as Part of MNR 104
3.4.3.1 Properties of PCB Mixtures 107
3.4.3.2 Characterization of PCB Weathering 109
3.4.3.3 Multivariate Model Fingerprinting Ill
3.4.3.4 Summary of PCB Fingerprinting for MNR 114
3.4.4 Assessing Natural Attenuation of PCDDs/PCDFs in Sediments 114
3.5 Assessing Sorption/Sequestation 115
4.0 FATE OF INORGANIC CONTAMINANTS IN SEDIMENTS 117
4.1 Relevance of Metals Behavior for MNR 117
4.2 Biogeochemical Processes that Affect Metal Behavior 117
4.2.1 Ionic Strength, Hardness, Alkalinity, and Temperature 118
4.2.2 pHandEh 118
4.2.3 Sediment Diagenesis and Establishment of Vertical Redox Profile 119
4.2.4 Iron and Manganese Oxides 120
4.2.5 Natural Organic Matter and Low Molecular Weight Organic Acids 122
4.2.6 Solubility Controls by Precipitation and Dissolution: The Formation of
Insoluble Complexes, Sediment Resuspension, and Oxidation 123
4.2.7 Spatial and Temporal Stability of Sedimentary Redox Boundaries 126
4.3 Biogeochemical Process Affecting Speciation and Bioavailability of Metals in Water
and Sediment 128
4.3.1 Metal Speciation: Adsorption, Complexation, and Solubility 130
4.3.2 Metal-Solid Partitioning 131
4.3.3 Metal-Organic Matter Interactions: Relevance to Bioavailability 133
4.4 Metal Specific Behavior 134
4.4.1 Arsenic 135
4.4.2 Cadmium 137
4.4.3 Chromium 137
4.4.4 Copper 138
4.4.5 Mercury 138
4.4.6 Nickel 139
4.4.7 Silver 140
4.4.8 Zinc 140
4.5 Sediment Sampling for Metals: Methods for Collection and Analytical Considerations 140
4.5.1 Collection of Sediments and Interstitial Water 141
4.5.2 In-Situ Analysis of Interstitial Water: Microelectrodes and Thin Films 142
4.5.3 Measuring Sediment-Water Exchange: Benthic Flux Chambers 143
4.5.4 Analysis of Key Geochemical Constituents in Sediments 143
4.5.4.1 pH 143
4.5.4.2 Ammonia in Pore Water 143
4.5.4.3 Total Organic Carbon Content 143
4.5.4.4 Particle Size Distribution (Percent Sand, Silt, and Clay) 144
4.5.4.5 Percent Water or Moisture Content 144
4.5.4.6 Salinity of the Pore Water (Marine Sediments) 144
4.5.4.7 Total Sulfides 144
4.5.4.8 Cation Exchange Capacity of Sediments 145
VI
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4.5.4.9 Redox Potential (Eh) of Sediments 145
4.5.4.10 Dissolved Oxygen 146
4.5.4.11 Dissolved Organic Carbon in Pore Water 146
4.5.4.12 Alkalinity and Hardness of Pore Water (Fresh Water Sediments) 146
4.5.4.13 Conductivity of Pore Water (Fresh Water Sediments) 147
4.6 Analytical Approaches to Metal Speciation in Sediments: Sequential Extraction,
SEM/AVS, and Spectroscopic Techniques 147
4.6.1 Analysis of Metals from Sediments: Sequential Extraction 147
4.6.2 The Simultaneously Extracted Metals/Acid Volatile Sulfide Concept 148
4.6.3 Limitations and Cautions in the Use of SEM/AVS 150
4.6.4 Promising Spectroscopic and Analytical Techniques 151
4.7 Model Approaches to Predicting Equilibrium Metal Speciation 152
4.7.1 Free Ion Activity Model 153
4.7.2 Windermere Humic Acid Model 153
4.7.3 Reactive Transport Modeling 153
4.8 Summary 153
5.0 LONG-TERM MONITORING AND SITE FORECASTING WITH PREDICTIVE MODELS 155
5.1 Monitoring Rationale and Strategies 155
5.2 Metrics for Long-Term Monitoring 156
5.2.1 Sediment Monitoring 157
5.2.2 Surface Water and Pore Water Monitoring 158
5.2.3 Biological Endpoint Monitoring and Ecological Monitoring 158
5.3 Predicting Long-Term Recovery 166
5.3.1 Predicting Recovery of Ecological Receptors 166
5.4 One-Dimensional Transport Modeling to Predict Changes in Surface Chemical
Concentrations 167
5.5 Numerical Models 169
5.5.1 Hydrodynamic Modeling 169
5.5.2 Sediment Bed Modeling 169
5.5.3 Sediment Transport Modeling 169
5.5.4 Integrating Models 172
5.6 Summary 172
6.0 REFERENCES 174
FIGURES
Figure 1-1. Conceptual Site Model Development - Hydrophobic Organic Contaminants (HOCs) 8
Figure 1-2. Conceptual Site Model Development - Inorganic Contaminants 9
Figure 2-1. Simplified Diagram of Major Sediment Transport Processes 14
Figure 2-2. Bioturbation Zones 19
Figure 2-3. Typical Trend in Critical Shear Stress as Sediment Composition Transitions from
Sand to Gravel 40
Figure 2-4. Propeller Action that Produces Scour 42
Figure 2-5. Annular Flume Configuration 45
Figure 2-6. Straight Flume Configuration Showing Upright Perspective (top) and Inverted
Perspective (bottom) 45
Figure 2-7. Sedflume Schematic 46
Figure 3-1. Chemical Structure and Carbon-Numbering System of HOC Compound Classes 50
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Figure 3-2. Relative Concentrations of Parent and Alkyl PAHs in Petrogenic Mixtures: Gasoline,
Diesel Fuel, and a Middle Weight Crude Oil 54
Figure 3-3. Relative Concentrations of Parent and Alkyl PAHs in Representative Pyrogenic
Mixtures: Coal Tar and Creosote 55
Figure 3-4. Positions ofOrtho,Meta, andPara Carbons in PCBs 62
Figure 3-5. Initial Steps in Biodegradation of PAHs by Prokaryotes (Bacteria) and Eukaryotes
(Fungi, Algae, Plants, Animals) 85
Figure 3-6. Comparison of PAH Composition and Concentration of a Heavy Fuel Oil #6 Based
on Analysis of the 16 TCL PAHs by Standard EPA Method 8270 and Analysis of the
Extended List of 43 Parent and Alkyl-PAHs by Modified EPA Method 8270 94
Figure 3-7. Profiles of the Naphthalene (TV) and Chrysene (C) Distributions in Pyrogenic (Black)
and Petrogenic (Gray) PAH Assemblages 98
Figure 3-8. THC Fingerprint (top) and PAH Histogram (bottom) of a Sediment Sample
Containing Hydrocarbons Attributed to Non-point Sources 101
Figure 3-9. Percent Loss of Total PAHs from Weathered North Slope Crude Oil in Subsurface
Sediments on the Shore at Sites Where Oil Has Persisted for 17 Years after the Exxon
Valdez Oil Spill 103
Figure 3-10. Congener Distribution Histograms for Five Common Aroclor Mixtures 108
Figure 4-1. Sequence of Microbially Mediated Reduction-Oxidation Reactions 121
Figure 4-2. Typical Redox Zones in Surficial Sediments in a Marine System Mediated by
Biological Reactions 122
Figure 4-3. Seasonal Variation of Sediment Oxygen Uptake Rate (Oxygen Consumption),
Sulfate Reduction Rate (Sulfide Production) in the Whole Sediment Column, and
Sediment Temperature 127
Figure 4-4. Mercury Speciation Plot, Hgtot = 10"3 M 131
Figure 4-5. Mercury Speciation Plot in a Solution of Sodium Chloride 132
Figure 4-6. Oxidation/Reduction Cycling of Iron and Sulfur is Driven by Microbial Respiration,
and Can Lead to Cr(VI) Reduction and Attenuation as Insoluble Cr(III) Precipitates 138
Figure 5-1. Example of Vertical 1-D Contaminant Transport Modeling of PCBs in Sediments
using: (a) Diffusion Only, (b) Diffusion Plus Deposition with Mild Benthic Mixing,
and (c) Diffusion Plus Deposition Plus Rapid Benthic Mixing 168
TABLES
Table 1-1. Sources of Sediment Contamination 5
Table 2-1. Common Grain Size Scale for Sediment Particles 16
Table 2-2. Common Coring Methods 20
Table 2-3. Common Radiological Age Dating Methods for Sediments 24
Table 2-4. Data Collection for Tier 1 and Tier 2 Sediment Transport Evaluation 32
Table 2-5. Critical Shear Stress for Particles Larger than 200 um 39
Table 2-6. Comparison of Devices for Measurement of Sediment Critical Shear Stress 44
Table 3-1. PAH and Related Compounds Typically Used in Hydrocarbon Fingerprinting 52
Table 3-2. Approximate Molecular Composition (%) and Physical Properties of Seven
Commercial Aroclors 57
Table 3-3. The IUPAC Numbering System for Polychlorinated Biphenyl Congeners and Log
KOW 58
Table 3-4. Toxicity Equivalency Factors (TEFs) of PCDDs, PCDFs, Non-Ortho- and Mono-
Ortho-PCBs, and Selected PAHs for Mammals from the World Health Organization
(WHO) and Relative Potency of PAHs in Fish 61
Vlll
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Table 3-5. Estimated Average Emission Factors (Mass of PCDDs/PCDFs Released per Mass of
Fuel Burned) and Total Annual Global Emissions of PCDDs/PCDFs to the
Atmosphere from Combustion Sources 63
Table 3-6. Physical/Chemical Properties of Importance in Environmental Fate Estimations of
Representative PAHs, PCBs, PCDDs, and PCDFs 68
Table 3-7. Mean Measured log Koc and log KBc Values for PAHs in More than 100 Historically
Contaminated Sediments Containing 0.2 to 8,600 |og/g Dry Weight Total PAHs 77
Table 3-8. Log Koc, Kd, and Retardation Factors (R) for Several PAHs in Sediments Containing
0.1 or 5% Organic Carbon 83
Table 3-9. Estimated Half-Lives (hours) for Selected PCBs, PCDDs, and PCDFs in Sediments 91
Table 3-10. Analytical Methods Frequently Used for Fingerprinting of Semivolatile
Hydrocarbons in Sediment 94
Table 4-1. Solubility Products for Selected Metal Sulfide, Carbonate, Phosphate, and
(Hydr)Oxides 125
Table 4-2. Dominant Adsorbed or Complexed Phases of Metals in Oxic and Anoxic Sediments 134
Table 5-1. Benthic Ecology Assessment Metrics and Analysis Tools 162
Table 5-2. Biological Indices 165
Table 5-3. Hydrodynamic Models 170
Table 5-4. Sediment Bed Models 171
Table 5-5. Sediment Transport Models 172
HIGHLIGHTS
Highlight 2-1. Lake Hartwell Surface Sediment PCB Trends 27
Highlight 2-2. Surface Sediment DDT, Lead, and PAH Trends in the United States and in White
Rock Lake Sediments 28
Highlight 2-3. Hypothetical Mass Balance Example 38
Highlight 3-1. Hydrocarbon MNRCase Study- Creosote Impacted Sediments 105
Highlight 3-2. Creosote Weathering Trends in Eagle Harbor Sediments 106
Highlight 3-3. Characterization of Lake Hartwell Surface Sediments by Comparison with Known
Aroclors and Aroclor Mixtures 110
Highlight 3-4. Evaluating PCB Dechlorination by Comparing PCB Congener Histograms 112
Highlight 3-5. Dechlorination ofMeta and Para Chlorines and Conservation ofOrtho Chlorines at
Lake Hartwell 113
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ACRONYMS AND ABBREVIATIONS
1-D one-dimensional
2-D two-dimensional
3-D three-dimensional
ADP acoustic Doppler profiler
AEP available for equilibrium partitioning
AhR aryl hydrocarbon receptor
AOM amorphous organic matter
APHA American Public Health Association
ASSET Adjustable Shear Stress Erosion and Transport
ASTM American Society for Testing and Materials
ATSDR Agency for Toxic Substances and Disease Registry
AVS acid volatile sulfide
BC black carbon
BSAF biota sediment accumulation factor
CB chlorobiphenyl
CBR critical body residue
CEC cation exchange capacity
CSM conceptual site model
CWA Clean Water Act
CYP cytochrome P450 mixed function oxygenase
DDT dichlorodiphenyltrichloroethane
DET diffusive equilibration in thin film
DGT diffusive gradients in thin film
DNAPL dense non-aqueous phase liquid
DO dissolved oxygen
DOC dissolved organic carbon
DOM dissolved organic matter
DRH TPH-diesel range
EDTA ethylene diamine tetraacetate
Eh redox potential
EPA U.S. Environmental Protection Agency
EqP equilibrium partitioning
ESB equilibrium partitioning sediment benchmark
EXAFS extended X-ray absorption fine structure
FCV final chronic value
FIAM Free Ion Activity Model
FID flame ionization detector
GC gas chromatography
GRH TPH-gasoline range
HOC
hydrophobic organic contaminant
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ICP-AES inductively-coupled plasma atomic emission spectrometry
ICP-MS inductively coupled plasma mass spectrometry
I.D. inner diameter
IOMD intraorganic matter diffusion
IUPAC International Union of Pure and Applied Chemistry
MDL method detection limit
MeHg monomethyl mercury
MFO mixed function oxygenase
MGP manufactured gas plant
MNR monitored natural recovery
MS mass spectrometry
NAPL non-aqueous phase liquid
NOAA National Oceanic and Atmospheric Administration
NOM natural organic matter
NRC National Research Council
NRMRL National Risk Management Research Laboratory
NS&T National Status and Trends
OCDD octachlorodibenzodioxin
OSI Organism-Sediment Index
PAH polycyclic aromatic hydrocarbon
PCA principal component analysis
PCB polychlorinated biphenyl
PCDD polychlorinated dibenzo-/?-dioxin
PCDF polychlorinated dibenzofurans
pH hydrogen ion activity
pHpzc pH at the point of zero charge
pKa acid dissociation constant
POC particulate organic carbon
ppm parts per million
PSD particle size distribution
PSEP Puget Sound Estuary Program
PVA polytopic vector analysis
QA quality assurance
QC quality control
RAO remedial action objective
Redox oxidation/reduction
RI remedial investigation
ROD Record of Decision
RRH TPH-residual (crude and heavy fuel oil) range
RTM reactive transport model
SEAWOLF Sediment Erosion Actuated by Wave Oscillations and Linear Flow
SEM simultaneously extracted metal
SIM selected ion monitoring
SOM soil organic matter
XI
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SPI sediment profile imagery
SQT Sediment Quality Triad
SRB sulfate-reducing bacteria
SUDAS Iowa Statewide Urban Design and Specifications
SVOC semivolatile organic compound
TCDD tetrachlorodibeno-/>-dioxin
TCL target contaminant list
TEA terminal electron acceptor
TEF toxicity equivalent factor
THC total extractable hydrocarbon
TOC total organic carbon
TPH total petroleum hydrocarbon
TRD technical resource document
UCM unresolved complex mixture
UNEP United Nations Environmental Programme
USAGE U.S. Army Corps of Engineers
USGS U.S. Geological Survey
WHAM Windermere Humic Aqueous Model
WHO World Health Organization
XANES X-ray absorption near edge structure
XAS X-ray absorption
XRD X-ray diffraction
xn
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ACKNOWLEDGMENTS
Development of this technical resource document (TRD) on monitored natural recovery
(MNR) has constituted a multi-year project spanning three U.S. Environmental Protection Agency (EPA)
contracts:
• Contract No. 68-C-00-185, Task Order 28
• Contract No. EP-C-05-057, Task Order 84
• Contract No. EP-W-09-024, Work Assignments 3-07 and 4-07
The scope of this TRD has changed several times during the conduct of this project to more
fully and accurately represent a companion or complimentary report to EPA's guidance on MNR in
Chapter 4 of the Contaminated Sediment Remedial Guidance for Hazardous Waste Sites published in
2005. Collaborative efforts between EPA and the project's prime contractor, Battelle (Columbus, OH),
over several mid-course corrections have resulted in a TRD that reflects the Agency's goals of providing
supportive information on methods for measuring and predicting MNR without infringing on the policy
guidance and technical protocols established in the above-mentioned guidance document.
Appreciation is extended to the following Battelle and subcontractor personnel who contributed
to authoring and revising this TRD:
Battelle Jerry Neff and Associates Environ International Corp.
Eric Foote, Project Manager Jerry Neff Victor Magar
Ryan Fimmen
Ramona Darlington Sea Engineering, Inc.
Gary Gill Craig Jones
Greg Durell
Kelly Quigley, Editor
Appreciation is also expressed to the following EPA personnel who provided invaluable
review and editing services in the areas of their expertise:
EPA-NRMRL* EPA-NHEERLA
Richard Brenner, Work Assignment Manager Marilyn Ten Brink
Marc Mills, Alternate Work Assignment Manager
Robert Ford EPA-Region 5
Barbara Butler Leah Evison
Kirk Scheckel
EPA-OSWER, OSRITI#
Stephen Ells
*National Risk Management Research Laboratory
#Office of Solid Waste and Emergency Response, Office of Superfund Remediation and
Technology Innovation
ANational Health and Environmental Effects Research Laboratory
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ABSTRACT
In 2005, the United States Environmental Protection Agency (EPA) published a document
entitled Contaminated Sediment Remediation Guidance for Hazardous Waste Sites (EPA, 2005a), which
provides technical and policy guidance for project managers and teams making risk management
decisions for contaminated sediment sites. This EPA guidance document addresses both in-situ and ex-
situ remedies for contaminated sediment sites, including, among others, dredging and excavation, in-situ
capping, and monitored natural recovery (MNR).
This report is a Technical Resource Document (TRD) on MNR intended to function as a
complement to Chapter 4 on MNR in the aforementioned EPA guidance document. As such, the purpose
of this TRD is to provide detailed information on field-scale methodologies and approaches that can be
used to measure and/or predict natural processes that contribute to the reduction of risks to human and
ecological receptors at contaminated sediment sites. Although the document includes information that
may be useful in developing a site-specific protocol, it is not a protocol or guidance document. The goals
of this TRD are to: 1) identify and describe natural physical, chemical, and biological processes normally
associated with recovery of contaminated sediments, and 2) discuss techniques and methods for
quantifying and assessing the rates and extent of those recovery processes that may be occurring at a
particular site. The number and types of measurement and analytical methodologies used for evaluating
MNR at contaminated sediment sites will be influenced by the size and complexity of the site, project
resources, available data, and the scope of decisions to be made.
This TRD consists of five technical sections that address different facets of MNR, including
an introduction and overview of MNR (Section 1); a discussion on sedimentation and contaminant
isolation (Section 2); consideration of the fate and transport of organic (Section 3) and inorganic (Section
4) contaminants in sediments; and a description of a six-point process for developing and implementing a
long-term monitoring plan to evaluate and forecast MNR progress (Section 5). A comprehensive set of
References for the five technical sections is provided (Section 6).
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1.0 INTRODUCTION
1.1 Purpose and Objectives
In 2005, the United States Environmental Protection Agency (EPA) published its
Contaminated Sediment Remediation Guidance for Hazardous Waste Sites (EPA, 2005a), which provides
technical and policy guidance for project managers and teams making risk management decisions for
contaminated sediment sites. This EPA guidance document addresses both in-situ and ex-situ remedies
for contaminated sediment, including dredging and excavation, in-situ capping, and monitored natural
recovery (MNR).
This report is a Technical Resource Document (TRD) on MNR intended to function as a
complement to Chapter 4 on MNR in the aforementioned EPA guidance document. As such, the purpose
of this TRD is to provide information on field-scale methodologies or approaches that can be used to
measure and/or predict natural processes that contribute to the reduction of risk to human health and
ecological receptors/resources at contaminated sediment sites. Although it includes information that may
be useful in developing a site-specific protocol, it is not in itself a protocol or guidance document. The
goal of this document is to present natural physical, chemical, and biological processes that contribute to
the recovery of contaminated sediments and to present methods that may be used to quantify and assess
those processes at sites. The number and extent of technical tools that should be used for evaluating the
appropriateness of MNR at contaminated sediment sites will be influenced by the size/magnitude and
complexity of the site, project resources, available data, and the types and scopes of decisions to be made.
Resources used to develop this document include primarily published peer-reviewed
literature, government literature, and the experience of the contributors to this TRD. Case studies are
introduced as appropriate to illustrate various monitoring and assessment methods. Some examples are
derived from the Lake Hartwell (Pickens County, SC) site, where, since 2000, EPA's National Risk
Management Research Laboratory (NRMRL) and Region 4 staff have been investigating natural physical,
chemical, and biological processes controlling the fate of polychlorinated biphenyl (PCB) contaminants in
sediments. Additional case studies or examples are also used to illustrate or demonstrate specific
processes or approaches.
This TRD includes the following sections:
1.0 Introduction provides an overview of MNR, introduces primary lines-of-evidence used
in support of MNR and other MNR considerations, and discusses the role of conceptual
site models (CSMs) in the site characterization and process of evaluating remedial
alternatives.
2.0 Sedimentation and Contaminant Isolation describes methods to measure sediment
deposition and contaminant burial processes. This section also focuses on methods to
measure sediment shear strength and hydrodynamic forces to estimate the potential for
contaminants to mobilize, thereby creating unacceptable risk.
3.0 Fate of Common Organic Contaminants in Sediments provides a broad discussion of
the current understanding of the fate and transformation of three categories of
persistent organic pollutants in sediments (/'. e., polycyclic aromatic hydrocarbons
[PAHs], PCBs, and polychlorinated dibenzo-/?-dioxins/polychlorinated dibenzofurans
[PCDDs/PCDFs]). This section includes techniques to evaluate bioavailability and
chemical fingerprinting methods to identify weathering and transformation processes
that can alter the mass, mobility, and relative toxicity of these contaminants.
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4.0 Fate of Inorganic Contaminants in Sediments provides information concerning
approaches to characterize the fate and transformation of inorganic contaminants.
These include evaluations of geochemical changes in metal speciation as a result of
oxidation/reduction (redox) processes that can affect solubility and bioavailability of
metals.
5.0 Long-Term Monitoring and Site Forecasting with Predictive Models provides resources
for development of a long-term monitoring program to support MNR. The long-term
monitoring program may focus on surface sediments (establishing concentration
reductions with time or evaluating and characterizing sediment erosion events), the
recovery of biological/ecological receptors, or both.
6.0 References are provided in this section.
1.2 MNR Processes and Mechanisms
MNR is a remedy that uses ongoing, naturally-occurring processes to contain, destroy, or
reduce the bioavailability or toxicity of contaminants in sediment (EPA, 2005a; National Research
Council [NRC], 1997). MNR typically relies on decreases in contaminant bioavailability and toxicity in
surface sediments. Throughout this document, "surface sediment" is defined as the site-specific
biologically active benthic layer at the sediment-water interface. This layer typically is 5 to 10 cm thick
in fresh water systems (EPA, 2005a) and can be as much as 1 m thick in estuarine and marine
environments (Apitz et al, 2002; Murdoch and Azcue, 1995). Bioavailability may be generally defined
as the extent to which living organisms can uptake chemical contaminants by active (biological) or
passive (physical or chemical) processes. Natural physical, chemical, and biological processes that
contribute to natural recovery of contaminated sediments may include sediment burial, sediment
erosion/dispersion, and contaminant sequestration/degradation (e.g., precipitation, adsorption, and
transformation). Each of these processes, discussed in greater detail below, can directly affect risk to site-
specific receptors associated with site-specific contaminants, and consideration should, therefore, be
given to understanding how each may apply to a given site and the ecological and human receptors
associated with specific sites.
1.2.1 Natural Sediment, Physical, Chemical, and Biological Processes. Risk reduction and the
recovery of ecosystem health or functionality depend on the following mechanisms that influence long-
term contaminant and sediment transport:
• Control of primary and secondary sources to prevent ongoing chemical releases into
surface sediments;
• Contaminant burial via sediment deposition and an assessment of whether burial is
sufficient to limit contaminant mobility and transport;
• Sediment erosion potential and associated contaminant exposure under a range of
reasonably expected flow events; and
• Contaminant sorption, solubility, chemical speciation, and transformation.
To the extent that physical, chemical, or biological processes reduce contaminant
bioavailability and toxicity in surface sediments, these processes may contribute to the natural recovery of
the biologically active zone of the sediment bed, and correspondingly to the recovery of ecological
resources. All of the processes discussed will be present at every site to varying degrees, and the
combination of these processes ultimately governs contaminant bioavailability and the associated site-
specific risk.
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1.2.1.1 Physical Processes. The dominant physical processes that affect the distribution of
contaminants in sediment are transport, surface sediment mixing (which includes both bioturbation
[Section 23.2 A} and hydrodynamic mixing), and pore water transport. Sediment transport includes
erosion, deposition, and water column transport of uncontaminated and contaminated sediment particles
and the potential for contaminant burial. Bioturbation and hydrodynamic mixing can result in and/or
enhance surface sediment mixing, and benthic fauna can act as vectors for transporting contaminants into
the food web. Bioturbation is often modeled as a physical process because the net result is the physical
mixing of sediments and exchange of sediment pore waters with the overlying water column. Pore water
transport includes diffusion from sediments and advection due to groundwater discharge. These physical
processes play an integral role in the chemical and biological processes in the system.
1.2.1.2 Chemical Processes. These processes are contaminant and site specific and define the
mobility and fate of contaminants in the environment. Chemical processes include a range of reactions
that govern the precipitation, adsorption, redox, transformation, and chemical speciation of a contaminant.
These processes may be directly or indirectly influenced by microbiological and physical processes that
control the flux of reactive chemical constituents entering and reacting with contaminants in sediments.
These processes influence the partitioning of a chemical between solid and aqueous phases within the
sediment bed and water column, thereby affecting the fate, transport, and bioavailability of the
contaminant. For example, the fate of metals depends on environmental factors such as hydrogen ion
activity (pH), redox characteristics, presence of sulfides, and organic carbon content. Likewise, the fate
of organic contaminants may also be affected by abiotic chemical processes such as photocatalysis and
sorption processes.
1.2.1.3 Biological Processes. According to the NRC (2001), "of the natural-attenuation processes,
biodegradation is generally considered the most desirable because it can result in elimination of risk."
Additionally, macrobiological processes, such as benthic mixing and bioturbation, can contribute to the
in-situ degradation or transformation of contaminants, which can affect mobility and, under certain
conditions, toxicity. For hydrophobic organic contaminants, the time frame required for biodegradation
can be greater than for risk reduction provided by burial and isolation processes, whereas biologically-
mediated transformations may be more immediate for inorganic contaminants. While chemical
transformation of inorganic contaminants is generally reversible, degradation of organic contaminants is
typically irreversible. When modeling biotransformation processes, it is important to understand
biotransformation kinetics, transformation products, changes in toxicity, and impacts on contaminant
mobility.
1.2.2 Risk Reduction Processes. The interactions of the three processes outlined above determine
the long-term risk trends at each site. Characterization of the influence of these processes on potential
human and ecosystem exposure to contaminants provides the technical basis for evaluation of MNR. The
primary risk reduction processes that are to be monitored to evaluate the success/failure of MNR to
mitigate site-specific risks include:
• Contaminant isolation/burial;
• Contaminant dispersion;
• Contaminant sequestration; and
• Contaminant transformation.
1.2.2.1 Contaminant Isolation/Burial. Natural deposition of clean (or cleaner) sediment can reduce
surface sediment contaminant concentrations with time. Burial occurs in depositional environments
where the rate of sediment deposition exceeds the rate of erosion and transport. Contaminant burial
results in compaction of underlying sediments, which typically reduces the vertical transport of water and
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dissolved components. Newly deposited sediments also provide additional reactive surface area for
contaminant sequestration. Burial processes typically reduce water column and biota exposure to vertical
diffusion and advection of contaminants from sediments and assist in physically isolating the
contaminants from the biologically active zone. Isolation also reduces resuspension of contaminated
sediments during high-flow events or storms. Finally, contaminant burial/isolation decreases the potential
for contaminant transport into the food chain by reducing contaminant exposures at the sediment surface.
1.2.2.2 Contaminant Dispersion. Erosion and hydrodynamic dispersion of contaminated sediments
can lead to localized contaminant concentration changes and, therefore, should be properly accounted for
in the CSM. These processes result in mixing of clean and contaminated sediments, which can reduce the
surface concentrations and potential bioavailability of the contaminants. However, reliance on dispersion
for recovery of contaminated sediment sites is problematic because it may increase contaminant loading
downgradient of the site. Furthermore, sediment erosion can expose buried sediment that often contains
higher contaminant concentrations than those typically found in surface sediments. Project managers
should carefully evaluate the effects of increased loading on receiving bodies where dispersion can result
in unacceptable risks downgradient from primary areas of concern (EPA, 2005a).
1.2.2.3 Contaminant Sequestration. Sorption, precipitation, and other sequestering processes can
reduce contaminant mobility and bioavailability. These processes may reduce the potential for vertical
diffusive and advective pore water transport of buried contaminants, which helps to ensure that
contaminants remain sequestered after burial. Sequestration is controlled by physical, chemical, and
biological activity of the sediment and the contaminant.
1.2.2.4 Contaminant Transformation. To consider contaminant transformation an operative process
for an MNR remedy, contaminants must not be converted to more toxic forms through biotic or abiotic
mechanisms and more optimally be transformed to less toxic compounds. Abiotic transformation
processes typical of sedimentary environments may include reduction, oxidation, mineralization, and
hydrolysis. Biotic processes most commonly involve organisms using the contaminant as an electron
donor or electron acceptor and converting the contaminant to a different chemical. The biotic
transformation process is termed biodegradation. Abiotic transformation processes occur without
enzymatic catalysis and include chemical reactions with reactive constituents in sediments (e.g.,
complexation or redox reactions involving sediment minerals and/or pore-water reactants) or the
overlying water column (e.g., photochemical reactions). Common examples of abiotic processes include
degradation of PAHs, partial dechlorination of PCBs, and precipitation of certain metals under reducing
and sulfide-rich conditions. Chemical weathering to less toxic products or less bioavailable forms can
provide long-term risk reduction. However, because weathering processes are often slow, reduction can
require years or decades and may be incomplete and, in some cases such as inorganic contaminants, may
be reversible. Biotic and abiotic processes often occur simultaneously and together may contribute to
reduced risk to human and ecological receptors.
1.3 Assessing MNR
1.3.1 Source History and Control. Per EPA's Contaminated Sediment Remediation Guidance for
Hazardous Waste Sites (2005), the first principle for site management is control sources early.
"As early in the process as possible, site managers should try to identify all
direct and indirect continuing sources of significant contamination to the
sediments under investigation. These sources might include discharges from
industries or wastewater treatment plants, spills, precipitation runoff, erosion of
contaminated soil from stream banks or adjacent land, contaminated
groundwater and non-aqueous phase liquid contributions, discharges from storm
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water and combined sewer outfalls, upstream contributions, and air deposition.
In summary, source control should be implemented to prevent recontamination
regardless of the selected remedial alternative" (EPA, 2005a).
The first step in documenting source control is the determination of historical and current
sources. Source control documentation relies primarily on historical records from prior remedial
investigations and cleanup efforts and past disposal records, historic photos, etc. Sites also may have
multiple point and non-point sources that can interfere with anticipated recovery. If evidence of source
control is unavailable, it may be necessary to conduct a field investigation to demonstrate that source
control has been achieved. Recognizing and unraveling multiple sources of contamination may require
historical records, source tracking, working with local and state agencies, and advanced chemical
forensics (EPA, 2004; Murphy and Morrison, 2007; Stout et al, 2003; Van Metre et al, 2000). Section
3.0 provides a detailed discussion of chemical fingerprinting for organic contaminants, which can be
critical to an accurate understanding of contaminant sources.
Potential sources of sediment contamination at many sites are often numerous and complex,
such as industrial discharges, urban runoff, combined sewer overflows, storm sewer systems, and others
(EPA, 2001a; EPA, 2002a; EPA, 2004; EPA, 2005a) (Table 1-1). For example, non-point sources, such
as urban runoff, can be a consistent and ongoing source of PAHs, PCBs, and metals and may negatively
impact chemical concentrations in surface sediments, even after control of a major point source (Apitz et
al., 2002; Brenner et al., 2001; Fletcher et al., 2008). Due to the diversity of industrial marine operations
and the ubiquitous nature of many anthropogenic contaminants, it can be difficult to ascertain the relative
contributions from multiple sources (Stout et al., 2003).
Table 1-1. Sources of Sediment Contamination
Discharges (direct or outfall) from
industrial waste and wastewater
treatment plants
Chemical spills into water bodies
Surface runoff from chemical
storage facilities and mine waste
piles and urban areas
Air emissions from power plants and
incinerators into water bodies by
precipitation or direct deposition
Seepage of contaminated
groundwater into water body
Erosion from flood plains and
agricultural areas
Disposal from docked or dry -docked
ships
Release of contaminants from ship
maintenance facilities
Infiltration from landfills
Adapted from EPA, 2005a.
In addition to identifying primary sources, it is often necessary to distinguish "external"
upland/watershed secondary sources (e.g., outfalls or non-point sources) from "internal" secondary
sources associated with releases from legacy sediments (e.g., resuspension of contamination from
historical releases and migration through pore water transport). Internal contaminant sources, including
high-risk sediment ("hot spots" - localized areas of high sediment contamination), can act as potential
reservoirs that release contaminants into the aquatic system and slow recovery processes. Sediment and
water column chemical concentrations also may be influenced by non-point sources that can be difficult
to control, such as atmospheric deposition to the watershed. These distinctions may be particularly
relevant if the boundaries of the site under consideration are not easily controlled.
The potential for sediment contamination as a result of groundwater discharge to surface
water should be considered during evaluation of source control. Organisms that inhabit the transition
zone between groundwater and surface water are at risk of exposure to contaminants that migrate through
pore water or directly contaminate surface water (EPA, 2008). It is, therefore, important to understand
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the groundwater-surface water interactions that occur in streams, rivers, estuaries, lakes, and wetland
settings.
1.3.2 Lines-of-Evidence. Since MNR relies on natural environmental processes to reduce the risk
posed by contaminated sediments to the environment, it is important that MNR be thoroughly evaluated
when it is being considered as a potential remedy. Systematically evaluating the feasibility of MNR as a
component of the sediment remedy is best achieved through the development of a CSM that adequately
captures the physical, chemical, and biological processes that control contaminant fate, transport, and
bioavailability. The CSM will likely be derived from analysis of historical and contemporary site data
and incorporate current and anticipated pathways of human and ecological exposure. The three main
questions that an MNR evaluation is designed to address are:
1) Is there evidence that the system is recovering over time?
2) What processes are controlling system recovery?
3) Is system recovery occurring at a rate sufficient to meet remedial objectives, and is it
sustainable?
These questions are answered using a multiple lines-of-evidence approach to evaluate the
feasibility of using MNR as the remedial alternative at contaminated sediment sites. As stated in EPA
guidance (EPA, 2005a), it is recommended that the following potential lines-of-evidence be evaluated:
• Long-term trend of contaminant levels in higher-trophic-level biota (e.g., piscivorous
fish);
• Long-term trend of water column contaminant concentrations evaluated over a typical
low-flow period of high biological activity (e.g., trend of summer low-flow
concentrations);
• Sediment core data demonstrating trends in historical surface contaminant concentrations
through time; and
• Long-term trends of surface sediment contaminant concentration, sediment toxicity, or
contaminant mass within the sediment.
One line-of-evidence approach is the Sediment Quality Triad (SQT) developed to explore the
relationship between contaminants in the sediment and the health of the supported biological community
(Sorensen etal, 2007). The SQT analysis is supported using multiple lines-of-evidence to explore the
cause and effect of chemicals on ecological receptors. The lines-of-evidence included in the SQT are
results of macrobenthic community, sediment toxicity, and sediment chemistry assessments. Sorenson et
al. (2007) utilized the SQT approach on six sites in the Lower Hackensack River, NJ. In four of the six
sites tested, they found that the macrobenthic community toxicity correlated with high contaminant
concentrations. In some instances of extreme toxicity, the microbial community responsible for
degrading the contaminants may be compromised and result in lower biodegradation rates. Using the
SQT approach, they were also able to identify specific metals that had no effect on the benthic biota even
though they were present at high concentrations. Sorensen et al. (2007) concluded that using multiple
lines-of-evidence is very important and should be coupled to modeling and other known methods of
evaluating toxicity. Long and Chapman (1995) used the SQT approach to measure sediment
contamination and toxicity in Puget Sound. They compared the three components of the SQT and
determined that chemical data alone were not reliable and stressed the importance of using multiple lines-
of-evidence to determine if a cause-and-effect relationship exists.
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1.3.3 Conceptual Site Model Development. The primary goals of site characterization are to: 1)
determine the nature and extent of contamination, and 2) evaluate the associated risk. The CSM is a
primary tool for evaluating site-specific processes influencing contaminant fate and transport in sediment
environments and estimating risk. EPA (2005) and the American Society for Testing and Materials
(ASTM, 2003) provide guidance for the development of a CSM. The CSM should specify primary
contaminant sources, primary release mechanisms, secondary sources, secondary release mechanisms,
contaminated media, exposure routes, and receptors (Adriaens et al., 2004). The CSM's identification of
the primary risk driver(s) at the site (e.g., small surficial area with high concentrations and widespread
low concentrations) ensures that remedies selected will appropriately address these risks. When
evaluating MNR, the general CSM can be refined to better characterize primary and secondary release
mechanisms and physical/chemical/biological processes that influence contaminant mobility and
bioavailability over time.
1.3.4 CSM Structure and Relevant Processes. A general conceptual model framework for a site
contaminated with organic compounds is presented in Figure 1-1. Potential contaminant pathways
between surface and buried sediments, the sediment-water interface and the water column, and biota in
the benthos and water column are illustrated in the figure (additional CSM examples, using different
formats, are shown in the Contaminated Sediment Remediation Guidance for Hazardous Waste Sites
[EPA, 2005a]). The figure illustrates how a conceptual model begins with an understanding of
contaminant sources and pathways that are unique to individual sites. CSMs should be developed with an
explicit directive towards mitigating the greatest risks at a site. For example, where remediation efforts
are focused on reducing sediment toxicity to benthic organisms, measures must be taken to evaluate the
evidence required to sufficiently mitigate the risk associated with benthic toxicity (/'. e., chemical data,
toxicity studies, and benthic community profiles). However, where MNR is being used to mitigate risk to
higher-trophic ecological receptors, such as piscivorous fish, birds, or humans, MNR needs to address
contaminant availability and biomagnifications through the food chain.
The model shown in Figure 1-1 focuses on the physical movement of sediment-associated
chemicals to and from the site, to and from the sediments, within the sediments, and to ecological
receptors. Identified processes include surface water transport of dissolved and particle-bound
contaminants; sediment suspension, settling, and burial; chemical partitioning; pore water transport;
chemical transformation or decay; and air/water exchange. Potential contaminated media include
sediments, pore water, surface water, air, and biota. At complex sites, contaminant transport processes
may be portrayed in multiple CSMs that separately characterize sources, sediment and aquatic media, and
chemical transport to human and ecological receptors.
Figure 1-2 presents a general conceptual model framework for a site contaminated with an
inorganic contaminant. The figure uses mercury as an example to show sources of mercury
contamination including municipal/industrial waste, mine drainage, and deposition. The fate of inorganic
contaminants is partly determined by the change in chemical speciation of the contaminant in the
sediments. Speciation of an inorganic contaminant also influences its toxicity, mobility, and
bioconcentration/biomagnification as well as other properties. Partitioning of inorganic contaminants in
sediments depends on the solid phases present or those being formed within the sediment. This topic is
discussed in further detail in Section 4 of this document. The risk receptors associated with metals
contamination in surface sediments, surface water, and groundwater are also illustrated in Figure 1-2.
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Surficial
Layer
(Surface S&fintent)
Figure 1-1. Conceptual Site Model Development - Hydrophobic Organic Contaminants (HOCs)
(adapted from Adriaens et a/., 2004)
When depicting the fate and transport of metals in the CSM, metals speciation must be
considered together with the types of inorganic and organic solid-phase components present in the
sediment that influence the toxicity, bioconcentration/biomagnifications, and partitioning of inorganic
contaminants in sediment. In the aqueous phase, the distribution and concentration of metals is
determined by ionic strength, specific inorganic co-contaminants, alkalinity, pH, and dissolved organic
carbon (DOC). The presence of organic carbon is important both in the aqueous and solid phases because
contaminant metals tend to form complexes with organic species. This complexation is at times
irreversible, decreasing the bioavailability of the metal contamination. Co-precipitation of toxic metals
with sulfide species occurs during diagenesis, reducing their bioavailability. In general, metals speciation
promotes partitioning to sediment when the pH and Eh of the surface water are high and a high
concentration of organic matter is present. However, when the pH, Eh, and organic matter concentration
are low, the metal may change speciation such that it partitions to the liquid phase and into the surface
water.
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SOURCES
RISKS
MECHANISM FATE AND TRANSPORT
Runoff
(Hg'& Hg")
Atmospheric
Ebullition Volatilization Deposition
(Hg°) (Hg») (Hg21
Transpiration
(Hg°)
Hg' Mining Waste
I
Hg' to Ground Water
Surface Water " Well
to Ground Water (Hg!
(Hg")
Diffusion
(Hg ) Settling Resuspension
(Hg" & MeHg) (Hg!* & MeHg)
cumulation &
magnification
(MeHg)
Ground Water Seepage
to Surface Water (Hg*)
Adsorption &
Ingestion
sediment_metals4.cdr
Figure 1-2. Conceptual Site Model Development - Inorganic Contaminants
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1.3.5 Management of Risk. The goal of selecting and implementing any remedial alternative is to
reduce risks to human health and/or ecological receptors/resources. Identified advantages of MNR
include its noninvasive nature (as compared to sediment removal by dredging or sediment burial by
capping), which may benefit ecological communities that are sensitive to disturbance, and comparatively
low implementation cost (EPA, 2005a). Disadvantages of MNR include longer times to achieve risk
reduction to acceptable levels and the lack of active controls to minimize potential future exposures of
contaminants left in place (EPA, 2005a). MNR should be considered when it would meet remedial
objectives in a reasonable time frame as compared to realistic estimates of the time to design, implement,
and complete a dredging or capping remedy (EPA, 2005a). The time frame to achieve risk reduction is
site specific and should be explicitly stated and understood. For each site, the time frame is based on a
decision rule (i.e., a specific cleanup level at which the contaminant is no longer considered a risk).
1.3.5.1 Risk of Remedy. Because all sediment remedial alternatives have advantages, disadvantages,
uncertainties, and risks when applied at a particular contaminated sediment site, management decisions
should be based on the relative net risks of each remedial alternative (EPA, 2005a). It is critical for
effective risk management that these uncertainties and risks be identified individually and
comprehensively for each site. Risks considered should include those risks introduced by implementing
the remedial alternatives as well as residual risks (EPA, 2005a) associated with any contaminated
sediments left in place (intentionally or unintentionally) after the remedial alternative has been completed.
This information on the risks of the remedial alternatives can then be used in comparing net risk reduction
among alternatives as part of the decision-making process for remedy selection (EPA, 2005a).
1.3.5.2 Adaptive Management. At many sites, and especially at complex sediment sites, an adaptive
management approach, whereby site assumptions are tested and re-evaluated as new information is
gathered, should be considered (EPA, 2005a). Whether an adaptive management approach is cost
effective is a site-specific decision (EPA, 2005a). Resources on adaptive management at sediment sites
include the NRC report Environmental Cleanup at Navy Facilities (NRC, 2003) and Adaptive
Management as a Measured Response to the Uncertainty Problem: Addressing Uncertainty and
Managing Risk at Contaminated Sediment Sites (Connolly and Logan, 2004).
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2.0 SEDIMENTATION AND CONTAMINANT ISOLATION
Natural sediment deposition can contribute to MNR through the gradual burial of
contaminated sediments (Brenner et al, 2004; Delaune and Gambrell, 1996; EPA, 2005a; Magar, 2001;
Voskov et al., 1991). However, because depositional environments are repositories for contaminated
sediment particles, source control is an integral component of MNR. Once the contaminant source is
controlled, natural contaminant weathering and sediment transport processes, including deposition of
cleaner sediments, can result in the burial and isolation of the contaminated sediment, resulting in a
decrease in surface sediment concentrations overtime. This reduction in surface sediment concentrations
should result in reduced risk from the contaminated sediment.
This section presents:
• Effects of erosion and deposition on the MNR of contaminated sediments;
• Dynamics of sediment transport;
• Measurement of surface sediment concentrations with time;
• Uncertainties in determining surface sediment concentrations with time; and
• Case studies where MNR of contaminated sediments successfully reduced contaminant
concentrations.
2.1 Contaminant Burial and Isolation
One of the primary processes responsible for successful remediation by MNR is the
deposition of cleaner sediments to effectively bury and isolate the contaminated sediments. With
successful source control, the deposition of cleaner sediments commonly results in lower surface
sediment contaminant concentrations over time. The depth of the surface sediments of interest must be
determined on a site-specific basis and will generally depend on both physical and biological processes
acting on the sediments and contaminants of interest. These processes are commonly dominated by:
• Physical and Chemical Processes - Sediment transport (erosion, deposition, and physical
mixing), diffusion, advection due to tidal pumping or groundwater, and chemical
reactions/transformations including gas migration; and
• Biological Processes- Benthic organism interaction with contaminants and sediments
through physical mixing of particles, pore water irrigation, and uptake.
The depth of this active upper region can vary significantly as a result of these processes.
The top 5 to 50 cm of sediment can be considered surface sediment depending on the dominant processes
at a particular site (Fletcher et al., 2008).
Surface sediments are mainly composed of partly decomposed organic material, deposited
soil and sediment, and weathered bedrock. Surface sediments may be rich in organic matter and,
therefore, possess a high capacity for contaminant adsorption to the sediment surface. Though sediment
particle sizes are controlled by flow conditions, morphology and biological processing, and upstream
mineralogy, surface sediments at typical contaminated sites are generally less than 2 mm in diameter
(SPAWAR Systems Center, 2003). Contaminants in surface sediments often pose the greatest risk of
chemical exposure to benthic and higher trophic ecological receptors and to humans through ingestion of
contaminated fish or direct contact with contaminated sediments. Thus, recovery of surface sediments in
the form of lowered contaminant concentrations and/or reduced chemical bioavailability is often the
immediate goal of sediment remediation processes.
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In depositional environments, natural sedimentation can result in contaminant burial and
reduced surface sediment concentrations overtime. Areas prone to sedimentation and sediment accretion
generally lie downstream of areas that are frequently eroded or receive soil and sediment particles from
natural processes, agricultural erosion, urban runoff, and oceanic or riverine sediments. Once deposited,
the sediment particles may remain in place and become covered by subsequent sediment deposition. The
gross rate at which the particles accrete and deposit in a sediment column is controlled by local variables
such as flow conditions, water chemistry, mineralogy, effective settling rates of particles and floes, bed
slopes, and bed armoring/roughness (Elimelech etal., 1998; Lick, 2010-; Rose and Appleby, 2005).
During periodic events (e.g., high river flow or storm waves), sediments may become resuspended and
transported off site. The effects of the processes must be determined in order to affirm potential MNR
sites are net depositional.
Following source control, reductions in surface sediment concentrations tend to be gradual
depending on the net sediment deposition rate, hydrodynamic and bioturbation mixing rates in surface
sediments, and the contribution of upstream contaminant flux into the watershed. Thus, even after source
control, the following factors may affect spatial and temporal changes in chemical distributions in surface
sediments:
• The presence of residual contamination can require decades to move through a
watershed. This contamination may re-contaminate areas that were previously
remediated or contaminate clean sediments that were deposited after the contaminated
sediments.
• Bioturbation and hydrodynamic mixing can change a contaminant's distribution in
surface sediment. Bioturbation refers to the agitation of surface sediments by benthic
organisms that process the surface sediments to live or search for food. The top layer of
the surface sediment inhabited by aquatic organisms is the biologically active zone. The
population and density of these organisms will be dependent on the local environment,
but may include sedentary burrowing organisms such as polychaetes, crustaceans, and
cephalopods. Additionally, hydrodynamic mixing occurs when regular currents cause the
surface sediments to resuspend and temporarily suspend in the water column (e.g., tidal
environments). These two processes affect contaminant concentration by mixing older,
deeper (potentially more contaminated) sediments into the surface layer, thereby slowing
contaminant burial and isolation.
• Extreme events that occur on an infrequent basis may be responsible for the net erosion of
previously buried contaminated sediments. The eroded sediments could recontaminate
local surface sediments or be transported off site.
2.2 Potential for Contaminant Mobilization
Typically, MNR is considered at sites that demonstrate a degree of inherent sediment bed
stability, which is why the sediment deposits formed and persisted historically. In conducting an MNR
assessment, the principal questions are: can sediment bed stability be expected to persist long term under
normal conditions, and can future extreme events cause unacceptable exposure and risk? To answer these
questions, sediment erosion potential should be evaluated under normal- to high-energy events such as
flood flows, extreme tidal fluctuations, wind-induced waves and currents, and ice scour. Man-made
sources of erosion such as propeller wash from passing vessels can also create elevated bottom shear
stresses leading to localized erosion (see Section 2.4.3.If). Benthic organisms also contribute to localized
erosion as they burrow and process sediment, which may cause sediments to become resuspended or
change overall sediment stability. The resuspension of contaminated sediment may result in unacceptable
exposure levels and harm to ecological receptors or human health. Therefore, erosion potential and long-
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term sediment accretion processes must be understood and quantified to provide a basis for the selection
of MNR as a remedy.
Sediment accretion is a naturally occurring process that results in the deposition,
incorporation, and accumulation of suspended sediment particles into the surface sediment in a water
body. This process is dependent on the physical and chemical properties of the suspended sediments and
the overlying current velocities. More information will be provided later on how these and other factors
affect sediment accretion rates.
The terms sediment scour and erosion describe the detachment and removal of sediment
particles from the sediment bed via hydrological shear stress imposed on the sediment bed. The bottom
shear stress responsible for removal of sediment particles from the sediment bed is caused by water
moving over the sediment bed due to currents or wave action (see Blake et a/., 2007; Van Rijn, 1993;
Yang et a/., 1996). Events of concern in evaluating erosion potential include high flow or flood events,
extreme tidal conditions, and high winds that may result in large waves and currents. Resuspension
processes may result in net erosion when the combination of re suspension, transport, and deposition
results in a loss of sediment from the area of interest. Scouring is generally associated with localized
high-water velocities, whereas erosion is typically used to denote net effects over a large area. Scouring
tends to leave a hole or impression in the area where it occurs, but erosion occurs more evenly across a
broader area.
2.3 Contaminant Transport via Sediment Transport Processes
At many contaminated sediment sites, contaminants are strongly sorbed to sediment particles
(an exception would be contaminant movement into sediment by a contaminated groundwater plume),
and, therefore, the physical and chemical processes that affect sediment particles have an influence on
contaminant fate and transport. As stated in the previous sections, the key to understanding sediment
transport is the characterization of the dominant processes involved in moving sediments. These
processes are sediment erosion, transport of sediments in the water column, and sediment deposition.
Other secondary processes can affect sediment transport (e.g., landslides and earthquakes), but these basic
processes govern the long-term sediment transport at a given site. The following sections briefly outline
these key processes (Figure 2-1) (Adriaens et al, 2004; Lick, 2010; Ziegler, 2002).
2.3.1 Sediment Transport Processes
2.3.1.1 Sediment Erosion. Sediment erosion and resuspension are the movement of particles from
the sediment bed into the overlying water column. Sediment erosion is initiated by mobilization and
subsequent transport of particles away from the localized area. Erosion can result in unacceptable
increases in risks to ecological receptors and/or humans by exposing buried sediments with substantially
higher contaminant concentrations and suspending contaminated particles in the water column.
Erosion is initiated by shear stress (TO) at the sediment-water interface exceeding a critical
value that results in a sediment particle being lifted from the sediment bed. Shear stress, or bed shear
stress, is produced by the friction of water and suspended sediment flowing over the sediment bed.
Resting sediment particles are in constant equilibrium between these drag forces from fluid shear, the lift
forces from flow over the particles, and the gravitational and cohesive forces that work to bind the
particles to the sediment bed. At a critical velocity, the combined drag and lift forces on the uppermost
particles of the sediment bed are greater than the gravitational and cohesive forces that bind surface
13
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Current
'•• .Suspended '
. ; « .
. °- • Sedimedf .•
• -. • . •
. •. ° Sediment Delivery
' • -from Upstream
Bottom
Shear
Stress
Erosion
Deposition
Biologically Active Zone
Bioturbation by
Benthic Organisms
Sediment Bed
Figure 2-1. Simplified Diagram of Major Sediment Transport Processes
sediments to the bed, dislodging them from their equilibrium positions. This velocity is related to the
critical shear stress for erosion (Tce), which is defined as the shear stress at which a small but accurately
measurable rate of erosion occurs. Initially, sediment erosion tends to occur in a few isolated locations.
However, as shear stress increases with increasing flow velocity, the movement of particles becomes
more sustained, causing a net erosive flux from the sediment bed (Roberts et al, 1998; Van Rijn, 1993;
Yang et al, 1996).
Shear stress is denoted as force per unit area (e.g., N/m2) and can be estimated based on
bottom-current velocities. It has been studied in detail for currents and waves and can be defined and
quantified mathematically given sufficient information regarding the hydrodynamics of the system
(Schlicting, 2000; Van Rijn, 1993; Ziegler, 2002).
Resuspension can also be caused by bioturbation, i.e., the disturbance of sediments by benthic
flora and fauna (Clarke et al., 2001). Benthic organisms utilize surface sediments as their habitat. These
organisms can process sediment during activities such as burrowing, feeding, and ventilating. The
activities of organisms and plant roots in the surface sediments lead to a vertical redistribution of
sediment particles. During biological processing of the bulk sediment, deeper sediments are transported
to the surface while surface sediments become buried or suspended in the water column. As the
organisms burrow, eat, and excrete the sediments, they may cause instability in sediments contributing to
erosion. Benthic organisms also can cause the sediment to become more cohesive through excretion of
biopolymers that bind the sediment. A further discussion of bioturbation is found in Section 2.3.2.4.
2.3.1.2 Sediment Transport. Once dislodged from their equilibrium position in the sediment bed,
sediment particles can be transported in the water column (either short or long distances) prior to
deposition. Two modes of transport are bedload transport and suspended load transport. Bedload
involves the transport of particles that move along the bed (i.e., rolling in contact with the bed) and
14
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saltating (i.e., bouncing) in a thin layer (Yang et al, 1996; Van Rijn, 1993). Suspended load transport
involves the suspension of particles into the water column and subsequent transport with the current. The
mode of transport for a given particle is largely affected by sediment properties and flow regime.
Bedload transport often dominates sediment transport in rivers with coarse-grained beds and
coastal regions where the current has insufficient energy to suspend the particles from the sediment bed.
It tends to be of less importance for fine-grained sediments; however, aggregates (i.e., clumps) of fine-
grained particles can move as bedload. Aggregates can also disaggregate into smaller particles; this
process increases the potential for suspended load transport.
Sediment particles transported as suspended loads move close to the velocity of the water. At
steady state, upward transport of sediment particles is balanced by gravitational particle settling and
deposition, which can result in sustained sediment suspension. From steady-state conditions, increasing
flow velocities will generally result in net sediment erosion once the critical shear stress has been reached,
whereas decreasing velocities will result in net sediment deposition. Vertical profiles of suspended, non-
cohesive sediment concentrations can be calculated based on particle size, a reference concentration, and
fluid velocity (Rouse, 1938; Van Rijn, 1993). Sediment transport can be coupled with hydrodynamics
and modeled to provide information on the deposition of various sediment classes to determine the
evolution of sediment layers and benthic habitat. Many such models are in use at contaminated sites.
EPA (2005) provides guidance for selecting and applying coupled hydrodynamic and sediment transport
models.
2.3.1.3 Sediment Deposition. Deposition is the process by which sediment particles settle out of the
water column and onto the sediment bed, causing net accretion of particles and subsequent burial of older
sediment. Suspended sediment in the water column will begin to settle when the settling velocities
exceed the forces that are maintaining the particle in suspension. These forces are a balance of
gravitational pull, fluid velocities, and fluid drag and are controlled by the physical and chemical nature
of the fluid (water) and the particles (size, shape, density, propensity to aggregate, surface charges of
particles, etc.). Once the settling velocities are sufficient to deposit the particles on the bed surface, the
particles will either be incorporated into the sediment bed or transported back into the water column. At
sufficiently low bed shear stresses, particles begin to accrete and remain with the sediment bed. As bed
shear stress decreases, the probability of settled particles remaining a part of the sediment bed increases.
This probability approaches 100% as the bed shear stress approaches zero. Deposition can happen at
either low or high water velocities depending on the characteristics of the suspended sediment particles.
For example, during flood events where the high suspended sediment concentration exceeds the carrying
capacity of the water, deposition can occur at velocities exceeding normal deposition thresholds.
As fine-grained particles interact in the water column, they may aggregate or flocculate,
especially at high suspended sediment concentrations. Flocculation tends to enhance sediment deposition
by increasing particle mass. This process depends on sediment type and surface charge, sediment
concentration, fluid velocity and shear, and water chemistry. In general, as sediments flocculate, they
form larger particles that settle and deposit faster than smaller individual particles. Techniques that have
been developed to predict flocculation and determine aggregate particle sizes are reported in Durban et al.
(1990), Lick (2010), and Bridges etal. (2008).
Differences in sediment accretion rates in aquatic systems are controlled by a variety of site-
specific habitat factors (e.g., geology, surrounding land use, current velocity, and organic matter). No
general rule-of-thumb can be used to assume the sediment accretion rate for a given site and rates must be
measured and/or inferred through a carefully designed analysis. It is generally necessary to determine
site-specific sedimentation rates to evaluate the use of MNR at a particular site. Subsequent sections will
discuss methods to characterize sediment accretion rates and sediment stability.
15
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2.3.2 Physical Properties Related to Sediment Transport. This section identifies and defines
sediment physical properties related to sediment erosion, transport, and deposition. Sediment physical
properties include sediment grain size, bulk density, and cohesiveness. Each of these properties is
quantifiable, and the methods of measurement and the influence of the properties on sediment transport
are described in this section.
2.3.2.1 Sediment Grain Size. Knowledge of the particle size distribution (PSD) (also known as
grain size distribution) is fundamental to understanding sediment transport processes. Sediment particle
sizes range from very fine clays (<0.24 (im) to boulders >0.25 m in diameter. The intermediate sized
particles include the silts and sands that make up the sediment beds of most aquatic environments.
Table 2-1 provides a description of the typical ranges of particle (or grain) size associated with PSD
classifications. Sediment grain size is measured based on phi ( = -Iog2 (d)
where d is the particle size diameter in mm (Krumbein, 1934).
(Eq.2.1)
Table 2-1. Common Grain Size Scale for Sediment Particles
(adapted from Blake et al., 2007)
Description
Boulder
Cobble
Large
Small
Gravel
Very coarse
Coarse
Medium
Fine
Very fine
Sand
Very coarse
Coarse
Medium
Fine
Very fine
Silt
Coarse
Medium
Fine
Very fine
Clay
Coarse
Medium
Fine
Very fine
Grain Size
(mm)
>256
128-256
64-128
32-64
16-32
8-16
4-8
2-4
1-2
0.5-1
0.25-0.5
0.125-0.25
0.0625-0.125
—
—
Grain Size
(urn)
—
—
—
1000-2000
500-1000
250-500
125-250
62.5-125
31.3-62.5
15.6-31.3
7.8-15.6
3.9-7.8
1.95-3.9
0.98-1.95
0.49-0.98
0.24-0.49
16
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Most often, natural sediments consist of a mixture of sediment grain sizes, and sediments are
described based on the relative proportions of each sediment type (e.g., sandy clay or silty sand). The size
and density of a particle will affect its ability to be scoured and transported under various hydrodynamic
conditions. For contaminated sediments, concern is generally placed with the smaller particle sizes that
have a high surface area-to-mass ratio and tend to sorb contaminants in the environment. The degree of
partitioning to sediment particles is controlled by surface area, surface charge, particle composition, and
the contaminant's physical and chemical properties.
Poppe et al. (2000) provide an overview of a number of methods and discussions for the
processing and interpretation of the data. To provide a brief context, only a few common methods are
mentioned below. For particles between 32 and 256 mm, templates with square openings can be used to
size fractions. Sieve analyses (ASTM, 2004) typically are used for particles between 0.0625 and 32 mm.
A settling tube may be used to determine the diameter for particles between 0.0625 and 2.0 mm (ASTM,
2007). Hydraulic settling methods are used for particles less than 0.0625 mm in diameter (ASTM, 2006).
These methods include the pipette method, which is considered the most reliable indirect method; the
bottom withdrawal method, which can be used if there is not enough material for the pipette method; and
the hydrometer method, which is relatively simple and can be accomplished at a lower cost but requires a
larger sample quantity (Syvitski, 2007).
2.3.2.2 Sediment Bulk Density. Bulk density (pb) is useful for classifying sediments and
quantifying transport properties. It is also used in combination with contaminant concentrations to
estimate contaminant mass inventory. The approximate density of the quartz and clay minerals that make
up the majority of sediment particles in the natural world is about 2.65 g/cm3 (Van Rijn, 1993). The bulk
density of a sediment bed is defined as the total mass of sediment and water per unit volume of bed
material. Bulk density varies with depth depending on the sediment type as seen for cohesive sediments
(see definition below). Bulk density generally increases with depth and time for cohesive sediments
because of consolidation and pore water displacement. Consolidation is defined as a reduction of space
between sediment particles due to an applied stress. This stress can be applied by overlying sediment
(overburden), increased depths of water, ice loadings, or periodic atmospheric pressure changes
(Skempton, 1969; Weller, 1959). As the bulk density increases, the potential for scour or sediment
erosion typically decreases due to consolidation (Jepsen et al., 1997; Mehta and McAnally, 1998). In
some sediments, anthropogenic materials (e.g., coke, coal, pitch, and construction debris) can measurably
decrease bulk density (Thomas, 2002).
2.3.2.3 Sediment Cohesiveness. Generally, sediments can be classified as cohesive or non-cohesive.
In cohesive sediments, interparticle forces are substantial, creating an attractive force (i.e., cohesion)
between particles. In non-cohesive sediments, interparticle forces are insignificant and gravitational
forces dominate the behavior of the sediment. Sediments composed primarily of clay-sized materials tend
to be cohesive because clay-like materials have surface ionic charges that promote strong interparticle
forces. Metallic or organic coatings on sediments also contribute to the interparticle forces making them
cohesive. Some components of cohesive sediments include inorganic clay minerals (e.g., silica, alumina,
montmorillonite, illite, and kaolinite) and non-clay minerals (e.g., quartz, carbonates, feldspar, and mica,
as well as organic material such as plant and animal detritus and bacteria). Fine-grained sediments,
especially clay minerals, tend to be more cohesive than coarse-grained sediments. Cohesive sediments
typically include those with significant fractions of silts and clays (<2 jam) or fine sands (<60 jam). Non-
cohesive sediments generally include particle diameters larger than 60 jam. In some cases, mixtures of
small percentages of clays into sand sediment can cause sandy sediments to behave in a cohesive manner
(Huang etal, 2006; Lick, 2010).
For non-cohesive sediments, sediment erosion and transport rates strongly correlate to
particle grain size with the transport rate declining with increasing particle size. This correlation does not
17
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hold for cohesive sediments where particle size alone cannot be used to predict transport rates (Mehta et
al, 1989; Mehta and McAnally, 1998; Roberts et al, 1998; Van Rijn, 1993) because of the cohesive
forces between particles. For cohesive sediments, empirical data are needed to quantify critical shear
stress and erosion rates, which can be obtained from devices for measuring sediment critical shear stress
(see Section 2.4.3.3a). Additionally, models have been developed to evaluate cohesive sediment
processes both empirically and mechanistically relative to how a sediment bed may behave over time. A
discussion of such models is generally beyond the scope of this document but McAnally and Mehta
(1998) presents an example of one such model for predicting aggregation of cohesive sediments. EPA
(2005) provides guidance for selecting and applying models at contaminated sediment sites.
2.3.2.4 Bioturbation. Although not a "sediment property," bioturbation can affect the physical
properties of surface sediments, namely bulk density and cohesiveness. Bioturbation occurs in the
uppermost layers of sediment (generally 10 to 15 cm) in which most benthic organisms reside (Boudreau,
1998; Clarke et al., 2001) though there are selected species that reside in deeper sediments (e.g., mud-
shrimp) (Clarke et al., 2001). Figure 2-2 illustrates that bioturbation activity can vary with depth. In-situ
characteristics such as particle size, dissolved oxygen (DO), amount and type of organic matter, salinity,
and pH will impact the benthic community and the extent of their activity within the biologically active
zone (Clarke et al., 2001). This, in turn, will impact the level of bioturbation that can affect physical
properties of surface sediments. Polychaetes (annelid worms), crustaceans (crabs, lobsters, and shrimp),
and mollusks (snails and clams) are the most common bioturbators in marine and estuarine environments.
Their mode of feeding and density has the greatest effect on sediment stability. Secretions associated
with tube building can bind sediment particles and increase sediment cohesiveness, whereas burrowing
can mix sediments and decrease cohesion and bulk density (Boudreau, 1998; Rhoads and Carey, 1997).
Young (1975) found that critical shear stress can be reduced as much as 50% by bioturbating organisms
in the laboratory. Conversely, Ravens and Gschwend (1999) discovered that algal mats, produced by
polysaccharide-secreting diatoms during fall and spring blooms, more than doubled sediment bed
stability.
The effects of bioturbation are site specific and can exhibit spatial and seasonal variations.
The net impacts of bioturbation are sometimes considered to be captured in bulk density and erodibility
measurements in surface and buried sediment (Mulsow et al., 1998). Direct measurements of
bioturbation rates and depths can be used in modeling the fate of chemicals, exposure concentrations, and
sediment-water fluxes that may result from event erosion. Measurements of bioturbation, however, are
not well suited for predicting erosion depth.
Bioturbation contributes to the porosity profiles in surface sediments; therefore, porosity is
widely used in the modeling of bioturbation. However, two contrasting theories exist. The first theory
suggests that bioturbation decreases porosity gradients by mixing pore water and sediment (Boudreau,
1986). The second theory contends that bioturbation does not modify porosity but only mixes particles in
the solid phase (Meysman et al., 2005). Therefore, assuming constant porosity, the second theory applies
Pick's Law of diffusion to evaluate radio tracer profiles developed from core analysis. Additionally,
cores can be analyzed for evidence of bioturbation by visual inspection for the tubes and tunnels in the
profile of the core (Orzech et al., 2001).
2.4 Measures of Surface Sediment Contaminant Concentrations
2.4.1 Measuring Changes in Surface Sediment Contamination. Several methods can be used to
quantify changes in surface sediment contamination. These methods include core analysis and surface
sediment analysis, described in detail below. Other approaches for detecting changes in surface sediment
concentration can be used as supportive lines-of-evidence. These include bathymetry that can be used to
evaluate changes in the depth to surface sediments and provide insights into erosional and/or depositional
18
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Bioturbation Activity
Surficial Bioturbation
(sediment completely mixed)
Mid-Depth Bioturbation
(significant numbers of organisms}
Deep Bioturbation (assumed low densities)
Figure 2-2. Bioturbation Zones (Clarke et a/., 2001)
environments. Also, watershed mass balance approaches can be used to estimate transport and fate
processes from source zone to deposition.
2.4.1.1 Core Analysis Methods. Chemical analysis of sediment cores can be used to create a vertical
chemical profile in sediments (ASTM, 2008; Brenner etal, 2002, 2004; EPA, 2001b, 2004; Murdoch and
MacKnight, 1994). In depositional environments, the vertical sediment column provides a historical
record of the deposition of sediment particles on the bed surface where the sediment-water interface
represents the date of sample collection and increasingly older sediments are found with increasing depth
below the surface. A typical sediment core profile in these environments includes uncontaminated
sediments at the deepest portion of the core, representing sedimentation before the first release of
contamination at the site. Above the uncontaminated deepest portion, buried sediment containing
maximum contaminant concentrations represents a period of maximum contaminant release into the
watershed. Above the maximum concentration horizon, decreasing concentrations approaching the
sediment-water interface represent recovery with time after source control or reduction. The uppermost
section of the core represents the sediment-water interface where benthic organisms, gas ebullition, and/or
erosion and deposition often maintain a high rate of vertical mixing. Not all sites necessarily exhibit
reduced surface sediment concentrations with time. Factors that can influence vertical contaminant
profiles in sediments with time include: 1) the extent and effectiveness of source control, 2) physical
transport and benthic mixing of surface sediment and contaminants, 3) extreme events that mobilize
contaminated sediment, 4) the presence of secondary sources that contribute to the suspension and release
of contaminants, and 5) sediment deposition rates and contaminant concentrations associated with those
sediments.
Table 2-2 outlines commonly-used coring methods and their advantages and disadvantages.
Different materials of construction and coring techniques are employed depending on the depth and type
of sample to be obtained (EPA, 200 Ib). As with most methods of analysis, several challenges are faced
when conducting chemical analysis of sediment cores (EPA, 200 Ib, 2008; Murdoch and MacKnight,
1994). According to Rothwell and Rack (2006), challenges lie in the development of: 1) standard
measurement and calibration methodologies, and 2) data analysis methods. In order to optimize
interpretation of the data and maximize its scientific value, new data visualization tools must be
developed. A uniform method of data archiving that will be made available to the entire scientific
community, thereby assisting in the interpretation of multiparameter data sets, is also urgently needed.
19
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Table 2-2. Common Coring Methods (adapted from EPA, 2001b)
Device/Dimensions
Fluorocarbon plastic or
glass tube (3.5 to 7.5 cm
inner diameter [I.D.] : <
120 cm long)
Hand corer with removable
fluorocarbon plastic or
glass liners (3.5 to 7.5 cm
I.D.:<120cmlong)
Box corer
Gravity corer, phleger corer
(3.5 cm ID. < 50 cm long)
Use
Shallow wadeable
waters or deep waters if
SCUBA available; soft
or semi-consolidated
deposits
Same as above except
more consolidated
sediments can be
obtained
Same as above but the
depth of the
unconsolidated
sediment must be at
least 1 m
Deep lakes and rivers;
semi-consolidated
sediments
Sample Depth (cm)
Oto 10
Oto 10
Oto 70
Oto 50
Sample Volume (L)
0.096 to 0.44
0.96 to 0.44
<30.0
<0.48
Advantages
• Preserves layering and
permits historical study
of sediment deposition
• Minimal risk of
contamination
• Rapid; samples
immediately ready for
laboratory shipment
• Same advantages as
fluorocarbon plastic or
glass tube
• Penetrates substrate
with greater ease
through use of handles
• Collects large,
undisturbed sample;
optimal for obtaining
intact subsamples
• Reduces risk of sample
contamination
• Maintains sediment
integrity relatively
well
• Penetrates with sharp
cutting edge
Disadvantages
• Small sample size
necessitates repetitive
sampling
• Small sample size
necessitates repetitive
sampling
• Requires careful
handling to prevent
spillage
• Requires removal of
liners before repetitive
sampling
• Barrel and core cutter
metal may
contaminate sample
• Difficult to handle
• Relatively heavy;
requiring larger vessel
and power winch to
deploy
• Requires careful
handling to avoid
sediment spillage
• Requires repetitive and
time-consuming
operation and removal
of liners due to small
sample size
-------
Table 2-2. Common Coring Methods (adapted from EPA, 2001b) (continued)
Device/Dimensions
Gravity corer, Kajak-
Brinkhurst corer (5 cm I.D.
< 70 cm long)
Benthos gravity corer (6.6
7.1 cm I.D. < 3 mlong)
Alpine gravity corer (3.5
cm I.D.)
Piston corers
MBH-53 piston corer
Use
Deep lakes and rivers;
soft fine-grained
sediments
Soft, fine-grained
sediments
Soft, fine-grained,
semi-consolidated
substrates
Ocean floor and large
deep lakes; most
substrates
Waters < 2 m deep with
extension rod; soft
deposits
Sample Depth (cm)
Oto70
Oto 3 m
<2m
3 to 20 m
<2m
Sample Volume (L)
<1.37
< 10.26
<1.92
5 to 40
<2
Advantages
• Collects greater
volume than the
phleger corer.
• Retains complete
sample from tube
because the core valve
is fitted to the core
liner
• Fins promote vertical
penetration
• Allows different
penetration depths due
to interchangeable
steel barrel
• Typically recovers a
relatively undisturbed
sediment core in deep
waters
• Piston provides for
greater sample
retention
Disadvantages
• Same as phleger corer
• Requires weights for
deep penetration so the
required lifting
capacity is 750 to
1,000 kg
• Requires vertical
penetration
• Compacts sediment
sample
• Lacks stabilizing fins
for vertical penetration
• May penetrate non-
vertically and
incompletely
• Requires a lifting
capacity of 2,000 kg
• Disturbs sediment
stratas and integrity
• Compacts sediment
sample
• Requires lifting
capacity of >2,000 kg
• Piston and piston
positioning at
penetration may fail
• Disturbs surface (0 to
0.5 m) layer
• Cores must be
extruded onsite to
other containers
• Metal barrels introduce
risk of metal
contamination
-------
Table 2-2. Common Coring Methods (adapted from EPA, 2001b) (continued)
Device/Dimensions
Boomerang corer (6.7 cm
ID.)
Vibracorer (5.0 to 7.5 cm
ID.)
Use
Ocean floor (up to
9,000 m deep)
Continental shelf of
oceans, large lakes;
sand, silty sand,
gravelly sand substrates
Sample Depth (cm)
1m
3 to 6 m
Sample Volume (L)
3.52
5.89 to 13.25
Advantages
• Requires minimal
shipboard equipment
so small vessels can be
used
• For deep profiles it
effectively samples
most substrates with
minimum disturbance
• Can be used in over 20
m of water depth
• Portable models can
be operated from small
vessels (e.g., 10 m
long)
Disadvantages
• Only penetrates 1.2 m
• Requires calm water
for recovery
• Loses 10 to 20% of
sample
• Labor intensive
• Assembly and
disassembly might
require divers
• Disturbs surface (0 to
0.5 m) layer
• Special generator may
be needed
• Heavier models
require larger boat and
power winch to deploy
to
to
-------
2.4.1.2 Surface Sediment Analysis. If the source of the contamination has been removed and clean
sediments are depositing (but coring is not a suitable technique), several methods are available for
analyzing surface sediments for reduction in contaminant concentrations. These methods also apply to
places where natural recovery is proceeding, as evidenced by decreases in contaminant concentrations,
without natural deposition and burial taking place, e.g., via biodegradation or metals reduction.
Surface sediment monitoring measures changes in chemical concentrations in surface
sediments over time. Surface sediment monitoring is most applicable when conducted at a large site over
a long time frame. However, spatial and temporal variability often overshadow short-term temporal
trends, so this technique often requires relatively large sample sets and long monitoring periods to identify
statistically representative changes in surface sediment concentrations (Connolly etal, 2005; Magar and
Wenning, 2006). Surface sampling also may be confounded by non-uniform sampling methods.
Sediment traps are devices used to collect surface sediment to evaluate suspended sediment
quality. Other surface sediment methods such as sediment dating and mass balance models only measure
sediment accumulation rates (Murdoch and MacKnight, 1994). Sediment traps are strategically placed
where deposition of sediments is high, the water column has a high concentration of suspended
sediments, and the water velocity is reasonably low to prevent resuspension of the material deposited in
the trap (Asper, 1987; Iowa Statewide Urban Design and Specifications [SUDAS], 2008). The outlet of
the sediment trap usually is designed to allow water to flow out but prevent loss of sediment sample. The
primary purposes for deploying sediment traps are to collect material being transported from the surface
water to the surface sediment (EPA, 1990). One method of constructing sediment traps is to connect a
large funnel to a collecting container surrounded by a steel frame and suspend it in the water column; a
few are fabricated from sieve cloths approximately 0.1 mm sieve size mounted on a steel frame (Bischoff
et a/., 2005).
Sediment traps can be simple to construct and are a cost-effective way to provide useful
information on the quality of local suspended solids. Sediment traps can cost from $500 to $7,000 to
construct and deploy. Sediment traps, if appropriately deployed, can provide an effective way of
intercepting and trapping water column sediment (Iowa SUDAS, 2008). Some of the limitations of
sediment traps are: 1) they require a large surface area to allow infiltration and settling of sediment, 2)
they may require upstream erosion control to ensure that the data generated are useable, 3) they usually
require some sort of protective fencing to prevent them from being distributed by humans and aquatic
animals, 4) they cannot be located in streams with high flow rates, and 5) they can provide biased results
in systems with bi-directional circulation. Sediment traps require regular maintenance during their
deployment and usually need to be removed after high rain events (EPA, 1990).
Radio dating techniques can be very useful in establishing a sedimentation rate for a site. In
sedimentary environments, depositional rates and ages of sediment horizons can be determined by the
distribution of certain radioactive isotopes. Those radioisotopes most commonly used for sediment
chronology are listed in Table 2-3. The age of the sediment containing a radioactive isotope with a
known half-life can be calculated by knowing the original concentration of the isotope and measuring the
percentage of the remaining radioactive material. The requirements for a radioisotope to be a candidate
for "dating" are that: 1) the chemistry of the isotope (element) is known; 2) the half-life is known; 3) the
initial amount of the isotope per unit substrate is known or accurately estimated; 4) the only change in
concentration of the isotope is due to radioactive decay; and 5) in order to be useful, it must be relatively
easy to measure.
23
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Table 2-3. Common Radiological Age Dating Methods for Sediments
Isotope
234Th
7Be
210pb
137Cs
239Pu
241Pu
Half-life
24 days
53 days
22.4
years
30.1
years
23,110
years
14.4
years
Origin
Naturally -occurring
daughter product
of238U.
Naturally found on
atmospheric particles
and in surface sediments
and soils.
Naturally found in air,
dust, soil, and sediment
as a daughter product of
radon.
Introduced from above-
ground nuclear weapons
testing. Peak production
occurred in 1963.
239Pu is virtually
nonexistent in nature. It
is made by
bombarding 238U with
neutrons in a nuclear
reactor.
241Pu is found in
association with 239Pu
and is virtually
nonexistent in nature.
Location in Sediment
Profile
0-10 cm depth; dependent on
sedimentation and mixing
rate.
0-15 cm depth; dependent on
sedimentation and mixing
rate.
Higher activities in surface
sediments, decreasing with
depth.
Normally subsurface;
dependent on sedimentation
rate.
Normally subsurface;
dependent on sedimentation
rate.
0-50 cm depth; dependent on
sedimentation rate.
Sediment Application'3'
Due to the short half-life, this
isotope is used to determine the
mixed layer in surface sediments.
Due to the short half-life, this
isotope is used to determine the
depth of the mixed layer in surface
sediments.
Ratio of 210Pb daughter
products 208Po and 210Po is used to
calculate excess 210Pb values,
which are then used to calculate
sedimentation rates and age
sediments to about 100 years old.
Useful for determining sediment
age and calculating and verifying
sedimentation rates.
Because it is an alpha emitter and
was used in bombs as part of
nuclear weapons testing, it was
deposited on the earth only during
specific times. It is used for dating
similarly to the 137Cs method.
Because it is an alpha emitter and
was part of nuclear weapons
testing, it was deposited on the
earth only during specific times. It
is used for dating similarly to
the 137Cs method.
(a) As a general rule, radioactive tracers are appropriate for estimating dates up to four half-lives.
Geochronology profiles using lead-210 (210Pb) or cesium-137 (137Cs) date sediment core
segments, resulting in an age profile with depth. The mechanisms that introduce 137Cs and 210Pb into the
sediment and the techniques used to interpret the depositional profiles of each isotope are different; using
both techniques facilitates independent estimations of deposition rates. Age-dating results provide
information on the temporal variations of contaminant release, sediment accumulation rates, and surface
mixing depths (Brenner et al, 2004; Matisoff etal, 2002a, 2002b; Koide etal, 1973; Van Metre and
Callender, 1997; van Metre et al., 1998, 2000). 137Cs is an event marker associated with atmospheric
nuclear testing that uses two specific events, the onset of measurable deposition and the period of
maximum deposition, to establish two sediment time horizons. 137Cs dating is most useful in identifying
strata deposited in the mid-1950s and early-1960s, periods of atmospheric nuclear testing, while 210Pb is
deposited continuously from the atmosphere and can be used to estimate more recent dates. The Pb
method uses the slope of the concentration to determine a sedimentation rate based on assumptions of a
relatively constant natural atmospheric deposition rate, a relatively constant rate of sediment deposition,
and a known rate of decay (Holmes, 1998). Because these radioisotopes have physicochemical properties
similar to sediment-bound hydrophobic contaminants, have known historical source loading histories, and
behave in a well-known manner once deposited, they can be used to reconstruct the historical loadings of
sediment-associated contaminants (EPA, 200Ib).
24
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137
Cs and 210Pb results are interpreted using the following methods (EPA, 200Ib):
• 137Cs Horizon Method. Where no local sources occur, 137Cs radioisotopes did not begin
to appear in soils/sediments until about 1954. Therefore, in most cases, the initial
(deepest) appearance of 137Cs in a core represents the 1954 horizon. The annual
sedimentation rate is established by dividing the depth to the 1954 horizon by the number
of intervening years since 1954.
• 137Cs Peak Method. The period of maximum atmospheric deposition of 137Cs occurred in
1963. These rates decreased sharply thereafter and were barely measurable by the late
1970s. The maximum 137Cs value in a core profile, therefore, is associated with the 1963
horizon. The annual sedimentation rate is calculated by dividing the depth to the 1963
horizon by the number of intervening years since 1963.
• 137Cs Focusing Factor. A comparison is made between the amount of 137Cs found in a
sample and the anticipated atmospheric deposition. The higher the ratio, the greater the
degree of sediment focusing in the area. A focusing value greater than 4 indicates a
highly depositional environment (Lockhart et a/., 1998).
• 210Pb Concentration Slope Method. 210Pb has a half-life of 22.26 years. Therefore, after
measuring the profile of 210Pb in the soil core, the sediment's age is estimated from the
degree of decay (Sowers etal, 2000).
7Be is produced by cosmic ray reaction with atmospheric nitrogen and oxygen. It is
transferred into sedimentary environments through precipitation. Once the 7Be is in the sediments, it
becomes associated with the solid phase. Because 7Be attaches strongly to particles, the highest measured
activity corresponds to the greatest sediment accumulation rate. 7Be has a half-life of 53 days, which
makes its effective range of applicability for dating sediment about 1 year. 7Be may also be used to
determine regional short-term sedimentation patterns. Another radioisotope, 234Th, with a half life of 24
days, can also be used to measure short-term sediment dynamics. In particular, analysis of 234Th in the
surficial layers of the sediment can reveal the extent of the mixing layer and the depth to which recent
sedimentation has occurred. The distribution of the radio nuclides 239+240pu in surface sediments can be
used as tracers in the modeling of bioturbation. 239+240pu become deposited to surface sediments by decay
of radon in the atmosphere and testing of nuclear weapons, respectively. The distribution of 210Pb
and 239+240pu in sediment profiles can be used to predict bioturbation rates and depth (Crusius et al.,
2004).
In many cases, all of the above techniques are used to calculate a sedimentation rate. When
more than one method returns a usable result, a systematic procedure is needed to determine which
technique produced the most reliable, or preferred, sedimentation rate. A typical procedure involves
comparing the computed sedimentation rates from the 137Cs horizon method, the 137Cs peak method, and
the 210Pb concentration slope method. If these three methods produce comparable sedimentation rates
(tolerance limits overlap), the method that produces the smallest tolerance interval may be selected as the
predicted sedimentation rate. Other characteristics of both the 210Pb and 137Cs activity profiles are also
considered when evaluating a sediment core to determine the sedimentation rate. For example, if
the 210Pb data are scattered and produce a low correlation coefficient or a wide tolerance interval, the lead
method may be considered less reliable. Similarly, the cesium peak method is considered unreliable if
there are multiple peaks in the 137Cs concentration profile or if there is no well-defined 1963 peak.
However, when core length is sufficient and both results are available, the 1954 cesium horizon method is
chosen most often as the benchmark for determining sediment accumulation rates.
25
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The coring depth and interval thicknesses for chemical and geochronological analyses depend
on such site-specific features as the expected contaminant depth, sedimentation rates, uniformity of
sediment deposition, surface sediment benthic mixing depths and rates, and the presence of secondary
contaminant sources. Sediment age dating also may be guided by the site-specific biological active zone
in surface sediment. Resources available for site characterization often limit the number of samples that
can be collected and analyzed, which, in turn, will dictate the temporal frequency, spatial distribution,
core depth, and interval (core segment) thickness of the sampling program. While more extensive coring
can provide information on the heterogeneity of sedimentation rates in a watershed and thinner core
segmentation provides more precise age-dating results, the numbers of cores and thickness/frequency of
core segment intervals must be weighed against the costs of associated sampling and analysis.
In determining the depth of sediment cores necessary to evaluate the potential
appropriateness of MNR, the focus should be on characterizing the potential exposure of ecological
receptors and reducing risks. Thus, sediment cores should be sufficiently deep to characterize and
differentiate horizons based on sediment contaminant concentrations that are relevant from a risk
perspective. Conversely, it is neither appropriate nor necessarily beneficial to extend sediment cores to
depths intended to characterize site conditions in the absence of human influence (e.g., prior to the
Industrial Revolution). Uniformity of the sediment column with respect to grain size distribution and
total organic carbon (TOC) facilitates age dating. Therefore, highly accurate age dating may be
impossible in sediments with significant vertical heterogeneity due to heterogeneous depositional
environments overtime (e.g., varying grain sizes and/or organic carbon content) or environments with
substantial resuspension and mixing (Brenner etal., 2002).
Sediment coring and vertical contaminant profiling, combined with radiochemical methods
for age dating, offer the strongest and most immediate measures of recovery in that they verify reduced
concentrations in surface sediments over time. An investigator can develop a work plan, collect sediment
cores, segment the cores for on- or off-site chemical analyses, analyze the sediments for contaminants of
concern, analyze sediments for radionuclides (e.g., 210Pb and 137Cs) and age date the sediment core
profiles, analyze the chemical data, and report the results in a short time period. Sediment coring does not
rely on repeated field monitoring over time. Using this approach, it may be possible to characterize the
rates of recovery of surface sediments quickly, thereby greatly diminishing the time required to perform a
feasibility study as opposed to conducting extensive long-term surface sediment monitoring. However,
sediment coring is most useful when deposition rates are high and sediments are homogenous. When
significant spatial variations exist at a site, core locations should be selected to adequately characterize the
range of sediment deposition and recovery. Examples of sediment coring, vertical contaminant profiling,
and sediment age dating are provided in Highlights 2-1 and 2-2. Vertical contaminant profiling and age
dating were used to evaluate historical changes in surface sediment concentrations in sediments at Lake
Hartwell, SC (Highlight 2-1) and White Rock Lake, Dallas, TX (Highlight 2-2) following source control.
The Lake Hartwell example demonstrates reduced surface sediment PCB concentrations following
industrial point source control. The White Rock Lake example illustrates reduced surface sediment
contaminant concentrations resulting from control of non-point sources. The elimination of non-point
sources of dichlorodiphenyltrichloroethane (DDT) and lead resulted in statistically significant decreased
contaminant concentration trends over time at White Rock Lake. However, when source attenuation was
not addressed for another non-point contaminant, the same sediment transport mechanisms that
contributed to reduced chemical concentrations in surface sediments also contributed to ongoing
deposition and accumulation of PAH-contaminated particles.
26
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Highlight 2-1. Lake Hartwell Surface Sediment PCB Trends
Sediment core profiles were used to establish vertical PCB concentration profiles, age date
sediments, and determine surface sedimentation rates and surface sediment contaminant-
reduction rates in 18 cores collected from 10 transects in Lake Hartwell, SC. Sediment age
dating was conducted using 210Pb and 137Cs concentration profiles in the sediment cores (Brenner
etal, 2004). PCB trends showed decreasing surface sediment concentrations since the late
1970s when EPA first regulated PCB use in the United States. The Lake Hartwell experience
provides an example of surface sediment reductions following removal of a point source.
Cores were taken to a minimum depth of 100 cm with trace concentrations of PCBs detected at
or below 100 cm. Results shown in the figures below indicate these deeper sediments were
possibly contaminated by other non-point sources deposited before the onset in 1955 of
discharges from capacitor manufacturing by the Sangamo-Weston Plant (the major source of
PCB contamination in Lake Hartwell). This deeper contamination also may have resulted from
migration of PCBs from overlying contaminated sediments. Maximum concentrations were
measured at -30 to 60 cm below the sediment-water interface, which corresponds to deposition
dates between 1960 and 1980. The peak concentrations were followed by a progressive decrease
in surface sediment concentrations overtime.
Sedimentation rates averaged 2.1 ± 1.5 gm/cm2/yr for 12 of 18 cores collected. Best-fit curves
were applied to the surface sediment recovery results to predict the amount of sedimentation and
time required to achieve clean-up goals stipulated in the 1994 Record of Decision (ROD). The
ROD cleanup requirement was 1.0 mg/kg t-PCBs, and two additional desirable targets,
0.4 mg/kg t-PCBs and 0.05 mg/kg t-PCBs, were also identified. It was determined that average
yearly surface sedimentation requirements to meet the three goals were 1.4 ± 3.7 cm, 11 ±
4.2 cm, and 33 ± 11 cm, respectively (Brenner et a/., 2004). Assuming the surface sediment
concentrations continue to decline towards background concentrations, surface sediments were
estimated to reach the 0.4 mg/kg cleanup level by 2020 and the 0.05 mg.kg level by 2041
(Brenner et a/., 2004).
eo.o
t-PCB Concentration (mg/kg)
50.0 40.0 30.0 20.0 10.0
Core T-L
y = 958Ln(x)-0.563
1^ = 0,9685
(»75
t-PCB Concentration (mg/kg)
60.0 50.0 40.0 30.0 20.0 10.0 0.0
CoreT-O
y=13.98Ln(x}- 0.251
P = 0.9095
1981*.
- 10
- 20
- 30
SO
60
- 70
- 80
?
Vertical Sediment Core Profile Illustrating Surface Sediment Recovery in
PCB-Contaminated Lake Hartwell Sediments for Two Cores at Transects T-L and T-O
(Brenner et a/., 2004)
(Reprinted with permission from ES&T.)
27
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Highlight 2-2. Surface Sediment DDT, Lead, and PAH Trends in the United States and in White Rock Lake Sediments
DDT, lead, and PAH trends were examined in sediments of U.S. lakes (EPA, 2004). Decreasing DDT trends were reported in 12 of the 22 lakes examined (Figure A),
and all 22 lakes had statistically significant decreasing trends in lead concentrations over time (EPA, 2004). These trends were attributed to decreased DDT use since
1960 and reduced anthropogenic lead releases since the switch in the 1970s to unleaded gasoline, as well as reduced industrial emissions.
Figure A
White Rock Lake (Dallas, TX) demonstrated surface sediment recovery following removal of non-point sources
of DDT and lead. Decreasing surface sediment concentrations are apparent in age-dated White Rock Lake
sediment core profiles for total DDT (DDT and ODD, plus DDE) (Figure B) and lead (Figure C) (Van Metre and
Callender, 1997; Van Metre et al., 2000; EPA, 2004). Sedimentation rates at White Rock Lake were estimated by
age dating at 1.13, 0.66, and 0.76 g/cm2/yr for the periods 1912-1952, 1953-1963, and 1964-1994, respectively
(Van Metre and Callender, 1997). \
DDT trends identified peak concentrations circa 1960, corresponding to peak DDT use in the United States,
followed by decreasing concentrations over time. Sediment lead concentrations also followed historical
atmospheric lead levels: peak concentrations (circa 1970) coincided with peak atmospheric levels, followed by
decreasing trends with shallower sediment depths.
In contrast, surface sediment PAH concentrations increased with time (Figure D). The trends appear to correlate
with vehicle use and urbanization in Dallas. PAHs often exhibit increasing trends with increased urbanization
(Van Metre et al., 2000) due to a variety of urban sources including power plant emissions, car/truck exhaust, and
oil leaks. Lead ([j/g)
Total DDT (M/kg) 0 20 40 60 80 100
^ Decreasing trend
= No trend
f Increasing trend
Surface Sediment Trends
in Continental U.S. Lakes
10
White Rock Lake
DDT Concentrations
2000-
0)
IS 1970-
Q
1960-
1940
White Rock Lake
Lead Concentrations
Historical
Atmospheric Lead
Total PAHs
0 2,000 4,000
|
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2.4.1.3 Bathymetry. Bathymetric changes may be studied to monitor sediment bed elevation changes
with time. Though this approach cannot detect contaminants directly, if the areas of contaminated
sediment are known, inference can be made about the contaminant transport by quantifying sediment
movement. Bed depth is obtained by echo sounding that involves a sound pulse traveling from the water
surface to the sediment bed. The sound pulse is reflected and monitored. Using the time it takes for the
sound to return and the speed of sound in water, a basic calculation can be applied to obtain depth
measurements. Repeating bathymetric tests over time in the same water body provides data that can be
interpreted to provide information on the erosion and depositional areas in the water body. However,
bathymetric measurements may not provide sufficient vertical resolution to measure relatively small
changes in sediment thickness, particularly over short periods. When comparing multiple bathymetric
surveys over time, it is necessary to ensure that measurement techniques, datum control, and resolution
are comparable. Bathymetric accuracy depends on instrumentation and survey procedures. The various
measurement techniques (both lateral and vertical) used in past and current bathymetric measurements are
subject to error functions, many of which have been explained in the literature and must be considered to
arrive at meaningful conclusions (Byrnes etal., 2002; Plant etal, 2002; Wilson and Richards, 2006).
2.4.1.4 Watershed Mass Balance. Watershed mass balance calculations are used to address
environmental concerns and are an integral part of watershed planning. Development of a watershed
mass balance can provide further evidence of the transport and deposition of increasingly clean sediments
over time. Mass balance calculations provide information on the manner in which contaminants,
sediments, nutrients, and water naturally cycle in a watershed and can assist in defining contaminant fate
and transport, the influx and outflow of nutrients, and changes in a watershed due to human impact. The
mass of water and the concentration of dissolved and suspended materials entering the watershed are
compared to that exiting the watershed. The solids mass balance should provide an average
sedimentation rate and be consistent with the weight-of-evidence from water column solids
measurements, sediment core profiles, geochronological core profiles, sediment trap data, and
bathymetric comparisons, though often on vastly different spatial scales.
Information obtained from watershed mass balance calculations can be incorporated into
models to more fully understand the processes that occur in a watershed. Johnson and Gerald (2006)
developed mass balance equations to determine the movement of nitrogen, phosphorous, and carbon in a
watershed. This information was then incorporated into the watershed model's solute transport
component to assist in determining the fate of these nutrients in watersheds that receive runoff waters.
Peters et al. (2006) evaluated both the monthly and annual water and solute mass balances of five
watersheds. The factors calculated to prepare the watershed mass balances included precipitation, solute
deposition, and stream discharge. The solutes contained in precipitation and stream discharge were
analyzed and included calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), ammonium (NH4),
chloride (Cl), nitrate (NO3), and sulfate (SO4). The primary purpose of conducting the watershed mass
balance at the five sites was to provide an index of water-resource degradation and determine how human
interactions and natural variations affect watersheds, thereby promoting more effective management of
these resources (Peters et al., 2006). Similar approaches are now being developed for other persistent
pollutants.
Some uncertainties may arise in evaluating watershed mass balances. At times, solute
concentrations are very low, making it difficult to ascertain the influx and outflow of solutes into the
system. Dry deposition of solutes is usually not measured and may be a significant source of
contaminants. Spatial uncertainties in rainfall amounts may cause a bias to occur in precipitation
measurements.
2.4.2 Uncertainties in Determining Reductions in Surface Sediment Contamination.
Combining sediment concentration profiles with age-dating results provides valuable information relative
29
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to the amount of time required to achieve surface sediment concentration goals. Nonetheless, uncertainty
exists when making future predictions based on past data trends, especially without a mechanistic
understanding or a verifiable model of contaminant fate processes. Surface sedimentation and recovery
may be affected by temporal changes in the sediment mass balance due to point-source discharge
controls, watershed growth and urbanization, or other factors. For example, in engineered reservoirs,
long-term stability of the sediment bed may rely on the long-term maintenance of dams and other
structures that form the reservoir. Thus, it is necessary to consider potential land use changes that may
impact sediment loads and environmental conditions.
Most data collected provide information on past sediment processes and contaminant
behavior, changes in future site and water body uses and potential changes to the watershed also must be
considered. Recreational and industrial site uses may require navigational dredging that could impact
natural recovery processes. Changes to the watershed, including changes in non-point source
contaminant loadings, and changes in sediment loads due to increased urbanization or changes in
agricultural practices can have long-term impacts. For example, Jaffe et al. (1998) report that from 1951
to 1983 the depositional rate in much of San Francisco Bay decreased as sediment supply diminished due
to cessation of hydraulic mining in the watershed, upstream flood-control, and water-distribution projects
that reduced peak flows.
Immediately following source control, surface sediment contaminant concentrations generally
experience a rapid reduction in contaminant concentrations; however, the rate of reduction typically
decreases substantially as concentrations decrease. This reduced recovery rate is due to such limiting
processes as surface sediment mixing, ongoing contaminant loading, and contaminant recycling through
resuspension and repeated cycles of biological contaminant uptake and decay. One way to look at the
life-cycle of a site is that exposure concentrations are initially controlled by point source loads, later by
ongoing loads from sediment deposits, and finally by uncontrolled non-point source loads (EPA, 1998).
Statistical analysis identifying upper prediction limits based on the data trends can temper
expectations of future recovery (Brenner et al., 2004), but it is important to assess whether it is
appropriate to use reductions in sediment concentrations over the past several decades as a basis for
predictions. It may be possible, for example, that a concentration plateau in surface sediments has been
reached where contaminants resurfacing from depth through advective transport and sediment mixing
processes, on-going contaminant loading, and contaminant recycling through the food web are at steady
state with reductions in surface sediment concentrations through natural burial. For these reasons, a well-
designed monitoring plan is a critical component of the natural recovery remedy. Defensible data are
necessary to determine the long-term extent of changes in chemical concentrations in surface sediments
and the extent to which contaminants persist in surface sediments and the ecosystem and pose a risk to
wildlife and humans.
Another consideration in predicting future recovery is that not all contaminants enter aquatic
environments as particle-bound contaminants. For relatively mobile contaminants, the "depositional
model" for natural recovery may not be appropriate. For example, coal-tar discharges, such as creosote
releases from former wood treatment sites, often include non-aqueous phase liquid (NAPL) that can be
transported via groundwater as it seeps to the sediment surface (Kueper et al., 2003; Patrick, 1998). For
NAPL seeps, vertical contaminant profiling may provide little information about recovery (Brenner et al.,
2002), which may depend more on hydraulically interrupting groundwater flow and thereby eliminating
upward flowing seeps than on deposition of clean sediments alone. Additionally, some metals due to
their relatively high solubility can migrate with advective groundwater transport through sediments.
Immobilization of contaminated sediments via geochemical processes in sediments, such as the
precipitation of divalent metals with reduced sulfides, may have a greater impact on reduced metal
bioavailability than burial and isolation by clean sediment. These limitations highlight the importance of
30
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a sound CSM that characterizes historical and potential ongoing sources, chemical fate and transport
processes, and sediment transport and burial processes. The CSM should be used to ensure that field
efforts focus on appropriate metrics that measure processes that influence recovery and, in particular,
address those with the greatest uncertainty. Notably, where rates and relative magnitudes of contaminant
transport and sedimentary processes are uncertain, sediment coring, age dating, and contaminant profiling
can better define those processes and refine the CSM.
2.4.3 Predicting Sediment Erosion and Transport: A Tiered Approach. A successful
application of MNR depends primarily on reductions in chemical bioavailability and toxicity in surface
sediments over time. In depositional environments, contaminant burial due to net clean sediment
deposition is often a major contributor to reduced contaminant bioavailability, which may be further
reduced through contaminant transformation, sequestration, and weathering. A phased approach can be
used to evaluate sediment transport processes. This approach begins with readily available data and
proceeds with more targeted site-specific data collection as necessary. Tier 1 involves estimation of
sediment erosion, transport, and deposition potential by measuring conventional sediment properties (e.g.,
sediment grain size) and estimating water velocities using readily available hydrodynamic data (e.g.,
rainfall records and U.S. Geological Survey [USGS] records). Tier 2 involves combining direct
measurements of sediment erosion, transport, and deposition potential (e.g., bulk density and critical shear
strength) with direct hydrodynamic measurements (e.g., currents and waves) to estimate sediment
transport potential under normal or high-energy events. Table 2-4 describes processes most commonly
associated with sediment transport and outlines data to support Tier 1 and Tier 2 investigations.
2.4.3.1 Tier 1 Estimates of Sediment Transport Processes. Multiple lines-of-evidence are used to
evaluate sediment transport in the Tier 1 analysis and support the overall interpretation of sediment
transport potential (Blake et al, 2007). The lines-of-evidence commonly used are included in Table 2-4.
The Tier 1 analysis relies on data typically collected during the remedial investigation (RI) phase.
Order-of-magnitude calculations are outlined below to demonstrate the steps required to
estimate erosion, transport, and deposition potential. For example, if, based on these calculations,
significant potential exists for erosion, then direct measurement of sediment shear strength (e.g., by
Sedflume) and hydrodynamic forces should be conducted as part of the Tier 2 analysis. The information
gathered from these analyses can then be used to assess the potential for unacceptable impacts from erosion.
The Tier 1 analysis takes the user through a series of steps to: a) estimate the processes
affecting sediment transport, and b) determine their likely impact. The steps involved are:
• Assemble a CSM by reviewing sediment transport patterns and hydrodynamic
characteristics.
• Evaluate the likelihood and magnitude of scour events. This establishes the minimum
acceptable storm or flow events against which MNR's effectiveness will be assessed.
• Estimate the sediment mass balance to determine whether the area is net depositional or
net erosional. (This may not be possible at all sites.)
• Calculate the bottom shear stress and critical shear stress. This information is used to
determine conditions under which erosion is likely to occur, including natural high-flow
events and man-made causes such as ship traffic and propeller wash.
• Based on the predicted shear stress, the potential depth of erosion from the "design"
event can be estimated.
31
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Table 2-4. Data Collection for Tier 1 and Tier 2 Sediment Transport Evaluation (modified from Blake et al., 2007)
Parameter | Suggested Tier 1 Data | Suggested Tier 2 Data \
Purpose
I
Questions Answered
Site Characteristics
Water body
configuration and
bathymetry (current
and historical).
Maps. National Oceanic and
Atmospheric Administration
(NOAA) or other historical
bathymetric charts.
Aerial photographs.
Information on current and
historical site use.
Detailed bathymetric survey
using single or multi-beam
mapping systems.
Shoreline surveys.
Side-scan sonar.
Bathymetry, topography, and historical
information are used to characterize the
physical boundaries of the site and define
relevant zones of influence.
Bathymetric/shoreline change analyses
define long-term depositional/erosional
characteristics and rates. Historical
information and aerial photographs can
identify sediment sources and sinks.
Is the site net depositional or
erosional? Have erosion and
deposition led to changes in water
depth?
Contaminant
sources.
Horizontal and
vertical distribution
of sediment
contaminants.
Geochemistry and field
parameters.
High resolution horizontal
and vertical sediment
contaminant distribution
data using sediment cores.
If flow is unidirectional and contaminant
sources and loading history are known,
sediment transport patterns can be
inferred from horizontal and vertical
contaminant distributions.
Have sediment contaminant
concentrations changed over time?
Has there been a major event causing
mixing of the surface sediments?
Have sediment contaminants been
transported to new areas due to
sediment transport processes?
Historical, current,
and future
anthropogenic
activities.
Identification of outfalls,
dredging and navigation
history, former or planned
construction/fill, future use,
and anticipated watershed
changes.
N/A
The influence of anthropogenic activities
must be taken into account during a
sediment transport analysis. Also,
changes in future site use could alter the
potential for sediment erosion, transport,
and deposition.
Have there been any anthropogenic
activities in the past that may have
contributed to transport of
contaminated sediments? Are there
plans to conduct any activities at the
site that can lead to sediment erosion,
deposition, or transport?
Water Column Properties
Waves, tides, and
currents. Salinity
and temperature.
Available regional or site-
specific data.
Hydrodynamic data may be
available fromUSGS, U.S.
Army Corps of Engineers
(USACE), or NOAA
gauging stations, including
rainfall data or other
watershed data.
Site-specific current
measurements: acoustic
Doppler profilers (ADPs),
velocimeters, current
meters, and Doppler
velocity logs. Tide and
wave measurements: ADPs,
directional wave meters, and
pressure sensors.
The dominant hydrodynamic forces drive
sediment transport; when hydrodynamic
measurements are combined with
suspended sediment measurements,
directions and quantities of sediment
transport can be described. Analysis of
water column transport properties is
necessary to determine sediment settling
properties and flux. Salinity/temperature
profiles to determine hydrodynamics and
behaviors or life cycles of aquatic
organisms.
What effect do tides and currents
have on sediment transport? What is
the potential for significant
bioturbation?
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Table 2-4. Data Collection for Tier 1 and Tier 2 Sediment Transport Evaluation (modified from Blake et a/., 2007) (continued)
Parameter
Suggested Tier 1 Data
Suggested Tier 2 Data
Water Column Pro
Suspended sediment
concentrations.
Water quality data from
USGS, state, or local
agencies.
Site-specific suspended
sediment concentrations:
measuring suspended solids,
optical backscatter of ADPs,
optical backscatter sensors, or
laser in-situ sediment
transmissometer.
Purpose
Questions Answered
nerties(Continued)
The quantity and characteristics of
suspended solids are used to calculate the
suspended sediment flux on or off site
and determine sedimentation or erosion
rates.
What is the quantity of suspended
solids in the water column, and
where did the suspended solids
originate?
Sediment Bed Properties
Horizontal and
vertical PSD.
Water content/bulk
density, TOC, and
sediment
stratigraphy.
Sediment shear stress
tests.
Sediment
accumulation rate.
Grain size data as
collected for the RI.
Water content data as
collected for the RI. TOC
data as collected for the
RI.
Available site data.
Sediment core
descriptions.
Estimated values for
sediment properties and
cohesiveness.
Bathymetric differences.
Dredging records.
Sieve analysis (>63 um),
laser diffraction methods
(<63 um), and optical
methods.
Higher density spatial
sampling using Phase I
analysis. Sub-bottom
profiler.
Surficial critical shear stress
and potential resuspension for
cohesive sediments: flume
studies. Sediment erosion
profiles for cohesive
sediments. Side-scan sonar.
Radioisotope analysis.
Sediment traps.
Sediment bed properties are used to infer
sediment transport characteristics. Data
also are needed for analytic and numeric
computations.
Bulk density helps to determine the
settling properties of the sediment.
Stratigraphic information is used to infer
depositional environments and sediment
bed erosion potential.
Sediment shear strength measurements
are used when working with cohesive
sediments to determine the potential for
sediment erosion and depths of erosion
during normal and high-energy events;
non-cohesive sediment behavior can be
predicted from grain size and bulk density
information. These measurements are
used to calculate sediment erosion
potential and degree of imbeddedness of
fine sediments in an armored sediment
bed.
Sediment accumulation rates can be used
to directly determine rates of burial of on-
site sediments. Also, these rates can
indicate the susceptibility to high-flow
sediment mixing or off-site transport.
According to the grain size of the
surface sediments, what is the origin
of the sediments? What fraction is
the contaminant associated with and
is this fraction susceptible to
transport?
What specific properties of the
sediment promoted settling?
Will the surface sediment be easily
eroded? Will deposition continue to
take place in the current depositional
environment? What is the erosion
potential of the surface sediment?
What is the likelihood of sediment
mixing or off-site transport due to net
erosion or, more often, high-flow
event erosion?
At what rate would contaminated
sediments be buried at the site?
How susceptible is the site to high-
flow event driven mixing of surface
sediments?
OJ
OJ
-------
Table 2-4. Data Collection for Tier 1 and Tier 2 Sediment Transport Evaluation (modified from Blake et a/., 2007) (continued)
Parameter
Suggested Tier 1 Data
Suggested Tier 2 Data
Purpose
Questions Answered
Sediment Bed Properties
Depth of biological
activity and
bioturbation.
Regional and site-
specific biological data
as available and as
collected for the RI.
Qualitative/quantitative
benthic surveys.
Sediment profile imaging.
Push-core observation.
Radioisotope profiles.
Redox measurements in
sediment.
Metals speciation.
Vertical physical transport of sediments
due to bioturbation must be understood
and quantified to characterize potential
depths to which contaminated sediments
may be exposed and/or transported. 7Be
and 234Th radioisotopes can be used to
measure actively mixed zones. Oxidized
layer of surficial sediment corresponds
with most actively mixed sediments.
Would bioturbation and other
biological activity reduce or increase
the rate of sediment recovery?
-------
2.4.3. la Using the Conceptual Site Model. A CSM should be developed and reviewed by
stakeholders and regulatory agencies before any sampling and analysis are conducted at the site. The
CSM is prepared using existing site information and should include a multi-dimensional (temporal and
spatial) description of the site (i.e., sources of contaminant, fate and transport of the contaminant through
exposure routes, and potential receptors). Both ecological and human risks should be identified. Since
site conditions change overtime, the CSM should be periodically updated to reflect current site conditions
(Apitz et al, 2005a; EPA, 2005a; Fletcher et al, 2008; SPAWAR Systems Center, 2003). The
development of the CSM initially should be guided by the site-specific conditions and will likely be
independent of remedy. Once the MNR remedy is selected, the CSM should be updated to reflect site and
mechanistic processes specific to the MNR remedy. Use of a CSM contributes to the determination of the
most cost-effective and efficient manner to protect the environment.
Once the CSM is developed, Tier 1 analysis should be used to identify the dominant sediment
transport processes at the site based on the site data collected. The CSM should then lead to the
development of sediment management questions: For example: 1) Could erosion of the sediment bed
lead to the exposure of contaminated sediments? 2) Could sediment transport lead to the redistribution of
contamination within the site or movement of contamination off site? 3) Will natural processes lead to
the burial of contaminated sediment by relatively clean sediment? 4) If a site is actively remediated,
could sediment transport lead to the recontamination of the site? Erosion, resuspension, transport and
deposition must be evaluated when preparing the CSM to guide the Tier 1 analysis (Blake et al., 2007).
2.4.3. Ib Mass Balance Estimates. The sediment mass balance is a model of all inputs and outputs of
sediment mass in a system. It can be used to determine whether the system is net depositional or
erosional and identify sediment transport directions and quantities. Preparation of the mass balance is
made easier if the necessary information is gathered during the initial RI phase.
The sediment mass balance is defined as follows:
Sediment mass inflow = Sediment mass outflow + Sediment erosion - Sediment deposition (Eq. 2.2)
Mathematically, the mass balance is expressed as follows:
QmCm - QoutCout - A(D -E) = Y (Eq. 2.3)
where Qin and Qout are the incoming and outgoing mass flow rates of water in volume per unit time; Cin
and Cout are the suspended sediment concentrations of the incoming and outgoing water in mass per unit
volume; Vis the volume of the water body; A is the surface area of the water body; the deposition rate (D)
minus erosion rate (E) in mass per unit area per unit time represents that change in sediment due to bed
exchange, and dC/dt is the change in suspended sediment in the water column over time, which is zero if
the system is in equilibrium or steady state. Flow rates are determined from field measurements or
gauging stations. Suspended sediment concentrations are measured. Values of D and E are more difficult
to determine but can be obtained from field monitoring of deposition and erosion. The same equation can
be rewritten in steady state as follows:
QmCm-QoutCout=A(D-E] (Eq.2.4)
The following conditions determine whether the site is depositional or erosional:
35
-------
• For Qm Cin > Qout Cout, then D > E and the area is a net depositional
environment.
• For Q^ Cin < Qout Cout, then D< E and the area is a net erosional environment.
• For Qin Cin = Qout Cout, then D = E and the sediment bed is in equilibrium with
the influent and effluent solids loads. Here, the assumption is made that the
system is at steady state.
During a flood or high-flow events, the above relationships may change, leading to deposition
in areas that are more prone to erosion and vice versa. Also, it is important to take into consideration the
scale of the area for which the mass balance is developed. Though the study area may have been
calculated as either depositional or erosional, there can be very localized scour or deposition occurring
due to obstructions, docks, flow restrictions, etc. Though these localized conditions may be small relative
to the site, they can play an important role in exposing or redistributing contaminants.
A conceptual sediment mass balance is shown in Highlight 2-3. The example emphasizes the
importance of understanding sediment inputs and outputs in characterizing the regional depositional
characteristics of an area. Calculation of a sediment mass balance cannot be carried out for all sites,
including coastal sites where definable inputs and outputs may not exist.
2.4.3.Ic Estimating Bottom Shear Stress. Shear stress is the force that water flow exerts on the
sediment surface due to waves, seiche flows, propeller wash, and/or currents. Turbulent shear stress (T)
can be calculated as follows:
r = pCfu2 (Eq. 2.5)
where p is the fluid density (kg/m3), Cf is the coefficient of friction, and u is the average water velocity
(m/s). Uncertainty may occur with the use of Equation 2.5 because of the error associated with
measurements or predictions of the coefficient of friction and errors with velocity estimates. During Tier
2 analysis, the velocity and coefficient of friction estimates can be improved with direct field
measurements of the vertical velocity profile (Cheng et al, 1999).
C/can be calculated for unidirectional flow using the following equation:
k2
(Eq. 2.6)
where k is von Karman's constant (0.42), z0 is the effective bottom roughness, and h is the water depth.
A first estimate of the effective bottom roughness is generally selected on the basis of the sediment bed
grain size distribution. Typical values for C/range from 0.002 to 0.004 in rivers and estuaries. The
coefficients of friction for environments where waves play a larger role involve more effort in their
computation and are described by Van Rijn (1993), Christoffersen and Jonsson (1985), and Grant and
Madsen(1979).
Knowledge of the average velocity of a river over the sediment bed is necessary when
estimating shear stress in rivers and estuaries. The average velocity of a river can be measured using
Doppler current meters, acoustic Doppler velocimeters, and electromagnetic current meters
(Westenbroek, 2006). The average velocity in a river at a given flow rate can also be obtained from flow
rating curves if the variation of the cross-sectional area with surface elevation is known. Flow rating
36
-------
curves provide an empirical estimate of velocity from flow rate measurements. The USGS has flow
rating curve data for most USGS-monitored rivers or streams (http://water.usgs.gov/). These data can
serve as a good resource for a first estimate of expected regional flow magnitudes.
37
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Highlight 2-3. Hypothetical Mass Balance Example
The hypothetical system is a lake that receives water and sediments from two rivers (refer to
conceptual mass balance figure below). The lake then discharges into a downstream river. The
lake is known to be net depositional. The average flow rates of the two inlet rivers are Qi and Q2,
and the sediment concentrations in the water column of these two rivers are Ct and C2,
respectively. The average flow rate and sediment concentration discharging out of the lake are Q0
and C0, respectively. The volume of the lake (V) can be calculated using the dimensions of the
lake hypsographic curves (a graph that shows the proportion of area that exists at various
elevations by plotting relative area against relative height), or simply using the volume of a cone
equation. In order to determine the mass balance in the lake several assumptions are made:
1) Flow is in one direction (from the two input streams to the discharge point of the lake)
2) Shear stresses are minimal, so no erosion occurs.
3) Tides are negligible.
To determine the mass balance, the steady-state mass balance equation used is:
QiCi+Q2C2-QoCo=V(D-E} (Eq. 2-3.1)
where E and D are erosion and deposition rates, respectively.
Flow rates are determined from field measurements or gauging stations. Suspended sediment
concentrations are measured. Values of D and E are more difficult to determine but can be
obtained from field monitoring of deposition and erosion.
River 1 Input
River 2 Input
Direction of Water Flow
Conceptual Mass Balance Example Showing a Lake Area with Net Deposition
38
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Resources that can be used to predict tides and associated currents in coastal regions in North
America are available from NOAA (http://co-ops.nos.noaa.gov). In many navigable locations, NOAA
has worked with local agencies to deploy real-time regional current and wave meters. These resources
can be used to determine order-of-magnitude shear stresses from waves and currents. More sophisticated
instrumentation for directly measuring velocity and shear stress is discussed in Section 2.4.33.a.
2.4.3. Id Estimating Critical Shear Stress. Critical shear stress is the minimum amount of shear stress
exerted by water currents that can initialize the movement of bottom particles. The erosion potential of
the sediments (which can be measured or calculated) determines the critical shear stress required to move
sediment particles and, therefore, determines whether contaminated sediments will become exposed or
mobilized. Sediments are composed of particles of varying sizes, and erosion generally begins with the
particles that are the easiest to move (require the least shear stress). A gradual increase in sediment
erosion is then experienced as the shear stress increases. This progression of erosion makes it a challenge
to define a specific critical shear stress for the sediment as a whole. Roberts et al. (1998) defines critical
shear stress as "the shear stress at which a small, but accurately measurable, rate of erosion occurs". In
their study, the critical shear stress (tce) was calculated using the following equation:
(Eq. 2.7)
where E is the rate of erosion assumed to be 10~4 cm/s, which is considered the rate at which a small but
accurately measurable movement of particles can be measured, and A, n, and m are constants that were
determined experimentally and are dependent on the size class of the sediment (Roberts etal, 1998).
Van Rijn (1993) estimated the critical shear stress using Shield's curve that determines
critical shear stress for erosion using particle diameter as follows. To simplify the calculation of critical
shear stresses, a dimensionless particle diameter, cf, is used:
d* =d
(A-04
U
(Eq. 2.8)
where d is the median particle diameter (cm); ps is the density of the particles (generally assumed as
2.65 g/cm3); v is the kinematic fluid viscosity (0.0117 cm2/s for salt water and 0.0112 cm2/s for fresh
water); and g is the acceleration due to gravity (980 cm/s2). Using a range of d" values for the sediment
bed, the critical shear stress, rce, in dynes/cm2 for either fresh water or salt water for a particle larger than
200 jam may be calculated as shown in Table 2-5. A typical progression of critical shear stress with grain
size is shown in Figure 2-3.
Table 2-5. Critical Shear Stress for Particles Larger than 200 um
Critical Shear Stress (dynes/cm2)
Tce
?ce
*ce
?ce
Tce
= 024d*-l[(ps-\)gd]
= 0.14d*-°64[(ps-l)gd]
= 0.04d*-°l[(ps-l)gd]
= 0.013d*029 [(ps -l)gd]
= 0.055 [(ps-\}gd\
Range of Valid d*
1 < d* < 4
4150
39
-------
if!
22
ro
0)
ro
u
O
.
«- V 4 =
Grain Size
Figure 2-3. Typical Trend in Critical Shear Stress as Sediment Composition
Transitions from Sand to Gravel (Adapted from de Linares and Belleudy, 2007.)
No single formula exists to calculate critical shear stress for smaller cohesive sediment
particles; however, a conservative estimate of 1 dyne/cm2 is often used (Ziegler, 2002; Gailani et a/.,
1991). Tier 2 analyses can be used to directly measure critical shear stress for cases where a high degree
of certainty is required. It is important to note that contaminants tend to be associated with cohesive
sediments. Although no universal equation exists, a simple sediment transport model (either a one-
dimensional vertical model or a more complex regional model) can be used to estimate the erosion
potential of cohesive sediments. The amount of sediment resuspended during cohesive sediment erosion
depends on the turbulent shear stress at the sediment-water interface and the level of consolidation
(Krone, 1962; Lick, 2010; Parchure and Mehta, 1985).
This information, including the bed porosity, can be used to calculate the mass of sediment
resuspended from a cohesive bed using the following empirical equation (Gailani etal, 1991):
r>r_,
(Eq. 2.9)
where E is the resuspension potential (mg/cm2), a0 and n are site-specific constants related to
resuspension properties of cohesive beds (n, the shear stress component, can vary from 2 to 3, while a0
can vary by an order of magnitude), Td is time after deposition (days), i is bottom shear stress due to
waves and currents, and icr is effective critical shear stress (typically 1 dyne/cm2 at the sediment surface).
The consolidation exponent (rri) depends on the depositional environment and varies from 0.5 to 2
depending on and inversely proportional to the energy of the body of water. This empirical equation is
used because no predictive analytical formulation has been developed to estimate sediment resuspension.
However, the maximum sediment resuspension (mg/cm2) can be estimated based on the maximum
expected shear stress using the following equation (Ziegler, 2002):
40
-------
= Tmax Ic
where d (Eq. 2.11)
This equation was developed using data obtained from annular flumes, a method used in Tier
2 analysis. Different flumes measure different physical properties, and, therefore, the values of maximum
sediment resuspension are dependent on the flume type used.
Once Emax is known, it can be used to calculate scour depth, but, generally, field studies must
be conducted to obtain site-specific values for a0 and n. Also, once Emax is known, the scour depth, Dscour,
(depth in cm), is calculated by the following equation:
(Eq-2-12)
— i
1000 Pdry
where pdry (g/cm3) is the measured sediment dry density.
2. 4.3. le Identifying Erosional Events. The most significant potential for unacceptable risks resulting
from sediment erosion occurs during high-energy events. Such high-energy events may include large
storms, extreme tidal events, floods, storm -induced waves, and dam releases that create conditions of high
shear stress. The frequency and intensity of such events should be considered in the MNR sediment
transport evaluation to assess the potential for sediment scour and exposure and transport of suspended
contaminated sediments and the impacts of these processes on potential risk to ecological receptors and
human health (EPA, 2005a). Often, a high-energy event with the probability of a 100-year flood
occurring in a given year is used as a design benchmark (EPA, 2005a).
At riverine sites, scour events typically involve high flows. USAGE (1993) developed a
Hydrologic Frequency Analysis manual to evaluate hydrographs and determine the frequency and
magnitude of high -flow events. For coastal and estuarine sites, storm activities typically most affect
regional sediment transport. USAGE later developed the Coastal Engineering Manual (2002) outlining
how to evaluate the maximum wave and water level conditions at a coastal or estuarine site. These manuals
can be used to predict the order of magnitude bottom shear stress expected during high-energy events. It
should also be noted that the river input into an estuary during such events can significantly alter flow
patterns in the region, in which case analyses for both the riverine and estuarine environments should be
conducted.
2.4.3.1f Propeller-Induced Scour. The sediment beds of navigable waterways may also be
susceptible to scouring from passing and moored ship traffic. Rotating ship propellers accelerate water in
order to move a ship. This water has high kinetic energy that can cause scour to a sediment bed. A ship
propeller has three components of velocity: the axial component with the rotation of the propeller that is
the main contributor and tangential and radial components that are perpendicular and parallel coincident
to the rotation of the propeller and parallel coincident to the radii. Ship propeller scour has presented an
engineering challenge in the past and has been studied in some detail. With known vessel and waterway
characteristics, empirical methods can be used to predict the scour depth as a function of time (Maynord,
2000). Ship propellers produce a wash or jet of localized swirling currents that induces scour on an
41
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erodible bed (Figure 2-4). The maximum equilibrium scour depths can be calculated as a function of
velocity (V0), propeller diameter (Dp), distance between propeller tip and sediment bed (Q, sediment
grain size (d50), and water density p (Sumer and Fredsoe, 2002).
H,
Figure 2-4. Propeller Action that Produces Scour
(Modified from Sumer and Fredsoe, 2002.)
2.4.3.Ig Wind-Induced Wave Erosion. Wind-induced waves are potentially important at shallow
depths (e.g., shoreline areas) and in areas with long fetches (i.e., areas over a body of water where the
wind flow is in the same direction). Areas that are prone to wind induced wave erosion are coastal
embayments, estuaries, and lakes. Wind effects can be modeled to predict waves and currents under
normal- or high-energy events. The best overall summary for predicting wind driven waves is the
USAGE Coastal Engineering Manual (2002). Using techniques outlined in Van Rijn (1993),
Christoffersen and Jonsson (1985), and Grant and Madsen (1979), combined wave and current shear
stresses can be computed so that the potential for sediment transport can be evaluated. Typical examples
can be found in Bailey and Hamilton (1997), Evans (2005), Hamilton and Mitchell (1996), Lou et al.
(2000), and Luettich et al. (1990).
2.4.3.2 Moving from Tier 1 to Tier 2 Sediment Erosion Characterization. The Tier 1 assessment of
sediment erosion potential is achieved by calculating hydrodynamic shear stress and sediment critical
shear stress values to determine order-of-magnitude erosion rates and scour depths. Tier 1 erosion rates
are based on the understanding that these rates vary with applied shear stress due to waves and currents
and as a function of sediment bulk properties. However, whereas sediment bulk properties (e.g., PSD,
moisture content, organic carbon, and bulk density) can be used to predict critical shear stress values of
non-cohesive sediments, they cannot readily be used to predict critical shear stress values for cohesive
sediments (Lick, 2010).
42
-------
Thus, if the Tier 1 assessment shows significant potential for unacceptable risks to ecological
receptors and/or human health due to erosion or if cohesive sediments are involved (thereby making it
difficult to estimate sediment erosion potential), then direct measurement of sediment shear strength and
hydrodynamic forces should be conducted as part of the Tier 2 analysis.
The magnitude and extent of sediment erosion under normal- and high-energy hydrodynamic
events is of paramount importance to MNR applications. Few sites are purely depositional. The extent to
which sediment erosion is acceptable can be assessed on a site-specific basis depending on the nature of
the vertical contaminant profile in the sediment, the potential risks to ecological receptors and human
health from erosion, and the potential for increased exposure.
Sediment erosion and scour can pose potentially unacceptable risks to ecological receptors
and human health by causing the following possible negative impacts on natural recovery processes:
• Sediment erosion and scour can remove overlying clean surface sediments and
expose buried sediments with higher contaminant concentrations. The scour
depth influences the magnitude of sediment exposure and the resulting surface
sediment contaminant concentrations after the scouring event.
• After erosion takes place, suspension of contaminated sediments in the water
column can increase exposures of aquatic animals to contaminants.
• Suspended contaminated sediments may be transported away from the site,
increasing the areal extent of sediment contaminants.
Not all scour or erosion, however, will result in unacceptable risk. Sediments eroded from
one part of a system may naturally be deposited in other parts of the system. Moreover, movement of
sediment itself may not necessarily create an unacceptable risk. EPA's Contaminated Sediment
Remediation Guidance (2005) notes that the key factor in evaluating the stability of contaminants in
sediment is whether movement of contaminated sediment or of contaminants alone is occurring or may
occur at scales and rates that will significantly change their current contribution to risks to human health
and/or ecological receptors.
At some sites, unacceptable risks to ecological receptors or human health due to impacts from
sediment erosion and suspension may persist. Because of these concerns, supporting evidence may be
required to evaluate sediment erosion after normal- or high-energy events. Modeling can greatly facilitate
predictions of future erosional events and sediment transport behavior, which can aid in the determination
of whether unacceptable risks to ecological receptors and/or human health may persist. However,
modeling is not always feasible, and at some sites, particularly smaller sites, modeling may not be cost
effective.
2.4.3.3 Tier 2 Estimates of Sediment Erosion and Transport. Tier 2 analyses are performed to
further the understanding of sediment processes and fill data gaps from a Tier 1 analysis. For cohesive
sediments, a great deal of uncertainty is associated with the prediction of erosion rates and scour depth
using the Tier 1 approach alone. The primary uncertainty resides in sediment erosion rates that depend on
critical shear stress, which are known to be highly heterogeneous from site to site and often within a
single site. This uncertainty can be compounded by additional uncertainties regarding current and wave
velocities. Accurate estimates of erosion rates and depths increasingly rely on site-specific, field-
measured critical shear stress and hydrodynamic velocities. Table 2-4, presented previously, provides a
list of information that can be collected to fill the data gaps remaining after a Tier 1 analysis. In some
cases, Tier 2 analysis recommends refinement of the procedures used in Tier 1 (i.e., applying site-specific
43
-------
measures as opposed to literature-derived values). In other cases, Tier 2 analysis suggests additional tests
and data that would present a better understanding of the site conditions.
2.4.3.3a Site-Specific Sediment Erosion and Critical Shear Stress Measurements. The difficulty of
predicting cohesive sediment erosion has led to the development of multiple flume configurations to
measure sediment erosion under known hydrodynamic conditions. The general approach involves
applying a known hydrodynamic force (i.e., a known flow rate through the flume) on in-situ or ex-si'tu
sediment columns, and measuring the amount of sediment erosion.
Typically, to conduct a flume study, the flow rate begins below the critical shear stress point
of the sediment where no erosion is observed. The flow rate is increased incrementally until the
sediments begin to erode. Erosion rates are then measured by the suspended solids concentration in the
flume effluent stream or the depth of erosion of the sediment surface. The flumes are also used to
measure critical shear stress to identify the critical flow rate at which a measurable amount of sediment is
eroded.
Methods used to measure sediment critical shear stress to estimate erosion at different current
velocities (usually under laminar flow) and corresponding hydrodynamic shear forces are identified in
Table 2-6. The table includes information on flow conditions used for each device, whether the device
can be used in situ or ex situ, the type of sediment transport measured, whether the device can measure
the erosion rate, the depth of erosion that can be measured, and the measurable range of shear stresses.
All of the devices allow for an indirect measurement of critical shear stress (TCS) in consolidated sediments
with clays, silts, and sand using well-defined flow velocities.
Table 2-6. Comparison of Devices for Measurement of Sediment Critical Shear Stress
Device
Straight
Flume
Annular
Flume/
Sea
Carousel
Sedflume
ASSET
Flume
SEAWOLF
Flume
Flow
Condition
Linear/
Oscillatory
Linear
Linear
Linear
Linear/
Oscillatory
In Situ
or
Ex Situ
Both
Both
Ex Situ
Ex Situ
Ex Situ
Measured
Transport
Total
Load
Suspended
Load
Total
Load
Suspended
Load +
Bedload
Total
Load
Net
Erosion
per Event
No
Yes
No
No
No
Erosion
Rate
Yes
No
Yes
Yes
Yes
Depth
Measured
Surficial
Layers
Surficial
Layers
0-3 m
0-3 m
0-3 m
Shear
Stress
Range
0-4 PA
0-1 PA
0-10+
PA
0-10+
PA
0-10+
PA
Reference
Ravens and
Gschwend,
1999
Maa, 1993;
Maa et al. ,
1995
McNeil
etal, 1996
Jepsen
et al, 2002
Roberts
etal.,
2003a
In-Situ flumes include the annular flume (Figure 2-5) and the straight flume (Figure 2-6).
These flumes are placed on the sediment bed and rely on water to be passed over the sediment bed to
erode and suspend surface sediments. The flumes themselves remain stationary. The annular flume is a
closed system that does not release sediments as they are suspended. Both of the in-situ flumes operate in
only the Surficial layers (a few cm).
44
-------
SECTION
Figure 2-5. Annular Flume Configuration (Image of a Sea Carousel, left, from Deltares hydrologic
research facility, the Netherlands; right schematic: USAGE, 2002.)
top. front view
Iron
trip
bottom, front view
Figure 2-6. Straight Flume Configuration Showing Upright Perspective (top) and
Inverted Perspective (bottom) (Ravens and Gschwend, 1999)
Annular flumes are limited in that they can only measure sediment resuspension and shear
stresses below 10 dynes/cm2 offeree and, therefore, can only be used in the top few millimeters of the
sediment bed (McNeil et al., 1996) as deeper depths cause a greater amount offeree to be applied to the
instrument. Prior to the development ofex-situ flumes, the in-situ annular flume was the leading method
of erodibility measurement for sediment transport studies (Lick et al., 1995). Either a bed of reconstituted
sediments or in-situ sediment in a closed circular flow system is subjected to erosion by applying rotation
of an overlying water column. At each velocity and associated shear stress, an experiment is run to
establish steady-state suspended solids concentrations in the water column, from which event-specific net
45
-------
erosion can be inferred. Subsequent analyses of this approach have identified artifacts, such as sediment
accumulation, along the walls of the annulus that may understate event net erosion.
Ex-Situ flumes include the Sedflume (Figure 2-7), the Adjustable Shear Stress Erosion and
Transport (ASSET) flume, and the Sediment Erosion Actuated by Wave Oscillations and Linear Flow
(SEAWOLF) flume.
TOP VIEW
PUMP
SIDE VIEW
fLOW — »•
?
10 Llll
i
V ^ ••'£*} '-'•"'Ts!
,,,,,,, ,•„
t^t^-3?*;
PUMP
CORE
PISTC* T^J
JACK
Figure 2-7. Sedflume Schematic
(Upper image adapted from McNeil et a/., 1996; Lick and McNeil, 2001.
Lower image courtesy of Sea Engineering.)
A Sedflume is a straight flume with a test section containing an open bottom through which a
rectangular or circular cross-section coring tube of sediment can be inserted. In general, the main
components of a Sedflume are the coring tube; the test section; an inlet section for uniform, fully-
developed turbulent flow; a flow exit section; a water storage tank; and a pump to force water through the
system. The coring tube, test section, inlet section, and exit section can be made of clear acrylic or poly-
carbonate to allow observation of sediment-water interactions. During the analysis, water is pumped
through the duct and the test section of the Sedflume. The core surface is moved upwards as necessary so
46
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that the sediment-water interface remains level with the bottom of the flume. Erosion rates are obtained
by measuring the remaining core length at different time intervals, measuring the plunger elevation
difference between each successive measurement, and dividing by the time interval. The duration of each
erosion test for a specified shear stress is dependent on the rate of erosion and generally is between 0.5
and 10 minutes. Flow rates that induce no measurable erosion also are recorded (Borrowman et al., 2006;
Lick, 2010; Stevens et al, 2008).
Sedflume measurements were carried out on sediment cores from San Francisco (Jaffe and
Foxgrover, 2006). The results revealed increasing critical shear stress values, decreasing erosion, and
increasing bulk density with sediment depth. Although age dates are not shown for these sediment cores,
it is clear that as sediment cohesiveness generally increases with sediment depth, it also increases with
sediment age. Other changes with increasing sediment depth also influence erosion by orders of
magnitude. These variations include increasing bulk density and decreasing porosity in sediments of
uniform chemical composition. The authors also found that biological processes such as degradation or
utilization of organic matter over time result in higher percentages of clay minerals with age and depth.
The ASSET and SEAWOLF flumes are modifications of the Sedflume (James etal, 2005).
The ASSET flume maintains the capabilities of the Sedflume, while also quantifying bedload and
suspended load transport. In addition to measuring erosion and the variation of erosion with depth below
the sediment-water interface, the SEAWOLF flume is able to analyze the impact of oscillatory flows on
the sediment erosion rate using two pistons that work in tandem at both ends of the flume. This function
may be used to better predict erosion in wave-dominated environments (James et al, 2005).
Despite being designed for ex-situ operation, these flumes are commonly field mobilized,
thereby minimizing the need for long-range transport and disturbance of sediment cores. The primary
advantages of ex-situ flumes are: 1) they can be used to profile an entire sediment horizon, providing
erosion rate profiles with sediment depth, 2) the erosion process is visible to the operator, permitting
better control of overflow rates and corresponding shear forces, 3) bedload/suspended transport and
erosion rates are measured independently because the use of sediment traps for recirculated water or
continuous flow from a source ensures that suspended sediments do not interfere with eroding sediments,
4) complicated underwater deployments are not required, and 5) cores may be reconstructed in the
laboratory to study the erosion rates during consolidation of deposited sediments. Estimates made with
ex-situ flumes often indicate much greater depths of scour than do in-situ annular flume estimates for the
same site and same assumed shear conditions (Roberts et al, 2003a). Models employing ex-situ flume
estimates have been developed and are widely used (Roberts etal, 2003a).
The primary disadvantage of ex-situ flumes is their reliance on external sediment cores and
the potential disruption of sediments during core collection and transport. For this reason, Sedflume
studies generally are conducted using push cores or by subsampling box cores. Theoretically, wall effects
during core collection and extrusion can also influence Sedflume results; however, wall effects are
reportedly negligible for 10-cm (width) by 15-cm rectangular cores (Roberts etal., 2003b). Much of the
erosion generated in ex-situ flume experiments is also observed to result in bedload. While this portion of
erosion has been successfully measured with ASSET, it may be a measurement artifact of other flumes,
leading to upward bias in erosion rates (Roberts et al, 2003b).
When considering MNR, the extent to which sediments can erode under normal- or high-
energy hydrodynamic conditions must be explored. By combining site-specific flow records with
measured erosion data, site managers can begin to develop a quantitative description of sediment erosion
potential. Some models use these data to predict long-term sediment transport trends. These models have
also employed annular flume estimates of erodibility (Reible, 2004).
47
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Because of potential artifacts in both in-situ and ex-situ flume methods, it is important to
validate estimates made with either method by incorporating them into sediment mass balances and
validating them against site-specific water column data and net burial rates inferred from the full range of
available evidence (James et al., 2005). Not all models can accommodate the results of the different types
of flumes; rather, specialized models are often required for different types of flume results. The challenge
of measuring and verifying erodibility measurements remains a source of uncertainty in sediment
transport modeling (Reible, 2004).
2.4.3.3b Water Column Hydrodynamic Studies. In combination with measurements of critical shear
stress and erosion rates of bed sediments, the hydrodynamic forces that drive erosion events must be
characterized. Most commonly, these data can be used in concert with measurements of critical shear
stress and erosion rate to predict the erosion of sediments as a result of high-energy events.
Hydrodynamic forces are site-specific and include river discharge, tidal currents, and wave action.
Erosion and resuspension events in riverine and estuarine environments can be measured at a
site by collecting both spatial and time-series measurements of suspended sediment concentrations and
current velocity in the water column. These measurements permit determination of the current velocity at
which sediments become resuspended, the concentration of sediment in suspension, and the height in the
water column to which sediments are being carried.
Extreme weather events, such as floods and hurricanes, can have significant effects on
sediment transport at a site. To predict the impact of extreme events, a statistical analysis can be
performed. Because extreme conditions are typically difficult to estimate accurately and often have
serious economic implications, a number of different techniques have been developed to determine the
probability and magnitude of extreme events in different systems (USAGE, 1993; 2002). Hydrodynamic
and sediment transport models also can be used to estimate sediment transport during high-energy events.
2.5 Summary
Successful application of MNR primarily depends on reductions in chemical bioavailability
and toxicity in surface sediments over time. Typically, this is accomplished in a depositional
environment. In depositional environments, contaminant burial due to net clean sediment deposition is
often a major contributor to reduced contaminant availability, which may be further reduced through
contaminant transformation, sequestration, and weathering. As stated in the previous sections, the key to
understanding the effectiveness of MNR is the characterization of the dominant processes involved in
moving sediments. A phased approach was outlined here that can be used to evaluate sediment transport
processes. This approach begins with readily available data and proceeds with more targeted site-specific
data collection as necessary.
48
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3.0 FATE OF COMMON ORGANIC CONTAMINANTS IN SEDIMENTS
3.1 Introduction
3.1.1 Monitored Natural Recovery. The primary goal of managing contaminated sediment is to
reduce health risks to aquatic/marine receptors, wildlife, and humans. Risk reduction can be
accomplished by human intervention (e.g., dredging, capping, and bioremediation) or by natural recovery
(Magar and Wenning, 2006). MNR is the management strategy used to determine if natural
transformation and fate processes are effective in reducing risks at a contaminated sediment site. MNR
involves leaving contaminated sediments in place and monitoring ongoing physical, chemical, and
biological processes that contain, transform, destroy, or otherwise reduce the bioavailability of the
contaminants until they are no longer a risk to receptors (NRC, 1997). Bioavailability is defined as the
extent to which a living organism can take up chemical contaminants by either active or passive
processes, or a combination of both. The physical, chemical, and biological processes that contribute to
the natural recovery of sediments contaminated with hydrophobic organic compounds (HOCs) that are to
be considered in this section include:
• Diffusion and volatilization of HOCs from sediments: Some low molecular weight HOCs
are sufficiently volatile or soluble that they can be lost rapidly from contaminated
sediments by dissolution, diffusion, and volatilization.
• Reduction in sediment mobility: Sorption of HOCs to stable organic or mineral phases in
sediments reduces their mobility.
• Chemical or biological transformation: HOCs can be transformed into degradation
products by abiotic and biotic oxidation or reduction reactions that may alter the mobility
or toxicity of the contaminants in question.
• Transport of HOCs in sediment pore water: HOCs can be transported through permeable
sediments by diffusion or advective processes, such as groundwater or tidal water flow.
3.1.2 Non-polar Organic Compounds of Concern. For sediment, some of the most common
contaminants of concern are non-polar organic compounds. These organic compounds are charge-neutral,
non-polar organic chemicals characterized by low aqueous solubility and an affinity for association with
dissolved or particulate organic matter and hydrophobic liquid, solid, and surface phases. Many HOCs
are produced and released to the environment in massive quantities by human activities. When entering
an aquatic environment, they tend to sorb to particles and accumulate in sediments. Many HOCs or their
degradation products are highly bioaccumulative and toxic to plants and animals because of their high
affinity for lipid phases of tissues; they can accumulate to hazardous concentrations in soils and
sediments, particularly near point sources. The behavior of different types of HOCs in marine and fresh
water bodies and associated sediments is controlled by the physical/chemical properties of the HOCs and
the receiving environment. HOCs of major environmental concern are those with:
• High relative toxicity (see below for a detailed discussion of relative toxicity, e.g.,
toxicity equivalent factors [TEFs]);
• Releases to the environment in large amounts from human activities; and
• High persistence in the environment.
HOCs with high toxicity include PAHs, PCBs, PCDDs, PCDFs, and halogenated pesticides and industrial
chemicals, such as DDT and polybromodiphenyl ether fire retardants. The HOCs that have been most
49
-------
studied for their environmental reactivity and toxicity include PAHs, PCBs, and PCDDs/PCDFs (Figure
3-1); therefore, these HOCs will be the focus of this section.
Hydrophobic
Organic Compounds
(HOCs)
Structure and Carbon
Numbering
Examples
Polycyclic Aromatic
Hydrocarbon
1.6.9-Tri methyl-
phctuinlli renc
Polychlorinated
Biphenyl
—,- 3.3".5,5'-Tclrachloro-
hipht-nyl
Polychlorinated
D ib en z o-p- d ioxi n
Di bcn/.o-f> -di n\ i n
cr o ci
2J:~.S-Tetrachlorodibenzo-p-dioxtn
Polychlorinated
Dibenzofuran
01
Dibcrvzofuran
ci
ci
Figure 3-1. Chemical Structure and Carbon-Numbering System of HOC Compound Classes
(An example of a typical chemical structure in each HOC class is included.)
50
-------
• Polycyclic aromatic hydrocarbons: PAHs are a class of hydrocarbons containing two or
more fused (sharing two carbons) aromatic rings (Figure 3-1). They are derived from the
incomplete combustion of organic matter (pyrogenic PAHs), from coal and petroleum
(petrogenic PAHs), or from anaerobic degradation of certain plant materials (biogenic
PAHs). PAHs may be classified further in terms of their analytical methodological
classes (e.g., total PAHs, dissolved PAHs, or extractable PAHs). The extractable class of
compounds refers explicitly to that fraction of the total PAHs that can be dissolved or
desorbed from a sorbed or otherwise solid phase through the use of an aqueous phase
extraction.
• Polychlorinated biphenyls: Biphenyl is composed of two benzene rings joined by a
single covalent carbon-carbon bond. PCBs are biphenyls containing one or more
chlorine atoms covalently bound to the biphenyl carbons. There are 209 possible PCB
congeners containing between one and 10 chlorines bonded to any of the 10 available
carbons on the benzene rings (Figure 3-1). Only about 130 congeners have been
identified in commercial PCB mixtures or environmental samples.
• Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans:
Dibenzodioxins and dibenzofurans are composed of two benzene rings joined by one or
two oxygen atoms (ether linkages), forming rigid coplanar (both benzenes oriented in the
same plane) molecules. Chlorinated dibenzodioxins and dibenzofurans contain one or
more chlorine atoms covalently bound to the benzene carbons (Figure 3-1). There are 75
possible PCDD congeners and 135 possible PCDF congeners containing between one and
eight chlorines. PCDDs and PCDFs are produced as unintended byproducts in the
manufacture of some types of industrial chlorinated organic compounds and by
combustion of organic matter containing chlorine or treatment of certain organic
wastewaters by chlorination. Most of the PCDDs/PCDFs in the environment originate
from combustion sources and reach soils and sediments by rainout or fallout of the vapor
phase and sorbed (to combustion soot) compounds.
3.1.2.1 Polycyclic Aromatic Hydrocarbons. PAHs are composed of two or more fused benzene
rings (Neff, 2002). Naphthalene (Ci0H8), consisting of two fused aromatic rings, is the lowest molecular
weight PAH. PAHs with up to six aromatic rings are commonly found in PAH-contaminated sediments.
PAHs with different numbers of fused benzene rings and degrees of alkylation vary in physical/chemical
properties (Table 3-1) that affect their fates and effects in the environment. PAHs can be formed by a
variety of mechanisms and classified as:
• Pyrogenic PAHs: Very rapid, high temperature (e.g., >700°C) incomplete combustion or
pyrolysis of organic materials;
• Petrogenic PAHs: Very slow (over millions of years) rearrangement and transformation
of biogenic organic materials at moderate temperatures of 100 to 300 °C to form fossil
fuels, such as coal and petroleum;
• Diagenic PAHs: Relatively rapid (days to years) transformation of certain classes of
organic compounds in anoxic soils and sediments; and
• Biogenic PAHs: Direct biosynthesis by organisms.
All four classes of PAHs can be found in water, soils, and sediments. Biogenic and diagenic
mixtures of PAHs in soils and sediments are composed of just a few specific PAHs. Those few PAHs
that are almost exclusively biogenic or diagenic usually are present at low concentrations compared to
concentrations of pyrogenic and petrogenic PAHs; however, perylene (a diagenic PAH) concentrations
51
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sometimes are high in organic-rich anoxic sediments (Venkatesan and Dahl, 1989). Biogenic/diagenic
PAHs are typically of little concern to MNR due primarily to their low relative concentrations in oxidized
surface sediments. Pyrogenic and petrogenic PAH mixtures in sediments usually are complex, composed
of hundreds of different PAHs, often at high concentrations; therefore, they represent a greater overall
risk to environmental health and safety.
Table 3-1. PAH and Related Compounds Typically Used in Hydrocarbon Fingerprinting
Analyte/Analyte Group
Naphthalene*
Ci -naphthalenes*
C2-naphthalenes*
C3 -naphthalenes*
C4-naphthalenes*
Biphenyl
Acenaphthylene*
Acenaphthene*
Dibenzofuran
Fluorene*
Ci-fluorenes*
C2-fluorenes*
C3-fluorenes*
Anthracene*
Phenanthrene*
Ci -phenanthrenes/anthracenes*
C2-phenanthrenes/anthracenes*
C3-phenanthrenes/anthracenes*
C/rphenanthrenes/anthracenes*
Dibenzothiophene
G! -dibenzothiophenes
C2-dibenzothiophenes
Abbr.
NO
Nl
N2
N3
N4
Bph
Acl
Ace
DdF
FO
Fl
F2
F3
AN
PO
PI
P2
P3
P4
DO
Dl
D2
Ring#
2
2
2
2
2
2
o
J
3
3
o
J
o
J
3
3
o
J
o
J
3
3
o
J
o
J
3
3
o
J
Analyte/Analyte Group
C3 -dibenzothiophenes
C4-dibenzothiophenes
Fluoranthene*
Pyrene*
G! -fluoranthenes/pyrenes*
C2-fluoranthenes/pyrenes
C3 -fluoranthenes/pyrenes
Benz(a)anthracene*
Chrysene*
Ci -chrysenes/benzanthracenes*
C2-chrysenes/benzanthracenes*
C3 -chrysenes/benzanthracenes*
C4-chrysenes/benzanthracenes*
Benzo(b)fluoranthene*
Benzo(k)fluoranthene*
Benzo(e)pyrene*
Benzo(a)pyrene*
Perylene*
Indeno(l,2,3-c,d)pyrene*
Dibenz(a,h)anthracene*
Benzo(g,h,i)perylene*
-
Abbr.
D3
D4
FL
PY
FP1
FP2
FP3
BaA
CO
CI
C2
C3
C4
BbF
BkF
BeP
BaP
Per
ID
DA
BgP
~
Ring#
3
3
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
6
5
6
~
Bold = 16 PAH priority pollutants identified in the Clean Water Act (CWA).
* = 34 PAHs identified in Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks
for the Protection ofBenthic Organisms: PAH Mixtures (EPA, 2003).
Source: Douglas et al., 2004
Although coal is generally considered an aromatic material (Berkowitz, 1988), concentrations
of extractable total PAHs usually are low, ranging from less than 0.01 to 0.8% (Stout and Emsbo-
Mattingly, 2008). Coal is derived primarily from the remains of land plants that accumulated as peat.
Upon burial, peat is converted to coal over millions of years of exposure to high temperatures and
pressures. Chemical rearrangements during slow diagenesis convert plant debris into a high molecular
weight, highly-aromatic, three-dimensional structure (Teichmuller, 1987). There is variation in the
number of fused aromatic rings per structural unit in coals of different ranks (hardness and maturity)
(Stout and Emsbo-Mattingly, 2008). The aromatic structures are linked together through linear and cyclic
aliphatic structures and oxygen and sulfur substituents. Most of the PAHs in coal are tightly bound in the
52
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coal structure; they typically do not leach out of coal. However, coal dust and particles have a large
surface area and, therefore, are more susceptible to leaching or to adsorption of dissolved PAHs; they may
contribute to the total extractable PAHs in sediments receiving coal-associated materials from aerial
deposition or runoff (Tripp et al, 1981; Shorted a/., 1999: Ahrens and Morrisey, 2005). It is important to
determine the contribution of coal dust PAHs and pyrogenic PAHs to the total PAHs in contaminated
sediments being monitored in MNR investigations. These PAHs typically are bound more tightly than
petrogenic PAHs to sediment particles and may contribute little to the PAH hazard of the contaminated
sediments (Neff etal, 2005).
Crude oil is derived from the thermal degradation and rearrangement of organic polymers
found in source rocks, often sedimentary shales, in the subsurface (Tissot and Welte, 1984). The organic
matter in the source rocks usually is derived primarily from sedimentation of dead plants and animals in
fresh water and marine sediments. Crude oil represents the material that has migrated away from the
source rocks to accumulate in a subsurface reservoir. Crude petroleum contains primarily small
molecules (i.e., those able to migrate) unless substantial weathering has occurred to the oil in the
reservoir. Most of the PAHs in crude oil are composed of two or three fused aromatic rings (Neff, 2002).
Typically, the largest molecules in oil are asphaltenes. Asphaltenes are large, complex compounds
composed primarily of 2- to 4-ring PAHs, heterocyclics (mostly thiophenes), saturated cyclic
hydrocarbons, and metal porphyrins linked by aliphatic chains (Gray, 2003). The PAHs in asphaltenes
are immobile and, therefore, have a low toxicity, similar to that of the PAHs in coal.
Crude oils can contain 0.2% to 7% total PAHs. However, some crude oils that have
weathered naturally in the geologic formation may not contain detectable concentrations of any PAHs
(Neff, 2002; Wang et al., 2003). The abundance of individual aromatic hydrocarbons in petroleum
usually decreases markedly with increasing molecular weight. In most cases, the one- through three-ring
aromatic hydrocarbons (i.e., benzene through phenanthrene) and related heterocyclic aromatic
hydrocarbons (such as dibenzothiophenes) account for at least 90% of the aromatic hydrocarbons that can
be resolved in crude oil by conventional analytical techniques (Neff, 2002). Higher molecular weight (4-
through 6-ring) PAHs, some of which are either known or suspected carcinogens, are much less abundant
in crude oils than they are in most pyrogenic PAH mixtures (Neff et al., 2005). Concentrations of
individual carcinogenic PAHs in crude oils range from below detectable levels (about 0.05 mg/kg oil) to
120 mg/kg (Kerr etal., 1999).
The method detection limit (MDL) is defined as the minimum concentration of a substance
that can be measured and reported with 99% confidence that the analyte concentration is greater than zero
as determined from analysis of a sample in a given matrix containing the analyte (EPA, 1986c) and can
set the lower limits of the ability to study trace-level contaminants in the environment. Modern analytical
methods, based on modifications of EPA Method 8270, in which analysis of purified sample extracts is
performed by capillary column gas chromatography with quantification by mass spectrometry (GC/MS)
operated in the selected ion monitoring (SIM) mode, can attain very low MDLs (Douglas et al., 2004).
MDLs for individual parent and alkylated PAHs in sediments range from 0.04 to 0.50 (ig/kg (parts per
billion dry weight). These detection limits are low enough to detect PAH in sediments below potentially
toxic concentrations.
The PAHs in coal and petroleum (i.e., petrogenic PAHs) often contain one or more methyl,
ethyl, butyl, or occasionally higher alkyl substituents on one or more of the aromatic carbons (Figure 3-1).
As a general rule, these alkyl PAHs are more abundant than the parent compounds in petroleum
(Youngblood and Blumer, 1975). Homologues with two to five alkyl carbons usually are more abundant
than the less or more highly alkylated homologues (Neff, 2002). Crude oil is refined to produce a wide
variety of fuels and hydrocarbon feedstocks (Neff, 1990). Refined petroleum products typically contain
the PAHs in the parent crude oil, as well as small amounts of PAHs produced by catalytic cracking and
53
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other refining processes. The PAH assemblage in different refined oils varies depending on the refining
processes used (i.e., distillation temperature range for the product). Gasoline and other light distillates
contain mainly monocyclic aromatic hydrocarbons and low molecular weight PAHs (i.e., naphthalenes
and fluorenes [Neff, 2002; Figure 3-2]). Diesel fuels and other middle distillate products may contain
aromatic hydrocarbons from benzene through fluoranthene (Figure 3-2). Heavy fuel oils, residual fuel oil
(bunker fuel), and crude oil may contain, in addition to the 2- and 3-ring PAHs, small amounts of higher
molecular weight, higher boiling 4- through 6-ring PAHs, such as chrysenes and benzopyrenes
(Figure 3-2). Petrogenic PAHs can enter fresh water and marine environments from naturally-occurring
oil seeps; erosion of coal, peat, and oil shale deposits; and anthropogenic sources such as oil and coal
spills, discharges of treated and untreated ballast and bilge water from oil tankers and other ships,
effluents from oil refineries, coastal and offshore production platforms, coal-fired power plants, road
runoff, storage tank leaks, and municipal wastewater treatment plants (NRC, 1985, 2003b).
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combustion mixture cools to form complex new molecular structures including PAHs, a process called
pyrolysis/pyrosynthesis (Neff, 1979). The PAH yield (or total PAH concentration from a single source)
depends on the composition of the organic material being combusted, the combustion temperature, and
the oxygen concentration. The PAH mixtures (relative PAH concentrations within single sources)
produced by pyrolysis of organic matter are complex and, unlike the mixtures in petroleum, are
dominated by 3- through 6-ring PAHs (Figure 3-3). In pyrogenic PAH mixtures, the dominant compound
in each homologous series is the unalkylated parent compound or a homologue with only one or two alkyl
substituents; more highly alkylated congeners are rare (Sporsol et al., 1983). Unalkylated phenanthrene,
fluoranthene, and pyrene often are the most abundant PAHs in pyrogenic PAH mixtures (Figure 3-3).
The differences in PAH compositions and ratios of parent to alkyl substituted PAH congeners can be used
to help distinguish between petrogenic and various types of pyrogenic PAH assemblages in
environmental samples (Stout et al, 2000, 2004).
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cracking of petroleum feed stocks to produce refined petroleum products (NeffetaL, 1994); and
aluminum smelting (Thrane, 1987; OSPAR, 2002) produce airborne particulates and solid wastes
containing very high concentrations of PAHs. Wood burning stoves, used frequently for domestic
heating, produce large amounts of PAHs that are concentrated primarily in the particulate fraction (soot
and ash) of the smoke (Ramdahl etal, 1982; Knight et al., 1983). Forest fires and open burning of brush
and plant wastes also are important sources of particulate emissions rich in PAHs (Sullivan and Mix,
1983; Freeman and Cattell, 1990).
PAHs produced during combustion of organic matter, as discussed above, also are present in
the exhaust gases and ash (or soot) produced during fossil fuel combustion. The lower-molecular weight
PAHs in the exhaust gases are primarily present in the vapor phase; the higher-molecular weight PAHs
are associated primarily with the particulate (soot) fraction (Dachs and Eisenreich, 2000). Soot is thought
to be an agglomerate of peri-condensed PAHs (Venkatesan and Dahl, 1989). PAHs associated with soot
particles are part of the particle structure or are tightly adsorbed to the particles through hydrogen bonding
and are not easily desorbed. Lower-molecular weight PAHs desorb slowly from airborne soot particles
into the vapor phase. Higher-molecular weight PAHs are tightly bound to soot particles (Halsall et al.,
1997). The higher-molecular weight PAHs are bound to the finest size soot particles, inhibiting photo-
oxidation and facilitating long-range transport in the air (Offenberg and Baker, 1999). The particulate
fractions of exhaust from gasoline and diesel-powered vehicles contain from 16 to 2,300 |o,g/g 4- through
6-ring PAHs (Takada et al, 1991; Oda et al., 1998). Fluoranthene and pyrene represent between 46%
and 88% of the total PAHs in diesel engine exhaust and 15% to 17% of the total PAHs in gasoline engine
exhaust. They are often the most abundant PAHs in fresh water and marine sediments in urban estuaries
(Stout et al., 2004). Nearly all the PAHs derived from vehicular exhaust are deposited within
approximately 50 m of roads (Harrison and Johnston, 1985; Hewitt and Rashed, 1990). Much of the
deposited PAHs, however, find their way to water bodies in surface runoff from land (Hoffman et al.,
1984; Sharma etal., 1994; Stein etal., 2006; Zhang etal., 2008) and are deposited in sediments.
The estimated total input of PAHs to the ocean from all sources (mostly petroleum and
combustion) is about 230,000 metric tons per year (Neff, 2002). Releases of petrogenic and pyrogenic
PAHs each account for about half the total annual releases to the ocean; however, about 95% of high-
molecular-weight, potentially carcinogenic PAHs entering the ocean each year are from pyrogenic
sources and 5% are from petrogenic sources. Concentrations of pyrogenic PAHs in near-shore fresh
water and marine sediments often are closely correlated with the abundance of spheroidal carbonaceous
particles (soot) (Broman et al., 1990). This indicates that PAHs remain associated with the soot after
deposition and usually are not modified by photooxidation, dissolution, or biodegradation during
transport in the air and water (Readman et al., 1984a,b; Readman et al., 1987; Ghosh et al., 2003;
Hartmann et al., 2004). Soot-associated PAHs in sediments, which are discussed in greater detail in
Section 3.3, have a low bioavailability to aquatic organisms (Farrington and Westall, 1986; Gustafsson et
al., 1997) and are extremely resistant to abiotic and biotic degradation. PAH assemblages that have been
identified in Cretaceous/Tertiary and Jurassic sedimentary deposits are thought to have been derived from
ancient forest fires (Venkatesan and Dahl, 1989; Killops and Massoud, 1992).
3.1.2.2 PolychlorinatedBiphenyls. PCBs are synthetic commercial HOCs that have no significant
natural sources. PCBs were produced in North America under the trade name Aroclor from 1929 to 1977.
EPA banned manufacturing, processing, distribution, and use of PCBs in the United States in 1979 (EPA,
2002a), when problems from their environmental persistence and threats to humans and the health of the
environment were recognized. However, they are still abundant in the environment because of their
extreme stability. Small amounts of PCBs are still in use for special applications.
PCBs were used in a variety of industrial applications (e.g., as dielectric, hydraulic, and heat
transfer fluids; plasticizers; flame retardants; carbon paper, etc.) and were produced under many different
56
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trade names in several countries, including the United States (Monsanto Corporation: Aroclor), Germany
(Bayer S.A.: Clophen), and Japan (Kanegafuchi Chemical: Kanechlor). In the United States, 6.3 x 10s kg
of PCBs were manufactured between 1929 and 1977 and sold as mixtures of chlorobiphenyls (CBs).
PCBs were manufactured and marketed in many countries as mixtures of 40 to 60 different congeners
with different numbers and positions of chlorine atoms on the aromatic carbons. The Monsanto
Corporation (Madison, NJ) produced nine technical grades of PCBs under the trade names Aroclors 1221,
1232, 1016, 1242, 1248, 1254, 1260, 1262, and 1268. The last two digits in the numerical suffix indicate
the percent chlorine by mass in the technical mixture, with the exception of Aroclor 1016 which contains
42% chlorine. Table 3-2 is a summary of the composition and chemical properties of the different
Aroclors. The extent of chlorination, isomeric composition, and resulting physical properties of each
mixture are unique, reflecting the reaction conditions and thermodynamics of the manufacturing process.
Although 209 congeners of PCBs are theoretically possible, the manufacturing process preferentially
creates a limited number of congeners.
Table 3-2. Approximate Molecular Composition (%) and Physical Properties of
Seven Commercial Aroclors
Composition and Properties
Biphenyl
Mono-chlorobiphenyl
Di-chlorobiphenyl
Tri-chlorobiphenyl
Tetra-chlorobiphenyl
Penta-chlorobiphenyl
Hexa-chlorobiphenyl
Hepta-chlorobiphenyl
Octa-chlorobiphenyl
Specific Gravity (@ 25/15. 5°C)
Absolute Viscosity (cp @ 38°C)
Solubility (ng/L @ 25 °C)
Vapor Pressure (mm Hg @ 25°C)
Aroclor
1221
11.0
51.0
32.0
4.0
2.0
0.5
-
-
-
1.18
5
200
0.0067
1232
6.0
26.0
29.0
24.0
15.0
0.5
-
-
-
1.27
8
-
0.0046
1016
<0.01
1.0
20.0
57.0
21.0
1.0
<0.01
-
-
1.37
20
240
0.0004
1242
-
1.0
17.0
40.0
32.0
10.0
0.5
-
-
1.38
24
240
0.0004
1248
-
-
1.0
23.0
50.0
20.0
1.0
-
-
1.41
70
54
0.0004
1254
-
-
-
-
16.0
60.0
23.0
1.0
-
1.50
700
12
0.00008
1260
-
-
-
-
-
12.0
46.0
36.0
6.0
1.56
resin
2.7
0.00004
Source: Agency for Toxic Substances and Disease Registry (ATSDR), 2000
Only approximately 130 individual congeners have been identified in commercial PCB
mixtures (Pereira, 2004). Individual PCB congeners contain between one and 10 chlorines covalently
bonded to the available aromatic carbons. Each PCB congener has been assigned a unique identifying
number by the International Union of Pure and Applied Chemistry (IUPAC). IUPAC numbers are based
on the numbers and positions of chlorines on the biphenyl molecule (Table 3-3). There is continuing
discussion of the PCB congener nomenclature (Mills et a/., 2007).
57
-------
Table 3-3. The IUPAC Numbering System for Polychlorinated Biphenyl Congeners and Log K0w
Congener
Number
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
82
83
84
85
86
87
88
89
90
Substitution Pattern
Biphenyl
2-
3-
4-
2,2'-
2,3-
2,3'-
2,4-
2,4'-
2,5-
2,6-
3,3'-
3,4-
3,4'-
3,5-
4,4'-
2,2', 3-
2,2',4-
2,2', 5-
2,2', 6-
2,3,3'-
2,3,4-
2,3,4'-
2,3,5-
2,3,6-
2,3',4-
2,3', 5-
2,3', 6-
2,4,4'-
2,4,5-
2,4,6-
2,4',5-
2,4',6-
2,3',4'-
2,3',5'-
3,3',4-
3,3',5-
3,4,4'-
3,4,5-
3,4',5-
2,2',3,3'-
2,2',3,3',4-
2,2',3,3',5-
2,2',3,3',6-
2,2',3,4,4'-
2,2',3,4,5-
2,2',3,4,5'-
2,2',3,4,6-
2,2',3,4,6'-
2,2',3,4',5-
Log Kow
4.09
4.46
4.69
4.69
4.65
4.97
5.06
5.07
5.07
5.06
4.84
5.28
5.22
5.29
5.28
5.30
5.16
5.25
5.24
5.02
5.57
5.51
5.58
5.57
5.35
5.67
5.66
5.44
5.67
5.60
5.44
5.67
5.44
5.60
5.66
5.82
5.88
5.83
5.76
5.89
5.66
6.20
6.26
6.04
6.30
6.23
6.29
6.07
6.07
6.36
Congener
Number
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
125
126
127
128
129
130
131
132
133
Substitution Pattern
2,2',3,4-
2,2',3,4'-
2,2', 3,5-
2,2', 3,5'-
2,2', 3,6-
2,2', 3,6'-
2,2',4,4'-
2,2',4,5-
2,2',4,5'-
2,2',4,6-
2,2',4,6'-
2,2',5,5'-
2,2', 5,6'-
2,2', 6,6'-
2,3,3',4-
2,3,3',4'-
2,3,3',5-
2,3,3',5'-
2,3,3',6-
2,3,4,4'-
2,3,4,5-
2,3,4,6-
2,3,4',5-
2,3,4',6-
2,3,5,6-
2,3',4,4'-
2,3',4,5-
2,3',4,5'-
2,3',4,6-
2,3',4',5-
2,3',4',6-
2,3',5,5'-
2,3',5',6-
2,4,4',5-
2,4,4',6-
2,3',4',5'-
3,3',4,4'-
3,3',4,5-
3,3',4,5'-
3,3',5,5'-
3,4,4',5-
2,3',4',5',6-
3,3',4,4',5-
3,3',4,5,5'-
2,2',3,3',4,4'-
2,2',3,3',4,5-
2,2',3,3',4,5'-
2,2',3,3',4,6-
2,2',3,3',4,6'-
2,2',3,3',5,5'-
Log Kow
5.69
5.76
5.75
5.75
5.53
5.53
5.85
5.78
5.85
5.63
5.63
5.84
5.62
5.21
6.11
6.11
6.17
6.17
5.95
6.11
6.04
5.89
6.17
5.95
5.86
6.20
6.20
6.26
6.04
6.20
5.98
6.26
6.04
6.20
6.05
6.13
6.36
6.35
6.42
6.48
6.36
6.51
6.89
6.95
6.74
6.73
6.80
6.58
6.58
6.86
58
-------
Table 3-3. The IUPAC Numbering System for Polychlorinated Biphenyl Congeners and
Log KOW (continued)
Congener
Number
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
Substitution Pattern
2,2',3,4',6-
2,2',3,5,5'-
2,2', 3,5, 6-
2,2',3,5,&-
2,2', 3,5', 6-
2,2',3,6,&-
2,2',3,4',5'-
2,2',3,4',6'-
2,2',4,4',5-
2,2',4,4',6-
2,2',4,5,5'-
2,2',4,5,&-
2,2',4,5',6-
2,2',4,6,6'-
2,3,3',4,4'-
2,3,3',4,5-
2,3,3',4',5-
2,3,3',4,5'-
2,3,3', 4,6-
2,3,3',4',6-
2,3,3',5,5'-
2,3,3',5,6-
2,3,3',5',6-
2,3,4,4',5-
2,3,4,4',6-
2,3,4,5,6-
2,3,4',5,6-
2,3',4,4',5-
2,3',4,4',6-
2,3',4,5,5'-
2,3',4,5',6-
2,3,3',4',5'-
2,3',4,4',5'-
2,3',4',5,5'-
2,3',4,4',5',6-
3,3',4,4',5,5'-
2,2',3,3',4,4',5-
2,2',3,3',4,4',6-
2,2',3,3',4,5,5'-
2,2',3,3',4,5,6-
2,2',3,3',4,5,6'-
2,2',3,3',4,5',6-
2,2',3,3',4,6,6'-
2,2',3,3',4,5',6'-
2,2',3,3',5,5',6-
2,2',3,3',5,6,6'-
2,2',3,4,4',5,5'-
2,2',3,4,4',5,6-
2,2',3,4,4',5,6'-
2,2',3,4,4',5',6-
Log Kow
6.13
6.35
6.04
6.13
6.13
5.71
6.29
6.13
6.39
6.23
6.38
6.16
6.22
5.81
6.65
6.64
6.71
6.71
6.48
6.48
6.76
6.45
6.54
6.65
6.49
6.33
6.46
6.74
6.58
6.79
6.64
6.64
6.74
6.73
7.11
7.42
7.27
7.11
7.33
7.02
7.11
7.17
6.76
7.08
7.14
6.73
7.36
7.11
7.20
7.20
Congener
Number
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
Substitution Pattern
2,2',3,3',5,6-
2,2',3,3',5,6'-
2,2',3,3',6,6'-
2,2',3,4,4',5-
2,2',3,4,4',5'-
2,2',3,4,4',6-
2,2',3,4,4',6'-
2,2',3,4,5,5'-
2,2',3,4,5,6-
2,2',3,4,5,6'-
2,2',3,4,5',6-
2,2',3,4,6,6'-
2,2',3,4',5,5'-
2,2',3,4',5,6-
2,2',3,4',5,6'-
2,2',3,4',5',6-
2,2',3,4',6,6'-
2,2',3,5,5',6-
2,2',3,5,6,6'-
2,2',4,4',5,5'-
2,2',4,4',5,6'-
2,2',4,4',6,6'-
2,3,3',4,4',5-
2,3,3',4,4',5'-
2,3,3',4,4',6-
2,3,3',4,5,5'-
2,3,3',4,5,6-
2,3,3',4,5',6-
2,3,3',4',5,5'-
2,3,3',4',5,6-
2,3,3',4',5',6-
2,3,3',5,5',6-
2,3,4,4',5,6-
2,3',4,4',5,5'-
2,3,3',4,4',5,5'-
2,3,3',4,4',5,6-
2,3,3',4,4',5',6-
2,3,3',4,5,5',6-
2,3,3',4',5,5',6-
2,2',3,3',4,4',5,5'-
2,2',3,3',4,4',5,6-
2,2',3,3',4,4',5,6'-
2,2',3,3',4,4',6,6'-
2,2',3,3',4,5,5',6-
2,2',3,3',4,5,5',6'-
2,2',3,3',4,5,6,6'-
2,2',3,3',4,5',6,6'-
2,2',3,3',5,5',6,6'-
2,2',3,4,4',5,5',6-
2,2',3,4,4',5,6,6'-
Log Kow
6.55
6.64
6.22
6.83
6.83
6.67
6.67
6.82
6.51
6.60
6.67
6.25
6.89
6.64
6.73
6.67
6.32
6.64
6.22
6.92
6.76
6.41
7.18
7.18
7.02
7.24
6.93
7.08
7.24
6.99
7.02
7.05
6.93
7.27
7.71
7.46
7.55
7.52
7.52
7.80
7.56
7.65
7.30
7.62
7.20
7.27
7.62
7.24
7.65
7.30
59
-------
Table 3-3. The IUPAC Numbering System for Polychlorinated Biphenyl Congeners and
Log KOW (continued)
Congener
Number
184
185
186
187
188
Substitution Pattern
2,2',3,4,4',6,6'-
2,2',3,4,5,5',6-
2,2',3,4,5,6,6'-
2,2',3,4',5,5',6-
2,2',3,4',5,6,6'-
Log Kow
6.85
7.11
6.69
7.17
6.82
Congener
Number
205
206
207
208
209
Substitution Pattern
2,3,3',4,4',5,5',6-
2,2',3,3',4,4',5,5',6-
2,2',3,3',4,4',5,6,6'-
2,2',3,3',4,5,5',6,6'-
Decachlorobiphenyl
Log Kow
8.00
8.09
7.74
7.71
8.18
Chlorinated biphenyl congeners are identified by the substitution pattern (location of chlorine on different aromatic
carbons). The estimated log octanol/water partition coefficient (log Kow) for each congener is included. The toxic
coplanar PCB congers are highlighted (Hawker and Connell, 1988; EPA, 2003).
Coplanar PCB congeners are of much greater environmental concern than non-coplanar
congeners because of their substantially greater toxicity to mammals, birds, and fish (Van den Berg et al.,
1998). The chronic toxicity of coplanar PCBs and PCDDs/PCDFs is expressed through their ability to
bind to and activate the aryl hydrocarbon receptor (AhR), which is a cytosolic ligand-activated
transcription factor. Activation of this receptor leads to induction (stimulation of enzyme synthesis) of
the cytochrome P450 (CYP) mixed function oxygenase system responsible for oxidation of many types of
natural (e.g., steroid hormones) and contaminant aromatic hydrocarbons (Safe, 1990). Intense induction
of various CYP forms by PCBs and PCDDs/PCDFs may lead to alterations in hormone functions and
production of toxic/carcinogenic metabolites. Coplanar aromatics, including PAHs, vary in their ability
to bind the AhR receptor, which correlates with toxicity (Van den Berg et al., 1998). Thus, the toxicity of
different PCB, PCDD, PCDD, and PAH congeners can be ranked in relation to the most toxic congener,
2,3,7,8-tetrachlorodibeno-p-dioxin (TCDD) (Figure 3-1) and expressed as TEF (Table 3-4).
The two benzene rings of a coplanar PCB congener can orient in the same plane, a
configuration similar to that of a PCDD or PCDF (see discussion below). Coplanar PCBs contain, at
most, two chlorines in the ortho position on the biphenyl molecule (Figure 3-4). A PCB with three or
four ortho chlorines cannot attain a coplanar configuration due to steric hindrance, a configuration
essential for metabolic activation and degradation by most prokaryotes and eukaryotes. Coplanar PCB
congeners include non-ortho, mono-ortho, and di-ortho congeners with zero, one, or two chlorines in
ortho positions, respectively. There are four non-ortho PCB congeners (numbers 77, 81, 126, 169) and
eight mono-ortho PCB congeners (numbers 105, 114, 118, 123, 156, 157, 167, 189) of major
environmental concern (Tables 3-3 and 3-4). The toxicity of coplanar PCB congeners increases with the
number of chlorines in meta and para positions. The most toxic coplanar PCB congeners (IUPAC
Numbers 126 and 169) have chlorines in both para positions and at least two meta positions. As a rule,
non-ortho PCB congeners are more toxic than mono-ortho congeners which, in turn, are more toxic than
di-ortho congeners (Safe, 1990; Van den Berg et al., 1998). However, mono- and di-ortho PCB
congeners usually are more abundant than non-ortho congeners in environmental matrices because the
latter are more biodegradable. Due to the substantially greater toxicity of coplanar PCB congeners (over
non-coplanar), coplanar PCBs represent a much greater environmental risk to humans via direct exposure
(i.e., environmental abundance and access) (Parkinson and Safe, 1987; Safe, 1984; Pereira, 2004).
Therefore, one focus on MNR investigations should be on monitoring the loss of non-ortho and mono-
ortho coplanar congeners from PCB-contaminated sediments.
60
-------
Table 3-4. Toxicity Equivalency Factors (TEFs) of PCDDs, PCDFs, Non-Ortho- and Mono-Ortho-
PCBs, and Selected PAHs for Mammals from the World Health Organization (WHO) and Relative
Potency of PAHs in Fish
Congener
TEF
Polychlorinated Dibenzo-p-dioxins (PCDDs)
2,3,7,8-TCDD
,2,3,7,8-PentaCDD
,2,3,4,7,8-HexaCDD
,2,3,6,7,8-HexaCDD
,2,3,7,8,9-HexaCDD
,2,3,4,6,7,8-HepatCDD
OctaCDD
1
1
0.1
0.1
0.1
0.01
0.0003
Non-tfrt/zo-Polychlorinated Biphenyls (PCBs)
3,4,4',5-TetraCB (81)
3,3',4,4'-TetraCB (77)
3,3',4,4',5-PentaCB (126)
3,3',4,4',5,5'-HexaCB (169)
0.0003
0.0001
0.1
0.03
Congener
TEF
Polychlorinated Dibenzofurans (PCDFs)
2,3,7,8-CDF
12,3,7,8-PentaCDF
2,3,4,7,8-PentaCDF
1,2,3,4,7,8-HexaCDF
1,2,3,6,7,8-HexaCDF
1,2,3,7,8,9-HexaCDF
2,3,4,6,7,8-HexaCDF
1,2,3,4,6,7,8-HeptaCDF
1,2,3,4,7,8,9-HeptaCDF
OctaCDF
0.1
0.03
0.3
0.1
0.1
0.1
0.1
0.01
0.01
0.0003
Mono-OrtAo-Polychlorinated Biphenyls (PCBs)
2,3,3',4,4'-PentaCB (105)
2,3,4,4',5-PentaCB(114)
2,3',4,4'5-PentaCB(118)
2',3,4,4',5-PentaCB (123)
2,3,3',4,4',5-HexaCB (156)
2,3,3',4,4',5'-HexaCB (157)
2,3',4,4',5,5'-HexaCB (167)
2,3,3',4.4',5,5'-HeptaCB (189)
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
Relative Potency of Polycyclic Aromatic Hydrocarbons (PAHs) in Fish
Phenanthrene
2-Ethylphenanthrene
Fluoranthene
5 -Methylchry sene
Inactive
0.00001
0.000000002
0.00065
Benz(a)anthracene
Benzo(a)pyrene
Dibenz(a,h)anthracene
Indeno( 1 ,2,3 -cd)pyrene
0.00020
0.00024
0.00027
0.00188
IUPAC numbers are included in parentheses for PCBs (Van den Berg et al., 1998, 2006; Barren et al., 2004).
61
-------
Ortho positions - 2,2'.6,6'
Mefa positions - 3,3',5,5'
Para positions - 4,4'
Most Toxic non-ortho PCBs
3,3',4,4',5-PentaCB
TEF-0.1
3,3',4,4',5.S'-HexaCB
TEF-O.C1
Cl
Figure 3-4. Positions of Ortho, Meta, and Para Carbons in PCBs
(Structures of the two most toxic non-ortho PCBs are shown based on their TEFs,
relative to the toxicity of 2,3,7,8-tetrachloro dibenzo-p-dioxin [Table 3-4].)
3.1.2.3 Poly chlorinated Dibenzo-p-Dioxins and Polychlorinated Dibenzofurans. PCDDs/PCDFs
are composed of two non-fused benzene rings. Unlike PCBs, the benzene rings are bound together by
one (PCDFs) or two (PCDDs) ether linkages (-O-); the second linkage in PCDFs is a carbon-carbon bond.
This creates a rigid molecular structure with the two aromatic rings on the same plane (coplanar
configuration). As with coplanar PCBs, each PCDD/PCDF binds with a different affinity to the AhR
receptor and, therefore, exhibits a different potency for eliciting biological effects (Safe, 1990; Ahlborg et
a/., 1994; Van den Berg etal, 1998). The potencies of the different PCDD/PCDF congeners can be
normalized to that of 2,3,7,8-TCDD, the most toxic PCDD (Table 3-4). By convention, TCDD is
assigned a TEF of 1.0, and the TEFs for other coplanar aromatic compounds with dioxin-like toxic effects
range from 0 to 1. When TEFs are derived based on the relative binding affinity to the AhR receptor or
induction of cytochrome P450, it is assumed that these biochemical responses correlate with
lexicologically important effects (Van den Berg et al., 1998). Coplanar PCDDs/PCDFs and PCBs bind to
the specific AhR receptor in eukaryotes, inducing production of cytochrome P450 mixed function
oxygenase (MFO) enzyme systems that oxidize aromatic HOCs to more polar, reactive products. The
biodegradation products of PCDDs/PCDFs and coplanar PCBs are more toxic than the parent compounds,
rendering the coplanar congeners more toxic than the non-coplanar congeners. Because PAHs also are
coplanar, some bind weakly to the AhR receptor, inducing chytochrome PI A activity. The strength of
binding to the AhR receptor determines the level of induction of MFO and the relative toxicity of the
HOC. The most active PCBs and PAHs have TEFs one-tenth less than that of 2,3,7,8-TCDD.
62
-------
PCDDs/PCDFs are not produced intentionally and have no commercial applications. They
are produced as byproducts in the manufacture of other chlorinated chemicals, such as pentachlorophenol
and defoliating herbicides, e.g., Agent Orange (a 50:50 mixture of 2,4-dichlorophenoxyacetic acid and
2,4,5-trichlorophenoxyacetic acid), during combustion or intensive chlorination of organic materials in
the presence of chlorine (Brzuzy and Kites, 1996; Pereira, 2004). An important source of PCDDs/PCDFs
in the environment is bleaching of wood pulp during paper manufacturing. Combustion processes are the
major source of PCDD/PCDF emissions to the environment and the cause of the ubiquitous
contamination of soils and sediments, with transport processes such as atmospheric dispersal from point
sources representing the primary vector for PCDD/PCDF translocation in the environment (Rappe et al,
1987; Brzuzy and Hites, 1996). Most of the PCDDs/PCDFs (typically more than 80%) produced during
waste incineration are sorbed to soot particles (fly ash), which is released into the atmosphere unless the
combustion gases are scrubbed or filtered (Yan et al., 2007). PCDDs/PCDFs are released into the
atmosphere adsorbed to fly ash in the combustion plume and can be transported over long distances; they
are ultimately removed from the atmosphere by dry or wet deposition and accumulate in soils and
sediments. The major combustion sources of PCDDs/PCDFs and the estimated annual mass emission
rates are summarized in Table 3-5 (Brzuzy and Hites, 1996). Total annual global emissions to the
atmosphere in 1996 were estimated to be 3,000 ± 600 kg, with the largest emission being from waste
incineration. Estimated global deposition of PCDDs/PCDFs from the atmosphere to the land and ocean is
12,500 ± 1,300 kg/year and 610 ± 1,500 kg/year, respectively, for an estimated total global deposition of
13,100 ± 2,000 kg/year (Brzuzy and Hites, 1996). Estimated global deposition is about four-fold times
higher than estimated global emissions, likely because emissions are underestimated, particularly for
third-world countries.
Table 3-5. Estimated Average Emission Factors (Mass of PCDDs/PCDFs Released per Mass of
Fuel Burned) and Total Annual Global Emissions of PCDDs/PCDFs to the Atmosphere from
Combustion Sources
PCDD/PCDF Fuel Source
Municipal waste incineration
Biomass combustion
Ferrous metals production
Cement kilns (burning hazardous wastes)
Cement kilns (no hazardous wastes)
Secondary copper smelting
Medical waste incineration
Unleaded fuel combustion
Leaded fuel combustion
Total emissions
Emissions Factor
(ug/g fuel burned)
13.0
0.04
0.5
2.6
0.2
39.0
22.0
320.0
2,800.0
Global Emissions
(kg/year)
1130 + 450
350 + 140
350 + 140
680 + 280
320 + 130
78 + 31
84 + 35
1 +0.4
11+5
3,000 + 600
Source: Brzuzy and Hites, 1996
3.2
Measuring Fate Processes and Natural Recovery
MNR relies on multiple lines-of-evidence to demonstrate reductions in the bioaccessibility
and concentrations of bioavailable HOCs in sediments and the resulting long-term ecological recovery
and risk reduction of the affected environment. Several lines-of-evidence should be considered in
sediment investigations to support implementation of MNR as a remedy option, including (Magar and
Wenning, 2006):
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• Documentation and confirmation of source control;
• Evidence of contaminant burial (isolation) and reduction of surface sediment
concentrations;
• Evidence of minimal numbers of macrofauna accessing buried, contaminated sediments
(bioturbation);
• Measurement of surface sediment mixing, including sediment resuspension events (e.g.,
floods and/or storm events) to estimate the active sediment benthic layer and determine
the surface sediment depth to which remedial objectives should be applied;
• Measurement of sediment stability to assess the risk of contaminant resuspension under
normal and high-energy events;
• Evidence of contaminant sequestration, transformation, and risk attenuation
commensurate with reduced availability and decreased concentrations;
• Modeling of long-term trends for possible recovery scenarios under various conditions, as
evidenced by decreasing concentrations of bioaccessible contaminant in surface
sediments, the water column (or pore water), and local biota;
• Monitoring ecological recovery and long-term risk reduction; and
• Knowledge of future site and institutional controls, and a consideration of the risk due to
degradation products and the presence of multiple stressors.
In the subsequent sections, focus will be placed on controlling sorption and sequestration of
HOCs in sediment. Abiotic and biological transformation of sediment HOCs will also be described.
Approaches for assessing HOC sorption, sequestration, and transformation will be discussed in detail.
3.2.1 Physical and Chemical Processes Affecting HOC Fates. It is critical to understand the
conditions under which HOCs are bioavailable and the processes by which they become sequestered. To
adequately plan and execute successful sediment remediation by MNR, it is essential to understand the
principles of HOC fate and transport in sediments (and in the environment as a whole). These processes
determine the mobility of HOCs in the aquatic environment and the conditions under which HOCs are
bioavailable. An integral part of managing sediment sites contaminated with HOCs, such as PCBs,
PCDDs/PCDFs, and PAHs, is estimating the biological risk as defined by the concentrations of mobile,
potentially bioavailable forms of HOCs in sediment and selecting natural recovery goals that are
protective of sediment-dwelling (benthic) organisms, aquatic life, wildlife, and human health.
Fundamental to the selection of recovery goals is an understanding of the mechanisms by which receptors
are exposed to bioavailable forms of HOCs.
Natural transformation, sequestration, and weathering of HOCs can contribute to risk
reduction and recovery of fresh water and marine sediment ecosystems via contaminant removal, reduced
access, detoxification, or reduction in bioavailability. Both biological (aerobic and anaerobic
biodegradation) and abiotic chemical reactions (oxidation/reduction) are responsible for contaminant
transformations. Sequestration usually is caused by strong sorption of HOCs to sediment organic phases,
particularly high-surface area carbonaceous particles, occlusion in pore spaces of particles, and burial in
poorly-irrigated, subsurface sediment layers that are inaccessible to surface- or sediment-dwelling biota
(Qiu and Davis, 2004). Additionally, HOCs weather naturally after deposition in sediments, particularly
in near-surface, aerobic layers. Weathering processes include evaporation, dissolution, diffusion, and
dispersion upon discharge from the point sources (i.e., diffusive and advective transport); abiotic
oxidation/reduction; and microbial biodegradation (Neff, 1990; Magar and Wenning, 2006). The
combined weathering processes can reduce the concentration and bioavailability of HOCs in sediment.
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Bioavailability has three components: bioaccessibility, biological availability, and
pharmacological availability (Semple et al, 2004; Reichenberg and Mayer, 2006). Bioaccessibility and
biological availability are the two components of bioavailability that are of greatest concern in MNR. A
contaminant in sediment is bioaccessible if it is in a location in the environment where living organisms
can come in direct contact with it either as dissolved (e.g., in pore water), participate (e.g., sorbed to
solids), or colloidal phases. Contaminants buried in deep sediment layers below the depth of bioturbation
(which is not a static or well-defined layer and can range from millimeters to meters in depth) or that are
tightly bound to sediment particles or large colloids (Cornelissen and Gustafsson, 2005; Hawthorne et al.,
2007b) may be inaccessible to sediment-dwelling and water column animals.
A chemical has biological availability if it is in a form that can move through or bind to the
external epithelia of an organism (e.g., skin, gill epithelium, gut lining, or cell membrane) (Neff, 2002).
The most bioavailable form of HOCs is the dissolved form; HOCs adsorbed to small colloids or weakly
adsorbed to particles may be slightly less bioavailable. Bioavailable compounds can move through
biological membranes in both directions passively, or they can be transported down physico-chemical
gradients by enzyme systems. When an aquatic plant or animal is exposed to a non-polar organic
chemical dissolved in the ambient water, the chemical partitions into tissue lipids until an equilibrium is
reached (Davies and Dobbs, 1984; Bierman, 1990). The octanol/water partition coefficient (Kow) for the
chemical is used as a surrogate for a lipid/water partition coefficient because solubility of many non-polar
organic compounds in octanol and biological lipids is similar and there are published values for Kow for a
large number of non-polar organic chemicals of environmental concern (Cornell, 1993). Tables 3-3 and
3-6 contain estimated or measured log Kow values for several PCB, PCDD/PCDF, and PAH congeners.
At equilibrium, the rates of absorption into and desorption from the lipid phase of the plant or animal are
equal. Active processes controlling bioconcentration include metabolic degradation of the HOCs to more
water-soluble products that are easily excreted by the organism. Some aquatic animals (most crustaceans,
fish, and higher vertebrates) can metabolize and excrete HOCs more rapidly than predicted by
partitioning theory, resulting in lower than predicted equilibrium tissue concentrations.
3.2.2 Application of HOC Partitioning in Sediment to MNR. The first step in reducing
bioaccessibility and bioavailability is to reduce the concentration of the dissolved contaminant in the
aqueous phase. Dissolution increases the concentration in the aqueous phase by partitioning from the
solid phase (e.g., dissolution or desorption) and, thereby, increases the bioavailability of sediment HOCs.
In contrast, in some instances, evaporation and the combination of abiotic and microbial degradation
reduce HOC concentrations in sediments and associated pore waters, decreasing the amount of HOCs
available for bioaccumulation by sediment-dwelling organisms. Therefore, an MNR evaluation should
include an assessment of the long-term risk reduction that is achieved as a result of reduction in
concentration and bioaccessibility of HOCs in sediment; these are achieved through natural burial
(isolation), capping, and the combined weathering processes (Qiu and Davis, 2004; Magar and Wenning,
2006). Burial or capping of HOC-contaminated sediments is a common MNR strategy (Palermo et al.,
1998; Brenner et al., 2004). However, contaminated sediment burial may not be required to achieve
acceptable risk because contaminant binding, sorption, or partitioning to solid phases often is sufficient to
sequester HOCs from the dissolved phase. Model predictions and empirical evaluations of partitioning of
HOCs from aqueous to non-aqueous (e.g., adsorbed/absorbed) in site-specific sediments is an essential
line-of-evidence for MNR. An evaluation of HOC partitioning can provide an estimate of the risk-based
allowable concentration of HOCs in sediments. For example, high-molecular weight PAHs, particularly
those from combustion sources, can be tightly bound to particulate organic carbon (POC), particularly
combustion soot carbon (Thorsen et al., 2004), in sediment, which may result in a large reduction in their
bioavailability to aquatic organisms (Neff et al., 2005).
Contaminant sequestration in sediments contributes to the recovery of fresh water and marine
sediment-associated ecosystems and reduces the health risk to wildlife and human receptors by reducing
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the biological accessibility and biological availability of in-place HOCs. Sequestration results from the
sorption of HOCs to organic coatings on sediment mineral particles, organic particles, and organic
colloids in sediment and the associated geochemical reactions with the sediment matrix and pore water.
The most bioavailable and toxic forms of HOCs are in solution in sediment pore water or the overlying
water column; sequestration of HOCs in sediment particles and colloids usually substantially reduces the
bioavailability and toxicity of in-place HOCs (Qiu and Davis, 2004). This reduction in soluble forms of
the contaminant reduces the concentration of dissolved HOCs in the aqueous phase and the efflux of
bioavailable forms of HOCs from sediments into the overlying water column. However, this binding may
also be responsible for diminished rates of contaminant degradation (Bressler and Gray, 2003).
The limiting step for accessibility of HOC contaminants to sediment-dwelling organisms
usually is the rate of dissolution/desorption from sediment particles. HOCs that partition from sediment
solids into sediment pore water may be subjected to diffusive and advective transport, which further
dilutes the HOCs. The net effect of the combined weathering process is to generally reduce
concentrations of the bioavailable forms of HOCs in site sediments - the first critical step in sediment
recovery. On the other hand, weathering and transformation alone (in some instances) may not be
sufficiently rapid to attain safe levels of certain HOCs, including higher molecular weight PCBs, PAHs,
PCDDs/PCDFs, and organochlorine pesticides, in sediments in a reasonable amount of time. However,
because of the strong sorption of high-molecular-weight HOCs to sediment TOC, there may be little risk
associated with the presence of high-molecular-weight HOCs in sediment, whether or not they are buried
by cleaner sediments.
3.2.3 Influence of Sediment-Water Partitioning on HOC Bioavailability. Burial and sorption to
sediment solids and colloids decrease the bioaccessibility and bioavailability of HOCs. Sorption-
desorption processes are one of the most important processes controlling the bioavailability of sediment-
bound HOCs, such as PAHs, PCBs, PCDDs/PCDFs, and several highly non-polar organochlorine
pesticides because these processes control the concentration of HOCs in solution in sediment pore water
and overlying waters (Lamoureux and Brownawell, 1999; White et al., 1999; Kraaij etal.,2001;
Kukkonen et al., 2004). An understanding of sorption-desorption kinetics and how they affect HOC
bioaccessibility and bioavailability, gained by gathering sufficient empirical HOC partitioning data for
different types of sediments, is critical for predicting exposure to and ecological risks of sediment HOCs
during MNR. The inherent uncertainty about the fractions of total sediment HOCs and their forms that
are bioavailable is the key element in quantifying exposure in human health and ecological risk
assessments that often are the basis for derivation of sediment quality criteria (EPA, 2003).
Due to their hydrophobic nature, non-polar organic chemicals, such as PAHs, PCBs, and
PCDD/PCDFs, tend to have a strong affinity for phase boundaries, such as the surface microlayer of the
water body and the surface of particles (Olsen et al., 1982). They also have a high affinity for low-
dissolved, colloidal, and solid organic phases in sediments. Because of the strong affinity of dissolved,
non-polar organic chemicals for solid or liquid organic phases, they tend to partition from the water and
sorb to available organic phases, such as tissue lipids of aquatic organisms, organic coatings on sediment
particles, and organic particles and colloids (e.g., humic materials and organic detritus) (Karickhoff et al.,
1979; Knezovich etal, 1987).
The concentrations of dissolved HOCs in sediment pore water and the overlying water
column are dependent on their relative affinities for inorganic and organic sediment and suspended
particles, DOC, and the dissolved phase. HOCs introduced into fresh or marine water bodies in
dissolved, particulate, and colloidal forms partition among the different phases according to their relative
affinities forthe different phases (Karickhoff et al, 1979; Karickhoff, 1981; Neff, 2002).
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The tendency for an HOC in solution to adsorb to suspended or deposited particles, a process
that removes HOCs from solution in the aqueous phase and, therefore, limits bioavailability, can be
expressed as a linear sediment-water partition coefficient, Kd. However, fresh water and marine
sediments are an extremely heterogeneous mixture of different particle types and sizes. Because of this
heterogeneity, the Kd for a particular HOC is widely variable, depending on the chemical composition
and grain size of the sediment used for the Kd determination. Strongest adsorption is to organic coatings
on inorganic and organic sediment particles and organic colloids. Therefore, some of this variability can
be reduced by normalizing Kd to the concentration of TOC in the sediment (Karickhoff et al., 1979;
Karickhoff, 1981). The TOC-normalized partition coefficient, Koc, can be calculated if the Kd and the
weight fraction of TOC in sediment are known according to the following formula:
Koc=Kd/foc (Eq.3.1)
where foc is the fraction of organic carbon in the sediment. The value for Koc can be estimated easily for
a particular compound because it is correlated to the octanol/water partition coefficient, Kow (Hansch and
Leo, 1979), and the aqueous solubility (Karickhoff et al., 1979) of the compound. There are published
values for Kow for a large number of HOCs of environmental concern (Chiou et al, 1982; Connell, 1993;
Schwarzenbach et al., 2003; Mackay et al., 2006). A regression of the log Kow versus the log Koc for
non-polar organic compounds spanning a wide range of molecular weights yields a good linear
correlation between the two partition coefficients (Karickhoff, 1981). The log Koc/log Kow regression of
Karickhoff (1981) for PAHs has the form:
Log Koc = 0.989 log Kow - 0.346 (Eq. 3.2)
EPA (2003) used a slightly different regression derived by Di Toro (1985) for estimating
equilibrium partitioning sediment benchmarks (ESBs) for a large number of HOCs, including PAHs:
Log Koc = 000028 + 0.983 log Kow (Eq. 3.3)
Farrington (1991) summarized several other regression equations for estimating log Koc
values of different types of HOCs from their log Kow values. The linear relationship between Kow and
Koc appears to be slightly different for different homologous series of PAHs, PCBs, and PCDD/PCDFs
(Vowles and Mantoura, 1987). However, there is a reasonably good correlation between Kow (and thus
Koc) and aqueous solubility for most PAHs, PCBs, and PCDD/PCDFs (Table 3-6).
The empirical or literature values of Kd, Koc, and Kow can be used to yield model predictions
of the concentrations expected in the dissolved phase and subsequently estimate bioavailability, toxicity
risk and the recovery time for HOC-contaminated sediments. Values for log Koc can be used to predict
leaching or partitioning of HOCs from sediment particles into sediment pore water and the overlying
water column. Log Koc and log Kow values also can be used to predict the bioaccumulation and toxicity
of HOCs to benthic marine animals, based on the equilibrium partitioning theory (Di Toro et al., 1991; Di
Toro and McGrath, 2001). HOCs with high log Koc have low bioaccessibility because they are tightly
bound to sediment organic matter (Qiu and Davis, 2004; Reichenberg and Mayer, 2006; Hawthorne et al.,
2007a; Burgess, 2009).
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Table 3-6. Physical/Chemical Properties of Importance in Environmental Fate
Estimations of Representative PAHs, PCBs, PCDDs, and PCDFs
Hydrophobic Organic
Contaminant
Naphthalene
2,6-Dimethylnaphthalene
Phenanthrene
Fluoranthene
Chrysene
Benzo(a)pyrene
Dibenz(a,h)anthracene
2,2'-DichloroCB
3,3',5,5'-TetraCB
2,2',4,5,5'-PentaCB
2,2',4,4',5,5'-HexaCB
2,2', 3, 3',5,5',6,6'-OctaCB
DecaCB
Dibenzo-p-dioxin
2-ChloroDD
2,7-DichloroDD
2,3,7,8-TetrachloroDD
1,2,3,4,7-PentachloroDD
1,2,3,4,7,8-HexachloroDD
OctachloroDD
2,8-DichloroDF
2,3,7,8-TetrachloroDF
2,3,4,7,8-PentachloroDF
1,2,3,4,7,8-HexachloroDF
OctachloroDF
Molecular
Weight
(g/mol)
128.2
156.2
178.2
202.3
228.3
252.3
278.4
223.1
292.0
326.4
360.9
429.8
498.7
184.0
218.5
253.0
322.0
356.4
391.0
460.0
237.1
306.0
340.4
374.9
443.8
Vapor Pressure
(Pa)
PAHs
36.8
9.27
0.113
8.7 x 10"3
1.1 x 10'4
2.1 x 10"5
9.1 x 10'9
PCBs
0.428
2.0 x 10'3
3.6 x 10"3
5.9 x 10"4
<1 x 10'6
<1 x 10"6
PCDDs
0.51
0.10
8.1 x 10'3
1.2 x 10"4
4.3 x 10'6
.5 x 10"6
.2 x 10'7
PCDFs
.5 x 10'2
.9 x 10"4
.7 x 10'5
3.6 x 10"6
1.0 x 10'7
Solubility
(mg/L)
31
1.7
1.1
0.26
9.0 x 1Q-3
4.0 x 10'3
6 x 1Q-4
1.2
1.2 x 1Q-3
6.7 x 10'3
8.6 x 10'4
4.0 x 1Q-4
7.0 x 10'6
0.84
0.28
4.0 x 1Q-3
2.0 x 10"3
1.2 x 10'3
4.0 x 10"5
7.0 x 10'7
1.4 x 1Q-2
4.1xlO"4
2.4 x 10'4
1.8 xlO"5
1.2X10'6
Log Kow
(unitless)
3.37
4.31
4.57
5.22
5.86
6.04
6.75
4.65
6.48
6.38
6.92
7.24
8.18
4.30
5.00
5.75
6.80
7.40
7.80
8.20
5.44
6.10
6.50
7.20
8.00
Data from Shiu et al., 1988; Mackay et al., 2006; Nakajoh et al., 2006
3.2.4 Sorption to Organic Colloids. Organic colloids and suspended mixed-phase organic solids
usually are present at higher concentrations in sediment pore water than in overlying water (Chin and
Gschwend, 1992; Mackay and Gschwend, 2001). Organic colloids are particles, generally less than 0.1 to
0.2 jam in diameter, that pass through conventional filters and cannot be sedimented by centrifugation
(Wells and Goldberg, 1991; Gschwend and Schwarzenbach, 1992). They usually are humic acids formed
from biodegradation of natural terrestrial, fresh water, and marine plant materials (Chin and Gschwend,
1991, 1992). They represent most of the DOC in most fresh water and marine sediments.
Natural organic colloids in sediments and the water column have a high affinity for
absorption and binding of HOCs (Wijayaratne and Means, 1984a,b). This association increases the
apparent solubility and concentration of low-solubility HOCs in the water phase. Apparent solubility of
HOCs tends to increase in sediment pore water as the concentration of DOC increases because the HOCs
form non-covalent associations with hydrophobic domains within the DOC molecular framework (Raber
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et a/., 1998). Sorption of HOCs to colloids plays an important role in HOC fate and transport because
HOC association with colloids: 1) behaves like dissolved solutes, facilitating advective transport in pore
water; and 2) reduces the concentration of bioavailable HOCs in pore water, limiting exposure to
sediment biota (EPA, 2003).
The significance of suspended colloid concentrations to HOC sorption dynamics is
underscored by previous studies of sorption processes during the development of the equilibrium
partitioning (EqP) theory. Gschwend and Wu (1985) showed that a large fraction of PCBs in sediment
pore water was sorbed to colloidal materials and proposed that equilibrium partitioning models should
include the colloidal phase for three conditions: purely dissolved, sorbed to non-settling particles or
macromolecular colloids, and sorbed to settling solids. Others proposed models for estimating the
distribution of HOCs among the aqueous phase solids of various compositions, such as humic substances
and other organic polymers (Chin and Weber, 1989; McCarthy and Jimenez, 1985).
Other studies highlighted the importance of the nature of the colloidal material on sorption
processes. For example, Chin and Gschwend (1992) measured organic carbon-normalized, colloid/water
partition coefficients, Koccoii0id, for pyrene and phenanthrene and found that the values varied
substantially between sediment samples collected from two different areas of Boston Harbor. The authors
hypothesized that the difference in colloid-binding coefficients was due to differences in organic matter
functional group characteristics in sediments from the two locations. Koccoii0id for pyrene was
substantially higher for the sample collected from the location where the sediment organic carbon phase
contained a higher concentration of lipids. Thus, for pyrene the greater affinity for colloids could be
explained either by sorption to colloidal lipids or to organic matter from the location where a greater
degree of binding was observed. Therefore, the characteristics of the sediment organic fraction of the
sediment are important for controlling HOC sorption and, consequently, HOC bioaccessibility.
McGroddy and Farrington (1995) measured concentrations of dissolved PAHs and PAHs
sorbed to colloids in sediments from Boston Harbor and calculated sediment-water distribution
coefficients. When PAHs are bound to the colloids in the water, the solid-water distribution coefficient is
lower than a coefficient for a system with little or no colloids present because PAHs sorbed to the colloids
contribute to the total PAH load measured in the water phase, rather than in the sediment phase.
However, the authors found that Kd values in the Boston Harbor sediments were much higher than
expected, despite the additional PAH load sorbed to colloids in the water, due to the presence of soot
particles in the sediment that contain high relative concentrations of PAHs. Black carbon (combustion
soot carbon) has a very high adsorption capacity for HOCs (discussed below). McGroddy and Farrington
(1995) noted that the ratios of Koccouold to Kocsediment for pyrene were greater for samples collected at one
location where there was greater anthropogenic activity and sediment lipid concentrations were higher.
The Koccolloid values for sediments from locations disturbed to a greater degree by anthropogenic activity
and urban discharges, compared to remote locations where anthropogenic disturbance is not significant,
contain colloidal material with a higher proportion of natural humic substances.
These studies collectively illustrate the importance of sorption processes of organic colloids,
including the importance of functional group composition of the organic material of the colloids in HOC
partitioning in sediments. However, this phenomenon is not limited to PAH distribution between
sedimentary (aqueous), suspended (colloidal), and solid phases. For example, the concentration of total
PCBs in pore water of heavily contaminated sediments from New Bedford Harbor, MA, increased with
depth in the sediment core (Brownawell and Farrington, 1986). The concentrations of many of the
higher-molecular weight, less soluble (typically those with greater chlorine content) PCB congeners
exceeded the reported solubilities of CB isomer groups in a mixture of Aroclor in sea water. Profiles of
DOC and PCB concentrations in sediment cores were similar, suggesting that PCBs were non-covalently
associated with DOC molecules and/or colloids, thereby increasing their apparent or effective solubilities
69
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in sediment pore water. The PCB concentration profiles of interstitial water as a function of depth
suggested a diffusional flux to the overlying water column.
Consideration of sorption to individual components within heterogeneous sorbent material is
necessary to more adequately describe fate and transport of HOCs in sediments that frequently display
non-linear sorption dynamics to organic carbon-rich sediment and sedimentary colloidal material. These
studies show the important role that colloidal and dissolved organic matter (DOM) can play in governing
HOC partitioning and sorption within sediments, and hence the large role they may have in determining
the fate and transport of HOCs in situ. Thus, at sites where MNR is implemented, the potential for
enhanced transport of HOCs, such as PAHs, PCBs, and PCDDs/PCDFs, due to sorption to more mobile
colloids must be considered and addressed to ensure that best estimates are made for retention or
movement of HOCs in the remediation site. To achieve this objective, a more comprehensive
mechanistic understanding of HOC sorptive processes to sedimentary material must include interpretation
of existing data via linear and non-linear modeling efforts.
3.2.5 Non-Linear and Non-Equilibrium Sorption. A fundamental assumption in using PAH
ESBs and other regulatory guidelines that are based on EqP is that the dissolved, bioavailable HOC
concentration is directly proportional to the concentration of HOCs sorbed to sediment organic matter
(EPA, 2003). The organic matter-normalized linear EqP model was proposed decades ago and was
corroborated by results from studies showing that the distribution of HOCs between soil or sediment
organic matter and water could be treated similarly to the partitioning of HOCs between an organic
solvent phase (octanol) and water (i.e., the linear relationships between Kow, Koc, and Kd as defined
above) (Lambert, 1968; Karickhoff et al., 1979; Chiou etal, 1983). However, the validity of the
assumptions inherent in the EqP approach has been challenged based on the results of several field and
laboratory studies.
An example of how the EqP model is used to establish cleanup criteria for PAH-
contaminated sediment sites is found in EPA's Procedures for the Derivation of ESBs for the Protection
ofBenthic Organisms: PAH Mixtures (EPA, 2003). PAH ESBs are a function of the sum of the
concentration of 34 individual PAHs or PAH congener groups in sediment, and concentrations below the
benchmark are considered to be non-deleterious to benthic organisms in a given sediment environment.
The ESB is estimated by calculating the concentration of total PAHs in sediment pore water that is in
equilibrium with the critical body residue (CBR) of total PAHs in tissues of sediment-dwelling
macrofauna in contact with the sediment. The CBR is the molar concentration of total PAHs in tissues
that cause harmful effects by neutral narcosis (McCarty et al., 1992). Equilibrium concentrations of total
PAHs in bulk sediment, sediment pore water, and animal tissues may be predicted from partition
coefficients between sediment and water and between water and animal tissues. Because dissolved PAHs
are the most bioavailable form in sedimentary environments, and because most toxicity data are for PAHs
in the dissolved phase, the final chronic falue (FCV) for total PAHs from the National Water Quality
Criteria Guidelines is used as the toxicity endpoint for PAHs in sediment pore water at equilibrium. The
FCV, usually reported as |o,g PAH/g sediment organic carbon, is defined as the concentration of PAHs in
sediment that is protective of the presence of aquatic life. FCVs for 18 or 34 PAHs are summed to obtain
a FCV for total PAHs. If the ratio of the measured concentration of total PAHs in sediment to the FCV is
>1, a risk of toxicity of the PAHs to sediment biota exists.
A necessary part of an MNR evaluation is to understand the physical and chemical factors
that cause differences between modeled and actual sediment-water distribution coefficients for HOCs.
Sound scientific evidence is available to support the use of the EqP model to make predictive estimates of
contaminant bioavailability for most situations (EPA, 2003). However, as previously acknowledged, the
EqP model may not accurately represent actual sorption processes that are occurring in some situations.
If the in-situ sorption processes are complex or not at equilibrium, the calculated exposures to HOCs may
70
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not be accurate descriptions ofin-situ exposures and the use of EqP models may result in either overly or
insufficiently protective sediment quality criteria. An evaluation of measured versus modeled
concentrations of HOCs will help in determining whether the physical forms of the HOCs and
physical/chemical conditions at a site are suitable for use of the EqP model to set remediation criteria and
natural attenuation goals.
Sorption isotherms of various HOCs (e.g., PAHs and PCBs) onto geologic substrates (e.g.,
sediments or aquifer solids) were found to be non-linear in several studies (Miller and Weber, 1986; Ball
and Roberts, 1991a; Weber et al, 1991, 1992). In these investigations, it was observed that the ratio of
sorbed HOC concentration to aqueous HOC concentration changed as a function of initial HOC
concentration. Further, sorption of HOCs in environmental systems often does not reach equilibrium in
time scales relevant to environmental fate and transport processes, and sorption must be described using
non-equilibrium (i.e., Freundlich) models, rather than strictly Langmuir-type sorption models that
inherently assume finite sorption sites and, therefore, a linear relationship between initial aqueous phase
concentration and observed adsorption (Ball and Roberts, 1991b; Brusseau et al., 1991). These
observations of non-linearity and non-equilibrium have been modeled primarily by describing various
sorption heterogeneities in geosorbent material (i.e., organic versus mineral phases) (Weber et al., 1992;
Brusseau et al., 1991), often related to physico-chemical properties of the specific sorbent components
with linear and non-linear and equilibrium and non-equilibrium sorption processes attributed differently
to the various compartments of the geosorbent material under investigation.
Weber et al. (1992) proposed that sorption in any natural system is inherently dependent on
many local sorption phenomena and that a distributed reactivity model is most appropriate. Investigations
of non-linear and non-equilibrium sorption components have resulted in a few general concepts for
explaining the observed results. These general types of models are described below and include
intraparticle diffusion; intraorganic matter diffusion (IOMD); and a greater degree of irreversible sorption
to "hard," "condensed," or "black" carbon (BC) sorbents. BC, synonymous with combustion soot,
charcoal, coal particles and kerogens, or activated carbon (Fernandez and Brooks, 2003), is of particular
significance to the discussion of non-linear and non-equilibrium sorption due to the high capacity of this
material to irreversibly absorb significant quantities of HOCs, particularly on a mass-normalized basis.
With the current available analytical techniques, the absolute amount of these materials is difficult to
quantify in environmental matrices. These materials also defy molecular-level description of the sorptive
surfaces. Weber et al. (1992) described and modeled the sorptive properties of these materials and
hypothesized that sorption to BC is described by partitioning to two types of organic carbon domains, soft
(or rubbery) and hard (or glassy). Sorption to soft carbon was characterized by equilibrium, linear
partitioning, and sorption to hard carbon was characterized by non-equilibrium and non-linearity.
Adsorption of HOCs to these organic matter domains is notoriously non-linear and irreversible, attributes
that are credited to contaminant diffusion into particles, the organic matter matrix, and absorption into the
glassy/rubbery domains of organic matter, all of which are affected by the composition of the native
sedimentary organic matter and the extent of diagenetic alteration of the sorbent matrix. Accardi-Dey and
Gschwend (2002) expanded on this premise by removing soft carbon from sediments by combustion, then
measuring sorption. By accounting for absorption into natural organic matter (NOM) and adsorption onto
BC, they were able to explain non-linear isotherms and the strong sorption of PAHs. At equilibrium, the
overall solid-water distribution coefficient (Kd) could be calculated using:
Kd = focKoc + fBcKBcCw" ' (Eq. 3.4)
where foc is the weight fraction of organic carbon in the solid phase and K0c is the organic carbon-
normalized distribution coefficient for the compound of interest. Cw is the truly dissolved concentration,
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fee is the weight fraction of BC in the solid phase, KBC is the BC-normalized distribution coefficient, and
n is the Freundlich exponent, appropriate for each PAH-BC combination of interest.
3.2.6 Intraparticle and Intraorganic Matter Diffusion. Wu and Gschwend (1986, 1988) and
Ball and Roberts (1991b) proposed models in which HOCs diffuse radially within mineral solids to
explain non-equilibrium sorption observed in earlier experiments. The general concept of these
intraparticle sorption models is that dissolved HOC molecules within the pores of mineral grains diffuse
in water from areas of high concentration to areas of low concentration. The rate of HOC diffusion is
affected not only by those parameters that affect diffusion in water (e.g., physical and chemical
characteristics of the HOC molecules, water temperature, etc.), but also the sorption and steric properties
(e.g., hydrophobicity and size) of the HOCs and the tortuosity of the substrate. Their movement is
retarded by sorption to mineral surfaces or steric hindrance as they move through the pores.
Hamaker etal. (1966) proposed IOMD to explain rate-limited sorption of organic chemicals.
Brusseau and Rao (1989a, 1989b) and Brusseau et al. (1991) proposed IOMD as the rate-limiting sorption
process to explain non-equilibrium partitioning. NOM has been described as a flexible, amorphous
polymeric material into which HOCs diffuse. As studies have demonstrated how organic matter
characteristics strongly affect sorption processes, the IOMD model was expanded to include two general
types of organic matter matrices — an expanded amorphous or rubbery polyacetal domain and a
condensed microcrystalline or glassy polystyrene domain (Weber and Huang, 1996; LeBoeuf and Weber,
2000; Xing and Pignatello, 1997; Weber and Young, 1997; Cornelissen et al., 2000).
Weber and Huang (1996) proposed that natural soil and sediment sorbents could be modeled
as consisting of three domains: a mineral nonporous domain; a condensed (hard, glassy) soil organic
matter (SOM) domain; and an expanded, amorphous (rubbery) organic matter domain. They
hypothesized that sorption within the condensed SOM is non-linear and that the rate is affected by sorbate
concentration and also the SOM characteristics.
3.2.7 Enhanced Sorption to Black Carbon Sorbents. Additional levels of model sophistication
have been invoked to explain observations of non-linear and non-equilibrium processes. In addition to
intraparticle diffusion and IOMD, the extent of sorption to BC sorbents (defined above) has been
identified as contributing to empirically observed sorption non-linearity based on the diagenetic state of
the organic material. Early studies of sorption, such as those by Grathwohl (1990) and Weber et al.
(1992), showed that the age characteristics of sorbent material significantly affect the extent, linearity,
and reversibility of sorption. Grathwohl (1990) and Achten and Hofmann (2009) demonstrated that
geologically older carbon sorbents, such as the organic matter in unweathered shale or anthracite (older)
coal, have greater capacity to adsorb HOC contaminants than do SOM or bituminous (younger) coal.
These results demonstrate an inverse relationship between the amount of oxygen-containing functional
groups and sorption, suggesting a positive correlation between the degree to which SOM had undergone
diagenesis and sorption strength. Furthermore, Weber et al. (1992) observed that sorption of 1,2,4-
trichlorobenzene to "hard" or "glassy" carbon was mechanistically different from sorption to "soft" or
"rubbery" carbon, the former showing non-linearity and the latter showing linear partitioning behavior.
McGroddy and Farrington (1995) expanded on insights from studies by Grathwohl (1990)
and others regarding the importance of sorbent characteristics in predicting sorption behavior. The
authors measured sediment and pore water PAH concentrations in sediment samples from three locations
in Boston Harbor. They calculated Koc values for several PAHs based on measured PAH concentrations
in sediment pore water and found that the measured pore water concentrations were much lower than
predicted by equilibrium partitioning models. In contrast, measured aqueous concentrations of PCBs with
log Kocs similar to those of the PAHs studied were in agreement with concentrations predicted by
equilibrium partitioning models. Based on calculations of the fractions of phenanthrene and pyrene that
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were available for equilibrium partitioning (AEP), they hypothesized that PAHs from pyrogenic sources
were preferentially associated with soot particles in sediments and were less AEP than PAHs from
petrogenic sources or pyrogenic PAHs from liquid sources (e.g., creosote). Thus, the source of PAHs
deposited to sediments as well as the sorbents present in sediments will strongly affect how PAHs will be
distributed among solid and aqueous phases (Neffet al., 2005). This distribution can affect the
concentrations of bioavailable PAHs to which benthic organisms are exposed and also must be considered
in establishing clean-up levels for a particular site.
3.2.8 Sorption of PAHs, Coplanar PCBs, and PCDDs/PCDFs to Black Carbon Sorbents.
Strong and often nearly irreversible sorption of PAHs, PCBs, and PCDD/PCDFs to BC has been observed
(Persson et al, 2002; Ghosh et al, 2003; Lohmann et al, 2005; Ghosh and Hawthorne, 2010; Werner et
al, 2010). Most of the BC in sediments is combustion soot composed of irregular, microscopic carbon
particles generated during combustion of organic matter. The presence of BC in sediments and its effects
on HOC binding play a key role in PAH, coplanar PCB, and PCDD/PCDF fate and transport. PAHs are
the HOCs that usually exhibit the strongest binding to BC (Readman et al, 1987; Broman et al, 1990),
partly because they are generated during the formation of combustion soot and adsorb strongly to the soot
particles as they cool (Fernandez and Brooks, 2003). Concentrations of pyrogenic PAHs and
PCDDs/PCDFs in near-shore marine sediments often are closely correlated with the abundance of
carbonaceous particles of soot, kerogen, and coal (Broman et al, 1990; Lohmann et al, 2002; Yunker et
al, 2002; Achten and Hofmann, 2009), indicating that these HOCs remain associated with the particles
after deposition and may not be extensively modified by photooxidation, dissolution, or biodegradation
during transport in the air and water (Prahl and Carpenter, 1983; Readman et al, 1987). Readman et al.
(1987) used a linear free energy sediment-water exchange model to simulate PAH partitioning and
exchange of individual unalkylated (primarily pyrogenic) PAHs in the surface mixed layer of sediment
and the overlying water column of the Tamar River Estuary, England. They showed that the
concentrations of PAHs in the sediments are between two and five orders of magnitude higher than those
expected from equilibrium partitioning with concentrations of PAHs in the associated water. Their
observations indicated that most of the PAHs in the sediments were sequestered on BC and were
chemically unreactive and resistant to abiotic transformations. Sorptive exchange with water and,
therefore, bioavailability of PAHs to marine organisms appeared to be restricted by the existence of
occluded and other micro-morphologically inert forms of particle-bound PAHs.
McGroddy et al. (1996) tested the hypothesis that only a fraction of PAHs in sediment is
AEP. They measured desorption and equilibrium sediment and pore water concentrations, calculated the
predicted aqueous concentrations based on existing equilibrium partitioning models, and compared their
results to calculated AEP values. Previously established EqP models overpredicted the aqueous PAH
concentrations. However, they found that by utilizing their equation for partitioning and the AEP values
calculated from a previous study they could predict the aqueous concentration of PAHs. In contrast to
PAHs, aqueous PCB concentrations were predicted well by the previous models. They concluded that the
PAHs that were not AEP were released to the environment by pyrolysis of organic matter and were
associated with the soot particles that were generated simultaneously. On a broader scale, they noted that
PAHs and PCBs interact with sediment solids via fundamentally different geochemical mechanisms. It is
important to note that the PCBs studied by McGroddy and Farrington (1995) and McGroddy et al. (1996),
PCBs 101 and 138, were non-coplanar. As discussed below, the degree of coplanarity of compounds
composed of benzene rings (PAHs, PCBs, and PCDDs/PCDFs) strongly affects sorption/desorption
behavior.
Irreversible binding of PAHs to BC renders pyrogenic PAH assemblages that are chemically
stable for long periods of time, especially after deposition in soil and sedimentary environments.
Desorption of PAHs from carbonaceous particles is very slow as indicated by the low bioavailability and
toxicity of pyrogenic PAHs in sediments to aquatic organisms (Cornelissen et al, 1998; Farrington and
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Westall, 1986; Gustafsson etal, 1997; Rust et al, 2004; Thorsen etal, 2004; Cornelissen and
Gustafsson, 2005). Particle-bound PAHs in marine sediments near Norwegian and Canadian aluminum
smelters (a source of PAH-rich soot particles) produce fewer adverse effects on local benthic
communities than expected based on laboratory studies with dissolved PAHs (Knutzen, 1995; Paine et al,
1996). The PAHs in effluents from aluminum smelters are pyrogenic and tightly bound to soot particles;
they have limited bioavailability to marine organisms (Naes et al., 1998).
An important aspect of understanding and quantifying the role of soot in HOC sorption is the
quantification of soot in soils and sediments. Gustafsson etal. (1997) developed a method for quantifying
soot in sediments. The method involves removing organic carbon by thermal oxidation, removing
inorganic carbonates by acidification, and measuring the remaining BC using carbon, hydrogen, and
nitrogen elemental analysis. The authors proposed that previously measured large Koc values for PAHs in
sediment could be explained by the partitioning of HOCs to high-affinity sorption sites of the measured
concentrations of BC particles, as evidenced by approximations of BC-water partition coefficients (KBCs)
that were obtained from studies of PAH sorption to activated carbon. By measuring PAH, organic
carbon, and BC concentrations in sediments, Gustafsson and Gschwend (1997) found that PAH sorption
correlated strongly with BC. They also calculated KBcS and hypothesized that PAH interactions with BC
were similar to interactions with activated carbon. The authors suggested that there may be TT-TT bond
OO J
interaction between planar HOCs and sorbents with a significant aromatic ring structure such as BC.
Gillette et al. (1999) showed that the concentration of PAHs on particles at the microscale
could be measured directly in a 40-(im diameter spot in the sample by microprobe laser desorption/laser
ionization mass spectrometry. In the past, studies of HOC sorption to geosorbents had to rely on
observations at the particle macro-scale. Ghosh et al. (2000) demonstrated that the majority of PAHs in
Milwaukee Harbor sediments are sorbed to the wood/coal fraction of sediment, though these types of
particles only comprise a small fraction of the total sediment mass. They used these mass spectral
measurements of PAHs on BC particles to demonstrate that PAHs are concentrated primarily on the outer
few microns of the coal-derived particles. Their data indicated that the concentrations of PAHs were
highest in patches of organic matter on sediment particles. This was the first particle-scale direct
observation of the association of HOCs with organic matter in specific locations on sediment particles and
provided a detailed view of how PAHs sorb to these types of particles.
Ghosh et al. (2000) also investigated the kinetics of desorption from the two general classes
of particles studied: more dense mineral particles, such as clay and sand; and less dense organic particles,
such as wood and coal-derived particles. They showed that PAHs desorbed from the less dense fraction
(e.g., wood and coal-derived particles) more slowly than from the denser mineral fraction. Higher
activation energies were associated with PAH binding to coal-derived particles than with binding to
clay/silt particles (Ghosh et al., 2001). Furthermore, Cornelissen et al. (2000) demonstrated rapid
desorption from linearly sorbing organic matter, but slow desorption from non-linearly sorbing sites
based on the nature of the non-covalent interactions between the condensed (glassy, more dense) and
amorphous (rubbery, less dense) domains of the organic sorbent material. These results have important
implications for MNR because desorption is an integral part of HOC fate and transport in sediments. For
example, if site sediment is likely to have significant amounts of coal- or soot-derived BC particles, PAHs
present in the sediment probably are less bioavailable for uptake by benthic organisms than if the PAHs
were associated with mineral phases in sediments (Cornelissen and Gustafsson, 2005; Neffet al., 2005).
Thus, higher sediment PAH concentrations may be acceptable due to the lower associated bioaccessibility
and risk.
These studies emphasize irreversible binding of PAHs to low density organic particles found
in sediment. However, PAHs (and also PCDDs/PCDFs) can be associated with BC particles beginning
with their release to the environment at their point of origin. BC is produced during the incomplete
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combustion of organic matter. Combustion produces a variety of PAHs that escape with the smoke or
exhaust gases and condense onto the BC particles as they cool (Neff, 1979). PCDDs/PCDFs produced by
combustion of organic matter in the presence of high concentrations of chlorine also are released to the
environment in the combustion gases and tend to condense on BC particles in the exhaust plume as it
cools. These PAHs and PCDDs/PCDFs are entrapped in the aromatic matrix of the BC particles. PAHs
in the fuel or those produced during combustion are entrapped in BC particles released to the
environment. Therefore, a feasible explanation for observations of a strong PAH association with coal-
derived particles and BC is co-generation in addition to strong sorption mechanisms.
In contrast to the situation with PAHs and PCDDs/PCDFs, PCBs rarely are produced or
released during combustion of wastes. Because of the aromatic structure, large surface area, and high Koc
of BC, BC particles deposited in fresh and marine water bodies scavenge dissolved PAHs, PCBs,
PCDDs/PCDFS, and other HOCs from the water or sediments (Ghosh et al, 2003; Lohmann et al, 2005).
The PAHs and PCDDs/PCDFs that condense on the cooling BC in the combustion gases and the HOCs
that adsorb to the BC particles in the environment are tightly bound to the BC particles and are deposited
in soils and sediments. Aerial deposition of BC to the Laurentian Great Lakes ranges from 0.02 to 0.89
mg/m2/year, representing annual mass loadings of BC that range from 2.3 x 103 tons onto Lake Superior
to 420 x 103 tons onto Lake Michigan. Only 0.01%, on average, of the total mass of PAHs in the lakes is
adsorbed directly from the water phase (Buckley et al., 2004). Because of their lower aqueous solubility
and higher Kocs, even less PCDDs/PCDFs associated with sediment BC is adsorbed from the water phase.
However, PCBs associated with sediment BC are adsorbed from dissolved and particulate phases in the
water. Thus, much of the pyrogenic PAHs, PCDDs, and PCDFs are tightly bound to the particulate BC
phase and have low accessibility and bioavailability to sediment-associated organisms. PCBs are less
tightly bound to sediment organic phases and may be more mobile in sediments and water.
Accardi-Dey and Gschwend (2003) proposed a new method for describing the distribution of
HOCs between water and sediment solids that contain relatively high concentrations (a few percent TOC)
of BC. They proposed that, by accounting for the presence of BC sorbents (using the BC-normalized
distribution coefficient, Kbc) in addition to Koc, they could quantify PAH distribution between water and
particulate phases. Lohmann et al. (2005) estimated Kbc values for other PAHs, PCBs, and PCDDs in
Boston and New York Harbor sediments based on Kbc values for pyrene and phenanthrene calculated by
Accardi-Dey and Gschwend (2002) and Accardi-Dey and Gschwend (2003), respectively. Kbc values for
most HOCs were one order of magnitude or higher than the corresponding Koc values.
Bucheli and Gustafsson (2000) measured Kbc for PAHs using diesel particulate matter as a
model BC sorbent. The Kbc values calculated were some of the highest solid-water distribution
coefficients ever measured and were up to 250 times higher than predicted Koc values. Their results
provided additional evidence to support the hypothesis that sorption to BC is a probable explanation for
the extensive and often irreversible PAH sorption in sediments shown in previous studies. Hawthorne et
al. (2007b) measured Koc and Kbc for parent and alkylated PAHs in more than 100 historically
contaminated sediments (mainly at manufactured gas plant [MGP] sites). Both Koc and Kbc values varied
by up to three orders of magnitude for individual PAHs in sediments containing 0.3 to 42% by weight
TOC, 0.1 to 30% BC, and total PAH concentrations (16 priority pollutant parent PAHs) from 0.2 to 8,600
Hg/g. PAH partitioning in sediment was not explained more clearly by the combined Koc and Kbc
partitioning models than by the Koc model alone. This was unexpected because log Kbc was larger than
log Koc for all PAHs in all sediments (Table 3-7). The fraction of TOC in sediment that is BC determines
the effects of PAH sorption to BC in modeling PAH partitioning in sediments. Evidence from several
recent studies suggests that sorption of HOCs in soils and sediments occurs as absorption within
amorphous organic matter (AOM) and adsorption to BC. Strong sorption to BC leads to: 1) solid-water
distribution coefficients higher (by up to two orders of magnitude) than what would be expected based
upon sorption to AOM; 2) lower-than-expected bioavailability of HOCs to sediment-dwelling organisms,
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including the microbiota responsible for HOC biodegradation in sediments; and, as a result, 3) much
slower-than-expected biodegradation of sediment HOCs (Cornelissen et al., 2005).
3.2.9 Partitioning of PAHs from Sediment NAPL. Petrogenic PAHs from petroleum and
pyrogenic PAHs from creosote and coal tar in sediments may be complexed with the colloidal and
particulate organic fractions of sediment. Additionally, PAHs can be associated with a NAPL, such as
petroleum or coal tar, or an oil coating on sediment particles. PCBs also may be present in sediment in an
oil-like NAPL phase, e.g., the mixtures of different chlorinated biphenyls in commercial PCB mixtures
(Table 3-2), such as Aroclor 1254, a viscous liquid. Since the affinity of PAHs and PCBs is higher for the
NAPL phase than for the sediment NOM and sediment pore water phases, but probably not for the
sediment BC phase, partitioning of PAHs or PCBs into solution in sediment pore water is controlled
primarily by the affinity of the HOCs for the NAPL phase (Eisenhut et al, 1990; Neff et al, 2005). No
information is available regarding the partitioning of HOC between NAPL and BC phases. The rate of
partitioning of HOCs from NAPL to sediment pore water phases is dependent on the relative surface area
of contact between the water and NAPL, the viscosity of the NAPL (which controls diffusion within the
NAPL layer), and the NAPL/water partition coefficients for the different HOCs. Thus, in estimating the
partitioning of PAHs or PCBs between the NAPL phase (petroleum, coal tar, creosote, and Aroclor) and
the dissolved phase, PAH and PCB concentrations should be normalized to some measure of total organic
matter in the NAPL.
The PAHs in sediment NAPLs are distributed among the dissolved (pore water), NAPL, and
sediment organic carbon phases of the sediment in accordance with their relative affinities for the three
phases. This distribution between the NAPL and water phases can be described by an oil/water partition
coefficient (Koll) (Lee et al., 1992a, 1992b), and the distribution between sediment organic carbon and
water can be described by Koc as discussed above. The Koc for most HOCs and colloidal-particulate
organic matter in sediments is lower than the Kow (Karickhoff, 1981; Di Toro etal, 1991), whereas the
K0ii for PAHs in most crude and refined petroleum products and liquid coal tars and creosote is about the
same as or higher than the Kow and tends to increase with average molecular weight of the NAPL material
(Shiuetal., 1990; Lee etal., 1992a, 1992b).
Use of Kow, Koc, and Koll to make predictive estimates of HOC partitioning behavior tends to
overestimate concentrations of dissolved PAHs in pore water for sediments that are contaminated
primarily with pyrogenic PAHs. However, partition coefficients can provide a reasonable upper-limit
estimate of dissolved phase PAHs in pore water of petroleum-, creosote-, or coal tar-contaminated
sediments if the oil or other NAPL phase is still liquid and in physical contact with sediment pore water
(Pope et al., 2010). The NAPL, particularly if it is crude oil or coal tar, may develop a surface "skin" of
resins-asphaltenes or other high-molecular-weight polar compounds, decreasing NAPL/water partitioning
(Nelson et al., 1996). When pore spaces are filled with NAPL or oil, the permeability of the sediment
may be substantially decreased, thus decreasing the effective NAPL/water interface and limiting
accessibility of the PAHs to partitioning into sediment pore water. Site-specific, empirically-determined
Kd values (particle-water partition coefficients) may be best for estimating sediment-water partitioning of
pyrogenic PAHs or PAHs from weathered crude oil.
Neff et al. (2006, 2011) observed that bioavailability to intertidal animals of PAHs from
subsurface deposits of weathered North Slope crude oil in shoreline sediments 12 years after the Exxon
Valdez oil spill was negligible. The PAHs in the oil, which had persisted buried in shoreline sediments
for 12 years after the spill, were observed to be immobile and have low bioaccessibility (Boehm et al.,
2007).
Thorsen et al. (2004) measured the bioavailability of sediment PAHs to benthic animals as
the biota sediment accumulation factor (BSAF: lipid-normalized PAH concentration in tissue/sediment
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organic carbon-normalized PAH concentration in sediment). BSAF values for petrogenic PAHs were
greater than those for pyrogenic PAHs, indicating that the petrogenic PAHs were more bioavailable than
the pyrogenic PAHs in sediments. Bioavailability of pyrogenic PAHs in sediment decreased with
increasing BC concentration in sediments, but bioavailability of petrogenic PAHs in sediment was not
affected by BC concentration, indicating that pyrogenic PAHs, but not petrogenic PAHs, were strongly
sorbedto BC.
Table 3-7. Mean Measured log Koc and log KBC Values for PAHs in More than 100 Historically
Contaminated Sediments Containing 0.2 to 8,600 \iglg Dry Weight Total PAHs
Polycyclic Aromatic Hydrocarbon
Naphthalene
2-Methylnaphthalene
1 -Methylnaphthalene
C2-Naphthalenes
C3 -Naphthalenes
C4-Naphthalenes
Acenaphthylene
Acenaphthene
Fluorene
Cl-Fluorenes
C2-Fluorenes
Phenanthrene
Anthracene
C 1 -Phenanthrenes/ Anthracenes
C2-Phenanthrenes/ Anthracenes
C3 -Phenanthrenes/ Anthracenes
C4-Phenanthrenes/ Anthracenes
Fluoranthene
Pyrene
C 1 -Fluoranthenes/Pyrenes
Benz(a)anthracene
Chrysene
Cl-Chrysenes
Benzo(b+k)fluoranthenes
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno( 1 ,2,3 -cd)pyrene
Dibenz(a,h)anthracene
Benzo(ghi)perylene
Measured Log K^
3.02
3.36
3.16
3.63
3.33
3.34
4.02
3.37
3.65
3.94
3.67
4.18
4.60
4.38
4.64
4.74
4.77
4.74
4.70
4.75
5.48
5.50
5.49
5.87
5.53
5.68
5.88
6.41
6.13
6.11
Measured Log Kbc
4.73
5.03
4.76
4.84
4.85
4.93
5.54
4.89
5.17
5.23
5.25
5.65
6.20
5.79
6.04
6.31
5.84
6.28
6.32
6.46
7.03
7.08
7.30
7.39
7.17
7.22
7.25
8.17
7.37
7.73
Source: Hawthorne et al., 2007b
3.2.10 Physical/Chemical Transformation
3.2.10.1 HOC Dissolution and Evaporation. Aqueous solubilities of PAHs, PCBs, and
PCDDs/PCDFs are low and decrease with increasing molecular weight (Table 3-6). Solubility also
decreases with decreasing ambient temperature and increasing salinity or dissolved solids concentration
(Doucette and Andren, 1988; Eastcottetal., 1988; ShiuetaL, 1988; Abrajano etal, 2005). Single-phase
fresh water solubilities of HOCs, such as those listed in Table 3-6, represent the theoretical maximum
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aqueous phase concentration of HOCs in sediment pore water at 25°C and ionic strength less than 0.5M
without DOC.
Most HOCs have a low volatility and do not readily evaporate from sediment or water.
Evaporation of HOCs from subsurface sediments is slow and is not a significant route of HOC loss from
contaminated fresh water and marine sediments. Most of the available information on evaporation of
HOCs is for hydrocarbon evaporation from slicks of spilled oil on the water surface (Neff, 1990).
Compounds in petroleum that boil at temperatures below about 250°C, or have vapor
pressures greater than about 0.1 mm Hg (13.3 Pa), tend to evaporate rapidly from the surface of the oil.
Included in this category are alkanes from methane to «-dodecane and aromatics from benzene through
naphthalene (Bobra et al., 1979). All PCB and PCDD/PCDF congeners have vapor pressures below 1 Pa
(Table 3-6), indicating their low volatility. The evaporation rates of different HOCs are directly
proportional to their vapor pressures (Table 3-6), which are inversely proportional to their molecular
weights (Mackay and Leinonen, 1975). A Canadian mid-weight crude oil exposed to the air and flowing
water at low light intensity for 24 days lost all «-alkanes up through Cio and all monoaromatic
hydrocarbons (Wang and Fingas, 1995). Evaporation of 44.5% of the oil mass resulted in a loss of 52%
of the total naphthalenes and 5.7% of the total fluorenes. Less than 1% of the total phenanthrenes,
dibenzothiophenes, and chrysenes were lost by evaporation. Thus, light aliphatic and aromatic
hydrocarbons up to at least dodecane and methylnaphthalene readily evaporate from deposits of
petroleum products on the water surface or on soils and sediments. Evaporation from buried oil deposits
is much slower and depends on the diffusion rate in the sediments (which is a function of porosity and
concentration gradients).
Low-molecular-weight saturated and aromatic hydrocarbons in air remain primarily in the gas
phase and do not adsorb to a significant degree to airborne particles (Dickhut and Gustafson, 1995a,
1995b; Gundel et al., 1995). Gas phase aromatic hydrocarbons are degraded rapidly by photolysis
(Atkinson and Arey, 1994). For example, naphthalene reacts rapidly with atmospheric hydroxyl radical
and has an atmospheric lifetime of approximately 8 hours. However, some of the photolytic reactions of
PAHs in the atmosphere produce mutagenic products (Atkinson and Arey, 1994). Mutagenic products of
PAH photooxidation in the atmosphere include 2-nitronaphthalene,
3-nitrofluorene, and 2-nitrobenzopyranone.
Very little HOCs evaporate from contaminated sediments. However, the presence of a wide
variety of low-molecular-weight PAHs and less chlorinated PCBs in the vapor phase of the atmosphere
indicates that some HOCs do transfer to the atmosphere in the form of vapor or particulate- or aerosol-
associated contaminants (Eitzer and Kites, 1991; Gigliotti et al., 2000; Su et al., 2007). A strong inverse
relationship exists between temperature and the vapor pressure of HOCs. Thus, the rate of evaporation of
volatile hydrocarbons from NAPL on the sea surface, dispersed oil in the water column, or in shoreline
sediments increases sharply as the temperature of the NAPL increases (Reijnhart and Rose, 1982;
Edgerton et al., 1987). Super-cooled liquid vapor pressures from the liquid phase of most PAHs, PCBs,
and PCDDs/PCDFs are much lower than 10 Pa at 25°C (Table 3-6). However, PAHs with molecular
weights up to that of pyrene are volatile enough that a significant fraction of the total PAHs may be
present in the vapor phase of a combustion plume (Gundel et al., 1995) or in the atmosphere. The
concentration of total PAHs (36 measured) in the vapor and particulate fractions of air samples from
Sandy Hook, a coastal community in New Jersey, are 2.8 to 42 ng/m3 and 0.15 to 4.0 ng/m3, respectively
(Gigliotti et al., 2000). The most abundant PAHs in the vapor phase are phenanthrene and
alkylphenanthrenes. The higher-molecular-weight PAHs that are produced by flame pyrolysis are
associated primarily with the BC particulate fraction (Dachs and Eisenreich, 2000). PAHs with molecular
weights lower than that of phenanthrene are present in the winter primarily in the vapor phase of arctic
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haze over northern Canada and Russia; higher-molecular-weight PAHs are bound to BC particles (Halsall
etal, 1997).
PCBs and PCDDs/PCDFs, despite their very low vapor pressures, do evaporate in small
amounts from water, soils, and sediments (Eitzer and Kites, 1989; Bobet et al., 1990; Su et al., 2007). Su
et al. (2007) measured 0.006 to 0.15 ng/m3 total PCBs in air over forested and cleared land in southern
Ontario. Higher concentrations have been recorded in air samples collected at heavily-contaminated
segments of the Snake River. These PCBs probably are derived from evaporation from the soil or water
surface. PCDDs/PCDFs enter the air primarily in emissions from incomplete combustion of municipal
and chemical wastes (Table 3-5) (Brzuzy and Kites, 1996; Friesen et al., 1996; Fiites, 1990). Because of
the steep decrease in vapor pressure with increasing chlorination, only the lower chlorinated congeners
persist in the vapor phase when the combustion gases cool.
In air, the lower chlorinated PCDDs/PCDFs in the vapor phase are removed from the
atmosphere primarily by photodegradation with an estimated half-life in hours (Koester and Kites, 1992;
Mackay et al., 2006). Particle-bound and vapor-phase higher chlorinated PCDDs/PCDFs photolyze more
slowly in the atmosphere, with air residence times of 1 to 3.7 days for some PCDDs/PCDFs (Kwok et al.,
1995). Harrad (1996) reported average concentrations in British urban air samples of 6.1 x 10"6 ng/m3 of
2,3,7,8-TCDD (the most toxic PCDD), 3.3 x 10'5 ng/m3 of 2,3,7,8-TCDF, and 0.11 ng/m3 of 2,2',5,5'-
tetrchlorobiphenyl. Nearly 100% of total PCDDs/PCDFs in the United Kingdom are in soil (99.5%) and
the top 5 cm of fresh water sediments (0.45%). Thus, much of the PCBs and PCDDs/PCDFs in the
atmosphere are derived from combustion emissions and evaporation from soil, surface waters, and
sediments. The atmospheric HOCs are distributed between the vapor and particulate phases in the air,
with higher concentrations of lower-molecular-weight HOCs than higher-molecular-weight HOCs in the
vapor phase.
Sediments act as reservoirs from which PAHs, PCBs, and PCDDs/PCDFs are slowly released
to water via desorption. In water, HOCs can be removed by photodegradation to oxygenated products or
less chlorinated derivatives and/or slow anaerobic/aerobic biodegradation with a half-life time in days
(Travis and Hattemer-Frey, 1987).
3.2.10.2 Electrophilic Substitution. PAHs, PCBs, and PCDDs/PCDFs are extremely stable in the
environment. However, they can undergo three chemical reactions: electrophilic substitution, oxidation,
and reduction. For many years, it was thought these reactions could occur abiotically (in the absence of
metabolic reactions of living organisms). However, there is growing evidence that abiotic chemical
transformation, except photolysis, of organic compounds usually occurs at extremely slow rates, but can
contribute to destruction or alteration of contaminants over years or decades, rates probably too slow for
MNR. However, most of these chemical transformation reactions also can be catalyzed by metabolic
activity of microorganisms in water, soils, and sediments (Vogel etal., 1987). Microbially-mediated
chemical reactions usually are much faster than the corresponding abiotic reactions.
Electrophilic substitution reactions involve exchanging a hydrogen atom on the organic
molecule for a substitute functional group (or alternative electron-rich nucleophile). These substitution
reactions include hydrolysis reactions, conjugation, and other functional group substitutions. Hydrolysis
is a chemical reaction occurring between an organic chemical and water or a hydroxide ion in the
following manner:
R-X + H2O = R-OH + H+ + X (Eq. 3.5)
R-X + OH = R-OH + X (Eq. 3.6)
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Substitution reactions are generally of the following form:
R-X + Nu = R-Nu + X' (Eq. 3.7)
where Nu is a nucleophilic group such as HS"1. Microbially-mediated electrophilic substitution reactions
usually are orders of magnitude more rapid than abiotic reactions under most physical/chemical
conditions in water and sediments (Vogel et al, 1987).
3.2.10.3 Oxidation/Reduction. Because oxidation and reduction reactions require removal or addition
of electrons, external electron acceptors or donors must be present in the reacting system. For example,
carbon atoms in hydrocarbons are in a reduced state and tend to undergo oxidation reactions such as
hydroxylation, halosyl oxidation, and epoxidation. Substituting halogen atoms for hydrogen atoms
effectively increases the oxidation state of the carbon, so highly halogenated organics are more
susceptible to reduction reactions such as hydrogenolysis, dihalo-elimination, and coupling.
Reduction processes of common inorganic electron acceptors in environmental systems (i.e.,
oxidizing agents) include reduction of O2 to H2O, nitrate to nitrite, SO4"2 to HS"1, and 2FT to H2.
Oxidative processes of inorganic electron donors (i.e., reducing agents) that are commonly available in
the environment include oxidation of H2 to 2FT and Fe+2 to Fe(OH)3. Oxidation or reduction of organics
can be carried out directly or catalyzed by microorganisms. Microbial mediated reactions typically occur
at higher rates than abiotic oxidation or reduction reactions.
PCDDs and PCDFs are chemically stable and not likely to be degraded at significant rates by
abiotic hydrolytic reactions under environmental conditions. Juttner et al. (1997) detected similar
concentrations of PCDDs/PCDFs at all levels in dated lake sediments dating back to the 17th century,
implying that little degradation occurred following burial in anoxic sediment layers.
3.2.10.4 Photooxidation. Abiotic photolysis is an effective and sometimes rapid mechanism of
degradation of PAHs, PCBs, and PCDDs/PCDFs in the air, surface waters, or deposits on the surface of
near-surface sediments (Atkinson, 1991; Neff, 2002; Pereira, 2004). PCDDs in soils and shallow-water
surface sediments appear to be resistant to photochemical degradation (Crosby et al., 1971). Photolytic
half-lives of PCDDs/PCDFs in surface waters range from 1 to 225 days in winter and up to 550 days in
surface sediments (Atkinson, 1991). PCBs and PAHs adsorbed to quartz sand and spiked into several
moist soils in the presence of a TiO2 catalyst were photooxidized rapidly when the soil was exposed to
sunlight (Krauss and Wilcke, 2002). Adsorption to SOM inhibited photooxidation. These studies show
that PCBs in surface layers of sediments may be degraded by photooxidation.
Photolysis is the most important degradation mechanism of gas phase PCDDs/PCDFs in the
atmosphere (Deriziotis, 2004). Photolysis rates are highest for the less chlorinated congeners and
decrease with increasing chlorination (Orth et al., 1989). The photolytic half-lives of TCDD and
octachlorodibenzodioxin (OCDD) were 0.4 to 17 hours and 6.8 to 82 hours, respectively, depending on
light intensity (Pennise and Kamens, 1996). Atmospheric PCDDs/PCDFs adsorbed to airborne particles,
particularly soot, are resistant to photolysis (Kwok et al., 1995).
The estimated atmospheric lifetime of vapor-phase PAHs and their photooxidation products
ranges from 1.3 hours for anthracene to 2.7 days for 1-nitronaphthalene (Atkinson and Arey, 1994).
Photodegradation rates of different PAHs vary widely; degradation rates may depend on concentrations of
PAHs and photosensitizers in the oil, and on the physical form of the PAH assemblage (Mill et al., 1981;
Valerio and Lazzarotto, 1985). PAHs bound to soot particles are less sensitive than dissolved PAHs to
photooxidation (Valerio and Lazzarotto, 1985; Kamens et al., 1988). Most dissolved alkyl-PAHs and
heterocyclic compounds are more sensitive to photolysis than are the parent, unalkylated compounds
80
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(Ehrhardt et al., 1992). High molecular weight four- through six-ring PAHs tend to be most sensitive to
photooxidation (Mill etal, 1981). The sensitivity of PAHs in solution to direct photolysis increase s with
increasing molecular weight. For example, the half-life of naphthalene (MW 128.2 daltons) in surface
fresh water in sunlight equivalent to 40°N latitude in mid-summer is 61 hours, compared to a half-life of 8
hours for phenanthrene (MW 178.2 daltons) and 0.54 hours for benzo(a)pyrene (MW 252.3 daltons).
Sorption of PAHs to suspended or bottom sediments or colloids may decrease or increase
photolysis rates. David and Boule (1993) showed that sorption to silica particles increases the rate of
photolysis of anthracene, phenanthrene, and benz(a)anthracene. Ferric oxide, montmorillonite clay, and
cellulose sorbents decrease photolysis rates. The major photolysis products of silica-sorbed PAHs are
quinones and hydrogen peroxide. PAHs associated with suspended particles of soot or CB are relatively
refractory to photooxidation reactions.
3.2.10.5 Advective and Diffusive Transport Affecting Dispersal of Sediment-Associated HOCs.
Dispersion and weathering processes affect the fate of HOCs and complex HOC mixtures (e.g., NAPLs)
in sediments. The most important of these are diffusion, advection, dispersion, and bed (sediment)
transport. Diffusion is the process whereby dissolved species in water are transported by random
molecular motion from an area associated with higher concentrations, usually the contaminated
particle/water interface, to an adjacent area associated with a lower concentration. Diffusion is too slow
to cause significant dispersion of the HOC in the absence of advection. Advection is movement of a
chemical or a mixture vertically or horizontally through an environmental medium, such as sediment, in
association with water movement through the medium. Migration of dissolved HOCs with sediment pore
water flow is considered advection. Dispersion of fine-grained particles and organic colloids in sediment
pore water facilitates the advection of particle-bound, dissolved colloid-bound, and dissolved HOCs, and
NAPLs vertically or horizontally in sediments and into the overlying water column (Neff et al., 1994;
Friedman et al., 2011). Combined diffusion and advection usually result in spreading of subsurface
HOCs and, in the process, the contaminant becomes diluted. Advective dispersion rarely is uniform; the
leading edge of migrating contaminant usually advances more rapidly than the centroid due to the wide
variability in actual permeability and flow paths within the porous medium.
The rate and extent of migration of HOC NAPLs composed of oils, coal tars, or creosote into
and through sediments depends on the viscosity, density, and interfacial tension of the NAPL and the
permeability of the sediment (Strain, 1986; Vandermeulen et al., 1988). NAPLs of crude, refined, and
residual petroleum; creosote; coal tars; and different PCB mixtures (e.g., Aroclors) vary widely in density,
interfacial tension, and viscosity. Most crude and refined/residual oils have densities less than that of
fresh and salt water and, therefore, will tend to migrate to and float on the upper interface of pore water in
periodically emergent sediments (e.g., intertidal zone of the ocean or estuary) and will not penetrate
submerged sediments. PAHs also may partition slowly from a buried oil NAPL and be advected to the
sediment surface by colloid transport or sediment bioturbation (Wilcock et al., 1996; White et al., 2005).
The water table in intertidal marine and estuarine sediments moves up and down with the tides, and
NAPLs can penetrate permeable intertidal sediments during the falling tides and be retarded there by the
reduced sediment permeability caused by the oil. Any upward percolation of water through the sediment
column with the incoming tide or submarine groundwater discharge will tend to push the NAPL upward
in the sediment (Strain, 1986).
Commercial PCB mixtures are denser than fresh water (Table 3-2), and, as a result, an
Aroclor NAPL tends to be advected horizontally or downward in pore water with substantial retardation
due to the high viscosity of the mixtures (Brenner et al., 2004). However, less chlorinated PCBs may
partition from the NAPL and adsorb to fine-grain silt/clay sediment particles and be carried with them to
the sediment surface (Lee et al., 2006). Advection of NAPL or dissolved/dispersed HOCs toward the
surface of fresh water and near-shore sediments also can occur due to upward groundwater flow through
81
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contaminated sediments into the overlying water column. Sediments containing high concentrations of
clay have very low hydraulic conductivities; diffusion and advection of low-molecular-weight, low
viscosity HOCs and NAPLs are very slow in such sediments (Mott and Weber, 1991).
Some heavy crude, residual oils, coal tars, and highly chlorinated Aroclors (classified as
dense non-aqueous phase liquids [DNAPLs]) are denser than water and sink when spilled in open water.
They are so viscous that their rate of penetration into and migration through any but the coarsest sand and
gravel/cobble sediments is very slow (Gerhard et a/., 2007). However, in locations where DNAPLs have
been disposed of or spilled on land or in disposal sumps, there may be substantial migration of even the
heavy compounds, viscous oil, tar, PCBs, and organic solvents through sand/silt sediment columns.
Sediments composed of fine-grained silt or clay particles, as well as sandy sediments containing more
than a few percent silt/clay, have very low permeabilities (Scheidegger, 1957).
Advective transport, dispersion, and diffusion rates for dissolved HOCs in soils and
sediments are controlled by the permeability of the media and the sediment-water partitioning behavior of
the HOCs and site sediment particles. As discussed above, the strength of HOCs binding to sediment is
dependent on the quality and concentration of organic matter in the sediment. Natural capping by fine-
grained silt/clay sediments may provide a barrier to the upward migration of HOCs (Brenner et al., 2004);
the effectiveness of that sediment barrier depends on the properties and thickness of the natural capping
material. Therefore, knowledge of sediment pore water flow characteristics is needed to evaluate the
significance of a contaminant's advection and diffusion characteristics and its potential to transport HOCs
laterally out of the contaminated site or vertically into the water column. The rate of migration of
dissolved HOCs is slow relative to groundwater transport due to interactions between the dissolved HOCs
and the soil or aquifer solids. Retention of the HOCs by the aquifer or sediment materials occurs when
the contaminant is sorbed onto the substrate (particularly clay-sized sediment particles), sediment organic
particles, or organic coatings on fine-grained sediment particles. The contaminant velocity can be
estimated using the groundwater flow rate at the site and the retardation factor for the contaminant of
interest (Domenico and Schwartz, 1998). The retardation factor, R, is derived by the following equation:
R=l+pbKoc/n (Eq. 3.8)
where, pb is the bulk or mass density of the porous medium, n is the porosity of the medium, and Koc is
the sediment organic carbon/water partition coefficient (see Section 3.2.3 for discussion of Koc). Table
3-8 summarizes the estimated retardation factors for several PAHs in soils or sediments containing
different concentrations of TOC. The two sediments used in the study contained either low
concentrations of TOC (0.1%, similar to 'cleaned' or washed sand) or relatively high concentrations of
TOC (5.0%, similar to nearshore sediments). These data clearly demonstrate the profound effect of
partitioning to organic fractions in contaminated sediments on the migration of sediment-associated
HOCs. From these data, it can be surmised that PAHs and other HOCs with R values greater than about
100 will migrate through sediment dissolved in pore water very slowly, if at all. High-molecular-weight
HOCs with high log Koc values (> 4.0 mL/g) generally will not migrate rapidly through sediments
containing higher than trace concentrations of TOC. This includes all PCB and PCDD/PCDF congeners;
they have log Kow values ranging from 4.3 to 8.2 (Mackay et al., 2006) (Tables 3-3 and 3-6).
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Table 3-8. Log Koc, Kd, and Retardation Factors (R) for Several PAHs in Sediments
Containing 0.1% or 5% Organic Carbon
PAH
Naphthalene
2-Methylnaphthalene
Dibenzothiophene
1 -Methylphenanthrene
Benz(a)anthracene
5 -Methylchry sene
Benzo(a)pyrene
Log Koc
(mL/g)
3.11
3.93
4.87
4.93
6.14
6.21
6.74
Kd (mL/g)
(0.1% OC)
1.30
8.50
74.13
85.11
1,380
1,622
5,500
R
(0.1% OC)
9
52
446
512
8,281
9,732
33,000
Kd (mL/g)
(5% OC)
65
425
3,707
4,256
69,000
81,091
275,000
R
(5% OC)
391
2,551
22,240
25,535
414,000
486,544
1,650,000
The sediment used to generate these data had a density of 1.80 g/cm and a porosity of 0.30.
Source :Neffe/ al., 1994
As discussed above, natural organic colloids in sediments and the water column have a high
affinity for absorption and binding of HOCs (Gschwend and Schwarzenbach, 1992). Because colloids
behave like dissolved macromolecules, they are readily transported by diffusion, advection, and
dispersion through sediment pore water flow. Transport of HOCs adsorbed to colloids differs from
transport of HOCs that are not sorbed. Despite their high Koc values, high-molecular-weight HOCs can
migrate through and out of sediments adsorbed to colloids. The retardation factors of HOCs adsorbed to
organic colloids are much lower than those of HOCs in solution in pore water. The reduction of the
retardation factor is approximately proportional to the ratio of the Koc colloid to the Koc. Thus, it is
important to measure colloid concentrations and their effect on HOC migration in site sediment when
performing a site-specific ecological risk assessment.
Although diffusion is a relatively slow process, diffusion-driven mass transport always occurs
if concentration gradients are present. For example, a diffusive flux for PCBs from sediments was
estimated for a 6.75-mile stretch of the Grasse River near the confluence to the St. Lawrence River at 22
g/d for the entire area (Ortiz et al., 2004). This estimate was calculated using a mass transfer coefficient
of 0.02 m/d and diffusion rate to show that diffusive flux was a significant mechanism for PCB transport
to the water column. Consequently, diffusion can produce a significant flux of contaminants through a
saturated porous medium in the absence of advection (Palermo et al., 1998), although diffusion is the
slower of these two processes. Thus, the mass flux of interest to MNR is usually driven by advection
(Fetter, 1994). Diffusion is expected to be significant for contaminated sediment layers that are in direct
contact with the water column, whereas burial of contaminated sediments will retard the diffusive
transport of contaminants to the water column. It should be noted that as contaminated sediments recover
(i.e., become less toxic), recovering populations of benthic organisms can mix sediments by bioturbation,
causing rates of advective transport to increase.
3.3
Biological Transformation of HOCs in Sediments
3.3.1 PAH Biodegradation. Nearly all fresh water and marine water bodies and sediments contain
populations of bacteria and fungi that are capable of biodegrading bioaccessible PAHs, PCBs, and
PCDDs/PCDFs to some degree (Leahy and Colwell, 1990; Abramowicz, 1995; Barkovski and Adriaens,
1996; Bressler and Gray, 2003). Water column and sediment bacteria and fungi exhibit considerable
diversity and adaptability in utilizing different types of organic molecules as a sole or supplemental
carbon source. Thus, after a period of time through the activity of local microbial communities,
sediments may be rendered non-toxic even though some HOCs are still present. Many groups of
microorganisms oxidize saturated hydrocarbons and, to a lesser extent, most low-molecular-weight
aromatic hydrocarbons and heterocyclic compounds completely to carbon dioxide during heterotrophic
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respiration (Bressler and Gray, 2003). Selected soil and sediment fungi are adapted to metabolizing plant
lignins, rich in aromatic structures and, thus, possess the enzymatic assemblage necessary to degrade
some PAHs completely to carbon dioxide (CO2) and water (Field et al, 1992; Cutright, 1995).
Weathering of petroleum in sediments usually results in a decrease in sediment toxicity due
to a decrease in the concentration of bioaccessible and bioavailable hydrocarbons, including PAHs and
their degradation products (Jonker et al., 2006). Often, however, PAHs and heterocyclic compounds are
metabolized only partially, yielding a variety of polar, oxygenated metabolites (Fedorak and Westlake,
1984; Bossert and Bartha, 1986; Kuhn and Suflita, 1989; Qiu and McFarland, 1991), some of which can
be more toxic than the parent PAHs. A variety of ketones, quinones, and organic acids may be formed
during incomplete biodegradation of high-molecular-weight PAHs in sediments and water (Pothuluri and
Cerniglia, 1994; Wischmann and Steinhart, 1997). In some cases, however, reactive intermediates of
PAH biodegradation become covalently and irreversibly bound to sediment organic matter (Qiu and
McFarland, 1991), ultimately mitigating the toxicity of these weathering products by rendering the
covalently bound adducts non-bioavailable.
Following contamination of sediments with petroleum, coal tar, or creosote, different
hydrocarbon classes are simultaneously or sequentially degraded by indigenous microbiota in sediments,
but at widely different rates (Atlas, 1995; Gough et al, 1992; Kaplan et al., 1996; Oudot et al, 1998;
Brassington et al, 2007). Low-molecular-weight «-alkanes with chain lengths of 10 to 22 carbons are
metabolized most rapidly, followed by isoalkanes and higher-molecular-weight «-alkanes, olefins,
monocyclic aromatic hydrocarbons, PAHs, and, finally, highly-condensed cycloalkanes, resins, and
asphaltenes. Degradation of some sulfur heterocyclics, such as dibenzothiophene and its alkyl
homologues, seems to require mixed microbial assemblages and co-substrates (Kropp et al, 1994;
Dyreborg et al, 1996). Usually these mixed microbial assemblages and co-substrates are present in oil-
contaminated sediments where dibenzothiophenes biodegrade at rates similar to those for PAHs of similar
molecular weight (Douglas et al, 1996; Burns et al, 1997). Following the Exxon Valdez oil spill, ratios
of various alkyl dibenzothiophenes to alkyl phenanthrenes in marine sediments remained stable for
several years and were useful for identifying the spilled oil in environmental samples (Douglas et al,
1996).
Highly-branched or cyclic alkanes and alkenes are resistant to biodegradation and tend to be
persistent in sediments (Robson and Rowland, 1987; Wang and Stout, 2007). Alkanes from Cn to C35
and PAHs from naphthalene to dimethylfluorenes were biodegraded nearly completely in bench-scale
microcosm incubation experiments during 1 month of exposure of a weathered light Arabian crude oil to
sea water containing a natural assemblage of marine bacteria (Dutta and Harayama, 2000). Only C4-
dibenzothiophenes and C6- and C7-phenanthrenes were resistant to biodegradation under these
experimental conditions. Crude oils and some heavy fuel oils contain a wide variety of complex cyclic
alkanes (steranes and triterpanes) derived from plant precursors during petrogenisis in source rocks.
These hydrocarbons are resistant to biodegradation in surface sediments and, therefore, are frequently
used as biomarkers of the sources and weathering of the petroleum residues in sediments (Wang et al,
2006).
Some high-molecular-weight PAHs, such as chrysene, dibenzanthracene, and perylene, are
degraded only very slowly in a variety of sediments (Bossert and Bartha, 1986; Heitkamp and Cerniglia,
1987). In 9 months, 40 ± 7% of total oil, 83 ± 6% of aliphatic hydrocarbons, 49 ± 10% of cyclic alkanes,
and 55 ± 18% of aromatic hydrocarbons were biodegraded in intertidal sediment plots contaminated with
a weathered, emulsified light Arabian crude oil (Oudot et al, 1998). Resins and asphaltenes were not
biodegraded during this time. However, after the 1991 Gulf War, indigenous microbes from waters and
sediments of the western Arabian Gulf degraded PAHs and related sulfur-containing heterocyclic
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compounds more rapidly than they degraded normal alkanes under low nutrient conditions (Fayad and
Overton, 1995).
Differences between prokaryote (bacteria) and eukaryote (algae, fungi, and higher organisms)
metabolic pathways for degrading PAHs may be important with respect to production of mutagenic or
carcinogenic byproducts. In bacteria, a dioxygenase enzyme system incorporates two oxygen atoms into
the aromatic ring structure to form a dioxethane intermediate (Figure 3-5). The dioxethane is oxidized
further to a c/s-dihydrodiol and then to various dihydroxy products, the most common of which are
catechols (Cerniglia, 1993; Wilson and Jones, 1993: Pothuluri and Cerniglia, 1994; Sutherland et al,
1995). These intermediates are not mutagenic or carcinogenic.
Most eukaryotes use a monooxygenase system, the cytochrome P450 MFO system, to
incorporate one oxygen atom into the aromatic ring structure to form an arene oxide, which either
isomerizes spontaneously to form a phenol, or is hydrated by epoxide hydrase to form a fra«s-dihydrodiol
or a phenol (Cerniglia, 1993; Sutherland et al., 1995) (Figure 3-5). As with prokaryotes, the dihydrodiol
may be oxidized to a dihydroxy product, such as a catechol.
Fungi, algae
02
Cyt p-450/Me!hane
Monooxygenase
White rot fungi
Polycyclic ^^^ H2O2
hydrocarbon'p^'ijgnin peroxidases,
laccases
Bacteria, algae
2^—
~V\C. OH S
xl^ 0^:
aH ^^^ Phenol
^^?O
R HA^SL H
^•0/%^ (f^l ,,,|OH
%® Vt*OH
R H
frans-Dihydrodio!
qumones
c/s
O-Glucoside
O-Glucuronide
O-Sulfate
O-Xyloside
Ring fission
c;s-Muconic
acid
r^COOH
WCOOH
H NAD* Orthoffission
^f-«iOH \ _ ^s-
•
OH
CatpchoiA
Dioxygenase
R
l_l Dehydrogenase R
ds-Dihydrodiol
I
alfi
Tc
Metalfission
'cHO
NADH+H
OH
i \
2-Hydroxyrnuconic
semialdehyde
Figure 3-5. Initial Steps in Biodegradation of PAHs by Prokaryotes (Bacteria) and Eukaryotes
(Fungi, Algae, Plants, Animals)
(Traws-dihydrodiols of some higher-molecular-weight PAHs produced by eukaryotes are
carcinogenic [redrawn from Cerniglia, 1993].)
85
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In those PAHs that can be enzymatically activated to mutagens or carcinogens (selected 4-
through 6-ring PAHs, such as benz[a]anthracene, benzo[a]pyrene, and dibenz[a,h]anthracene)
(Table 3-4), the reactive metabolites that interact with cellular DNA to induce cancer or mutation are
fra«s-diol-epoxides (Hall and Grover, 1990; Guengerich, 1992; Luch and Baird, 2005). For example, the
most carcinogenic metabolite of benzo(a)pyrene is benzo(a)pyrene-7,8-diol-9,10-oxide (Hall and Grover,
1990). The prokaryotic (bacterial) pathway does not produce arene oxide and epoxide intermediates;
therefore, the products of bacterial degradation of PAHs are less mutagenic and carcinogenic than the
products of PAH biodegradation by fungi and higher organisms.
Crude oil contains only traces of mutagenic/carcinogenic PAHs. Most of the PAHs in crude
oil are low-molecular-weight, 2- and 3-ring aromatics, related heterocyclic aromatic compounds (e.g.,
dibenzothiophene), and their alkylated homologues. These compounds are toxic but not
mutagenic/carcinogenic (Neff, 2002; Di Toro et al., 2007; McGrath and Di Toro, 2009). Microbial
degradation of these low-molecular-weight PAHs produces a variety of phenols, quinones, dihydrodiols,
alcohols, and acids as short-lived intermediates (Liu etal, 1992; Volkering and Breure, 2003). Some of
these intermediates are more toxic than the parent compounds (Aprill et al., 1990; Wang et al., 1990;
Belkin et al., 1994; Hund and Traunspurger, 1994); however, these intermediates do not persist long in
aerobic water and sediments (Heitkamp and Cerniglia, 1987). Ultimately, the low- and medium-
molecular-weight aromatic hydrocarbons in petroleum are degraded to low-molecular-weight organic
acids, most of which are not toxic. Under oxidizing conditions, these organic acids are degraded rapidly
by sediment microbiotato CO2 and water and can serve as important nutrients (primarily as a readily
degradable form of carbon) for sediment microorganisms.
All available evidence indicates that rates of hydrocarbon degradation are much lower under
anoxic than under aerobic (oxygen-rich) conditions, even though many species of bacteria and fungi are
able to metabolize petroleum hydrocarbons, including PAHs and heterocyclic compounds, in the absence
of oxygen (Johnston, 1970; Bauer and Capone, 1985; Kuhn and Suflita, 1989; McFarland and Sims,
1991; Karthikeyan and Bhandari, 2001; Rothermich et al., 2002; Seo et al., 2009). Part of the negative
effect of low oxygen concentrations on the microbial degradation of hydrocarbons in sediments may be
related to availability of primary nutrients (nitrogen and phosphorus). Mineralization of NOM, resulting
in release of inorganic nitrogen and phosphorus, is very slow in anoxic sediments. Hydrocarbons being
rich in carbon but deficient in nitrogen and phosphorus are an inadequate nutrient source but a rich carbon
source for microbes in the absence of exogenous sources of available primary nutrients. Addition of
primary nutrients may stimulate hydrocarbon degradation in anoxic and hypoxic sediments (Scherrer and
Mille, 1989; Mills et al., 2004). Excess nutrient addition, however, may favor alkane over PAH
biodegradation. During early attempts to bioremediate oiled sediments on the shore following the Exxon
Valdez oil spill, the mineralization potential of indigenous bacteria for aromatic hydrocarbons was high;
however, a year later (after extensive application of fertilizers), the mineralization potential was higher for
alkanes than for aromatic hydrocarbons (Sugai et al., 1997).
Availability of a suitable alternative to oxygen as the electron acceptor may also limit PAH
degradation in anoxic sediments. The predominant electron acceptor processes in anoxic sediments are
iron-, manganese-, and sulfate-reduction and methanogenesis; sulfate is abundant in sea water and marine
sediments, so sulfate-reduction, mediated by anaerobic sulfate-reducing sediment bacteria, is the
quantitatively most important source of electrons for hydrocarbon oxidation in anoxic marine sediments
(Ashtok and Saxena, 1995; Sharak Genthner etal., 1997; Rothermich etal., 2002). Coates etal. (1996,
1997) reported that naphthalene, methylnaphthalene, phenanthrene, fluorene, and fluoranthene, but not
pyrene and benzo(a)pyrene, were degraded to CO2 and water under sulfate-reducing conditions in heavily
contaminated sediment from San Diego Bay, CA. PAH oxidation likely is performed by anaerobic,
sulfate-reducing bacteria (SRB) in these sediments. Biodegradation of naphthalene in anaerobic sediment
columns is more efficient when sulfate rather than nitrate or manganese is available as the electron
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acceptor (Langenhoff et al., 1996). Sharak Genthner etal. (1997) reported only limited PAH
biodegradation under nitrate-reducing, sulfate-reducing, and methanogenic conditions in creosote-
contaminated anaerobic sediments. A wide variety of organic acids accumulate in small amounts in
petroleum-contaminated groundwater as a result of anaerobic microbial degradation of saturated and
aromatic hydrocarbons under anoxic conditions (Cozzarelli et al., 1994). Thus, hydrocarbons including
PAHs from oil, coal tar, creosote, and other combustion sources may be persistent in anoxic or hypoxic
sediments, particularly if the sediments are fine-grained and contain high concentrations of organic matter
(Burns etal., 1993; Floodgate, 1994; Xia and Wang, 2008). The degradation rate of low-molecular-
weight PAHs as reported for the sediments evaluated in these studies was inversely related to sediment
organic carbon concentration (Hinga, 2003) and also to the concentration of expandable clay in the
sediments (Hwang and Cutright, 2003).
The main limitation on both the degradation rate and extent for PAHs in sediments is
bioaccessibility and bioavailability (Weissenfels et al., 1992; Cerniglia, 1993; Richnow etal., 1993;
Bressler and Gray, 2003). PAHs, in particular the higher-molecular-weight 4- through 6-ring PAHs
(including the pro-carcinogens [PAHs that can be activated to carcinogens enzymatically, as described
above]), have low solubilities and tend to bind strongly to sediment organic matter (Tan and Tomson,
1990; Amy et al., 1991; Scheunert etal., 1992; Weissenfels et al., 1992; Richnow etal., 1995;
Carmichael et al., 1997; Reemtsma and Mehrtens, 1997) or polymerize to form complex new compounds
that can be found in the asphaltene phase of the weathered oil (Bossert and Bartha, 1984; Qiu and
McFarland, 1991). As part of the asphaltene phase, these contaminants are not bioavailable to the local
microbial communities for biodegradation. Richnow et al. (1993) reported that a large fraction of the
PAH metabolites produced by microbes during degradation of an oil in contaminated soil from Germany
were covalently bonded to soil humic substances through stable ester bonds. Selective chemical
degradation methods were required to release the PAH metabolites from the soil humic substances that
were tightly sorbed to the soil humic material. These covalently-bonded or physically-sorbed PAHs had
low bioavailability and toxicity. Sorption of low-molecular-weight PAHs, such as naphthalene, to
organic colloids in sediments does not affect their bioavailability or rate of microbial degradation
(Meredeth and Radosevich, 1998); however, this trait could be due to the low relative mass of PAHs
sorbed to colloids relative to that sorbed onto solids. It is widely accepted that only desorbed (dissolved)
PAHs are bioavailable for biodegradation by sediment microorganisms (Bressler and Gray, 2003). This
appears to be true for PAHs that are tightly bound to BC (and unavailable) in sediments (Beckles et al.,
2007). However, Xia and Wang (2008) demonstrated that hydrocarbon degradation was more rapid at the
sediment particle-water interface than in the aqueous, pore water phase in sediments. Bressler and Gray
(2003) showed that bacteria on the surface of hydrated sediment particles are able to translocate and
metabolize lightly adsorbed PAHs. Thus, sorption of PAHs and other hydrocarbon-derived HOCs may
not completely inhibit biodegradation and bioavailability.
PAHs are potentially bioavailable to sediment microorganisms and higher benthic and water-
column organisms when they remain in the oil phase (NAPL) in the sediment; their bioavailability is
reduced when the oil weathers, increasing its viscosity, as described above; the PAHs are covalently
bound to the asphaltene phase; or the oil weathers to a solid asphaltic mass. However, if PAHs are
leached by water-washing of the oily sediment into pore water or surface water, they tend to sorb to
natural sediment organic matter, mostly high-molecular-weight humic materials. After long equilibration
times, these sorbed PAHs become tightly bound to the sediment organic fraction and become less
bioavailable to sediment organisms. PAH metabolites produced by bacteria and fungi, as well as
photooxidation products of hydrocarbons, are highly reactive and tend to react or complex with sediment
organic matter. Some PAH degradation products become covalently bound to sediment organic matter
and can only be analytically detected by destructive pyrolysis of the sediment organic matter (Richnow
et al., 1995). This tight binding or sequestration of PAH degradation products in the sediment has the
effect of decreasing their bioavailability, microbial metabolism, and toxicity to sediment plants and
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animals (Alexander, 1995). Thus, after a period of time, the sediment may become non-toxic due to low
PAH bioavailability even though PAHs may still be present at concentrations exceeding established
threshold values.
The scientific literature contains several estimates of the rates of degradation of petroleum
hydrocarbons, including PAHs, in water and sediments. Atlas and Bronner (1981) estimated that
petroleum hydrocarbons from the 1978 Amoco Cadiz crude oil spill were being degraded at a rate of
1.5 g/m2/month in the upper 5 cm of intertidal sediments along the Brittany coast of France. After
8 years, concentrations of alkanes that were originally present at concentrations of 2,400 to 17,000 |o,g/g,
and aromatic hydrocarbons that were originally present at concentrations of 740 to 5,500 |o,g/g, had
dropped to near background levels in soft intertidal sediments near the spill site (Page et al, 1989).
Massie et al. (1985) estimated that the rate of degradation of naphthalene in off-shore marine sediments
near oil production platforms ranges from 0.06 to 93.6 g/m2/month; the degradation rate of
benzo(a)pyrene rarely exceeds 0.8 g/m2/month.
About 40% of the crude oil spilled from the tanker Exxon Valdez in Prince William Sound,
AK, in March 1989 was washed onto the coarse boulder/cobble/gravel shore sediments and accumulated
in subsurface deposits. The buried oil weathered rapidly in the first few years after the spill. Boehm et
al. (1995) estimated half-lives for total petroleum PAHs in intertidal sediments during the year and a half
after the spill of 2 months in the upper intertidal zone and 3.8 months in the lower intertidal zone. By the
summer of 1991, 2 years after the spill, PAH degradation half-lives had increased to the range of 7.4 to
16.0 months, likely due to reduced leaching and bioavailability of PAHs from highly weathered oil in
shoreline sediments. In the year after the spill, the oxidation rate potential for PAHs by microbial
communities in intertidal sediments ranged from 5 to 30 |o,g PAHs mineralized/g dry sediment/day (Sugai
et al., 1997). Numbers of hydrocarbon-degrading bacteria increased in intertidal and subtidal sediments
from the spill zone during the year after the spill (Braddock et al, 1995). Some oil deposits on the shore
developed an asphalt-like coating (Ramaswami et al., 1994) or sank into coarse upper intertidal sediments
and were protected from weathering by an armor of rocks and boulders. PAHs in these deposits were
nearly immobile, and biodegradation was very slow. Percent loss relative to the original total PAHs from
buried oil residues on the shore increased slowly from an average of 66% in 1989-1993 to an average of
82% in 2004-2006 (Atlas and Bragg, 2007). Most of the oil residues on the shore within a few years after
the spill had >70% PAH depletion, and the PAH fractions were resistant to further biodegradation.
3.3.2 PCB Biotransformation. The compositions of PCB congener mixtures present in the
environment differ substantially from those of the original, technical Aroclor mixtures released to the
environment. This is because several processes collectively referred to as "environmental weathering"
change the composition of PCB mixtures over time after release into the environment. This weathering is
a result of the combined effects of processes such as differential volatilization, solubility, sorption,
anaerobic dechlorination, and aerobic and anaerobic biodegradation; these processes result in changes in
the composition of PCB mixtures overtime and differences in PCB compositions during trophic transfer
and biomagnification in marine food webs (Froese et al., 1998). Generally, less-chlorinated PCBs are lost
most rapidly due to volatilization and metabolism, while more-highly-chlorinated PCBs often are more
resistant to degradation and volatilization and sorb more strongly to particulate matter (Abramowicz,
1995). More-highly-chlorinated PCBs tend to bioaccumulate to a greater degree than do less-chlorinated
PCBs in tissues of animals and have a high potential to biomagnify in fresh water and marine food webs
(Hoekstrae/'a/., 2003).
Biodegradation of PCBs in sediments requires consortia of microorganisms with broad
specificity and modes of oxidative or reductive attack (Seo et al., 2009). There are three general ways
that PCBs are biodegraded:
-------
• Aerobically as a growth substrate: aerobic bacteria degrade less-chlorinated PCB
congeners as a source of biomass accretion (Costa et al, 2004).
• Aerobically by cometabolism: aerobic bacteria or fungi use non-polar organic substrates
as a primary energy source and, in the process, biodegrade some PCB congeners
(Abramowicz, 1995).
• Anaerobically: sediment microbes reductively dechlorinate more-highly-chlorinated
PCBs by replacing chlorines on the biphenyl skeleton with hydrogens (Brown et al.,
1987; Zanaroli et al, 2006; Bedard, 2008).
Several aerobic sediment microorganisms can completely mineralize some less-chlorinated
PCB congeners through aerobic oxidation (Abramowicz, 1995; Van Briesen et al, 2004). More-highly-
chlorinated PCB congeners can be reductively dechlorinated by anaerobic bacteria, including sulfate-
reducing and methanogenic bacteria, in anoxic sediments producing less chlorinated mono-, di-, and tri-
chlorobiphenyls (Brown et al, 1987; Zanaroli et al, 2006). If conditions are right, complete removal of
PCBs from contaminated sediments can be accomplished by sequential activities of anaerobic and aerobic
bacteria (Evans etal, 1996). Gradients of oxygen concentration with depth in sediment control the
oxidative and reductive processes (Van Briesen et al, 2004), with most degradation occurring in the
transition region of the sediment column, I.e., the redox potential discontinuity where redox potential (Eh)
is near zero.
The more-highly-chlorinated PCB congeners are reductively dechlorinated in the suboxic
layers of sediment below the redox potential discontinuity. Anaerobic bacteria couple oxidation of
sediment organic matter to reductive dechlorination of PCB congeners serving as the electron acceptor.
Under aerobic conditions, the less-chlorinated PCB congeners are metabolized by aerobic
microorganisms with the PCB congener as the sole or secondary carbon source and electron acceptor.
The major limitation to these processes being carried out concurrently in time and space is
that one process requires oxygen and the other is inhibited by the presence of oxygen. The sequential
oxidative and reductive degradation of PCBs can take place at and just above and below the redox
potential discontinuity in the upper layer of sediments, particularly if microscopic algae or aquatic plants
occupy the near-surface layers of the sediments. The plants cause the depth of the redox potential
discontinuity to sink deeper in sediments during the day when oxygen is released into the sediments by
photosynthesis and to rise toward the surface at night when plant respiration removes oxygen from the
sediments (Catallo, 1999; Aldridge and Ganf, 2003).
Aerobic PCB biodegradation involves the initial oxidation of the biphenyl nucleus by the
addition of oxygen at the 2,3-position by a 2,3-dioxygenase enzyme, with subsequent dehydrogenation of
the catechol followed by ring cleavage (Harkness et al, 1993; Abramowicz, 1995). This pathway leads to
the production of chlorobenzoic acid intermediates that can build up and inhibit pure cultures of PCB
degrading organisms, but that are readily hydrolyzed by other aerobic bacteria in diverse microbial
communities.
Aerobic biodegradation of PCBs usually is limited to the congeners containing fewer than
four chlorine atoms, but a few strains of aerobic bacteria have the ability to degrade tetra-, penta-, and
hexa-chlorobiphenyls (Bedard etal, 1986, Abramowicz, 1990). Because the majority of congeners in
Aroclors 1221, 1232, 1016, and 1242 contain fewer than four chlorine atoms, it is possible to demonstrate
significant levels of PCB mass removal with aerobic biodegradation. In fact, reductions of between 50%
and 85% by mass have been reported in sediments contaminated with up to 1,000 parts per million (ppm)
of Aroclors 1221 through 1248. One study reported a 67% drop in the molar concentration of weathered
Aroclor 1248 from soil slurry microcosms with low organic carbon content (Evans et al, 1996). The
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main disadvantages of aerobic PCB biodegradation include: 1) the inability of microorganisms to break
down the more-highly-chlorinated, more toxic congeners, and 2) the limited availability of oxygen in
most contaminated sediment environments, rendering most sediment layers anaerobic at depths more than
a few millimeters from the sediment-water interface. The absence of or very slow aerobic biodegradation
of more highly-chlorinated PCBs results in their persistence in aerobic environments.
In contrast to aerobic biodegradation that destroys PCBs via ring cleavage, microbially-
catalyzed anaerobic, reductive dechlorination simply removes chlorine substituents from PCBs,
particularly from the more highly-chlorinated congeners with three or more chlorines. In general,
reductive dechlorination preferentially removes chlorines from the meta and para positions and replaces
them with hydrogen atoms, resulting in depletion of highly-chlorinated PCB congeners accompanied by
an increase in concentrations of less-chlorinated ort/2o-substituted PCB congeners (Natarajan et al., 1998;
Seeger et al., 2001; Zanaroli et al., 2006; Bedard, 2008). Because the most toxic PCB congeners are
those with chlorines in the meta and para positions in the biphenyl molecule and no more than two
chlorines in the ortho position (coplanar PCBs), reductive dechlorination causes a substantial reduction in
carcinogenicity and dioxin-like toxicity of PCB mixtures in sediments (Quensen et al., 1998;
Abramowicz, 1995; Zanaroli et al., 2006). Dechlorination contributes to detoxification of PCB mixtures
primarily through two mechanisms: 1) elimination of coplanar congeners that may exhibit dioxin-like
toxicity (McFarland and Clarke, 1989; NRC, 2001; Quensen et al, 1998), and 2) the transformation of
generally more-toxic, higher-chlorinated congeners to less-toxic, less-chlorinated congeners (ATSDR,
2000).
The less-chlorinated PCBs also have a lower potential than do more highly-chlorinated PCBs
for bioaccumulation. For example, 2-chlorobiphenyl and 2,2-dichlorobiphenyl display an approximately
450-fold decrease in the tendency to bioaccumulate in fish compared with tri- and tetra-chlorinated PCBs
(Abramowicz and Olson, 1995). Furthermore, the PCB mixture becomes more susceptible to aerobic
degradation, and by lowering the chlorination level of the mixture, the tendency of the mixture to
bioaccumulate is also reduced.
Several approaches have been attempted to enhance the microbially-catalyzed reductive
dechlorination of PCBs. Researchers have attempted to stimulate dechlorination by amending
microcosms with carbon substrates (e.g., fatty acids, glucose, methanol). Amendment of anaerobic
bacterial consortia in sediments resulted in shortened lag times or increased initial rates of dechlorination,
but the overall extent of PCB dechlorination was not significantly increased (Abramowicz and Olson,
1995). Amendment with glucose or methanol stimulated preferential dechlorination of highly-chlorinated
(>5 chlorines) PCBs in lake sediments (Natarajan et al., 1998). Others have attempted to stimulate
dechlorination by adding individual poly chlorinated or polybrominated congeners to microcosms. The
process is designed to selectively enhance populations of organisms that can use the supplied congener as
an electron acceptor. In one instance, this strategy reduced hexa- through nona-chlorobiphenyls by 79%
in sediments contaminated with Aroclor 1260; the resulting dechlorination products were predominately
tri- to penta-chlorobiphenyls (Abramowicz and Olson, 1995). This approach may not be applicable to /'«-
situ sediment remediation efforts due to the potential for release of halogenated biphenyls into the
environment, but these results do suggest that PCB dechlorination can be stimulated.
Although PCBs in sediments can be biodegraded by aerobic and anaerobic bacteria, the rates
of degradation are too slow, even under nutrient stimulation, to allow adequately rapid attenuation by
MNR alone. Sinkkonen and Paasivirta (2000) estimated the degradation half-lives of PCDDs, PCDFs,
and PCBs in air, soils, and sediments (Table 3-9). Estimated half-lives in sediment of different PCB
congeners ranged from 26,000 hours for 2,4,4'-trichlorobiphenyl to 333,000 hours for 2,2',3,4,4',5,5'-
heptachlorobiphenyl. The most toxic PCB congener (PCB 126) has an estimated half-life in sediment of
87,600 hours (10 years).
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Table 3-9. Estimated Half-Lives (hours) for Selected PCBs, PCDDs, and PCDFs in Sediments
Compound
Half-Life
Compound
Half-Life
Polychlorinated Biphenyls (PCBs)
2,4,4'-Trichloro-BP (PCB 28)
3,3',4,4'-Tetra-BP (PCB 77)
2,3',4,4'5-Penta-BP (PCB 118)
2,3',4,4',5-Penta-BP(PCB 126)
26,000
87,600
60,000
87,600
2,2',4,4'5'-Penta-BP (PCB 138)
2,2',4,4',5,5'-Hexa-BP(PCB 153)
3,3',4,4',5,5'-Hexa-BP (PCB 169)
2,2',3,4,4'5,5'-Hepta-BP (PCB 180)
165,000
165,000
165,000
333,000
Polychlorinated Dibenzo-p-Dioxins (PCDDs)
2,3,7,8-Tetra-CDD
1,2,3,7,8-Penta-CDD
1,2,3,4,7,8-Hexa-CDD
900,000
1,000,000
2,400,000
1,2,3, 6,7,8- Hexa-CDD
1,2,3,7,8,9- Hexa-CDD
Octachloro Dibenzodioxin
550,000
700,000
1,300,000
Polychlorinated Dibenzofurans (PCDFs)
2,3,7,8-Tetra -CDF
1,2,3,7,8-Penta-CDF
2,3,4,7,8- Penta-CDF
550,000
450,000
550,000
1,2,3,4,6,7,8-Hepta-CDF
1,2,3,4,7,8,9- Hepta-CDF
Octachlorodibenzofuran
350,000
200,000
250,000
Compounds with dioxin-like toxicity (Table 3-4) are highlighted. Source: Sinkkonen and Paasivirta (2000)
3.3.3 PCDD and PCDF Biotransformation. PCDDs/PCDFs in sediments are primarily tightly
adsorbed to organic particles, including BC. These adsorbed PCDDs/PCDFs are chemically stable and
immobile and have low bioaccessibility to sediment-dwelling bacteria and fungi. The estimated half-lives
of different PCDD/PCDF congeners in sediment range from 200,000 hours (22.8 years) for 1,2,3,4,7,8,9-
heptachlorodibenzofuran to 2,400,000 hours (274 years) for 1,2,3,4,7,8-hexachlorodibenzodioxin (Table
3-9). The most toxic PCDDs (2,3,7,8-tetrachlorodibenzodioxin and 1,2,3,7,8-pentachlorodibenzodioxin)
have half-lives in sediments of 900,000 to 1,000,000 hours (Table 3-9). An earlier estimate of the half-
life of 2,3,7,8-tetrachlorodibenzodioxin was 12,000 to 144,00 hours based on oxidation (Ward and
Matsumura, 1978), indicating the importance of mixed oxidation/reduction in mineralization of these
compounds.
Several strains of sediment bacteria and fungi that are capable of oxidizing or dechlorinating
most PCDD/PCDF congeners have been isolated. Species such as Sphingomonas wittichii, Pseudomonas
veronii, Cordyceps sinensis, and Dehalococcoides spp. are capable of enzymatically degrading PCDDs,
and S. wittichii has been used successfully to bioremediate contaminated fly ash (Simon, 2006; Johnson,
2008). Biodegradation pathways for PCDDs/PCDFs are similar to those discussed above for PCBs.
There are several pathways of aerobic oxidation and anaerobic reductive dechlorination of PCDD/PCDFs
by bacteria and fungi. Sediment bacteria and fungi use several enzymatic mechanisms to biodegrade
PCDD/PCDFs (Chan, 2008). These pathways include oxidative degradation by dioxygenase-containing
aerobic bacteria, bacterial and fungal cytochrome-P450 enzyme systems, fungal lipnolytic enzymes,
reductive dechlorination by anaerobic bacteria, and direct ether ring cleavage by fungi containing
esterase-like enzymes.
The aerobic bacterium, Burkholderia sp., is able to biodegrade dibenzofurans and
dibenzodioxins by biphenyl dioxygenase enzymes (Kasuga et al, 1997; Seeger et al, 2001). Dioxins and
dibenzofurans are attacked at the quasi-ortho position (the benzene carbons to which the oxygen[s] are
attached) and the immediate adjacent carbons. The angular attack by biphenyl dioxygenases is the main
route of dibenzodioxin oxidation. Later dioxygenation leading to dihydrodiols is the major oxidative
reaction for dibenzofuran. The major biodegradation product of dibenzodioxin is a trihydroxylated
diphenyl ether. The major biodegradation product of dibenzofuran is a dihydrodiol. These metabolites
are degraded further to catechols with a substantial decrease in toxicity. Other oxidative degradation
products include chlorophthalates or salicylates and chlorophenols (Nam et al., 2006). Some aerobic
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sediment bacterial species can biodegrade PCDDs with up to six chlorine atoms (Yamazoe et al, 2004;
Nam et al., 2006). The rate of these aerobic bacterial mediated reactions decreases sharply with
increasing chlorination of the dibenzodioxin and dibenzofuran molecules.
Reductive dechlorination appears to be the main pathway of microbial PCDD/PCDF
biodegradation in anoxic sediment layers (Adriaens and Grbic-Galic, 1994; Barkovskii and Adriaens,
1996; Bunge et al, 2003; Johnson, 2008). The more highly-chlorinated congeners (octachloro-DD and
octachloro-DF) are dechlorinated most rapidly, and the dechlorination rate appears to decrease with
decreasing levels of chlorination (Gaus et al., 2002). Thus, vertical PCDD/PCDF profiles in sediment
cores often exhibit a decrease in concentrations of highly-chlorinated congeners with depth in the
sediment core. Reductive dechlorination of PCDDs/PCDFs has been reported to be very slow under
normal environmental conditions (Deriziotis, 2004).
White rot fungi (Phanerochaete spp.) also are able to biodegrade PCDDs/PCDFs under
aerobic conditions (Gold et al., 1994; Takada et al., 1996). Biodegradation is initiated by extracellular
peroxidase catalized oxidation reactions that generate quinone intermediates. Quinones are reduced
enzymatically to peroxides. Several cycles of oxidation and reduction or oxidation, reduction, and
methylation successively remove more chlorines from the dioxin and dibenzofuran molecules. A lignin
peroxidase is responsible for cleaving the C-O-C bonds. The dechlorinated molecules are converted to
1,2,4,5-tetrahydroxybenzene and 1,2,4-trihydroxybenzene that are subject to ring opening and further
degradation. The rate of biodegradation by the fungal enzymes increases with increasing chlorination of
the PCDDs/PCDFs.
Given the very high toxicity of some PCDDs/PCDFs to animals, including humans, and their
extreme stability and low degradation rates in sediments, natural attenuation, managed by MNR, may not
be a sufficiently rapid remediation strategy unless the PCDD/PCDF-contaminated sediments can be
effectively sequestered by irreversible sorption and/or burial. MNR then would be used to ensure that the
toxic chemicals are not leaching from the buried deposit. Rigorous MNR would be required because
many aquatic animals are able to metabolize PCDDs/PCDFs, producing reactive oxidation products that
are highly toxic and possibly carcinogenic in the aquatic food web and the animals, including man, who
depend on it. Although PCDD/PCDF metabolism in animals is ecotoxicologically important, it does not
contribute to appreciable reduction in total mass loadings of PCDDs/PCDFs in sedimentary
environments.
3.4 Assessing Contaminant Transformation
An integral part of an MNR evaluation is consideration of both: 1) the nature and extent of
HOC contamination in sediments and their source(s) (particularly for sediments that contain similar suites
of HOCs introduced from multiple sources), and 2) the rate and extent of contaminant transformation and
mineralization. For example, PAHs often are introduced from multiple sources, e.g., MGP and aluminum
smelter wastes, creosote, petroleum residues, urban runoff, and aerial deposition of PAHs from
combustion exhaust. Early in the MNR evaluation, attempts to understand the potential for long-term
recovery and risk reduction of PAH-contaminated sediment sites were facilitated by the identification of
sources and interpreting concentration distributions and changes overtime. A chemical forensic approach
known as chemical fingerprinting employs high-resolution analytical methods and interpretative
techniques to identify and differentiate unique chemical characteristics of organic mixtures from different
sources. This approach is a critical tool in MNR for attributing particular chemical fingerprints to
different sources and for indentifying and characterizing weathering processes in environmental samples.
Hydrocarbon fingerprinting methods have evolved rapidly in the last few years. Several publications
describe these methods (e.g., Stout etal, 200la, 2003a, 2003b; Fabbri etal, 2003; Christensen etal,
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2004; Burns et al, 2006; Wang et al, 2006; Arey et al, 2007; Ebrahimi et al, 2007; Malmquist et al,
2007; Wang and Stout, 2007).
A universal characteristic of chemical fingerprinting methods is that they provide
significantly greater chemical detail than standard analytical (e.g., EPA SW-846) methods. Greater
chemical information permits sources to be differentiated and chemical weathering processes to be more
accurately quantified (Douglas et al., 2004). This is particularly useful for urban watersheds where
pervasive and persistent background chemical sources may create a chemical concentration threshold in
sediments. Chemical fingerprinting plays an important role in MNR by providing the chemical detail
necessary to:
• Identify or confirm chemical sources in sediments;
• Monitor chemical concentration changes and transformation processes due to weathering;
• Determine the kinetics of various weathering processes; and
• Establish sediment ambient or background conditions.
Using chemical fingerprinting as part of the MNR evaluation requires that analytical
strategies and goals for field investigations be developed carefully. It is necessary to balance costs with
project goals because chemical fingerprinting methods tend to be more detailed and costly than
conventional analytical methods. For example, a site assessment could be optimized by applying higher-
cost fingerprinting methods to a small sample set and using lower-cost conventional analytical tools to
make a broad characterization of the full extent and distribution of chemicals of concern. The U.S. Navy
has demonstrated the benefit of combining low-cost, rapid sediment characterization techniques (e.g.,
immunoassay screening) with chemical fingerprinting methods to identify contaminant sources in
sediments (Stout et al., 2003b).
Chemical fingerprinting can be more powerful when coupled with information about both
spatial concentration gradients and temporal variations relative to known, or suspected, sources where
data are acquired through the collection of sediment core samples to provide historic records of
contaminant deposition and transformation (Brenner et al., 2001). Spatial and temporal trends (e.g.,
sediment age and/or contaminant age in sediment) can impact the distribution and weathering of
chemicals in the environment. Chemical fingerprinting can be combined with historical information,
spatial distributions, and temporal distributions to better differentiate sources, historical depositional
events, and weathering trends, the combination of which will ultimately determine the expected rate of
sediment restoration via MNR.
3.4.1 Analytical Methods Used in Support of Fingerprinting. The most common methods for
the analysis of regulated semi volatile organic compounds (SVOCs) in sediments are modifications to
EPA SW-846 Method 8015 and Method 8270 (Table 3-10) (EPA, 1997b, 2007a,b). Modifications are
required because the original EPA methods provide insufficient information to differentiate hydrocarbon
source or weathering patterns in samples. For example, the target contaminant list (TCL) for EPA
Method 8270 includes only 16 unalkylated PAHs (Table 3-1), which is an insufficient number to
distinguish unique sources, particularly petrogenic mixtures, or different degrees of weathering in
sediments (Douglas and Uhler, 1993). An appropriately-modified EPA Method 8270 (8270M) can
simultaneously generate with much greater sensitivity (~1 ng/g [parts per billion: ppb] is generally needed
for fingerprinting analyses) the required regulatory data (i.e., TCL PAH concentrations) plus additional
SVOCs for chemical fingerprinting (Table 3-1). Douglas et al. (2004) reported MDLs for PAHs in
sediments on the order of 0.5 ng/g dry wt, which is more than 1,000 times lower than the 660 ng/g
sensitivity specified by EPA Method 8270. Such modifications are permitted within EPA SW-846
methods provided the fundamental SW-846 performance criteria are maintained or exceeded (EPA,
2007b).
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Table 3-10. Analytical Methods Frequently Used for Fingerprinting of Semivolatile
Hydrocarbons in Sediment
Measurement
Method
Target Compounds
Utility for Fingerprinting of Contaminants
Modified EPA
Method 8015
Total extractable hydrocarbons (THC)
Total aliphatic and aromatic hydrocarbons
C8 to C44 normal and branched-chain
hydrocarbons
Distinguishing resolved vs. unresolved
compounds in a chromatogram
THC Quantification
Develop diagnostic source indices
Develop diagnostic weathering indices
Establish background characteristics and THC
concentrations
Modified EPA
Method 8270
Priority pollutant PAHs
Alkyl homologues of priority pollutant PAHs
S-, N-, O-containing heterocyclic aromatics
Petroleum biomarkers
PCB congeners
Quantitate >50 semivolatile hydrocarbons
Develop diagnostic source indices
Develop diagnostic weathering indices
Establish background characteristics and PAH
concentrations
Source: Stout et al, 2002
The advantage of greater chemical detail, i.e., using a more extensive analyte list and
markedly lower detection limits, is demonstrated in Figure 3-6. Here, the distribution and concentration
of PAHs and related compounds in a heavy fuel oil #6 (from an oil-fired electric power plant) analyzed by
standard EPA Method 8270 is compared to data from analysis by the modified EPA Method 8270 that
includes a longer analyte (43 parent PAH and alkyl-congener groups). The larger number of target
analytes included in the modified analytical method substantially improves the ability to distinguish PAH
sources and recognize PAH weathering in sediments. It also shows that the standard method grossly
underestimates the concentration of total PAHs.
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The analytical methods used in support of chemical fingerprinting generally include the
following optimizations:
• Fingerprinting analyte lists for PCBs and PAHs tend to be more extensive than those
obtained by standard SW-846 analyses (Plumb, 2004). PCB congener analyses typically
include 50 to 110 individual and co-eluting congeners (NRC, 2001). PAH analyses often
include both non-alkylated PAHs (commonly comprised of the 16 priority pollutant
PAHs identified under the CWA) and their alkylated derivatives, representing more than
40 individual PAHs or congener groups.
• Analytical protocols are optimized for better chromatographic separation of individual
compounds, thereby providing greater chemical specificity.
• Analytical protocols are optimized to achieve lower detection limits than required by
standard SW-846 analyses (typically up to two or three orders of magnitude lower),
thereby reducing or eliminating the ambiguous non-detects at elevated detection limits.
• Detailed quality assurance/quality control (QA/QC) protocols are used to ensure accurate
and precise data sets that allow confident and defensible characterization of samples.
• Statistical or numerical data analysis approaches often are used to help minimize
subjectivity in establishing the degree of chemical similarity or dissimilarity among
samples.
3.4.2 Hydrocarbon Fingerprinting and Weathering Processes. PAHs that are present in the
environment, from any source, are ultimately derived from the degradation and rearrangement of natural
organic precursors of microbial, plant, and animal origin (Neff, 1979). Four types of PAH assemblages
can be identified in water, soil, sediment, and tissues of organisms. As discussed in Section 3.1.2.1, PAH
assemblages in environmental compartments are of petrogenic, pyrogenic, diagenic, or biogenic origins.
Advanced fingerprinting methods can differentiate PAH assemblages from the four sources. The
petrogenic and pyrogenic assemblages are of major environmental concern because the numbers and
concentrations of PAHs in them are much larger than in diagenic and biogenic assemblages and much of
the petrogenic and pyrogenic PAH mixtures are released to the environment by human activities. PAH
source fingerprinting can identify multiple sources of pyrogenic and petrogenic PAH assemblages,
differentiate pyrogenic and petrogenic PAH assemblages from one another, and differentiate these
sources from diagenic and biogenic assemblages in environmental samples. PAH fingerprinting also can
document the rate and extent of weathering of pyrogenic and petrogenic PAH assemblages.
"Urban background" PAHs in sediments are derived from numerous chronic, relatively low-
flux, point and non-point sources. Characterization of these urban background PAHs and differentiation
of the contribution of these PAHs from PAHs present at a site that originate from non-urban sources is
particularly important in MNR studies and is a topic for further discussion in Section 3.4.2.3.
PAHs that are frequently analyzed as part of environmental investigations, particularly
hydrocarbon fingerprinting studies, are listed in Table 3-1. The TCL PAHs (the 16 EPA priority pollutant
PAHs) identified in Table 3-1 and shown graphically in Figure 3-6 usually are analyzed as part of
remedial investigations; however, as shown later, the TCL PAHs alone usually are insufficient for
satisfying the objectives of hydrocarbon fingerprinting.
As discussed above, PAHs are subject to physical, chemical, and biological weathering
processes in sediments. Lower-molecular-weight, 2- and 3-ring PAHs are more susceptible than high-
molecular-weight, 4- through 6-ring PAHs to loss from sediments by dissolution, volatilization, and
biodegradation. Higher-molecular-weight PAHs are sensitive to photooxidation but are persistent in
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sediments, particularly in anoxic layers, because of their low solubilities, high affinities for sorption to
sediment organic matter (particularly BC), resistance to degradation, and protection from photooxidation.
Weathering and degradation can reduce the concentrations, mobility, and bioavailability of
PAHs in sediments. Weathering of higher-molecular-weight, 4- through 6-ring PAHs usually is slow and,
in some cases, may not be measurable over reasonable time periods, particularly if the PAHs are
sequestered in a fine-grained, low-permeability sediment layer. In general, hydrocarbon weathering is
characterized by a progressive decrease in the proportion of lower-molecular-weight hydrocarbons and
the persistence of higher-molecular-weight hydrocarbons (Brenner et al., 2001; Stout etal, 200 la; Bence
et al., 2007). These patterns of PAH loss from sediments may reduce or eliminate acute exposures to
lower-molecular weight compounds (e.g., naphthalene and phenanthrene and their alkyl homologues) that
tend to be more mobile and bioavailable in the environment due to their higher solubility.
3.4.2.1 Hydrocarbon Source Fingerprinting. Sediments often contain hydrocarbons from a variety
of sources. The ability to recognize different sources of hydrocarbons depends primarily on the
interpretation of data acquired by two analytical methods identified in Table 3-10. An overview of the
interpretation of these data is provided in this section. A more detailed and comprehensive discussion of
hydrocarbon source characterization and fingerprinting is provided by Wang and Stout (2007).
Modified Method 8015 (8015M) provides a chromatographic fingerprint and concentrations
of the total hydrocarbons in sediments. Method 8015M uses a solvent to extract organic material from the
sediment sample, silica gel to remove most of the non-hydrocarbon organic material from the extract, and
high resolution gas chromatography with flame ionization detection (GC/FID) to determine the total
hydrocarbon concentration in the sample and to reveal a unique chromatographic pattern useful in
fingerprinting activities. Method 8015M is useful for distinguishing distillate from residual range
petroleum sources, and for comparing the relative abundances of easily recognized compounds such as
normal alkanes («-alkanes), selected isoprenoids (e.g., pristane and phytane), and some PAHs.
Method 8015 has been used routinely as a first-level identification of both petroleum product
type and weathering state (EPA, 2007a). The GC/FID method differentiates and quantifies different types
of refined and residual petroleum in environmental samples, including total petroleum hydrocarbons
(TPH)-gasoline range (GRH), TPH-diesel range (DRH), and TPH-residual (crude and heavy fuel oil)
range (RRH). These types of hydrocarbon assemblages are based on quantification of the area under
chromatographic peaks in a certain segment of the gas chromatogram corresponding to hydrocarbons of a
defined carbon number and boiling point range. The area under the chromatogram (including both
aromatic and aliphatic hydrocarbons) between C6 and Cio is defined as GRH; the area under the
chromatogram between Cio and C24 is defined as DRH; and the area under the chromatogram >C24 is
defined as RRH.
A GC/FID chromatographic trace or TPH fingerprint is produced as part of the TPH analysis.
TPH is defined as the sum of the concentrations of the resolved compounds in the chromatogram in the
carbon range of n-alkanes Ci0 to C40 plus the concentration of the unresolved complex mixture (UCM:
the hump on the chromatogram composed of a complex mixture of unresolved hydrocarbons) between
n-alkanes Cio and C40. Due to evaporative losses of the more volatile hydrocarbons during the solvent
extraction process for the TPH analysis, only the higher-molecular-weight, less volatile hydrocarbons
(> Cio) are measured by this method. The UCM contains hundreds of individual compounds that cannot
be resolved by conventional GC and appears as a broad unresolved "hump" above the baseline of the
chromatogram. Often the TPH concentration is divided into distinct carbon ranges that are quantified
using different types of fuel oils as standards. For example, the hydrocarbon concentration in the DRH
range of n-alkanes usually is quantified using a commercial diesel distillate product. Although reported
as DRH, the hydrocarbon source material need not be diesel fuel or even from a petrogenic source. All
96
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hydrocarbons from any source including pyrogenic sources in the effective carbon range of CIQ to C24 are
captured and reported as DRH. Another carbon range fraction typically reported is RRH and includes
mixtures of heavy hydrocarbons, including heavy fuel oils, residual oils, and middle- and heavy-weight
crude oils, represented by the hydrocarbon concentration range of «-alkanes C2s to C40.
Source characterization begins with visual examination of the THC chromatogram. For
instance, peak patterns and intensities in the THC fingerprint are useful for differentiating different fuels
or refined oil types (e.g., kerosene, diesel, or bunker oil), and for comparing the relative abundances of
easily recognized compounds such as «-alkanes, selected isoprenoids (e.g., pristane and phytane), and
some PAHs (Stout and Wang, 2007). Different refined petroleum products (gasoline, diesel fuel, heavy
fuels, and lubrication oils), crude and residual oils, and pyrogenic sources (coal tar and creosote) have
distinctive THC chromatographic features that can be used to identify and distinguish these sources in
sediments. The UCM or "hump" can be identified in chromatograms of weathered and sometimes fresh,
middle- and heavy-weight refined, residual, and crude oils (Jones etal, 1983; Gogou etal, 2000;
Frysinger et al, 2003), but is not characteristic of most pyrogenic hydrocarbon mixtures (e.g., coal tar).
The UCM in petroleum-contaminated sediments contains a complex mixture of weathering-resistant and
recalcitrant hydrocarbons that are persistent in sediments (Reddy et al., 2002).
3.4.2.2 Differentiating Petrogenic from Pyrogenic PAH Signatures. Although fingerprints and
concentration measurements of TPHs can be used to distinguish a variety of hydrocarbon sources, their
use for ecological and human health risk assessments usually focuses on PAHs in sediments due to their
toxic, mutagenic, and carcinogenic characteristics, and persistence in sediments. As discussed previously,
the two major types of complex PAH assemblages in the environment are pyrogenic and petrogenic.
Pyrogenic and petrogenic PAH assemblages can be distinguished easily on the basis of their alkyl PAH
distributions (Giger and Blumer, 1974; Youngblood and Blumer, 1975; Lee etal., 1977; Laflamme and
Kites, 1978) and the relative concentrations of low- and high-molecular-weight PAHs present.
Crude oil (a petrogenic source) is refined, i.e., distilled, to separate hydrocarbon fractions
spanning different boiling point ranges (Gary and Handwerk, 1984) to produce light distillates (gasoline,
kerosene, and jet fuel), middle distillates (diesel fuel #2, fuel oil #2, and fuel oil #4), and residual oils
(lube oils, fuel oil #6, bunker C fuel, and paving asphalt). The PAH composition is unique among the
discrete refined oil fractions, reflecting the distillation boiling point range for each refined petroleum
product. Refined petrogenic products contain primarily PAHs that were present in the parent crude oil.
Because distillation temperatures are relatively mild (<550°C), only small amounts of new, highly-
condensed PAHs are formed during catalytic cracking of the heavy distillate fractions. Figure 3-2 shows
the PAH profiles (fraction of total PAHs represented by each PAH) of typical gasoline, diesel fuel, and
crude oil.
Major pyrogenic PAH sources include fuel combustion exhaust emissions, combustion ash
and other debris, and high-temperature combustion residues (tars and black carbon) from MGPs
(associated with coal or petroleum coking) and steel and aluminum smelting, and commercial products
from these items such as creosote and various tar products. Because of the high temperature of formation
(>700°C), pyrogenic PAH assemblages are dominated by the more thermally stable linear and
unalkylated PAHs.
PAH assemblages in the environment contain parent PAHs and alkyl PAHs with one or more
alkyl carbons bonded to aromatic carbons. The relative abundance of parent PAHs and their alkyl
homologues in PAH assemblages in environmental samples is used frequently for identifying and
characterizing hydrocarbon sources and interpreting the effects of natural weathering processes.
Figure 3-2 shows the PAH profiles for three petrogenic samples (gasoline, diesel fuel, and crude oil), and
Figure 3-6 shows the PAH concentrations in heavy fuel oil #6. Figure 3-3 illustrates the PAH profiles for
97
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two pyrogenic samples (coal tar and creosote). The petroleum samples are relatively enriched in the
naphthalene homologous series PAHs (NO - N4) and the phenanthrene series (PO - P4). The gasoline and
diesel fuel contain only traces of a few 4- through 6-ring PAHs, due to fractionation during the distillation
process. The heavy fuel oil (Figure 3-6) contains relatively high concentrations of alkyl
fluoranthenes/pyrenes and chrysenes. Alkyl-PAHs are more abundant than the parent PAHs in all of the
petrogenic PAH assemblages (Figure 3-7).
Coal Tar -
Black
1.00
•- CM n
CJ LJ
U U U U U
Figure 3-7. Profiles of the Naphthalene (N) and Chrysene (Q Distributions in
Pyrogenic (Black) and Petrogenic (Gray) PAH Assemblages (from Neff et a/., 2005)
In contrast to the petroleum samples, the PAHs in the pyrogenic source material are enriched
in higher-molecular-weight, 4- through 6-ring PAHs (Figure 3-3). Creosote, a distilled product of coal tar
or petroleum, has a noticeably higher concentration of naphthalene than coal tar. The parent PAHs are
more abundant than any of the corresponding alkyl homologues (Figure 3-7).
PAH concentrations usually are markedly different in petrogenic and pyrogenic source
materials, and this difference can be useful in characterizing PAH sources and extent of weathering in
environmental samples. The unweathered crude oil and distillate fuel samples in Figure 3-2 contain only
0.6% to 2.8% dry weight PAHs; the heavy fuel oil, a residual oil fraction, contains about 9% total PAHs.
The pyrogenic coal tar and creosote samples in Figure 3-3 contain 27% and 20% dry weight PAHs,
respectively. Some heavy coal tars from coal coaking at MGPs may contain more than 80% total PAHs
(Neff, 1979). Even small quantities entering waterways can contribute significant quantities of PAHs to
sediments because of the high relative percentage of PAHs in these pyrogenic source materials.
Environmental weathering often removes the parent PAHs more rapidly than their alkylated
homologues from pyrogenic PAH assemblages. Therefore, the PAH profile of a weathered pyrogenic
PAH assemblage may resemble the bell-shaped profile of a fresh petrogenic PAH assemblage. When
fresh and weathered mixed petrogenic and pyrogenic PAH assemblages are encountered, a more in-depth
analysis that extends beyond simple visual evaluation of TPH and PAH fingerprints may be necessary.
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3.4.2.3 Understanding Urban Background Sediments. The characterization of urban background
PAHs is important for source control and development of sediment remediation strategies. Understanding
and characterizing the contaminant contribution at an MNR site that can be attributed to urban-industrial
background in sediments is essential for recognizing and assessing the inputs and contributions to
hydrocarbon contamination of sediments from point (including upland sites) and non-point sources (Stout
et al, 2004). Sediment remediation strategies, including MNR, must consider the potential for
recontamination or concentration plateaus in surface sediments due to continuing inputs from persistent
background PAHs from uncontrollable urban sources (air deposition, road runoff, asphalt leachate, etc.).
The urban background hydrocarbons present in sediments are derived and delivered from
non-point sources and diffuse transport processes (e.g., storm water runoff, surface runoff from asphalt
roadways, direct atmospheric deposition, and small but persistent discharges) (Stout et al., 2004). Urban
background PAHs enter waterways from numerous chronic non-point pyrogenic and petrogenic sources
and are added to PAHs from the targeted point sources that contaminated the site undergoing remediation.
Pyrogenic PAHs usually are most abundant and sources include exhaust emissions (including BC) from
internal combustion engines and burning of fossil fuels, wood, and other organic matter, rainout and dry
fallout of particulate and vapor phase PAHs from the atmosphere, and PAHs associated with BC from
wear of vehicle tires (Neff, 1979). Petrogenic urban background sources include road runoff containing
lubricating and hydraulic oils, fuel oils from accidental spills, and leachate and wear from asphalt
pavement.
These PAHs enter waterways via atmospheric deposition and in combined sewer overflows
and storm water runoff. Although urban background sources contain both pyrogenic and petrogenic PAH
signatures, the urban background PAH assemblage in urban fresh water and estuarine sediments usually is
dominated by pyrogenic PAHs (Eganhouse et al., 1982; Stout et al., 2004; Brown and Peake, 2006; Stein
et al., 2006).
The pyrogenic PAHs in urban waterway sediments usually come primarily from deposition of
particulate and vapor phase PAHs from the urban atmosphere. Simcik et al. (1999) reported that
atmospheric deposition is the major source of PAHs to the sediments and water column particulate phase
of Lake Michigan off Chicago. Most of the PAHs are from combustion sources: 48% from coal
combustion, 26% from natural gas combustion, 14% from coke ovens, and 9% from gasoline and diesel
vehicular emissions. Much of the petrogenic PAHs in urban runoff comes from storm water runoff from
parking lots, highways, and gas stations (Smith et al., 2000).
No single "representative" urban background fingerprint or hydrocarbon concentration exists
since the total PAH concentration, relative age of the PAH contamination, and mixture of PAHs varies
spatially (i.e., across different urban watersheds) and also geologically. The residues of combustion
byproducts from fossil fuels, however, have produced a modern urban background PAH signature in areas
affected by hydrocarbon contamination. Background hydrocarbon assemblages tend to be dominated by:
1) a variably-shaped UCM in the residual (> C2o) range, and 2) a variable distribution of 4- to 6-ring TCL
non-alkylated PAHs (Stout et al., 2004). The mean TPH concentration in 280 sediment samples from
nine well-studied urban waterways on the east and west coasts of the United States was 415 mg/kg (dry
wt); the total TCL PAHs concentration was less than 20 mg/kg in 96% of the sediments studied. TPH
and PAH fingerprints of a characteristic urban background sediment sample collected from the
Wyckoff/Eagle Harbor Superfund Site (Bainbridge Island, Washington) are shown in Figure 3-8 (Stout
et al., 200 Ib). Notably, PAHs from each homologous series exhibit a skewed pyrogenic pattern despite
the presence of a petroleum component, indicative of an urban source PAH signature of contamination.
Recognizing an urban background PAH signature in sediments is an important component of
an MNR study. Doing so can help distinguish the urban background component of PAHs in sediments
99
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from hydrocarbons introduced from point sources. The potential for sediment recontamination due to the
persistent influx of background PAHs should be addressed in any sediment remedy, including MNR
(Brenner et al, 2001).
3.4.2.4 Characterization of Hydrocarbon Weathering. Fingerprinting analyses can provide
information about the state and rate of hydrocarbon weathering in addition to characterizing and
differentiating hydrocarbon sources in sediment samples. Fingerprinting to characterize hydrocarbon
sources is virtually indistinguishable from fingerprinting to characterize weathering processes. Both rely
on detailed chemistry to identify and characterize unique chemical hydrocarbon patterns. For MNR, it is
very valuable to estimate the rate of weathering loss due to all processes of individual and total PAHs
from contaminated field sediments. However, because of the extreme variability in loss rates and the
number of physical, chemical, and biological factors in the environment affecting loss rates, it usually is
not possible to accurately estimate weathering rates of PAHs in sediments.
The effects of weathering processes (i.e., dissolution, volatilization, photooxidation, and
biodegradation) on concentrations and compositions of hydrocarbon assemblages in sediments have been
studied in both natural (Connan, 1984; Palmer, 1993) and controlled laboratory conditions (Huesemann,
1995; McMillen et al., 1995). PAH weathering processes and their effects on the composition and
concentrations of PAHs from different sources in sediments were discussed above in Sections 3.2 and 3.3.
Most studies of hydrocarbon weathering in sediments have focused on biodegradation
because, under oxidizing (aerobic) conditions for sediments at moderate temperatures (5 to 30°C),
microbial biodegradation is the most rapid weathering process. All non-alkylated (parent) PAHs
biodegrade aerobically, including the TCL PAHs (Douglas et al., 1992; Prince and Drake, 1999; Nadalig
et al., 2000; LeBlanc and Brownawell, 2002). Some PAHs also degrade under anaerobic conditions that
are associated with microbially-mediated sulfate reduction (Coates et al., 1996, 1997; Rothermich et al.,
2002), nitrate reduction (Milhelcic and Luthy, 1991; Durant et al., 1995), and methanogenesis (Sharak
Genthner et al., 1997). Sulfate-reducing conditions are particularly relevant for marine sediments due to
high natural sulfate concentrations (~28 mM) in sea water. Nitrogen reduction and methanogenesis
usually are more important in anoxic fresh water sediments where sulfate concentrations may be low.
The relative rates of degradation of the different hydrocarbon classes in petroleum in sediments are
discussed in Section 3.3.1.
Rates of PAH biodegradation vary widely depending on PAH source and form (see
Section 3.3), molecular weight, structural configuration, oxygen concentration in and permeability of
sediment, and, for anoxic sediments, electron acceptor availability (Bressler and Gray, 2003). Pyrogenic
PAH assemblages, particularly if they are adsorbed to BC, weather more slowly than do petrogenic PAH
assemblages, which are often associated with NAPL (oil, coal tar, or creosote). Generally, PAH
biodegradation rates decrease with increasing numbers of aromatic rings, increasing molecular weight,
and decreasing solubility (Prince et al, 2003). Parent, non-alkylated PAHs usually degrade more rapidly
than their alkylated derivatives, and biodegradation rates decrease with increasing alkylation (Elmendorf
et al, 1994; MacGillivray and Shiaris, 1994; Burns et al, 1997; Prince et al, 2003). Different alkyl
congeners of some PAHs may biodegrade at different rates (Elmendorf et al., 1994; Wang and Fingas,
1995; Mazeas and Budzinski, 2002). This tendency usually is attributed to steric hindrances of various
methyl group positions.
100
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Response (mV) X4481
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Figure 3-8. THC Fingerprint (top) and PAH Histogram (bottom) of a Sediment Sample Containing
Hydrocarbons Attributed to Non-point Sources (from Stout et a/., 2001a)
Detailed composition data (individual and total PAHs and hydrocarbon biomarkers) are
needed for fresh source samples (samples of the spilled crude oil, heavy fuel oil, coal tar, or creosote) and
sediment samples that are collected periodically over several years at the contaminated sediment site in
order to characterize chemical and biological weathering of PAH mixtures in sediments. Rates of
biodegradation of PAHs in crude oil and some pyrogenic (e.g., coal tar) NAPLs in sediment can be
assessed by comparing mass reduction rates of individual and total PAHs to those of petroleum biomarker
compounds. Petroleum biomarkers are non-polar organic compounds in the original source sample that
are highly resistant to biodegradation or other weathering loss (Wang and Stout, 2007; Peters et a/.,
2008). A C30-hopane, 17ot(H)hopane, or 21p(H)hopane is used frequently as a biomarker in crude and
heavy fuel oils. If this biomarker is not analyzed or is not present in a source sample (e.g., middle
distillate fuel oils, coal tar, or creosote), a congener group of higher-molecular-weight PAHs, such as total
C0-C4 chrysenes, can be used. Most light and middle distillates, such as gasoline and diesel fuel, do not
contain any high-molecular-weight degradation-resistant PAHs or petroleum biomarkers, so it may not be
possible to use biomarker measures of PAH depletion in sediments containing these hydrocarbon
mixtures. Depletion (loss) of individual or total PAHs in oil-, coal tar-, or creosote-contaminated
sediment can be estimated with the equation:
% loss of component X = [1 - (Cx/Ccon)w/(Cx/Ccon)s] x 100
where: Cx = mass concentration of component x in oil
Ccon = mass concentration of conserved species (biomarker) in oil
w = weathered sample
s = source or reference sample.
(Eq. 3.9)
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Atlas and Bragg (2007) used this approach to estimate the depletion of petrogenic PAHs from
residues of the North Slope (Alaskan) crude oil buried in intertidal sediments after the Exxon Valdez oil
spill. Source samples of unweathered crude oil from the tanker and approximately 200 oil-contaminated
subsurface sediment samples were analyzed for individual and total PAHs and sterane/triterpane
petroleum biomarkers by advanced analytical methods (Douglas et a/., 2004). Initial loss of PAHs from
sediments was rapid, caused primarily by water-washing and volatilization. Total PAHs were depleted by
an average of 66% in oil-contaminated sediment samples collected during the summer after the spill
(Figure 3-9). However, depletion was extremely variable in the 4 years after the spill, with depletions
ranging from less than 20% to more than 90%. Mean PAH depletion from buried oil residues in intertidal
sediments increased from 64% to 66% in 1989 through 1993 to 73% in 1999 through 2003 and 82% in
2004 through 2006. PAH degradation was observed to be most rapid shortly after the spill, with the
degradation rate observed to decrease substantially within the 10 years after the spill. However, PAH
degradation was still occurring 17 years after the spill, but at a low rate. The weathered, spilled oil
remaining in intertidal sediments 10 years or more after the spill was sequestered in fine sediment layers
under boulder-cobble armor and was not readily bioaccessible for continued microbial biodegradation.
Stout et al. (200 Ib) provides examples of unweathered and weathered petrogenic and
pyrogenic sources. Knowledge of specific PAH transformation processes permits the prediction of how
weathering alters the PAH distribution in sediments. PAH weathering is expected to:
• Preferentially reduce the proportion of lower-molecular-weight (2- and 3-ring) PAHs,
thereby increasing the proportion of higher-molecular-weight (4- to 6-ring) PAHs; and
• Preferentially reduce the proportion of non-alkylated PAHs relative to alkylated PAHs,
thereby increasing the proportion of alkylated PAHs.
The weathering trends for petrogenic and pyrogenic PAH sources can reduce or eliminate
acute exposures to lower-molecular-weight PAHs (e.g., naphthalene, phenanthrene, and their alkyl
homologues) that are more mobile in the environment than the higher-molecular-weight PAHs.
Weathering of higher-molecular-weight (4- to 6-ring) PAHs often is slow as the relative concentrations
(as percent of total PAHs [tPAHs]) of high-molecular-weight PAHs in the oil residues in sediments often
increase during weathering, even though the total mass of oil residues and PAHs in sediment decreases
with weathering. The rate of natural attenuation of high-molecular-weight PAHs in sediments from oil,
coal tar, and creosote spills may not be sufficient (decades) to warrant long-term surveillance by MNR
unless the sediments are buried and inaccessible to surface biological receptors.
3.4.2.5 Summary of Hydrocarbon Fingerprinting and Weathering Processes. Hydrocarbon
characterization from the application of hydrocarbon fingerprinting (i.e., forensics), particularly PAH
fingerprinting, is essential for providing valuable site information regarding identification and
differentiation of contaminant sources including urban/industrial background PAH levels and sources; the
magnitude and extent of hydrocarbon weathering; the impact of weathering on the distribution of PAHs in
sediments; and, ultimately, providing critical lines-of-evidence to evaluate the suitability of MNR.
102
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100
in
o
O
E
3
OT
I/I
O
8/11/1987 5/7/1990 1/31/1993 10/28/1995 7/24/1998 4/19/2001 1/14/2004 10/10/2006 7/6/2009
Date
Figure 3-9. Percent Loss of Total PAHs from Weathered North Slope Crude Oil in
Subsurface Sediments on the Shore at Sites Where Oil Has Persisted for 17 Years
after the Exxon Valdez Oil Spill
(Based on sum of chrysenes as the hydrocarbon biomarker; average percent total
PAH losses relative to original concentrations are given for four time intervals [from
Atlas and Bragg, 2007].)
A thorough understanding of the sources of hydrocarbons in site sediments and the fate and
transport of PAHs in sediments is needed for studies intended to monitor the recovery of ecological
receptors at hydrocarbon-contaminated sediment sites. This is particularly true for unidentified sources
that can persist after known sources are controlled. The pervasive example of one such persistent source
is urban background that must be characterized and evaluated independently from known point sources.
Obviously, unrecognized hydrocarbon sources could easily confound an MNR analysis by erroneously
increasing unresolved surface sediment concentration trends and/or surface sediment chemical
concentration plateaus, or contributing to recontamination after remedy implementation.
To illustrate this approach, a forensic case study from the Wyckoff/Eagle Harbor Superfund
Site (Bainbridge Island, WA) is shown in Highlights 3-1 and 3-2. As part of a program to achieve a better
understanding of the mechanisms contributing to the natural recovery of ecological resources at
contaminated sediment sites, EPA conducted a study of sources and fate of hydrocarbon contamination at
the Wyckoff/Eagle Harbor Superfund Site (Stout et a/., 2001b; Brenner et a/., 2001). Chemical forensic
analyses were employed to identify hydrocarbon sources, including creosote, that were used as a wood
preservative at the former Wyckoff facility.
The urban sediments in Eagle Harbor were contaminated with urban background and
creosote. The relative contributions of different PAH sources were determined through fingerprinting
techniques (as described above). The fingerprinting analysis became a much more powerful tool when
coupled with PAH spatial concentration gradients and temporal relationships relative to known or
suspected point sources identified through historical research. This study site demonstrates how a
coupled spatial and temporal understanding of PAH contamination obtained through the characterization
of sediment core profiles, combined with radiogenic isotopic age-dating (e.g., 137Cs or 210Pb; Brenner et
103
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a/., 2001) permits an evaluation of the PAH signatures present in sediments deposited at different
locations and time intervals, which may be used to better recognize the contribution of historical and
modern sources to the MNR study area.
3.4.3 Chemical Fingerprinting of PCBs as Part of MNR This section summarizes techniques
for characterization of PCB source and weathering patterns through the use of chemical fingerprinting
techniques as applied specifically to the analysis of PCBs. As with PAHs and other HOCs, the fate of
PCBs in sediments is controlled by sorption, dissolution, volatilization, and biotransformation/
biodegradation processes. Weathering of PCBs in sediments by these processes was discussed earlier in
Sections 3.2 and 3.3.2. The number and positions of chlorines on the biphenyl molecule and the
oxidation/reduction condition of the PCB-contaminated sediments affect the rate of attenuation of
complex PCB mixtures in sediments. Sorption is the dominant process influencing the fate of PCBs in
sediments, and is of particular significance because strength of sorption influences the rate of
biodegradation and other weathering processes by controlling the concentration of dissolved
(bioavailable) PCBs in sediment pore water and PCB accessibility to sediment microbes (Hickey, 1999).
The strength of sorption of PCB congeners to sediment organic matter (proportional to log Koc: Table 3-
6) increases with degree of chlorination of the biphenyl molecule.
The primary factors affecting PCB biotransformation are the number and pattern of chlorine
substituents and the redox state of the sediments. PCBs behave similarly to PAHs and PCDDs/PCDFs in
aerobic sediments; low-molecular-weight PCBs (e.g., < 3 chlorines) are preferentially weathered and
biodegraded. The ability of aerobic bacteria to degrade PCBs decreases with increased chlorination;
congeners with five or more chlorines are relatively recalcitrant to aerobic biodegradation (Bedard et al,
1986; Abramowicz, 1990; Abramowicz, 1995).
Under anaerobic conditions, the primary metabolic pathway is reductive dechlorination in
which chlorine removal and substitution with hydrogen by bacteria results in a chemically reduced
organic compound with fewer chlorines (Brown et al., 1987; Mohn and Tiedje, 1992; Zanaroli et al.,
2006). Highly chlorinated PCB congeners are reductively dechlorinated with the accumulation of mono-,
di-, and tri-chlorobiphenyls (Bedard and Quensen, 1995; Wiegel and Wu, 2000). Reductive
dechlorination preferentially removes chlorines from the meta and para positions and replaces them with
hydrogen atoms. Dechlorination results in a decrease in the toxicity of complex PCB mixtures in
sediments (Section 3.3.2).
Forensic techniques may be used to identify different commercial PCB mixtures (e.g.,
Aroclors) and their weathering state, and to differentiate the various complex physical, chemical, or
biological processes that are altering the composition of mixtures over time. Johnson et al. (2002)
provides a comprehensive discussion of environmental forensics for PCBs. Examples of PCB congener
interpretations and fingerprinting analyses are included in this section to aid the reader in understanding
the application of environmental forensics for PCB mixtures, which may also be relevant to other
halogenated hydrocarbon compounds in the environment, such as PCDDs/PCDFs, that are susceptible to
reductive dechlorination. EPA's NRMRL has been characterizing biological, chemical, and physical
MNR processes at the Sangamo-Weston/Twelvemile Creek/Lake Hartwell Superfund Site since 2000.
104
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Highlight 3-1. Hydrocarbon MNR Case Study - Creosote Impacted Sediments
Eagle Harbor is a shallow marine embayment on Bainbridge Island, located approximately 10 miles
due west of Seattle, WA. Sediment cores (<100 cm) were collected from 10 locations. Cores were
age-dated with 210Pb and 137Cs (Brenner et al., 2001), and chemical fingerprinting was used to
characterize PAH sources and weathering processes (Stout etal, 200Ib). Fingerprinting revealed
three primary hydrocarbon sources (urban background, natural background, and creosote), as well as
various degrees of source weathering. Principal component analysis (PCA) was used to identify the
spatial distribution of the natural background-, urban background-, and creosote-dominated samples;
weathered PAH signatures; and source mixtures. Creosote signatures were mostly in sediments from
the vicinity of the former creosoting operation. However, the absence of creosote near the center of
the harbor was unexpected. In this area, surface sediments were dominated by urban background,
attributed primarily to ferry terminal traffic on the north shore of the harbor (Brenner et al., 2001).
Natural background signatures were observed only in buried sediments where age-dated deposition
preceded industrialization and urbanization of the area. Urban background and creosote signatures
probably obscured the natural background signature at shallower depths.
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Creosote, urban runoff, and natural background distributions in Eagle Harbor sediment cores
(adapted from Brenner et a/., 2001). The vertical columns represent the tops and bottoms of
vertically profiled sediment cores (~40 to ~70 cm deep).
105
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Highlight 3-2. Creosote Weathering Trends in Eagle Harbor Sediments
This highlight shows selected examples that exhibited varying degrees of PAH weathering. Weathering
not only changes the chemical fingerprint, but also can result in irreversible sorption to sediments.
Eagle Harbor sediments with a dominant creosote chemical signature were examined to evaluate the
extent of creosote weathering to determine the natural recovery potential of the creosote-impacted
sediments. The shift from unweathered to more highly-weathered creosote was characterized by a
progressive decrease in the percentage of low-molecular-weight (2- and 3-ring) PAHs. The severely
weathered creosote sample contained a very low percentage of 2- and 3-ring PAHs, but exhibited a
sharp increase in the proportion of fluoranthene and higher-molecular-weight PAHs. However, because
of mass reduction in creosote during weathering, concentrations of all PAHs decreased in the sediment
(Brenner et a/., 2001).
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Eagle Harbor (from Brenner et a/., 2001)
106
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3.4.3.1 Properties of PCBMixtures. As discussed in Section 3.1.2.2, a PCB molecule is composed
of a biphenyl molecule with chlorines bonded to between one and 10 of the available benzene carbons
(Figure 3-4). The possible 209 PCB congeners are distinguished by the number and position of the
chlorine atoms on the biphenyl molecule (Table 3-3). The degree of chlorination (homologue groups) and
physical properties of the seven most common Aroclors are summarized in Table 3-6, and congener
profiles of five Aroclors are summarized in Figure 3-10. One of the most important considerations when
evaluating PCBs in environmental samples is whether to measure Aroclors, homologue groups, or
individual congeners. Detailed forensic analysis of PCB mixtures used to identify the source of the
contamination by comparison with the original Aroclor mixture and extent of degradation (e.g.,
dechlorination) in environmental samples typically will quantify 50 to 110 of the most environmentally
relevant congeners.
The selection of which measure of PCBs should be a site-specific decision based on
information about the sources of PCB contamination, concerns about congener-specific toxicity, the need
to characterize PCB weathering processes, comparability between multiple data sets, and the cost of
analyses (EPA, 2005a). The NRC's (2001) A Risk Management Strategy for PCB-Contaminated
Sediments provides an overview of PCB analytical methods for Aroclor, congener, and homologue
analyses. Aroclor-equivalent methods generate a single t-PCB concentration based on comparison of the
sample chromatogram with those of the original Aroclor formulations. Even though t-PCB analyses are
relatively inexpensive and may be useful for general assessment and screening of PCB concentrations in
sediments, they provide little information on PCB sources and long-term fate.
At many sites, PCBs no longer resemble their original formulations due to weathering. PCB
homologue and/or PCB congener analyses are necessary when more detailed information is required. For
homologue and congener analyses, t-PCB concentrations are determined by summing congener or
homologue concentrations. Summing congeners or homologues provides more accurate t-PCB
concentration values than the Aroclor t-PCB method (NRC, 2001).
Congener analyses are necessary to identify sources and to evaluate congener distribution
changes overtime or distance from a source. Congener analysis is the most reliable approach for
characterizing the PCB contamination in environmental samples that have undergone significant changes
from the original source material or if the PCB mixtures in site sediments are derived from releases of
multiple commercial PCB mixtures. Homologue methods are similar to the congener method except that
they quantify entire homologue groups to decrease analytical and QA/QC times and costs. A dedicated
PCB homologue analysis can be performed concurrently with a congener analysis if GC/low resolution
mass spectrometry-SIM is used.
Congener analyte lists are based on the relative toxicity of the individual congeners and their
relative abundance in commercial Aroclor mixtures and the environment. Because PCB congener
behavior and transformation processes are well understood, characterization of PCB sources and
weathering patterns can be obtained with a well selected set of approximately 50 to 100 congeners.
Table 3-3 summarizes the complete PCB congener analyte list; most analyte lists are relatively consistent
among laboratories, though they may differ by some congeners and the total number of congeners
resolved. About 97% to 99% of the PCB congeners in Table 3-3 are targeted in most environmental
samples and analyses. Eighteen congeners are targeted in the National Status and Trends (NS&T) and
EPA Environmental Monitoring and Assessment Program methods (NRC, 2001). The sum of these 18
congeners typically makes up between 45% and 55% of the PCBs in many environmental samples (more
if the congener distribution is predominantly higher in molecular weight than Aroclor 1254, and less if the
distribution has a lower molecular weight than that of Aroclor 1248).
107
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Selecting the most appropriate analytical method to characterize PCB-contaminated
sediments, which can affect long-term project costs, is important to the level of interpretation possible
with the data and for comparison across multiple data sets. Whereas analytical costs may be lower using
the NS&T Aroclor method, this method often provides insufficient information for source identification
and characterizing fate and transport that is possible through a more detailed fingerprinting approach.
Hence, long-term costs may be higher if samples have to be reanalyzed, more samples are necessary, or if
MDLs are high, resulting in higher assumed contaminant concentrations for samples below the MDL.
Resolving Aroclor mixtures, distinguishing among various sources, and quantifying PCB weathering in
sediments generally requires the specificity provided by congener or, to a lesser degree, homologue
analyses. As is the case with other aspects of MNR, the level of sampling and type and degree of
chemical analysis should be tailored to the risk and ecological importance of the PCB-contaminated site.
The second challenge is that most Aroclor mixtures begin to weather immediately upon
release into the environment. Weathering can significantly alter congener distribution patterns making it
difficult, and sometimes impossible, to identify the original source. The resulting congener distribution
may not resemble any Aroclor, or it may resemble a more- or less-chlorinated Aroclor mixture depending
on the type of weathering (Chiarenzelli et al., 1997; Magar et al., 2005a). The similarities between
congener histograms from field samples and source patterns are usually determined by visual observation.
However, they can also be evaluated by calculating the cosine theta similarity metric (cos 0) between the
individual sample compositions compared to Aroclor source and alteration patterns (Magar et al., 2005a;
Johnson et al., 2000, 2002; Davis, 1986). The cos 9 metric calculates the cosine of the angle between two
multivariate vectors, in this case the two matrices formed by the 50 to 100+ congeners included in the
analysis for each sample. A cos 9 value of 0 would indicate two completely dissimilar, orthogonal
vectors. A cos 9 value of 1.0 would indicate two identical vectors.
An example of sample source characterization for surface sediment samples collected from
the Sangamo-Weston/Twelvemile Creek/Lake Hartwell Superfund Site is presented in Highlight 3-3.
Field sampling and forensics analyses were used to characterize surface sediment congener distributions
and relate those distributions to historically-released Aroclor mixtures.
3.4.3.2 Characterization of PCB Weathering. As discussed in Sections 3.2 and 3.3.2, PCB mixtures
undergo several weathering processes following release to the environment including dissolution,
volatilization, sorption to and desorption from dissolved and particulate organic matter, and aerobic and
anaerobic biodegradation by water, soil, and sediment bacteria and fungi. The most important weathering
mechanisms for PCB mixtures in sediments include oxidative and reductive biodegradation, sorption, and
dissolution. These processes may be influenced by sedimentation, sediment organic carbon quality and
concentration, sediment redox potential, and abiotic (bed transport) and biological (bioturbation) mixing
of sediments. The significance of dissolution, photooxidation, and volatilization processes become
greater in contaminated surface sediments that are resuspended and re-enter the water column or that are
directly exposed to the atmosphere at low water levels (e.g., intertidal marine sediments).
Evaporation, dissolution, and aerobic microbial degradation selectively remove the less-
chlorinated PCB homologue groups from PCB mixtures in sediments. Photooxidation and anaerobic,
bacterial reductive dechlorination selectively remove the more highly-chlorinated PCB homologue groups
from sediments. Hydrophobicity, which is proportional to log Kow and associated log Koc, increases and
vapor pressure and aqueous solubility decrease with increasing levels of PCB chlorination (Table 3-4).
The most highly-chlorinated, most hydrophobic congeners are approximately 10,000 times more
hydrophobic than their least chlorinated, most mobile counterparts. Thus, the strength of sorption to
sediment organic matter increases and evaporation and dissolution decrease with increasing PCB
chlorination, decreasing the mobility and bioavailability of the more highly-chlorinated PCB congeners.
109
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Highlight 3-3. Characterization of Lake Hartwell Surface Sediments by
Comparison with Known Aroclors and Aroclor Mixtures
At Lake Hartwell, SC, a search of historical records revealed that the former Sangamo-Weston plant used
Aroclors 1016, 1242, and 1254 (Magar et al, 2005a, and references therein). Congener distributions in
surface sediment samples were characterized by comparing them with various mathematically-derived
mixtures of these three Aroclors. The surface sediment congener distributions most strongly resembled a
50/50 mixture of Aroclors 1242 and 1254, particularly beginning from PCB26 (2,3',5-TCB) (Magar et al,
2005a).
A cosine 0 similarity metric (cos 0) analysis was conducted to determine the similarity between surface
sediment histograms and that of the 50/50 Aroclors 1242/1254 mixture. The cos 0 values were near 1.0,
suggesting similarity between field samples and the Aroclor mixture. The cos 0 for the histograms shown
in this highlight was 0.85, and the average cos 0 value for 18 surface sediment samples (0 to 5 cm) was
0.648 + 0.241. The similarity of the histograms was influenced by the degree of chlorination. For mono-
through trichlorobiphenyl congeners, the average cos 0 was 0.562 + 0.105, compared to 0.875 + 0.054 for
tetra- through hexachlorobiphenyl congeners and 0.934 + 0.045 for hepta- through decachlorobiphenyl
congeners. The poorer correlation among the lower-chlorinated congeners could have been due to: 1) the
onset of dechlorination in the upper 5 cm, resulting in the accumulation of lower-chlorinated congeners, 2)
weathering during sediment transport, resulting in the loss of lower-chlorinated congeners, or 3) a
combination of these phenomena. These factors, and the fact that the surface sediments did not resemble a
single Aroclor, demonstrate the difficulties associated with comparing sample congener distributions with
known Aroclor congener distributions.
At Lake Hartwell, deeper sediments exhibited a decreasing resemblance to Aroclors or Aroclor mixtures
due, presumably, to their age and long exposure to dechlorination reactions. Surface sediments most
closely resemble source patterns because they have undergone the least amount of dechlorination;
however, they may experience aerobic degradation of the lower chlorinated congeners. Eventually, when
these surface sediments are buried over time, they too will become anaerobic and will undergo
dechlorination; with time, the congener distributions will look less and less like their sources.
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(b) a 50/50 distribution of Aroclors 1242 and 1254 (from Magar et al., 2005a)
110
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Evidence for PCB dechlorination can be obtained by comparing congener histograms for
multiple samples that have been anaerobic for different lengths of time (Zanaroli et al., 2006). In
laboratory studies, PCB-contaminated sediments containing inocula of different anaerobic bacteria are
incubated under anaerobic conditions for long periods of time. The sediments are then sampled and
analyzed periodically, enabling dechlorination rates and patterns to be monitored. In the field,
dechlorination must be inferred by comparison of congener distributions from in-situ samples with known
or inferred source distributions. Historical documents may provide information about the types and
amounts of Aroclor mixtures used and released into the environment. Comparison of the congener
distribution of historical sources may be inferred from record searches that reveal the types of Aroclor
mixtures used in association with a source. Another strategy to surmise original PCB source and/or the
specific Aroclor mixture responsible for a contaminated area is to compare surface sediment samples with
progressively older, buried sediment samples (Bzdusek et al., 2006). Implicit in this second approach is
the assumption that the surface sediments are characteristic of historical PCB sources, i.e., when buried
sediments once resided on the sediment surface, their congener distributions resembled the current surface
sediment distributions.
Highlights 3-4 and 3-5 illustrate examples of PCB dechlorination in sediments from the Lake
Hartwell site. In Highlight 3-4, congener shifts are compared visually and by subtracting the congener
distribution in surface, less-altered PCBs from those in buried, extensively-dechlorinated PCB residuals.
The result is a graph of the net dechlorination observed at a particular location in the lake. In
Highlight 3-5, Lake Hartwell dechlorination was examined by plotting relative ortho and meta plus para
chlorine concentrations per biphenyl molecule with sediment depth. The loss of meta plus para chlorines
and the conservation of ortho chlorines provided very strong evidence of PCB dechlorination with
sediment depth and age. Bzdusek et al. (2006) performed similar studies with Lake Harwell sediment
cores and obtained similar results.
PCB dechlorination can be an important natural process leading to a reduction in toxicity of
PCB-contaminated sediments and recovery of sediment-water ecosystems (as discussed above).
However, the positive impact of dechlorination (e.g., reduced mass and reduced toxicity) must be
understood in the context of where in the sediment bed these reactions occur. Dechlorination is a
progressive, anaerobic process. Most near-shore fresh water and marine sediments become anoxic at a
depth of 0.5 to 10 cm below the sediment surface, depending on factors such as the concentration of
biodegradable organic matter in the sediments, pore water flows, bioturbation, etc. Since reductive
dechlorination is performed by obligate anaerobic bacteria, the extent of dechlorination, which usually
reflects the time since deposition, increases with sediment depth so that surface sediments, which
typically are aerobic and support benthic receptors and pose the greatest risk of environmental exposure,
are likely to exhibit the least PCB dechlorination. The most highly-dechlorinated PCBs usually are in the
deepest sediment layers deposited after the introduction of commercial PCBs. Consequently, whereas
PCB dechlorination is likely to provide limited short-term risk reduction, it is a potentially beneficial
process that, overtime, can lead to the detoxification of buried sediments.
3.4.3.3 Multivariate Model Fingerprinting. Multivariate chemometric models, such as polytopic
vector analysis (PVA), have been used frequently to characterize sources and alteration patterns of
chlorinated organic compounds in complex environmental settings (Johnson et al., 2000, 2002; Johnson
and Quensen, 2000; Imamoglu et al., 2004; Barabas et al., 2004). The advantage of PVA is that it is a
mathematical/statistical technique valued as an exploratory data analysis method that can be used to
identify end-member fingerprints with minimal assumptions about contributing source patterns and/or
alteration mechanisms. PVA provides estimated compositions of contributing PCB fingerprints directly
from the analysis of the ambient data. PVA also provides estimates of the relative contribution of each
end member in each sample. Only after the end-member patterns are resolved are they compared to
111
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Highlight 3-4. Evaluating PCB Dechlorination by Comparing PCB Congener Histograms
Congener distributions for surface and buried sediment samples from Lake Hartwell sediment reveal a
congener shift from higher- to lower-chlorinated congeners with sediment depth and age (Magar et a/.,
2005a). Comparison of surface (a) and buried (b) sediment PCB profiles, represented in (c) as the
histogram of the difference of surface and buried sediment samples, demonstrated concentration
decreases in the tetra- through decachlorobiphenyl homologues, and corresponding increases in mono-
through trichlorobiphenyls. Negative values in (c) represent a net concentration loss, while positive
values represent a net concentration gain.
Though dechlorination is shown to be extensive, most occurred after burial. Surface sediments (a)
exhibit the least dechlorination. Dechlorination can contribute to long-term risk reduction by reducing
more toxic PCB congeners in buried sediment, particularly if there is a risk of sediment erosion and
resuspension.
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Congener distributions showing dechlorination characteristics for Lake Hartwell sediments
Magar etal.,2W5a)
(from
112
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Highlight 3-5. Dechlorination of Meta and Para Chlorines and Conservation of
Ortho Chlorines at Lake Hartwell (from Magar et a/., 2005b)
PCB dechlorination was measured in Lake Hartwell sediment cores by plotting ortho chlorines and
meta plus para chlorines per biphenyl molecule (i.e., moles chlorine/mole PCBs) with sediment depth.
The loss of meta plus para chlorines and the conservation of ortho chlorines with sediment depth
showed that the PCBs underwent reductive dechlorination after burial. Notably, ortho chlorines were
remarkably well conserved for more than five decades, since the first appearance of PCBs (ca. 1950 to
1955).
Dechlorination rates were determined by plotting the numbers of meta plus para chlorines per biphenyl
with sediment age. The average dechlorination rate was 0.094 ± 0.063 mol Cl/mol PCBs/year, and the
time required per chlorine removal ranged from 4.3 to 43.5 years, with an average of 16.4 ±11.6 years.
Though dechlorination tended to be very extensive in buried Lake Hartwell sediments, it was not
always consistent from core to core or at various depth intervals within a single core, as shown below.
The reason for this variability in dechlorination extent could not be determined, but it did not appear to
correlate with such factors as PCB concentration, total organic carbon, or age.
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Number of ortho (•) and meta p\uspara (A) chlorines per biphenyl molecule with sediment depth
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literature-reported source and alteration patterns (e.g., Aroclor compositions, PCB dechlorination
patterns, and other weathering patterns).
PVA is particularly useful for large data sets. Comparison of paired samples, such as those
shown in Highlight 3-3, would be difficult to perform for every sample pair in a large data set. PVA
makes it possible to: 1) determine dominant sources and alteration patterns in a large data set, 2) calculate
the distribution of end members in each sediment sample, and 3) model the distribution of end members
in multiple sediment cores. PVA methods are described in detail by Johnson et al. (2002) in Introduction
to Environmental Forensics and summarized by Johnson and Ehrlich (2002). The reader is referred to
Johnson et al. (2000) and Magar et al. (2005a) for examples demonstrating the use of PVA for
identification of PCB source and alteration patterns.
The discussion above describes a single multivariate fingerprinting method, PVA. While
PVA is a valuable, proven method for resolving certain complex source mixtures, it is not the only
113
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mathematical tool available for such analyses. PCA is another method that has often been employed to
distinguish different sources in less complex sediment mixtures (Johnson et al., 2002). Three other
methods described by Johnson et al. (2002) for use with complex source mixtures are target
transformation factor analysis, source apportionment by factors with explicit restrictions, and positive
matrix factorization. Because different methods are better suited for different data situations,
practitioners are encouraged to consider each of these methods when evaluating data sets.
3.4.3.4 Summary ofPCB Fingerprinting for MNR. PCB dechlorination and weathering can affect
the concentrations and distributions of PCB congeners in sediments. Many factors can influence the rate
and extent of PCB dechlorination and other weathering processes such as PCB concentrations, the
presence of co-contaminants, redox conditions, and sediment age. Several fingerprinting techniques and
detailed PCB congener analysis methods can be used together to better understand surface sediment
sources and dechlorination processes. Evidence of dechlorination can be observed in the net loss of
chlorines with depth, the preferential loss ofmeta and para chlorines, and dechlorination patterns
characteristic of patterns reported in the literature by others.
Contaminant weathering and biotransformation processes in sediments can collectively
contribute to reduced exposure and the recovery of sediment-water ecosystems at contaminated sediment
sites. At PCB-contaminated sediment sites, dechlorination, leading to reduced mass and reduced toxicity,
may be an important component of natural recovery. However, dechlorination typically occurs slowly
and mainly in buried, anaerobic sediments and it increases with sediment depth and age. Thus,
dechlorination of biologically active, near-surface sediments rarely is complete and residual mono-
through tri-chlorobiphenyl PCB congeners commonly persist. Whether dechlorination provides sufficient
risk reduction to justify MNR must be assessed on a site-specific basis, depending on the nature of the
sediment environment as it relates to promoting or restricting dechlorination processes, the formation of
dechlorination intermediates and end products, and the presence or absence of ecological receptors. In
fact, the greatest potential for natural dechlorination processes to provide long-term benefits (i.e.,
ecosystem recovery) is likely to be found at sediment sites that exhibit anaerobic conditions and limited
benthic resources.
3.4.4 Assessing Natural Attenuation of PCDDs/PCDFs in Sediments. The characteristics of
PCDDs/PCDFs and their weathering, including biodegradation, in the environment were discussed in
Sections 3.1.2.3 and 3.3.3. PCDDs/PCDFs are unintended byproducts of manufacture of certain
commercial chlorinated hydrocarbons (pentachlorophenol, Aroclor, and 2,4,5-trichlorophenoxy acetic
acid contain trace impurities of mixed PCDDs/PCDFs), chlorine bleaching of wood pulp in paper
manufacture, and combustion of organic material in the presence of chlorine. There are 75 possible
PCDD congeners and 135 possible PCDF congeners. The tetrachloro-DDs/DFs contain the most
congeners (Safe, 1991). Fifteen congeners containing chlorines in the 2-, 3-, 7-, and 8-positions are
extremely toxic (Table 3-4) and are of major concern in sediments contaminated with chlorinated HOCs.
However, the most highly-chlorinated congeners, octachlorodibenzo-/>-dioxin and
octachlorodibenzofuran, are so insoluble and bind so strongly to sediment organic matter (log Kow > 8.0:
Table 3-6) that they are not bioavailable or toxic to aquatic organisms (Berends et al., 1997) and do not
biomagnify in aquatic-terrestrial food chains (Broman et al., 1992).
The fingerprinting methods described above for PCBs (Section 3.4.3) can be used to
determine possible sources of PCDDs/PCDFs in environmental samples (O'Keefe et al., 1996). Most of
the PCDDs/PCDFs in sediments, however, are from diffuse, non-point sources (e.g., atmospheric
deposition of combustion products), and their homologue and congener compositions, relative
concentrations, and weathering patterns have not been well characterized.
114
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Brzuzy and Kites (1996) compared the homologue profiles of globally-averaged
PCDD/PCDF atmospheric deposition to typical single-source incinerator emissions. The most abundant
homologue in deposited mixtures was octachlorodibenzofuran, followed by heptachlorodibenzofuran.
Incinerator emissions contained nearly equal amounts of dibenzodioxin homologue groups with five, six,
and seven chlorines and dibenzofuran homologue groups with seven or eight chlorines. The differences
could have been caused by degradation of all dibenzodioxins and the less-chlorinated dibenzofurans in the
atmosphere by photooxidation or in sediments by biodegradation, or to the presence of one or more
additional major sources of PCDD/PCDF emissions with substantially different homologue distributions
than the incinerator emissions.
O'Keefe etal. (1996) used PCA to compare concentrations of PCDDs and PCDFs in
sediments from two interconnected lakes in New York, Lake Champlain and Lake George. Two different
PCDD/PCDF congener profiles were present in surface sediments from Lake George. Sediments near
residential areas and power boat marinas had the highest levels of PCDDs/PCDFs, dominated by hepta-
and octa-CDDs and tetra- and octa-CDFs. However, the PCDD/PCDF profiles in the sediments
resembled those in sediments from remote lakes where atmospheric deposition was the most likely source
of contamination, suggesting that the dioxins/furans in Lake George sediments were not from boating
activities. Lake Champlain sediments also contained two PCDD/PCDF congener profiles, but these were
different from those in Lake George sediments. The most heavily-contaminated samples contained high
concentrations of octa-CDD. Some of these high samples also contained elevated concentrations of
several tetra-CDDs, primarily highly toxic 2,3,7,8-tetrachlorodibenzodioxin. The likely source of these
PCDDs/PCDFs is the bleached kraft pulp and paper mills on the shores of Lake Champlain.
Gaus etal. (2002) evaluated congener profiles of PCDDs in Queensland, Australia sediments.
There was an increase in the relative concentrations of less-chlorinated PCDDs and a decrease in the
relative concentrations of the more highly-chlorinated PCDDs with depth and time of deposition in
sediment cores. Sequential reductive dechlorination of OCDD to TCDDs in anoxic sediment layers was
the most likely cause of these changes. Because this process is slow relative to the rate of sediment
deposition, the most abundant PCDDs at all depths in sediment were the heptachloro- and hexachloro-
dibenzodioxins. Tetrachlorodibenzodioxin congeners (the most-toxic congeners: Table 3-4) were most
abundant in the deeper sediment layers. The most toxic congener, 2,3,7,8-tetrachlorodibenodioxin,
however, was not present in the Queensland sediments or in sediments from the Mississippi River; Osaka
Bay, Japan; and Hong Kong, China. Gaus et al. (2002) hypothesized that sediments from all of these
locations contained PCDD/PCDF assemblages from multiple, poorly-characterized sources, including
releases of contaminated commercial chlorinated hydrocarbons and incineration.
3.5 Assessing Sorption/Sequestration
The only HOCs in fresh water, estuarine sediments, and marine sediments that are of
ecological importance are those in forms and locations that can interact with ecological receptors causing
sublethal and lethal responses that may disrupt local biological populations and ecological communities.
The HOC must be in a form that is bioavailable to ecological receptors, including sediment microbes (see
Section 3.2.1). The most bioavailable form of most HOCs is the dissolved species. The HOC must also
be in a location where potential receptors can come in contact with it or with HOCs dissolving
(partitioning) from the adsorbed, or otherwise sequestered, HOC inventory in sediments. Therefore,
sorption, sequestration, and burial govern the relative bioavailability and hazard of HOCs in sediments.
As discussed in Section 3.3.2, sorption and sequestration of HOCs in sediments vary widely
depending on the hydrophobicity (driven by compound polarity and functional group characteristics) of
the HOC (proportional to log Kow) and surficial sediment properties. The physical form and chemical
milieu in which the HOC or HOC mixture was introduced into sediments is an important determinant of
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the subsequent in-situ behavior of the HOC. Petrogenic and some pyrogenic PAHs often are still
associated with a NAPL phase in sediments. Pyrogenic PAHs often enter water bodies and are deposited
in sediments tightly bound to soot particles from the combustion source. Mixed petrogenic/pyrogenic
PAHs associated with coal tars and creosote are often associated with highly viscous or solid particles in
sediments. The petroleum PAHs often partition from the oil NAPL phase more easily than the pyrogenic
PAHs partition from the organic particle or NAPL phase. Therefore, it is important, as an early phase of
MNR, to determine the physical/chemical forms and sources of PAHs in site sediments and their
association with the sediment matrix (Neff et al., 2005).
PCB and PCDD/PCDF mixtures may also be present in sediments in NAPL or adsorbed to
organic phases (coatings on particles, BC, and colloids) as discussed in Sections 3.2.4 and 3.2.7. Most
PCDDs/PCDFs that enter sediments are from combustion sources and are introduced to aquatic systems
adsorbed to soot particles; they partition similarly to pyrogenic PAHs. In contrast, PCBs usually are
introduced to aquatic systems from releases of oily commercial mixtures (e.g., Aroclor) and may partition
in sediments more similar to petrogenic PAHs (Section 3.2.8).
Sources of PAH, PCB, and PCDD/PCDF mixtures in sediments sometimes can be identified
by fingerprinting techniques (Sections 3.4.2.1, 3.4.3, and 3.4.4). Different mathematical relationships can
be used to estimate partitioning from NAPL phases (oil, coal tar, and creosote) or sorbed phases into the
dissolved (bioavailable) phase in water. Organic particle/colloid-water partitioning has been studied
more thoroughly and should be used for an initial assessment of sediment-water partitioning of HOCs
from all sources.
Equilibrium partitioning models provide only a first estimate of actual partitioning behavior
due to high variability in sorption/desorption and sequestration of different HOCs in sediments with
different geochemistry and concentrations and quality of organic matter. Because BC adsorbs PAHs and
PCDDs/PCDFs much more effectively than predicted by conventional sediment organic matter/water
partition coefficients (Koc), some of the variability in predicted sorption behavior of these chemicals can
be reduced by correcting or accounting for the fraction of BC in site sediments (Accardi-Dey and
Gschwend, 2002; Hawthorne etal, 2007b).
In order to make site- and HOC-specific predictions of aqueous phase HOC concentrations,
more accurate, site-specific data can be obtained by determining log Koc empirically in sediment samples
from cores collected at several locations at the study site. Hawthorne et al. (2005, 2006, 2007b) outline
an approach to measuring log Koc for PAHs in sediments from the vicinity of former MGP sites.
Sediment samples are collected and processed quickly according to EPA (200Ib) recommendations. Wet
sediments are centrifuged at moderate speed to separate sediment solids from pore water. Colloids are
precipitated from the water phase by alum flocculation. Target analytes (PAHs, PCBs, or
PCDDs/PCDFs) are extracted and analyzed from all three phases by GC/MS. If soot carbon
concentration is measured in an aliquot of the dry sediment, the distribution of particulate-phase HOCs
between soot and natural organic carbon can be estimated (Cornelissen and Gustafsson, 2005; Cornelissen
et al., 2005). An example of data generated by this protocol is summarized in Table 3-7, where partition
coefficients to soot (Ksc values) are greater than Koc values by at least an order of magnitude, illustrating
the importance of considering BC (in this case, soot) material in sediments as HOC sorbent materials.
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4.0 FATE OF INORGANIC CONTAMINANTS IN SEDIMENTS
4.1 Relevance of Metals Behavior for MNR
An understanding of the biogeochemical processes in sediment is of particular importance for
evaluating the effectiveness of MNR for metals-contaminated marine, estuarine, and fresh water
sediments. Many environmental variables govern the chemical state of metals in sediment, which in turn
affect their mobility, bioavailability, and toxicity, making natural recovery of metals-contaminated
sediments difficult to predict. Much of the current understanding of the role of chemical processes in
controlling risk is focused on the geochemical transformations resulting from changes in pH and redox
potential that can profoundly affect speciation, solubility, and, consequently, bioavailability of metals and
organometallic species. For any site where MNR is implemented, determination of metal speciation is of
critical importance for estimates of bioavailability and risk. For some metals, the free metal (or aquo ion)
may be the most toxic form to organisms; however, for other metals, such as mercury, biologically
transformed species (e.g., methyl mercury) pose the greatest risk. The redistribution of a contaminant
from soluble to insoluble species through the formation of independent solid phases, precipitates, co-
precipitates, or adsorption onto sediments may reduce the bioaccessibility of the contaminant. For
example, formation of relatively insoluble metal sulfides under reducing conditions may effectively
reduce the risk posed by metal contaminants if reducing conditions are maintained. Environmental
variables that influence metal mobility and bioavailability in sediments include pore water pH, alkalinity,
major ion composition/concentration, sediment grain size, sediment mineralogy, redox conditions, and the
amount of sulfides and organic carbon present in the sediments. Furthermore, the resident biological
community affects many chemical processes in sedimentary environments. Sediment dwelling organisms
impact the physical/chemical properties of surface layers of the sediment through mixing of surface
sediments with deeper layers and facilitate transport of oxic surface water into deeper layers in sediments
where conditions are often sub-oxic or anoxic.
The success of MNR as a risk reduction approach typically is dependent on understanding
the dynamics of the contaminated environment and the fate, mobility, and bioavailability of the
contaminant under the geochemical conditions in that specific sedimentary environment. The natural
processes of interest for MNR may include a variety of processes that, under favorable conditions, act
without human intervention to reduce the mass, mobility, or concentration of bioavailable/toxic forms of
contaminants in the sediment bed. Natural processes that result in metal sequestration, immobilization,
stabilization, or reduced bioavailability may include physical, biological, and chemical mechanisms
such as redox and sorption. This section focuses specifically on: 1) chemical processes of metals in
sediments, 2) behavior of specific metals in sediments, 3) sediment sampling and analytical
considerations, and 4) analytical approaches to metal speciation in sediments.
4.2 Biogeochemical Processes that Affect Metal Behavior
The distributions and concentrations of metals in sediments will depend on a large number of
factors, with the most important being the conditions related to the aqueous phase (e.g., ionic strength,
specific inorganic co-contaminants, alkalinity, pH, and DOC), but also on the types and abundance of
metal-binding phases present in the sediments, such as particulate sulfide (acid volatile sulfides [AVS]),
oxides of iron and manganese, clay minerals, and organic matter (Besser et al, 2003; Lee et al, 2004;
Simpson et al., 2004; Simpson and Batley, 2007). These factors affecting metal speciation (i.e., aqueous
phase concentration) and, by extension, bioavailability, are discussed in detail in this section.
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4.2.1 Ionic Strength, Hardness, Alkalinity, and Temperature. The ionic strength of surface and
interstitial pore water will influence the bioavailability of metals and metal compounds. The dissolved
salts in waters having a high ionic strength may compete for biological uptake with charged metal species
or may influence the thermodynamic equilibria by altering the ratios of metal species in solution (Stumm
and Morgan, 1981). This phenomenon is most relevant to marine and estuarine environments (e.g., Riba
et al, 2003). Ultimately, the concentration of the various organic and inorganic cations and anions,
which collectively contribute to the overall ionic strength, influence the distribution of metal species in
the aqueous phase.
Hardness, mainly determined by calcium (Ca2+) and magnesium (Mg2+) in most natural
waters, affects metal behavior and bioavailability by competing with metals for binding with DOM,
anions, and biological membranes (e.g., gills) (Hodson et al., 1978; Pagenkopf, 1983; Benson and Birge,
1985; Lauren and McDonald, 1986). Increased concentration of hardness ions can, therefore, interfere
with an organism's ability to take up potentially toxic trace metals due in part to the common divalent
characteristics of hardness cations and common metal contaminants. Natural seawater (salinity 35 g
dissolved salt/L) contains approximately 420 mg/L calcium and 1288 mg/L magnesium and,
consequently, is extremely hard. The metals most affected by hardness include common divalent metal
contaminants (e.g., cadmium, chromium, cobalt, copper, manganese, nickel, and zinc), but insufficient
data exist for the development of hardness-based toxicity equations for some metals (e.g., cobalt, lead,
manganese) (e.g., Chapman et al., 1980; Pagenkopf, 1983; Bradley and Sprague, 1985; Stubblefield et al.,
1991; Diamond et al., 1992). Although hardness historically has been considered an independent factor
in determining the bioavailability and toxicity of metal and metalloid contaminants, modern predictive
approaches used for toxicity estimates (e.g., the Biotic Ligand Model; see EPA, 2007c) typically consider
hardness in conjunction with other factors, such as pH, alkalinity, and DOC.
Alkalinity refers to the acid neutralizing capacity of the sampled water. For a water body
containing dissolved carbonate and no other strong bases, the alkalinity will be approximately equal to the
sum of dissolved bicarbonate, carbonate, and hydroxide ion (see Stumm and Morgan [1996] for extended
definition and formal calculations of alkalinity). Alkalinity affects bioavailability in a manner analogous
to hardness, but involves the carbonate anion instead of the Ca2+ and Mg2+ cations. Increased alkalinity
will reduce metal bioavailability through the formation of metal carbonate complexes. For some metals
(e.g., copper and lead), it has been found that in addition to hardness, alkalinity also may have an
influence on bioavailability (Shaw and Brown, 1974; Andrew, 1976; Davies, 1976; Nelson et al., 1986);
however, the mechanism by which alkalinity governs metal speciation and, consequently bioavailability,
was not elucidated. For a regional site manager, alkalinity may be used as an initial indicator of several
factors that govern metal speciation, including the formation of carbonate complexes, carbonate
precipitates, and hydroxide precipitates; the sorption of trace metals onto the formation of a bulk
carbonate/hydroxide solid phase; or a combination of these processes.
Temperature exerts an important effect on metal speciation because most chemical reaction
rates are highly sensitive to temperature changes (Elder, 1989). Equilibration rates of metals between
dissolved and sorbed phases in sediment increase with increasing pore water pH and temperature
(Simpson et al., 2004). Temperature may also affect quantities of metal uptake by an organism because
biological process rates in unacclimated organisms typically double with every 10°C temperature
increment (Luoma, 1983; Prosi, 1989). Because increased temperature may affect both influx and efflux
rates of metals, net bioaccumulation may or may not increase (Luoma, 1983). Temperature also has an
indirect influence on metal cycling through its effect on organisms and the biogeochemical processes
involving trace metals that they mediate.
4.2.2 pH and Eh. pH and Eh are important factors governing metal speciation, solubility, and
sorption/desorption to mineral and/or organic surfaces, which, in turn, affect metal transport and
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bioavailability in aquatic systems. The system pH influences the solubility of oxyhydroxide, carbonate,
phosphate, and sulfide minerals and adsorption-desorption processes. The pore water of marine and
estuarine sediments, like the overlying seawater, is strongly buffered, and pH changes typically are not as
significant as in fresh water sediments. The buffer capacity of the sediment solid phase is even greater
than that of seawater, but reactions involving solid phases can be slow (Simpson and Batley, 2007).
Addition of dissolved cationic metals to sediment tends to release hydrogen ions (FT) to solution via
electrostatic exchange or proton displacement reactions as a result of surface complexation, increasing
acidity, and reducing pore water pH. Although mineral solubility is a function of pH, most metal
hydroxide minerals have very low solubility under pH conditions typical of natural waters (pH 6 to 8).
The solubility of metal hydroxide minerals usually increases with decreasing pH, and more dissolved
metals become potentially available for incorporation in biological processes. Furthermore, increased
solution acidity results in increased free metal ion concentration in solution due to competition with
protons for exchange sites or electrostatic repulsion from the increased net positive charge of mineral
surfaces.
A key factor determining the speciation and biogeochemistry of multi-valent trace metals
(e.g., arsenic and chromium) in sediment is the Eh of the system. System redox influences both the
oxidation state of the contaminant (e.g., Cr[III] versus Cr[VI]) and the stability of various mineral phases
that affects solid-phase partitioning of the contaminant (e.g., iron oxides versus iron sulfides). A redox
reaction occurs when electrons are transferred from an electron donor (the reductant) to an electron
acceptor (the oxidant). The redox condition of any environment can be described either as reducing,
where the tendency is for electron-deficient species (e.g., oxides of Fe[III] or Mn[IV]) to gain electrons,
or oxidizing, where electron-rich species (e.g., reduced organic carbon) tend to lose electrons.
Biogeochemical processes often facilitate the transfer of electrons between chemical constituents,
resulting in oxidation and reduction. Different important oxidation/reduction reactions occur at different
Eh values; the critical MnO2/Mn2+ and Fe2O3/Fe2+ redox reactions occur at about +10 to +50 mV, and the
SO42"/H2S and CO2/CH4 reactions occur at about -70 to +50 mV, depending on pH (Stumm and Morgan,
1996; Drever, 1997). Field measurements ofin-sifu redox potential should be regarded with some caution
as typical methods that rely on a platinum working electrode do not necessarily reflect the distribution of
redox species for a given redox couple (EPA, 2002b). Whereas pH-buffering processes may occur
rapidly, electron transfer processes are often kinetically slow and the actual concentrations of
reduced/oxidized species of a given element in a specific environment may vary greatly from predictions
based solely on thermodynamic considerations. In sedimentary environments, pH and Eh control the
solubility and concentration of the major metal species through the dissolution of redox-active mineral
oxides (e.g., Fe/Mn oxides) and desorption of those elements associated within or adsorbed onto the
structural matrix of these oxides (Kimball and Wetherbee, 1989; Van Cappellen and Wang, 1996).
4.2.3 Sediment Diagenesis and Establishment of Vertical Redox Profile. In surficial sediments,
a major process that dictates the chemical speciation and biogeochemistry of trace elements is microbial
respiration of organic matter, which, in turn, controls the redox potential of the sediments and
concomitantly influences pH and alkalinity. In surficial sediments, this is referred to as early diagenesis.1
In early diagenesis, fresh sediment in the uppermost layer is transformed by a suite of biogeochemical
reactions, all of which combine to lower the free energy of the system (Sundby, 2006). Microbial
processes drive organic matter oxidation in sediments through the utilization of a series of terminal
electron acceptors (TEAs) (Figure 4-1). The TEAs are utilized in the order of decreasing free energy
yield with the major components (in order) being O2, NO3" and NO2", MnO2(s), FeOOH(s) or
Fe(OH)3(s), and SO42" (Stumm and Morgan, 1996). Once all of the TEAs are consumed, oxidation of
organic matter occurs by methanogenesis (e.g., CO2 + 4 H2 —»• CH4 + 2H2O or CH3COOH —» CH4 +
1 Diagenesis is the process of chemical and physical change in deposited sediment during its conversion to rock.
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CO2). This preference of TEAs results in a vertical layering of redox conditions in near surface
sediments (Figure 4-2) (Froelich etal, 1979). This figure represents atypical marine system; however,
the spatial scale of this vertical distribution may change depending on the actual fluxes of the different
TEAs into the sediment layer. The vertical concentration distributions of several TEAs in sediments
utilized by microorganisms in the oxidization of organic matter to carbon dioxide are depicted in Figure
4-2. The preference in the use of oxidants by microorganisms results in a layering of reduction zones,
each layer driven by a different class of microorganism. As illustrated, there is often considerable overlap
of the reduction zones, which can be exacerbated by bioturbation, resulting in a smearing of the redox
zones. In open ocean sediments, the abundance of organic matter delivered to sediments is typically low
and early diagenesis rarely proceeds beyond nitrate reduction into sub-oxic diagenesis. In coastal areas
where the delivery of organic matter can be high, sulfate reduction is usually the dominant oxidant
pathway due to the abundance of sulfate in marine systems. In fresh water sediments, the spatial scale of
the vertical stratification of redox strata may be compressed relative to marine sediments, with the redox
transitions from nitrate reduction to sulfate reduction occurring within the uppermost 4 cm (Wersin et al.,
1991). The vertical distribution of TEAs shown in Figure 4-2 may vary for different sites, including
changes in overall depth and the degree of overlap between TEA distributions. In addition, depending on
depth of surface water and flux of degradable organic carbon, the transition from oxidizing to reducing
conditions may occur within the water column, at or some depth below the sediment-water interface.
Methane production is not normally seen in marine systems, but is more common in fresh water systems
where sulfate levels are far less abundant. This layering can directly affect bioavailability of metals by
controlling the redox state of the metal and indirectly by providing a barrier to diffusive transport due to
the oxidative precipitation of insoluble iron and manganese phases near the sediment-water interface
(Stumm and Morgan, 1996).
4.2.4 Iron and Manganese Oxides. The iron/manganese oxide component of sediments is an
important, even controlling, repository for a wide variety of metals in the majority of systems. Iron
hydrous oxide particles have diameters ranging from tens of nanometers to hundreds of microns
depending on the physical and chemical conditions where the oxides were formed and degree of atomic
substitution (Cornell and Schwertmann, 1996). These small dimensions give them a large surface area
per unit mass and a strong propensity to adsorb as surface films on larger, clay-sized particles. During
initial weathering, estuarine mixing, or groundwater upwelling, iron and manganese often precipitate near
the sediment-water interface as the reduced forms encounter oxic conditions. Conversely, as oxidized
forms of iron and manganese oxides encounter reducing conditions, a combination of biotic and abiotic
processes acts on these minerals, causing reductive dissolution and/or the transformation to new mixed-
valence oxide phases. For iron oxides, this process can be observed in the transformation of crystalline
ferric oxides (commonly found as hematite, goethite, ferrihydrite, and/or amorphorous phases) to mixed
valence magnetite and/or green rust minerals to ferrous minerals depending on the specific geochemical
conditions at each site. For manganese oxides, oxidation of buried Mn(II) minerals results in a vertically
dynamic distribution of Mn(FV) and Mn(III) minerals. For manganese oxides, upward diffusion of
reduced phases results in oxidation/precipitation of Mn(III) and Mn(FV) oxides. As these oxides are
buried, they are reduced to Mn(II) and/or Mn(III) species (Anschutz et al, 2005). Oxidation and aging of
Mn(II) minerals is also the result of microbiological (enzymatic) transformation (Tebo et al., 2004). In
redox active sediments (e.g., at the sediment-water interface), the action of a multi-copper enzyme was
observed to facilitate the transformation of reduced manganese to manganese oxide similar to birnessite
(Tebo et al., 2004). Biogenic Mn(FV) oxides are significant adsorbents for trace metals governing the in-
situ speciation and bioavailability of metals cations such as Cu, Cd, Co, Ni, Zn, Sn, Ca, Hg, Se, and Pb
(Fuller and Harvey, 2000; Tebo et al., 2004).
In high-alkalinity waters with sufficient dissolved carbonates, ferrous iron may precipitate as
carbonate solids (e.g., FeCO3, siderite), whereas in sedimentary environments with elevated phosphate,
the formation of iron phosphate minerals (e.g., Fe3[PO4]2, vivianite) may occur. In some cases, the
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presence of minerals such as siderite are inferred on the basis of equilibrium considerations and not direct
spectroscopic evidence (Wersin et a/., 1991). During ferric oxide reduction, dissimilatory iron reducing
bacteria have been shown to use ferrihydrite as a TEA, transforming the ferric hydroxide to mixed-
valence Fe(II)/Fe(III) oxides including magnetite (Fe3O4) and green rust (fougerite, a clay-like mineral)
(Behrends and Van Cappellen, 2007; Glasauer et a/., 2003). During the iron oxide mineral
transformations, the sequestration of inorganic contaminants may occur via adsorption, surface
complexation, or co-precipitation processes, all of which may operate separately or concurrently
depending on specific site conditions.
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Nitrate reduction D+L -19.6
Fermentation F+L -6.4
Suffate reduction G+L -6.1
Methane fermentation H+L -5.6
N2 fixation J+L -4.8
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Ferrous oxidation A+N -21.0
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Figure 4-1. Sequence of Microbially Mediated Reduction-Oxidation Reactions
(Source: Aquatic Chemistry, W.S. Stumm and J.J. Morgan, 1981, reprinted with permission by
John Wiley and Sons, Inc., New York City, p. 460)
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Concentration of Oxidant
Open
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Diagenesis
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Bioturbated Layer (1 cm-10 cm)
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BIOTURBIDITY.CDR
Figure 4-2. Typical Redox Zones in Surficial Sediments in a Marine System Mediated by Biological
Reactions (Adapted from Froelich et al., 1979.)
Both ferric and manganese oxides display prominent pH-dependent, variable-charge surface
electrostatic properties. The different iron and manganese oxides have different surface pKa values:
when the pH equals the surface pKa, the surface is considered to have a net zero charge, also known as
the pH at the point of zero charge (pHpzc). When the pH is less than the pKa, the surface has a net
positive charge, and when the pH is greater than the pKa, the surface has a net negative charge. Surface
pKa values for manganese oxides are typically lower than for iron oxides with reported values of pHpzc
for different manganese oxides ranging from 2.7 (5-birnessite [Scott and Morgan, 1995]) to 4.6 for
hollandite (McKenzie, 1989) to 6.2 for naturally occurring manganese oxide-containing sediments
(Amirbahman et al., 2006). In contrast, the pHpzc for common Fe(III) oxide minerals ranges from 8 to 9.
As such, Fe(III) oxides are typically good sorbents for metals and metalloids from circumneutral to
slightly acidic pH, whereas Mn(FV) oxides are good sorbents for metal cations from slightly acidic to
basic pH.
4.2.5 Natural Organic Matter and Low Molecular Weight Organic Acids. Nearly all
contaminant metals, particularly those that exist as cations and/or cationic hydrolysis species, may form
complexes with organic ligands, which are ubiquitous features of sedimentary environments. NOM in
sediments exists in a dynamic equilibrium between dissolved phases and non-aqueous phases (usually
adsorbed onto clay or oxide surfaces, or suspended colloidal particles). DOM, usually an operationally
defined pool of NOM that passes a 0.45-jam cutoff filter, contains functional groups predominantly
comprised of oxygen-, nitrogen-, or sulfur-containing moieties, which are able to form complexes with
dissolved metals. Four classes of humic substances can be differentiated based on molecular weight and
solubility behavior: humin, humic acid, fulvic acid, and low-molecular-weight organic acids (Jonasson,
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1977; Aiken et al., 1985). Of these, only humin is virtually insoluble. Humic and fulvic acids are
heterogeneous, polydisperse, electrolytic macromolecules with a strong tendency to adsorb to particles,
surfaces, and complex metals and to bind organic molecules. Humic acids are higher molecular weight
and more aromatic in character than are fulvic acids and, therefore, typically are not dissolved in sediment
pore waters where the pH is usually less than 9. At pH values above 9, humic acids can become more
important in the binding, adsorption, and precipitation of metals via coagulation. Fulvic acids have lower
molecular weights compared to humic acids. Accordingly, they are more water soluble and are
characterized by a lower C:O ratio, indicative of a greater number of carboxylate functional groups and,
therefore, a greater metal-binding capacity than humic acids on a per-carbon basis. For example, the
average C:O ratio of humic acids is between 0.47 and 0.57 as compared to between 0.64 and 0.74 for
fulvic acids (Sparks, 2003). Even lower in molecular weight and more water soluble are the low-
molecular-weight organic acids. These tend to be comprised of simple mono- and di-carboxylic acids
such as acetate, lactate, oxalate, malonate, lactate, phthalate, salycilate, and many others. Nearly all
forms of NOM are found to co-occur with metals. Even the most rigorous attempts to isolate NOM
substances will result in NOM containing some metals as well as metal oxides, which are often quantified
as "ash-content" during elemental analysis. This indicates that many metals are irreversibly bound to
NOM and not likely to be accessible for biotic uptake.
In addition to direct metal complexation, NOM in sediments will form coatings on sediment
particles (clays, oxides, etc.) and will act as new reactive surfaces having a high affinity for trace metals.
Small particles, with large reactive surface area to mass ratios, have the capacity to adsorb trace metal
contaminants and may adsorb metal contaminants from the dissolved phase. It has been observed that
NOM coatings of iron oxide particles can govern the formation and growth of these particles and
determine the ultimate morphology and reactivity of iron oxide particles in the natural environment
(Ferret et al., 2000). These carbon-iron participates were further observed to contain significant
quantities of other elements and may, thus, be a significant ternary phase for the partitioning of trace
metals in sedimentary environments where iron and carbon, two environmentally ubiquitous elements, co-
occur (Ferret et al., 2000). Furthermore, as DOM forms metal complexes, the metal ions act as bridges,
binding together several DOM molecules, gradually increasing the molecular size of the complexes, and
eventually creating discreet colloid-sized particles. These particles can flocculate, deposit on the
sediment surface, and become buried within the sediment. Irrespective of the precise mechanism, metal
contaminants bound to particulates still may have a significant toxic effect on some benthic communities
through the ingestion of the particulates.
4.2.6 Solubility Controls by Precipitation and Dissolution: The Formation of Insoluble
Complexes, Sediment Resuspension, and Oxidation. Precipitation of insoluble metal phases is a
significant process facilitating the sequestration of toxic metal contaminants in sediments (Porter et al.,
2004). Solids precipitation may lead to metal sequestration via three principle routes: 1) precipitation of a
pure-phase mineral when sufficient metals and ligands are present, 2) co-precipitation in the case where
the formation of a solid phase captures a metal contaminant within the mineral matrix, and 3) the sorption
of a metal contaminant onto surfaces of a freshly precipitated solid-phase sorbent material. Commonly-
occurring solid phases in sedimentary environments include hydroxide, carbonate, phosphate, and sulfide
minerals, although this list is not exhaustive. Geochemical conditions of the sediment environment must
meet certain criteria in order for these solid phases to form. For example, high alkalinity waters, or
waters of karst bedrock environments, are characterized by elevated levels of dissolved carbonates,
conditions where precipitation of carbonate minerals may occur.
Eutrophic conditions, especially in areas impacted by agriculture or animal husbandry,
commonly contain high levels of phosphate. In these areas, precipitation of phosphate minerals in
sediments (e.g., Fe3[PO4]2 [vivanite] or Pb5[PO4]3OH [hydroxypyromorphite]) may occur (Porter et al.,
2004; Scheckel and Ryan, 2004). In areas where metal contaminants are predominantly present as
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relatively soluble minerals (e.g., PbSO4 [anglesite]), amendments can be made (e.g., phosphate, sulfide,
or lime) in order to facilitate the formation of insoluble and relatively stable mineral phases (e.g.,
anglesite transformation to pyromorphite via phosphate addition; Porter et al., 2004). However,
consideration must be given to temporal changes in redox status, which may cause oxidation (e.g.,
dissolution of sulfide precipitates) or reduction (e.g., dissolution of iron/manganese oxide geosorbents)
and lead to the mobilization of the sequestered contaminant of concern.
Sediments that receive large amounts of organic carbon from photosynthesis (autochthonous)
in overlying waters or transport from terrigenous plant debris (allochthonous) are often highly anoxic.
Low redox potential in this type of environment can promote microbial iron and sulfate reduction and,
consequently, sulfide mineral deposition (e.g., Kanaya and Kikuchi, 2004). During diagenesis,
potentially toxic metals such as arsenic, cadmium, copper, mercury, lead, and zinc may co-precipitate
with insoluble sulfides (e.g., mackinawite or pyrite), form discrete sulfide phases (e.g., PbS [galena], CdS
[hawleyite or greenokite], or HgS [cinnabar or metacinnabar]), or adsorb to the surfaces of freshly
precipitated sulfide phases (e.g., arsenite adsorption onto FeS [mackinawite] or FeS2 [pyrite]) and through
each of these processes become increasingly unavailable to benthos (Morse, 1994; EPA, 2005c).
Pyritization (i.e., formation of insoluble sulfide mineral phases) and/or depyritization (i.e., oxidation and
solubilization of sulfide mineral phases) of trace metals are important processes in controlling
bioavailability of many trace metals, especially in estuarine and marine environments (Morse, 1994). The
importance of sulfide phases is underscored by their very low solubilities relative to carbonate or
phosphate minerals (Table 4-1). Although sulfides have been identified as a main factor for decreasing
the solubility of some metals in sub-oxic sediments, toxicity might not be seen even if the sulfide pool
becomes exhausted. This implies the importance of other binding phases (e.g., organic ligands and
particulate and/or colloidal iron or manganese oxides [Miiller and Sigg, 1990]) that also contribute to the
reduction of metal dissolution and bioavailability (see Section 4.3). Understanding of fundamental
geochemical conditions, such as those outlined in this section (e.g., alkalinity, pH, redox, etc.), should be
used to guide regional assessments for the potential of solid-phase precipitation as a viable MNR process.
The solubility and toxicity of chromium, cobalt, and selenium are not influenced by the
amount of sulfide in the sediment layer to the same extent as are other metals, such as those illustrated in
Table 4-1. The fate, mobility, and toxicity of these metals are influenced more strongly by the presence
of organic material and iron and manganese oxyhydroxides and the redox state of the sediment. For
example, Berry et al. (2004) showed that chromium solubility may be observed to correspond to sulfide
concentration even though chromium does not form stable sulfides, presumably due to the rapid reduction
of chromate to insoluble Cr(III) precipitates. The solubility of chromium in sediment pore water is
affected by sediment redox potential; chromium tends to precipitate as chromic hydroxide (Cr[OH]3)
(solubility product, 6.3 x 10"31) in sub-oxic sediments, where sulfide minerals concurrently increase in
abundance.
Arsenic is unusual because its reactivity and fate have been observed to differ in marine (high
sulfate) versus fresh water (low sulfate) sediments. Arsenic is adsorbed mainly to iron and manganese
hydroxides in oxidized sediment layers; as redox potential declines, it is released from the Fe/Mn oxides
and, in high sulfide environments, may precipitate as orpiment (As2S3) or realgar (AsS) or as inclusions
in other metal sulfide solid phases, such as pyrite (Porter et al, 2004). In fresh water sediments with low
sulfate concentrations, arsenate released during reductive dissolution of Fe/Mn oxides may be reduced to
arsenite and become mobilized or bind to sediment organic matter. At low Eh and low sulfate
concentrations, arsenite may precipitate as arsenolite (As2O3), which is soluble (equilibrium
concentration of H3AsO3 is reported to be 10"°68 M in equilibrium with arsenolite; Yue and Donahoe,
2009) and may diffuse upward into the overlying water column (Porter et al., 2004).
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Table 4-1. Solubility Products for Selected Metal Sulfide, Carbonate, Phosphate, and (Hydr)Oxides
Sulfide
MnS (alabanite)
FeS (mackinawite)
NiS (millerite)
ZnS (wurtzite)
PbS (galena)
HgS (metacinnabar)
CuS (covellite)
CdS (greenockite)
Ag2S (acanthite)
logio KSD
12.84(a)
18.80(d)
20.97(d)
22.50(a)
27.5 l(a)
51.66(a)
36.10(a)
27.07(a)
49.02(a)
Carbonate
MnCO3
FeCO3
NiCO3
ZnCO3
Pb3(C03)2(OH)2
HgC03
Cu2(OH)2CO3
CdCO3
Ag2C03
logio KSD
9.3(c)
10.7(c)
6.9(c)
10.0(c)
45.46(b)
16.1(c)
33.8(c)
13.7(c)
ll.l(c)
Phosphate(e)
MnHPO4
Fe3(P04)2
M3(P04)2
Zn3(P04)2
Pb3(P04)2
n/a
Cu3(P04)2
Cd3(P04)2
Ag3P04
logio KSD
5.74(a)
36.0(c)
31.30(b)
36.70(b)
43.5(c)
35.12(b)
32.60(b)
17.6(c)
(Hydr)Oxides
Mn(OH)2
Fe2O3
Ni(OH)2
Zn(OH)2
PbO
Hg(OH)2
Cu(OH)2
Cd(OH)2
AgOH
logio KSD
12.8(c)
40.63™
17.20™
15.55™
15.09™
25.40™
1936™
14.27™
770(b)
(a) From Lindsay, 1979
(b) From Benjamin, 2002
(c) From Morel and Hering, 1993
(d) From Van den Hoop et al. , 1 997
(e) n/a indicates no record of insoluble phosphate mineral
to
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4.2.7 Spatial and Temporal Stability of Sedimentary Redox Boundaries. The sequestration of
toxic metals through precipitation, co-precipitation, or adsorption processes is reversible. Through
dissolution of the solid phase, precipitated or adsorbed metals are released into the dissolved phase. This
is commonly caused by shifts in redox potential. Shifts in pH can cause surface electrostatic conditions to
change dramatically, resulting in the release of adsorbed species. Of critical importance to effective
natural attenuation of sequestered metals is the knowledge of how both natural (e.g., tidal or bioturbation)
and anthropogenic (e.g., dredging) forces can induce shifts in Eh and/or pH, and how those shifts affect
metal speciation in sediments. For example, a shift from reducing to oxidizing conditions can lead to the
oxidative dissolution of Cr(III) minerals, or release trace metals associated with the oxidation of sulfide
minerals. Alternatively, a shift from oxidizing to reducing conditions can lead to the desorption of metals
adsorbed to Fe(III)/Mn(IV) oxides. Resuspension of sediments (e.g., in the event of storms, tidal surges,
or dredging) could result in a potential increase in dissolved concentrations of metals in the surface water,
primarily related to environmental conditions promoting the shift of trace metals from the particulate state
to the dissolved state. However, the kinetics of oxidation/reduction processes are often slow relative to
time-scales of resuspension events, and the dissolution process may require days to years before release of
solid-bound metals are detected. Van Den Berg et al. (200 Ib), for example, collected data on metal
remobilization during a large-scale experimental dredging project and found that dredging activities did
not notably influence dissolved concentrations of trace metals in the water column for the studied site.
These observations could be related to a relatively slow oxidation of metal sulfides or to rapid
resequestration of liberated trace metals by freshly formed manganese and/or iron (hydr)oxides.
Spatial and temporal stability of sediment redox boundaries and processes can be disrupted,
altered, or generally affected by the introduction of water containing a unique geochemical composition,
such as salt water intrusion in a fresh water coastal lake or groundwater upwelling in a precipitation-fed
bog (Whitmire and Hamilton, 2008). Groundwater discharge can introduce a seasonal flux of electron
donor/acceptors or nutrient cations into lacustrine or riverine systems (Sebestyen and Schneider, 2004;
EPA, 2005d). For example, high-iron groundwater upwelling into a fresh water lake system may result in
iron oxide precipitation at the oxic-anoxic boundary, or salt water intrusion into a fresh water coastal
lake/marsh can produce drastic changes in the predominant microbiological processes that govern the
oxidation state of sedimentary redox-active metals. Salt water intrusion into a fresh water system
represents a significant perturbation to the system. Anaerobic Fe/Mn reducers and/or methanogens can
comprise the majority of microbial community structure in the absence of sulfate reducers, and the
introduction of sulfate from sea water can promote SRB activity. Competition by SRBs for labile organic
substrate can inhibit the activity of methanogens and/or Fe/Mn reducers (Canavan et al., 2006). This shift
in anaerobic community structure has implications for the fate of mercury in contaminated sediments.
Several studies have reported dynamic seasonal and spatial (horizontal and vertical) behavior
of sulfide minerals in natural systems (Koretsky et al., 2003; Roychoudhury, 2007). Three interconnected
factors may help to explain the observed patterns reported in the literature regarding the spatial and
temporal variation in sedimentary redox conditions: 1) diagenetic processes (biotic and abiotic reactions
that alter the chemical form of many of the inorganic and organic constituents of the sediment), variations
in temperature, and oxygen and organic carbon content influencing the local flora that, in turn, influence
sedimentary redox processes via rhizosphere processes, 2) the stability of the reduced metal precipitates
and/or complexes with respect to oxidation, and 3) bioturbation.
In response to the activity of local flora, redox boundaries in shallow sediments may shift on
seasonal and even diurnal time scales. In shallow waters and wetlands, the metabolic activity of algae
(periphyton) living on the surface of sediments or attached vascular plants (marsh grasses) may produce a
diurnal vertical shift of the sediment redox potential discontinuity depth (defined as the depth in sediment
where Eh is 0). The surface layer of sediment becomes reducing during the night when algae at the
sediment-water interface and/or marsh plants are respiring, decreasing ambient oxygen concentrations.
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The surface layer is oxidized during the day when the algae and marsh grasses are photosynthesizing,
producing a net increase in oxygen concentration. This process is of particular importance to mercury-
contaminated sediments where transformation of inorganic mercury to methyl mercury (a potent
neurotoxin) during reducing episodes may promote the release of mercury to the overlying water column
and bioaccumulation of methyl mercury (Bloom et al., 1998). The transient nature of sulfides depends on
the particular metal sulfide. The oxidation of iron monosulfides in sediments cannot be taken as
indicative of the oxidation of other metal sulfides that may be more stable (Table 4-1; Peterson et al,
1996). Simpson et al. (1998) demonstrated that, although FeS and MnS are labile and rapidly oxidizable,
CdS, CuS, PbS, and ZnS are kinetically stable for several hours.
Seasonal variation in flow rates or storms that induce an influx of oxygenated (sea) water can
result in rapid oxidation of anoxic sediment and, thereby, release significant proportions of associated
metals (Koretsky et al., 2003; EPA, 2005c). Although considerable site-to-site variability exists due to
climatic differences, in general, reducing conditions conducive to the formation of metal sulfides tend to
be more prominent at the end of the summer and during fall and revert to more oxidizing conditions in the
winter and spring (Howard and Evans, 1993; Van Den Hoop et al., 1997; Grabowski et al., 2001;
Roychoudhury, 2006). Sediments tend toward anoxic conditions during summer months due to
diminished fresh water inputs, higher water temperatures, an increased rate of particulate organic matter
(e.g., phytoplankton and detritus) deposition, and increased biological activity, which leads to increased
oxygen consumption, general hypoxia, and more reducing conditions closer to the sediment-water
interface (Kraus and Bragin, 1988; Koretsky et al., 2006). Microbial degradation of organic matter in
sediments depletes sediment oxygen and increases sediment sulfide concentration via sulfate reduction
(Figure 4-3).
02 SO/
25-
75
20-
50
£
o
25
5-
0 0
Sulfate Reduction
Oxygen Uptake
Temperature
-25
-20
-15
O
-10
-5
10
Summer
12 2
Winter
6 8 10
Summer
12
Figure 4-3. Seasonal Variations of Sediment Oxygen Uptake Rate (Oxygen Consumption), Sulfate
Reduction Rate (Sulfide Production) in the Whole Sediment Column, and Sediment Temperature
(Adapted from Jorgensen, 1977.)
In addition to temporal shifts in sediment redox potential, a vertical redox gradient is created
from microbial respiratory processes (as illustrated in Figures 4-1 and 4-2). Most often, metal sulfide
concentration increases with increasing sediment depth and is linked to the redox gradient present in the
sediment (EPA, 2005c). This increase may occur over a small sediment distance (less than 1 cm) (Van
Den Berg et al., 1998, 200la, 200Ib). The observed vertical gradient in sediment metal sulfide
127
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concentration is caused mainly by the oxidation of sulfides near the sediment/overlying water interface.
A factor that may contribute to this oxidation is sediment bioturbation (De Witt et al., 1996), which may
carry oxygen to deeper sediment layers due to the burrowing activity of benthic organisms and govern
vertical trends of Fe(III) minerals and/or sulfate (Kostka et al., 2002; Hyun et al., 2007). The effect of
bioturbation on a broader scale is to push the sub-oxic zone of sediments, especially in tidal or littoral
zones, to great depths; the spatial distribution of aerobic decomposers, Fe/Mn reducers, and sulfate
reducers may exhibit considerable overlap (Koretsky et al., 2005). Since the oxidation process is often
relatively slow, the rate of conversion of insoluble metal sulfide to dissolved metal in the pore water
occurs slowly but still may be of significance to metal bioavailability in environments that are not
dynamic. On the other hand, any surficial layer of metal sulfide that becomes dissolved in the pore water
as a result of metal sulfide oxidation will not simply build up in the pore water and remain there. Rather,
it will be subject to diffusion from the pore water into the overlying water as it is produced. Given that
the sediment-water interface is quite thin, this diffusive flux will tend to offset any increase in pore water
metal levels that occur as a result of the oxidation process. Furthermore, pore water metal concentrations
will not necessarily be entirely available to benthic organisms since any metal that is present in the pore
water has the potential to form non-bioavailable metal complexes with other pore water ligands on DOC,
thereby further reducing the potential for toxicity.
4.3 Biogeochemical Process Affecting Speciation and Bioavailability of Metals in Water and
Sediment
All sediments contain metals. The metals in fresh water and marine sediments originate from
both natural and anthropogenic sources and are present in different physical and chemical forms (Gleyzes
et al., 2002; Canavan et al., 2007). The two major pathways available for metal incorporation from
sediments into higher trophic level aquatic species are: 1) ingestion of metal-enriched sediment,
suspended particles, and metal-contaminated food, and 2) direct uptake of solution-phase (i.e., strictly
dissolved) species (Luoma, 1989; Simpson and Batley, 2007). Consequently, knowledge of geochemical
reactions of metals in both water and sediment is necessary to understand controls on metal
bioavailability in natural water (Luoma, 1989). Bioavailability studies indicate that aquatic organisms
bioaccumulate free metal ions (i.e., hydrated metals ions or aquo ions) from solution quite efficiently;
similarly, terrestrial species bioaccumulate dissolved metals more efficiently than via direct particulate
matter ingestion (Luoma, 1983; Newman and Jagoe, 1994). Consequently, geologic and/or
environmental conditions that enhance dissolved metal concentrations (e.g., lower pH) result in greater
metal bioavailability. Indirect controls, such as larger particle or sediment size, also can result in greater
bioavailability of metals by reducing effective surface area available for adsorption of increasing
dissolved metal concentrations. Metal assimilation from ingested particulate matter may be of
significance in some situations because metals are highly concentrated in this form (Luoma, 1989).
Sediments are composed of inorganic detrital particles derived from weathering of crustal
rocks and in-situ precipitation of oxide minerals in upwelling groundwater plumes (e.g., ferrous iron
containing groundwater). They typically contain a variety of metals, mostly in a stable mineral lattice.
Heavy minerals and many clay minerals are rich in one or more metals. Most of the metals in these solid,
stable forms in coastal sediments are derived from natural chemical weathering, physical erosion, and
surface runoff from land. However, anthropogenic inputs from such sources as mine tailings, dredge
material, and oil well drilling muds may be locally important. The metals that are present in solid
sediment particles are classified as residual or detrital metals; residual metals may represent up to about
90% of the total metals in some sediments (Loring, 1982). These residual metals are not bioavailable.
Residual metals in sediment tend to be extremely persistent; therefore, it is important in MNR of metals-
contaminated sediments to differentiate between residual and potentially more mobile, bioavailable metal
fractions in sediments.
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The detrital metals in sediments are present as precipitates from the overlying water or
sediment pore water, or they are adsorbed to or complexed with various sediment components and may be
bioavailable. In oxidized sediments, metals may be adsorbed to clay particles or to iron, manganese, and
aluminum oxide coatings on clay particles or associated with particulate organic matter. During shifts in
redox potentials, metals may be released from sorbed phases into sediment pore water as aqueous and,
therefore, bioavailable forms. As the concentration of oxygen in sediment decreases, usually because of
microbial respiratory utilization of organic matter, the iron, manganese, and aluminum oxy-hydroxide
coatings begin to dissolve, releasing adsorbed metals. In oxygen-deficient and sulfur-rich sediments,
many metals react with sulfide produced by SRB to form insoluble metal sulfides. Microbial degradation
of organic matter may also release metals that were intercalated within the DOM macromolecular
framework. Certain bacteria are able to methylate some metals, such as mercury, arsenic, and lead, to
organic species that are more bioavailable and have high potential to bioaccumulate compared to non-
methylated forms.
The chemical species (or forms) of metals in sediments (e.g., aquo ions, hydrolysis species,
dissolved complexes, adsorbed species, pure-phase precipitated solids, and trace-metal co-precipitated
solids) profoundly affect the bioavailability and toxicity of the metals to aquatic/marine plants and
animals (Nelson and Donkin, 1985). Each metal has unique physical and chemical properties that
influence their speciation in sediments and pore water and, thus, their relative bioavailability to aquatic
receptors. Metals in forms sequestered from the aqueous phase generally are not bioavailable to
sediment-dwelling organisms. Metals in solution or colloidal suspension in sediment pore water, or that
are readily desorbed (leached) into the aqueous phase by small changes in oxygen concentration, pH, or
redox potential, are much more bioavailable. The free or aquo ions (e.g., Cu[H2O]42+) are considered the
most bioavailable forms of most metals, with mercury being a notable exception (Newman and Jagoe,
1994; RibaetaL, 2003; Bartacek etal, 2008; Keung etal., 2008).
As discussed in Section 4.2, fluctuations in redox status, due to natural or anthropogenic
forces, can exert a profound effect on metal speciation and bioavailability. However, where
thermodynamic considerations might predict drastic changes in chemical speciation, the kinetics of these
changes may not lead to observed toxic effects corresponding to thermodynamic calculations. Kinetics of
redox processes are specific for each metal, and, as such, rates of metal redox changes must be considered
on a site-specific basis depending on the particular metal contaminant at the given site. For example,
Sundelin and Eriksson (2001) provide evidence of minimal bioavailability of certain metals (e.g.,
cadmium, zinc, and copper) after oxygenation and subsequent remobilization of bulk sedimentary metal
sulfides. In this study, it was demonstrated that after long-term oxygenation of sediment cores (3 to 7
months), cadmium, zinc, and copper remained relatively unavailable to a benthic organism (Monoporeia
affinis, a benthic amphipod) compared to the lead and mercury co-contaminants, although the solubility
products of the different sulfide minerals would predict greater stability of mercury sulfide.
Long-term stability suggests that other ligands (in addition to sulfide) are important for metal
bioavailability. Buykx et al. (2000), using a modification of the Tessier sequential extraction protocol,
demonstrated that aeration of sediments for 3 weeks had little effect on the distribution of extractable
nickel, copper, and lead across the operationally-defined pools typical of the Tessier sequential extraction
method. Zinc and cadmium were observed to be released as sulfide levels decreased, but subsequently
were bound as carbonates or adsorbed to other binding phases. These results should be taken with a
cautionary note as all operationally-defined protocols are limited in their ability to translate to in-situ
metal speciation. However, these results are consistent with the findings of Mahony et al. (1996) and
DiToro et al. (2001) regarding metal binding to other geosorbents (e.g., organic carbon and metal
oxides/hydroxides) in sediments, where adsorption (e.g., onto iron and manganese hydrous oxides
occurring in sediment) can influence the distribution and bioavailability of metals. For example, Zhuang
et al. (1994) investigated the effect of aeration on cadmium bioavailability in sediments in a series of lab
129
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aeration experiments in batch reactors during periods of approximately 1 month. During aeration, the
concentration of metal ions associated with sulfide minerals and pyrite decreased. At the same time,
increases were noted in the concentrations of hydrous iron and manganese oxides and these materials
became increasingly more important in the binding of cadmium. Following aeration, more than 50% of
the cadmium was associated with the extractable iron and manganese components and approximately 2%
of the cadmium released during the oxidation of sulfide minerals entered into the liquid phase. However,
in the case of highly contaminated sediments, a 2% release still may be significant.
4.3.1 Metal Speciation: Adsorption, Complexation, and Solubility. Adsorption, which occurs
when dissolved metals are attached to surfaces of solid-phase precipitates or suspended particulate
material (notably oxides of iron, manganese, and/or aluminum; clay; and/or organic matter), is principally
governed by a combination of: 1) pH/Eh, 2) the pH-dependent electrostatic characteristics of the
individual mineral phases present within a given specific sedimentary environment (Campbell and Stokes,
1985; Cusimano etal, 1986), 3) the different affinity for adsorption of metal cations and anions present
in solution, and 4) the availability of particulate surfaces and the total dissolved metal content (Bourg,
1988; Elder, 1989). Particle size and total surface area available for adsorption are important factors in
adsorption processes and can affect metal behavior and subsequent bioavailability (Luoma, 1989). Small
particles with large surface area-to-mass ratios allow more adsorption than an equivalent mass of large
particles with small surface area-to-mass ratios. Reduced adsorption results in more metals remaining in
the dissolved phase, which can increase metal bioavailability. The sorptive capacity of sediment particles
is less in marine than in fresh water sediments because of the high ionic strength of sea water.
The "adsorption edge", defined as the pH range over which the rapid change in sorption
capacity occurs, is unique to each metal. For example, metals have different hydrolysis constants (a
function of pFf) as well as different complexation constants with other common dissolved constituents
(e.g., carbonate, chloride, and phosphate) collectively governing the macroscopic affinity of metal
contaminants for surface complexation or ion-exchange adsorption. For sites with complex mixtures of
metal contaminants, adsorption of different metals occurs over a large pH range. Cadmium and zinc tend
to have adsorption edges at higher pH values than do iron and copper, and, consequently, they are likely
to be more mobile and more widely dispersed at typical environmentally-relevant pH values. Adsorption
edges also vary with the concentration of the complexing agent; increasing concentrations of the
complexing agent increase the pH of the adsorption edge (Bourg, 1988). Major cations such as Mg+2 and
Ca+2also compete for adsorption sites with other metals and can reduce the amount of metal adsorption
(Salomons, 1995).
To illustrate how solution phase speciation and complexation can affect solubility,
adsorption, and, by extension, bioavailability, an example of mercury speciation (Figures 4-4 and 4-5) and
adsorption onto a simple, low-activity, 1:1 clay mineral (kaolinite) is discussed below. Several studies
have shown that in the absence of complexing ligands or surficial columbic effects, the Hg(OH)2°
complex is the predominant sorptive species owing to the inherent solubility of the other charged species,
Hg2+ and HgOFf (Sakar et al., 1999, 2000; Kim et al., 2004). In the absence of overwhelming
electrostatic repulsive interactions between charged surfaces and ionic mercury species, a typical pH-
sorption edge for mercury occurs at a pH between 3.5 and 4, roughly corresponding to the pKa of the
hydrated mercury species (pH 3.2, Figure 4-4) (Sakar et al., 1999). This observation alone indicates that
simple electrostatic attraction between a negatively charged surface and positively charged ions is not the
process governing the sorption of inorganic mercury from aqueous solution.
130
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Figure 4-4. Mercury Speciation Plot, Hgtot = 10 3 M
(Mercury speciation was modeled using MINTEQ ver. 2.53.)
In the presence of a simple electrolyte, sodium chloride, experimental and model studies
indicate that mercury complexation with chloride results in the formation of Hg-Cl complexes with
greater intrinsic solubility than the hydrolysis species (Hg[OH]x+[2"x]) (Sakar et al, 2000). Using 0.01-M
NaCl as the background electrolyte, the pH absorption edge shifts to a pH of 7, an observation explained
by the higher solubility of the dominant solution species HgCl2°, HgCl+, HgClOH, and HgCl3" that
dominate at pH values up to 7.5 (Figure 4-5). Above pH 7.5, the dominant mercury species is Hg(OH)2°,
which is the mercury hydrolysis species with the highest observed tendency for sorption in the absence of
additional background salts. The observation of diminishing mercury sorption and the increasing pH of
the mercury adsorption edge with an increasing chloride concentration has been attributed to the
formation of Hg-Cl complexes, which are more stable in the aqueous phase and less prone to sorption,
resulting in a greater aqueous phase pool of mercury for biotic processing in sediments containing
dissolved chloride.
4.3.2 Metal-Solid Partitioning. The partitioning of metals between sediment and interstitial pore
water is affected strongly by the presence/absence of reactive geosorbents (e.g., organo-clays and oxides),
the affinity of dissolved metals species for those sorbents, the chemical and phase speciation of the
sediment-bound metals (e.g., bound to sulfides, organic matter, or iron hydroxides), the pH and redox
conditions, and the physical forces acting on the sediment (e.g., bioturbation and tide-induced sediment
resuspension) (Calmano et al., 1993; Warnken et al., 2001; Atkinson et al., 2007). Particle size affects
the accessibility and relative surface area of the different metal-binding phases. High pore water DOC
concentrations may lead to an increase in pore water metal concentrations (although not necessarily in
biologically available forms) (Besser et al., 2003). Sulfides characteristically possess reactive surfaces
important in controlling pore water concentrations of B-type metals, those metals in the iron, cobalt,
nickel, copper and zinc groups (e.g., silver, gold, cadmium, and mercury). Metal binding by POC and
iron hydroxide phases is pH-dependent, with the adsorption edge typically being in the pH range of 5 to 7
(Lion et al., 1982; Millward and Moore, 1982; Stumm and Morgan, 1996). Additionally, sediment-water
partition coefficient (Kd) values typically decrease substantially as pH decreases, although the effect of
pH on metal adsorption can vary widely across metals depending on the columbic and solubility
131
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Figure 4-5. Mercury Speciation Plot in a Solution of Sodium Chloride
(Mercury speciation was modeled using MINTEQ ver. 2.53.)
characteristics of the hydrolysis species (Stumm and Morgan, 1996; Tessier et al, 1996; Trivedi and Axe,
2001). In spiked-sediment experiments, Simpson et al. (2004) found that metals in pore water
equilibrated faster in sediments having high concentrations of metal-binding sites (e.g., particulate sulfide,
organic matter, and iron hydroxide phases) and large surface areas (e.g., fine, silt/clay sediments) versus
in sandy sediments having low binding capacities. Additionally, these authors found that equilibration
rates were faster at higher pH values and at higher temperatures.
Commonly occurring iron(III) oxides (e.g., hematite, goethite, and ferrihydrite) have surface
pKa values ranging from 8 to 9, whereas manganese oxides have surface pKa values less than 5 (see
Section 4.2). Therefore, at circumneutral pH values common in interstitial pore water of sedimentary
environments, manganese oxides tend to sorb metal cations, rather than oxyanions, with high affinity.
Ferric hydroxide is able to adsorb trace metals predominantly as cations (Cr3+, Pb 2+, Cu 2+, Zn 2+, Ni2+,
and Cd2+) in neutral to high pH levels, and predominantly as anions (SeO4~2, CrO4"2, and AsO4"3) in
neutral to mildly acidic pH levels (Patoczka et al., 1998). Thus, sorption behavior is related to pH, and
each metal ion has its own optimum pH range for adsorption (Farley et al., 1985). Under circumneutral
pH conditions, trace metals tend to bind directly to the OH groups of iron and manganese oxides and
hydroxides, whereas binding to organic matter coatings of oxide particles is observed under more acidic
(pH < 5) conditions (Tessier et al., 1996). The unusually high adsorption and scavenging capacities of
manganese oxide/hydroxide minerals provides one of the main controls of dissolved inorganic
contaminant concentrations in aquatic sediments where manganese minerals are present (Young and
Harvey, 1992; Dong etal, 2000; Amirbahman etal, 2006).
Manganese hydrous oxides are some of the strongest oxidants naturally found in the
environment (Tebo et al., 2004) and, as such, participate in a wide variety of redox reactions including
oxidation of Se(IV) to Se(VI), Cr(III) to Cr(VI), and As(III) to As(V), thereby influencing toxic metal
availability by oxidative precipitation (e.g., arsenic) or solubilization (e.g., chromium) (Huang, 1991;
Manceau and Charlet, 1992; Scott and Morgan, 1995; Amirbahman et al., 2006). Interaction of metals
with even small amounts of manganese oxides has been reported to decrease the dissolved metal
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concentration by several orders of magnitude (Wei and Murray, 1991; Fuller and Harvey, 2000; Kay
et al., 2001). Manganese oxide minerals can adsorb or incorporate substantial amounts of metals such as
copper, cobalt, cadmium, zinc, nickel, strontium, lead, calcium, iron, radium, mercury, arsenic, and
selenium (Dong et al., 2000; Foster et al., 2003; Nicholson and Eley, 1997; O'Reilly and Hochella, 2003;
Webbed al., 2006).
4.3.3 Metal-Organic Matter Interactions: Relevance to Unavailability. Oxygen-containing
functional groups (e.g., alcohols, phenols, and carboxylic acids) prefer to complex hard metals, such as
Al(III) and Fe(III). Nitrogen-containing functional groups (e.g., amines and aniline compounds) and
sulfur-containing functional groups (e.g., thiols) tend to bind with soft metals, such as Hg(II) and Cu(II).
The formation of metal-DOM complexes can profoundly affect metal fate, transport, and bioavailability
in sediments by increasing metal solubility through surface complexation-dissolution processes,
promoting metal agglomeration and particle nucleation resulting in decreased solubility, or promoting
metal sorption by facilitating the formation of ternary mineral-NOM-metal complexes. The amount of
metal that can be complexed by DOM-associated organic ligands is metal dependent. Analytical
techniques have been developed within the past two decades to demonstrate that the ligands responsible
for complexing several metals (e.g., copper, nickel, zinc, cobalt, lead, and iron) in marine and fresh water
systems are organic in nature, not inorganic as was often previously assumed (Capodaglio et al., 1990;
Donat and Bruland, 1990; Xue and Sigg, 1993; Miller and Bruland, 1994; Rue and Bruland, 1995; Wells
et al., 1998; Ellwood and Van Den Berg, 2001). In many cases, organic ligands are responsible for
binding the majority, in some cases close to 100%, of dissolved metals present in aqueous systems.
The interaction of metals with DOM in aquatic systems has been described by several
conceptual models, illustrating a range of processes in contrasting environments. One model is of metal
complexing ligands of low concentration, but with high metal-binding specificity and affinity having their
origin in the evolution of biological systems (Bruland et al., 1991). Organic ligands with multiple
binding sites on a single discrete molecule with high metal binding affinity are referred to as chelating
agents. Examples of chelating agents of these types include phytochelatins and siderophores, which are
minor components of the overall organic carbon pool in the environment but whose environmental
significance is underscored by high metal-binding constants and each chelating agent typically
demonstrating a strong affinity for a specific metal. This type of interaction often is used to define metal-
organic interactions in open ocean surface waters but has not been demonstrated in metal-organic
associations occurring in sediments and interstitial pore waters. This is likely the consequence of the
abundance of molecularly-uncharacterized organic substances in sediments that have undergone
significant diagenetic alteration. Thus, specific siderophores or chelatins would be expected also to have
experienced molecular transformations and, thus, not be readily identified by common isolation
techniques. However, the metal binding properties of DOM for a range of metals has been modeled using
a two-domain model with both high-affinity and low-affinity binding sites. The high-affinity sites are
analogous to metal-chelate associations, indicating that high-affinity metal binding domains within DOM
may be biogenic in origin. Metals bound within the macro-molecular framework of DOM typically are
considered less bioavailable, as they are not readily accessible for uptake by microorganisms. Addition of
humic material has been shown to reduce toxic effects of copper and cadmium in sediments, and
adsorption of the metals onto the humic material was invoked to explain the diminished toxicity (Nadella
et al., 2009).
Another conceptual model of trace metal complexation by organic ligands in aquatic systems
has been described in which metals are associated with and trapped within colloidal aggregates. Metal
availability is governed by steric constraints and mass transport limitations (Honeyman and Santschi,
1984; Mackey and Zirino, 1994; Santschi et al., 1997). This model has been used to describe metal
speciation in pore waters as well as the interaction of trace metals with organic matter coated on the
surface of particles in sediments. While there are a paucity of studies, evidence exists that the physical
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form or physical speciation of many trace metals in interstitial water of sediments and in overlying water
consists of colloidal particles and further that associated trace metals have a dynamic cycling between
water and sediments (Guoet al, 2000; Santschi etal, 1997; Shank etal, 2004a, 2004b; Skrabal etal,
1997, 2000, 2006). It also is well recognized that organic matter coats the surface of sedimentary
particles and serves as a binding site for the adsorption of trace metals (Hunter, 1980; Santschi etal.,
1997). This process likely is most important in surficial sediments during early diagenesis and probably
does not play a significant role in deeper sediments where the abundance of NOM is usually observed to
decline with depth.
4.4
Metal Specific Behavior
The total concentration of metals in sediment typically is not a reliable indicator of sediment
toxicity because of the effect of sediment chemistry on metals speciation and bioavailability (Apitz etal.,
2005a; Berry et al., 1999, 2004; Boothman et al., 2001). Redox chemistry and pH strongly influence the
solubility and availability of metals, particularly those with multiple valence states. Solubility and
mobility of some metals (e.g., chromium) decrease under anaerobic/low-redox conditions, while they
increase for others (e.g., arsenic). The pH and Eh of sediment pore waters can differ from the overlying
aquatic environment and, in general, determine the redox state of the metal. Table 4-2 shows the
dominant phases of metals in oxic and anoxic sediments. In this table, it can be seen that the speciation of
divalent metals is influenced strongly by labile sulfides in sediments containing high total sulfur
concentrations (marine, estuarine, and some fresh water sediments). In most cases, interactions between
metal contaminants and reduced sulfur result in the formation of insoluble, non-bioavailable, metal
sulfide precipitates. While chromium does not form an insoluble sulfide precipitate, its solubility and
bioavailability still are influenced by sulfides and Fe(II) due to their acting as electron donors to catalyze
the reduction of soluble Cr(VI) to insoluble Cr(III).
Table 4-2. Dominant Adsorbed or Complexed Phases of Metals in Oxic
and Anoxic Sediments (from Brown and Neff, 1993)
Metal
Arsenic
Cadmium
Chromium
Copper
Lead
Mercury
Nickel
Tinw
Zinc
Associations in Oxic
Sediments
Fe/MnOx = AsO4~J
Fe/MnOx, OM/S, -CO3
OM, FeOx
OM, Fe/MnOx
Fe/MnOx
OM
Fe/MnOx
TBT-C1-OH-CO3
Fe/MnOx, OM
Associations in Anoxic
Sediments
As2SO3, AsS, FeAsS
CdS
OM, Cr(OH)3
Cu2S, CuS, FeCuS
PbS
HgS,OM
OM, MS, organic thiols
TBT-S, -OH, -CO3
ZnOM/S
(a) Only butyltins are considered.
Fe/MnOx = = AsO4"3 = indicates arsenate - oxide sorption complex
CO3 = carbonates
FeOx = iron oxyhydroxides
Fe/MnOx = iron and manganese oxyhydroxides
OM = organic matter
S = sulfides (dominant species given)
TBT-C1, -OH, -CO3, and -S = tributyltin chloride, hydroxide, carbonate, and sulfide
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For divalent metal cations, including silver, cadmium, copper, nickel, lead, and zinc, the
presence of reduced sulfur is a useful predictor of anoxic sediment toxicity (Ankley et al., 1991; Berry
et al., 1999; Boothman et al., 2001). Sulfide reacts with divalent metals to form highly insoluble
compounds that are not bioavailable (Allen et al., 1993; Ankley et al., 1991; Berry et al., 1999;DiToro
et al., 1992; EPA, 2005b). It follows in theory (Di Toro et al., 1992) that divalent transition metals do not
cause toxicity in anoxic sediments until the reservoir of sulfide is exhausted. This concept is known as
the simultaneously extracted metal (SEM)/AVS theory and is discussed in Section 4.6. Sulfide also reacts
with mercury to form highly insoluble compounds. However, because mercury presents unique problems,
particularly under anaerobic conditions where it can be methylated, mercury is not included in the
standard SEM analysis and, if expected to be present, the sediments may require direct toxicity testing for
the presence of methylated forms (discussed in detail later in this section).
The geochemistry of divalent metals often present in trace quantities relative to more
abundant iron and manganese minerals (e.g., cadmium, copper, lead, mercury, nickel, and zinc) is unique
because, in the presence of excess sulfides, divalent metals will displace the iron and manganese moieties
of aqueous FeS and MnS to form more insoluble metal sulfides. This renders the metals unavailable for
uptake by biota. In aerobic sediments, these divalent metals will associate with negatively charged
surface sites on either permanently- or variably-charged minerals, such as alumino-silicates or
iron/manganese oxyhydroxides, respectively. It stands to reason that for most divalent metals, redox
chemistry (e.g., the reductive precipitation of a sulfide mineral or the precipitation of insoluble Cr[III]
phases) plays a much larger role in MNRthan sediment burial. If redox chemistry sufficiently reduces
metal toxicity, additional measures intended to promote reduced toxicity and recovery of ecological
receptors may not be required. However, under such conditions, it would be imperative to examine the
long-term spatial and temporal stability of the anaerobic environment to ensure the long-term stability of
the metal-complex precipitate as the reversion to aerobic conditions could result in mineral oxidative
dissolution and subsequent release of toxic metals.
4.4.1 Arsenic. Arsenic has four oxidation states (V, III, 0, and -III) and is highly redox sensitive
in the environment. In oxic environments, As(V) is the most stable form, while As(III) is predicted to
form in moderately reducing conditions. As(III) can be reduced to As(0) under highly reducing
conditions, although it is rarely seen in the natural environment. At low pH values, arsenic forms the
covalent compound AsS (realgar), which has low solubility in water. The redox reactions of arsenic tend
to be slow and are mediated by microorganisms and algae (Meng et al., 2003; Rhine et al., 2005).
Inorganic arsenic species tend to predominate in the environment, although there are a number of
methylated organic compounds that have been identified, with the mono- and dimethyl arsenicals being
the most commonly observed species in aqueous systems (Anderson and Bruland, 1991).
Solid-phase arsenic speciation in sediments is one determinant of arsenic's potential mobility.
Arsenic in more labile phases can be mobilized readily by changing redox conditions or other chemical
changes, while arsenic that has become sequestered in mineral phases is unlikely to be mobilized by
typical biogeochemical processes in sediments. Sequential extraction techniques for assessing the
solid-phase speciation and mobility of arsenic in sediments (see Section 4.6.1) tend to be different from
those often used for the transition metals (Hudson-Edwards et al., 2004; Haus et al., 2008). A major
focus of methods developed for the solid phase speciation of arsenic is determination of As(III) and
As(V) since commonly-employed extraction techniques focus on cationic metals and arsenic species are
anionic (Keon et al., 2001; Georgiadis et al., 2006).
Arsenic behavior in sediments is driven by two major processes: 1) interaction with the redox
cycling of iron during early diagenesis in surficial sediments, and 2) interaction with reduced sulfur and
the formation of solid phases that are more recalcitrant and reduce arsenic mobility. The interaction with
reduced sulfur tends to occur in deeper sediments where reduced sulfur is more abundant, but can vary
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considerably depending on the total concentrations of reduced sulfur in the sediments. Arsenic often is
found associated with iron mineral phases through adsorption to their surfaces. When Fe(III)
(oxyhydr)oxides are reductively dissolved, arsenic can be released to sediment pore waters. In anoxic
sulfide-rich sediments, arsenic, as As(III), may be adsorbed to iron sulfide phases as an FeAsS-like
precipitate (Bostick et al., 2004). The chemical nature of the arsenic surface complexes on iron sulfides
has been shown to transform on a time scale of weeks into an As2S3 (arsenolite) solid phase (Bostick
etal., 2004). Thus, sulfide-rich sediments have been identified as potential semi-permanent sinks for
arsenic. In settings where reactive forms of iron exceed available sulfur, the formation of pyrite and/or
greigite may impede the formation of arsenic-sulfide minerals (Wilkin and Ford, 2004; Toevs et al,
2008). In more acidic sediment systems or marine systems undergoing sulfate reduction, precipitation of
arsenic-sulfide minerals may dominate (e.g., Neff, 2002; O'Day et al., 2004).
In fresh water systems low in sulfur, arsenic is complexed primarily to iron oxides. In one
study in fresh water having sediments low in sulfate and high in iron, the fate and transport of arsenic was
coupled intimately to sedimentary iron redox dynamics (Root et al., 2007). At the sediment-water
interface, arsenic was found primarily as As(V) adsorbed to ferric oxides. Just below the sediment-water
interface (~4 cm), As(V) was reduced to As(III) and arsenite was found to be complexed to iron oxides as
bidentate-binuclear inner-sphere complexes. Below the oxic/anoxic transition zone (~10 cm), iron oxides
were found to be transformed to mixed-valence Fe(II)/Fe(III) green rust and the release of arsenic at
greater depths was concurrent with the reductive dissolution of solid iron phases (Root et al., 2007). In
general, arsenic is more mobile in sediments that undergo regular fluctuations in redox status, and is more
stable (less bioavailable) in sediments where the water level and redox status are more stable (O'Day
et al., 2004; Haus et al., 2008). In fresh water sediments with low sulfate concentrations, arsenate
released from Fe/Mn oxides during redox decline is reduced to arsenite and binds to sediment organic
matter when NOM is available. At low Eh values, much of the arsenite may be present as arsenolite,
which is slightly soluble and may diffuse upward into the overlying water column. Thus, arsenic is
generally more bioavailable in fresh water than in marine sediments.
Most studies evaluating arsenic behavior in sediments have been relatively short
investigations of 2 years or less. Many of the above processes have been elucidated from these studies.
Longer-term observations of arsenic dynamics in contaminated sediments, although less common,
underscore the importance of such observations for the application of MNR to arsenic-contaminated
sediments. In one study, Senn et al. (2007) explored the long-term (>30 year) fate of arsenic in a dimictic
eutrophic lake. Arsenic was added to this lake in the 1960s to control harmful aquatic macrophytes, with
application principally to the littoral zone (Senn et al., 2007). This lake experiences bottom-water anoxia
during the spring-summer-winter months, during which time arsenic is reduced from As(V) to As(III),
and concentrations were observed to increase in the water column along with concentrations of dissolved
iron. As iron concentrations increased, the water column became super-saturated with respect to iron-
bearing minerals and the arsenic ultimately was found to adsorb to these iron particulates and settle to the
hypolimnetic sediments. Unexpectedly, the epilimnetic sediments from the littoral zone, which were oxic
throughout the year, were observed to be a strong net source of arsenic to the water column. This
observation was attributed to the high concentration of allochthonous organic carbon in these near-shore
sediments, which resulted in anoxia just beneath the sediment-water interface. Upward diffusion of
solubilized arsenic was found to enter the water column, adsorb to particulate iron (e.g., hydrous iron
oxides), and settle to the hypolimnetic sediments (Senn et al., 2007). A prominent diffusive flux of
arsenic from the epilimnetic sediments characterized by oxic conditions of the sediment-water interface
indicates the importance of the relative kinetics of concomitantly-operative processes, such as reductive
dissolution, diffusive/advective flux, ferric oxide precipitation, and arsenic adsorption, on the evolution of
pore water geochemistry (§engor et al., 2007). This case study serves as an example that long-term
monitoring of contaminated sediments selected for MNR is required to critically evaluate the results of
system biology, geochemistry, and hydrology on long-term contaminant fate.
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4.4.2 Cadmium. In anoxic environments, a majority of particulate cadmium is complexed by
insoluble organic matter or bound to sulfide minerals (Kersten, 1988). In studies of cadmium speciation
in anoxic marine and estuarine sediments, nearly all measured cadmium has been found to be present as
cadmium sulfide (Lee and Kittrick, 1984; O'Day etal, 2000). Greenockite (CdS) has extremely low
solubility under reducing conditions, thereby decreasing cadmium bioavailability. Oxidation of reduced
sediment or exposure to an acidic environment results in transformation of insoluble sulfide-bound
cadmium into more mobile and potentially bioavailable hydroxide, carbonate, and exchangeable forms
(Kersten, 1988).
As anoxic sediments containing cadmium are exposed to oxic waters, as much as 50% of the
cadmium sulfide was found to be oxidized within a 90-day period in one study (Carroll et al., 2002).
Cadmium in oxidized sediments may be associated primarily with the carbonate plus Fe/Mn oxide
fractions of the sediment (Rosental et al., 1986). Most of the remainder is associated with the
organic/sulfide fraction. Only about 1% is in the completely non-bioavailable residual fraction, indicating
that cadmium associated with oxidized sediments is likely to be moderately mobile and bioavailable
(Samantetal., 1990).
4.4.3 Chromium. Elevated concentrations of chromium in soils, sediments, and groundwater
usually result from anthropogenic inputs associated with the use of chromium in electroplating, tanning,
pigments, corrosion inhibition, wood preservation, and other industrial processes (Papp, 1994; Johnson et
al., 2006). Chromium has two oxidation states in aquatic systems, chromate (Cr[VI]) and trivalent
chromium (Cr[III]). Cr(VI) is fairly soluble and can be toxic to aquatic organisms and plants and
carcinogenic to humans; Cr(III) is fairly insoluble and tends to be much less toxic and is even essential in
animal and human nutrition (Katz and Salem, 1994; Nriagu and Nieboer, 1988; Berry et al., 2004; EPA,
2002b, 2005a; Costa, 2003). An exception to Cr(III) insolubility can result if metal chelating agents
produced by indigenous organisms are present that can dissolve amorphous chromium hydroxides and
bind with Cr(III) (Carbonaro etal, 2008).
Processes or environments that promote Cr(VI) reduction are of great interest because they
represent conditions that should lead to minimal chromium toxicity. Hence, the reduction of Cr(VI) to
Cr(III) can be considered a clean-up strategy without changing the total chromium content of a soil
(James, 2001). Moreover, it has been hypothesized that sediments that are anoxic, as evidenced by the
presence of AVS, should contain no Cr(VI) and also have minimal or no toxicity (Berry et al., 2004;
Rifkine/1a/.,2004).
The reduction of Cr(VI) in aquatic sediments is linked intimately to the biogeochemical
cycling of iron, sulfur, and DOM as facilitated by microbial activity (Buerge and Hug, 1998, 1999; Guha,
2004; Lee et al., 2008) (see Figure 4-6). Cr(VI) is reduced readily to Cr(III) by various chemical and
biochemical processes in anoxic or even moderately sub-oxic sediments (Morse and Rickard, 2004;
Morse, 1994; Berry et al., 2004; Magar et al., 2008). If present, Cr(VI) observed in sediments usually is
bound tightly to sediment organic matter and iron oxide coatings on clay particles, or is co-precipitated
with iron sulfides (Schropp et al., 1990; Olazabal et al., 1997; Shtiza et al., 2008). Chromium is not
known to form sulfides, carbonates, or phosphates (Mayer, 1988); thus, most of the chromium in these
sediments likely either is bound to organic matter or is present as the stable trivalent hydroxide.
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Microbial
Respiration
(biotic)
Organic Carbon(reduced)
Inorganic Carbon(oxidlMdj
CO2, CO32
Coupled
Iron/Sulfur
Cycling
Fe(lll), SO
Fe(ll), S
Chromate
Reduction
(abiotic)
Figure 4-6. Oxidation/Reduction Cycling of Iron and Sulfur is Driven by Microbial
Respiration, and Can Lead to Cr(VI) Reduction
and Attenuation as Insoluble Cr(III) Precipitates
4.4.4 Copper. In sediments containing high concentrations of organic matter, copper is associated
primarily with the organic/sulfide fraction or extractable organic matter (Luoma, 1985). Much of the
remainder of the copper in oxidized sediments is associated with the reducible iron and manganese oxides
(Prohic and Kniewald, 1987). In anoxic sediments, copper may undergo a variety of reactions with
different inorganic and organic sulfur species to form a variety of soluble and insoluble complexes (Shea
and Helz, 1988). Polysulfide complexes with Cu(I) are soluble, so the dominant form of copper in
solution in the pore water of anoxic sediment layers is CuS(S5)"2. The dominant forms of copper in the
solid phase of sediment include chalcocite (Cu2S), covellite (CuS), and possibly chalcopyrite (CuFeS2)
(Shea and Helz, 1988). These sulfides have low solubility and bioavailability.
4.4.5 Mercury. Mercury sources to the environment are quite varied and include both natural
(e.g., volcanic activity, soil degassing, and forest fires) and anthropogenic processes (e.g., burning of
fossil fuels and medical waste, cement manufacturing, mining, and a variety of industrial processes)
(United Nations Environmental Programme [UNEP], 2002; Pacyna etal, 2006; Selin etal, 2007). These
sources enter the aquatic environment primarily through atmospheric deposition and municipal and
industrial discharges. Recent estimates place the annual amounts of mercury released into the air by
human activities at between 50% and 75% of the total yearly input to the atmosphere from all sources
(EPA, 1997a). Once introduced to the aquatic environment, mercury quickly partitions to solid phases
and settles to sediments (Santschi, 1988). Most sedimentary mercury in natural aquatic environments is
associated with humic and other organic materials as well as oxide and sulfide minerals (Ravichandran,
2004; Merritt and Amirbahman, 2007). Several studies have shown that mercury is less bioavailable in
sediment that is rich in organic matter (Luoma, 1989). Mercury reacts strongly with free sulfide to form
insoluble sulfide mineral precipitates (HgS, cinnabar, and metacinnabar) or is bound as surface complexes
with organic matter containing sulfur (Ravichandran, 2004). Vertical profiles of mercury in fresh water
sediments indicate sulfide controls on mercury speciation, where zones of sulfide mineral precipitation act
as a net sink for mercury (Merritt and Amirbahman, 2007). Mercury also forms a number of soluble
polysulfide complexes that enhance the solubility of HgS in pore waters of sediments (Benoit et al.,
1997).
Microbial methylation of mercury in water and sediments leads to the formation of
monomethyl mercury (MeHg), which is the toxic form of mercury that bioaccumulates through the food
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chain (Wiener et al, 2003). Methyl mercury species are more readily assimilated and retained by aquatic
organisms than are inorganic forms of mercury (Mason et al., 1995, 1996). Most mercury methylation
takes place in suboxic sediment layers (Gagnon et al., 1996; Gilmour et al., 1998) and is considered to be
performed primarily by sediment-dwelling, SRB (Goulet et al., 2007). The relationship between SRB
activity and mercury methylation may be a consequence of the marine and estuarine environments where
many of these observations have been made. There is evidence that iron-reducing bacteria (Geobacter
sp.) also may be able to methylate mercury (Fleming et al., 2006).
In general, factors that promote SRB activity also are influential in the production of MeHg
(Goulet et al., 2007). These parameters include the total mercury concentration in water or sediment,
oxic/anoxic conditions, temperature, and concentrations of DOM, sulfate, and sulfide. High temperatures
generally stimulate bacterial activity. In waters low in sulfate, increasing sulfate can stimulate
methylation. In all waters, the sulfate-reduction process and the subsequent build-up of sulfide appears to
limit the production of MeHg, perhaps by limiting the fraction of Hg2+ that is available for methylation
(Benoit etal, 1997; Marvin-DiPasquale etal, 2005).
Dissolved MeHg in sediment pore waters can comprise a significant portion of the total
dissolved mercury present (Gagnon et al, 1996; Bloom et al., 1998; Choe et al., 2004). Although much
of the dissolved MeHg in sediment pore waters actually is complexed to DOM or exists in association
with colloidal organic matter, it should be considered potentially bioavailable to sediment-dwelling
organisms. Movement of MeHg from anoxic pore water into the overlying water column occurs through
sediment-water exchange processes, which can be enhanced by benthic fauna (Gill et al., 1999; Choe et
al., 2004).
Several pathways exist for the removal (detoxification) of MeHg in water and sediment.
Oxidative bacterial demethylation of MeHg can occur under both anaerobic and aerobic conditions
(Marvin-DiPasquale and Agee, 2003; Marvin-DiPasquale et al., 2003). Sunlight also can degrade MeHg
via photolysis. In addition, bacteria may reduce Hg(II) to Hg(0), thereby eliminating the precursor of
MeHg and generating a form of mercury (Hg°) that degasses to the atmosphere. Because of rapid
interconversions between inorganic and organic mercury species in oxidized and reduced layers of fresh
water and marine sediments, MeHg typically comprises <1% of the total mercury in sediments (Berman
and Bartha, 1986; Choe et al., 2004). Implications of the above-described studies suggest a management
strategy for a mercury-contaminated site in which primary sources and hot spots of mercury are removed,
followed by monitoring of aged mercury present at reduced concentrations in the remaining part of the
site.
4.4.6 Nickel. In oxidized sediments, much of the potentially bioavailable nickel is complexed to
iron and manganese oxides (Luther et al., 1986). Nickel forms weak coordination complexes with
oxygen donors such as carboxylate, hydroxyl, and other oxy-ligands (e.g., humic and fulvic acids, clays,
and metal oxides) (Wood, 1987). It also becomes tightly bound to anionic groups of bacterial
polysaccharides (Wood, 1987). Nickel forms stable, insoluble complexes with sulfides and organic thiols
in anoxic sediment layers (Wood, 1987). However, most of the nickel (often more than 90%) in relatively
uncontaminated sediments is in the residual fraction, associated primarily with oxide minerals such as
magnetite and silicates (Loring, 1982). In the presence of clay minerals, short-term nickel sorption results
in the formation of a nickel-aluminum layered double hydroxide phase (Ford et al., 1999; Scheckel et al.,
2000). The formation of a layered nickel-aluminum double hydroxide phase has been observed following
reaction of aqueous nickel with clay minerals (e.g., Ford et al., 1999). The chemical stability of this
association increased with time and was consistent with the ultimate incorporation of nickel into a new
phyllosilicate mineral (Peltier et al., 2006). Thus, the bioavailability of nickel due to interactions with
clays, oxides, and NOM in sediments usually is low.
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4.4.7 Silver. Silver introduction into aquatic systems originates principally from industrial
processes utilizing silver (e.g., photofinishing and electroplating) via wastewater discharge; more recent
sources include use of silver nanoparticles as antimicrobial agents (Sanudo-Wilhelmy and Flegal, 1992;
Flegal etal, 1996; Bothner etal, 2002; Squire etal, 2002; Blaser et al., 2008). Silver is particle reactive
and is quickly delivered to sediments via adsorption onto suspended matter or incorporation into
biological matter (Santschi, 1988; Connell et al., 1991; Sanudo-Wilhelmy and Flegal, 1992; Benoit etal.,
1994; Wen et al., 1997). Measurements of silver in sediments, therefore, can be used to delineate zones
impacted by wastewater discharges into the aquatic environment and assess changes in environmental
quality associated with these discharges. Interstitial pore water profiles suggest that silver is released at
the sediment-water interface during early diagenesis in near surface sediments (Rivera-Duarte and Flegal,
1997). In anoxic sediments, silver likely is sequestered in sulfide (Ag2S) or selenide (AgSe or Ag2Se)
solid phases as evidenced by coincident increases in silver and selenium at depth in sediments and
association with AVS in sediments (Gobeil, 1999; Crusius and Thomson, 2003). Exposure of anoxic
sediments to oxygenated conditions can lead to a mobilization of silver (Crusius and Thomson, 2003).
Numerous studies have shown that free ionic silver (Ag+) or neutrally complexed forms of silver (e.g.,
AgCl2°) are toxic to aquatic biota, while most other complexed forms of silver are far less toxic (Nebeker,
1982; Nebeker et al, 1984; EPA, 1987a; Rodgers etal, 1997; Reinfelder and Chang, 1999; Guadagnolo
et al, 2000). MNR may be a viable option for silver as suggested by long-term monitoring efforts in San
Francisco Bay and elsewhere that show decreases in silver in water, sediments, and biota on time scales
of decades (Bothner et al, 1998; Squire et al, 2002; Flegal et al, 2005). The potential effectiveness of
MNR for silver contamination would need to be evaluated on a site-by-site basis to determine if the rate
of silver reduction is sufficient to achieve the remedial goals in a relevant period of time.
4.4.8 Zinc. Zinc speciation and bioavailability are influenced by biological transformations during
the aging process (Webb et al, 2000). Adsorption of zinc to oxide particles, such as iron oxides, is the
dominant short-term sink for zinc. As these particles age, especially in anoxic or sub-oxic environments,
zinc becomes associated with sulfides (Webb et al, 2000). In slightly basic, anoxic marsh sediment
environments, zinc is effectively immobilized and is not bioavailable (Gambrell et al, 1991), presumably
through the formation of insoluble zinc sulfide precipitates. Substantial amounts of zinc are released to
solution if this sediment is oxidized or exposed to an acidic environment. Very high abundances of
soluble zinc are present under well-oxidized conditions at pH 5 to pH 6.5. Conversely, low abundances
of soluble zinc are present at pH 8 under all redox conditions and at pH 5 to 6.5 under moderately and
strongly reducing conditions (Gambrell et al, 1991). At the sediment-water interface, soluble zinc can be
diminished significantly by sorption onto ferric oxide mineral phases. As the redox potential declines and
reduction of ferric oxides commences, zinc can be incorporated into magnetite (Fe3O4) or siderite
(FeCO3) (Cooper et al, 2000). In polluted river environments, most zinc is scavenged by non-detrital
carbonate minerals, organic matter, and oxide minerals and is less mobile than cadmium (and perhaps less
mobile than lead) (Prusty et al, 1994). Elevated chloride content decreases adsorption of zinc to
sediment (Bourg, 1988).
4.5 Sediment Sampling for Metals: Methods for Collection and Analytical Considerations
Mineralization of organic matter by benthic microorganisms in sedimentary environments
results in the depletion of oxygen, utilization of alternate TEAs, and ultimately a vertical redox gradient
within the sediment profile. Metal concentrations and speciation that develop under reducing conditions
will be sensitive to exposure to oxygen in air; thus, collection of sediments for metals analysis must aim
to preserve in-situ conditions prior to analysis. Changes in sedimentary geochemical properties, most
notably through the diffusion of oxygen into anoxic sediments, may alter the distribution of mineral
phases in sediment samples (e.g., conversion of iron sulfides to iron oxides) such that an assessment of
in-situ conditions would be impossible. Therefore, maintaining the integrity of sediments collected for
metals analysis is a primary concern when monitoring a site for natural recovery of metal contaminants.
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This section will briefly discuss the issues and approaches for air-sensitive collection of whole sediments
and interstitial pore water, evaluation of sediment-water exchange, and analysis of metals in sediments
and pore water.
For a complete discussion of the advantages and disadvantages of various types of sediment
collection devices, the reader is directed to Methods for Collection, Storage, and Manipulation of
Sediments for Chemical and Toxicological Analyses: Technical Manual (EPA, 2001b). The scope of this
discussion is sediment collection and analysis relating specifically to the aims and objectives of sampling
for MNR of metals-impacted sedimentary environments.
4.5.1 Collection of Sediments and Interstitial Water. It is recommended that the collection of
sediments for metals analysis as part of assessing the viability of MNR for a contaminated site be
conducted in a manner that maintains the original conditions of the sediments as much as possible for the
purposes of understanding in-situ geochemistry. Primary factors that dictate sample integrity include: 1)
preserving the original redox conditions, and 2) avoiding contamination of the sample during sediment
acquisition.
Nearly all sediment sampling devices can be sorted into three categories: 1) dredge samplers,
2) grab samplers, and 3) core samplers. The former two devices have use in the collection of sediments to
evaluate depositional rates and sediment thickness or the collection of benthic organisms. For sediment
collection aimed at the preservation of in-situ geochemical conditions, the core sampling devices are
preferred. Core samplers are preferred because they maintain the integrity of the sediment profile and are
much less destructive than either dredge or grab samplers. Grab samplers can be used reliably for
physicochemical characterization and toxicity testing provided the samplers are closed when the sediment
sample is retrieved, are relatively full of sediment, and do not appear to have lost the superficial fines.
The materials used for sediment sampling should be chosen such that they minimize sources
of contamination. If sediment samples are to be analyzed for trace concentrations of metals, the sampler,
if constructed of metal, should be coated with an inert non-sorptive coating, such as Teflon® or Kynar®, to
prevent contamination. Often, polypropylene sleeves with rubber caps on either end are used to avoid
sample contamination from the coring device. Samples of sediment in the core sampler or grab sampler
should be sub-sampled with a pre-cleaned plastic or Kynar®-coated scoop or mini-corer. This technique
avoids obtaining a sample that is in contact with the walls of the sampling device. Additionally, whether
using a grab or a core-type sampler, all materials should be pre-cleaned prior to filling with the sample.
Containers used to transport or store the sample should be purged with an inert gas (e.g., nitrogen) prior to
and after filling, and the containers should be filled completely if the sample will not be frozen prior to
analysis. Samples should be processed in a glovebox or similar apparatus under an inert, oxygen-free
environment.
During collection of interstitial waters, the priority is to recover and stabilize the samples
such that oxidation and/or volatilization are prevented. Many interstitial pore waters have long residence
times and are assumed to be at thermodynamic equilibrium with the sediments. In many cases, the pore
waters are in equilibrium with a partial pressure of CO2 greater than that of the ambient atmosphere. As
such, considerable off-gassing may occur from the moment of collection with accompanying increases in
pH. In general, there are two approaches for the collection of sediment pore waters: in-situ and ex-situ
methods (EPA, 2001b). In-situ methods are preferable if toxicity testing is of primary concern and
typically involve suction techniques (EPA, 200Ib). However, these approaches typically yield low
volumes of pore water. Ex-si tu methods are preferable when toxicity testing is not the primary objective
and when greater volumes of pore waters are desired. Typical methods for ex-situ pore water collection
include centrifugation, sediment squeezers, and pressurized/vacuum devices (EPA, 200Ib). Containers
used for pore water collection should be filled completely to minimize alteration of contaminant
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bioavailability resulting from changes in DO content. Procedures for stabilization are dependent on the
analyses to be performed. When non-volatile compounds are the target analytes, acidification often is
recommended, while organic carbon and methane may be stabilized with saturated mercury chloride
(Mudroch and MacKnight, 1994).
Additional considerations upon retrieval of diffusion pore water samplers include:
• Handling of samplers should be conducted within a nitrogen- or argon-purged inert
atmosphere chamber, such as a glovebox or glove bag. Any caps or membranes should
not be removed until they are within the inert atmosphere chamber.
• Pore water from the samplers should be decanted within the nitrogen/argon-purged
glovebox into a bottle of the appropriate volume to eliminate any headspace before
shipment to the lab.
• For the analysis of sulfides, pore water can be decanted into media bottles containing zinc
acetate and sodium hydroxide preservative that prevents the oxidation of sulfides.
Transfer of pore water must be conducted within the inert atmosphere chamber. Samples
must be handled to prevent aeration to avoid volatilization or oxidation of sulfide, both of
which artificially lower the actual sulfide concentration that ultimately will be measured.
• For many metals (e.g., Resource Conservation and Recovery Act metals), acidification
usually is required. Acidification is conducted typically with nitric or sulfuric acid
depending on the exact metals being analyzed. For example, sulfuric acid is
recommended to acidify samples for arsenic analysis because nitric acid will oxidize
As(III) to As(V) (Electric Power Research Institute, 1986). Preparation of samples for
the analysis of Cd, Cr, Cu, Pb, Hg, Ni, Se, Ag, and Zn is described in EPA (1986a).
• All sample containers should be sealed in zip-lock plastic bags and placed in a cooler on
ice for shipment to the analytical laboratory.
4.5.2 In-Situ Analysis of Interstitial Water: Microelectrodes and Thin Films. Microelectrodes
have become an important tool for looking at sub-millimeter level depth distributions of redox sensitive
components involved in early diagenesis in surficial sediments. To date, electrochemical methods with
millimeter-scale resolution have been developed for oxygen (O2), sulfide (HS"/H2S), manganese (Mn2+),
iron (Fe2+), iodide (I"), pH, nitrate + nitrite (NOX), nitrous oxide (N2O), carbon dioxide (pCO2), redox
potential, hydrogen (H2), conductivity, and temperature. In addition, microelectrode techniques have
been developed for the measurement of several trace elements (copper, lead, cadmium, and zinc) in
sediments (Brendel and Luther, 1995; Nolan and Gaillard, 2002; Sundby etal, 2005; Luther etal, 2008).
Limitations of microelectrode application to geochemical characterization of sediments include depth of
the microelectrode penetration and the small number of commercially-available microelectrodes
specifically used for environmental monitoring.
Thin film probes are created by layering a diffusive gel of known thickness between the
medium to be investigated (ambient water or interstitial pore fluid) and any medium capable of reacting
with the analytes of interest (Davison and Zhang, 1994). The gel is held in a rigid plastic frame with a
window to allow exposure to the matrix being studied (Davison et a/., 2000). A filter membrane usually
is placed over the exposure window to prevent mechanical damage and biological fouling of the gels.
The technique of diffusive equilibration in thin films (DET) establishes equilibrium between solutes in
pore waters and in a hydrogel that contains 95% water (Davison, 1991). For trace elements, the reacting
medium is often a chelating ion exchange resin, such as Chelex®, that is contained in a thin layer of gel
(Warnken et a/., 2004). By controlling the pore size of the diffusive gel, it is possible to exclude
particulate phases, large macromolecules, and colloids and, hence, achieve a physical measurement of the
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metal speciation in solution (Warnken et al, 2004). Thin film probes also have been developed to
measure sulfide in sediments (Teasdale et al, 1999; DeVries and Wang, 2003), phosphorous in soils
(Mason etal, 2008), arsenic (Campbell etal, 2008), and mercury (Cattani etal, 2008). Davison (1991)
developed a DET technique for measuring iron and manganese in interstitial pore waters of sediments at
millimeter-scale resolution. The method subsequently was expanded to include other trace metals
(Morford et al., 2003). A related technique, diffusive gradients in thin films (DGT), expands on the DET
technique to quantify both horizontal and vertical gradients of trace metals and metalloids in fresh water
and marine sediments (Zhang et al., 1995, 2002; Davison et al., 1997; Tankere-Muller et al., 2007;
Campbell et al., 2008).
4.5.3 Measuring Sediment-Water Exchange: Benthic Flux Chambers. Benthic flux chambers
are in-situ sampling devices designed to obtain a measure of the exchange of constituents in interstitial
pore water with overlying water. Flux chambers also are used to measure metabolic processes driven by
microorganisms in sediments. Flux chambers are typically small surface area (< 0.5 m2) enclosures that
are placed over the sediments and capture a portion of the ambient bottom water that is in direct contact
with the sediments. The concentration of the constituent of interest is monitored over time in the captured
water, and an exchange flux is determined from the change in concentration of the constituent in the
chamber over the deployment period. Deployment periods vary from a few hours in near-shore
environments to several days in deep sea environments. Chambers used in shallow areas tend to be
simple devices that are deployed and sampled by Scuba divers. Flux chambers used in deep water (e.g.,
the open ocean) are much more sophisticated and deployed from a landing platform that controls
placement and sampling intervals. Benthic flux chambers have been used in marine and fresh water
systems to determine the exchange of trace metals, nutrients, and organic carbon, as well as quantify
benthic respiration (DO) (Burdige and Homstead, 1994; Rowe etal., 1994; Gill etal., 1999; Berelson et
al, 2002, 2003; Warnken etal, 2000, 2001, 2003, 2008).
4.5.4 Analysis of Key Geochemical Constituents in Sediments
4.5.4.1 pH. Sediment pH is one of the single most important factors controlling speciation and
equilibrium for many chemicals including sulfide, ammonia, cyanide, and metals, all of which ionize at
different pH values defined by their acid dissociation constant (pKa). Metal (Cr, Cd, Cu, Ni, Pb, and Zn)
speciation and bioavailability are known to be affected by pH (Schubauer-Berigan and Ankley, 1991).
Generally, pH is measured using a pH meter consisting of a potentiometer, a glass electrode, a reference
electrode, and a temperature compensating device. A circuit is completed through the potentiometer
when the electrodes are submersed. General purpose pH electrodes are available in a wide variety of
configurations for in-line and submersion applications. Detailed methods for measuring pH in water and
sediment are described in EPA (1983, 1986a).
4.5.4.2 Ammonia in Pore Water. Nitrogen, a nutrient associated with over-enrichment of aquatic
environments, exists in several forms including ammonia. Ammonia is highly soluble in water where it is
found in an un-ionized form (NH3) and an ionized form (NH4+). The extent of ionization is dependent on
pH, temperature, and salinity (in sea water). Ammonia in sediments and pore water is generally a product
of microbial degradation of nitrogenous organic material such as amino acids (Ankely etal., 1990).
4.5.4.3 Total Organic Carbon Content. The TOC content in sediment is a measure of the total
amount of oxidizable organic material. TOC is the sum of DOC, POC, and colloids. TOC is an
important parameter in sediments because it is a major determinant of non-ionic organic chemical
bioavailability (DiToro et al, 1991). Metal bioavailability is affected by the amount of TOC present in
sediments. TOC usually is expressed as a percentage of the bulk sediment and used to normalize the dry-
weight sediment concentration of a chemical to the organic carbon content of the sediment. EPA
Equilibrium Partitioning Guidelines estimate bioavailability as a function of contaminant concentration
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sorbed to sediment organic carbon and contaminant concentration in pore water under equilibrium
conditions (EPA, 1994a). Several methods have been used for measuring organic carbon content in
sediments, including wet oxidation titration, modified titration, and combustion after removal of
carbonate by the addition of HC1 and subsequent drying. EPA methods (1986a, 1987b), including
SW-846 and 430/9-86-004, often are used to measure TOC.
4.5.4.4 Particle Size Distribution (Percent Sand, Silt, and Clay). Particle size is used to assess the
physical characteristics of sediments. Because particle size influences both chemical and biological
properties, it can be used to normalize chemical concentrations and account for some of the variability
found in biological assemblages or laboratory toxicity testing (EPA, 2000). Particle size can be
characterized in varying detail. The broadest divisions that generally are considered useful for classifying
particle size are the percentages of gravel, sand, silt, and clay. However, each of these size fractions can
be subdivided further so that additional characteristics of the size distribution (e.g., elemental distribution,
surface area analysis, etc.) can be determined (Puget Sound Estuary Program [PSEP], 1986). Both PSEP
(1986) and EPA (1995) provide recommended methods for measuring sediment PSDs.
4.5.4.5 Percent Water or Moisture Content. Water content is a measurement of sediment moisture
and usually is expressed as a percentage of the whole sediment mass. It is known to influence toxicity
and is used to aid in the interpretation of sediment quality investigations. Sediment moisture content is
calculated as the difference between the wet and dry masses of the sediment following oven drying at
50°C to 105°C to a constant mass. Percent water is used to convert sediment concentrations of substances
from wet weight to dry weight. Methods for determining moisture content are described by Plumb
(1981). Additional methods are provided in EPA (1987b).
4.5.4.6 Salinity of the Pore Water (Marine Sediments). Salinity is a measure of the mass of
dissolved salt in a given mass of solution. The most reliable method to determine true or absolute salinity
is complete chemical analysis. However, this is time consuming and costly. Therefore, indirect methods
are more suitable. Because the colligative properties of water (e.g., salinity, density, and conductivity)
co-vary, one property can be measured to estimate the value of another property. Indirect methods
include conductivity, density, sound speed, or refractive index (American Public Health Association
[APHA], 1995). Because sea water contains relatively constant ion ratios, chloride (the most abundant
anion) can be measured and related to the total salt concentration; salinity is then calculated from the
empirical relationship between it and the indirect measurement. Conductivity measurements have the
greatest precision but respond only to ionic solutes (APHA, 1995). Density measurements respond to all
solutes. APHA (1995) recommends the electrical conductivity method because it is sensitive and easily
performed. APHA (1995) also recommends the density method using a vibrating flow densitometer.
EPA (1986a) methods also may be consulted. A salinity refractometer can be used for quick readings of
salinity in solutions such as sea water. These refractometers are easy to read, non-corrosive, and
lightweight. They have dual scales and an adjustable focus. Temperature and non-temperature
compensating refractometers are available.
4.5.4.7 Total Sulfides. Total sulfides represent the combined amounts of acid-soluble H2S, HS", and
S2" in a sample. Sulfides are often measured because they are common in some sediments, particularly
those that are anoxic, and they can be toxic to aquatic organisms. PSEP (1986) describes a method to
measure total sulfides in sediments. Oxygen is removed from the sample using nitrogen gas, methyl
orange and hydrochloric acid are added, and the mixture is heated. Amine solution and iron chloride are
added to develop a colorimetric reaction product, and sample absorbance is measured
spectrophotometrically.
Potentiometric methods for measuring sulfides in aqueous samples are described by APHA
Method 4500 (APHA, 1995). Sulfide ions are measured using a sulfide ion-selective electrode in
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conjunction with a double-junction, sleeve-type reference electrode. Potentials are read using a pH meter
or a specific ion meter having a direct concentration scale for the sulfide ion. Samples are treated with
sulfide anti-oxidant buffer, which fixes the solution pH at a highly alkaline level and retards air oxidation
of sulfide ion in solution. This ensures that measured sulfide represents the total sulfides as S2" ion, rather
than as HS" or H2S found at lower pH values. APHA Method 4500 (APHA, 1995) provides both
qualitative and quantitative methods to determine aqueous sulfide concentrations. Qualitative methods
include the antimony test, the silver-silver sulfide electrode test, the lead acetate paper test, and the silver
foil test. Quantitative methods include the photometric method, the automated photometric methylene
blue colorimetric methods, and the iodometric titration method for standardizing stock solutions.
4.5.4.8 Cation Exchange Capacity of Sediments. Cation exchange capacity (CEC) is a parameter
that provides information relevant to metal bioavailability studies (Black, 1965). Cations, such as
positively charged elements (e.g., calcium, magnesium, hydrogen, and potassium), are attracted to
negatively charged surfaces of clay and organic matter. A continuous exchange of cations occurs
between sediment and water. CEC is a measure of the sediment's ability to retain cationic elements. It is
also a measure of clay activity and mineralogy, which is used to calculate mineralization rates and
leaching rates and to predict interactions with contaminants. The degree of CEC is dependent on the type
and amount of suitable surfaces such as organic matter and clay. High cation exchange capacities are
associated with high clay content, and high organic matter and changes in CEC are typically associated
with changes in the organic carbon content and pH of the sediment. Organic matter generally supplies a
greater number of exchange sites than clay particles. CEC can be measured by treating samples with
ammonium acetate such that all exchangeable sites are occupied by the NH4+ ion, digesting the samples
with sodium hydroxide during distillation, and then titrating to determine the ammonium ion
concentration. The amounts of exchangeable cations are expressed in milliequivalents of ammonium ion
exchanged per 100 g of dried sample. More detailed methods are provided in Bascomb (1964), Black
(1965), Klute (1986), and EPA (1986b).
4.5.4.9 Redox Potential (Eh) of Sediments. Redox (Eh) is a measure of the oxidation-reduction
potential of sediments. Measurements of Eh are particularly important for metal speciation and
determining the extent of sediment oxidation. Eh values below approximately -100 millivolts may
indicate biologically important sulfide concentrations if the sediment contains significant amounts of
sulfur. As discussed, some trace metals form insoluble complexes with sulfides, rendering them
unavailable for uptake by biota. Since free ionic metals generally are thought to possess the greatest
toxicity potential, measuring the conditions that control binding dynamics, such as pH and Eh, is critical.
Potentiometric measurements of Eh using a millivolt reader can be obtained with a platinum
electrode and a standard hydrogen electrode (Plumb, 1981). APHA (1995) does not recommend using the
standard hydrogen electrode, as it is fragile and impractical. Instead, their method recommends the use of
a silver-silver-chloride or calomel reference electrode. APHA (1995) also recommends a graphite rather
than a platinum electrode for sediments. Once the Eh equilibrium is reached, the difference between the
platinum or graphite electrode and the reference electrode is measured. This potential then is normalized
to report the Eh of the system relative to the standard hydrogen electrode. For a more detailed
explanation on how to calculate the Eh, see APHA (1995).
A number of problems are associated with the accurate measurement and interpretation of Eh
in sediments, particularly in marine sediments. Therefore, considerable attention should be paid to the
use of proper equipment and techniques. Some of the problems identified by Whitfield (1969) and
Murdoch and MacKnight (1994) include measurement inaccuracy resulting from disturbance of the
sediment sample during insertion of the electrode, instability and poor reproducibility of the
measurements, and differential responses of platinum electrodes under different environmental
conditions. It is recommended that published studies on the problems associated with measuring and
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interpreting sediment Eh be consulted before any attempt is made to measure this parameter in sediment
samples (Berner, 1963; Morris and Stumm, 1967; Whitfield, 1969).
4.5.4.10 Dissolved Oxygen. Measures of DO refer to the amount of oxygen contained in water and
define the living conditions for oxygen-requiring (aerobic) aquatic organisms. DO concentrations reflect
an equilibrium between oxygen-producing processes (e.g., photosynthesis) and oxygen-consuming
processes (e.g., aerobic respiration, nitrification, and chemical oxidation), and the rates at which DO is
added to and removed from the system by atmospheric exchange (aeration and degassing) and
hydrodynamic processes (e.g., accrual/addition from rivers and tides versus export to ocean).
The two standard methods for measuring DO concentrations are membrane electrodes and the
Winkler (iodometric) titration method. Membrane electrodes are the most practical for in-situ
determinations and continuous monitoring protocols. The biochemical oxygen demand is another
standard procedure used to determine the oxygen requirements of raw wastewater and treated effluents. It
measures the oxygen utilized during a specified period of organic matter degradation. The solubility of
oxygen is affected non-linearly by salinity and water temperature. Therefore, DO measurements often are
expressed as percentage saturation values as this parameter is independent of temperature and salinity.
Most instrumentation for DO measurement can convert DO to % DO when the salinity,
temperature, and altitude are known. Spot measurements of DO or % DO are not very useful. The full
diurnal range of DO concentrations is required for proper data interpretation and can be used as an
indicator of primary production (i.e., the accumulation of organic carbon and concomitant formation of
oxygen via photosynthesis). Diurnal DO changes can be tracked overtime using moored, continuously-
recording DO sensors. At a minimum, measurements should be taken at mid-day and dawn to
approximate the diurnal range.
4.5.4.11 Dissolved Organic Carbon in Pore Water. DOC often consists of humic substances and is
the fraction of the organic carbon pool that is dissolved in water and passes through a 0.45-um glass fiber
filter. DOC is an indicator of the chemically reactive organic fraction and accurately measures the
dissolved organic load. Sediment pore waters can be rich in humic acids, which can bind metals. Gilek
etal. (1996) measured DOC using a TOC apparatus and infrared detection of CO2. Borga etal. (1996)
measured DOC using flow-injection analysis interfaced with inductively-coupled plasma atomic emission
spectrometry (ICP-AES). Three methods for measuring DOC, including the combustion-infrared method,
the persulfate-ultraviolet oxidation method, and the wet-oxidation method, have been adapted from
APHA Method 5310 (APHA, 1995) for TOC. Adjustments for inorganic carbon interference may be
required.
4.5.4.12 Alkalinity and Hardness of Pore Water (Fresh Water Sediments). Studies have shown that
toxic effects of metals are influenced by alkalinity as it alters speciation and bioavailability. For a formal
definition of alkalinity and hardness, the reader is referred to Stumm and Morgan (1996), as well as to
Section 4.2 of this document. APHA (1995) recommends a color-change titration method to measure
alkalinity. The sample is titrated with standard acid to a designated pH, and the endpoint is determined
electrometrically (/'. e., pH meter) or by the color change of an internal indicator. The inflection points
along the curve are used to determine the alkalinity of the sample. The color-change titration method is
most commonly used. Hach (Method 8202) has developed a portable water chemistry kit based on the
APHA (1995) color-change titration method and an additional method using sulfuric acid with a digital
titrator (Hach, Method 8203).
Hardness is the concentration of metallic cations, with the exception of alkali metals, present
in water samples. Generally, hardness is a measure of the concentration of calcium and magnesium ions
in water. Hardness usually is expressed as a calcium carbonate equivalent in mg/L. The APHA describes
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two methods to measure hardness: 1) the calculation method, and 2) the ethylenediaminetetraacetate
(EDTA) titrimetric method (APHA, 1995). The calculation method is based upon the results of separate
determinations of calcium and magnesium ions, and hardness is expressed as milligram equivalents of
CaCO3 L"1. In the titrimetric method, calcium and magnesium ions in water are sequestered by the
addition of EDTA. The endpoint of the reaction is measured by means of Chrome Black T3, which is red
in the presence of calcium and magnesium ions and blue when both are sequestered. APHA recommends
the calculation method because it is more accurate. The method uses direct determinations of calcium
and magnesium to calculate hardness. Hach has developed portable water chemistry kits (Methods 8222,
8204, 8030, 8226, 8213, 8338, and 8329) for a variety of hardness determinations using a
spectrophotometer or titration methods with a decision tree for selecting the appropriate procedure. Three
of the Hach methods were adapted from APHA Method 2340 (APHA, 1995): 1) the burette and 0.020-N
titrant method (8222), 2) the ManVer 2 burette and 0.020-N titrant method (8226), and 3) the burette
titration method (8338). The APHA EDTA titration method most often is used.
4.5.4.13 Conductivity of Pore Water (Fresh Water Sediments). Conductivity is a measure of the
ability of an aqueous solution to carry an electric current. This ability is dependent on the presence of
ions in the solution, the concentration of the ions, their mobility and valence, and temperature. Solutions
of inorganic compounds are usually good conductors, while those of organic compounds are usually poor
conductors. Calcium, potassium, sodium, and magnesium chlorides and sulfides enhance conductivity.
Meters can be used to measure the degree to which electrical current can travel through water. The unit
of measure is 1 mS/m = 1 millisiemens/meter or 1 (iS/cm = 1 microsiemens/centimeter. The reading is
related to the amount of ions in the water, with a higher conductivity indicating higher amounts of ions.
While traditional chemical tests for hardness measure calcium and magnesium, they do not consider other
ions (e.g., sodium); thus, conductivity is a better indicator of a solution's ionic strength than is hardness.
4.6 Analytical Approaches to Metal Speciation in Sediments: Sequential Extraction,
SEM/AVS, and Spectroscopic Techniques
4.6.1 Analysis of Metals from Sediments: Sequential Extraction. Low levels of trace metals
occur naturally in the environment, but elevated levels in sediment are generally associated with
anthropogenic contaminant loads. Metals may partition among several phases in sediments (Gambrell et
al, 1976). From an environmental perspective, the most important forms of metals in sediments are those
that are bioavailable to benthic organisms, either directly or following diagenic transformations. Great
care must be taken in collecting and handling soil and sediment samples to prevent changes in pH, redox
potential, and water content that may change the chemical forms and associations of the metals in the
soils and sediments (Kersten and Forstner, 1986, 1991).
Sequential extraction or leaching schemes have been used extensively to partially
characterize the phase associations of metals in sediments and to identify the fraction or fractions of total
metal that are, or could become, bioavailable (Tessier and Campbell, 1987). None of these selective
extraction techniques is completely specific for a particular metal fraction in sediment, and none
adequately defines the bioavailable fraction (Luoma and Bryan, 1979, 1981; Salomons and Forstner,
1984). However, the ultimate design behind a sequential extraction of a sediment is to characterize the
partitioning of a metal phase between a more mobile (i.e., exchangeable, or more bioavailable) fraction
and a less mobile (residual, or less bioavailable) fraction (Filgueiras et al., 2002). These selective
extraction techniques are reasonably selective when applied to oxidized, sulfide-poor sediments; however,
they are not sufficiently selective for use with anoxic, sulfide-rich sediments (Rapin et al., 1986). In
sulfur-rich anoxic sediments, it was noted that copper, iron, and zinc were particularly sensitive to the
maintenance of oxygen-free conditions during sample acquisition and pre-treatment (Rapin etal., 1986);
thus, anaerobic conditions are of the utmost importance in maintaining sample integrity for sequential
extraction approaches to understanding metals speciation/bioavailability in sediments. Selective
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extraction approaches are bolstered when coupled with quantitative spectroscopic measures, such as
X-ray diffraction (XRD) (Ryan et al, 2002; Haus et al, 2008) Furthermore, it should be noted that
sequential extraction methods do not yield a quantitative and conclusive description ofin-situ metal
speciation or metal partitioning into the various solid phases due to: 1) the redistribution of metals during
the sequential extraction procedure, 2) non-selectivity of the reagents for the various relevant pools, 3)
incomplete extraction of the target metals from the various solid phases, and 4) the precipitation of new
minerals during the extraction procedure (Burton et al., 2006; Bacon and Davidson, 2008; Haus et al.,
2008).
Three to six fractions usually are defined in sequential extraction schemes (Salomons and
Forstner, 1984). These include: 1) exchangeable cations, 2) carbonates, 3) easily reducible phases,
4) moderately reducible phases, 5) organic plus sulfide fraction, and 6) residual fraction.
In some extraction schemes, two or more fractions are combined and extracted with a single
extractant. The exchangeable metal cations (Fraction 1) are extracted with a solvent that contains cations
that are more strongly complexed to the binding sites on sediment particles than are the metal cations.
Metal ions bound to the carbonate fraction of sediments can be extracted with a weak acid. Metals in this
fraction include those adsorbed to inorganic carbonate particles and those incorporated into the calcium
carbonate crystal lattice during biodeposition of skeletal carbonates (mainly calcium carbonate) or
precipitation of carbonates. Carbonate precipitates are known to form in fresh water environments,
especially where calcium carbonate minerals are found; however, these are generally special cases and
carbonate minerals are more frequently encountered in salt water or brackish environments.
The residual or detrital fraction contains metals that are: 1) tightly bound to or incorporated
into the crystalline lattice of clays, other silicates, or heavy minerals, 2) precipitated or co-precipitated as
stable heavy metal sulfides (e.g., cinnabar and pyrite), or 3) adsorbed to crystalline iron oxides or highly
refractory organic matter (e.g., humic acids). These residual metals usually are extracted with mixtures of
concentrated strong acids (e.g., aqua regia) at a high temperature. Hydrofluoric acid may be added to the
aqua regia to dissolve metals associated with silicate minerals. Generally, at least 50% of most metals
and occasionally as much as 98% of some metals are associated with the residual fraction of sediments.
Residual metals are not bioavailable to plants and animals.
4.6.2 The Simultaneously Extracted Metals/Acid Volatile Sulfide Concept. Building on the
sequential extraction methods described above, it was observed that metals bound to the organic fraction
of soil or sediment can be extracted with a hot acidic solution of hydrogen peroxide and nitric acid or
weak (1-M) hydrochloric acid (Tessier and Campbell, 1987). These two extractants also extract the more
labile metal sulfides (Allen et al., 1993; Ankley, 1996). The observation that the potentially bioavailable
metals (Fractions 1 through 4) are extracted along with AVS with weak 1-M HC1 led to the development
of the SEM/AVS approach of quantifying the bioavailable fraction of metals in sediments. According to
Huerta-Diaz et al. (1998), AVS equals the sum of amorphous FeS, mackinawite (FeS), and greigite
(Fe3S4) and can be defined as those sulfides that are readily extracted by a cold extraction of the sediment
in 1-M HC1. The analytical method, purge and trap, involves conversion of solid sulfides to gaseous H2S,
which then is purged from the system and trapped in an aqueous solution. The trapped sulfide may be
detected with a sulfide probe or by following a wet chemistry method (DiToro et al., 1990). SEM can be
defined as the sum of metals extracted under the same conditions as AVS. The equivalent release of
sulfide (AVS) and metal, however, does not necessarily mean that the metal is bound by sulfide alone.
SEM, therefore, is essentially an operationally-defined pool of metals theorized to be associated with
sulfides and any other metal-bearing phase that is extracted in cold 1-M HC1 (Allen et al., 1993). For
example, metal sorbed onto iron oxides and POC also will be extracted. As with operationally-defined
pools of metals in sedimentary environments, which are defined by an experimental laboratory
manipulation, a mechanistic understanding of chemical reactivity is necessarily limited. Cautionary
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considerations for the implementation of the SEM/AVS approach to understanding site- and metal-
specific bioavailability are elaborated below (Section 4.6.3).
In intertidal sediments, where sulfide occurs in significant concentrations, the reductive
dissolution of Fe(III) oxyhydroxide phases is followed by the formation of FeS(aq), FeS(s), and FeS2(S)
(Theberge and Luther, 1997). The formation of pyrite in anoxic conditions is rapid and occurs by
reaction between H2S or (S°) and aqueous (or solid) FeS (Luther, 1991; Rickard, 1997; Rickard and
Luther, 1997). Di Toro et al. (1990, 1992) have proposed an SEM/AVS model based on the recognition
that AVS is a reactive pool of solid-phase sulfide that is available to bind with metals to form insoluble
metal sulfide complexes that are not bioavailable. Since FeS has a higher solubility product (Ksp) than do
other trace metal sulfides (MeS), metals will displace the iron to form the more insoluble MeS under
equilibrium conditions, as illustrated by the reaction below. This reaction reflects the stoichiometric
relationships between iron sulfides and other metal sulfide phases with lower solubilities, but is not
intended to invoke a mechanistic interpretation for the in-situ formation of metal sulfide precipitates, or
define the time frame required for this exchange to complete
Me2+ + FeS(s) = MeS(s) + Fe2+ (Eq. 4.1)
It is important to note that each cationic metal has a different binding affinity for sulfide
(EPA, 1994a; Stumm and Morgan, 1996). Currently, there is considerable debate regarding the relative
affinities of each of the metals. Relative affinities of metals for sulfide increase as the log Ksp decreases,
which is an equilibrium consideration and does not take into account kinetic considerations for the
formation of different sulfide minerals. Mercury and copper have the lowest log Ksp values (Table 4-1),
indicating that the affinity of these metals for AVS is higher than that of the other metal cations. The
mercury concentration in sediments, however, is nearly always much lower than the concentrations of all
other metals, so it doesn't compete with other metals for complexation with AVS. It usually is assumed
that, at equilibrium, copper preferentially reacts with AVS, displacing all other metals. If the available
AVS is not completely saturated by copper, then the remaining metals react in the following order: lead,
cadmium, zinc, and nickel. In this model, the fraction of copper in the sediment that is considered as
bioavailable and potentially toxic is defined as follows:
Cub = ([CuSEM]—[AVS]) x (MWCu) (Eq. 4.2)
where
Cub = fraction of copper that is bioavailable
[CuSEM] = molar concentration of copper as defined by simultaneous extraction
[AVS] = molar concentration of AVS
MWCu = molecular weight of copper (mg/mole).
The concentration (mg/kg sediment) of bioavailable copper in sediments is:
Cubl0available = [Cusedlment] X Cub (Eq. 4.3)
where [Cusediment] is the concentration of total copper in sediment.
The SEM/AVS difference provides insight into the extent of either the additional metal
binding capacity with reduced sulfur or the magnitude by which AVS binding has been exceeded. When
organism response is considered, this metric can indicate the potential magnitude of importance of other
metal binding phases and which metals may be of concern (Hansen etal., 1996). For example, the
SEM/AVS model predicts that when the measured AVS concentrations exceed the concentration of SEM
(SEM/AVS molar ratio < 1), the pore water levels office metal ions should be very low, resulting in the
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prediction of minimal toxicity (Chapman etal, 1998). Alternatively, when SEM exceeds AVS, higher
pore water concentrations of bioavailable forms of the metal(s) are predicted, possibly leading to metal
toxicity. Recently Di Toro et al. (2005) extended the SEM/AVS-based approach by coupling it to a biotic
ligand model with more explicit consideration of POC as a metal-binding phase. This approach has been
successful in predicting toxicity, or lack thereof, in metal-contaminated sediments (Di Toro et al., 2005).
The bioavailable fraction of the other metals in sediment may be determined in the same manner
following the order described above. For each successive metal, the molar concentration of AVS applied
should be decreased according to the molar concentration of the preceding metal; when the concentration
of AVS is zero, all remaining metals are assumed to be bioavailable.
4.6.3 Limitations and Cautions in the Use of SEM/A VS. The major limitations in the use of the
SEM/AVS model stem from the fact that the measurement of AVS is operationally defined and does not
provide a mechanistic understanding of metals speciation, transformation, or bioavailability. Several
fundamental misconceptions regarding the recovery of iron sulfide minerals are addressed by a collection
of review articles devoted to this topic (Luther, 2005; Meysman and Middelburg, 2005; Rickard and
Morse, 2005). In these review articles, the authors examine the reactivity of different iron sulfide
minerals and the molecular nature of sulfur recovered by the AVS extraction. Where there has been a call
to abandon the use of AVS due to the inherent limitations of an operationally-defined reservoir of
sedimentary sulfur (Rickard and Morse, 2005), others have tempered these criticisms with the utility of
the AVS method in understanding sulfur and trace metal biogeochemistry in sediments (Luther, 2005;
Meysman and Middelburg, 2005). Their synopses, in addition to the studies mentioned below, describe
the pertinent issues regarding the use of the SEM/AVS method for estimating metal speciation, reactivity,
and bioavailability in sediments.
The principal limitation of the SEM/AVS protocol for understanding metal speciation and
bioavailability in sediments is due to the sensitivity of sediments to geochemical conditions, such as pH
and redox. For example, artificial changes in metal speciation may occur during operational
manipulations of the sediment samples. These changes may occur from the moment of sampling to
laboratory manipulations during extraction and can bias interpretation of AVS/SEM analyses. During
sample collection, care must be taken to ensure sample integrity to avoid changes in metal speciation of
the in-situ sulfide phases, particularly oxidation upon exposure to atmospheric conditions. Secondly,
metals speciation can be unintentionally altered due to the formation of insoluble precipitates and/or the
formation of other adsorbed species onto those newly precipitated phases during the acid extraction
stages, which can shift metal speciation from that found in the original sediments (Wilkin and Ford, 2002;
Scheckel et al., 2003; D'Amore etal., 2005). The re-adsorption of copper, lead, and cadmium and re-
precipitation of lead (in phosphorous-containing soils) during sequential extractions has been documented
(Rendell etal., 1980; Scheckel etal., 2003). The precipitation of arsenic sulfides (orpiment or realgar)
during acid extraction of arsenic-containing sediments has also been observed (Wilkin and Ford, 2002).
Furthermore, a considerable quantity of metals may be contained within a sulfide mineral or precipitate
that is not extractable by HC1; however, this reservoir may be susceptible to oxidation and subsequently
bioavailable (Morse, 1994; Cooper and Morse, 1998). For example, Morse observed that the degree of
HC1 soluble trace metals in marine sediments increased three-fold after exposure to oxic sea water
(Morse, 1994). Therefore, the use of a weak acid extraction can underestimate trace metal bioavailability
due to under-extraction of metals associated with oxidizable phases and also due to the precipitation of
new solid phases as a consequence of the acid extraction used for the SEM/AVS analysis.
In some cases, toxicity was observed when, according to the SEM/AVS model, no metals
should have been bioavailable (i.e., SEM « AVS) (Lee etal., 2000; O'Day etal., 2000). For example,
in laboratory incubation experiments, metal accumulation in two clam species was observed when the
concentration of SEM was only a fraction of AVS. The total level of metals that were bioaccumulated
displayed a linear relationship with total metals irrespective of AVS or pore water metal concentrations
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(Lee et al, 2000). In an example of a field-scale test of the SEM/AVS model, metal bioavailability and
toxicity were evaluated in sediment from Naval Air Station Alameda (O'Day et al., 2000). In this case, it
was ultimately determined that the observed toxicity was not caused by metals, but was due to the
presence of either other contaminants that were not measured or to naturally-occurring geochemical
conditions that were detrimental to the organism used for the toxicity test. Toxicity tests in surficial
sediments resulted in no or low toxicity; however, deeper sediment (30 to 60 cm) displayed high toxicity.
The observed toxicity of the deeper sediments was attributed to the lack of DO and high levels of
ammonia, and was not linked directly to the high levels of metal contaminants (e.g., cadmium, lead,
chromium, zinc, copper, and nickel) (O'Day etal., 2000). Additional factors may contribute to observed
toxic responses, which emphasize the importance of thorough geochemical sediment characterization, and
the limitations of relying solely upon the SEM/AVS model for evaluating sediment toxicity.
The presence of reactive geosorbent materials, in addition to sulfides, that are able to
sequester metals from the aqueous phase presents an additional limitation to the SEM/AVS model. For
example, when extractable metals exceed the molar equivalent of AVS, sediment phases other than AVS
such as organic sorbents and/or clay minerals, will become important in metal binding (Oakley et al.,
1981; Luoma and Davis, 1983; Tessier and Campbell, 1987; Rivera-Duarte and Flegal, 1997; Trivedi and
Axe, 2001). Metal binding by POC and iron hydroxide phases is dependent on pH with the highest
amounts being adsorbed in the range of pH 5 to pH 7 (Lion et al., 1982; Millward and Moore, 1982;
Stumm and Morgan, 1996). Sediment-water partition coefficient (Kd) values typically decrease
substantially as pH decreases, or as negatively charged surface sites become protonated and electrostatic
considerations become less favorable for metal cation adsorption (Stumm and Morgan, 1996; Tessier et
al., 1996; Trivedi and Axe, 2001). Thus, when [SEM - AVS] > 0 (i.e., excess of labile metal cations), an
exposure risk is to be presumed; however, toxicity might not be observed due to metal uptake by organic
matter, oxide, and/or clay mineral phases.
Further limitations of this approach stem from interpretations of data from acid extractions of
sulfide minerals. As noted, the most common approach taken is to measure the concentrations of H2S
and SEM following leaching of the sediment with dilute acid, typically 1-M HC1, although other acids
and acid strengths also have been used. Rickard and Morse (2005) argue that: 1) because AVS is
operationally defined, it should never be used as directly equal to sedimentary FeS, 2) different protocols
(i.e., acid strength, temperature, leaching time, and use of antioxidants) leach different amounts of AVS
materials, in some instances varying by five-fold with the same sediment, 3) the SEM concentration also
varies with time and location, 4) it is not a reasonable expectation that aqueous S(-II) components and
sulfide minerals undergo the same reactions with sedimentary trace metals, 5) it cannot be assumed that,
for any given AVS yielding S(-II) concentration, they have similar influences on the toxicity of metals,
6) because of complex biogeochemical dynamics, sediments can have very different AVS depth
distribution patterns, which precludes that there is a universally correct sampling interval for the
AVS/SEM method, and 7) AVS may undergo major concentration changes on time scales of hours to a
few days to seasonally in response to hypoxia/anoxia in overlying waters resulting in the AVS-to-SEM
ratio constantly changing. They conclude that all of these complications ultimately result in confusion in
drawing conclusions about potential metal toxicity or exposure risks in sediments.
4.6.4 Promising Spectroscopic and Analytical Techniques. Modern instrumental methods being
applied to the analysis of trace metal speciation include: 1) electrochemistry (e.g., differential pulse
polarography), 2) spectrophotometry (e.g., silver diethyldithiocarbamate), 3) atomic absorption/emission
spectroscopy coupled to chromatography, 4) X-ray synchrotron techniques, 5) inductively coupled plasma
mass spectrometry (ICP-MS; also coupled to a chromatographic technique), and 6) neutron activation
(PSEP, 1997; D'Amore et al., 2005). X-ray methods, at the time of the publication of this document,
offer the most promise in understanding in-situ metal speciation, with potential for bridging gaps in
understanding metal speciation and bioavailability. Examples of X-ray methods to determine metal
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speciation in environmental soils and sediments include: 1) synchrotron X-ray fluorescence (Isaure etal,
2002; Manceau etal., 2004), 2) X-ray emission (Isaure etal., 2002), X-ray absorption (XAS) (Scheckel
et al., 2003), 3) X-ray absorption near edge structure (XANES) (Manceau etal., 1992; Bang and
Hesterberg, 2004), and 4) extended X-ray absorption fine structure (EXAFS) (Ford etal., 1999; Scheckel
etal, 2000; Randall etal, 2001; Isaure etal, 2002; Manceau etal, 2004; Scheckel and Ryan, 2004).
These studies offer promising insight into both the technical limitations of sequential extraction methods
and an improved understanding of the in-situ metal coordination environment of inorganic contaminants
in sedimentary environments.
The development of many of the aforementioned methods and techniques are aimed
specifically at identification of in-situ metal speciation. Metal speciation drives the interaction between
metals and the overall environmental milieu. For example, metals uptake by local biota is dependent on
the metal species present and, by extension, ecological risks are determined by metal speciation and not
by total metal concentration. The suite of analytical techniques for understanding metal speciation can be
separated into those applied to solid phase-associated metals and those that can be applied to aqueous
phase speciation. Analysis of solid phases typically exploits three principal properties of solids:
1) the ability of surface-associated metals to absorb or reflect (diffract) X-rays (these
techniques include XRD and X-ray absorption methods including XAS, EXAFS, and
XANES);
2) the ability of solids to interact with magnetic fields (techniques useful for magnetic solids
include nuclear magnetic resonance, electron paramagnetic resonance and Mossbauer
methods); and
3) the characteristic vibrational frequencies that are unique to different solid surficial metals
and metal-associated functional groups. Techniques used for vibrational frequency
analysis include infrared spectroscopy and Raman spectroscopy.
Analysis of dissolved phase species typically are accomplished by colorimetric/
spectrophotometric methods that suffer from a general lack of specificity, a lack of sensitivity, and the
potential for artificial changes in speciation during sample treatment. Electrochemical methods (e.g.,
differential pulse polarography and stripping voltammetry) are sensitive to the unique redox potentials of
metals with multiple valence states. Therefore, the presence of multiple metals in complex environmental
samples can be analyzed simultaneously. However, application of electrochemical methods to in-situ
metal speciation is restricted by the depth to which electrodes can penetrate sediments and to the limited
range of commercially available electrodes that are sufficiently robust for use in environmental settings.
ICP-MS or ICP-AES allows for the determination of many metals at sub-ppb levels with little
pretreatment (Crecelius and Bloom, 1987; Berry et al., 1999). Another commonly used instrumental
method to analyze sediments for metals is atomic absorption spectrometry (PSEP, 1997).
4.7 Model Approaches to Predicting Equilibrium Metal Speciation
It is well established that many geochemical factors influence the speciation of metals in
water and sediments. Nearly all the mathematical models that are used to predict metal speciation and
bioavailability in sediments are based on thermodynamic equilibrium conditions. Although it is
understood that most sedimentary systems are not truly at equilibrium, thermodynamic models allow us to
make educated predictions of how a particular system will change, for example, with shifts in pH, Eh, or
total ligand concentration (Porter et al., 2004). Several of the paradigms that explain the relationships
between these geochemical factors and metal bioavailability and toxicity are described below.
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4.7.1 Free Ion Activity Model. As discussed, the formation of organic and inorganic metal
complexes and metal sorption to particulate material reduces metal bioavailability and toxicity in the
water compartment (Pagenkopfe^ a/., 1974; Sunda and Guillard, 1976; Sunda and Hansen, 1979;
Pagenkopf, 1983; Di Toro et al., 2001). As a result, the relationship of metal toxicity to total or dissolved
concentrations can be highly variable depending on ambient water chemistry (Di Toro et al., 2001).
Initially, the free ion activity was considered the best index of metal toxicity, resulting in the formulation
of the Free Ion Activity Model (FIAM). The FIAM is a model that describes how variations in the levels
of metals can be explained on the basis of metal speciation and metal interactions with the organisms
(Morel, 1983; Paquin et al., 2002). Being based on thermodynamic estimates of metal speciation, the
FIAM contains the implicit assumption that toxic effects are proportional to the flux of the metal in
question and predict a first-order linear relationship between biological accumulation and free metal ion
aqueous concentration. This assumption has been demonstrated to underestimate metal uptake by
microorganisms, especially in situations where organisms may possess active uptake transport
mechanisms (Hassler and Wilkinson, 2003).
4.7.2 Windermere Humic Acid Model. A number of chemical speciation or equivalent models
provide good characterization of the metal species in a solution containing inorganic ligands and well-
characterized organic ligands. As binding of metals to organic matter is often one of the most dominating
processes in natural water, it is essential that such speciation models include an accurate description of
organic matter reactions with trace metals. The Windermere Humic Aqueous Model (WHAM, Model V)
was developed to simulate chemical equilibrium of waters, sediments, and soils dominated by NOM
(Tipping, 1994). WHAM was the outcome of a series of models developed to describe NOM chemistry
and interactions with metals. The capabilities of WHAM (Model V), along with extensive calibration to
published datasets, make this a comprehensive model for simulation of metal chemistry where
interactions with NOM are important.
4.7.3 Reactive Transport Modeling. Reactive transport models (RTMs) are highly beneficial for
simulating interplay between metal speciation and complex chemical reactions and describing how these
processes influence metal transport processes both spatially and temporally in the environment on a
variety of scales. RTM has been used to describe early diagenesis processes in sediments (Wang and Van
Cappellen, 1996; Boudreau, 1999). RTMs applied to sediments consider the reaction couplings among
the principal reduction-oxidation elements (carbon, oxygen, nitrogen, sulfur, iron, and manganese). They
can consider dissolved, interfacial, and solid-state chemical species, and incorporate major
biogeochemical reaction pathways (e.g., organic matter destruction and sulfate reduction) and transport
processes, both biotic (bioirrigation and bioturbation) and abiotic (diffusion). Examples of the use of
RTMs in the sedimentary environment include prediction of metal cycling in fresh water sediment
(Canavan et al, 2007), modeling pH distributions in aquatic sediments (Jourabchi et al., 2005),
describing organic matter mineralization in fresh water lakes (Canavan et al., 2006), and predicting
sediment oxygen demand from carbon and nitrogen cycling (Hantush, 2007). One modeling tool
currently available for conducting RTM is the Biogeochemical Reaction Network Simulator described in
Aguilera et al. (2005) and Jourabchi et al. (2005). Additional information is available online from the
RTM group at: http://www.geo.uu.nl/~rtm/index.php?page=intro.
4.8 Summary
A decision to implement MNR at a site requires a thorough understanding of the
biogeochemical behavior of the elements of interest and the specific environmental conditions at the
given site that will influence chemical and phase partitioning and biological interactions. The behavior
and fate of trace elements and metalloids in the environment are complex and depend on many
interrelated chemical, biological, and environmental processes. In sediments and surface water, factors
that may influence metal toxicity and mobility include the presence, abundance, and forms of reduced
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sulfur, redox conditions, pH, acid-buffering capacity, abundance of organic matter and iron and
manganese oxide minerals, and the chemical and phase speciation of the specific metal or metalloid. A
number of modeling tools are available that can help to integrate these processes and make predictions
about behavior and fate in relation to the MNR decision-making process.
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5.0 LONG-TERM MONITORING AND SITE FORECASTING WITH
PREDICTIVE MODELS
5.1 Monitoring Rationale and Strategies
Potential risks posed by contaminated surface sediments, the rate of change in surface
sediment chemical concentrations, and the rate of recovery of ecological resources are site-specific
features that will influence the effectiveness of MNR and guide any corresponding long-term monitoring
requirements. Long-term monitoring in support of MNR may focus on surface sediments (establishing
concentration reductions with time or evaluating and characterizing sediment erosion events), recovery of
biological/ecological receptors, or both. Monitoring plans should be based on a clear understanding of
remedial action objectives (RAOs), which in turn require familiarity with site-specific risk assessments,
cleanup levels for sediment, remedial goals for surface water and fish tissue where applicable, and target
time frames for achieving the goals of confirming continuation of natural recovery. RAOs for MNR may
include attainment of risk-based contaminant concentrations in surface sediment, reduction of chemical
mobility to achieve risk-based chemical concentrations in pore water or sediment, and/or achievement of
target contaminant concentrations in biota.
Most sites requiring remedial action, such as MNR, will have site-specific sediment, aquatic,
or biotic target concentrations considered protective of human health and ecological receptors. These
targets form the basis of long-term recovery goals expected to be achieved in a reasonable time period.
The goals of long-term monitoring should be to: 1) evaluate the extent to which chemical concentration
targets are achieved, and 2) determine whether measured chemical and biological recovery continues at
acceptable rates. Monitoring may include surface sediment sampling overtime, sediment coring, pore
water sampling, water column monitoring, and biota tissue monitoring. In addition to establishing
expectations of future surface sediment chemical concentrations, the MNR CSM should form the basis of
the long-term monitoring program to determine sampling locations, sampling methods, sample counts,
sampling frequency, and monitoring duration.
The following six-point process for developing and implementing a long-term monitoring
plan is presented in EPA (2005). This section adds to EPA's (2005) discussion by focusing on MNR
monitoring goals.
Step 1. Identify Monitoring Plan Objectives
Step 2. Develop Monitoring Plan Hypotheses
Step 3. Formulate Monitoring Decision Rules
Step 4. Design the Monitoring Plan
Step 5. Conduct Monitoring Analyses, and Characterize Results
Step 6. Establish Management Decision Guidelines
Short- and long-term monitoring may be considered as the collection of field data (chemical,
physical, and/or biological) to determine present conditions at a particular point in time and/or a trend in
conditions over a period of time for specified environmental parameters or characteristics, relative to
clearly defined management objectives. Likewise, for both short- and long-term monitoring, clearly
identified temporal boundaries for the monitoring program should be established. The period of time
designated for evaluation of short- and long-term performance may be related to a 5-year project review
(e.g., ROD 5-year review) or to a site-specific environmental condition. The data, methods, and time
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endpoints should be directly related to the RAOs and cleanup levels or remediation goals for the site. The
success of the long-term monitoring plan can be evaluated according to the EPA (2005) measure of
remedy effectiveness, delineated according to short- and long-term evaluation criteria:
1) Short-term remedy performance - Have the sediment cleanup levels been achieved?
2) Long-term remedy performance - Have the sediment cleanup levels been reached and
maintained for at least 5 years and thereafter, as appropriate?
3) Short-term risk reduction - Do data demonstrate a reduction in fish tissue levels, a
decrease in benthic toxicity, and/or an increase in species diversity or other community
indices after 5 years?
4) Long-term risk reduction - Have the remediation goals in fish tissue been reached, or has
ecological recovery been accomplished?
The long-term monitoring plan implemented for MNR should include quantitative metrics to
measure long-term performance according to the above evaluation criteria. Short- and long-term metrics
should be included as well as contingencies for situations where MNR may not perform according to site-
specific RAOs.
5.2 Metrics for Long-Term Monitoring
Long-term monitoring is an essential component of MNR and should be included in any
remedial plan. Long-term monitoring, as discussed above, is useful for short-term assessment and long-
term remedy evaluation. Long-term monitoring can also be used to confirm predictions developed during
the RI phase, such as predicted sediment deposition/erosion rates, corresponding changes in surface
sediment contaminant concentrations with time, predicted sediment stability under normal- or high-
energy events, predicted contaminant weathering processes, and predicted recovery of ecological
receptors in response to physical changes in the environment. Thus, careful selection of the appropriate
monitoring metrics consistent with the CSM is critical to the evaluation of model predictions and remedy
effectiveness with field-scale analytical data. Given the broad range of environmental variables
potentially available for monitoring, selection of monitoring metrics should be considered within a
conceptual system designed to reduce the amount of data required for long-term site evaluation of MNR
remedy effectiveness. The tiered analysis approach, introduced in Section 2, provides the necessary
framework to develop a cost-effective long-term monitoring plan.
The objective of the tiered analysis approach is to reduce progressively the overall quantity of
data required to execute site-wide monitoring for MNR evaluation and quantification. The tiered analysis
approach, described in detail for application to metals contaminated groundwater (EPA, 2007c), consists
of four tiers for evaluation of a site being considered for MNR:
1) Demonstration of contaminant containment
2) Determination of the mechanism and extent of contaminant containment
3) Determination of the capacity of the sediments for contaminant attenuation
4) Design and implementation of a site-specific, long-term monitoring program.
The purpose of Tiers 1 through 3 is to guide site Remedial Project Managers in selecting site
characterization data to evaluate sites for implementation of MNR. Field sampling approaches and
appropriate models for Tiers 1 and 2 as applied to sediment transport and stability are discussed in
Section 2. Tier 4 specifically details elements of a long-term monitoring strategy to evaluate MNR
performance for sites where MNR has already been selected as the site remedy. The two objectives of
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Tier 4 analysis are to: 1) develop a monitoring program, and 2) identify alternative remedies that can be
implemented at sites where MNR is failing as the principal remedy. The first objective in Tier 4 analysis
is the focus of this section. Any contingency plans for alternative remedies should be developed on a site-
specific basis.
An important aspect of developing a monitoring plan for recalcitrant or non-degradable
contaminants is the identification of surrogate monitoring data that can serve as a trigger for impending
MNR failure. For example, changes in water quality parameters such as pH or alkalinity might provide
an indication that the system is chemically evolving to a state that is not favorable for continued
immobilization. The same observation could be offered for a change in contaminant speciation that might
change toxicity or bioavailability. This example relates to chemical parameters, but one could envision
changes in system hydraulics due to land development/modifications or other factors becoming a concern.
The bottom line is that MNR typically is selected based on knowledge of system function, so, in addition
to contaminant analyses (typically expensive, so they are conducted infrequently), it is important also to
monitor surrogate indicators that may be less expensive and easier to maintain on a more frequent basis.
Long-term monitoring may include monitoring of sediments, surface water and pore water,
and/or biota. The following sections describe considerations for each of these media.
5.2.1 Sediment Monitoring. Where sediment erosion is a concern, a baseline sediment
assessment strategy should be established, including monitoring of surface sediment concentrations and
bathymetry. Sediment bathymetry can be monitored after high-energy events to evaluate bed elevation
changes and compare with model-predicted behavior. Water column monitoring can also be used to
support sediment stability assessments by monitoring water column suspended solid concentrations
and/or turbidity and flows to back calculate the amount of suspension or deposition of sediments during
normal- and high-energy events.
Bathymetric changes are considered a means of properly documenting the accretion of
sediments at a site. Comparative bathymetry is an important and valuable technique in establishing
historic sediment stability and accretion. However, practitioners must consider carefully the nuances of
performing these types of comparisons. These comparisons are particularly subject to several error
functions that arise from the various measurement techniques (both lateral and vertical) used in many
bathymetric measurements.
Uncertainties in comparative bathymetry commonly arise from inaccuracy, imprecision,
and/or lack of comparability in the following areas:
• Location control precision: Inaccuracy always exists in the horizontal plane when
determining the position of each given sounding. For example, the degree of location
control, and associated uncertainties, varies greatly when comparing survey location
results with global positional system values.
• Depth measurement precision: Inaccuracies in measurements made in the vertical plane
result from such factors as resolution, equipment characteristics, calibration, datum,
survey stability, the effects of vessel velocity, and echo sounding sensitivity to sediment
materials.
The reader is referred to the engineering and design manual for hydrographic surveying
(USAGE, 2004) for descriptions of the methods used over the past century to measure water depths and
prepare bathymetric surveys and for method-specific expected levels of precision and accuracy. For a
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discussion of methodologies to compare bathymetry, the reader should consult Herzog and Bradshaw
(2005).
Although comparisons made from sediment monitoring provide relatively straightforward
measures of physical and geochemical changes during MNR, the information derived from sediment
monitoring presents several challenges:
• Obtaining a sufficient number of samples representative of heterogeneous site conditions
can be difficult.
• Detecting small changes in contaminant concentrations over time may be difficult.
• Monitoring for long periods (years or decades) may be required before evidence of
biological recovery is statistically significant.
• Linking surface sediment and water column contaminant concentrations to contaminant
concentrations in ecological receptors may be difficult.
Sediment sampling strategies as part of a broader effort to evaluate MNR effectiveness
should include considerations of the depth interval of concern, discrete or composite sample analysis,
measurement of physical characteristics that affect bioavailability, and comparability to historical site
data and relevant data available from other sources (such as regional monitoring programs). Sampling
frequency and density should be scaled appropriately for the site. Spatial and temporal scales should be
consistent with existing hydrodynamic forces (e.g., combined base flow and storm flow, tidal mixing, and
tidal excursion), sedimentation rates, chemical concentration ranges, source locations, and the anticipated
rate of change in surface sediment chemical concentrations. Some sites may require years or decades to
achieve significant reductions in contaminant concentrations, particularly when surface sediment
concentrations asymptotically approach cleanup targets, or when high variability in the data limits the
ability to discern relatively small changes in contaminant concentrations. Thus, when developing a long-
term sediment-monitoring program, it is important to recognize the degree to which spatial heterogeneity
and temporal variability may influence results.
5.2.2 Surface Water and Pore Water Monitoring. Surface water sampling typically provides
short-term information relative to water column contaminant concentrations, but often does not provide
information regarding advective or diffusive flux from the sediment unless monitored over discrete spatial
and temporal intervals. Pore water monitoring may be used as a more attractive predictive metric to
assess direct exposures of benthic organisms to surface sediment contaminants, particularly where the
total concentration of contaminants in surface sediments is an unreliable indicator of sediment toxicity.
Surface water monitoring is less common than is sediment monitoring but may be valuable if it is possible
to demonstrate reduced exposure or risk and recovery of ecological receptors. This is especially true
where aging of the contaminant in the sediment results in reduced toxicity via sorptive sequestration or
encapsulation, two processes that do not necessarily degrade, mineralize, or transform the contaminant.
For metals, particularly, but also for some organic contaminants, sediment pore water monitoring may
provide a more direct measure of ecological exposures than would whole sediment analysis. If MNR
relies on geochemical stability and reduced metal bioavailability, a long-term monitoring plan for metals
could include pore water monitoring in lieu of whole sediment sampling. This approach would likely be
predicated on prior demonstration that pore water contaminant concentration provides a more reliable
indicator of toxicity than sediment contaminant concentration for a given site.
5.2.3 Biological Endpoint and Ecological Monitoring. In many sediment site risk assessments,
biological endpoints serve as the primary line-of-evidence for assessing human health and/or ecological
protection. Depending on the specific site conditions, relevant data often include fish and invertebrate
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monitoring of key biological endpoints such as tissue chemistry/residues, acute and/or chronic sediment
toxicity bioassays, and community analysis. For this assessment, it is important to consider whether
adequate and comparable biological endpoint data are available to support an evaluation of temporal
trends. The site-specific risk assessment will likely define the relevance or appropriateness of the
endpoints.
Establishment of historical trends should include sufficient sampling to provide adequate
evaluations of temporal trends of the biological endpoint of interest to define spatial heterogeneity and
temporal variability inherent at the site. Specifically, data should be available for a time period over
which recovery could be expected, considering such factors as the life cycle and age of the biological
community being addressed. The timing of sampling events and the number and location of samples
collected also can have a significant impact on the confidence placed in the trend analysis.
Potential confounding factors to trend analysis with biological endpoints include:
• Differences in field collection methods for different sampling events (e.g., changes in
protocol for field filtration of samples, or use of different pore water samplers);
• Changes in laboratory analytical methods for a parameter during the monitoring period;
• Population dynamics;
• Fish migration patterns;
• Food chain dynamics;
• Seasonal variations in abundance or condition; and
• Spatial variation in organism:
o Habitat quality
o Density
o Lipid content
o Age
o Sex
o Size
o Reproduction cycles.
Reducing or eliminating confounding factors may be achieved through selection of sampling
or analysis methods that avoid introduction of artifacts in the collected data, as well as data evaluation
approaches that incorporate explicit checks on procedure implementation and/or review of supplemental
data that serve as indicators of inadequate data quality. Using appropriate data sets, trend analyses of
relevant biological endpoints often can be used to corroborate risk reductions and biological recovery as
may be indicated by chemical data. However, statistically valid methods for testing the significance of
identified trends (parametric and nonparametric tests) should be employed only after testing and
controlling for other potential confounding factors.
Though monitoring changes in contaminant concentrations in biota can be more challenging
than monitoring changes in surface water and sediment, biota can provide direct measures ofin-situ
contaminant bioavailability, which reflects toxicity, exposure, and risk. Contaminant concentrations in
tissues and bioaccumulation, however, are not in and of themselves biological effects, but may correlate
with biological effects.
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Biological monitoring may include:
• Direct measurement of contaminant concentrations in tissues of resident, typically native,
species;
• Deployment of non-resident species within contained benthos samplers and measurement
of contaminant concentrations in tissues over time;
• Determination of the potential for bioaccumulation using food-web models or biological
surrogates;
• Measurements of fecundity, recruitment, and size- or age-class structure of populations;
and
• Benthic community surveys.
Tissue concentrations alone do not necessarily correlate with deleterious biological effects.
Despite the uncertainties involved in linking upper-trophic-level fish tissue concentrations to site-specific
sediment concentrations, fish tissue measurement is often a valuable tool for assessing risk to humans and
regional availability of environmental contaminants to fish. Edible fish sampling and analysis methods
are established and documented elsewhere, as are considerations for species/size/age selection, specific
tissues to be analyzed, sample compositing, and archiving (EPA, 2000). Tissue contaminant
concentrations are not the only information provided by fish sampling. Other information gathered
through fish sampling includes important metrics of the overall health of individual fish, such as gross
morphological abnormalities, evidence of disease, and body condition, as well as information on the
health of the overall population, such as fecundity, recruitment, and size- or age-class structure. These
data can be useful in evaluating MNR effectiveness. For example, liver lesions in mummichogs were
positively correlated with t-PAH concentrations in sediments from the Elizabeth River, VA (Vogelbein
and Unger, 2003). Liver lesions have also been linked to sediment contaminants in Puget Sound flatfish
and PAH metabolite concentrations in the bile of flatfish collected from Vancouver Harbor, British
Columbia, Canada (Stein et a/., 1990; Myers et a/., 2000; Stehr et a/., 2004). Toxicity and
bioaccumulation tests also can be conducted over time to monitor changes in ecosystem toxicity.
Not all metrics are appropriate at every site; rather, monitoring metrics should be selected
based on their ability to provide information relevant to the remedial goals for the individual site. The
concentrations of contaminants in ecological receptors may be affected by a multitude of factors unrelated
to sediment contaminants, such as diet from other sources (Fisk et a/., 2001; Herbert et a/., 2000) and
individual age, sex, size, and reproductive stage. Thus, ecological monitoring is not necessarily as simple
as monitoring contaminant concentrations in tissues over time. The following influences should be
considered when sampling contaminant concentrations in biota:
• Annual or seasonal variability in uptake and food-web resources;
• Annual or seasonal variability in species abundance and distribution;
• Reproductive state and spawning condition;
• Sampling methods reproducibility with time and space;
• Collection of sufficient biota mass to detect chemical concentrations;
• Collection of sufficient individuals to establish site-specific spatial and temporal trends to
overcome natural variabilities in contaminant concentrations and effects;
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• Coordination of sampling among multiple trophic levels for estimating accumulation
factors; and
• Use of reference sites to obtain comparable data and determination of whether reference
sites should be relatively pristine, or comparably urbanized or industrialized but
otherwise unaffected by the contaminant of concern.
The comparison of site environmental monitoring data to similar data collected at comparable
reference areas is a widely-used approach for evaluating remedy effectiveness. A reference area is
defined as a site, preferably located in the same water body or river system, with similar physical and
geochemical sediment characteristics as the contaminated site and demonstrated not to be impacted by the
contaminated site. It may not be possible to identify a reference area within the same water body or river
system. In most cases, the difficulties in identifying a single reference site that contains all of the
essential environmental and ecological features desired for evaluating the site can be overcome by using
several reference sites that together contain all of the desired qualities (EPA, 1994b; Chapman et al,
1997; Hunt etal, 2001; SPAWAR Systems Center, 2003; Apitz etal, 2005b). These reference sites may
or may not be proximal to the site.
Biological monitoring may also include benthic community monitoring. Not all metrics or
data analysis approaches are appropriate at every site. The biological monitoring program should be
designed to address site-specific questions and/or remedial goals (EPA, 2005e). The specific parameters
or metrics for a site are selected to provide information about several independent community features.
Table 5-1 lists several data analysis approaches (e.g., classification and statistical) and their advantages
and limitations for benthic community assessments. Individual parameters are analyzed and values are
scored according to a predetermined scale to provide a snapshot of the ecosystem by consolidating
multiple complex variables into a single number or set of numbers. This classic benthic community
ecology approach requires the collection of discrete sediment samples using grab samplers or box corers
of various sizes (Blomqvist, 1991; Somerfield and Clarke, 1997), consideration of the size of the samplers
and quantity/quality of the sample taken by grab samplers (Blomqvist, 1991), and the mesh size to sieve
samples after collection (Bachelet, 1990; James etal, 1995; Schlacher and Wooldridge, 1996a, 1996b).
Sampling methods (sampler type, size, and sieve size) must be consistent between monitoring events.
Once data have been collected, it is necessary to determine whether the data actually indicate
that natural recovery is taking place. Potential approaches include:
• Reference area comparisons;
• Sediment Quality Triad analysis;
• Biotic index analysis; and
• Organism-Sediment Index analysis.
Data analysis tools that incorporate sediment chemistry and toxicity measurements, and their
advantages and limitations, are summarized in Table 5-2.
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Table 5-1. Benthic Ecology Assessment Metrics and Analysis Tools
Metric/Analysis Tool
Description, Approach
Potential Advantages
Potential Limitations
Abundance and biomass
General features of population or
community; abundance may
provide an indication of condition
of community relative to stress.
Biomass may be used with
abundance to evaluate level of
disturbance or recovery.
Use consistent sampling methods
(e.g., grab type, size, sieve size);
sort samples to remove >95%
organisms in sample.
Somewhat straightforward measurement.
Biomass can be used as a surrogate for a
functional aspect of community
(secondary production).
Less than dramatic changes in abundance are
difficult to interpret; can be cyclic, dependent on
stochastic events; no change in abundance does not
mean no change in community; may require
multiple samples to reduce variability; characterizes
structural, not functional, element; costly and time
consuming.
Biomass is a destructive method of analysis that
requires collection of a separate sample; often,
programs use wet weight, which is a very poor
measure of biomass.
Diversity/
species richness
General community feature; may
indicate community resilience and
provide an indication of
community condition relative to
stress.
Use consistent sampling methods
(e.g., grab type, size, sieve size);
sort samples to remove >95%
organisms; identify to lowest
practical unit.
Sample "richness" (number of species)
is straightforward measurement;
relatively easy to understand.
Requires consistent taxonomy over life of
monitoring program; indices increase complexity
(e.g., common index, Shannon diversity, varies with
the logarithm used in calculation); sample richness
varies with abundance and does not vary linearly
with area sampled; taxonomic identifications are
costly and time consuming; species counts are
inadequate measures of diversity.
Dominant taxa
Describes communities by most
abundant taxa; used to characterize
community (e.g., Nucula-Nephtys).
Count numbers of each species
present; determine relative
abundance.
Simplistic characterization of
community structure.
Assumes that numerical dominance equals
functional importance.
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Table 5-1. Benthic Ecology Assessment Metrics and Analysis Tools (continued)
Metric/Analysis Tool
Description, Approach
Potential Advantages
Potential Limitations
Indicator taxa:
• Pioneer/opportunistic
species
• Bioturbator species
• Valued ecosystem
component species
• Functional groups
Select key species, populations, or
communities to indicate
environmental impacts or recovery
because not all parameters of
potential interest can be measured.
Requires knowledge of life history
characteristics of species;
select species carefully; indicators
that have desired characteristics are
preferable; recognize that
abundance is often not an
indication of value.
May provide cost- and time-effective
means to assess ecosystem impacts;
appeals to managers because of focus on
aspects of ecosystem thought to have
most importance.
Pioneer species may indicate time since
disturbance and/or state of
recolonization; functional groups have
been linked to various successional
stages that occur during recovery from
impacts.
Bioturbators indicate potential
contribution of biota to surface sediment
mixing.
Correlation between indicator and desired metric
may vary over large area; selected taxon may limit
ability to detect impact or recovery; indicator may
be influenced by "natural" environmental factors.
Consensus about "value" may not be easy to reach;
value may vary with changing environmental,
social, and political situations.
Functional groups may be difficult to characterize;
species may occupy different groups, especially
feeding groups, in different situations.
Classification analyses
Group samples based on overall
similarity in species composition
and relative abundance; also used
for non-biological data.
Identify and count species;
measure other parameters of
interest; run standard software.
Somewhat more realistic view of the
community structure as it incorporates
relative abundance of species.
Results often depend on method chosen to
determine similarity; decision rules for using
similarity levels to determine important clusters of
samples are not well defined and are often arbitrary.
Statistical tests
Compares population or
community data (abundance,
species numbers, indicator species,
etc.) across time and/or space.
Identify and count species;
measure other parameters of
interest; run standard software.
Establishes statistical relationships
between measured populations over time
or space.
Lack of statistical difference does not equal lack of
population or community change; statistical
significance may not equal biological significance.
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Table 5-1. Benthic Ecology Assessment Metrics and Analysis Tools (continued)
Metric/Analysis Tool
Description, Approach
Potential Advantages
Potential Limitations
Multivariate analyses
Reduces highly complex
environmental and community data
to a simpler, more easily
understood form.
Select appropriate analytical
approach; run standard software.
Integrates multi-disciplinary data;
provides visual output; allows tracking
of temporal changes; can distinguish
between two compositionally dissimilar
communities that have the same
univariate (abundance, species numbers)
structure.
Despite sophistication of approach, tool is still
correlative and does not indicate cause-effect
relationship; output often hard to describe; many
approaches have little guidance for selecting
method.
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Table 5-2. Biological Indices
Analysis Tool,
Data Inputs
Description, Approach
Advantages
Limitations
Sediment Quality Triad:
Measurements of stress sensors
(contaminants); measurements of effects
(toxicity); measurements of species
richness/diversity (benthos)
Comparative, three-value interpretation of
habitat quality by comparison to reference
area.
Reference area may be pristine or polluted;
use multivariate analytical techniques (see
Section 5.2.3).
Uses data from three types of
studies to evaluate habitat;
graphical output makes it easy to
visualize differences.
Decision depends on selection
and nature of reference or
polluted area; costly and labor
intensive.
Biotic Indices (Index of
Biological/Benthic Integrity):
Sediment grab samples, water samples,
and fish trawls; sediment geochemistry;
pore water properties; bulk chemistry;
infauna
Single, integrative value interpretation of
habitat quality.
Analyze individual parameters; score values
according to prepared scale; calculate
average per discipline (water, infauna, etc.);
calculate overall value.
Summarizes many parameters
into single value; can
incorporate data from several
disciplines.
Scoring system is usually
arbitrary; overall value can
mask problem areas; based on
"health" analogy that may not
apply to ecosystems; based on
structural features, not
functional.
Organism-Sediment Index (OSI):
Sediment profile imagery (SPI) camera
images; successional stage, redox
potential discontinuity, and gas voids
Single, integrative value interpretation of
habitat quality.
Determine values for individual metrics;
score according to prepared scale. OSI
range is +11 to -10; higher score indicates
better habitat quality.
Summarizes parameters from
three categories (relative percent
difference, successional stage,
and gas voids) into single value
describing habitat quality.
Developed for northwest
Atlantic estuarine, muddy
habitats; needs calibration for
use elsewhere; scoring system
somewhat arbitrary.
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5.3 Predicting Long-Term Recovery
The evaluation of long-term MNR effectiveness requires predictions of whether and how
natural processes will reduce risk to human health and ecological receptors/resources overtime. Models
can integrate a broad range of physical, geochemical, and biological processes germane to sediment
recovery. These may include, but are not limited to, sedimentation, sediment transport via hydrodynamic
suspension, benthic mixing, chemical partitioning (sorption), and chemical transformation, the collective
use of which improves the understanding of long-term chemical transport and behavior in aquatic
environments. After empirically characterizing sediment deposition, recovery, and sediment erosion
potential, sediment transport models can be applied to estimate long-term recovery of ecological
receptors/resources. These models are discussed in Section 2. Data collected during site characterization
are used to identify the specific contaminant and geochemical constituents of the sediment. These data
are used in chemical speciation models to estimate organic contaminant sediment dynamics (e.g.,
sorption, transformation, and degradation) as described in Section 3 or to estimate metal speciation as
described in Section 4. This section (Section 5) describes how long-term ecological models could be
linked to chemistry and sediment transport models to estimate the likelihood and trajectory of recovery of
resident ecological receptors. By "model," this document refers to a wide range of mathematical
representations of aquatic, sediment, chemical, and biological processes, ranging from relatively simple
spreadsheet models to highly complex, multi-dimensional fate and transport models. The appropriate
complexity of models for a site will depend on the available data, project resources, site size, and scopes
of decisions to be made. The goal of this section is to provide an overview of how models can be
integrated to evaluate MNR processes (e.g., long-term chemical behavior), the recovery of ecological
resources, and the reduction in risk to ecological receptors and human health.
Models should be selected so that the site's dominant processes are defined and represented
with sufficient accuracy to provide a confident prediction of future trends. Section 5.3 presents some of
the important considerations for selecting models to represent the dominant contaminant transport and
transformation processes, including water column hydrodynamics, sediment bed processes, sediment
transport, and contaminant transport. The remainder of this section summarizes the key considerations
associated with modeling in support of MNR and identifies available modeling tools and their advantages
and disadvantages. Section 5.4 discusses the application of one-dimensional modeling in support of
MNR, and Section 5.5 covers numerical models for calculating sediment bed stability and transport.
5.3.1 Predicting Recovery of Ecological Receptors. Predicting the recovery of ecological
receptors sensitive to sediments contamination (e.g., bottom feeding fish and piscivorous mammals and
birds) requires, among other things, an understanding of the key indicators (i.e., biological endpoints) that
should be assessed at different levels of organization (e.g., organisms or populations) and the use of
clearly defined data quality objectives. Indicators or endpoints may include:
• Chemical concentrations measured in organisms or populations representing important
trophic links (food-web components) that can be used to model or predict effects on
organisms; and
• Organism abundance, population, growth rate, and species diversity.
Tissue contaminant concentrations can be predicted by combining the sediment concentration
expected from MNR with a site-specific BSAF (Burkhard, 2009). The resulting tissue concentration then
can be used as an exposure point estimate in food-web modeling of expected risk to upper-trophic-level
organisms. This approach assumes that site-specific accumulation factors have been developed in the
field or laboratory using bioaccumulation tests with synoptic sediment chemistry or literature values. For
large sites that might encompass a range of ecological settings in which monitored biological exposure
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occurs, it may be necessary to verify that the estimated BSAF value or range of values adequately
represents site variability (Wong etal, 2001; Burkhard etal, 2005; Melwani etal, 2009).
Food-web models, despite their complexity, provide a useful framework for identifying all
contaminant sources to the receptor organism and a technique for estimating the magnitude of change in
surface sediment concentrations to effect a reduction in an impacted species (EPA, 2009). If the
ecological risk assessment performed as part of a baseline risk assessment in the remedial
investigation/feasibility study is available, the same model may be used to evaluate pre- and post-
implementation of the sediment MNR remedy.
Food-web models typically require site-specific validation to ensure that the model output
adequately describes the actual food web at the site. Otherwise, a high level of uncertainty persists in
exposure and risk estimates. Significant sources of uncertainty typically identified in ecological risk
assessments and pertaining to the evaluation of sediment contaminant exposure and risk to ecological
receptors include:
• Size, age, sex, reproductive state, and behavior (i.e., competition for food) of receptor
individuals;
• Percentage of time a receptor forages at the site (site use factor);
• Level of association of the receptor with sediment (trophic level of receptor);
• Percentage of time that prey forages or spends at the site;
• Level of association of prey organisms with the sediment (trophic level of prey);
• Percentage of the receptor diet represented by each prey organism; and
• Population dynamics of receptor and prey species.
In addition to focusing on remedy effectiveness at the individual organism level, models also
may consider species populations and the larger ecological community at a site. This line of investigation
would be accomplished either by modeling specific target populations of concern or surrogate indicator
species.
5.4 One-Dimensional Transport Modeling to Predict Changes in Surface
Chemical Concentrations
Site-wide model approaches are presented in this section to predict changes in chemical
concentrations using one-dimensional (1-D) advection/diffusion models. These models require input on
chemical species and how they partition, and with what affinity, to the sediment solids and pore water.
Potential transport processes include diffusion, advection, and bioturbation (often modeled as a "special"
kind of advection). For example, diffusive flux may predominant at sites where concentration and
geochemical gradients are driven by mixing with overlying water. In contrast, sites with significant
groundwater upwelling through the sediment bed may be governed by advective transport. Advective
processes are also likely in coastal sites where tidal action influences groundwater movement. The
relative influence of these processes will depend on site conditions, particularly the hydrologic setting.
Advection/diffusion models are useful tools to discern those processes that most directly govern
contaminant transport at a given site and, therefore, should be included among the monitoring design
components (Jones and Lick, 2001; Lick, 2010; Wu and Gschwend, 1986).
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To assess sediment concentration-reduction rates in the sediment bed, vertical 1-D models
can be used if assumptions are made about the rates of deposition and erosion and overlying water
column concentrations of contaminants. Figure 5-1 shows the results of a 1-D model used to estimate
changes in surface sediment PCB concentrations at a hypothetical PCB-contaminated site (Battelle, 2004;
Battelle, 2006). The figure presents three model runs: the first run (Figure 5-la) includes diffusive
processes but no deposition; the second run (Figure 5-lb) adds 0.5 cm/year deposition of clean sediments;
and the third run (Figure 5-lc), which also includes 0.5 cm/year deposition, increases the net diffusion
rate by increasing the bioturbation rate to simulate increased benthic biological activity and mixing. The
model includes contaminant partitioning (Kd = 1.2 x 105 L/kg), diffusion (Dm = 10"7 cm2/s), deposition
rate (deposition = 0.5 cm/yr), and an effective diffusion rate due to bioturbation (Db = 10"7 cm2/s for
Figures 5-la and 5-lb and 10"5cm2/s for Figure 5-lc). Db in this model decreases with depth.
In this example, contaminant transport is dominated by deposition and surface mixing. In
Figures 5-lb and 5-lc, deposition drives the peak contaminant concentrations downward, resulting in 52
cm burial at 0.5 cm/yr over a 100-year period (i.e., the peak migrated from 23 cm to 75 cm below the
sediment-water interface in Figure 5-lb and from 23 to 90 cm in Figure 5-lc). Mixing resulted in the
gradual reduction of surface sediment concentrations and the asymptotic approach to zero at the sediment
surface. The greater mixing coefficient used for Figure 5-lc resulted in increased mixing, which resulted
in greater contaminant spreading and slower rates of change in surface sediment chemical concentrations.
These processes are commonly observed at contaminated sites (Lick, 2010; Wu and Gschwend, 1986).
o
5
10
~ 15
o^
!c 20
f
° 25
30
35
0 500 1000 1500 2000 2500
PCB Sediment Concentration (ppb)
0 1000 2000 3000 40*9 MOO 600D TOKO 8W9
PCB Sedmemt Concentration (ppbj
0 MOO 2000 MOO 4W» 10*0 WOO 7000 SOW
PCB Sedment Concentration (ppb)
Figure 5-1. Example of Vertical 1-D Contaminant Transport Modeling of PCBs in Sediments
using: (a) Diffusion Only, (b) Diffusion Plus Deposition with Mild Benthic Mixing, and (c) Diffusion
Plus Deposition Plus Rapid Benthic Mixing (Developed by Dr. Craig Jones, Sea Engineering, Santa
Cruz, California.)
The simplicity of the 1-D modeling approach is desirable for sites without the data or
resources to support more complex monitoring. A 1-D model could be applied repeatedly to different
areas of a site to evaluate chemical concentration profiles at different locations. This model approach
does not incorporate individual depositional or erosional events, except to apply a net depositional or
erosional term in the model.
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5.5 Numerical Models
Sediment transport models commonly begin with a hydrodynamic model that is calibrated to
site conditions. Sediment transport and chemical transport models build on the hydrodynamic model, and
these models may be linked to a food chain model. Models mathematically characterize one or more of
the following processes with different levels of detail depending on the model application and available
data:
• Sediment transport processes, which can influence long-term contaminant transport;
• Contaminant partitioning between solid and aqueous phases (Ghosh et al, 2000, 2003;
Deane etal., 1999; Lick etal., 2004);
• Bioturbation, which can influence chemical transport in surface sediments and chemical
flux at the sediment-water interface (EPA, 2005e); USAGE (2001) presents a
comprehensive review of the effects of bioturbation and provides suggested rates and
effective depths for various regions throughout the United States; and
• Pore water chemical transport, which depends on pore water hydrodynamics, chemical
solubility, and partitioning (Lick etal., 2004).
The remainder of this section discusses hydrodynamic, sediment bed, and sediment transport
modeling. Detailed comparisons of available modeling tools are provided in Tables 5-3, 5-4, and 5-5.
Considerations associated with merging models are discussed in Section 5.5.4.
5.5.1 Hydrodynamic Modeling. Water column and sediment transport processes are often
significant factors resulting in reduced exposure and risk at a given site. A hydrodynamic model, which
typically focuses on the movement of water, can predict hydrodynamic shear forces at the sediment
surface. The hydrodynamic model must be able to be interfaced with a sediment bed model (focus on
movement of sediment), a contaminant transport model (focus on movement of contaminant), and
possibly the food-web model (focus on biological uptake and associated toxicity of contaminant). If
selected models do not easily share data, it may require significant effort to ensure accurate data transfer
among models. Table 5-3 lists commonly available hydrodynamic models along with their respective
advantages and limitations. A majority of these are finite difference models that use finite difference
equations to approximate the solutions to ordinary differential equations. In contrast, finite element
methods use linear algebraic equations to approximate the solutions to partial differential equations.
5.5.2 Sediment Bed Modeling. Sites that consider MNR may require some level of sediment bed
modeling. The chemical concentrations and fluxes in the sediment bed and at the sediment-water
interface are ultimately responsible for determining the long-term success of MNR. Modeling of long-
term trends in sediments should accurately represent dominant physical, chemical, and biological
processes. Table 5-4 shows various commonly-used sediment modeling frameworks along with
advantages and disadvantages of each. A thorough and site-specific evaluation of each model should be
conducted to judge the appropriateness of the model for predicting long-term recovery.
5.5.3 Sediment Transport Modeling. Accurate modeling of long-term sediment transport
generally requires higher-level numerical modeling with site-specific measurements of the sediment bed
critical shear strength and hydrodynamic shear forces. Table 5-5 summarizes several commonly-used
sediment transport models. For each model, the table indicates if site-specific sediment bed and
hydrodynamic data can be incorporated. Those that can incorporate site-specific measurements will
typically have less uncertainty because they are constrained by measured data.
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Table 5-3. Hydrodynamic Models
Hydrodynamic Model
SEDZL
SMS - RMA2
EFDC
ECOM
MIKE 3 and MIKE 21
DELFT-3D
CH3D
Primary Application
2-D coastal and inland
finite difference
hydrodynamic model
2-D coastal and inland
finite element
hydrodynamic model
3-D coastal and inland
finite difference
hydrodynamic model
3-D coastal and inland
finite difference
hydrodynamic model
2-D and 3-D costal
finite difference
hydrodynamic models
fromDHI
3-D coastal and inland
finite difference
hydrodynamic model
(Delft Laboratories)
3-D generalized
curvilinear grid
("boundary-fitted
grid") for estuaries,
lakes, and coastal
waters with very fine
grid-resolution
Potential Advantages
Robust model that has been
verified extensively at dozens
of unique locations; more
extensively verified than most
hydrodynamic models.
User-friendly interface and
pre- and post-processing.
Well-verified hydrodynamic
model. USAGE supported.
EPA-supported, public -domain
model with user-friendly
interface. Based on the well-
verified Princeton Ocean
Model.
Well-verified 3-D
hydrodynamic model based on
Princeton Ocean Model.
Robust model with user-
friendly interface. Widely
used commercially. Well-
verified 3-D hydrodynamics.
Robust model with user-
friendly interface. Well-
verified 3-D hydrodynamics.
Combines long-term
hydrodynamic circulation data
with water quality parameters
such as temperature, salinity,
and nutrient concentrations.
Has been used to model high-
energy events, such as coastal
storm surge and hurricane
events.
Potential Limitations
Application requires high-
level modeling and Fortran
programming skills.
Significant pre/post-data-
processing requirements.
Difficult to interface finite
element modeling with the
most common contaminant
transport models.
Custom model applications
present difficulties for non-
specialists. Still undergoing
EPA revision.
HydroQual, Inc., proprietary
model. No fee for use and
readily available to the public.
Expensive modeling package.
Code is not open source, so
custom applications are not
easily developed.
Expensive modeling package.
Code is not open-source, so
custom applications are not
easily developed.
Considerable knowledge of
hydrodynamics is required to
use the model effectively.
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Table 5-4. Sediment Bed Models
Sediment Bed Model or Reference
Recovery (Ruiz et al., 2000)
Palermo et al, 1998
DiToro, 2001
Lick et al, 2004
Primary Application
Long-term recovery
of contaminated
sediment beds
Contaminant
transport in sediment
beds after remedial
cap installation
Various models of
chemical species flux
from sediments
Hydrophobic
chemical transport
model
Advantages
Standardized model for
sediment and chemical
transport covering a
wide range of chemical
species.
Standardized model for
cap design and
contaminant transport in
sediments and caps.
Provides generalized
equations for flux of
most chemical species
from sediments.
Techniques are easily
applied to different
systems.
Provides detailed
mechanistic descriptions
of hydrophobic chemical
transport in natural
systems. Includes non-
equilibrium partitioning.
Disadvantages
Does not provide a
detailed mechanistic
treatment of contaminant
sorption processes.
Assumes equilibrium
partitioning.
Geared specifically to
contaminant penetration
in remedial caps. Does
not provide a detailed
mechanistic treatment of
contaminant sorption
processes. Assumes
equilibrium partitioning.
Parameterized techniques
can oversimplify
important mechanisms.
Assumes equilibrium
partitioning.
Limited to hydrophobic
contaminants. Has not
been widely applied.
Proprietary model not
available in the public
domain.
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Table 5-5. Sediment Transport Models
Sediment
Transport Model
SED2D
EFDC
SEDZL
ECOM-SED
SEDZL-J
Primary Application
Cohesive and
noncohesive sediment
transport model
Cohesive and
noncohesive sediment
transport model
Cohesive and
noncohesive sediment
transport model
Cohesive and
noncohesive sediment
transport model
Cohesive and
noncohesive sediment
transport model
Advantages
Integrated with RMA2
hydrodynamics package in a
user-friendly environment.
USAGE supported.
Integrated with EFDC 3-D
hydrodynamics. Includes
multiple size classes, bedload
transport, and bed armoring.
EPA supported.
Integrated with SEDZL 2-D
hydrodynamics model. Based
on site-specific sediment data.
Has been verified at a number
of contaminated sediment sites.
Utilizes SEDZL model coupled
to 3-D hydrodynamics model.
Integrated with SEDZL 2-D
hydrodynamic model. Based on
site-specific sediment
measurements of shear strength
with depth into sediments.
Limitations
Only allows a single grain
size per simulation.
Generally not applicable for
contaminant transport
applications.
Based on dated cohesive
sediment dynamics.
Limited to 2-D
hydrodynamics. Based on
surficial sediment
measurements only.
Based on surficial sediment
measurements only.
Hydroqual, Inc. proprietary
model. No fee for use, but
not readily distributed.
Currently limited to 2-D
hydrodynamics. Has not
been applied widely.
5.5.4 Integrating Models. For MNR, virtually all modeling efforts are expected to examine
processes at the sediment bed level, ideally using site-specific data. Complex hydrodynamic and
sediment transport processes may benefit by merging hydrodynamic, sediment bed, sediment transport,
and chemical speciation/partitioning models.
In the simplest scenario, a sediment transport model may quantify the net sediment deposition
(i.e., burial) processes that result in the slow but persistent burial of contaminated surface sediments.
With increasing complexity, temporal variations in sediment transport can alter the distribution of
contaminants in the sediment bed. Sediment transport processes may also be responsible for
redistribution of contaminants on and off site. In these cases, hydrodynamic, sediment transport, and
sediment bed models may be merged. The merging of the models also can affect the efficacy and
uncertainty of models. Merging models can reduce errors and uncertainty that may be introduced through
incompatible modeling assumptions or incompatible time and spatial scaling between individual models.
5.6
Summary
Establishing an effective monitoring plan requires an understanding of the rates of attenuation
and driving processes, site-specific remedial goals, ecology, hydrodynamics, water chemistry, and
sediment transport processes. It is particularly important that site managers and regulatory oversight staff
be familiar with these parameters. A one-size-fits-all approach is not available or appropriate for
monitoring biological and ecological responses (i.e., recovery) to reduced chemical exposure via natural
processes at sediment sites. Long-term monitoring plans need to be flexible so that the data collected can
172
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be adjusted as necessary to meaningfully measure recovery of ecological resources. An adaptive
management approach, whereby site assumptions are tested and re-evaluated as new information is
gathered, is critical for effective MNR and should be adopted where feasible (EPA, 2005e). Whether an
adaptive management approach is cost effective is a site-specific decision (EPA, 2005e). Resources for
adaptive management at sediment sites can be found in Environmental Cleanup at Navy Facilities (NRC,
2003). Scientists and resource management agencies in the United States and elsewhere increasingly
understand that monitoring programs must have the flexibility for refinement during the course of
measuring reduced exposure and recovery of ecological resources at a sediment MNR site.
A variety of tools and endpoints likely are needed and appropriate for each site. Sediment
and water column chemical and physical characterization and monitoring frequently will dominate long-
term monitoring programs. These measurements provide rapid, relatively inexpensive, and readily
interpretable information on the state of the ecosystem. While such measurements can provide
biologically relevant information regarding contaminant exposure and bioavailability, it is important to
incorporate direct measures and estimates of recovery of ecological receptors. In some cases, species
may recover faster or slower than indicated by sediment or water column recovery rates.
Aquatic biota may be monitored directly by collecting various species and measuring
contaminant concentrations and responses in collected samples. Laboratory toxicity and/or
bioaccumulation studies provide well-established and understood tools to measure chemical effects on
biota. Sites may also be characterized by species abundance and diversity. Taxonomic indices may be
used to compare sample sets. Such indices risk oversimplification, but nonetheless may provide a
baseline for comparing sample sets over time and qualitatively assessing recovery at a site. When
assessing recovery, long-term monitoring plans should recognize the large spatial and temporal scales
governing MNR evaluation.
Models are generally important tools for evaluating the effectiveness of MNR in reducing
risk because they facilitate the prediction of long-term recovery of ecological receptors. The appropriate
modeling complexity will depend on available data, project resources, site size and complexity, and the
scope of decisions being made. Numerical models are useful tools for evaluating the cumulative long-
term effects of natural processes on chemical exposure, bioavailability, and risk reduction. The scale of
the model should be appropriate to the scale of the site and available resources; full-scale models may not
always be necessary. A simple, vertical, 1-D model may suffice and more effectively use resources
available for some sites. At others where empirical lines-of-evidence adequately support the MNR
decision framework, modeling may not be necessary. For larger and more complex and dynamic sites,
numerical models may be needed to incorporate the combined effects of changes in chemical loadings,
biological and chemical degradation processes, and natural transport and mixing processes that occur over
a wide range of spatial and temporal scales. Food-web and population-based models are also important
tools for evaluating and predicting the direction and pace of the recovery of ecological receptors due to
changes in bioavailability of sediment contaminants.
173
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