c/EPA
EPA/600/R-16/322 | October 2016 | www.epa.gov/research
United States
Environmental Protection
Agency
Methods and Metrics for
Evaluating Environmental
Dredging at the Ashtabula
River Area of Concern
(aoc) ^
Office of Research and Development
National Risk Management Research Laboratory
Land Remediation and Pollution Control Division
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EPA/600/R-16/322
October 2016
Methods and Metrics
for Evaluating Environmental Dredging
at the Ashtabula River Area of Concern (AOC)
by
Battelle
Columbus, OH 43201
and
Integral Consulting Inc.
Santa Cruz, CA 95060
Contract No. EP-C-05-057
Task Order 50
Contract No. EP-W-09-024
Work Assignments 2-13 and 3-07
Contract No. EP-C-11-038
Task Order 30
Co-Principal Investigators
Marc Mills, Richard Brenner, and Joseph Schubauer-Berigan
Land Remediation and Pollution Control Division
National Risk Management Research Laboratory
and
James Lazorchak and John Meier (r)
Systems Exposure Division
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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Notice/Disclaimer
The U.S. Environmental Protection Agency (EPA), through its Office of Research and
Development (ORD), funded and managed, or partially funded and collaborated in, the research
described herein. It has been subjected to the Agency's peer and administrative review and has
been approved for publication. Any opinions expressed in this report are those of the authors
and do not necessarily reflect the views of the Agency; therefore, no official endorsement should
be inferred. Any mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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Foreword
The U.S. Environmental Protection Agency (U.S. 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, U.S. 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 ground water; prevention and control
of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and
private sector partners to foster technologies that reduce the cost of compliance and to anticipate
emerging problems. NRMRL's research provides solutions to environmental problems by:
developing and promoting technologies that protect and improve the environment, advancing
scientific and engineering information to support regulatory and policy decisions, and providing
the technical support and information transfer to ensure implementation of environmental
regulations and strategies at the national, state, and community levels.
International concern about contaminated sediments is increasing, mainly because sediments are
viewed as long-term pollutant sinks for compounds such as polychlorinated biphenyls (PCBs),
polycyclic aromatic hydrocarbons (PAHs), metals, and other contaminants of concern (COCs).
Large areas of contaminated sediment accumulation are known to pose a threat to benthic,
aquatic, and terrestrial ecosystems as well as human health. Sediment contamination exists in
every region and state of the Nation, negatively impacting overlying surface waters and
surrounding ecosystems. To date, three primary technologies have been applied to the
remediation of contaminated sediment sites: 1) engineered capping with imported clean material
such as sand, 2) monitored natural recovery (MNR) wherein the contaminant source is known to
have been removed and natural capping with indigenous clean sediment is allowed to cover or
bury the contaminated sediment over a long period of time, and 3) environmental dredging that
relies on rapid mechanical removal of the contaminated sediment layer and subsequent off-site
confined disposal. Environmental dredging was selected as the remedy of choice for remediation
and cleanup of the Ashtabula River Area of Concern (AOC), a highly contaminated sediment site
in northeastern Ohio. PCBs constituted the primary COC for this site, with PAHs and inorganic
chemicals comprising secondary COCs. Dredging was carried out from the fall of 2006 through
the fall of 2007 on this AOC. The site was extensively characterized in the spring and summer
of 2006 prior to the onset of dredging. A comprehensive evaluation and monitoring program
conducted by U.S EPA then ensued: 1) during the dredging period, 2) immediately following
dredging in early 2008, and 3) over the next 3 years through 2011 to assess long-term recovery.
This report summarizes and interprets the results of this 6-year study to monitor pollutant fate
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and transport and ecosystem recovery through the use of bathymetry; sampling and chemical
analysis of sediment, water, and indigenous fish; and deployment and follow-up retrieval and
analysis of macrobenthos and passive samplers.
Cynthia Sonich-Mullin, Director
National Risk Management Research Laboratory
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Abstract
International concern about contaminated sediments is increasing as sustainable practices are
needed to maintain our water resources and waterways as important economic, commercial,
recreational, and community resources. Sediments often serve as long-term sinks for legacy
pollutants, such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs),
inorganics, and other emerging and known contaminants of concern (COCs). Large areas of
contaminated sediment accumulation are known to pose a threat to benthic, aquatic, and
terrestrial ecosystems, as well as human health. Sediment contamination exists in every U.S.
EPA Region and state of the Nation, negatively impacting overlying surface waters and
surrounding ecosystems, and ultimately the health and quality of life for surrounding
communities.
To date, three primary management strategies have been applied to remediate contaminated
sediment sites: 1) engineered caps, 2) monitored natural recovery (MNR), and 3) environmental
dredging or a combination of these approaches. Engineered capping relies on the isolation of
the contaminant from receptors and, more recently, may incorporate sorptive or reactive media to
mitigate or treat contaminants that may migrate through the cap. MNR depends on monitoring to
verify the source control actions and natural processes to isolate, degrade, and/or control the
release of contaminants are progressing as predicted. Environmental dredging utilizes rapid
mechanical removal of the contaminated sediment followed by isolation of the contaminated
sediment from potential receptors. Modern sediment remediation generally uses a combination
of these strategies to optimize environmental protection and the cost of remediation.
U.S. EPA's Office of Research and Development (ORD) has an interdisciplinary research
program to evaluate the effectiveness of risk management strategies and develop innovative
treatment technologies. These projects have investigated and documented methods and
approaches to assess remediation projects in the short term (project driven goals) and over
longer-term restoration and recovery periods (programmatic goals). Research described in this
report focuses on the development of methods and approaches to conduct a remedy effectiveness
assessment (REA) on environmental remediation projects. In this research effort, several
monitoring and sampling approaches were developed, standardized, and demonstrated on a
sediment remediation project at the Ashtabula River initiated in 2006 by U.S. EPA's Great Lakes
National Program Office (GLNPO) under the Great Lakes Legacy Act (GLLA). Environmental
dredging was utilized on approximately 1.2 miles in a lower reach of this river in northeastern
Ohio. PCBs constituted the primary COC for this site, with PAHs and inorganic chemicals
comprising secondary COCs. Hydraulic dredging was carried out from the fall of 2006 through
the fall of 2007 on this GLLA project. Extensive site characterization was conducted by
GLNPO, ORD, and their partners at Federal and State agencies in the spring and summer of
2006 prior to the onset of remediation.
In partnership with GLNPO and concurrent with the dredging project, a comprehensive research
effort was carried out by ORD on the Ashtabula River to develop assessment and monitoring
methods along biological, chemical, and physical lines of evidence (LOEs). These LOEs can be
used in a weight of evidence (WOE) framework to assess sediment remedies. Utilization,
monitoring, and evaluation of these methods and LOE approach began prior to the onset of
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environmental dredging of the Ashtabula River in 2006 and continued during and following
dredging through 2011.
This project report summarizes and interprets the results of this 6-year study to develop and
assess methods for monitoring contaminant fate and transport and ecosystem recovery through
the use of biological, chemical, and physical assessment methodologies such as:
1) comprehensive sampling of and chemical analysis of contaminants in surface, suspended, and
historic sediments; 2) multi-level real time water sampling and analysis of contaminants in the
water column during remediation; 3) sampling, chemical analysis, and development of
alternative toxicity endpoints for indigenous fish; 4) innovative bathymetry, suspended sediment,
and plume monitoring and modeling approaches; 5) multi-purpose macrobenthos collection
techniques for determining benthic condition and contaminant exposure; and 6) passive sampler
technology and deployment techniques.
The results of this project demonstrated that the application of multiple LOEs can be utilized on
various spatial and temporal scales to inform a project manager on the short- and long-term
impacts of sediment remediation. Using multiple LOE-based metrics and a WOE framework,
specific mechanisms and processes can be characterized to quantify the short- and long-term
impacts of a selected remedy on the surrounding ecosystem.
The objective of this specific research project was to develop and demonstrate selected
biological, chemical, and physical monitoring methods that can be integrated and applied on
future remediation projects for conducting REAs. As the initial product of this new integrated
approach, an REA is currently being prepared for the Ashtabula River project by GLNPO and
ORD and will be reported separately.
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Acknowledgements
The support and participation of many researchers, administrators, and support staff were
necessary to carry out a multi-year project of this scope and magnitude. Funding provided by the
National Risk Management Research Laboratory (NRMRL) of the U.S. Environmental
Protection Agency (U.S. EPA) and the U.S. EPA Great Lakes National Program Office
(GLNPO) to enable this project to be conducted is gratefully acknowledged. Collaborative
efforts and mutual support between NRMRL, GLNPO, and U.S. EPA's National Exposure
Research Laboratory (NERL) provided a forum for exchanging ideas and concepts and were
vital to the success of this project. The partnership and cooperation engendered on this study has
already begun to pay dividends on other projects. The excellent service and attention to detail of
the project's contractor, Battelle, simplified and optimized the implementation of complex
sampling and analytical programs that generated the project's large and comprehensive dataset.
The cooperation of GLNPO's dredging contractor, J.F. Brennan Company, Inc., in providing
dredge data and welcome advice was essential in relating research field measurements to
sediment inventories and dredging operations.
The authors of this report, Lisa Lefkovitz, Heather Thurston, Stacy Pala, Eric Foote, Greg Durell,
Paul Sokoloff, Matt Fitzpatrick, Jessica Tenzar, John Hardin, and Carlton Hunt from Battelle;
Craig Jones and Grace Chang from Integral Consulting, Inc. (a subcontractor to Battelle); and
Jason Magalen of HDR, Inc., along with U.S. EPA Co-Principal Investigators Marc Mills*,
Richard Brenner, Joseph Schubauer-Berigan, James Lazorchak, and John Meier4 wish to express
their appreciation to the following individuals for their substantial and valuable contributions to
this research undertaking:
Battelle
Greg Headington
Jim Hicks
Bob Mandeville
Shane Walton
U.S. EPA/NRMRL
U.S. EPA/GLNPO
Scott Cieniawski
Amy Pelka
Marc Tuchman
Pat Clark
Terry Lyons
Paul McCauley
Dennis Timberlake
Roger Yeardley
U.S. EPA/NERL
J.F. Brennan Company,
Inc.
Mark Binsfeld
Paul Olander
Formerly
The McConnell Group
Brandon Armstrong
Jason Berninger
Mark Berninger
Herman Haring
Paul Weaver
Ken Fritz
Brent Johnson
Paul Wernsing
* Corresponding Investigator: mills.marc@epa.gov
* Now retired from the U.S. Environmental Protection Agency.
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Table of Contents
Notice/Disclaimer i
Foreword ii
Abstract iv
Acknowledgements vi
List of Figures ix
List of Tables xv
List of Appendices xvi
Acronyms and Abbreviations xvii
1.0 Introduction 1
1.1 Description of Proj ect Area 3
1.2 Project Goals and Objectives 3
1.3 Proj ect Summary 4
2.0 Experimental Approach 11
2.1 Sediment Mapping 11
2.1.1 Bathymetry 11
2.1.2 Sidescan Sonar 12
2.2 Plume Tracking 12
2.3 Sediment 16
2.3.1 Sediment Cores 16
2.4 Passive Samplers 19
2.4.1 Passive Samplers: Semipermeable Membrane Device 20
2.4.2 Passive Samplers: Solid Phase Micro-Extraction 27
2.5 Macrobenthos Sample Collection 28
2.6 Caged Clams and Worms 32
2.7 Indigenous Fish 32
2.8 Chemical and Physical Analytical Methods 33
2.8.1 Chemical and Physical Analysis of Sediment Samples 33
2.8.2 Chemical Analysis of Water Samples 34
2.8.3 Chemical Analysis of Tissue Samples 35
2.8.4 Chemical Analysis of Passive Samplers 36
2.9 Data Management and Data Evaluation 36
2.10 Quality Assurance/Quality Control 39
3.0 Results 42
3.1 Bathymetry 42
3.2 Resuspension Survey during Dredging 47
3.2.1 Plume Tracking 47
3.2.2 Resuspended Sediment Mass 62
3.2.3 Link to Contaminant Distribution 69
3.3 Sediment 74
3.3.1 Comparison of tPCB(Zc) Concentrations in Pre- and Post-Dredge Cores.. 75
3.3.2 Surface Sediment PCBs Trends 84
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3.3.3 General Surface Sediment PCB Trends 93
3.4 Biological Samplers 104
3.4.1 Macrobenthos Tissue and Co-located Sediment and Water Chemical Results
104
3.4.2 Indigenous Brown Bullhead 126
3.4.3 PCB Results in Indigenous Brown Bullhead Fish 126
3.4.4 PAH Results in Indigenous Brown Bullhead Fish 128
3.5 Passive Samplers as Biological Surrogates 132
3.5.1 Water Column SPMDs 133
3.5.2 Sediment SPMDs 141
3.5.3 Solid Phase Microextraction Devices 148
4.0 Discussion 155
4.1 Macrobenthos Tissue Concentrations using Artificial Substrate Samplers 155
4.1.1 Macrobenthos ANOVA 157
4.1.2 Macrobenthos PCA 162
4.1.3 ANOVA Analysis of Surface Sediment for Macrobenthos Stations 164
4.1.4 Surface Sediment PCA 166
4.1.5 Macrobenthos Water ANOVA 167
4.1.6 PCA for Waters from Macrobenthos Stations 169
4.1.7 Comparison of Macrobenthos Tissue and Co-located Sediment and Water
PCBs 169
4.2 SPMDs 172
4.2.1 Correlation between SPMDs and Co-Located Sediments and Waters 173
4.2.2 Water Column SPMD ANOVA 176
4.3 Indigenous Fish 178
4.3.1 ANOVA for Fish Tissue Chemistry 179
4.3.2 PCA for Fish 181
5.0 Conclusions 183
5.1 Water Sampling during Dredging - Turbidity Measurements 183
5.2 Water Sampling during Dredging- Resuspended Sediment Mass Measurements... 185
5.3 Water Sampling during Dredging - Link to Contaminant Distributions 186
5.4 Contaminants in Surface Sediment 187
5.5 Macrobenthos 188
5.6 Indigenous Fish Tissue Contaminant Concentrations - Brown Bullhead 188
5.7 Semipermeable Membrane Device (SPMD) 190
5.8 Summary 191
6.0 References 192
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List of Figures
Figure 1-1. Location of the Ashtabula River Environmental Dredging Project in Ashtabula,
OH 2
Figure 1-2. Ashtabula River Dredging Project and ORD Study Area (River Stations 181+00
to 170+00) 2
Figure 2-1. June 2007 Survey Whole Water Sample Collection Locations and Dredge
Positions of the Michael B 14
Figure 2-2. July 2007 Survey Whole Water Sample Locations and Dredge Positions of the
Michael B. ("MB") and the Palm Beach ("PB") 15
Figure 2-3. Sediment Core Sample Locations in the Ashtabula River Residual Study Area for
Pre- (2006) and Post- (2007, 2009, and 2011) Dredging, respectively 17
Figure 2-4. Sediment SPMD Deployment Locations 21
Figure 2-5. Water Column SPMD Deployment Locations 22
Figure 2-6. Typical SPMD Rack Design for Deployment of SPMDs on the Surficial
Sediment 23
Figure 2-7. Top View and Angle View of the SPMD Spider Carrier (EST, St. Joseph, MO). 23
Figure 2-8. Full View and Cross-Sectional View of the Perforated Stainless Steel Carrier with
Five Spiders 24
Figure 2-9. Macrobenthos Samplers Used at Ashtabula River (Left -H-D artificial substrate
plate sampler; Right - Samplers hanging in fish cages during deployment) 29
Figure 2-10. Macrobenthos Deployment at the Ashtabula River and Conneaut Creek Reference
Site Locations (inset shows Conneaut Creek Reference Location) 30
Figure 3-1. Pre-Dredge Bathymetric Survey 43
Figure 3-2. Bathymetric Differences in meters between 2007 and 2009 for the ORD Study
Area of the Ashtabula River Showing Sediment Coring Locations 44
Figure 3-3. Bathymetric Differences in meters between 2007 and 2011 for the ORD Study
Area of the Ashtabula River Showing Sediment Coring Locations 45
Figure 3-4. Schematic Depicting the Stationary Turbidity Probe and ADCP Upstream and
Downstream of Dredging Activities 48
Figure 3-5. Dredging Region on the Ashtabula River and Fixed Monitoring Station Locations.
49
Figure 3-6. Histograms to Determine Frequency of Occurrence for Turbidity (A), and TSS
(B) 50
Figure 3-7. Up-looking Acoustic Doppler Current Profiler (ADCP) (Teledyne RD
Instruments 1200 kHz Workhorse Sentinel ADCP [Poway, CA]) on Bottom-
Mount Platform for Measuring TSS 51
Figure 3-8. Log-linear Relationship between ABS and TSS 51
Figure 3-9. A: Depth-Resolved Time Series of TSSabs iti Derived from ABS Computed from
Echo Intensity Measured by the Upstream (South) Bottom-Mounted ADCP 52
Figure 3-10. A: Depth-Resolved Time Series of TSSabs iti Derived from ABS Computed from
Echo Intensity Measured by the Downstream (North) Bottom-Mounted ADCP. 53
Figure 3-11. Time Series of TSS Derived from Optical Turbidity (blue) and Acoustical
Backscatter (ABS; red) for Data Collected at the Upstream (South) Site
Comparing Methods at about (A) 1 m below the Surface and (B) 1 m above the
Bottom and for Data Collected at the Downstream (North) Site Comparing
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Methods at about (C) 1 m below the Surface and (D) 1 m above the Bottom 53
Figure 3-12. TSS as a Function of Cross-Channel Distance and Depth; Example Comparisons
between TSS Derived from Optical Turbidity Measurements Collected on the
MDWS (A and C) and Acoustic Backscatter Measurements Collected from a
Vessel-Mounted ADCP (B and D) 54
Figure 3-13. LISST Measured Total Volume Concentration vs. TSS as Measured by the
Optical Turbidity Sensors Mounted on the MDWS for LISST Profiles
Corresponding to MDWS Transects 56
Figure 3-14. Three Dimensional Volumetric Plot of TSSplume Derived from
TSSTURB.MDWS Progressive Transects Collected on June 2, 2007 57
Figure 3-15. Three Dimensional Volumetric Plot of TSSplume Derived from
TSSTURB.MDWS Progressive Transects Collected on June 5, 2007 57
Figure 3-16. Normalized Plume Strength (NPS) as a Function of Cross-Channel Width and
Water Depth Determined for Progressive Transects Collected on May 31, 2007.58
Figure 3-17. 3-D Volumetric Plot of NPS for the Transects shown in Figure 3-16 59
Figure 3-18. Normalized Plume Strength (NPS) as a Function of Cross-Channel Width and
Water Depth Determined for Progressive Transects Collected on June 4, 2007.. 60
Figure 3-19. 3-D Volumetric Plot of NPS for the Transects shown in Figure 3-18 61
Figure 3-20. Example Computations for the Cross-Sectional Area of A): A Transect Affected
by the Dredge Plume; B): The Dredge Plume (cells containing significant plume
signature) 61
Figure 3-21. Estimates of the A) Total Volume of Water Affected by the Dredge; B) Total
Volume of the Dredge Plume 62
Figure 3-22. Absolute Value of the Total Mass of Dredge Sediment per Hour of Dredge
Activity as a Function of Distance from the Dredge 64
Figure 3-23. Rate of Sediment Resuspended by the Dredge as a Function of the Proportion of
Cutter Surface Area Exposed to Dredging, Q 67
Figure 3-24. Rate of Sediment Resuspended by the Dredge as a Function of Cutter Tip Speed,
Vs 67
Figure 3-25. Rate of Sediment Resuspended by the Dredge as a Function of Cutter Rotation
Speed, a 68
Figure 3-26. tPCB(Zc) in MDWS Samples Collected "at Upper Surface" and "Upper Mid-
Water" Water Depths from Each Station and Distance (meters) from Dredge from
Selected Stations 70
Figure 3-27. tPCB(Zc) in MDWS Samples Collected at "Lower Mid-Water" and "Near
Bottom" Water Depths from Each Station and Distance (m) from Dredge to
Selected Stations 71
Figure 3-28. Linear Relationships between PCB Concentration and TSS for the (A) Dissolved,
(B) Particulate, and (C) Dissolved Plus Particulate Phases of PCB 72
Figure 3-29. Volumetric Plot of the PCB Plume, Estimated from the Linear Relationship
between Dissolved Plus Particulate PCB Concentration and TSS and MDWS and
ADCP Transect Data Collected on June 4, 2007 73
Figure 3-30. Absolute Value of the Total Mass of Dredge PCB per Hour of Dredge Activity
(units of grams) as a Function of Distance from the Dredge 74
Figure 3-31. Sediment Core Sample Locations in the Ashtabula River Study Area (Pre- and
Post-Dredging) 75
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Figure 3-32. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 170 and 171 (A = West Side of River, B = East Side of
River) 77
Figure 3-33. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 172 and 173 (A = West Side of River, B = East Side of
River) 78
Figure 3-34. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 174 and 175 (A = West Side of River, B = East Side of
River) 79
Figure 3-35. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 176 and 177 (A = West Side of River, B = East Side of
River) 80
Figure 3-36. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 178 (A = West Side of River, B = Middle of River, C =
East Side of River) 81
Figure 3-37. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 179 (A = West Side of River, B = Middle of River, C =
East Side of River) 82
Figure 3-38. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 180 (A = West Side of River, B = West Middle of River,
C = East Middle Side of River, D = East Side of River) 83
Figure 3-39. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and 2011)
Cores at Transect 180 (A = West Side of River, B = West Middle of River, C =
East Middle Side of River, D = East Side of River) 84
Figure 3-40. Surface Sediment tPCB(Zc) Concentration (mg/kg dry) from Pre-Dredge (2006)
and Post-Dredge (2007 and 2011) 86
Figure 3-41. Surface Sediment tPCB(Zc) Concentrations from 2006 (Pre-Dredge); Created by
Earth Vision 2D Minimum Tension Gridding Algorithm using a 6.1-m x 6.1-m
(20-ft x 20-ft) Grid Spacing 88
Figure 3-42. Surface Sediment tPCB(Zc) Concentrations from Cores Collected in 2007 (1
Year Post-Dredge); Created by Earth Vision 2D Minimum Tension Gridding
Algorithm using a 6.1-m x 6.1-m (20-ft x 20-ft) Grid Spacing 89
Figure 3-43. Surface Sediment tPCB(Zc) Concentrations from Cores Collected in 2011 (4
years Post-Dredge); Created by Earth Vision 2D Minimum Tension Gridding
Algorithm using a 6.1-m x 6.1-m (20-ft x 20-ft) Grid Spacing 90
Figure 3-44. Principal Component Analysis Based on the PCB Congener Composition of
Surface Segments in the Ashtabula River Study Area during Four Study Phases
(two before dredging and two after dredging) 92
Figure 3-45. Surface Sediment (top 0.15 m) tPCB(Zc) Concentrations (mg/kg, dry wt) 94
Figure 3-46. Surface Sediment (top 0.15 m TOC (%) Concentrations 95
Figure 3-47. Surface Sediment (top 0.15 m) tPCB(Zc) Concentration Approximation Contours
(mg/kg, dry wt) Data 96
Figure 3-48. Surface Sediment (top 0.15 m) TOC-normalized tPCB(Zc) Concentration
Approximation Contours (mg/kg OC) Based on the Studies 1-4 Data 97
Figure 3-49. Average Lipid Content in Macrobenthos Samples over Time and by Location. 106
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Figure 3-50. Lipid-Normalized tPCB(Zc) in Macroinvertebrates by Location and Year 107
Figure 3-51. Contribution of PCB Homologs in Percent Aroclor on Ashtabula River
Macrobenthos Sample Locations (2006-2011): A) Upstream; B), Fields Brook;
C) Turning Basin; D) River Bend; E) Conneaut Creek Reference 109
Figure 3-52. Lipid-Normalized tPAH16 (A) and tPAH34 (B) Concentrations in Macrobenthos
Sampled from the Ashtabula River (2006-2011) 110
Figure 3-53. Percent Fines in Surface Sediments from the Ashtabula River Macrobenthos
Sample Locations (2007-2011) 113
Figure 3-54. Total Organic Carbon (%) in Surface Sediments from the Ashtabula River
Macrobenthos Sample Locations (2007-2011) 114
Figure 3-55. tPCB(^c) in Sediments by Macrobenthos Sample Location and Year 118
Figure 3-56. Organic Carbon-Normalized tPCB(^c) in Sediments by Macrobenthos Sample
Location and Year 118
Figure 3-57. Percent of tPCB(£c) as Contribution of PCB Homologs in Surface Sediment
Collected from the Ashtabula River Macrobenthos Sample Locations (2006-
2011) 119
Figure 3-58. tPAH16 (A) and tPAH34 (B) Concentrations (mg/kg dry wt.) in Surface
Sediments from the Macrobenthos Sample Locations in the Ashtabula River
(2007-2011) 120
Figure 3-59. Organic Carbon-Normalized tPAH16 (A) and tPAH34 (B) Concentrations (mg/kg
OC) in Surface Sediments from the Macrobenthos Sample Locations in the
Ashtabula River (2007-2011) 121
Figure 3-60. Average tPCB(£c) in Water Macrobenthos Samples by Location and Year 123
Figure 3-61. Percent tPCBs as Contribution of PCB Homolog Data for Water Column Samples
from the Ashtabula River Macrobenthos Stations (2007-2010) 124
Figure 3-62. Average Water tPAH16 (A) and tPAH34 (B) Concentrations (ng/L) in Benthic
Water Samples by Location and Year 125
Figure 3-63. Average Lipid Content with Error Estimates (Standard Deviations) in Indigenous
Brown Bullhead Collected from the Ashtabula River and Conneaut Creek 127
Figure 3-64. tPCB(Zc) Concentrations (mg/kg wet wt [A], and mg/kg lipid-normalized [B])
with Error Estimates (Standard Deviations) in Indigenous Brown Bullhead
Collected from the Ashtabula River and Conneaut Creek 129
Figure 3-65. Percent of tPCB(Zc) as Homolog Contributions in Brown Bullhead Collected
from the (A) Ashtabula River and (B) Conneaut Creek Reference (2006-2011).
130
Figure 3-66. tPAH16 (wet wt [A] and Lipid-Normalized [B]); and tPAH34 (wet wt [C] and
Lipid-Normalized [D]) Concentrations in Indigenous Brown Bullhead with Error
Estimates (Standard Deviation) Collected from the Ashtabula River and Conneaut
Creek Reference (2006-2001) 131
Figure 3-67. tPCB(Zc) Concentration per SPMD Suspended in the Water Column 135
Figure 3-68. tPCB(Zc) Concentrations in Co-located Whole Water Samples 136
Figure 3-69. 2006 PRC- and Non-PRC-corrected Water Column SPMD tPCB(Zc)
Concentrations Compared to Co-located Whole Water tPCB(Zc) and TSS
Concentrations 137
Figure 3-70. 2008 PRC- and Non-PRC-corrected Water Column SPMD tPCB(Zc)
Concentrations Compared to Co-located Whole Water tPCB(Zc) Concentrations.
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137
Figure 3-71. 2011 PRC- and Non-PRC-corrected Water Column SPMD tPCB(Zc)
Concentrations Compared to Co-located Whole Water tPCB(Zc) Concentrations.
138
Figure 3-72. Inter-annual Comparison of tPCB(Zc) Concentrations (Average and Standard
Deviation of 11 Stations in 2006 and 2011; 10 stations in 2008) for PRC- and
Non-PRC-corrected Water Column SPMDs to Whole Water Concentrations... 138
Figure 3-73. Percent of tPCB(Zc) as Homolog Distributions for (A) Water Column SPMD
Samples and (B) Co-located Water Column Samples from the Ashtabula River
(2006, 2008, and 2011) 140
Figure 3-74. tPCB(Zc) Concentration per SPMD Placed on Surface Sediments from the
Ashtabula River (2006 [n=21], 2008 [n=22], and 2011 [n=ll]) 142
Figure 3-75. tPCB(Zc) Concentrations in Ashtabula River Surface Sediment Samples Co-
located with Sediment SPMDs (2006 [n=6], 2008 [n=8], and 2011 [n=ll]) 143
Figure 3-76. Comparison of Average tPCB(Zc) Concentrations in Ashtabula River Sediment
SPMDs and Co-located Sediment Samples (2006 [n=7], 2008[n=8], and
2011| n 11|) 143
Figure 3-77. Percent of tPCB(Zc) as Homolog Distributions for (A) SPMDs Placed on Surface
Sediments, and (B) Co-located Sediment Samples from the Ashtabula River
(2006, 2008, and 2011) 145
Figure 3-78. Estimated Porewater Concentrations (PRC- and Non-PRC-corrected) Compared
to Co-located Water Concentrations for 2006, 2008, and 2011 147
Figure 3-79. Inter-Annual Comparison of tPCB(Zc) Concentrations for Estimated Porewater
Concentrations (PRC- and Non-PRC-corrected) to Measured Whole Water
Column Concentrations 148
Figure 3-80. tPCB(Zc) Concentration per SPME Suspended in the Water Column in the
Ashtabula River (2006 and 2008) 149
Figure 3-81. tPCB(Zc) Concentrations in Water Samples Co-located with SPMEs in the
Ashtabula River (2006 and 2008) 150
Figure 3-82. Percent of tPCB(Zc) as Homolog Distributions of the Water Column SPME
Samples (A), Co-located Water Samples (B), Sediment SPME Samples (C), and
Co-located Sediment Samples (D) from the Ashtabula River (2006 and 2008). 152
Figure 3-83. tPCB(Zc) Concentration per SPME Placed on Surface Sediments from the
Ashtabula River (2006 and 2008) 154
Figure 3-84. tPCB(Zc) Concentrations in Surface Sediment Samples Co-located with SPMEs
from the Ashtabula River (2006 and 2008) 154
Figure 4-1. Least Square Means for Lipid-Normalized Macrobenthos tPCB(Ec) (A); tPAH16
(B); and tPAH34 (C) (mg/kg Lipid) Measurements in Fields Brook, Turning
Basin, and River Bend Stations by Year with 95% Confidence Intervals 161
Figure 4-2. Least Square Means for Lipid-Normalized Macrobenthos tPCB(Ec) (A), tPAH16
(B), and tPAH34 (C) (mg/kg lipid) Measurements in Turning Basin, Fields Brook,
and River Bend Stations by Area with 95% Confidence Intervals 163
Figure 4-3. PCA for Macrobenthos tPCB(Zc) (All Stations, All Years) 164
Figure 4-4. Least Square Means for tPCB(Ec) Normalized to TOC (mg/kg Dry) Sediment
Sample Measurements Associated with Macrobenthos Samples by Area with 95%
xiii
-------
Confidence Intervals 166
Figure 4-5. PCA Showing PCB Congeners in Surface Sediment Co-located with
Macrobenthos 167
Figure 4-6. Least Squares Means for tPCB(Ec) (ng/L Liquid) Sample Measurements
Associated with Macrobenthos Samples by Year with 95% Confidence Intervals.
168
Figure 4-7. PCA Showing PCB Congeners in Waters with Macrobenthos Samples 170
Figure 4-8. Correlation Plot between tPCBs(Zc) in Macrobenthos Tissues and Co-located
Sediments (TOC Normalized) and Waters 171
Figure 4-9. PCA Showing PCB Congeners in Macrobenthos Tissue and Co-located Surface
Sediments and Waters 172
Figure 4-10. Correlation between Water Column SPMD and Co-located Whole Water Sample
tPCB(ZC) Concentrations 173
Figure 4-11. Correlation between Water Column SPMD Estimated Water and Co-located
Whole Water Sample tPCB(ZC) Concentrations 174
Figure 4-12. Correlation between 2006 Water Column SPMD (ng/SPMD) and Co-located
Whole Water Sample tPCB(ZC) Concentrations by Stations 174
Figure 4-13. Correlation between 2008/2011 Water Column SPMD (ng/SPMD) and Co-
located Whole Water Sample tPCB(ZC) Concentrations by Station 175
Figure 4-14. Correlation between Sediment SPMD (ng/SPMD) and Co-located Sediment
Sample tPCB(ZC) Concentrations 175
Figure 4-15. Correlation between Sediment SPMD (ng/L) and Co-located Whole Water
Sample tPCB(ZC) Concentrations 176
Figure 4-16. Least Squares Means for tPCB(Zc) Normalized to Lipids (mg/kg Lipid)
Calculated using tPCB(Zc) Fish Sample Measurements by Area with 95%
Confidence Intervals 181
Figure 4-17. Least Squares Means for tPCB(Zc) Normalized to Lipids (mg/kg Lipid)
Calculated using Common Congener Fish Sample Measurements by Area with
95% Confidence Intervals 181
Figure 4-18. PCA using tPCB(Zc) for Brown Bullheads from the Ashtabula River from
2006 through 2011 182
xiv
-------
List of Tables
Table
1.1:
Table
2.1:
Table
2.2:
Table
2.3:
Table
2.4:
Table
2.5:
Table
2.6:
Table
3.1:
Table
3.2:
Table
3.3:
Table
3.4:
Table
3.5:
Table
3.6:
Table
3.7:
Table
3.8:
Table
3.9:
Table 3.10:
Table 3.11:
Table
3.12:
Table
3.13:
Table
3.14:
Table
3.15:
Table
4.1:
Table
4.2:
Table
4.3:
Summary of Assessment Methods by Year, Number of Samples, and Parameters
Analyzed 5
Summary of SPMD Deployment Years and Locations 25
Summary of SPME Deployment Years and Locations 28
Summary of Macrobenthos Sampling Locations and Years 31
Surface Sediment Samples Collected during Macrobenthos Deployment (D) and
Retrieval (R) Events 31
Water Samples Collected during Macrobenthos Deployment (D) and Retrieval
(R) Events 32
Indigenous Brown Bullhead Catfish Collected from the Ashtabula River and the
Conneaut Creek for PCB Analysis 33
Sedimentation Rates at Sample Core Locations 46
Operational and Environmental Variables Used as Input for the Empirical Model
to Determine the Rate of Sediment Resuspended by the Dredge 66
tPCB(Zc) Concentrations (mg/kg) of Surface Sediment from Pre-Dredge (2006),
Post-Dredge (2007), and Post-Dredge (2011) 85
Average tPCB(Zc) Concentrations (mg/kg) for 30 Surface Sediment Samples
Collected in the Ashtabula River Study Area during Four Study Phases (two
before dredging and two after dredging) 91
Sample Data used to Characterize General River Surface Sediment Trends 98
tPCB(Zc), TOC, and TOC-normalized tPCB(Zc) Concentrations in Surface
Sediment Samples from Studies 1 through 4. Study 1-3 PCB data are based on
Aroclors and Study 4 on Congeners 98
Average tPCB(Zc) Concentrations in Surface Sediment and Sediment Trap
Samples Collected from the Area at the Confluence of Strong Brook and the
Ashtabula River, Upstream of the Turning Basin, and Downstream of Fields
Brook 102
Spatial Variability in Macrobenthos Samples Collection at Each Location 105
Number of Macrobenthos Samples Collected at Each Location 105
Number of Co-located Sediment Samples Collected at Macrobenthos Sample
Locations Ill
Comparison of tPCB(Zc), tPAH16, and tPAH34 in Surface Sediments Collected
during Deployment and Retrieval at the Macrobenthos Sample Locations in the
Ashtabula River (2007-2011) 117
Number of Co-located Water Samples Collected at Macrobenthos Sampler (H-D)
Locations 122
Number of Indigenous Fish Samples Collected 126
Number of Water Column SPMDs and Co-located Water Samples Collected.. 133
Number of Sediment SPMDs and Co-located Sediment Samples Collected 141
Summary of Macrobenthos Study Samples used in ANOVA 156
ANOVA Model Results for Raw and Lipid-Normalized Macrobenthos Factors.
158
Least Square Means and Confidence Intervals for Lipid-Normalized
Macrobenthos Factor Results with Significant Pairwise Comparisons by Year. 158
xv
-------
Table 4.4: Least Square Means and Confidence Intervals for Lipid-Normalized
Macrobenthos Factor Results with Significant Pairwise Comparisons by Area. 159
Table 4.5: Means for Lipid-Normalized Macrobenthos Chemical Measurements by Year for
Upstream and Conneaut Creek Reference 162
Table 4.6: Means for Lipid-Normalized Macrobenthos by Measurement for Upstream and
Conneaut Creek References 162
Table 4.7: Screening ANOVA Model Results for Sediment Samples Associated with
Macrobenthos Sample Factors 165
Table 4.8: Least Square tPCB(Ec) Means and Confidence Intervals for Sediment Sample
Measurements Associated with Macrobenthos Samples with Significant Pairwise
Comparisons by Year 166
Table 4.9: ANOVA Model Results for Water Sample Measurements Associated with
Macrobenthos Sample Factors 168
Table 4.10: Least Squares Means and Confidence Intervals for Water Sample Measurements
Associated with Macrobenthos Samples Factors 168
Table 4.11: Means for tPCB(Ec) (ng/L Liquid) Sample Measurements by Year for Upstream
and Conneaut Creek Reference 169
Table 4.12: Results of the Two Way ANOVA for Water Column SPMDs and Co-located
Water Samples 177
Table 4.13: Least Squares Means and Confidence Intervals for SPMD tPCB(Ec) (ng/SPMD).
177
Table 4.14: Least Squares Means and Confidence Intervals for Estimated Water
Concentrations using PRCs 178
Table 4.15: Least Squares Means and Confidence Intervals for Co-located Water
Concentrations 178
Table 4.16: Brown Bullhead Samples Collected from 2006 through 2011 in the Ashtabula
River and the Conneaut Creek Reference Location 178
Table 4.17: ANOVA Model Results for Fish Factors 180
Table 4.18: Least Squares Means and Confidence Intervals for Fish Sample Measurements
with Significant Pairwise Comparisons by Area 180
List of Appendices
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F:
Appendix G:
Appendix H:
Appendix I:
Sea Engineering Report
Bathymetry
Battelle's 2012 Source Tracking Report
2006, 2009, and 2011 Core Logs
PCBs, Grain Size, and Total Organic Carbon in Sediment Cores
Macrobenthos Summary Tables
Fish Summary Tables
SPMD/SPME Co-located Sediment and Water Summary Tables
Observed Measurements by Year and Site (Macrobenthos and Fish)
xvi
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Acronyms and Abbreviations
ABS
acoustic backscatter
ADCP
acoustic Doppler current profiler
ANOVA
analysis of variance
AOC
Area of Concern
ASTM
American Society for Testing and Materials
BSC
beam spread correction
BUI
beneficial use impairment
CERCLA
Comprehensive Environmental Response, Compensation and Liability Act
COC
chemical of concern
CSM
conceptual site model
DOC
dissolved organic carbon
EI
echo intensity
EST
Environmental Sampling Technologies
GC/MS
gas chromatography/mass spectrometry
GFF
glass fiber filter
GLLA
Great Lakes Legacy Act
GLNPO
Great Lakes National Program Office
GPS
global positioning system
H-D
Hester-Dendy
ICI
Integral Consulting, Inc.
ID
identification
IGLD85
International Great Lakes Datum of 1985
LISST
laser in situ scattering and transmissometry
LOC
level of chlorination
LOE
line of evidence
MBS
multi-beam sonar
MDWS
multi-depth water sampler
MSE
mean square error
NERL
National Exposure Research Laboratory
NPL
National Priorities List
NPS
normalized plume strength
NRMRL
National Risk Management Research Laboratory
NTU
nephelometric turbidity units
xvii
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OBS
optical backscatter system
ORD
Office of Research and Development
PAH
polycyclic aromatic hydrocarbon
PCA
principal component analysis
PCB
polychlorinated biphenyl
PDMS
polydimethylsiloxane
POC
particulate organic carbon
ppb
parts per billion
PRC
Performance Reference Compound
PSD
particle size distribution
QA/QC
quality assurance/quality control
QAPP
quality assurance project plan
REA
remedy effectiveness assessment
SEI
Sea Engineering, Inc.
SF
Superfund
SIM
selected ion monitoring
SOP
standard operating procedure
SPMD
semipermeable membrane device
SPME
solid phase micro-extraction
sss
side scan sonar
TOC
total organic carbon
TSS
total suspended solids
USACE
U.S. Army Corps of Engineers
U.S. EPA
U.S. Environmental Protection Agency
USGS
U.S. Geological Survey
VOC
volatile organic compound
vss
volatile suspended solids
WOE
weight of evidence
xviii
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1.0 INTRODUCTION
A research program to develop methods and metrics to assess remediation of contaminated
sediments is being conducted by the U.S. Environmental Protection Agency's (U.S. EPA's)
Office of Research and Development (ORD). Between 2002 and the present, U.S. EPA ORD
has been collaborating with U.S. EPA's Great Lakes National Program Office (GLNPO) and
U.S. EPA's Superfund Program to develop, validate, and demonstrate methods and metrics along
biological, chemical and physical lines of evidence (LOEs) to assess and evaluate remedy
effectiveness on projects carried out on contaminated sediment sites. This research is currently
being conducted within the Sustainable and Healthy Communities research program within ORD
(U.S. EPA, 2016a).
In order to conduct research studies on the impacts of remedial efforts and resultant recoveries
achieved, ORD initiated discussions with GLNPO starting in 2005 to form a partnership to
access contaminated sediment sites undergoing remediation. GLNPO, via its Great Lakes
Legacy Act (GLLA) mandate, is charged with undertaking and overseeing the remediation and
restoration of contaminated sediment sites in the Great Lakes Areas of Concern (AOCs). ORD,
through its research mission, is directed to evaluate the application and efficacy of remediation
and restoration of contaminated sites. Based on these mutual interests, in 2006, U.S. EPA's
National Risk Management Research Laboratory (NRMRL) and National Exposure Research
Labortory (NERL), hereafter collectively referred to as ORD, and GLNPO entered into an
agreement to jointly initiate a comprehensive project to develop and evaluate methods and
metrics for evaluating remedy effectiveness and conducting long-term monitoring on the
Ashtabula River AOC in Ashtabula, OH (Figure 1-1). Environmental dredging was selected by
GLNPO for the Ashtabula River to manage sediments contaminated with polychlorinated
biphenyls (PCBs) and other chemicals. PCBs constituted the primary chemicals of concern
(COCs) for this project. Additional COCs, including polycyclic aromatic hydrocarbons (PAHs)
and inorganic contaminants, were also monitored during this study.
Environmental dredging activities were carried out on approximately 1 mile of the Ashtabula
River (Figure 1-2) beginning in the fall of 2006 and ending in the fall of 2007. GLNPO led the
Ashtabula River environmental dredging operations, which consisted of hydraulic removal of
sediment from the red outlined area in Figure 1-2 (just south of the "Upper Turning Basin"
[River Station 194+00] north to the 5th Street Bridge [River Station 139+00]). Dredging
operations were performed by J.F. Brennan Company, Inc., a private marine contractor
headquartered in La Crosse, WI, as described in U.S. EPA (2010). The dredging was conducted
in two stages between September 9, 2006 and October 14, 2007, using a combination of 8-in. and
a 12-in. hydraulic swinging-ladder cutter-head dredges and resulted in the removal, transport,
and dewatering of approximately 496,600 yd3 of contaminated sediment. A more detailed
description of dredging activities is provided in the EPA ORD report titled "Field Study on
Environmental Dredging Residuals" (U.S. EPA, 2010).
1
-------
Ashtabula River
Project Site
SCALE IN MILES
Figure 1-1. Location of the Ashtabula River Environmental Dredging Project in
Ashtabula, OH.
Fields Brook
5th Street
Bridge
EXPLANATION
Figure 1-2. Ashtabula River Dredging Project and ORD Study Area (River Stations
181+00 to 170+00).
2
-------
1.1 Description of Project Area
The Ashtabula River lies in northeast Ohio, flowing into Lake Erie's central basin at the City of
Ashtabula (Figure 1-1). Its drainage basin covers an area of 137 mi2 (355 km2), with 8.9 mi2 (23
km2) in western Pennsylvania. Major tributaries include Fields Brook, Hubbard Run, and
Ashtabula Creek. The City of Ashtabula, with a population of 19,124 (2010 census), is the only
significant urban center in the watershed. The rest of the drainage basin is predominantly rural
and agricultural.
The industrial area of Ashtabula is concentrated around the upstream reach of Fields Brook from
Cook Road downstream to State Highway 11. Concentrated industrial activities, historical and
current, exist around Fields Brook (east of the Ashtabula River) and east of the Ashtabula River
mouth. Up to 20 separate industrial manufacturing activities have operated in the area since the
early 1940s. Industrial facilities ranging from metal fabrication to chemical production currently
operate on site. The decades of manufacturing activity and waste management practices at the
industrial facilities resulted in the discharge and release of hazardous substances to Fields Brook
and its watershed, including the floodplain and wetlands area. This contamination resulted in
Fields Brook being listed on the Superfund Program's National Priorities List (NPL) in 1983.
Sediments in portions of the Ashtabula River are contaminated with COCs, including PCBs.
Fields Brook and its five tributary streams that drain their 5.6-mi2 (15-km2) watershed have been
identified as a primary source of contamination into the Ashtabula River. The PCBs were
delivered into the river historically from Fields Brook, a stream that drains into the Ashtabula
River in the area of the upper Turning Basin (Figure 1-2). The eastern portion of the watershed
drains Ashtabula Township, and the western portion drains the eastern section of the City of
Ashtabula. The 3.5-mile (5.6-km) main channel of Fields Brook begins south of U.S. Highway
20, about 1 mile (1.6 km) east of State Highway 11. From this point, the stream flows
northwesterly, just under U.S. Highway 20 and Cook Road, to the north of Middle Road. The
stream then flows westerly to its confluence with the Ashtabula River immediately upstream of
the railroad bridge and Upper Turning Basin.
Sediments at the Fields Brook Superfund (SF) site were also contaminated with volatile organic
compounds (VOCs), PCBs, PAHs, heavy metals, phthalates, and low level radionuclides. VOCs
and heavy metals, including mercury, lead, zinc, and cadmium, have been detected in surface
water from Fields Brook and the Detrex tributary. Contaminants detected in fish include VOCs
and PCBs. The site posed a potential health risk to individuals who ingested or came into direct
contact with contaminated water from Fields Brook and with contaminated fish or sediments. A
Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) cleanup
of Fields Brook was completed in 2003 (U.S. EPA, 2016b).
1.2 Project Goals and Objectives
The goal of this U.S. EPA ORD research project was to develop, assess, and demonstrate
methods and metrics for evaluating the efficacy of environmental dredging of contaminated
sediments in the Ashtabula River AOC. This report presents the results of those studies wherein
the methods and metrics evaluated were developed along biological, chemical, and physical
3
-------
LOEs. These multiple LOEs can be integrated into a weight of evidence (WOE) framework to
assist in conducting a remedy effectiveness assessment (REA). The REA is then used to
evaluate the efficacy of the remediation process in meeting remedy objecties set by the project
manager. An REA was not prepared for this report. A comprehensive REA for the Ashtabula
River AOC using data generated on this project along with other relevant data is currently being
synthesized and prepared by GLNPO and ORD and will be reported separately.
The methods and metrics developed for this project were tested and evaluated during multiple
phases of the Ashtabula River AOC remediation effort (pre-, during, and post-dredging) and
targeted physical, chemical, and biological characterizations of the sediment, water column, and
associated ecosystem from 2006 through 2011. The primary objectives of the ORD research,
therefore, were to:
• Evaluate selected methods and metrics for measuring and documenting pre-, during,
and post-dredging physical, chemical, and biological conditions; and
• Evaluate selected methods and metrics for characterizing and predicting residual
contamination following environmental dredging.
1.3 Project Summary
The methods and metrics used on this project were developed along biological, chemical, and
physical LOEs. ORD, GLNPO, and Battelle implemented the field programs and collected the
required samples following U.S. EPA-approved protocols described in project specific quality
assurance project plans (QAPPs) (U.S. EPA, 2006, 2007, 201 la). Samples were analyzed by
ORD, Battelle, and its subcontracted laboratories.
The research study involved samples collected, metrics measured, and methods applied through
all stages of the remediaton project (pre-, during, and post-dredging). The characterizations of
sediment, water column, and ecosystem quality were conducted from 2006 through 2011. Table
1.1 lists the measurements and the methods employed, their intended use, and the timeframe in
which they were employed relative to dredging activities.
Extensive pre-dredging characterization was completed in the summer of 2006. Subsequently,
numerous sediment resuspension, sediment mapping (bathymetry and sidescan sonar), and
ecosystem measurements were made during the dredging activities in 2007. Post-dredging
characterization of sediment residuals and ecological indicators started in the fall and early
winter of 2007. Post-dredging and long-term monitoring studies continued during 2008, 2009,
2010, 2011, and 2015. After dredging was completed, physical, chemical, and biological uptake
measurements of dredging residuals1 were implemented using complementary techniques with
an emphasis on measuring the quantity of COCs in the various matrices over time. Particular
emphasis was given post-dredging to measuring the quantity and composition of the
contaminants in dredge residuals in the sediment and the fraction of contaminated sediment
removed by the dredging operation (i.e., estimating contaminated sediment removal efficiency).
1 Dredging residuals in the context of this report refer to contaminated sediment found at the post-dredging surface
of the sediment profile, either within or adjacent to the dredging footprint.
4
-------
Table 1.1: Summary of Assessment Methods by Year, Number of Samples, and Parameters Analyzed.
LTI
Measurement
Method
LOEa
Use
Pre-
Dredge
2006
# of
samples
Du ring-
Dredge
2007
# of
sample
s
Post-
Dredge
2007
# of
samples
Post-
Dredge
2008
# of
sample
s
Post-
Dredge
2009
# of
samples
Post-
Dredge
2010
# of
samples
Post-
Dredge
(# of
samples
2011)
Total
Number of
Samples (as
applicable)
Sediment Surface and Sediment Resuspension
Bathymetry -
Water
Depth/Sediment
Elevation
Multi-beam
sonar
P
Defines bottom depth and
allows visualization of the
sediment surface change
over time
Yesb
Yesb
Yesb
Yesb
Yesb
NA
Sediment
Surface
Imagery
Side scan
sonar
P
Imagery of the dredge
outline captured at specific
moments in time to identify
possible sources of
residuals
Yesb
NA
Turbidity in
Dredge Plume
Multiple optical
turbidity
sensors
(optical
backscatter
sensors
[OBS])
mounted on
the multi-depth
water sampler
(MDWS)
P
Plume tracking,
resuspended sediment
mass, link to contaminant
distribution; derivation of
TSS by direct comparison
with co-located water
Yesb
NA
Turbidity in
Dredge Plume
OBS mounted
1 m from the
surface and 1
m above the
bottom on
fixed
stationary
moorings
(upstream and
downstream of
the dredging
operations)
P
Derivation of TSS
concentration by direct
comparison with co-located
water samples analyzed for
TSS
Yesb
NA
Turbidity in
Dredge Plume
Downlooking,
vessel-
mounted
ADCP
P
Derivation of TSS
concentration, comparison
to TSSjurb.mdws
Yesb
NA
Current Velocity
Uplooking,
bottom-
mounted,
moored
P
Temporally resolved current
velocities and direction
upstream and downstream
of the dredging operations
Yesb
NA
-------
Table 1.1 (continued): Summary of Assessment Methods by Year, Number of Samples, and Parameters Analyzed.
CT)
Measurement
Method
LOEa
Use
Pre-
Dredge
2006
# of
samples
Du ring-
Dredge
2007
# of
sample
s
Post-
Dredge
2007
# of
samples
Post-
Dredge
2008
# of
sample
s
Post-
Dredge
2009
# of
samples
Post-
Dredge
2010
# of
samples
Post-
Dredge
(# of
samples
2011)
Total
Number of
Samples (as
applicable)
acoustic
Doppler
current profiler
(ADCP)
(upstream and
downstream of
dredging
operations)
Plume, Particle
Volume and
Size
Distribution
Laser in situ
scattering and
transmissomet
ry (LISST)
vertical
profiles
P
Derivation of volume
concentration, bulk particle
density for use in
comparison to TSSTUrb.mdws
Yesb
NA
Total
Suspended
Solids (TSS)
Concentration
Water samples
collected from
the MDWS
P
Plume tracking,
resuspended sediment
mass, link to contaminant
distribution; Discrete water
samples collected to
determine TSS in the water
column to correlate with
vessel-mounted optical
turbidity and acoustic
backscatter (ABS)
measurements
148
TSS°
148
Total
Suspended
Solids
Concentration
Water samples
collected at
the stationary
mooring
locations
P
Discrete water samples
collected to determine TSS
in the water column to
correlate with moored
optical turbidity and ABS
measurements
45
TSS°
45
Total PCBs in
Water Column
MDWS
discrete water
samples -
unfiltered
C
Determine Total PCB mass
concentrations and mass in
dredge plume
148
PCBd
(CONe,
HOMO,
GS9
148
Dissolved PCBs
in Water
Column
MDWS
discrete water
samples -
filtered
c
Determine dissolved PCB
mass and concentration in
dredge plume
155
PCBd
(CONe,
HOMf)
155
Sediment Depth Profile
1 II 1 1 1 1 — 1 1 — 1 1
-------
Table 1.1 (continued): Summary of Assessment Methods by Year, Number of Samples, and Parameters Analyzed.
Du ring-
Post-
Pre-
Dredge
Post-
Dredge
Post-
Post-
Post-
Dredge
2007
Dredge
2008
Dredge
Dredge
Dredge
Total
2006
# of
2007
# of
2009
2010
(# of
Number of
# of
sample
# of
sample
# of
# of
samples
Samples (as
Measurement
Method
LOEa
Use
samples
s
samples
s
samples
samples
2011)
applicable)
Sediment
Vibracoring
P
Measure the physical
328
180
No
160
415 from 30
and hydraulic
characteristics of intact
GS9, WETh
GS9,
chemical
GS9,
stations
piston coring
cores as a function of depth
from 30
WETh
analysis
WETh
stations
from 30
of 2009
from 28
stations
core
stations
samples
from 30
stations
Sediment
Vibracoring
C
Measure the chemical
369
58
180
No
165
415 from 30
and hydraulic
(PCB) characteristics of
PCBd
PCBd
PCBd
chemical
PCBd
stations
piston coring
intact cores as a function of
(CONe,
(CONe,
(CONe,
analysis
(CONe,
depth
HOMf,
HOMO,
HOMf),
of 2009
HOMO,
PCB IA),
OTHER
OTHER
core
PAH,
OTHER
from 30
samples
OTHER
from 30
stations
from 30
from 28
stations
stations
stations
Biological and Passive Samplers for Measuring Contaminant Uptake
Macrobenthos Samplers and Co-located Sediment and Water
Macro-
Macrobenthos
B
Measure chemical uptake
8
8
8
8
10
12
54
invertebrates -
samplers
(PCBs and PAHs) in
PCBd
PCBd
PCBd
PCBd
PCBd
PCBd
from
macrobenthos during
(CONe,
(CONe,
(CONe,
(CONe,
(CONe,
(CONe,
Macrobenthos
dredging operations
HOMO,
HOMO,
HOMO,
HOMO,
HOMO,
HOMO,
Stations
PAH',
PAH,
PAH',
PAH,
PAH,
PAH',
OTHER
OTHER
OTHER
OTHER
OTHER
OTHER
Sediment/
Sediment
C
Measure chemistry (PCBs
4
4
8
12
10
5
51
Surface
push core
and PAHs) in surface
PCBd
GS9,
GS9,
GS9,
GS9, TOO,
GS9,
Sediment from
sampler - top
sediments co-located with
(CONe,
TOO
TOO,
TOO,
PCBd
TOO,
Macrobenthos
0.15m
Macrobenthos stations
HOMO
PCBd
PCBd
(CONe,
PCBd
Stations
during dredging operations;
(CONe,
(CONe,
HOMO,
(CONe,
compare spatial and
HOMO,
HOMO,
PAH'
HOMO,
temporal trends
Q
PAH'
PAH'
PAH'
O
PCBd
(CONe,
HOMO,
PAH'
Water - from
Water grab
C
Measure chemistry (PCBs
4
8
8
12
10
42
Macrobenthos
sampler
and PAHs) in water
PCBd
PCBd
PCBd
PCBd
PCBd
Stations
samples co-located with
(Integration
(CONe),
(CONe),
(CONe),
(CONe),
Macrobenthos stations
)
PAH'
PAH'
PAH'
PAH'
during dredging operations;
compare spatial and
temporal trends
-------
Table 1.1 (continued): Summary of Assessment Methods by Year, Number of Samples, and Parameters Analyzed.
Measurement
Method
LOEa
Use
Pre-
Dredge
2006
# of
samples
Du ring-
Dredge
2007
# of
sample
s
Post-
Dredge
2007
# of
samples
Post-
Dredge
2008
# of
sample
s
Post-
Dredge
2009
# of
samples
Post-
Dredge
2010
# of
samples
Post-
Dredge
(# of
samples
2011)
Total
Number of
Samples (as
applicable)
Semipermeable Membrane Device/Solid Phase
/licro-extraction (SPMD/S
PMEs) and Co-located Sediment and Water
Sediment
Semipermeable
Membrane
Device (SPMD)
SPMD
deployed on
sediment
surface
C
Measure PCB uptake in
samplers positioned on the
sediment surface
25
PCBd
(CONe,
HOMO
26
PCBd
(CONe,
HOMO
13
PCBd
(CONe,
HOMO
64
Water
Semipermeable
Membrane
Device (SPMD)
SPMD
deployed in
water column
c
Measure PCB uptake from
the water column
12
10
13
35
PCBd
(CONe,
HOMf)
PCBd
(CONe,
HOMO
PCBd
(CONe,
HOMO
Sediment Solid
Phase Micro-
extraction
(SPME)
SPMD
deployed on
sediment
surface
c
Measure PCB uptake in
samplers positioned on the
sediment surface
14
PCBd
(CONe,
HOMO
15
PCBd
(CONe,
HOMO
29
Water Solid
Phase Micro-
extraction
(SPME)
SPMD
deployed in
water column
c
Measure PCB uptake from
the water column
6
10
16
PCBd
(CONe,
HOMO
PCBd
(CONe,
HOMO
Sediment/
Surface
Sediment from
SPMD/SPME
Stations
Sediment
push core
sampler - top
0.15m
c
Measure PCBs in surface
sediments co-located with
SPMD/SPME stations
during dredging operations;
compare spatial and
temporal trends
10
PCBd
(CONe,
HOMO GS9,
TOO
11
PCBd
(CONe,
HOMO
GS9,
TOO
11
PCBd
(CONe,
HOMO
GS9,
TOO
32
Water - from
SPMD/SPME
Stations
Water grab
sampler
c
Measure PCBs in water
samples co-located with
SPMD/SPME stations
during dredging operations;
compare spatial and
temporal trends
10
PCBd
(Integration
)
12
PCBd
(CONe,
HOMO
12
PCBd
(CONe,
HOMO
33
Fish and Bivalves
Indigenous Fish
(Brown
bullhead [BB],
channel catfish,
shiners)
Electroshockin
g
c
Measure PCB uptake in fish
during dredging operations
10 BBk
PCBd
(CONe,
HOMO,
PAH',
OTHER;
9 BBk
PCBd
(CONe,
HOMO,
PAH,
OTHER
10 BBk
PCBd
(CONe,
HOMO,
PAH',
OTHER
10 BBk
PCBd
(CONe,
HOMO,
PAH,
OTHER
20 BBk
PCBd
(CONe,
HOMO,
PAH,
OTHER
40 BBk
PCBd
(CONe,
HOMO,
PAH',
OTHER
150
45
Channel
Catfish
6 Shiner
PCBd
(HOMO,
OTHER
PCBd
(CONe,
HOMO,
-------
Table 1.1 (continued): Summary of Assessment Methods by Year, Number of Samples, and Parameters Analyzed.
Measurement
Method
LOEa
Use
Pre-
Dredge
2006
# of
samples
Du ring-
Dredge
2007
# of
sample
s
Post-
Dredge
2007
# of
samples
Post-
Dredge
2008
# of
sample
s
Post-
Dredge
2009
# of
samples
Post-
Dredge
2010
# of
samples
Post-
Dredge
(# of
samples
2011)
Total
Number of
Samples (as
applicable)
PAH',
OTHER
Caged Bivalves
Caged bivalve
deployment
C
Measure PCB uptake in
bivalves (Corbicula
fluminea) during dredging
operations
10 stations
0
No survival
Caged Worms
Caged worm
deployment
c
Measure PCB uptake in
worms (Lumbriculus
variegatus) during dredging
operations
10 stations
No survival
0
Water Column for Overall Site Characterization
Post-Dredge
Water Column
Samples
Grab samples
for PCB and
PAH analyses
c
Whole water sampled at the
centerline/midpoint of each
transect at mid-water depth
to measure PCBs and
PAHs in the water column
prior to and following
dredging
14
PCBd
(HOMf)
13
PCBd
(CONe,
HOMf),
PAH'
11
PCBd
(CONe,
HOMf),
PAH'
15
PCBd
(CONe,
HOMf),
PAH'
53
NA = Not applicable
aP = physical; B = biological; C = chemical
bYes = electronic data sampling effort/survey was conducted during this period
°TSS = Total suspended solids
dPCB = Polychlorinated biphenyl
eCON = PCB congeners (analysis)
fHOM = PCB homolog (analysis)
9GS = grain size (particle size distribution)
hWET = gravimetric wet weight of sample
'PAH = Polycyclic aromatic hydrocarbon
'TOC = total organic carbon
kBB = brown bullhead
-------
The results for the dredging residuals studies were summarized in a comprehensive interpretive
report titled "Field Study on Environmental Dredging Residuals: Ashtabula River, Volume I.
Final Report" (U.S. EPA, 2010).
The biological characterization methods developed and demonstrated were designed to evaluate
ecosystem recovery following remediation of contaminated sediments. The biological studies
were initiated prior to dredging in 2006 and continued through the period of dredging operations
and extended post-dredging through 2011 (4 years of annual post dredging assessment).
Macrobenthos contaminant concentrations was sampled using Hester-Dendy (H-D) artificial
substrate samplers deployed to collect macrobenthos. Additional biological evaluations were
conducted immediately post-dredging and included assessing native brown bullhead catfish, and
deployment of bivalves (Corbicula fluminea) and oligachaetes (Lumbriculus variegatus) to
measure chemical uptake; however, limited survival during these deployments curtailed further
study. Additionally, the potential uptake of the contaminants into organisms was measured with
passive samplers known as semipermeable membrane devices (SPMDs) and solid phase micro-
extraction (SPME). These samplers were placed in contact with surface sediment and in the
water column. The results from these biological and ecosystem investigations were summarized
in a comprehensive interpretive report entitled "Data Report on Ecosystem Monitoring for the
Ashtabula River Environmental Dredging Project" (U.S. EPA, 201 lb).
To characterize the potential for contaminant mass redistribution during dredging, a water
sampling program was implemented during dredging operations. This characterized the extent
of the suspended solids plume generated by dredging activities, including estimating the volume
and concentrations of suspended sediments over time, estimating the concentrations of PCB and
PCB mass associated with the suspended sediments over time, and estimating the mass of the
resuspended sediment and associated PCBs in the post-dredge residuals.
This report presents a summary of the research conducted by U.S. EPA ORD during the
Ashtabula River Environmental Dredging Project, including brief summaries of the previously
published U.S. EPA reports (USEPA 2010 and 2011). Data from all monitoring years (2006 to
2011) were integrated to address the ORD research program goals.
Results from this ORD study and of studies performed by U.S. EPA's partners will also be used
to address GLNPO's project goal to conduct a remedy effectiveness assessment and to support
beneficial use impairment (BUI) removal. BUI removal and data to support delisting the AOC
will be addressed GLNPO at a later date.
Section 2.0 of this report summarizes the experimental approach and provides summary level
details of the methods used to conduct the program. Section 3 presents the results utilizing the
methods described in Table 1.1. Section 4 provides an assessment of the methods applied during
the research project and their utility in characterizing environmental conditions relative to the
remediation activities. Section 5 considers the uncertainties relative to the project goals and
overall conclusions derived from the study. Section 6 provides references cited in this report.
10
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2.0 EXPERIMENTAL APPROACH
Pre-, during-, and post-dredging field studies were conducted from 2006 to 2011. These studies
adhered to U.S. EPA-approved QAPPs (U.S. EPA, 2006; 2007; 201 la). The QAPPs described
the projects' purposes and goals, field collection methods, analytical methods, and quality
assurance/quality control (QA/QC) requirements for each year.
Field sampling activities carried out before, during, and after dredging consisted of a multiple
LOEs approach using physical, chemical, and biological measurements to understand the
transport and fate of the COCs resulting from environmental dredging and the impacts to
ecosystem endpoints.
The ORD research program evaluated a range of physical, chemical, and biological sampling
devices and measurements for characterizing residuals resulting from contaminated sediment
dredging. These research studies were designed to reduce the uncertainty surrounding the use of
these methods for evaluating future remediation and recovery monitoring of contaminated
sediment sites.
ORD specifically focused on fate and transport of COCs using bathymetry, plume tracking
(transport), and physical and chemical characterization of the sediment prior to remediation and
in post-dredge residuals. Biological and passive samplers were used for estimating the uptake
(fate) of organic chemicals in biota. The research further examined the use of chemical
characteristics of the PCBs (i.e., congeners, homologs) and changes between pre- and post-
remediation for measuring long-term recovery following environmental dredging. The study
further compared and contrasted the chemical composition in organism and passive samplers to
the original sediments and post-remediation residuals to support the assessment method
evaluation. Co-located passive samplers, sediment, and water data enabled comparison of the
passive sampler data for potential use in the remedy effectiveness assessment.
The following describes the data collection approach for each of the methods described in Table
1.1.
2.1 Sediment Mapping
2.1.1 Bathymetry
The bathymetry of the study area was mapped during several surveys by multi-beam sonar/side
scan sonar (MBS/SSS). The sonar system was deployed by boat to survey the river's sediment
surface prior to dredging (2006), following dredging operations in 2007, and again in 2009 and
2011. Section 2.3.2 of the Dredge Residuals Report (U.S. EPA, 2010) details the MBS/SSS
bathymetric survey methods utilized in 2007 and 2009, which was conducted by ORD, Battelle,
and Integral Consulting, Inc. (ICI). Bathymetry was measured in 2011 by the U.S. Army Corps
of Engineers (USACE) Buffalo District.
11
-------
The sediment surface was also mapped daily to the extent possible during dredging. The
bathymetric variability and dredge cut slump progression was documented and representations of
the modified riverbed developed post-dredging.
Survey data quality was assured with daily verification of proper system operation and
verification of the accuracy (satellite corrected) of the global positioning system (GPS) position,
and vessel heading data. MBS calibrations and soundings were verified daily with sound
velocity casts in the water column and bar checks2 for the sonar depth offsets. The vertical
survey control was verified by comparing the water level logger real-time output with manual
elevation measurements at the location of the logger.
2.1.2 Sidescan Sonar
SSS was used to qualitatively identify materials on the sediment surface prior, during, and post-
dredging. SSS surveys were used to characterize the extent of debris prior to dredging. Debris is
suspected to result in higher dredge residuals due to inefficiency and disruption of the bedded
sediment during dredging. In addition, SSS during dredging over short time intervals allowed
for the identification of cut failures due to sediment sloughing.
2.2 Plume Tracking
In May, June, and July of 2007, Battelle and ICI with EPA ORD collected water quality data to
evaluate the character and extent of the suspended solids plume generated due to Ashtabula
River dredging activities, including estimating the volume and concentrations of suspended
sediments over time, estimating the PCB concentrations and mass associated with the suspended
sediments over time, and estimating the mass of the resuspended sediment and associated PCBs
contributing to the residuals after dredging.
ICI deployed submerged water quality moorings with mounted optical turbidity sensors and
acoustic Doppler current profiler (ADCP) platforms at distinct upstream and downstream
locations to measure turbidity (nephelometric turbidity units; NTU), current velocity, and
acoustic backscatter (ABS) at stationary locations.
An additional downward-looking ADCP was mounted from a vessel to measure current velocity
and ABS at cross-channel spatially varying locations in the river, upstream and downstream of
the dredging operation. Battelle's multi-depth water sampler (MDWS) was installed on the same
vessel as the ADCP and was used to collect water samples coinciding with plume locations
identified from real-time turbidity data. Optical backscatter system (OBS) sensors were mounted
at each of the four sampling depths of the MDWS to provide continuous, multi-depth turbidity
measurements. The water samples and optical turbidity were collected simultaneously with
ADCP measurements to allow for correlation between sensor turbidity measurements and TSS
measurements. Periodically, discrete water samples were collected and analyzed for total
suspended solids (TSS) and PCB concentrations.
2 Bar checks are made using a flat object held at predetermined distances beneath the MBS transducers to ensure
proper depths are recorded.
12
-------
Turbidity measurements collected with the MDWS were recorded for bank-to-bank river (cross-
river) transects both upstream and downstream of the dredging activities. The data from the
MDWS and ADCP were used to develop multi-dimensional maps of sediment plumes in the
remediation area. The MDWS and ADCP were first deployed on June 1-9, 2007 (Figure 2-1) and
produced turbidity data for 260 transects over 9 days. A second survey conducted between July
23 and 25, 2007 produced depth specific turbidity data for 70 transects over 3 days (Figure 2-2).
Particle size distributions (PSDs) of the suspended sediment were determined using a laser in situ
scattering and transmissometry (LISST) sensor that was vertically profiled at approximately mid-
channel during ADCP and MDWS vessel-based sampling. A LISST-100X, type B was deployed
in the center of channel cross-sections for a percentage of transects measured with the ADCP and
MDWS (Sea Engineering, Inc. [SEI], 2007; Appendix A to this report). The type B sensor
measured the size distribution for particles between 1.4 and 231.0 |im in diameter using laser
diffraction technology. Discrete water samples were also collected for laboratory measured
particle size determination to correspond with the LISST measurements.
Details of the field collection activities and data analyses are provided in the full SEI report
provided in Appendix A.
The exact transect spacing was determined in the field and selected to spatially (horizontally and
vertically) characterize the sediment plume associated with dredging operations. Similarly, the
frequency of surveys was subject to change after initial dredge plume assessment. Surveys of
this type occurred several times per day.
The MDWS's multi-depth water collection capability enabled simultaneous collection of water
samples at selected depth intervals. The suspended solids fraction of the water sample was
analyzed for particulate-associated PCBs. The aqueous fraction was analyzed for dissolved
PCBs. Whole water split samples were analyzed for turbidity, TSS, PSD, and total organic
carbon (TOC).
In all, a total of 45 whole water samples were collected and analyzed for turbidity, TSS, and
PSD, and a total of 148 samples were filtered for total and dissolved PCB analyses as described
in Section 2.9.2.
The ADCP was used to measure surface water velocities for future application of sediment
transport models to estimate particle and contaminant flux in the water column. The boat-
mounted ADCP was mounted to the boat hull in a downward-looking position from the water
surface. Surveys with the ADCP occurred concurrently with collection of water samples using
the boat-mounted MDWS to determine the water column flux (mass transport rate) of sediments
and contaminants and quantify the amount of suspended material in the water column.
13
-------
. Oownstrtim
AR054
• £'
ARM* oARrft
,a wrr
Water Sample
Collection
Locations
Survey
Date
• June 2007 Survey
¦ June 2007 Dredge Location
SCALE IN METERS
Note: Code for dredge location = month/day
Figure 2-1. June 2007 Survey Whole Water Sample Collection Locations and Dredge
Positions of the Michael B.
14
-------
Downstream
ARJ09 O .°AR331
Jaek'sMsrinc
Water Sample
Collection
Locations
Survey
Date
July 2007 Survey
July 2007 Dredge Location
SCALE IN METERS
Note: Code for dredge location: PB (Palm Beach) or MB (Michael B) and month/day
Figure 2-2. July 2007 Survey Whole Water Sample Locations and Dredge Positions of the
Michael B. ("MB") and the Palm Beach ("PB").
15
-------
When possible, three transects were run while: 1) the dredge was operating at a single location,
2) turbidity was visually present, and 3) river flow was in one direction. One transect was
performed close to the dredge, one mid-plume, and one far-plume. Due to dredge operations,
vessel traffic, and river flow conditions, it was not always possible to collect data along the three
target transects relatively coincidental. The following are descriptions of near-dredge, mid-
plume, and far-plume.
• Near-dredge refers to a transect located as close as safely possible to the dredge. This
distance was estimated as typically <15 m.
• Mid-plume was in a location approximately midway between the dredge and where
evidence of the plume was not distinguishable based on visual observations. This
distance was typically between 30 and 60 m.
• Far-plume was at the edge of the visible plume. This distance was typically between
60 and 120 m.
2.3 Sediment
Sediment was collected in support of a number of different field studies including:
• Deep sediment cores collected pre- (2006) and post- (2007, 2009, and 2011) dredging
to determine the historical physical and chemical profiles and to estimate dredging
residuals (U.S. EPA, 2010).
• Surface sediment cores (0 to 10 cm) were collected at positions co-located with both
passive samplers (SPMDs, SPMEs) and with the macrobenthos samplers (H-Ds) to
allow correlation of passive samplers and macrobenthos tissue concentrations with
sediment from the same locations over time and space.
The following describes sediment collections.
2.3.1 Sediment Cores
A total of 30 sediment cores were collected from the study area prior to dredging in 2006, again
upon completion of dredging in 2007, and in long-term monitoring in 2009 and 2011 (Figure
2-3). These 30 stations served as repeated monitoring positions for evaluating dredge residuals.
The name and geoposition are provided in U.S. EPA (2010). The following describes the sample
collection activities and the core processing procedures and strategy for each event.
2.3.1.1 Sediment Core Collection
Pre-dredge 2006 Sediment Cores. Sediment cores collected prior to dredging, where sediment
thickness was at its maximum extent, were collected using a vibracoring method. All pre-dredge
sediment cores were sampled to the point of refusal. Consistent with the original plan, it was
presumed that the area consisting of transects T181 to T177 would be dredged to a depth
confined by the bedrock layer, while dredging would continue only to 6 m below the IGLD85
(International Great Lakes Datum of 1985) in the area containing transects T177 to T170,
leaving a layer of soft sediment above bedrock. As such, collection of sediment cores to refusal
16
-------
ensured that the pre-dredge sediment cores would be as deep as or deeper than the target cut line.
In this way, a total of 16 sediment cores were collected in the area that was planned for dredging
to bedrock and 14 sediment cores were collected in the soft sediment area. When coupled with
chemical analysis, this allowed for a full PCB vertical profile of the sediment above and below
the target cut line.
Pre-dredging sediment cores were collected from a vessel with a pneumatic vibracore sampler.
The vibracore consisted of a vibratory head connected to a 10-cm outside diameter steel or
stainless steel tubing with a stainless steel core cutter and catcher. Core tubing was lined with a
pre-cleaned polyethylene tube of approximately 6 to 8 mil thickness. Cores were collected until
refusal from native bedrock or to approximately 1.0 m below target dredge depth.
Post-dredge 2007 Cores. The post-dredge sediment cores were collected in November 2007
following completion of dredging in June 2007; cores were collected using a hand push core
sampler or a hydraulically-driven piston core device. Samples were collected to a maximum
depth of approximately 1.5 m in some areas, with the intention to capture the post-dredge surface
sediment and native (un-dredged) sediment below. The cores were delivered intact to the
laboratory for processing and analysis.
N\
Figure 2-3. Sediment Core Sample Locations in the Ashtabula River Residual Study Area
for Pre- (2006) and Post- (2007, 2009, and 2011) Dredging, respectively.
LTM 2009 Cores. The 2009 cores were collected from 30 stations using either a vibracore or,
where leaf deposits prevented penetration of the vibracore unit, a hand-driven piston core device.
17
-------
A core was collected at each station and a duplicate core at five of the 30 stations (T170A,
T173A, T176A, T179A, and T181C). The cores were processed and physically characterized in
the fall of 2011, but not analyzed for PCBs as a cost cutting measure. Core samples collected in
2011 were used to determine additional post-dredge PCB profiles.
LTM2011 Cores. In 2011, 35 sediment cores were collected (28 sample locations and seven
duplicate core samples) using a pneumatically-driven piston core. Two stations (T177A and
T179A) were not sampled because the surface elevation was determined to be below the bottom
depth of the previously collected core (2009) indicating potential scouring of the location.
2.3.1.2 Core Processing
Following penetration and removal, the core enclosed in the liner was removed from the core
tube, examined for integrity and volume, labeled and stored upright until processing. The 2006
cores were cut into manageable lengths for shipment and "reconstructed" in the laboratory for
further processing as well as physical and chemical characterization. Cores collected in 2007,
2009, and 2011 were of shorter length and not cut in the field for shipment. They were
processed entirely in the laboratory. Cores collected in 2009 were held in a refrigerator and
processed in parallel with the 2011 cores.
Cores were partitioned into segments of various length depending on physical characteristics,
and each segment was photographed and identified with a placard containing the project name,
date, sample station identification (ID), and a measuring tape showing the length of the core.
Cores were described following American Society for Testing and Materials (ASTM) Procedure
D2488-93 (ASTM, 1993). Features such as sediment type (silt, clay, sand, etc.), color,
consistency, sedimentary structure, and odor were documented. Core material was extracted
from each polyvinyl chloride sleeve for analysis by splitting the sleeve lengthwise and removing
sediment from the internal portion of the core to avoid sediments that may have adhered to the
sidewalls of the sleeve during coring.
Sediment samples were transferred to a pre-cleaned stainless steel bowl using a cleaned stainless
steel spoon. The sediment was homogenized to a uniform color and consistency and then
distributed into the appropriate pre-labeled, certified-clean containers (U.S. EPA, 2007).
2006 Core Processing. Each sediment core collected in 2006 was cut with a portable hand or
battery-powered saw into intervals of 30 cm or less in the field and submitted to the laboratory
for processing and analysis. Sediment core lengths were "reconstructed" on the laboratory bench
top, and photographs of each core were taken and recorded. The length of each sediment
segment was determined upon physical observation of the core with greater delineation focused
in the range of the target cut line or dredge depth. The segments were based on sediment
characteristics and previously collected cores/segments for comparison between sampling years.
Each core segment was processed further by mixing in a laboratory blender for approximately 5
minutes before analysis. Core segments were analyzed for PCB congeners, TOC, PSD, and bulk
density.
18
-------
2007 Core Processing. Post-dredge sediment cores were processed in the same manner
previously described. The sediment segment thickness decreased in the range of the target cut
line, and the frequency of segments in this depth range increased. The post-dredge bathymetric
survey, as well as visual observations of note and consideration of the target cut line elevation,
played a major role in the decision process as to where the post-dredge sediment cores would be
sectioned.
Sediment cores collected during the pre- and post-dredge sampling events from each sampling
station were aligned vertically using elevation data to compare pre- and post-core segments and
determine their relationship to the dredge cut line. Several parameters were used independently
and in combination to verify alignment for pre- and post-dredge core comparisons. These
included water depth information, core lengths, refusal depth, and pre- and post-dredge
bathymetric survey data. Post-dredge core sections were processed and analyzed for the same
parameters as the pre-dredge core sections.
2009 and 2011 Core Processing. In the fall of 2011, the 2009 and 2011 cores were processed
and analyzed in a similar manner as noted above for the 2006 and 2007 cores. The 2009 and
2011 cores were segmented based on the 2007 segmentation plan and were virtually aligned with
the 2007 core data using elevation data. For the 2009 and 2011 cores, material was processed in
15-cm segments from the water surface interface down and from the 2007 surface elevation up
leaving an odd length core segment in the middle. Any material below the 2007 bottom
elevation was segmented in 15-cm intervals. Segment sizes from the section of the core that
overlapped with the 2007 core ranged from 1.5 to 7.5 cm.
For cost efficiency and to reduce the number of samples for analysis, the 2011 subsamples were
composited following a compositing scheme prepared by Battelle and accepted by U.S. EPA.
When the core material appeared to be new depositional material (based on elevation data), the
top 15 cm were collected first and then advancing 30-cm intervals were collected until the 2007
surface elevation was met. The subsamples taken from the subsection of the core that
corresponded with the 2007 surface elevation were composited based on 2007 total PCB
analytical data (<1 part per million [ppm], 1 to 10 ppm, 10 to 50 ppm, and >50 ppm). The
composited sample intervals ranged from 3 to 30 cm. Any material collected below the bottom
depth of the 2007 core was not analyzed.
Water samples were collected prior to deployment and retrieval to avoid sampling particulate
material entering the water column during equipment placement. Water was collected with a
Van Dorn or Niskin type sampler. Care was taken to prevent sampler contact with the bottom to
avoid disturbing bottom sediments. Sample jars were filled with site water, placed on ice, and
distributed to the analytical laboratories for PCB, PAH, TOC, and TSS analysis (U.S. EPA,
2007).
2.4 Passive Samplers
Two types of passive sampling devices were deployed for 28 days to mimic biological uptake of
COCs from either the water column or sediment surface. These consisted of SPMDs and
SMPEs. The SPMDs were deployed on the sediment surface (Figure 2-4) and in the water
19
-------
column (Figure 2-5) for 28 days at 10 to 11 stations (depending on year) in 2006, 2008, and
2011. SPMEs were only deployed in conjunction with the water column and sediment SPMDs
in 2006 and 2008. The following subsections describe the devices themselves and the
deployment methodologies.
2.4.1 Passive Samplers: Semipermeable Membrane Device
The SPMDs used for this program were composed of flat, low-density polyethylene tubing
containing a thin film of a pure, high-molecular-weight lipid (triolein). The triolein oil was
spiked with a known amount of a surrogate or performance reference compound (PRC). The
PRC was used to estimate the sampled water volume using a formula developed by the U.S.
Geological Survey (USGS) that takes into account partition coefficients and the concentration of
remaining PRC at the time of retrieval and is discussed further in Section 3.6. PRC was spiked
into the triolein oil batch at a mass of 50 ng of each PCB congener per SPMD sample. The PRC
matrix consisted of the following mixture of PCBs in hexane each year:
• 2006: PCB 38, PCB 50
• 2008: PCB 29, PCB 38, PCB 150, PCB 166
• 2011: PCB 8, PCB 186
These PCB congeners were selected as the field PRCs because they were all good indicators of
target PCB behavior and were not detected in prior characterization of the site. Over the course
of the project, different analytes were used for this PRC matrix to minimize co-elutions with the
target analytes.
SPMDs were deployed on the surficial sediments using a device called an "SPMD rack" (Figure
2-6) and in the water column using a device called a "spider carrier" (Figures 2-7 and 2-8)
(Schubauer-Berigan et al., 2012). The SPMD racks were designed and provided by U.S. EPA;
the spider carriers and SPMDs were provided by Environmental Sampling Technologies, Inc.
(EST) of St. Joseph, MO.
Table 2.1 identifies the sampling locations where the sediment and water column SPMDs were
deployed. SPMD racks were deployed at Stations 1 through 21 for the 2006 and 2008 sampling
events. Water column SPMD canisters were deployed at 10 of the 21 stations. In 2011, SPMD
racks were deployed on the sediment surface and SPMD canisters were deployed in the water
column at 10 of the 21 locations from 2006 and 2008.
20
-------
Legend
Sediment SPMDs
Q 2006
O 2008
100 Meters
Figure 2-4. Sediment SPMD Deployment Locations.
-------
Legend
Water Column SPMDs
100 Meters
Figure 2-5. Water Column SPMD Deployment Locations.
22
-------
Rod
Metal Screen
Covering Bottom
(Shown Partially
Removed)
Rod
PLAN VIEW
FROM BELOW
SPMD Rack
Design 1 (Original)
Brass Screw
NOT TO SCALE
ELEVATION VIEW
END VIEW
Center Eye Bolt
(Welded On)
T=T
PLAN VIEW
FROM ABOVE
Figure 2-6. Typical SPMD Rack Design for Deployment of SPMDs on the Surficial
Sediment.
SPMD
- Stainless Steel
Roller Teflon Coated
Tension '
Spring
SPIDER CARRIER
TOP VIEW
Teflon Covered
Stainless Steel Roll er
Center Post
(Tube)
(S.S.) Plate
Total Weight
Loaded Spider
Carrier -268 grams
36" Std. SPMD with Mounting
Loops: -5 grams
SPIDER CARRIER
ANGLE VIEW
Figure 2-7. Top View and Angle View of the SPMD Spider Carrier (EST, St. Joseph, MO).
23
-------
Tether Ring Tether Ring
Threaded
Screw-On Cap
Total V\feight
Empty: ~ 1,150 grams
304 Stainless Steel Construction
FIVE CARRIER CANISTER
SIDE VIEW
Removable End Cap Perforated Shell
c=
Stacked Carriers
CROSS-SFCTION OF FIVF CARRIFR CANISTFR
SIDE VIEW
Figure 2-8. Full View and Cross-Sectional View of the Perforated Stainless Steel Carrier
with Five Spiders.
2.4.1.1 Sediment SPMDs
The sediment SPMDs were deployed using racks that were loaded with five individual SPMD
ribbons (91.4 cm long by 2.5 cm wide) that were extended the full length of the rack and fixed to
rods on each end of the unit by slipping the rod through each looped end of the SPMD (Figure 2-
6). Nitrile gloves were worn during SPMD handling to prevent contamination of the ribbons.
After loading, a protective stainless steel mesh screen was attached to the bottom of the rack and
a chain was attached to the carrier eyebolt. The rack was lowered into the water column and set
on top of the sediment surface. A chain was extended away from the unit and used to recover the
rack after the 28-day deployment period.
During retrieval, each unit was brought to the surface via the chain attached to the rack. Once on
deck of the research vessel, the SPMDs were removed from the samplers. Each SPMD was
lightly rinsed using site water to remove excess sediment that adhered to the ribbon, and then all
24
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five ribbons were transferred into a common hexane-rinsed can for shipment to EST for
processing and dialysis (extraction).
Table 2.1: Summary of SPMD Deployment Years and Locations.
Station
ID
2006
2008
2011
SPMD-
S
SPMD-
W
SS
w
SPMD
-S
SPMD
-W
s
s
w
SPMD-
S
SPMD
-W
SS
w
PS-01
-
-
s
s
s
s
s
PS-02
•/
-
-
-
S
-
-
-
-
-
-
-
PS-03
¦/*
•/
•/
S*
•/
•/
•/
•/
•/
•/
•/
PS-04
./**
•/
s
s
S*
s
s
s
s
s
s
s
PS-05
S
s
s
S*
s
s
s
s
s
s
s
PS-06
-
-
-
s
-
-
-
-
-
-
-
PS-07
•/
-
-
-
s
-
-
-
-
-
-
-
PS-08
s
•/
•/
•/
s
•/
•/
•/
•/
•/
•/
•/
PS-09
s
-
-
-
s
-
-
-
-
-
-
-
PS-10
s
-
-
-
s
-
-
-
-
-
-
-
PS-11
s
-
-
-
s
-
-
-
-
-
-
-
PS-12
s
•/
•/
•/
s
-
•/
^(C)
•/
•/
•/
•/
PS-13
s
-
-
-
s
-
-
-
-
-
-
-
PS-14
s
-
-
-
s
-
-
-
-
-
-
-
PS-15
s
•/
•/
•/
s
•/
•/
•/
•/
•/
•/
•/
PS-16
s
-
-
-
s
-
-
-
-
-
-
-
PS-17
s
-
-
-
s
-
-
-
-
-
-
-
PS-18
s
-
-
-
s
-
-
-
-
-
-
-
PS-19
s
-
-
-
s
-
-
-
-
-
-
-
PS-20
s
-
-
-
s
-
-
-
-
-
-
-
PS-21
s
-
-
-
s
-
-
-
-
-
-
-
PS-22
-
•/
•/
•/
s
•/
•/
•/
•/
•/
•/
•/
PS-23
-
s
s
s
s
s
s
s
s
PS-24
-
s
s
s
s
s
s
s
s
PS-25
-
s
•/
-
s
s
s
•/
s
s
s
SPMD-S: Sediment SPMD
SPMD-W: Water SPMD
SS: Surface Sediment
W: Surface Water
No sample deployed at this station.
"*" Duplicate sediment SPMD racks deployed at this station. Average of duplicates used in data
evaluation.
(a) 2006 and 2001 water samples collected during deployment of SPMD samplers only.
Water samples were collected during deployment of SPMD only at Station 12. No water column
SPMD or water samples were collected during retrieval at Station 12 in 2008.
2.4.1.2 Water Column SPMDs
SPMDs were deployed in the water column using large canisters that were supplied by EST.
Water column SPMDs were shipped to the field mounted on the spider carrier (see Figure 2-7).
Each spider carrier contained one full-length of SPMD ribbon (90 cm long by 2.5 cm wide) that
25
-------
was "woven" through spindles on the spider carrier to maximize surface area for exposure and
uptake. For each water column deployment, a total of five spider carriers were stacked onto a
central post within a perforated stainless steel carrier canister (Figure 2-8). The canister was
secured with a screw-top lid. The canister's holes allowed for ample movement and circulation
of water through the device once it was deployed into the water column.
Water column SPMD deployments were attached to the chain of the SPMD rack, with the rack
serving to anchor the water deployment in place. Each water column canister was fitted with a
subsurface buoy so that the canister was allowed to float approximately 1 m above the sediment
surface. Water column deployments were the first to be retrieved from a given station to
minimize impacts from disturbed sediments. Each canister was brought to the surface, and the
top of the canister was removed. Each of the five spider carriers was removed from the canister
and transferred into a hexane-rinsed can with the SPMD left in place on the carrier and shipped
to EST for extraction. The spider carriers from a station were combined as a single sample.
2.4.1.3 Surface Sediments
Surface sediment samples were collected (top 10 cm) at locations corresponding to deployments
for passive samplers (SPMD/SPME). For most years, surface sediments were collected from
these same locations when the equipment was retrieved. Table 2.1 identifies the sampling
locations where surface sediment was collected in association with SPMDs.
A stainless steel ponar sampler was used to collect surface sediment samples in 2006 through
2010. In 2001, the Undisturbed Surface Sediment (USS) sampler, a unique sampler developed
by U.S. EPA/NERL's Environmental Sciences Division, was used to collect surface sediment
samples. At least one sample from each location was photographed in the grab or core sampler
after placing a placard containing the project name, date, and sample station ID on the sampler.
The top 10 cm of each grab sample or core was transferred to sample containers by transferring a
portion of sediment with pre-cleaned stainless steel spoons. Sediment that contacted the walls of
the grab sampler or core barrel was not included in the sample. Each sample's general
characteristics (e.g., sediment type [silt, clay, sand, etc.], color, consistency, sedimentary
structure, and odor) were recorded on a Sediment Characterization form (Appendix D).
Sediment from each location was field homogenized to a uniform color and consistency by hand
using stainless steel utensils. Homogenized samples were placed on ice and shipped to pre-
approved analytical laboratories for PCB, PAH, PSD, and TOC analysis.
2.4.1.4 Water Column Samples
Whole water samples were collected at passive sampler (SPMDs, SPMEs) stations to correlate
water column data with passive sampler results. Table 2.1 identifies the sampling locations
where water column samples were collected in association with SPMDs.
Water samples were collected approximately 15 to 30 cm above the sediment-water interface.
This depth was deemed sufficient to collect water samples as close to the sediment surface as
possible while taking caution not to disturb the bottom sediments. Water samples were collected
with aNiskin sampler.
26
-------
2.4.2 Passive Samplers: Solid Phase Micro-Extraction
In 2006, SPME devices consisted of a fiber optic material with an external non-polar coating
which was used to accumulate non-polar organic compounds, such as PCBs, at a known rate
based on equilibrium partitioning. Commercially available SPME fibers (Supelco, Part# 57341-
U) were purchased and affixed inside a 6-in. stainless steel mesh Geoprobe™ well screen that
was modified with a removable screw cap. Each mesh "container" was fixed with a thin gauge
steel wire to the outside of either an SPMD water column deployment or the inside of the
protective screening of the SPMD rack.
At retrieval, it was found that the wire tie used to fix the SPMEs to the SPMD deployments
corroded considerably over the 28-day deployment period and most of the SPMEs that were
attached to the outside of the water column SPMD carriers were lost, as well as most of those
that were attached to the SPMD racks.
In 2008, an alternative deployment approach and SPME material, similar to that described in
Burgess et al. (2015), was used. SPMEs were derived from a fiber optic material (Fiberguide,
Inc.) and cut to length in the laboratory. The fibers consisted of a polydimethylsiloxane (PDMS)
coating. This material was demonstrated to be equivalent to commercially available SPME
devices for a wide range of hydrophobic analytes. For field sampling, the disposable fibers
provided a significant reduction in cost over the commercially available SPME fibers.
The SPME fiber was cut into 3-cm long pieces. The specifications of each fiber piece were as
follows:
• Fiber piece length: 3 cm;
• PDMS coating thickness: 10 [j,m;
• Diameter of silica core: 210 [j,m;
• Diameter of fiber piece (PDMS coating + silica core): 230 [j,m
• Volume of PDMS coating: 0.207 [j,L;
• Density of PDMS coating: 1.05 (J,g/(J,L;
• Weight of PDMS coating: 0.22 jag.
These SPMEs were transferred into a stainless steel mesh pouch (Burgess et al., 2015). Each
pouch was pre-cleaned and wrapped in aluminum foil for shipment to the site. The stainless
steel pouch was fixed inside the water column SPMD carriers and inside the mesh screen of the
SPMD racks. For each location, two SPME samplers were deployed. The duplicate SPME
sampler served as a backup sample in the event the primary sample was compromised during the
sampling. The SPMEs were retrieved after the 28-day deployment period. All SPMEs including
the duplicates were retrieved from each location and shipped to the laboratory for processing,
extraction, and analysis. Table 2.2 summarizes the deployment locations for 2006 and 2008 and
the collection locations for co-located surface sediment and water samples.
27
-------
Table 2.2: Summary of SPME Deployment Years and Locations.
Station
ID
2006
2008
SMPE-S
SPME-W
SS
w
SPME-S
SPME-W
SS
W
PS-01
-
PS-03
PS-04
PS-05
PS-06
•/
-
-
-
-
-
-
PS-07
S
-
-
-
-
-
-
PS-08
s
PS-10
s
-
-
-
-
-
-
PS-11
s
-
-
-
-
-
-
PS-12
s
^(C)
PS-15
s
PS-22
-
-
PS-23
-
-
PS-24
-
-
PS-25
-
-
-
SPME-S: Sediment SPME
SPME-W: Water SPME
SS: Surface Sediment
W: Surface Water
No sample deployed at this station.
"*" Duplicate sediment SPME racks deployed at this station. Average of duplicates used in data
evaluation.
(a) 2006 and 2001 water samples collected during deployment of SPMD samplers only.
(b) 2008 water samples collected during deployment and retrieval of SPMD samplers.
(c) Water samples were collected during deployment of SPMD only at Station 12. No water column
SPMD or water samples were collected during retrieval at Station 12 in 2008.
2.5 Macrobenthos Sample Collection
Macrobenthos artificial substrate samplers were used to collect macrobenthos tissue for chemical
analysis. Each deployment system consisted of units called Hester-Dendy (H-D) multi-plate
samplers (Figure 2-9). Each sampler consisted of eight square pieces of tempered hardboard
plate set up with increasing top to bottom spacing intervals (Figure 2-9); see Lazorcheck et al.
(2015) for more details.
The entire sampler was held together with an eyebolt and wing nut assembly. The plates (7.6 cm
x 7.6 cm) and spacers (2.5 mm thick) were placed on the eyebolt so that there were three single
spaces, three double spaces, and one triple space between the plates. The total surface area of the
sampler, excluding the eyebolt, was approximately 924 cm2. Six individual samplers were
attached onto a 1.2 m x 0.9 m x 0.6 m size wire mesh fish cage. Each wire mesh cage was
weighted with a brick and positioned such that the H-D samplers extended from the top of the
box to within 0.3 m of the sediment surface. Each box was anchored to the shoreline with a
metal chain. Two macrobenthos samplers were deployed at each station for a total of 40
individual H-D samplers per location.
28
-------
The macrobenthos samplers were deployed in the Ashtabula River at four stations from 2006 to
2011. These locations were designated Upstream, Field Brook, Turning Basin, and River Bend
(Table 2.3, Figure 2-10).
Figure 2-9. Macrobenthos Samplers Used at Ashtabula River (Left -H-D artificial
substrate plate sampler; Right - Samplers hanging in fish cages during deployment).
Two additional macrobenthos samplers were deployed at a Conneaut Creek reference location in
2009, 2010, and 2011. Conneaut Creek is approximately 22 km east of the Ashtabula River and
also flows into Lake Erie outside of the Ashtabula River AOC.
The macrobenthos deployments were recovered after 28 days of exposure and transferred to U.S.
EPA field staff for processing. U.S. EPA enumerated and recorded the organism species and
calculated community structure parameters (these data are not addressed in this report). U.S.
EPA also provided composite macrobenthos tissue samples for chemical analysis (lipids, PCBs,
and PAHs). Eight composite macrobenthos samples were processed and analyzed in 2006, 2007,
and 2008; 10 composites were processed and analyzed in 2009, 2010, and 2011 (Table 2-3).
Table 2.4 identifies the sampling locations where co-located surface sediment was collected, as
described in Section 2.4.1.3. Table 2.5 identifies the sampling locations where co-located water
column samples were collected, as described in Section 2.4.1.4.
29
-------
Brookp
Conneaut Creek
Reference Site
Kilometers
onneaut
Area
of
Detail
North
Kings ville
Legend
2009
200 Meters
Figure 2-10. Macrobenthos Deployment at the Ashtabula River and Conneaut Creek
Reference Site Locations (inset shows Conneaut Creek Reference Location).
30
-------
Table 2.3: Summary of Macrobenthos Sampling Locations and Years.
River
Mile
Area
(RM)
Site Description
2006
2007
2008
2009
2010
2011
Upstream
(UP)
2.33
Located approximately 1,000 m
up river above the confluence of
Fields Brook and the Ashtabula
River
V
V
V
V
V
V
Fields Brook
(FB)
1.58
Located in Fields Brook
approximately 50 m upstream
from the mouth of the brook
V
V
V
V
V
V
1.65
Located along the north bulkhead
of the Turning Basin; however,
Turning Basin
this station was transferred from
V
V
V
V
V
V
(TB)
the Turning Basin to northwest
side of the railroad bridge during
dredging in the Turning Basin
-0.9
Located at the northern bulkhead
of the River Bend. The during-
River Bend
(RB)
dredge sampler deployment was
located approximately 100 m to
the west of the bulkhead due to
dredging in that area.
V
V
V
V
V
V
Conneaut
Creek
-1.5
Approximately 22 km east of the
Ashtabula River and also flows
NS
NS
NS
V
V
V
Reference
into Lake Erie outside of the
(CC)
Ashtabula River AOC
NS = No sample collected
Table 2.4: Surface Sediment Samples Collected during Macrobenthos Deployment (D) and
Retrieval (R) Events.
Area
2006
2007
2008
2009
2010
2011
Upstream (UP)
R
D/R
D/R
D/R
D/R
D/R Composite
Fields Brook (FB)
R
D/R
D/R
D/R
D/R
D/R Composite
Turning Basin (TB)
R
D/R
D/R(c)
D/R
D/R
D/R Composite
River Bend (RB)
R
D/R
D/R(d)
D/R
D/R
D/R Composite
Conneaut Creek Reference (CC)
NC
NC
NC
D/R
D/R
D/R Composite
NC = not collected
D = Deployment
R = Retrieval
(a) Deployment samples were not collected in 2006; the retrieval samples were analyzed for PCBs (i.e.,
PAHs and TOC were not measured);
(b) The 2007 retrieval samples were not analyzed for TOC; TOC data from 2007 deployment samples were
used to normalize the 2007 retrieval data and for graphics and statistical analysis.
(c) The 2009 Turning Basin retrieval sample was not analyzed for TOC; 2009 TOC data from the Turning
Basin deployment were used to normalize the data and for graphics and statistical analysis.
(d) The 2009 River Bend retrieval sample was not analyzed for TOC; 2009 TOC data from the River Bend
deployment were used to normalize the data and for graphics and statistical analysis.
31
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Table 2.5: Water Samples Collected during Macrobenthos Deployment (D) and Retrieval (R)
Events.
Area
2006(a)
2007
2008
2009
2010
2011
Upstream
R
D/R
D/R
D/R
D/R
NC
Fields Brook
R
D/R
D/R
D/R
D/R
NC
Turning Basin
R
D/R
D/R
D/R
D/R
NC
River Bend
R
D/R
D/R
D/R
R(b)
NC
Conneaut Creek Reference Area
NC
NC
NC
D/R
D/R
NC
NC = not collected
D = Deployment
R = Retrieval
(a) 2006 retrieval samples (no deployment data) were analyzed for PCBs by
integration method; no PAHs were analyzed.
(b) No water sample collected at River Bend in 2010 during the macrobenthos
deployment.
2.6 Caged Clams and Worms
Asian clams (Corbicula fluminea) and fresh water oligochaetes {Lumbriculus variegatus) were
deployed in 2006 to assess PCB bioaccumulation in tissue over a 28-day exposure period. Clams
and worms were deployed in August 2008 during the same event in locations where water
column SPMDs were deployed. They were retrieved in September 2008. Asian clams obtained
from Alum Creek, Alum Creek State Park, OH, were captured and transported to the site for
deployment. The clams appeared to be healthy upon arrival at the project site but showed signs
of stress from overnight storage. Therefore, the clams were aerated, the water was changed, and
the clams were then deployed in cages in the water column at Stations 1, 3, 4, 5, 8, 12, 15, 22,
23, 24, and 25 as planned under a permit issued by the Ohio Department of Natural Resources.
A maximum of 50 Asian clams were deployed per cage. The caged clams were positioned
approximately 1 m above the sediment water interface.
Lumbriculus variegatus were deployed in polyethylene mesh cages following methods similar to
those described by Burton et al. (2005). The Lumbriculus cages were co-located with clam
deployments but positioned on the sediment surface. Approximately 4 g of Lumbriculus
variegatus were weighed and transferred into each mesh cage for deployment.
The clam and worm cages were retrieved after 28 days. All of the clams died during
deployment, and no Lumbriculus variegatus were found in any of the cages. Based on the
unsuccessful deployment in 2006, no further bivalve or worm deployments were used in this
study.
2.7 Indigenous Fish
U.S. EPA NERL collected indigenous brown bullhead (BB) catfish from the Ashtabula River
and the Conneaut Creek (Reference Location) from 2006 through 2011. The indigenous fish
were collected using an electroshocking method. The Ashtabula River fish were submitted to
Battelle for analysis of PCB homologs and congeners, PAHs, percent moisture, and total lipids.
32
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The Conneaut Creek Reference fish were submitted to U.S. EPA NERL for chemical analysis of
PCBs. Table 2.6 summarizes the number of fish collected from the Ashtabula River and the
Conneaut Creek.
Table 2.6: Indigenous Brown Bullhead Catfish Collected from the Ashtabula River and
the Conneaut Creek for PCB Analysis.
Area
2006
2007
2008
2009
2010
2011
Ashtabula River
10
9
10
10
10
13
Conneaut Creek Reference
1
9
10
0
10
13
Note: Additional fish were collected in both the Ashtabula River and the Conneaut
Creek Reference Area and were examined for anomalies/lesions, tumors, and
histopathology. These data are not discussed in this report.
2.8 Chemical and Physical Analytical Methods
The method of analyses for the various samples collected was thoroughly described in the QAPP
and associated addenda (U.S. EPA, 2006, 2007). The subsections below briefly summarize the
specific analytics conducted during this study.
2.8.1 Chemical and Physical Analysis of Sediment Samples
PCBs and PAHs. Sediment samples were extracted and analyzed for PCB homologs, PCB
congeners, and PAHs by Battelle at its laboratory located in Duxbury, MA. If both PCB and
PAH analyses were required, the extract was quantitatively split 50:50. One-half of the extract
was analyzed for PCBs using gas chromatography/mass spectrometry (GC/MS) in the selected
ion monitoring (SIM) mode, and the other half of the extract was analyzed for PAHs using a
separate GC/MS system in the SIM mode (U.S. EPA, 2007). The PAH analysis is based on
SW846 Method 8270C. The PCB homolog and PCB congener analyses were based on U.S.
EPA Method 1668A and SW846 Method 8270C. In 2006, PCB homologs were measured based
on a calibration using the first and last congeners of each level of chlorination. Approximately
140 individual PCB congeners were also analyzed. In subsequent years, PCB homologs were
determined by summing the individual PCB congeners within each level of chlorination (LOC).
Total PCBs were determined by summing the individual congeners, henceforth referred to as
tPCB(Zc). Section 2.10 presents these calculations in more detail. All results were reported in
Hg/kg dry weight.
Particle Size Distribution. A quantitative determination of the distribution of particle sizes in
sediment was performed by Applied Marine Sciences (League City, Texas) (2006, 2007, 2008)
and by Columbia Analytical Services (Kelso, WA) (2009, 2010, 2011) following ASTM D422
and Standard Operating Procedure (SOP) AMS-2103. The distribution of particle sizes larger
than 74 microns (#200 sieve) (i.e., gravels and sands) was determined by sieving, while the
distribution of particle sizes less than 74 microns (i.e., silts and clays) was determined using a
hydrometer. The results were reported as percent on a dry weight basis.
33
-------
Total Organic Carbon. TOC analyses were performed by Applied Marine Sciences (League
City, Texas) (2006, 2007, 2008) and by Columbia Analytical Services (Kelso, WA) (2009, 2010,
2011) following SW-846 Method 9060A and AMS SOP-2201, CAS SOP-9060M, and CAS SOP
D4129-82M. All results were reported in percent carbon on a dry weight basis.
Percent Moisture. Percent moisture was determined by each laboratory conducting soil,
sediment or tissue analyses to determine the amount of water present in sample aliquots. Percent
moisture was determined as the percent ratio of wet to dry weight for each analytical aliquot. All
results were reported as percent moisture for each analytical laboratory.
Bulk Density. Bulk density for sediment samples was measured by Applied Marine Sciences
(League City, Texas) (2006, 2007, 2008) and by Columbia Analytical Services (Kelso, WA)
(2009) following AMS SOP-2305 and CAS SOP ASTM El 109-86, which were based on ASTM
Method C29/C29M. Bulk density is a measure of the weight of sediment per unit of total
volume of sediment mass. Two values were reported with this method. The first was the dry
bulk density, which is the mass of oven-dried sediment per unit volume, and the second was the
wet bulk density, which is the mass of sediment at the natural moisture content per unit volume.
Both dry and wet bulk densities were reported in g/cm3.
2.8.2 Chemical Analysis of Water Samples
Water samples collected using the MDWS were filtered at the laboratory on a pre-cleaned l-|j,m
glass fiber filter (GFF) for PCB analysis. In general, 2 L of water were filtered for each sample.
Particulate PCBs were analyzed according to the sediment extraction method noted above
(Section 2.8.1) and reported in ng/g wet weight. Dissolved PCBs were determined from analysis
of the filtrate, as described below for a whole water sample (U.S. EPA, 2007).
Water samples were extracted and analyzed for PCB homologs, PCB congeners, and PAHs. If
both PCB and PAH analyses were required, the extract was quantitatively split 50:50. One-half
of the extract was analyzed for PCBs using GC/MS in the SIM mode, and the other half of the
extract was analyzed for PAHs using a separate GC/MS system in the SIM mode. The PAH
analysis was based on SW846 Method 8270C. The PCB homolog and PCB congener analyses
were based on U.S. EPA Method 1668A and SW846 Method 8270C. The calibration used to
quantify the PCB homologs utilizes the first and last congeners of each LOC. The calibration
also consists of approximately 140 individual PCB congeners, which were calibrated at the same
time as the LOC congeners. This allows the ID and quantification of individual congeners in the
sample in case reexamination of the data was requested in the future. All results were reported in
ng/L. All methods are described in detail in the QAPPs for each phase of the research (U.S. EPA,
2006, 2007).
Total Organic Carbon in Water. TOC in water samples was determined as both dissolved
organic carbon (DOC) and particulate organic carbon (POC) following a laboratory SOP based
on U.S. EPA Method 415.1. Analysis was conducted by Applied Marine Sciences (League City,
TX) (2006, 2007, 2008) and by Columbia Analytical Services (Kelso, WA) (2009, 2010, 2011)
following AMS SOP-2202 and CAS SOP- SM 5310 C. Samples were measured, and if needed,
adjusted to a pH of < 2. In some cases, samples were filtered to remove particulate matter. POC
34
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was determined by analyzing the particles isolated by filtration using Millipore AP40 (GFF 0.7
|j,m) 47-mm diameter filters for TOC. DOC was measured by analyzing the filtrate for TOC.
All results were reported in mg/L.
Total Suspended Solids and Volatile Suspended Solids. TSS and volatile suspended solids
(VSS) in the water samples were determined following a laboratory SOP based on U.S. EPA
Methods 160.2 and 160.4. Analysis was conducted by Applied Marine Sciences (League City,
TX) (2006, 2007, 2008) and by Columbia Analytical Services (Kelso, WA) (2009, 2010, 2011)
following AMS SOP-2306 for TSS and VSS and CAS SOP- SM 2540 D for TSS. Water
samples were filtered through a weighed GFF, and the residue retained on the filter was dried to
a constant weight and weighed. The increase in weight represented the TSS. The filter and TSS
were then combusted and cooled several times until a constant weight was obtained. The weight
loss after this process represented the VSS fraction. All results were reported in mg/L.
Turbidity. A turbidity meter was used to measure turbidity in the water samples. Readings, in
NTUs, are based on a comparison of the intensity of light scattered by a sample under defined
conditions against the intensity of light scattered by a standard reference solution (U.S. EPA,
2007).
Particle Size Distribution. Particle size distributions were determined using laser diffraction
analysis in the ICI laboratory (Santa Cruz, CA) using Sediment Grain Size SOP Rev. 1.2.
Sediment samples were dispersed in water and inserted into a Beckman Coulter LS 13-320 laser
diffraction particle analyzer. Each sample was analyzed in three 1-minute intervals and the
results of the three analyses were averaged. The 13 -320 laser diffraction particle analyzer
adheres to ISO 13320-1 1999-11-01 (Particle Size Analysis - Laser diffraction methods).
2.8.3 Chemical Analysis of Tissue Samples
PCB Homologs, PCB Congeners, andPAHs in Tissues. Tissue samples including fish and
macrobenthos were analyzed for PCB homologs, PCB congeners, and PAHs by Battelle
(Duxbury, MA). All methods are described in detail in the QAPPs for each phase of the research
(U.S. EPA, 2006, 2007). Prior to extraction, tissue samples were homogenized using a stainless
steel tissuemizer (for smaller macrobenthos samples) and a meat grinder (for larger fish
samples); larger volume samples were cut into smaller pieces prior to grinding. Homogenized
tissue samples were extracted and analyzed for PCBs following the relevant QAPPs (U.S. EPA,
2006, 2007). One-half of the extract was analyzed for PCB homologs using GC/MS in the SIM
mode, and the other half was analyzed for PAHs using a separate GC/MS in the SIM mode. The
PAH analysis is based on SW846 Method 8270C. The PCB homolog and PCB congener
analyses were based on U.S. EPA Method 1668A and SW846 Method 8270C. The calibration
used to quantify the PCB homologs utilizes the first and last congeners of each level of
chlorination. The calibration also consists of approximately 140 individual PCB congeners,
which were calibrated at the same time as the LOC congeners. This allows the ID and
quantification of individual congeners in the sample in case the reexamination of the data was
requested in the future. All results were reported in |_ig/kg wet weight.
35
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Percent Moisture in Tissues. Percent moisture in the tissue samples was determined
gravimetrically by Battelle (Duxbury, MA) following EPA QAPP where the difference between
the wet and dry weights determines the percent moisture. All results were reported as percent
moisture.
Percent Lipids in Tissues. Percent lipids (as total extractable organics) in the tissue samples was
determined gravimetrically by Battelle (Duxbury, MA) following SOP BDO-5-190, where the
percent lipid is determined by a gravimetric analysis of extract residue after the solvent has been
evaporated. All results were reported as percent lipids on a wet weight basis.
2.8.4 Chemical Analysis of Passive Samplers
PCB Homolog and PCB Congener Analyses of SPMDs. All SPMD samples were extracted by
EST (St. Joseph, MO) that holds a patent on the extraction (dialysis) process. Prior to extraction,
EST recorded the length, width, and weight of all five SPMD ribbons per sampler. The extract
was analyzed at Battelle (Duxbury, MA) following EPA QAPP for PCB homologs or PCB
congeners using GC/MS in the SIM mode, which was based on SW846 Method 8270C and U.S.
EPA Method 1668A. The calibration used to quantify the PCB homologs utilizes the first and
last congener of each LOC. The calibration also consists of approximately 140 individual PCB
congeners, which were calibrated at the same time as the LOC congeners. This allows the ID
and quantification of individual congeners in the sample in case the reexamination of the data
was requested in the future. All results were reported in ng/SPMD. The PRCs were quantified
as target PCB congeners, in total |ig. This value was compared with the amount initially added
to the SPMDs to calculate a percent recovery. Surrogate standards were added to the SPMD
samples by EST prior to dialysis (extraction). Battelle provided the surrogate solution and
directed EST regarding spiking amounts for each sample. Additional QC samples (method
blanks and blank spikes) were prepared and extracted at EST, and the QC and sample extracts
were shipped to Battelle for sample clean-up, concentration, and analysis.
PCB Homolog and PCB Congener Analyses of SPMEs. The extraction and analysis of SPME
samples were conducted at Battelle (Duxbury, MA). The extract was analyzed for PCB
homologs using GC/MS in the SIM mode, which is based on SW846 Method 8270C. The PCB
homolog calibration and quantification procedure was based on Method 1668A. The calibration
used to quantify the PCB homologs utilizes the first and last congeners of each level of
chlorination. The calibration also consists of approximately 140 individual PCB congeners,
which were calibrated at the same time as the LOC congeners. This allowed the ID and
quantification of individual congeners in the sample in case the reexamination of the data was
requested in the future. All results were reported in ng/SPME.
2.9 Data Management and Data Evaluation
Calculation of Total PCBs and Total PAHs. Total PCBs were determined by the sum of
approximately 140 individual PCB congeners, henceforth referred to as tPCB(Zc). Non-detected
values were included at one-half the method detection limit for summing. Similarly, PCB
homologs for the 10 LOCs were determined by summing the individual congeners within each
LOC. For statistical analyses of total PCBs using principal component analyses (PCAs),
36
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however, non-detected individual congeners were considered to be zero. Additional screening of
data used for PCA was performed to reduce outliers and uncertainty (Battelle, GeoChem Metrix,
U.S. Navy SPAWAR, and U.S. EPA ORD, 2012).
Total PAHs were calculated as either the sum of the 16 priority pollutant PAHs or as total PAHs
summing both the priority pollutant PAHs and the alkylated PAHs. All non-detects were
considered as one-half the method detection limit for summing purposes. Total PAHs calculated
as a sum of the 16 PAHs are henceforth referred to as tPAH16; total PAHs calculated as a sum of
the 34 PAHs are henceforth referred to as tPAH34.
Statistical Comparison Analyses. To look for significant change over time (deployment year)
and space (deployment location) for macrobenthos, SPMDs, and their co-located sediments and
waters, the following analysis of variance (ANOVA) model was fitted to the naive average
response for individual areas and years by the ANOVA procedure in STATA MP version 13.0
(http ://www. stata.com/features/):
Yy = n + Areai + Yearj + % (Equation 2-1)
where Y,:, is the observed average response for the /th year at the z'th area, [j, is an overall constant,
and 8y are the random error terms, assumed to be distributed as Normal with mean 0 and variance
a2.
All significant model effects were noted, along with the estimated model r-square and overall
model variance expressed as mean square error (MSE). Residuals were examined for
homoscedasticity and normality and the response data appropriately transformed if indicated.
For each of these models, the following data were tabulated:
• The correlation coefficient (r2) for the model - the degree to which this model
explains the overall variability seen in the data
• The mean square error (MSE) - the remaining variation unexplained by the model
• The p-values for whether fixed effects area and year were significantly different from
zero.
When a model effect was determined to be significant, the model was used to create least square
mean estimates for each effect level, along with 95% confidence intervals. Pairwise
comparisons were calculated using bonferroni-adjusted p-values to assess the relationships
among levels.
Least square means were used in the ANOVA analyses that are presented for all statistical
comparisons. In a statistical design with two factors, the least square means for one factor are
the means for that factor averaged across all levels of the other factor. For example, when data
were collected for each location (Turning Basin, Fields Brook, and River Bend) by year (2006,
2007, 2008, 2009, 2010, and 2011), the least square means for location would find the mean for
each location, say Turning Basin, regardless of the year in which the data were collected. The
least square means for the other locations would be found in a similar way. Results of ANOVA
analyses are provided in Section 4.0.
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Principal Component Analyses (PCA). PCA was used to assess how multiple sampling
approaches and methodologies compare measuring PCB congeners and changes in congener
composition over time. PCA was used to compare PCB congener compositions in macrobenthos
samples and SPMDs, as well as their co-located sediment and water samples. PCA was also
used to examine the composition of PCBs in indigenous fish collected throughout the study area
in multiple years. A total of 79 PCB congeners were used (out of a possible 140 congeners in the
analytical method) in the PCA. The 79 congeners used in the PCA analysis were selected
because they were consistently detected across samples.
PCA determines a sequence of orthogonal linear combinations of variates that achieve maximum
variance of each linear combination. The principle components are ordered in the sequence from
greatest variance to least. The methodology is useful for reducing the dimensions of multivariate
data to conceptual dimensions that are useful for separating observations and identifying clusters
of similar observations. Clusters of observations indicate common combinations of attributes
that may be of predictive or diagnostic value. For example, it would be informative to know
how macroinvertebrate, sediment, and water samples are differentiated in the first principle
components and whether certain matrices cluster more similarly with any of the Aroclor
references.
PCA is sometimes performed directly on the observed values and sometimes on the standardized
values. The standardized values for each variate are computed by subtracting the variate mean
and dividing by the variate standard deviation. Among the observations, many low congener
concentrations and occasionally 'spikes' in concentration occur. The maximum values across the
congener concentrations differ by as much as two orders of magnitude. Performing PCA on
scaled observations is commonly used when the variates measure attributes that are on scales that
have no common units. Although the congeners have common units of concentration,
performing the PCA on the unsealed observations results in certain congeners having
substantially more influence in the PCA results due to differences in the magnitudes of spikes
across the congeners. This results in all but a few isolated observations being crowded at one or
the other end of the first and/or second principle component axes. For this reason, the PCA
analysis was carried out on the scaled variates, providing more meaningful separation between
the majority of the observations on plots of the first two principle components.
The PCA analysis was accomplished using the prcomp function in R. PCA was conducted on
observations for each matrix separately and also on the combined observations. In all cases, the
observations from the reference location (Conneaut Creek) and the Aroclor samples were
omitted in determination of the principle components. The loadings (the linear coefficients) for
the first and second principle components of each analysis were applied to the congener values
of each sample to determine the sample's coordinates on the plots of the first two principle
components. The coordinates for the reference location observations and the Aroclor samples
were similarly determined. The observations are indicated in the plots with the concatenation of
the two-character abbreviation of the location and the two-digit abbreviation (i.e., UP06) of the
year of observation. The results of the PCA analyses performed by matrix are color-coded by
location.
38
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Area
Symbol
Color
Upstream
UPxx
Purple
Fields Brook
FBxx
Blue
Turning Basin
TBxx
Yellow
River Bend
RBxx
Red
Conneaut Creek
Reference
RFxx
Green
For the PCA performed on the observations combined across the matrices, the observations are
color-coded by matrix (macroinvertebrate indicated by yellow; sediment indicated by green;
water indicated by blue). Aroclor samples are indicated with the letter 'A' concatenated with the
four-digit code identifying the Aroclor.
PCA was also conducted on the 59 fish tissue observations on the same 79 PCB congeners. The
observations were labeled by number and two-digit year, separated by a period (e.g., 01.06).
Results were color-coded by year:
Year
Color
2006
Magenta
2007
Grey
2008
Blue
2009
Purple
2010
Green
2011
Yellow
PCA results for sediment, macrobenthos, SPMDs, and fish are provided in Section 4.0.
2.10 Quality Assurance/Quality Control
This multi disciplinary research project was a collaborative effort of the U.S. EPA ORD national
research laboratories NRMRL and NERL, in coordination with their U.S. EPA program office
partner GLNPO. Each organization had project objectives specific to their mission. Organizing
this research effort required the coordination of the multiple U.S. EPA entities over a multiyear
period.
The U.S. EPA quality system is integral to this effort, providing policy and procedures that are
implemented in all aspects of the project to ensure that the data generated from each discipline
would be of a type and quality necessary and sufficient to achieve project objectives. The U.S.
EPA quality system encompasses management and technical activities related to the planning,
implementation, assessment, and improvement of environmental programs that involve:
39
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• the collection, evaluation, and use of environmental data
• the design, construction, and operation of environmental technology
Consistent with the requirements of the U.S. EPA quality system, the participating U.S. EPA
organizations have implemented Quality Management Plans to define the specific processes and
procedures that each U.S. EPA organization uses to ensure implementation of the U.S. EPA
quality system. The following QA tools were implemented during the project:
• A systematic planning approach was implemented to develop acceptance or
performance criteria for all work covered by the U.S. EPA quality system, defined in
the QAPP. A QAPP was developed and approved for use by Battelle and the U.S.
EPA quality staff for each project effort, before any data collection activities were
initiated in the field or laboratory. QAPPs that were developed and implemented for
this project are identified in the relevant sections of this report and in the references
section.
• SOPs were implemented for all applicable field and laboratory activities to ensure
consistency in the collection of samples, operation of environmental technologies,
and generation of environmental data in the field and in the laboratory.
• Appropriate training was provided for staff to ensure that quality-related
responsibilities and requirements as defined in the QAPPs were understood, and that
SOPs were implemented for all applicable activities. This ensured that research
activities are conducted in a consistent and reproducible manner, with the intent that
the research data produced would meet project data quality objectives and/or
acceptance criteria for usability to achieve project objectives.
• Technical assessments (e.g., technical systems assessment, data quality audits) were
scheduled and performed by U.S. EPA and/or Battelle quality staff to verify that the
QAPP requirements and SOPs were implemented during the project. A technical
systems assessment was performed by Battelle as required by the QAPP developed
for Stage 1 of the project. The on-site field audit was conducted for the ORD
Ashtabula River study by a Battelle QA Officer. The audit assessed the compliance
of field sampling procedures with the QAPP and applicable SOPs. Activities
observed included the retrieval of SPMDs, collection of sediment samples, and QC
samples, collection of water quality data, and field documentation practices.
• Data were reviewed and verified by research staff after collection and audited by the
Battelle QA staff to ensure that the type, quantity, and quality were sufficient to reach
conclusions stated in this report and ultimately to achieve project objectives.
The data review process identified exceedances of acceptance criteria and applied appropriate
qualifiers to the data to indicate limitations to the data that could affect data usability and the
ability to reach conclusions with respect to project objectives. Limitations to the data are
identified in the relevant subsections of this report.
Furthermore, it is a requirement that all U.S. EPA quality system elements "flow down" to the
contractor support entities. U.S. EPA quality system specifications are incorporated into all
applicable U.S. EPA-funded agreements and are defined in 48CFR46. An important element of
40
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this system for contracted analytical services is certification by an independent accrediting
organization, such as the National Environmental Laboratory Accreditation Conference. This
certification ensures that data are collected according to standard procedures and methodologies
under a quality system that is equivalent to ANSI/ASQC E4, which is the basis of the U.S. EPA
quality system.
41
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3.0 RESULTS
3.1 Bathymetry
Bathymetric surveys were conducted before and after dredging in 2007 and following dredging
in 2009 and 2011. The 2007 pre- and post-dredge bathymetry results were reported previously
(U.S. EPA, 2010). This section provides a summary of the bathymetric change from 2009 to
2011 and the overall change after dredging as of the 2011 survey.
Each bathymetric survey covered the extent of the GLLA dredge project area; however,
consistent with U.S. EPA report (2010), a greater interpretative focus has been placed on the
River Run where the 30 transect cores were collected and analyzed in 2006, 2007, and 2011.
Bathymetric data for the other parts of the river are provided in Appendix B. Battelle's
contractor, SEI/ICI conducted the 2009 survey (as well as the 2006 pre-dredge and 2007 first
post-dredge survey discussed in U.S. EPA [2010]). The 2011 bathymetric survey was conducted
by USACE, and the data were provided to Battelle. Note that in all of the following figures the
horizontal datum is NAD83 and the vertical datum is IGLD85.
The pre-dredge bathymetry shown in Figure 3-1 indicates shallow water and increased sediment
thickness along the eastern bank of river between T181 and T176. This area corresponded with the
highest pre-dredge PCB concentrations observed at approximately 3 m below the pre-dredge
sediment surface. The water column depth ranged from approximately 0.9 to 3 m deep in the
extended study area. A narrow channel was evident running from upstream at T181 to the
downstream extent of the study area at T170. Figure 3-1 also shows the extent of dredging on the
east bank just south of T181 that commenced prior to the first bathymetric recording. Sediment had
been dredged to a depth of approximately 6 to 7 m below the water surface (IGLD85).
The post-dredge bathymetric difference maps are shown in Figures 3-2 and 3-3 for the 2009 to
2007 and 2011 to 2007 differences, respectively. Dredging in the River Run was completed on
approximately June 18, 2007. The bathymetric differences between post-dredge years 2007 and
2009 are shown in Figure 3-2. The differences between post-dredge years 2007 and 2011 are
provided in Figure 3-3.
The post-dredge sediment surface was measured to be between 6 to 7 m below the Lake Erie
datum of IGLD85 in most locations. The target dredge depth (>6 m or bedrock elevation) was
achieved within the ORD study area. It is understood when interpreting these bathymetric data
that the timing of such electronic surveys plays an important role in defining what is being
measured. As the unconsolidated sediment is becoming more consolidated over time, it is
expected that the sediment surface elevation may change. Also, the unconsolidated sediment
may be more susceptible to scour or erosional events. It is realized that additional research will
be needed to identify optimal timing for collecting these data with specific consideration given to
site-specific conditions.
Bathymetric differences between 2007 and 2009 and between 2009 and 2011 were used to develop
sedimentation rates (Table 3.1) at sample core locations on the 10 transects of the River Run.
42
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T171-A
* T172-A
T172
Meter;
T173-A
T173-B
• T174-A
T174-B
T175-A
T175-I!
T176-A
T176-B
T177/A
T177-B
T178-A
T178-B
T178-C
T179-A
T179-B
T180-B
T181-A
T180-C
T181-B
1180-1)
T1811)
T172-B
T173-A
T173-B
• T174-A
T174-B
T175-A
T175-B
T176-A
T176-B
T177*A
T177-B
T178-A
T178-B
T178-C
T179-A
T179-B
T180-B
T180-C
1180-1)
I 181-1)
as surveyed on May 16-18, 2007.
Results are valid for only this date and time,
depths are referenced to the Lake Erie
Water Datum of 1GLD1985 (which is 173.5 meters).
Pre-Dredge Survey Bathymetry (m)
Pre-Dredge Core Locations
Greater than 6 in
5 m to 6 m
4 m to 5 m
3 m to 4 m
2 m to 3 m
1 m to 2m
Less than 1 m
—i—
80 48'0-W
Note: Horizontal (latitude/longitude) datum is NAD83, and vertical datum (elevation) is IGLD85.
Figure 3-1. Pre-Dredge Bathvinetric Survey.
43
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T170B
T171A
T171B
Meter?
T172A
T172B
T173A.
T173B
T174AJ174B
T175A
T175B
T176A
T177A
T177B
T178A
T178B
T178C
T179A
I t
' TJ79B
T180A
T180B
T181A
T180C
T181B
T180I)
TtfllC
T181D /
OVHD CAB
' GL 131 FT REP
B&SCULE BRIDGE
HOR CL 112 FT'
VERT CL 11 FT
Riverside V.-'jht CLb
«o-4rsrw
80'4eirw
) 4OT"W
J
8C47-H5-W
-n—*-
Bathymetric Difference (m)
2009 • 2007 Surveys
2007 Sediment Cores [ J 0.0 m to 0.5 m
1 m Contours I | 0.5 m to 1.0 m
More than 2.5 ni Erosion ~ 1.0 m to 1.5 m
-2.5 m to -2.0 m ~ 1.5 m to 2.0 m
-2.0 m to -1.5 m ~ 2.0 m to 2.5 m
-1.5 m to -1.0 m | | 2.5 m to 3.0 ni
-1.0 m to -0.5 m More than 3.0 m Deposition
-0.5 rn to 0.0 in
l- ¦ -• ¦} ,¦ > ML
Figure 3-2. Bathymetric Differences in meters between 2007 and 2009 for the ORI) Study
Area of the Ashtabula River Showing Sediment Coring Locations.
44
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Bathymetric Difference (m)
2011 - 2007 Surveys
2011 Sediment Cores 0.0 m to 0.5 m
1 m Contours 0.5 m to 1.0 m
More than 2.5 m Erosion 1.0 m to 1.5 m
T170-A
More than 3.0 m Deposition
Meter:
T173-B
T174A.
T175-A
T178-A
T178-B.
T180-
T179C
T180 B
B1 B T180-D
TJ81-C jflBbD-Dup'
OVHO CAB
|GL 131 FT REP
BASCULE BRIDGE
HOR CL 112 ft
VERT CL 11 FT
Rivprsidp Vacht Cl-b
0O'47'5O"W
T180:
Figure 3-3. Bathymetric Differences in meters between 2007 and 2011 for the ORD Study
Area of the Ashtabula River Showing Sediment Coring Locations.
45
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Table 3.1: Sedimentation Rates at Sample Core Locations.
2011 Core
Locations
Total Sedimentation
since 2007 (cm)
2009 2011
Annual
Sedimentation
(cm/yr)
2009 2011
Avg Annual Sed
(cm/yr)
2007-2011
T170-A
52.30 153.02
26.15 38.26
32.20
T170-B
28.46 121.31
14.23 30.33
22.28
T171-A
38.25 173.81
19.12 43.45
31.29
T171-B
42.31 136.15
21.16 34.04
27.60
T172-A
62.47 150.70
31.24 37.67
34.45
T172-A Dup
40.79 150.70
20.40 37.67
29.03
T172-B
40.83 138.96
20.42 34.74
27.58
T173-A
42.17 159.69
21.09 39.92
30.50
T173-B
37.97 149.03
18.99 37.26
28.12
T174-A
33.44 131.89
16.72 32.97
24.85
T174-B
38.03 129.37
19.01 32.34
25.68
T175-A
11.04 94.55
5.52 23.64
14.58
T175-B
68.07 149.61
34.04 37.40
35.72
T176-A
14.84 57.04
7.42 14.26
10.84
T176-B
68.22 170.55
34.11 42.64
38.37
T177-B
89.73 147.92
44.86 36.98
40.92
T178-A
8.89 17.06
4.45 4.27
4.36
T178-B
29.17 59.43
14.59 14.86
14.72
T178-C
124.21 210.43
62.11 52.61
57.36
T179-B
68.10 201.85
34.05 50.46
42.26
T179-B Dup
68.10 186.10
34.05 46.52
40.29
T179-C
165.02 222.33
82.51 55.58
69.05
T180-A
15.16 n/a
7.58 n/a
7.58
T180-A Dup
15.16 n/a
7.58 n/a
7.58
T180-B
34.88 208.63
17.44 52.16
34.80
T180-C
124.76 246.65
62.38 61.66
62.02
T180-D
91.01 247.45
45.50 61.86
53.68
T180-D Dup
91.01 247.45
45.50 61.86
53.68
T181-A
22.44 43.67
11.22 10.92
11.07
T181-B
12.81 193.22
6.41 48.30
27.36
T181-C
81.94 196.60
40.97 49.15
45.06
T181-D
92.48 197.61
46.24 49.40
47.82
T181-D Dup
92.48 197.61
46.24 49.40
47.82
While hydrodynamic measures were not included in this investigation, there are areas of
sediment deposition in 2009 and 2011 that are consistent with pre-dredge survey information
46
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(Figure 3-1). Transects 181 to 176, toward the east side of the Ashtabula River continue to be
highly depositional area.
3.2 Resuspension Survey during Dredging
The resuspension study implemented during dredging operations was designed to evaluate the
effectiveness of the individual methods used to characterize the short-term suspension and fate of
sediment during dredging. This section describes the results of these methods to estimate the
volume and concentrations of suspended sediments over time, and to estimate the PCB
concentrations and PCB mass associated with the suspended sediments over time, as well as to
estimate the mass the resuspended sediment and associated PCB compounds contributed to the
residuals in the Ashtabula River.
As described in Table 1.1, a series of electronic data surveys were conducted and water samples
were collected simultaneously from various depths immediately up and downriver of active
dredging using Battelle's MDWS to determine sediment resuspension and settling during
dredging operations. An ADCP was used simultaneously to record flow dynamics of the system
and to identify resuspended sediment plumes. Stationary and mobile OBSs were also deployed
up and downriver of dredging operations to monitor for the existence of turbidity plumes created
by sediment resuspension during dredging. Additional details of the during-dredging water
column survey are provided in Appendix C and the QAPP (U.S. EPA, 2007).
3.2.1 Plume Tracking
A variety of TSS measurements were collected during dredging in an effort to quantitate the
temporal and spatial distribution of TSS so that resuspension and the total volume of the dredge
plume could be estimated. These measures consisted of up-looking optical turbidity probes
stationed upstream and downstream of dredge activity and vessel-mounted optical turbidity
probes positioned at various depths on Battelle's MDWS. ADCP units were co-located with the
same stationary and vessel-mounted turbidity units. Each were positioned to measure optical
backscatter while executing transect runs above and below the active dredge. Additionally, a
LISST unit was deployed at discrete points co-located with specific MDWS water sample
collection points.
Optical Turbidity Probes. Optical turbidity sensors (YSI 6-series sondes) were deployed on
fixed moorings between May 19 and June 9, 2007, and again between July 22-25, 2007 (Figure
3-4) (see SEI, 2007 [Appendix A]). Two sets of turbidity sensors were deployed at upstream
(south) and downstream (north) locations (Figure 3-5) 1 m below the surface and 1 m above the
bottom. Initially, the upstream mooring array was positioned 200-250 m south of the active
dredge zone, and the downstream mooring was located 500-600 mnorth of this zone. As the
dredge advanced downstream (north), the upstream mooring position remained stationary, while
the downstream monitoring equipment was repositioned northward, as needed, to remain at least
150 m downstream of the dredge.
Each turbidity probe was calibrated to provide measurements of turbidity in standard NTUs,
which required further calibration to correlate to TSS concentrations. Turbidity measurements
47
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were directly compared to TSS derived from water samples collected at the mooring locations at
water depths consistent with optical turbidity probe mounting depths on May 19 and 31, June 4-5
and 7-10, and July 22-24, 2007.
60 Flash/Min Red Blinking Light
Sub Surface Marker Float
Turbidity Probe
Surface Marker Float
Pick Up Buoy
Turbidity Probe
ADCP Current Meter
Recovery Drag Line
Clump Weights
Figure 3-4. Schematic Depicting the Stationary Turbidity Probe and ADCP Upstream and
Downstream of Dredging Activities.
Concurrently collected NTU and TSS data were filtered for outliers by using 20-bin histograms
to determine NTU and TSS frequency of occurrences (Figure 3-6) (Emery and Thomson, 1997).
All negative values and values with a frequency of occurrence less than 5 were removed from the
dataset. Least-square linear regression with forced zero intercept was then performed for NTU
vs. TSS. The resulting best fit slope was 0.83. The correlation coefficient, r2, was 0.69 for data
points within 1.25 standard deviations of the best fit line (Figure 3-6; in red) and r2 = 0.17 for all
filtered data points (not shown).
Time series of TSS were estimated from measured turbidity following:
Common to long deployments of optical sensors in productive waters, the optical turbidity data
suffered from biofouling. Hence, time series of turbidity-derived TSS were manually filtered to
remove periods when data indicated that the optical sensor was obstructed. Data were also
corrected for sensor calibration differences caused by different optical responses between the
calibration standard and the in situ sediments by assuming minimum values of TSSturb ih of 5
mg/L and 10 mg/L for near-surface data and near-bottom data, respectively. These values were
based on near-bottom and near-surface TSS minimums determined by ADCP ABS.
TSS n Rij.m = 0.83 * NTU
(Equation 3-1)
48
-------
II
Uu tn» VmHmvrXKS IJV
Dredging Reg.on
Monitormg Station Locations
Adi^*Fiw_
'«r>: il Sun* y Ccord
Progressive Survey <6/10/2007)
Bathymetry (m)
Figure 3-5. Dredging Region on the Ashtabula River and Fixed Monitoring Station
Locations.
Acoustic Backscatter - Fixed Stations. Acoustic backscatter was measured using ADCPs
(Teledyne RD Instruments 1200 kHz Workhorse Sentinel [Poway, CA]) deployed in two
different locations (Figure 3-5), co-located with the optical turbidity sensors: upstream (south)
and downstream (north) (SFX 2007). The ADCPs were bottom-mounted, up-looking (Figures
3-4 and 3-7) to provide high temporal and vertical resolution current information as well as echo
intensity (EI), which was used to compute ABS for direct correlation to TSS. Computations of
ABS were made following ICI's internal processing techniques, which are based on acoustical
theory (Shulkin and Marsh, 1962; Thorne et al., 1991; Gartner, 2004). Briefly, EI, measured in
counts, was converted to EI in decibels using factory provided instrument and beam specific
scale factors. The beam spread correction (BSC) was then computed. BSC is the two-way
transmission loss due to beam spreading and is related to the slant distance to the source of the
return echo and a transducer near-field correction that accounts for non-spherical spreading of
acoustic energy close to the transducer (Downing et al., 1995). The acoustic absorption of water
49
-------
(WA) was calculated (Shulkin and Marsh, 1962) using ADCP measured temperature values and
the freshwater assumption (salinity equal to zero). WA is related to the hydrographic properties
of the water column and the slant distance to the source of the return echo. ABS was then
computed following:
ABS = 10 logio(EI) -I- BS + WA
(Equation 3-2)
30
>»
o
C
a)
3
a*
15
o
c
a)
3 10
a*
a)
L.
u- 5
0 100 200
Turbidity (NTU)
35
300
50 100
TSS (mg/L)
150
W 15
5 10 15 20 25 30 35
Turbidity (NTU)
Figure 3-6. Histograms to Determine Frequency of Occurrence for Turbidity (A), and TSS (B).
(The outlier filtering criteria [values less than 0 or whose frequency of occurrence is less than 5]
are indicated with red lines. Linear relationship between optical turbidity and TSS (C). The best
fit linear regression and 1.25 standard deviations of the best fit line are shown in blue.)
Resulting depth-resolved time series of ABS were correlated to TSS derived from water samples
collected at the mooring sites on May 19 and 31. June 4-5 and 7-10, and July 22-24, 2007. A
log-linear relationship was developed with co-located, concurrent ABS and TSS data (Figure 3-
8). The least square log-linear regression fit was sati sfactory with no data filtering performed.
The resulting slope (m = 0.035) and intercept (b = -1.4) values are comparable to those obtained
in other aquatic systems (riverine, estuarine, and coastal). The correlation coefficient, r2, for data
within 1.25 standard deviations of the best fit line was 0.64; r2 = 0.31 for the full data set.
50
-------
Figure 3-7. Up-looking Acoustic Doppler Current Profiler (ADCP) (Teledyne RD Instruments
1200 kHz Workhorse Sentinel ADCP [Poway, CA|) on Bottom-Mount Platform for Measuring
TSS.
2. i
1.
c/T
-------
Upstream (South) Mooring
19-May 25-May 31-May 07-Jun 13-Jun 20-Jun 26-Jun 03-Jul
Date (2007)
100
~"
O)
E,
^ 50
c
2
CO
I-
0
19-May 25-May 31-May 07-Jun 13-Jun 20-Jun 26-Jun 03-Jul
Date (2007)
Figure 3-9. A: Depth-Resolved Time Series of TSSabs.™ Derived from ABS Computed from
Echo Intensity Measured by the Upstream (South) Bottom-Mounted ADCP.
(The y-axis represents distance above transducer [meters], B: TSSabsih derived from ABS
measured at bin 1, or nearest to the bottom [approximately 1 m above bottom]).
Acoustic Backscatter - Mobile Measurements. ABS was also computed from vessel-mounted
ADCP transect data for comparison to TSS derived from optical turbidity sensors that were mounted
on the Battelle MOWS. Vessel-mounted ADCP data were first gridded to a 3-m horizontal grid
spacing and a 0.3-m vertical grid spacing to match the cell sizes of the TSS data derived from the
optical turbidity sensors mounted on the MOWS (TSSturb.mdws). ABS data from the vessel-
mounted ADCP were then computed from the gridded EI data, and TSS from the vessel-mounted
ADCP (TSSabs.v) was estimated for each transect using the log-linear relationship obtained from
moored ADCP data:
TSSabs.v = 10(0 035 *ABS~ L4) (Equation 3-4)
The least square linear regression correlation coefficient between TSSabs.v and TSSturb mdws was at
times excellent (> 0.9) and at times poor (< 0.25). Poor relationships were generally found during
periods of high frequency current direction shifts associated with Lake Erie seiche effects (SEI,
2007), which resulted in noisy ADCP EI signals (Figure 3-12; A and B). Excellent relationships
were generally found during periods associated with constant flow direction (Figure 3-12; C and D).
These results indicated that the use of boat-mounted ADCPs is suitable for aquatic systems with
constant flow directions or lower frequency current direction shifts (e.g., river or tidal estuary) and is
52
-------
an excellent method with which to obtain co-located TSS and current information with high
temporal and spatial resolution.
Downstream (North) Mooring
a 1.5
19-May 25-May 31-May 07-Jun 13-Jun 20-Jun 26-Jun 03-Jul
Date (2007)
100
50
-------
(A)
Distance (m)
IS)
o cn o
©
-A
D
Q
to
"O
5
CO
3
cn
cn -»¦
©
to
o
ro
cn
w
cn
(B)
tssturb.mdws
-------
pp (g/cm3) = m * LISSTvc + b
(Equation 3-5)
where m and b are the slope and intercept of the best-fit linear regression line between LISSTvc
(|iL/L) and TSSturb.mdws (mg/L).
Although the correlation coefficients of the best fit between LISSTvc and TSSturb.mdws at times
exceeded 0.9 (median r2 for all LISST profiles was 0.6; Figure 3-13), the LISST method to
derive TSS was determined to be infeasible for this project. Resulting bulk particle density
values (i.e., the slope of the best fit lines) ranged between 0.14 and 0.83 g/cm3, which were
extremely low. The bulk density of inorganic particles is typically 2.65 g/cm3. The primary
shortfall of using the LISST to derive TSS for this project was the assumption that all particles
are in the size range as measured by the LISST, type B (between 1.4 and 231.0 |im) and that the
bulk density of particles was constant with depth. Other limitations included:
• Indeterminate sampling locations of the LISST profile relative to the TSS transect
• Dissimilar sampling times of the LISST profile and MDWS transect.
Methods and Metrics for Identifying the Plume. The data used for the development of methods
and metrics for identifying the dredge plume were TSS transects derived from optical turbidity
data collected using the Battelle MDWS system (TSSturb.mdws) and depth-resolved time series
of TSS estimated from the upstream (south) mooring ADCP data (TSSabs ih). Subsequent
transects collected while progressing toward and away from the dredge operation area, hereafter
referred to as progressive transects, were evaluated for plume signatures following the methods
described below. The data ranged from sets of three to 10 transects collected from greater than
1000 m upstream to greater than 1000 m downstream of the dredge. Progressive transects were
collected between May 31 and June 2 and between and June 4 and 10, 2007.
The first step necessary for identifying the dredge plume was to determine background TSS and
subtract it from TSS collected during progressive transects, where:
TSSpiume = TSSturb.mdws - TSSback. TSSback (Equation 3-6)
for each sampling day was assumed to be equal to the minimum values of TSSabs iti at each bin
depth of the upstream (south) mooring ADCP recorded for each particular sampling day. In this
manner, TSSback was allowed to vary with the environmental conditions of the Ashtabula River.
The upstream (south) mooring was chosen because its water depth was greater than that at the
downstream (north) mooring; therefore, it provided TSSback for a larger portion of the water
column. The background TSS profile was interpolated to the MDWS measurement depths
(between surface and 6 m with a grid spacing of 0.3 m) and subtracted from TSSturb.mdws at
each vertical and horizontal grid cell. Negative values of TSSpiume were not allowed, i.e., TSSback
was set equal to TSSturb.mdws when TSSback was found to be greater than TSSturb.mdws. The
major limitation in this method was that TSSback was assumed to be constant across-channel.
Examples of TSSpiume are shown in Figures 3-14 and 3-15 (more volumetric plots can be found in
Appendix A).
55
-------
Following plume identification, it was necessary to distinguish the dredge plume, TSSdredge, from
elevated TSS levels from other sources, i.e., Lake Erie or Ashtabula River flow. TSSdredge was
determined for each set of progressive transects by computing the along-shore gradient of
TSSpiume, where. TSSdredge A1SSpiume/Ax, ATSSpiume is the change in JTSSpiume from one
progressive transect to the next for each grid cell and Ax is the along-channel distance from the
center point of the dredge operating area to the location of the TSSturb.mdws transect. Transect
distances upstream (south) of the dredge operating area were negative, and transect distances
downstream (north) of the dredge operating area were positive (as shown in Figures 3-14 and 3-
15); therefore, Ax was always positive. A transect grid was created at the origin (i.e., the dredge
site, where x = 0); TSSorigin was assumed to be equal to the maximum value of TSS collected
over all TSSturb.mdws (-140 mg/L).
Figure 3-13. LISST Measured Total Volume Concentration vs. TSS as Measured by the
Optical Turbidity Sensors Mounted on the MDWS for LISST Profiles Corresponding to
MDWS Transects.
(The best fit lines are shown for multiple measurements indicated by varied colors.)
A negative value of TSSdredge upstream of the dredge indicated that particular value of TSSpiume
originated from sources other than the dredge, i.e., Ashtabula River flow. Similarly, a positive
value of TSSdredge downstream of the dredge area indicated elevated TSS originating from Lake
Erie. In order to map only TSS originating from the dredge, TSS values associated with tion-
dredge related processes were set equal to zero.
Maps of Plume Extent during Identifiable Dredging Events. Dredge plume strength was
computed from TSS values determined to originate from the dredge (non-zero values of
TSSdredge). All positive values of TSSdredge upstream and the absolute value of all negative values
120
J 100
°0 50 100 150 200 250
LISSTVC UiUL)
56
-------
of TSSdredge downstream were normalized to the largest value of TSSdredge computed for all
transects in a particular set of progressive transects:
NPS = |TSSdredge|/ |TSSdredge_max| (Equation 3-7)
where NPS is "normalized plume strength". An NPS value of 1.0 indicated strong dredge plume
signature and an NPS of 0 indicated no dredge plume signature (Figures 3-16 through 3-19).
Lett\«8W
'"stream / Ups(rt
Dredge
distance H
Figure 3-14. Three Dimensional Volumetric Plot of TSSpluine Derived from
TSSTURB.MDWS Progressive Transects Collected on June 2, 2007.
(The cross-shore distance was 60 m, and the along-shore distance covered by the transects was
approximately 1200 m).
DTnSlr°am I "Pstream
Dredge
Along-shore distance (m)
Figure 3-15. Three Dimensional Volumetric Plot of TSSpluine Derived from
TSSTURB.MDWS Progressive Transects Collected on June 5, 2007.
(The along-shore distance covered by the transects was approximately 2000 m.)
57
-------
Ln
00
Figure 3-16. Normalized Plume Strength (NPS) as a Function of Cross-Channel Width and Water Depth Determined for
Progressive Transects Collected on May 31, 2007.
The Dredge Region and Transect Locations by Number are Indicated on the Map. (The transect number and downstream distance
from the dredge are indicated above each panel. Stronger dredge plume signatures are shown in red, and weaker signatures are shown
in blue; black indicates no dredge plume signature [NPS = 0], Note the different NPS scales in each panel.)
River
Bend
Distance (m)
Fields Brook
5th Street
Bridge
Distance (m)
Distance (m)
ISWA'LiBII KJIKI EiTiEKSl
fSMlUEKSg
BaBaggaasMMB L'Ei
#8; 69
Distance (m)
1 0
1
#13; 478 m
¦tf*1
WJ m
0.1
0.08
,2
E.
y
0 06 m
5 3
a
I *
0.04
I 5
0.02
1 6
d
¦
0
20 40 60
Distance (m)
EXPLANATION
TRANSECT LOCATION
DREDGING AREA BOUNDARY
-------
4- N
Downstream
Dredge
AI°ng-shore distance (m)
Figure 3-17. 3-D Volumetric Plot of NPS for the Transects shown in Figure 3-16.
Estimates of the volume of water affected by dredge activity were calculated following the
methods described here. Each transect in a set of progressive transects was evaluated for
significant plume signature, which was defined as a grid cell exhibiting NPS greater than or
equal to 0.1 or at least 10% of maximum TSSdredge. If no grid cells contained NPS values of at
least 0.13, the transect was not included in the calculations. Once all transects were evaluated,
the cross-sectional area of each transect identified to contain significant plume signature(s) was
computed. Cross-sectional areas were determined by: 1) calculating the width of each vertical
bin through summation of the number of cross-channel grid cells containing data and multiplying
by the horizontal grid cell spacing (3 m), 2) multiplying the width of each vertical bin by the
vertical grid cell spacing (0.3 m), and 3) summing all areas (Figure 3-20A). Each transect's
cross-sectional area was then multiplied by the along-channel distance between it and the next
transect identified to contain significant plume signature(s). The results were summed to
estimate the total volume of water affected by the dredge (Figure 3-21 A). This method assumes
that the channel width remained constant between two subsequent progressive transects.
3 The value of 0.1 was chosen to represent error in TSS estimates. It is based on cumulative experience in
estimating TSS from optical and acoustical backscatter. Note that this value of 0.1 is used as a minimum criterion to
estimate the volume of water affected by dredge activity and the total volume of the dredge plume based on
instantaneous transect data. It lias no bearing on cumulative mass.
59
-------
CT)
O
Figure 3-18. Normalized Plume Strength (NPS) as a Function of Cross-Channel Width and Water Depth Determined for
Progressive Transects Collected on June 4, 2007.
The Dredge Region and Transect Locations by Number are Indicated on the Map. (The transect number and downstream distance
from the dredge are indicated above each panel. Stronger dredge plume signatures are shown in red, and the weaker signatures are
shown in blue; black indicates no dredge plume signature [NPS = 0], Note the different NPS scales in each panel.)
0 40
Distance (m)
Distance (m)
Distance (m)
Distance (m)
Fields Brook
Distance (m)
5th Street
Bridge
Distance (m)
0 40
Distance (m)
EXPLANATION
TRANSECT LOCATION
DREDGING AREA BOUNDARY
-------
g
sz
Q.
5
Q
Figure 3-19.
39 rn * 0 3 m
42 m x 0 3 m
42mx03m
42 mx 0 3m
42mx03m
42 m x 0 3 m
39mx 0 3m
39mx03m
39mx03m
39m x 0 3 m
36mx03m
36 m x 03m
36mx03m
36 m x 0 3 m
33 m x 0 3m
33m x 03 m
33 m x 0 3 m
27 m x 0 3 m
18mx03m
207 9 m2
3mx0 3m
3 m x 0 3 m
3m x 0 3 m
3mx03m
12mx0 3m
15mx03m
11 7 m2
Figure 3-20. Example Computations for the Cross-Sectional Area of A): A Transect
Affected by the Dredge Plume; B): The Dredge Plume (cells containing significant plume
signature).
Similar computational procedures were followed in order to estimate the total volume of the
dredge plume (Figure 3-21B). However, instead of calculating the width of each vertical bin
through summation of the number of cross-channel grid cells containing data, the width of each
vertical bin was determined by summing the number of grid cells containing NPS values greater
than or equal to 0.1 (Figure 3-20B). Results indicate that the total volume of water affected by
the dredge plume varied between approximately 30 m3 and 130 rrr and the total volume of the
dredge plume varied between 20 m3 and 120 m3.
Dredge
4— N
Downstream and Upstream -t
0.7 jz
0.6
0.5 f=
0.4 =
Along-shore distance (m)
3-D Volumetric Plot of NPS for the Transects shown in Figure 3-18.
m
61
-------
Upstream
Figure 3-21. Estimates of the A) Total Volume of Water Affected by the Dredge; B) Total
Volume of the Dredge Plume.
(The upstream and downstream components of each volume estimate are also shown and
labeled.)
3.2.2 Resuspended Sediment Mass
The rates generated for TSS by dredge activity and the estimates of mass transported away from
the dredge operation were used to estimate resuspended mass at discrete time periods, as well as
totaled over the entire dredging activity.
Water Column Sediment Flux Calculations. Sediment fluxes were calculated using transect
data collected repeatedly in the same location at a set distance from dredging activity over a
sustained period of time, hereafter referred to as grouped transects. Ten sets of grouped transect
data were collected between June 7 and 9, 2007. These data ranged from sets of three to 14
transects collected over periods of time between 20 minutes to greater than 2 hours. The
locations of grouped transects were from more than 1000 m upstream to greater than 450 m
downstream of the dredge.
The current velocity from the vessel-mounted ADCP and TSS derived from optical turbidity
sensors mounted on the MDWS were used to calculate water column sediment fluxes. Sediment
flux was defined as follows:
F = Q * C, (Equation 3-8)
where F is flux in units of mass per time, Q is flow rate in units of volume per time, and C is
concentration in units of mass per volume. Therefore, it was necessary to compute Q using
transect ADCP data and multiply derived Q with TSS to derive sediment flux. To avoid making
assumptions about the direction of the boat traverse relative to the along-channel flow (i.e.,
eliminate the effects of boat crabbing) straight cross-channel transect lines normal to the river
banks were used in the computations. These straight transect lines were determined by taking
the average start and end points in each set of grouped transects and drawing a straight line
62
-------
between these two points. Unit vectors were calculated for the straight cross-channel line
(representing the transect line) and the line normal to the cross-channel line (representing the
flow line). This method assumed that on average, the boat's start and end points were
perpendicular to the channel.
The flow rate of each measurement grid cell was then determined as follows:
dQ = U * dA, (Equation 3-9)
where U is the along-channel current velocity (see Equation 3-12) and dA is the area of the grid
cell:
dA = dl * dz, (Equation 3-10)
where dl is the straight, cross-channel transect line and the vertical grid spacing is dz. The
vertical grid spacing was always 0.3 m and:
dl = Sx * dlx + Sy * dly, (Equation 3-11)
where Sx and Sy were the east and north components of the transect line unit vector and dlx and
dly were the east and north distances travelled by the boat as measured by the ADCP bottom
track system. Similarly, the unit vector normal to the cross-channel transect line was used to
compute the along-channel current velocity, U:
U = Nx * u + Ny * v, (Equation 3-12)
where Nx and Ny are east and north components of the flow line unit vector and u and v are the
east and north components of current velocity as measured by the ADCP. Again, because unit
vectors along and normal to a straight, cross-channel transect line were used, no assumptions
were made about the direction travelled by the boat relative to the along-channel flow direction,
and hence the effects of boat crabbing were eliminated.
The results for computations of dQ were validated by summing dQ over all grid cells to derive
total flow rate for each measured transect. The sign of the flow rate was then compared to the
velocity direction as measured by the North mooring ADCP. Positive flow rates represented
downstream flow, and upstream flow was represented by negative Q (i.e., a right-hand
coordinate system was used). With the exception of transects collected during or just after
periods of high frequency directional shifts caused by the Lake Erie seiche effect, the flow rate
directions were in agreement with current velocity directions recorded by the downstream (north)
mooring ADCP.
Following validation of flow direction, total Q for each transect was computed by summing dQ
of all cells along with the assumption that the values of dQ in cells affected by the near-surface
ADCP blanking distance (1.02 m) were equal to the values of dQ in the uppermost measured bin.
Sediment flux, F, was calculated by multiplying transect Q by the average value of TSSpiume
(TSSturb.mdws - TSSback) over all cells of each transect.
63
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Estimates of TSS Generated by Dredge at Multiple Time Periods. The sediment fluxes
determined for each transect in a group were integrated over the time period of grouped transect
collection to derive Total Mass of Sediment (kg) per group (and per time period of grouped
transect collection). This value was divided by the time passed during each particular grouped
transect collection (in hours) to derive Total Mass of Sediment per hour. This was repeated for
each of the 10 groups of transects.
In order to estimate the generation of TSS by the dredge, any values of Total Mass of Sediment
that were determined to point toward the dredge, i.e. negative values downstream of the dredge
and positive values upstream of the dredge, were assumed to be from factors other than dredging
(e.g., natural Ashtabula River flow or Lake Erie seiche) and set equal to zero. All other values
were determined to represent the total mass of dredge sediment per hour of dredging.
The absolute value of the total mass of dredge sediment per hour of dredge activity was plotted as
a function of distance from dredge, and a power-law fit was applied to the trend (Figure 3-22).
This enabled the prediction of sediment mass generated by the dredge as a function of distance
from the dredge.
150
L.
I
c
^ 100
«/>
<0
2
c 50
-------
shown in Figure 3-22. This empirically-based function can be used to estimate the generation of
TSS by the dredge. For example, at less than 25 m from the dredge, an average of 100 kg of
sediment was measured during an hour of dredging time. This implies that during 8 hours of
dredging, 800 kg of sediment were generated in the direct vicinity of the dredge operations. The
grand total of TSS generation during the entire dredging activity can similarly be estimated for
varying distances from the dredge. However, it should be noted that this does not necessarily
reflect what mass has left the project area. For example, material that is resettled and
subsequently dredged later is not quantified with the methodology outlined here.
Estimates of Generation of TSS by Dredge: Comparison with Analytic Methods. Predictions
of the generation of solids mass by the dredge were accomplished by using the cutterhead dredge
dimensional model presented by Hayes et al. (2000). The dimensional model was developed
using stepwise regression analysis to determine empirical relationships between resuspended
sediment data and cutterhead dredge operational and environmental variables. The following
procedures were followed to predict the rate of sediment resuspended by the dredge, g (units of
kg/hr):
The total surface area of the cutter, Ac, and the surface area of the cutter exposed during
dredging, Ae, were computed.
Ac = (7T2 Lc dc) / 4 (Equation 3-13)
Ae = QAc (Equati on 3 -14)
where Ac and Ae are in units of m2, Lc and dc are the length and diameter of the cutter (units of
meters), respectively, for a 0.3 m cutterhead dredge, and Q is the proportion of the cutter that is
exposed during dredging.
The rate of sediment resuspended by the dredge for port-to-starboard swings (gps; kg/hr) was
calculated.
gps = 1.3147 | Vs - araicl1'864 [Ae / (dcLc)]14143 (Equation 3-15)
The rate of sediment resuspended by the dredge for starboard-to-port swings (gsp; kg/hr) was
calculated.
gsp = 1.3147 | Vs + araicl1'864 [Ae / (dcLc)]14143 (Equation 3-16)
where Vs is the swing velocity at the tip of the cutter (m/s) and a is the cutter rotation speed
(rotations per second).
The rate of sediment resuspended by the dredge, g, was computed as the average of gps and gsp.
The operational and environmental variables used as input for the empirical model for a 12 in.
cutterhead are presented in Table 3.2 ("Input"). Sensitivity analysis for the variables were
investigated; upper and lower limits for the operational variables were determined following
Hayes and Wu (2001) (Table 3.2; "Sensitivity Analysis").
65
-------
Table 3.2: Operational and Environmental Variables Used as Input for the Empirical
Model to Determine the Rate of Sediment Resuspended by the Dredge.
Lc (m)
dc(m)
Q
Vs (m/s)
a (rps)
9 (kg/hr)
Input
0.8
1.0
0.5
0.3
0.3
24.8
Sensitivity
Analysis
0.8
1.0
0.35-0.65,
every 0.02
0.05-0.65,
every 0.05
0.1 -0.5,
every 0.033
0.16 (min) -
1015 (max)
The rate of sediment resuspended by the dredge determined by the empirical model of Hayes et
al. (2000) was 24.8 kg/hr. This value is more than four times less than the dredge sediment
resuspension rate determined from measurements (100 kg/hr at less than 25 m from the dredge).
There are several potential sources of discrepancy, namely measurement bias and unknown
operational variable information. Measurement bias could have been due to sediment that
remained suspended in the water column that was advected upstream and downstream by the
Lake Erie seiche effect. Thus, the sediment plume could have been evaluated repeatedly,
resulting in a bias toward higher measured suspended sediment loads.
The operational and environmental variables used as input to the empirical model were estimated
based on literature (Hayes et al., 2000; Hayes and Wu, 2001) and could have significant impact
on predictions of the rate of sediment resuspended by the dredge. The effects of input variables
were evaluated with sensitivity analysis (Figures 3-23 through 3-25). Results indicate that the
proportion of cutter surface area exposed to dredging, Q, had the strongest effect on resuspension
rate determinations. The rate of sediment resuspended by the dredge varied by nearly four orders
of magnitude, from 0.16 kg/hr to greater than 1000 kg/hr for Q varying by only ±0.15 of 0.5
(Hayes et al., 2000 suggested Q = 0.5) and all other variables set equal to those presented in
Table 3.2, "Input". Cutter tip swing speed, Vs, had the least effect on g; dredge resuspension
rate varied between 23 kg/hr and 32 kg/hr for swing velocities between 0.05 m/s and 0.65 m/s.
Dredge sediment resuspension rate was moderately affected by variations in cutter rotation
speed, a. Cutter rotation speed was varied between 6 rpm and 30 rpm (0.1 rps and 0.5 rps),
resulting in resuspension rates between 5 kg/hr and 61 kg/hr.
Results from sensitivity analysis indicate that the measured rate of dredge resuspension within 25
m of the dredge of approximately 100 kg/hr was within values of resuspension rates determined
by the empirical model presented by Hayes et al. (2000). It is important to note that measured
rates of sediment resuspension are difficult to compare directly to estimates computed from
analytical methods due to the lack of knowledge about the operational parameters as well as
modifications to operational parameters over dredging time periods. It is certain that, for
example, the proportion of cutter surface area exposed to dredging was not constant over the
entire time period of dredging operations. As we would assume an even distribution of the solids
over the area resuspension rates is observed.
66
-------
Figure 3-23. Rate of Sediment Resuspended by the Dredge as a Function of the Proportion
of Cutter Surface Area Exposed to Dredging, Q.
(All other operational variables used in the empirical model by Hayes et al. [2000] are indicated
at the top of the plot.)
L£ = 0.8 m: dc = 1 m: = 1.97 m2; A( = 0.987 mz; a = 18 rpm
-
a»
JC.
CD
Vs (m/s)
Figure 3-24. Rate of Sediment Resuspended by the Dredge as a Function of Cutter Tip Speed, Vs.
(All other operational variables used in the empirical model by Hayes et al. [2000] are indicated
at the top of the plot.)
67
-------
Lc = 0.8 m; d = 1 m; Ac = 1.97 m2: At = 0.987 m2; Vs = 0.3 rrVs
a-
-£
a (rpm)
Figure 3-25. Rate of Sediment Resuspended by the Dredge as a Function of Cutter Rotation
Speed, a.
(All other operational variables used in the empirical model by Hayes et al. (2000) are indicated
at the top of the plot.)
Estimates of Residual Solids Mass and Thickness Generated due to Resuspension. Residual
solids mass generated by dredging activities can also be estimated from the empirical
relationship shown in Figure 3-22. If we define that residual solids mass is dredge material in
suspension at a distance of greater than 1000 m from dredge operations, then 1 hour of dredge
operations resulted in less than 5 kg of residual solids mass.
In order to estimate the potential residual thickness generated by the dredge, the maximum
generation rate of approximately 100 kg/hr from Figure 3-22 can be used to conduct an order of
magnitude analysis. In this analysis, it is assumed that the 100 kg of sediment is evenly deposited
along a small stretch of river with an area of 1,000 m2. Dividing the 100 kg of sediment
generated each hour by a conservatively low dry surface sediment density typical of fine
sediment (500 kg/m3) and by the area over which the sediment is deposited (10,000 m2), a
deposition rate of 0.2 mm/yr is calculated. In a standard 8 to 10 hours/day of dredging,
approximately 2 mm of residuals could be expected in a 10,000 ni2 region of channel. It is
important to note that the calculation represents the maximum solids generation measured and
assumes that all the sediment deposits over a moderate area of the channel.
68
-------
3.2.3 Link to Contaminant Distribution
Figures 3-26 and 3-27 show the tPCB(Xc) concentrations in water column samples that were
collected at varying depths and distances from the dredge using the MDWS. These water sample
data were collected over a period of 8 days in June and July 2007 at four different depths in the
water column while dredging was occurring in the area. The water depths collected using the
MDWS ranged from approximately 0.2 to 5.6 m. In the laboratory, water samples
(approximately 2 L) were filtered through a glass fiber filter (pore size 1 (j,m) to create two
fractions for analysis, a dissolved water sample that went through the filter and a particulate
sample on the filter that was measured as a mass and then converted to a water volume based on
the original water sample volume. The tPCB(Xc) concentrations in both the dissolved-phase
(filtered) and particulate-phase (from the glass fiber filter) are shown for each station as a
function of depth of the MDWS (upper surface, upper mid-water, lower mid-water, near bottom).
In general, PCB concentrations increased with depth and decreased with increased distance from
the dredge footprint. The tPCB(Xc) concentrations in the upper surface and upper mid-water
samples are very similar and possibly represent general water column concentrations in this
region not related to dredging activities. The near bottom and lower mid-water samples indicate
that tPCB(Xc) concentrations increase with closer proximity to the dredge location. In general,
the tPCB(Xc) concentrations in the dissolved fraction didn't change throughout the four water
column depths. The increased tPCB(Xc) concentrations observed in the particulate fraction was
likely attributable to the increased TSS found nearer to the actual dredging activity. In addition,
based on plume tracking studies discussed elsewhere in this report, TSS was found to be higher
near the sediment surface, where hydraulic dredging was occurring, and decreased substantially
near the surface. The fact that the dissolved concentrations did not appear to change with
distance or depth is likely due to the fact that these particulates settled out relatively quickly after
dredging activity stopped, therefore, not allowing the water column to equilibrate with the
elevated particulate concentrations. This is evidenced by the observed data and represented by
the power-law model shown in Figure 3-22. Further discussion regarding the relationship
between TSS and particulates is presented below.
Relationships between dissolved, particulate, and dissolved plus particulate PCB concentrations
with TSS were determined for each of the four different sampling depths and for the total of all
samples collected in the month of June; July data were excluded due to a lack of concurrent
ADCP data. Least-squares linear regression analysis with a forced zero-intercept was used to
quantify the correlations. The results of the linear correlations between PCB and TSS are shown
in Figure 3-28.
Minimal depth-dependence was noted for the correlation between PCB and TSS. The strongest
correlation was found for the dissolved plus particulate phase; the correlation coefficient was 0.7.
Therefore, the resulting correlation determined for dissolved plus particulate PCB vs. TSS was
used to estimate PCB mass in the water column for specific events and totaled during the entire
dredging activity.
Three-dimensional volumetric plume plots of estimated PCB concentrations can be found in
Appendix A. The PCB dredge plume was determined using gradients of TSSpiume, similar to
methods described previously, and the relationship between dissolved plus particulate PCB and
69
-------
TSS. Recall that the dredge plume, TSSdredge, was determined for each set of progressive
transects by computing the along-shore gradient of TSSpiume. A negative value of TSSdredge
upstream of the dredge indicated that that particular value of TSSpiume originated from sources
other than the dredge, i.e. Ashtabula River flow. Similarly, a positive value of TSSdredge
downstream of the dredge area indicated elevated TSS originating from Lake Erie. TSS values
associated with non-dredge related processes were set equal to zero. The remaining values of
TSSdredge were multiplied by the regression coefficient shown in Figure 3-28 (C) to derive
PCBdredge (Figure 3-29).
Upper surface
1400
1200
1000
.f. 800
u
W
ds 600
u
** 400
200
0
I Particulate ¦ Dissolved
*Station within 50 m of dredge
AStation within 100 m of dredge
rrttl
¦ I ill11111 ill¦ ¦11i.i¦
CO U) 1/1
in (N
^rLn^HOOLnoLnmcrio^mroT-tcn
ocococccococococQcccocccocococa:
<<<<<<<<<<<<<<<<
en oo oo m (N in
in m o-j
1400
1200
1000
^ 800
u
M
ds 600
u
** 400
200
0
Upper mid-water
¦ Particulate ¦ Dissolved 'stationwi
thin 50 m of dredge
thin 100 m of dredge
| "Station wi
1
1,
1 ¦
ll . 1
II
|
1.
Ill
1
¦
1 1 . 1 1 1 1 . 1 . 1 1 . 1
1 1 ¦ 1 ¦ ¦
NmNINinTOlTUlNNHm'JMCO
^rin^Hoomoinmcrio^mroT-tcn
OCCCOCCCOCOCOCOCQCCCOCOCOCOCGCa:
<<<<<<<<<<<<<<<<
oi co co in in in
in m fN
^inujooiDinujoi
^ in (N
CN (N fN
Note: Stations are ordered from least to greatest distance from dredge (no distance data are available for Stations
AR-116, AR-119, AR-121, AR-186, AR-209, AR-210, and AR-233).
Figure 3-26. tPCB(Ec) in MDWS Samples Collected "at Upper Surface" and "Upper Mid-
Water" Water Depths from Each Station and Distance (meters) from Dredge from Selected
Stations.
Note: If no symbol is shown next to a station, then that station was greater than 100 m from the
dredge.
70
-------
1400
1200
1000
*5&
^ 800
u
M
co 600
u
~ 400
200
0
r*.mr^rNm<3-(7i^-u3r-.r-.*-im*-iu3C7iom
^iriTHooLnoirifocriOTHmfriTHaifoaiofnfo^HirifOfNoo^LrifN'H'HfNcoo^m
cpcp'Hr^r^rsifncprncpr|jfnf,nrn'-HcprnorsimmmofriO'Hmooo^H'H'H'HfNrsifN
cccccccccccccccco^cccccc^ccccQ^cccccccccccccrcccccccccccccrcrcr crcrcrcrQ:
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
******** <<<<<<<<<
Near Bottom
1400
1200
1000
_j
*5&
^ 800
u
M
co 600
u
** 400
200
0
r^mr».rNm<3-(7i^-u)r-.r-.*-im
-------
where TSSpiume is TSSback subtracted from measured TSSturb.mdws for each transect. PCB
values derived from TSS were converted from units of ng/L to mg/L, averaged over each
transect, then multiplied by Q. PCB fluxes calculated for each transect in a group were integrated
over the time period of grouped transect collection to derive total mass of PCBs (g) per group
(and per time period of grouped transect collection). This value was divided by the time passed
during each particular grouped transect collection (in hours) to derive total mass of PCBs per
hour. This was repeated for each of the 10 groups of transects.
PCB
PCB
dissolved
particulate
800
1000
PCB = 7.98 * TSS
R2 = 0.655
PCB = 4.35 * TSS,
R2 = 0.462
800
O)
600
c 400
400
Q- 200
••
200
0 10 20 30 40 50 60 70 80 90 100
TSS (mg/L)
100
TSS (mg/L)
1500
—1200
D>
-E- 900
6
c
o
u 600
CO
U
a.
300
0
0 10 20 30 40 50 60 70 80 90 100
TSS (mg/L)
Figure 3-28. Linear Relationships between PCB Concentration and TSS for the (A)
Dissolved, (B) Particulate, and (C) Dissolved Plus Particulate Phases of PCB.
(The four sample collection depths are denoted by different colored symbols: near-bottom =
blue, lower mid-water = green, upper mid-water = yellow, and near-surface = red. Least-squares
linear regressions for all depths are shown in black with 95% confidence limits in gray.
Regression and correlation coefficients are indicated.)
72
-------
<-N
Dredge
15 'ream
Figure 3-29. Volumetric Plot of the PCB Plume, Estimated from the Linear Relationship
between Dissolved Plus Particulate PCB Concentration and TSS and MDWS and ADCP
Transect Data Collected on June 4, 2007.
(The cross-shore distance was 60 m, and the along-shore distance covered by the transects was
approximately 1000 m [negative distances are upstream of the dredge and vice versa].)
In order to estimate the generation of PCBs by the dredge, any values of Total Mass of PCBs that
were determined to point toward the dredge, i.e., negative values downstream of the dredge and
positive values upstream of the dredge, were assumed to be from factors other than dredging
(e.g., natural Ashtabula River flow) and set equal to zero. All other values were determined to
represent the total mass of dredge PCB per hour of dredging.
The absolute value of the total mass of dredge PCBs per hour of dredge activity was plotted as a
function of distance from dredge, and a power-law fit was applied to the trend (Figure 3-30).
This enabled the prediction of PCB mass generated by the dredge as a function of distance from
the dredge.
Estimates of Residual PCB Mass Generated Due to Resuspension. PCB residual solids mass
generated by dredging activities can also be estimated from the empirical relationship shown in
Figure 3-30. Assuming that residual solids mass is defined as dredge material in suspension at a
distance of greater than 1000 m from dredge operations, then 1 hour of dredge operations
resulted in less than 0.06 g of PCB residual solids mass. Two hours of dredge activities thus
generated less than 0.12 g of PCB residual solids mass.
73
-------
2
s_
5 1.5
c
§
-------
characterization efforts: 1) conducted herein, and 2) in conjunction with other discrete
investigations concerning potential ongoing sources and post-dredging surface surveys as further
outlined in Table 3.3. Surface sediments (generally 10 to 15 cm in depth) that were collected
from other locations throughout the river in conjunction with ecosystem-related measurements
(biological and passive samplers) are presented in Sections 3.4 and 3.5, respectively, along with
the results from the co-located measurements.
Fields Brook
5th Street
Bridge
[SOURCE JF BRENNAN CO . 2007. GOOGLE EARTH 2009)
Figure 3-31. Sediment Core Sample Locations in the Ashtabula River Study Area (Pre-
and Post-Dredging).
Red line indicates the boundary of the GLLA project area.
3.3.1 Comparison of tPCB(Ec) Concentrations in Pre- and Post-Dredge Cores
Figures 3-32 through 3-39 compare tPCB(Sc) concentration profiles in cores collected pre-
dredge (2006) and post-dredge (2007 and 2011). The color-coded dashed lines across each
figure represent the sediment surface elevations at the time the cores were collected. Pre-dredge
surface sediment elevations occur (in most cases) 0.15 m above the highest elevation identified
in each figure, as each point represented on a figure is the midpoint of the core segment that was
analyzed (i.e., the top core interval analyzed in the 2006 pre-dredge cores was 0.3 m). The
tPCB(Zc) concentrations are based on the sum of 117 PCB congeners; congeners that comprise
more than 98% of the total PCBs in all Aroclors and most environmental PCB contamination.
The set of 117 PCB congeners (including co-reported co-eluting congeners) that were common
75
-------
to all four sampling events were used to produce the tPCB(Xc) to be able to compare results.
The method detection limit is included in the summation for congeners that were not detected.
In general, the pre-dredge cores (shown in red; 2006) were collected at an elevation of 163 to
173 IGLD85 m and to a depth of approximately 5.5 to 7 m. Complications from year-to-year
from leaf mats, debris, etc. caused deviations from the goal of year-to-year comparisons in some
cases. Subsequent core collections targeted elevations below the project cut line to permit for
year-to-year comparisons. Nonetheless, the 2007 post-dredge cores (shown in blue) were
collected at an elevation of 166 to 170 IGLD85 m and to a depth of approximately 0.8 to 0.9 m.
These 2007 cores revealed significant increases in PCB concentration, which is consistent with
other observations here and as expected given the dredged residuals profile reported in U.S. EPA
(2010). Note that in some cases, the 2007 sediment surface elevation increased as much as 0.6 m
in sampling locations and as much as 3 m at T181D due to high sedimentation rates in that
portion of the river as noted in Section 3.1. Leaf litter and other detritus made it difficult to
obtain a deeper 2007 core sample at T181D. Increased PCB concentrations at approximately
mid-depth at all transect locations and negligible or low PCB concentrations at maximum depth
were observed.
In 2011, cores were collected at an elevation of 166.5 to 170 IGLD85 m. Surface sediment PCB
concentrations had returned to pre-dredge levels or lower due to significant sedimentation of
cleaner sediments from 2007 to 2011. The exception was at T178B where far less sedimentation
was observed relative to all other sample locations.
76
-------
Station Station
T170A T170B
10
174
173
172
171
170
169
168
167
166
165
2006
2007
2011
10
174F
173
172
171
170
o 169
o 168
167
166
165
164
2006
2007
2011
Station
T171B
Station
T171A
Elevation = IGLD85 meters
Figure 3-32. tPCB(Ec) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 170 and 171 (A = West Side of River, B = East Side of River).
77
-------
Station
T172A
Station
T172B
S 169
ns
> 168
w 168
167
166
165
Station
T173A
Station
T173B
= 169
o
S 169
S 168
at
lu 168
Elevation = IGLD85 meters
Figure 3-33. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 172 and 173 (A = West Side of River, B = East Side of River).
78
-------
Station Station
T174A T174B
174
173
172
171
170
169
| 168
w
167
166
165
164
2006
2007
2011
2
173
172
171
170
169
o 168
5 167
166
165
164
163
2006
2007
2011
Station
T175A
-2-1 0 1 2-2
10 10 10 10 10 10
173
172
171
170
E- 169
168
167
166
165
164
2006
2007
2011
163
172
171
170
169
165
164
163
2006
2007
2011
162
Station
T175B
-10 12
10 10 10 10
Elevation = IGLD85 meters
Figure 3-34. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 174 and 175 (A = West Side of River, B = East Side of River).
79
-------
Station
T176A
Station
T176B
§ 169
2 167-
3> 166
10
10
< 2006
¦ 2007
Station
T177B
Station
T177A
E 168
o 171
Elevation = IGLD85 meters
Figure 3-35. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transects 176 and 177 (A = West Side of River, B = East Side of River).
80
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Station
T178A
172
171
170
169
I. 168
c
o
- 167
¦4—1
TO
>
0)
m 166
165
164
163
2006
2007
2011
162
172
171
170
169
.2 168
£ 167
166
165
164
163
2006
2007
2011
Station
T178B
-.n"1 „.1 2
10 10 10 10
Station
T178C
-2-10 1 2
10 10 10 10 10
176
175
174
173
E. 172
I 171
I 170
169
168
167
2006
2007
2011
166
Elevation = IGLD85 meters
Figure 3-36. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 178 (A = West Side of River, B = Middle of River, C = East Side of
River).
81
-------
Station Station
T179A T179B
2
10
172
175
171
174
170
173
169
172
168
171
167
IS 170
166
169
165
168
164
167
163
166
2006
2007
2011
2006
2007
162
165
Station
T179C
-2-1012
10 10 10 10 10
176
175
174
173
I. 172
c
o
™ 171
ro
>
aj
HI
170
169
168
167
2006
2007
2011
166
Elevation = IGLD85 meters
Figure 3-37. tPCB(Ec) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 179 (A = West Side of River, B = Middle of River, C = East Side of
River).
82
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Station
T180A
Station
T180B
S 168
w 167
10
173 -
172 -
171 -
170 -
F
c
169 -
o
ns
168 -
ai
LU
16/ -
166-
165 -
164-
10
10
10
10
Station
T180C
Station
T180D
£ 170
Elevation = IGLD85 meters
Figure 3-38. tPCB(Zc) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 180 (A = West Side of River, B = West Middle of River, C = East
Middle Side of River, D = East Side of River).
83
-------
Station
T181B
Station
T181A
Station Station
T181C T181D
10"2 10"1 10° 101 102 10"2 10"1 10° 101 102
Elevation = IGLD85 meters
Figure 3-39. tPCB(Ec) Concentrations (mg/kg) in Pre- (2006) and Post-Dredge (2007 and
2011) Cores at Transect 180 (A = West Side of River, B = West Middle of River, C = East
Middle Side of River, D = East Side of River).
3.3.2 Surface Sediment PCBs Trends
Shown in Table 3.3 are surface sediment tPCB(Zc) concentration data for 2006, 2007, and 2011
and the surface sediment segment interval that was analyzed based on visual observations of the
cores and distinct horizons to be analyzed to target residuals in 2007 and recently deposited
84
-------
sediment in 2011. tPCB(Xc) concentrations are plotted in Figure 3-40, and surface contours of
the pre- and post-dredge events are shown Figures 3-41 through 3-43, respectively. All surface
contouring was conducted using a grid method/program, the Earth Vision 2D Minimum Tension
gridding algorithm, and a 20 ft by 20 ft grid spacing. The 2006 tPCB(Xc) surface sediment
concentrations were variable across all sample locations and averaged 1.12 mg/kg. Seven
stations (T174A, T176B, T178A, T179B, T179C, T180D, and T181B) had tPCB(Zc) surface
concentrations greater than 1.00 mg/kg, including T176B with the highest concentration of 8.61
mg/kg. These sampling locations were in moderate to high depositional zones within the study
area (U.S. EPA, 2010).
Table 3.3: tPCB(Ec) Concentrations (mg/kg) of Surface Sediment from Pre-Dredge (2006),
Post-Dredge (2007), and Post-Dredge (2011).
Sediment
Core ID
Pre-Dredge 2006
Post-Dredge 2007
Post-Dredge 2011
Segment
Length
(m)
tPCB(Ec)
(mg/kg
dry)
Segment
Length
(m)
tPCB(Ec)
(mg/kg
dry)
Segment
Length
(m)
tPCB(Ec)
(mg/kg
dry)
T170A
0.3
0.581
0.06
4.29
0.2
0.188
T170B
0.3
0.734
0.09
5.71
0.09
0.211
T171A
0.3
0.641
0.03
18.9
0.2
0.190
T171B
0.18
0.224
0.06
37.7
0.2
0.275
T172A
0.3
0.652
0.09
20
0.2
0.210
T172B
0.3
0.209
0.05
4.57
0.2
0.526
T173A
0.3
0.849
0.03
4.09
0.2
0.276
T173B
0.3
0.369
0.06
4.65
0.2
0.454
T174A
0.3
1.14
0.06
3.35
0.2
0.258
T174B
0.3
0.235
0.01
8.34
0.2
0.125
T175A
0.2
0.175
0.05
12.0
0.2
0.267
T175B
0.1
0.152
0.03
7.01
0.2
0.121
T176A
0.2
0.495
0.1
3.21
0.2
0.340
T176B
0.3
8.61
0.02
6.51
0.2
0.0762
T177A
0.2
0.430
0.2
7.06
NA
NA
T177B
0.3
0.740
0.03
10.2
0.2
0.479
T178A
0.3
3.32
0.05
4.44
0.2
1.42
T178B
0.2
0.427
0.2
8.89
0.2
1.02
T178C
0.3
0.890
0.09
14.1
0.2
0.228
T179A
0.3
0.203
0.1
1.48
NA
NA
T179B
0.3
1.82
0.06
9.16
0.2
0.143
T179C
0.3
1.25
0.02
7.04
0.2
0.312
T180A
0.3
0.162
0.2
0.651
0.1
0.207
T180B
0.3
0.284
0.09
11.1
0.2
0.186
T180C
0.3
0.540
0.03
12.9
0.2
0.658
85
-------
Table 3.3 (continued): tPCB(Ec) Concentrations (mg/kg) of Surface Sediment from Pre-
Dredge (2006), Post-Dredge (2007), and Post-Dredge (2011).
Sediment
Core ID
Pre-Dredge 2006
Post-Dredge 2007
Post-Dredge 2011
Segment
Length
(m)
tPCB(Ec)
(mg/kg
dry)
Segment
Length
(m)
tPCB(Ec)
(mg/kg
dry)
Segment
Length
(m)
tPCB(Zc)
(mg/kg
dry)
T180D
0.3
3.12
0.03
7.35
0.2
0.575
T181A
0.3
0.587
0.1
1.18
0.2
0.0371
T181B
0.3
3.13
0.09
2.62
0.2
0.0859
T181C
0.3
0.821
0.05
4.93
0.2
1.17
T181D
0.3
0.744
0.09
11.2
0.2
0.561
Minimum
—
0.152
—
0.651
—
0.0371
Maximum
--
8.61
--
37.7
--
1.42
Average
—
1.12
—
8.48
—
0.379
NA = No data available. No sediment core collected.
22
20
18
> 16
i—
T5
0J3 14
a-t
a, 10
CO
2 8
+¦»
6
4
2
0
Figure 3-40. Surface Sediment tPCB(Zc) Concentration (mg/kg dry) from Pre-Dredge
(2006) and Post-Dredge (2007 and 2011).
The 2011 surface concentrations (average tPCB(Xc) was 0.379mg/kg) were almost three times
(2.95) lower than the 2006 surface concentrations (1.12 mg/kg). The areas with the highest
concentration of PCBs in 2006 (concentrations greater than 1.0 mg/kg) were less than 0.20
mg/kg in 2011.
¦ 2006 ¦ 2007 ¦ 2011
—
-ir.
i. j.
l~
r->0Q00CQa"}Cr}a"}ooOo
-------
The percent fines in the surface sediment samples was variable across the 30 sediment cores.
For example, the percent fines ranged from 14.3% to 99.8% across all locations and years (2006,
2007, and 2011), with an average of 73.1%. Examination of the cores from 2006 indicate that
pre-dredge surface sediments consisted generally of stratified sand and clay, with the deeper
intervals comprised mainly of clay silt and then clay before reaching the bedrock layer
(Appendix D). In 2007, the sediment cores were characterized as being mainly clay mixed with
silt or fine sandy clay. Although not analyzed for tPCB(XC), 2009 sediment cores were
comprised of mostly clay silt with traces of fine sand and organic matter (i.e., leaf matter). In
2011, fine sandy clayey silt with surface organic matter dominated the sediment type in the cores
collected. Therefore, over time, there appeared to be a slight fining in surface sediments from
pre-dredge (2006) to post-dredge (2007, 2009, and 2011) collections. This change in sediment
characteristics would be expected as a result of the changes in the velocity profiles across the
channel after dredging.
The TOC concentrations in the surface sediments were also variable ranging from 0.39% to
6.48%), with an average of 2.57%. A common means of assessing these bulk sediment properties
is to represent the sediment grain size (as percent fines) vs. TOC under the premise that grain
size relates to TOC. The Ashtabula River bulk sediment properties showed the expected positive
slope, but the correlation was not a strong one (R2 = 0.127). The correlation between PCB
concentrations and percent fines in the sediment samples was even weaker (R2 = 0.0844). Note
that in all cases the data were highly variable, hence the confidence in the suggested correlations
is low. No relationship was observed, and these data are presented in Appendix F.
Table 3.4 shows the average tPCB(Xc) concentrations in the surface segment from sediment
cores collected in 2006, pre-dredging in 2007, post-dredging in 2007, and in 2011 (4 years post-
dredging). Because the focus of the sediment collections was to target residuals in 2007 and
recently deposited sediment in 2011, the sediment depth that the surface segment represented
varied, making it challenging to interpret both the PCB concentration and PCB composition data.
The surface sediment samples collected before dredging had comparable average PCB
concentrations in 2006 and 2007 (1.12 and 1.41 mg/kg, respectively). The surface sediment PCB
concentrations were much higher shortly after dredging (averaged 8.37 mg/kg) compared to
before dredging, which can most likely be attributed to dredged residuals with significant
contributions from the highly contaminated sediments dredged from depth (generally 2-3 m) in
2007; the sediments from all depths that were dredged were mixed during the dredging
operations, and some were re-deposited as surface sediment. These results and this phenomenon
were previously discussed in the U.S. EPA 2010 report. The average surface sediment tPCB(Zc)
concentrations were significantly lower in 2011 (averaged 0.358 mg/kg), indicating that
sediments with lower PCB concentrations have been deposited in the study area after dredging.
This sediment deposition was supported by a bathymetric survey conducted over the project area
(Section 3.1), which measured an average of 0.16 m of deposition since 2007 (Table 3.1).
87
-------
tPCB(Ec)
(mg/kg)
5.00
Batteiie
Scale in Meters
Ashtabula River
I're-Dredge 2006 PC B Concentration (mg/kg)
ASHTABUI A COUNTY - OHIO
CHECKED 8*
Figure 3-41. Surface Sediment tPCB(Zc) Concentrations from 2006 (Pre-Dredge); Created
by EarthVision 2D Minimum Tension Gridding Algorithm using a 6.1-m x 6.1-m (20-ft x
20-ft) Grid Spacing.
88
-------
¦. .
tPCB(Ec)
^¦(mg/kg)
5.00
MAX =
2.00 377
i.oo mg/k9
0.50
0.20
0.0
Batfeiie
Ashtabula River
Post-Dredge 2007 PCB Concentration (mg/kg)
Scale in Meters
ASHTABULA COUNTY - OHIO
| F'L|cB. CONTOURS 2007 POST DRFDGF. CQr|DATE09/M
Figure 3-42. Surface Sediment tPCB(Ec) Concentrations from Cores Collected in 2007
(1 Year Post-Dredge); Created by EarthVision 2D Minimum Tension Gridding Algorithm
using a 6.1-m x 6.1-m (20-ft x 20-ft) Grid Spacing.
89
-------
tPCB(Ec)
¦ (mg/kg)
H 5.00
MAX -=
2.00 -j 42
mg/kg
Battelle
\shtabula Kiver
Pwt-Ditdy 2011 PCB Camrtitfai
ASHTABULA COUNTY - OHIO
Figure 3-43. Surface Sediment tPCB(Sc) Concentrations from Cores Collected in 2011 (4
years Post-Dredge); Created by Earth Vision 2D Minimum Tension Gridding Algorithm
using a 6.1-m x 6.1-m (20-ft x 20-ft) Grid Spacing.
90
-------
Table 3.4: Average tPCB(Ec) Concentrations (mg/kg) for 30 Surface Sediment Samples
Collected in the Ashtabula River Study Area during Four Study Phases (two before
dredging and two after dredging).
Sampling Year/Event
tPCB(Ec)
Concentration
(mg/kg, dry weight)
Average Depth of
Samples (m)
No. of Samples
(coring
locations)
2006
1.12
0.3
30
2007 Pre-Dredging
(surface grabs)
1.41
0.1
20
2007 Post-Dredging
8.37
0.1
30
2011
0.358
0.2
33
(a) Includes five duplicates; the samples were from 28 locations.
The PCB composition, which was similar for the four different surface sediment sample sets,
was consistently dominated by the historic Aroclor 1248 PCB source. This was the case with the
2006 samples and the 2007 samples collected both before and after dredging as described earlier
(U.S. EPA, 2010). This was also the case for the samples collected in 2011 that are included
with the 2006 and 2007 samples in the principal component analysis (PCA) shown in Figure
3-44. The PCA in Figure 3-44 was computed using PCB congener data; similar results were
obtained using PCB homolog data. The PCA does not show any clear separation of samples by
sampling event, indicating that the Aroclor 1248 source is the primary contributor to the PCB in
all of these samples including the less contaminated sediments deposited in recent years. The
few samples shown in Figure 3-44 that are separated from the main sample cluster in the PCA
are samples with low PCB concentrations that include an unusually high relative contribution
from PCB209; they also represent samples from various sampling events.
Other sampling and data analysis also indicated a contribution of Aroclor 1260 to the surface
sediments in the main stem of the river before dredging. That contribution of Aroclor 1260 now
has been shown to be detectable only in the confluence of Strong Brook and the Upper Turning
Basin area; it no longer appears to be detectable in the sediments farther downstream (U.S. EPA,
2012). The ability to detect recent contributions from a second source was only possible using
data for shallow surface sediment samples from the top few centimeters of sediment. The
"surface sediment" data from the sediment cores illustrated in Table 3-5 and Figure 3-44 were
collected to a depth from 0.1 to 0.3 m, on average, for the four sampling events and represent
longer and differing time periods. This greater sampling depth makes it difficult to compare the
data from the different sampling events to each other, and it also presents problems in
distinguishing between current and recent contamination. It is therefore recommended the top
0.03 m consistently be isolated for analysis in future studies that include coring in different
years, and that other core segmenting strategies (e.g., based on observations) be applied to the
remaining sediment below 0.03 m depth.
91
-------
NJ
T180A-07-S
Most samples from most sampling
events cluster together, and near
Aroclor 1248
T180A-07
T179A-06
A1242
T178B
«7
T181A
Aroclor 1248
" T180B-06
T180A-06
Aroclor 1248:1260 (1:1)
A1254
Aroclor 1260
A1260
Factorl
Note: Sample ID codes = Station ID followed by Collection Year. "DUP" = duplicate sample.
Figure 3-44. Principal Component Analysis Based on the PCB Congener Composition of Surface Segments in the Ashtabula
River Study Area during Four Study Phases (two before dredging and two after dredging).
-------
3.3.3 General Surface Sediment PCB Trends
In addition to the primary data sets collected to accomplish specific objectives specified in
Section 2, further efforts were pursued throughout the project time span of this ORD study that
were jointly executed by U.S. EPA ORD and U.S. EPA GLNPO. The data accumulated from
four of these efforts are related and relevant to the understanding of the surface sediment
conditions (as of 2011) beyond the ORD Study Area boundaries and encompass a surface
sediment interpretation of the entire GLLA dredge footprint. Table 3.5 outlines the four primary
investigations that were used to make the interpretations discussed in this section.
The tPCB(Zc) concentrations in the 79 surface sediment samples from Studies 1 through 4 are
presented in Table 3.6, along with TOC concentrations and the TOC-normalized tPCB(Zc)
concentrations; this includes the samples representing the upper segment of the eight cores
collected in Study 3. The tPCB(Zc) (and TOC and TOC-normalized tPCB(Zc)) concentrations
of all 37 samples from the eight Study 3 sediment cores are presented in Appendix C. The
tPCB(Zc) and TOC concentrations are presented geographically in Figures 3-45 and 3-46,
respectively. Figures 3-47 and 3-48 show the concentration-extrapolated estimated tPCB(Zc)
and TOC-normalized tPCB(Zc) concentrations, respectively. The tPCB(Zc) data for the Studies
1-3 samples are based on Aroclors, and the Study 4 data are based on PCB congeners.
It should be noted that the two-dimensional concentration contours in Figures 3-47 and 3-48 are
modeled concentrations. The contouring is highly dependent on the data extrapolation
algorithms, the physical shape of the area being contoured, and the concentration distribution.
These contoured representations should only be used to obtain an estimate of the concentrations
and may not accurately represent the concentrations across the full area being depicted. The
PCB and TOC concentrations at the specific station locations are the only concentrations that are
known, and those are presented in Table 3.7.
93
-------
Explanation:
¦tPCB(Sc) (mg/kg)
•Rp(39
RF-34
RF-32
-.RF-30J
RF-23;
RF-22
[RFf2jj]
RF-20
P'K&^RglT.
jswmnrab
rFghfmrr..
¦St j,15iRF^5j
rfJHoI
iRF£i:4j
£5921
SR-1 5i
G 1 r.
¦srW
SR-13
SR-1 2
rsRimi
[SRT10]
Meters
Meters
Figure 3-45. Surface Sediment (top 0.15 in) tPCB(Sc) Concentrations (mg/kg, dry wt).
94
-------
Explanation:
Total Organic Carbon (%)
O <5%
'RF-r39
IRFJ36V
[sxhm
O 5-10%
O 10-15 %
• >15 %
RF-32'
IRFJ3T11
[RF-3OT
R^27j
RF-26"
RF-24
1RF/23J
RF-22
[RF;-2n]
Rgj20j
:st-25SRFj;i9:
IRFJ17,
RF-26]
lRF^4j
IpJgJ
SR-15
SR-14;
SR-1 2
rSRH'Q]
Meters
Meters
Figure 3-46. Surface Sediment (top 0.15 in TOC (%) Concentrations.
95
-------
Explanation
• Sampling Location
tPCB(Sc) (mg/kg)
pWnlKfrgjd»?^]
:rf-39
RF-36
'RFJ35I
RFJ34
RF-33'
St-5.
RF.-32 <
RF-31
RF-30' •
V I RF-28 A
[RF-26.
'
RF-24
RF-23-
[Rra]
RFJ20]
St-15R523
rfZbI
[RF?14i
:RKi2t
RF-11
IRF-10
?SRyl3]
¦SR?2l
iG-1-2w
W TG-04MG-0"5
Cj2 yp \
•p Iwv
^G-1l|G^03^pn
Meters
Figure 3-47. Surface Sediment (top 0.15 m) tPCB(Zc) Concentration Approximation
Contours (mg/kg, dry wt) Data.
96
-------
Explanation:
• Sampling Location
"tPCB(Sc) (mg/kg OC)
M > 100
|R p"«|K
IRFJ363
RFf34
¦RF-33'
fet-5.
RF-32
|RR-3i1,
RF-301
RF-28
RE?27i
^P#RFJ
RF-24 Jfl
[RF£26!
[St-24
|RFj23j
RF-22
B'620l
St-12
St-25lRRliy
IRETlfl
rf'^«R
' St-15 RF/lJ
M
RF-12j
RF-11
IRF-101
SR-1 5,
^SRjTJ]
fSR-L.131
1SRI121
rsRii;n
rsRii;o]
Meters'
Figure 3-48. Surface Sediment (top 0.15 m) TOC-normalized tPCB(Sc) Concentration
Approximation Contours (mg/kg OC) Based on the Studies 1-4 Data.
97
-------
Table 3.5: Sample Data used to Characterize General River Surface Sediment Trends.
Study Number and Data Sources
# Sites/
Samples
PCB
Aroclor
PCB
Congeners
Surface sediment (top 0.15 m) samples collected in 2011 to calculate
surface weighted average concentration at 54 locations throughout
Ashtabula River and analyzed for PCB Aroclors, including from the
area at the confluence of Strong Brook and the Ashtabula River, the
Turning Basin, the mouth of Fields Brook, and upstream and
downstream. These are Stations 40 RF (RF-1 through RF-40) and
14 SR (SR-1 through SR-15; no SR-6) stations, and samples with ER
sample IDs.
GLNPO Data, (see Appendix C of this report)
54
X
Surface sediment samples (top 0.15 m) collected in 2011 at 17
locations in the area at the confluence of Strong Brook and the
Ashtabula River and analyzed for PCB Aroclors. These are Stations
G-01 through G-17.
GLNPO Data, (see Appendix C of this report)
17
X
Sediment core samples collected in 2011 at eight locations in the
area at the confluence of Strong Brook and the Ashtabula River and
segmented to represent multiple depths and times of deposition (the
top segment was generally sediment deposited on top of the sand
cap applied after dredging, or the top 0.15 m; the top segment depth
ranged from 0.03 to 0.2 m) and analyzed for both Aroclors and PCB
congeners. The tPCB(Ec) concentrations (as the sum of the Aroclors
and/or sum of PCB congener concentrations) and the detailed PCB
congener information were used. These are Stations C-1 through C-
8 (37 samples total).
GLNPO Data, (see Appendix C of this report)
8
(37
samples)
X
X
Surface sediment samples (top 0.15 m; range 0.1 to 0.2 m) collected
in 2011 at 15 SPMD (11) and macrobenthos (4) sampling stations
throughout the Ashtabula River and analyzed for PCB congeners.
The tPCB(Ec) concentrations and the detailed PCB congener data
were used, (see Section 3.4 of this report)
15
X
Table 3.6: tPCB(Zc), TOC, and TOC-normalized tPCB(Zc) Concentrations in Surface
Sediment Samples from Studies 1 through 4. Study 1-3 PCB data are based on Aroclors
Station ID
tPCB(Zc)
(mg/kg dry wt)a
Total Organic
Carbon (%)
TOC-normalized
tPCB(Ec) mg/kg OCa
Study 1
RF-1
0.94
2.91
32.3
RF-2
18
4.79
376
RF-3
ND
1.83
ND
RF-4
0.4
1.77
22.6
RF-5
0.086
2.38
3.61
RF-6
0.39
1.47
26.5
RF-7
0.28
3.99
7.02
98
-------
Table 3.6 (continued): tPCB(Ec), TOC, and TOC-normalized tPCB(Zc) Concentrations in
Surface Sediment Samples from Studies 1 through 4. Study 1-3 PCB data are based on
Station ID
tPCB(Sc)
(mg/kg dry wt)a
Total Organic
Carbon (%)
TOC-normalized
tPCB(Ec) mg/kg OCa
RF-8
1.2
2.06
58.3
RF-9
0.094
0.54
17.3
RF-10
0.076
3.14
2.42
RF-11
0.52
3.39
15.3
RF-12
0.49
2.42
20.3
RF-13
0.056
3.14
1.78
RF-14
ND
1.81
ND
RF-15
0.15
2.38
6.3
RF-16
0.16
1.51
10.6
RF-17
0.091
1.76
5.17
RF-18
0.043
0.86
4.99
RF-19
0.41
2.54
16.1
RF-20
1.34
1.83
73.2
RF-21
ND
4.24
ND
RF-22
ND
1.4
ND
RF-23
3.1
1.35
230
RF-24
0.15
1.38
10.9
RF-25
0.82
1.24
66.1
RF-26
0.44
17.1
2.57
RF-27
0.29
3.72
7.80
RF-28
0.18
1.88
9.57
RF-29
0.17
1.66
10.2
RF-30
0.12
2.01
5.97
RF-31
0.25
1.89
13.2
RF-32
0.25
4.22
5.92
RF-33
0.36
1.82
19.8
RF-34
0.24
1.53
15.7
RF-35
ND
2.3
ND
RF-36
0.52
5.75
9.04
RF-37
ND
1.91
ND
RF-38
ND
1.79
ND
RF-39
0.4
1.41
28.4
RF-40
0.5
26.9
1.86
SR-1
ND
0.8
ND
SR-2
ND
1.71
ND
SR-3
0.11
2.31
4.76
SR-4
ND
3.14
ND
SR-5
ND
0.54
ND
99
-------
Table 3.6 (continued): tPCB(Ec), TOC, and TOC-normalized tPCB(Zc) Concentrations in
Surface Sediment Samples from Studies 1 through 4. Study 1-3 PCB data are based on
Station ID
tPCB(Sc)
(mg/kg dry wt)a
Total Organic
Carbon (%)
TOC-normalized
tPCB(Ec) mg/kg OCa
SR-7
0.069
0.98
7.02
SR-8
ND
0.70
ND
SR-9
2.9
1.73
168
SR-10
ND
2.85
ND
SR-11
ND
1.21
ND
SR-12
0.046
1.91
2.41
SR-13
ND
3.79
ND
SR-14
0.432
2.45
17.6
SR-15
0.163
2.24
7.28
Study 2
G-01
0.49
2.66
18.4
G-02
1.55
3.92
39.5
G-03
1.8
11.4
15.8
G-04
1.87
7.81
23.9
G-05
1.48
13.4
11
G-06
3.7
12.5
29.6
G-07
0.69
3.23
21.4
G-08
3
6.67
45
G-09
1.63
4.63
35.1
G-10
0.94
3.68
25.5
G-11
0.34
2.56
13.3
G-12
1.44
6.55
22
G-13
1.33
4.44
30
G-14
0.76
2.85
26.7
G-15
2.46
6.48
38
G-16
1.71
5.66
30.2
G-17
0.86
4.34
19.8
Study 3
C-1
2.04
14.4
14.2
C-2
0.212
0.85
24.9
C-3
1.9
4.52
42.1
C-4
2.37
5.31
44.8
C-5
0.391
3.94
9.93
C-6
0.284
2.33
12.2
C-7
0.596
7.14
8.35
C-8
0.018
3.28
0.55
Study 4
FB*
9.75
3.04
321
100
-------
Table 3.6 (continued): tPCB(Ec), TOC, and TOC-normalized tPCB(Zc) Concentrations in
Surface Sediment Samples from Studies 1 through 4. Study 1-3 PCB data are based on
Station ID
tPCB(Ec)
(mg/kg dry wt)a
Total Organic
Carbon (%)
TOC-normalized
tPCB(Ec) mg/kg OCa
RB*
0.387
2
19.4
TB*
2.93
2.04
144
UP*
0.009
1.55
0.56
Station 1
3.33
2.15
155
Station 3
0.014
1.17
1.19
Station 4
0.2
1.73
11.6
Station 5
0.101
1.98
5.12
Station 8
0.191
1.42
13.5
Station 12
0.294
1.78
16.5
Station 15
0.502
1.55
32.4
Station 22
0.317
1.46
21.7
Station 23
0.235
1.7
13.8
Station 24
1.17
1.77
66.2
Station 25
0.409
6.80
6.01
(a) Study 1-3 PCB data are based on Aroclor analysis, and Study 4 PCB data are based on congener
analysis. Samples represent the top 0.15 m of sediment.
*FB = Field Brook
*RB = River Bend
*TB = Turning Basin
*UP = Upstream
The TOC concentrations of the surface sediment samples ranged from less than 1% to more than
20%, but were between 1% and 5% for most samples, and averaged 3.6%. The Strong Brook
confluence samples had, on average, slightly higher TOC content than the main stem river
samples, but the two samples with the highest TOC concentrations were collected in the northern
part of the main stem (samples RF-26 and RF-40). The TOC concentrations varied somewhat
geographically, but the PCB concentrations were not notably controlled by the TOC content. If
the TOC content controlled the PCB concentration, the non-normalized sediment PCB
distribution (Figure 3-47) would be similar to the TOC-normalized distribution (Figure 4-48),
and the high PCB concentrations (Figure 3-45) would primarily be at locations with high
sediment TOC content (Figure 3-46), which is not the case.
The tPCB(Zc) concentrations in the Study 1-4 surface sediment samples ranged from not
detected (in 15 of the 79 samples) to 18 mg/kg, dry wt, and averaged 0.98 mg/kg, dry wt. A few
of the samples from the main stem of the river had tPCB(Zc) concentrations above 1.0 mg/kg
(SR-9, St-1, RF-20, RF-23, and St-24), but most of the main stem surface sediments had
tPCB(Zc) concentrations below 0.5 mg/kg, dry wt (Table 3.7 and Figures 3-45 and 3-46).
101
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Table 3.7: Average tPCB(Ec) Concentrations in Surface Sediment and Sediment Trap
Samples Collected from the Area at the Confluence of Strong Brook and the Ashtabula
Location
Average tPCB(Ec)
Concentration (mg/kg dry wt)
Studies 1-4 (top 0.15 m)
Confluence of Strong Brook and Ashtabula River(a)
1.94
(n=27)
Upstream of Turning Basin(b)
0.053
(n=14)
Downstream of Fields Brook(c)
0.365
(n=36)
The samples from the confluence of Strong Brook and the Ashtabula River are those
collected within the Jack's Marine North slip area. The outer-most samples included in the
calculation were C-8 (Studies 1-4).
Upstream of Turning Basin samples are samples that include and are upstream of RF-5
(Study 1), but do not include those from slips and water bodies that are not part of the
main stem of the river (SR-3, SR-7, and SR-9; Study 1) or the clear outlier sample (St-1;
Study 4).
Downstream of Fields Brook samples are samples that include and are downstream of
RF-14 (Study 1), but do not include those from slips and water bodies that are not part of
the main stem of the river (RF-21, RF-26, and RFR-27; Study 1).
The PCB concentrations were notably higher in the surface sediments collected in the Strong
Brook Confluence than in the main stem of the river, with most samples having a tPCB(Xc)
concentration greater than 1.0 mg/kg, dry wt. The highest tPCB(Zc) concentrations were
measured in the sample collected in Fields Brook (9.75 mg/kg, dry wt) and a sample collected in
the inner part of the Strong Brook Confluence (RF-2; 18 mg/kg, dry wt). No samples were
collected in Strong Brook for Studies 1-4.
Most of the main stem of the river, including locations shortly downstream of the confluence of
Fields Brook and Strong Brook and the Ashtabula River had surface sediment PCB
concentrations that were substantially lower than those measured near Strong Brook (Figure 3-47
and Table 3.7). The samples from upstream of the influence of Fields Brook and Strong Brook
had an average tPCB(Zc) concentration of 53 ng/g (Table 3.7), which can be considered
background PCB levels that might have been the concentrations in much of the Ashtabula River
in the absence of the Fields Brook and Strong Brook sources. The samples from the main stem
of the Ashtabula River below the mouth of Fields Brook had an average tPCB(^c)
concentrations of 0.365 mg/kg, or approximately seven times higher than upstream
concentrations. The tPCB(^c) concentration in the surface sediment samples from near Strong
Brook averaged 1.94 mg/kg, which is about five times higher than the PCB concentrations in the
102
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Ashtabula River downstream of Fields Brook and about 36 times higher than the concentrations
of the surface sediments from upstream in the Ashtabula River.
The tPCB(^c) concentrations in the main stem of the river were generally below 0.500 mg/kg,
dry wt and comparable to the surface sediment concentrations measured prior to dredging in
2007 (U.S. EPA, 2010). The surface sediment PCB concentration were, as noted, higher in the
Study 1-4 samples from the area at the confluence of Strong Brook and the Ashtabula River and
had not returned to PCB levels that were comparable to the rest of the river following the
dredging in 2007. This indicates that the sediments that have deposited in this area in recent
years have higher PCB concentrations than those that have deposited in the rest of the river,
suggesting there may be an active source of PCBs entering this slip area. The relatively high
PCB concentration in the one sample collected in Field Brook, and the generally lower PCB
concentrations in the sediments from upstream than downstream of the mouth of Fields Brook,
imply that Fields Brook was a low level source of PCBs to the Ashtabula River. However, after
a source study completed by U.S. EPA and Ohio EPA, a source was remediated within the
Strong Brook watershed (U.S. EPA, 2012).
It should be noted that the surface sediment samples in Studies 1-4 are of the top 0.15 m of
sediment and, thus, represent sediment that may have been deposited over many years and not
necessarily what is currently introduced to the surface of the sediment (i.e., not necessarily
currently active contamination sources). An additional 39 samples were collected in 2012 to
enable more detailed study of potential sources of the PCBs. Those samples provide more
reliable information on potential current sources, and that work is described in detail in U.S.
EPA, 2012 (Appendix C). In summary, the results suggest that in 2012: 1) there was PCB input
to the Ashtabula River upstream of the confluence of Strong Brook and Fields Brook and the
Ashtabula River, 2) there was a source of Aroclor 1260 upstream in Strong Brook, 3) Fields
Brook may still have been contributing some PCBs resembling Aroclor 1248, 4) the surface
sediment PCB concentrations near Strong Brook and in the Ashtabula River were lower than pre-
dredging levels in 2007, and 5) the type of PCB contamination in the most contaminated deeper
sediments near Strong Brook appear to be different from that of the surface sediments.
Although detailed PCB compositional information is not available for the Studies
1-2 samples, the laboratories did report concentrations for separate Aroclor formulations for the
Studies 1-3 samples (Table 3.6). Aroclor data can be used to obtain some general compositional
information, in this case with the caveat that different laboratories can approach Aroclor
identification differently and three separate laboratories were used to generate the Studies 1-3
Aroclor information. Some inconsistencies in Aroclor identifications were observed in a few
Study 1 samples (e.g., elevated concentrations of Aroclor 1254 in samples RF-2, RF-23, and SR-
9), which is inconsistent with other samples from close proximity and from past investigations,
which have primarily identified Aroclors 1248 and 1260 in the sediment (mixtures of Aroclors
1248 and 1260 can be misidentified as Aroclor 1254 if care is not taken during the
identification). The results for the Study 2 surface sediment samples identify Aroclor 1254
(along with Aroclor 1260) in all 17 of those samples from the area at the confluence of Strong
Brook and the Ashtabula River, which is inconsistent with other investigations, suggesting
laboratory inconsistency (those 17 samples were the only samples analyzed by a single
laboratory). Nonetheless, most of the surface sediment samples collected in this area near Strong
103
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Brook identified Aroclor 1260 as the most abundant Aroclor, and most of the samples from the
main stem of the river (and the deeper, historically deposited Study 3 samples) had Aroclor 1248
identified as the predominant Aroclor, suggesting possible differences between historical and
present PCB sources.
The combination of: 1) elevated PCB concentrations in the Strong Brook confluence compared
with the main stem of the Ashtabula River, and 2) a difference in the identified Aroclors, suggest
that in recent years those two general areas may have been subjected to PCB contamination
contributed primarily from different sources. Results of the source identification studies
summarized above are described further in Appendix C (U.S. EPA, 2012) of this report.
3.4 Biological Samplers
This section summarizes the PCB and PAH results for the macrobenthos and indigenous fish
tissue samples. In addition, this section includes the chemical results for co-located water and
sediment samples for each matrix. Additional analytical data tables are provided in Appendix F.
3.4.1 Macrobenthos Tissue and Co-located Sediment and Water Chemical
Results
This section summarizes the results of the chemical analyses of macrobenthos tissue samples
collected from the macrobenthos artificial substrate samplers (H-Ds) and the results of the
physical and chemical analyses of the co-located sediment and water samples. Data are shown
for the four deployment locations in the Ashtabula River designated as Upstream, Fields Brook,
Turning Basin, and River Bend. These locations were sampled from 2006 through 2011.
Additional results are presented for the Reference location (Conneaut Creek) that was sampled in
2009-2011. Sample locations were previously provided in Figure 2-10. A description of the
procedures employed for macrobenthos sampler deployment and collection of the co-located
samples is described in Section 2.5.
The location of the four Ashtabula River deployment stations varied from year-to-year (Figure
2-10) because the Turning Basin and River Bend macrobenthos stations were moved from the
target locations in anticipation of dredging in these areas (Table 3.8). The variation in
deployment locations is important to note as sediment and water characteristics can change
significantly over these spatial and temporal scales depending on hydrodynamic factors driving
different contaminant and sediment transport and other considerations.
The H-D samplers were deployed for approximately 28 days to allow sufficient time for
colonization by macrobenthos. Surface sediment (top 0.15 m in 2009 through 2011 collections)
and water samples (approximately 0.30 m above the sediment/water interface) were typically
collected at the time the H-D samplers were deployed and retrieved. Macrobenthos tissue from
the H-D samplers, surface sediment, and water were analyzed for PCB congeners and PAHs.
The percent lipid was measured in the macrobenthos samples; the surface sediment samples were
analyzed for grain size and TOC, and water for TSS/VSS.
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Table 3.8: Spatial Variability in Macrobenthos Samples Collection at Each Location.
Station
Description
Upstream (UP)
2007 through 2011 stations located 730 m upstream of 2006 location
Fields Brook (FB)
2007 through 2011 station within 90 m upstream of 2006 location
Turning Basin (TB)
2006 through 2011 stations varied by 200 - 215 m
River Bend (RB)
2007 through 2001 stations located approximately 180 m north of the 2006
location
PAH and PCB data are provided on both a mg/kg wet weight basis and on a mg/kg lipid
(normalized) basis. Summary tables of results for tPCB(Xc), tPAH164, tPAH344, and percent
lipids are provided in Appendix F. The sediment data are presented as both TOC and percent
fines normalized and are summarized in Appendix F. Graphical representation of tissue and co-
located sediment and water concentrations by location and over time are given below.
3.4.1.1 Macrobenthos Tissue Data
Dates and numbers of samples are summarized by location in Table 3.9. Fields Brook samples
were not collected in 2009 because the macrobenthos samplers deployed at this location were
vandalized. Chemical analysis results were averaged by location and year and are presented
below.
Table 3.9: Number of Macrobenthos Samples Collected at Each Location.
Year
Upstream
Fields Brook
Turning Basin
River Bend
Conneaut Creek
Reference
Total
2006
2
2
2
2
0
8
2007
2
2
2
2
0
8
2008
2
2
2
2
0
8
2009
2
0
2
2
2
8
2010
2
2
2
2
2
10
2011
4
2
2
2
2
12
Total
14
10
12
12
6
54
The lipid (g lipid/g tissue) content of the individual macrobenthos samples ranged widely
(0.0005 to 0.04 g/g [0.05 to 4%]) over the study period. Moreover, the lipid content of duplicate
macrobenthos samples varied greatly, with some field duplicates similar (e.g., 0.0063 and 0.0084
g/g) and others very different (e.g., 0.02 and 0.0052 g/g). The Upstream location produced the
lowest average lipid value (-0.007 g/g) in 2009 and the highest average lipid value (-0.21 g/g) in
2008 (Figure 3-49). The macrobenthos lipid content from the Turning Basin macrobenthos
samples were the most consistent over the study period; those from the Upstream location were
most variable. Other than the Turning Basin, the lipid content generally increased from the 2006
4 tPAH16 = sum of the 16 priority pollutant PAHs; tPAH34 = sum of 16 priority pollutant PAHs and 18 alkylated
PAHs.
105
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pre-dredge values, typically falling in the 0.07 to 0.015 g /g tissue range after 2007. Attempts to
relate the lipid content to station location or to sampling year did not show any statistically
significant correlations. However, as a standard practice and to ensure the data were normalized
with respect to bioaccumulation, the PCB and PAH data were normalized to lipid content for all
graphing and statistical analyses.
The average tPCB(Zc) concentration (404 mg/kg lipid) for the 2006 Fields Brook samples was
four times higher than for the Turning Basin (104 mg/kg lipid) (Figure 3-50) and 11 times higher
than the average of the Upstream and River Bend samples (34 mg/kg lipid). Samples from the
Upstream and River Bend locations were similar to each other from 2006 through 2011, with
consistent decreases through 2011 at both locations. In contrast, slight increases in
macrobenthos samples from the Fields Brook and Turning Basin in 2010 and 2011, respectively,
were measured. However, the Turning Basin concentration decreased again in 2011. The 2009
to 2011 Upstream and River Bend lipid-normalized PCB data were more similar to the Conneaut
Creek Reference (note: the Reference Area was not sampled before 2009). An abnormal
influence on the 2009 Upstream PCB results from the very low lipids is not apparent in the data.
0.025
O
3
0.02
.SP
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B 0.015
CD
3
"P
'q.
-25 o.oi
T3
Q.
CD
CUD
ro
k-
o
^ 0.005
-•-Upstream
-¦-Fields Brook
-~—Turning Basin
-X-River Bend
-A-Conneaut Creek Reference
PRE-DREDGE DURING-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE
2006 2007 2008 2009* 2010 2011
* Fields Brook data missing in 2009.
Figure 3-49. Average Lipid Content in Macrobenthos Samples over Time and by Location.
106
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450
Upstream
Fields Brook
400
-^Turning Basin
-X-River Bend
-^Conneaut Creek Reference
350
m
M
£ 250
U
W
g 200
a.
+¦»
150
100
PRE-DREDGE DURING-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE
2006 2007 2008 2009* 2010 2011
* Fields Brook data missing in 2009
Figure 3-50. Lipid-Normalized tPCB(Zc) in Macroinvertebrates by Location and Year.
The lipid-normalized PCB congeners in the macrobenthos samples were also evaluated as 10
homolog groups (Figure 3-51). The Fields Brook PCB homolog composition was similar across
all years, with small contributions to the total PCB concentration from the bi, di, and tri
homologs and dominance by the tetra and penta homologs. Relatively small contributions from
these three homologs were also detected in the macrobenthos at the other locations with two
exceptions. One was the higher percent contribution of the bi, di, and tri homologs at the
Turning Basin and especially the Upstream locations in 2006. The Upstream site was further
notable in that the bi, di, and tri homologs were relatively moderate contributors to the total PCB
concentrations across all 6 years of the study. One other post-remediation change was the lower
percent contribution of the hexa, hepta, and octa homologs at the River Bend and Turning Basin
locations from 2007 through 2011.
While the total PCB concentrations at the Reference were similar, homolog composition for each
of the 3 years sampled was variable and different from year-to-year (Figure 3-51). The penta
concentrations were higher in 2009, and the tri concentrations lower in 2011. The variability is
due to the total PCB content being very low and skewing the homolog data.
Total PAH concentrations in the macrobenthos samples calculated as the sum of 16 priority
pollutant PAH compounds (tPAH16) and the sum of the 34 priority pollutant (16) and alkylated
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(18) PAHs (tPAH34) were both plotted (Figure 3-52). Generally, the year-to-year and site-to-
site lipid-normalized total PAH trends (i.e., increases and decreases) were similar between the
two datasets, although the 2006 34 PAH concentrations were more distinctly separated from the
consistently lower River Bend samples. The higher 34 PAH totals may reflect a somewhat
different petroleum source impacting those specific locations during that time frame. Evaluation
of the specific distribution of the individual PAH compounds may provide further evidence of
this; however, PAH distribution was not evaluated in this report.
The 2006 Total 16 PAH (lipid-normalized) macrobenthos community concentrations were
similar among the Upstream (251 |ig/g lipid), Fields Brook (143 |ig/g lipid), and Turning Basin
(175 |ig/g lipid) sites (Figure 3-52). The Total 16 PAH concentrations decreased among these
locations by 2011 and were similar to concentrations at the River Bend and Reference locations
(ranging from 10 |ig/g lipid to 37 |ig/g lipid).
An exception to the observations above was the Total 16 PAH concentration in 2009 at the
Upstream location (515 |ig/g lipid), which was five times higher than any other concentration
observed. Investigation to determine whether the data were anomalous and should be removed
from the dataset found the lipid content for the two Upstream samples in 2009 was unusually low
(0.05 and 0.09% lipid) compared to the other samples which ranged from 0.21% to 4% lipid.
Moreover, the 2009 Upstream macrobenthos PAH concentration was high in only one of the two
replicates (0.480 mg/kg wet weight vs. 0.061 mg/kg wet weight; 961 mg/kg lipid vs. 68 mg/kg
lipid). The high replicate appears to have caused the high average for this 2009 location. An
audit of the lipid conversion formula did not find any issues. Analytical trends in results for
surface sediment and water samples collected at the same location and time were similar.
However, there were no compelling reasons to a priori exclude the 2009 Upstream data from
statistical analyses so the values were retained in subsequent evaluations.
108
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100%
100%
100%
100%
2006 2007 2008 2009 2010 2011 2006 2007 2008 2009 2010 2011
E
u
g 40%
CO
£ 30%
«4—
O
£ 20%
-------
Upstream
Fields Brook
Turning Basin
River Bend
Conneaut Creek Reference
400
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009*
POST-DREDGE
2010
POST-DREDGE
2011
Upstream
Fields Brook
Turning Basin
River Bend
Co nneaut Creek Reference
s 400
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009*
POST-DREDGE
2010
POST-DREDGE
2011
: Fields Brook samples lost in 2009
Figure 3-52. Lipid-Normalized tPAH16 (A) and tPAH34 (B) Concentrations in Macrobenthos Sampled from the
Ashtabula River (2006-2011).
-------
3.4.1.2 Surface Sediment Data Associated with Macrobenthos Locations
Surface sediments were collected to support the evaluation of the macrobenthos data by
addressing how well the chemical data from macrobenthos samples represent changes in the
chemical composition of the Ashtabula River sediments. Since the comparison required
collection of surface sediment at time scales representative of each macrobenthos exposure site
and period, the sediment data were collected to describe the temporal trends at the individual
locations. The location distribution also enabled understanding of the relatively fine spatial scale
variability that represents responses to the 2006/2007 remediation project. The temporal and
spatial variability was anticipated to provide aggregate means to test the representativeness of the
macrobenthos data to detect and characterize the system's response to remedy and the variability
and trends.
Co-located sediment samples were generally collected at deployment and then again at retrieval
(Table 3.10). However, in some years (i.e., 2006 and 2011), only one or the other was collected
as summarized in Table 3.10. For years in which sediment was collected at both deployment and
retrieval, the sediment data were averaged for that station and year.
Table 3.10: Number of Co-located Sediment Samples Collected at Macrobenthos Sample
Locations.
Conneaut Creek
Year
Upstream
Fields Brook
Turning Basin
River Bend
Reference
2006
1 (R)
1 (R)
1 (R)
1 (R)
0
2007
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
0
2008
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
0
2009
2 (D, R)
2 (D, R)
4 (D, R)
2 (D, R)
2 (D, R)
2010
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
2011*
1 (D/R)
1 (D/R)
1 (D/R)
1 (D/R)
1 (D/R)
Total
10
10
12
10
5
D = deployment; R = retrieval
* In 2011, the sediment samples collected during deployment and retrieval were composited for
each location and analyzed as a single composite rather than as two separate samples as was
done in previous years.
3.4.1.3 Bulk Sediment Properties
A basic characteristic of sediments is the relationship of organic carbon to the sediment grain
size distribution. Most often this is represented by the correlations of the percent fine grained
sediment (i.e., <63 |im [silt plus clay]) fraction and percent TOC (or OC). Typically, sediments
that have higher percent fines contain higher TOC content, as generally represented by a linear
correspondence with positive slope. Moreover, the organic carbon content of the sediments is
the sediment phase that interacts with organic contaminants (i.e., given a constant load of
chemicals such as PCBs, high contaminant concentrations are expected as organic carbon
content increases).
Ill
-------
The relationship between the two fundamental bulk sediment properties vs. time was explored
for the individual macrobenthos sample locations to understand temporal changes in the
sediments. The relationship between the two bulk sediment parameters was also assessed to
characterize differences among the sediment locations to bind or sorb contaminants. Note that
the 2006 sediment samples were not analyzed for either grain size distribution or TOC content.
The percent fines in the macrobenthos surface sediment samples were variable within sample
locations. For example, the percent fines ranged from 10.8% to 98.6% with an average of
66.2%. The River Bend location generally appears to have the highest fine-grained sediments at
>80 % (Figure 3-53), but not always as a slight coarsening of sediment appears in the 2008 and
2010 data relative to the other years. The Reference, Turning Basin, and Fields Brook locations
generally had moderately fine sediments (60 to 80% fines). The lowest percent fines content was
measured at Fields Brook in June 2007. This result appears to be an anomaly as all other percent
fines content from this location was greater than 30 %. The percent fine-grained sediment at the
Fields Brook and Upstream locations also appears to increase through 2010, although close
examination of the data shows high variability between the deployment and recovery times
within a given year at Fields Brook (Figure 3-53 and Appendix F). In contrast to the apparent
fining of the sediments at these locations, the sediments from the Reference and Turning Basin
may have experienced slight coarsening by 2011. The most consistent trend in grain size
changes is observed at the Upstream location, where all but June 2009 macrobenthos recovery
sediment tended to fall on a linearly increasing percent fines trend line.
These data demonstrate that the surface sediments in the study region are dynamic and
potentially influenced by longer-term and local factors. Changes in cross-sectional area will
result in changes in the velocity profiles across the channel. For example, when the channel is
dredged, the velocities during any given river flow will decrease proportional to the increase in
cross-sectional area. As the channel fills in with sediment over time, the velocities will increase
until the channel reaches a dynamic equilibrium where the long-term accumulation is in a
dynamic balance with the ranges of flows and associated velocities mobilizing sediment from the
channel. In addition, there appears to be an apparent change (increase) in the fine-grained
sediment content of the macrobenthos sediments between the H-D samplers deployment and
retrieval times.
The TOC concentrations in the surface sediment samples co-collected along with the
macrobenthos samples were also variable ranging from 0.32% to 3.38%, with an average of
2.05%). Only four samples within the macrobenthos surface sediment dataset had less than 1%
TOC (Figure 3-54). The TOC data also appear to change within a deployment site, with TOC
level often less in the recovery vs. deployment sediments. The TOC changes between
deployment and recovery are substantial with TOC differences measured over the 28-day
deployments ranging between 0.5% and 1.5%. These changes could reflect local site
heterogeneity or changes in TOC in recent deposition due to flow or seasonal changes.
The trends in the TOC data also point to an overall decrease in the organic carbon content of the
sediments at the H-D deployment locations. This may be a result of the alterations in flow due to
the changes in the flow cross section as a result of dredging. Only the Upstream location
displayed a tendency towards increasing TOC; the TOC at all other locations generally decreased
112
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A
80 -
70 -
60 -
50 -
40
30 -
20
10 -
0 --
2008 2009
Date
B
70
60
10
0
2008 2009
Date
100
90
30
20
10
2008 2009
Date
bU
2008 2009
Date
LU
2008 2009
Date
2006 = Pre-Dredge; 2007 = During Dredge; 2008 - 2001 = Post-Dredge
A = Upstream; B = Fields Brook; C = Turning Basin; D = River Bend; E = Conneaut Creek
Figure 3-53. Percent Fines in Surface Sediments from the Ashtabula River Macrobenthos
Sample Locations (2007-2011).
113
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A
2008 2009 2010
Date
2008 2009 2010
Date
B
D
2008 2009 2010
Date
2008 2009 2010
Date
2006 2007
2008 2009
Date
2006 = Pre-Dredge; 2007 = During Dredge; 2008 - 2001 = Post-Dredge
A = Upstream; B = Fields Brook; C = Turning Basin; D = River Bend; E = Conneaut Creek
Figure 3-54. Total Organic Carbon (%) in Surface Sediments from the Ashtabula River
Macrobenthos Sample Locations (2007-2011).
over the study period, but in a highly variable manner. TOC data, similar to grain size data,
reflect a dynamic situation both in the short term (1 month) and long term (~6 years). This is
likely due to limited data and high variability, seasonal changes, and flow alterations.
114
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A common means of assessing these bulk sediment properties is to represent the sediment grain
size (as percent fines) vs. TOC under the premise that grain size relates to TOC. The Ashtabula
River bulk sediment properties do not have a consistent relationship among the deployment
locations. Two locations (Fields Brook and Turning Basin) displayed a negative slope (note the
Conneaut Creek Reference slope was also negative); two locations (Upstream and Turning
Basin) showed the expected positive slope. Note that in all cases the data are highly variable,
hence the confidence in the suggested correlations is low. No relationship was observed, and
these data are presented in Appendix F.
3.4.1.4 Co-located Surface Sediment PCBs
As observed in the bulk sediment properties, the sediment PCB results varied systematically
between the deployment and recovery samples (Table 3.11). For example, the Fields Brook
deployment and retrieval samples were substantially different in all years, with the retrieval
concentrations of tPCB(Zc) being substantially higher than deployment concentrations. There
were also differences in deployment and retrieval concentrations of tPCB(Xc) at the Turning
Basin location. However, the trend was not consistent over time. In 2007, retrieval
concentrations were greater, while in 2011, deployment concentrations were lower. These trends
at Fields Brook and the Turning Basin locations follow those observed for percent fines
(Appendix F), with finer sediments having increased tPCB(Xc) concentrations and coarser
sediments having lower tPCB(Xc) concentrations. To better integrate the sediment chemistry
data for each macrobenthos sampling event, the decision was made to average the deployment
and retrieval concentrations for each location and year in order to examine spatial and temporal
trends.
The tPCB(Xc) concentrations in the sediments from the macrobenthos deployment locations
varied from 1 mg/kg dry to -10 mg/kg dry (Figure 3-55). However, only the 2011 Fields Brook
value was greater than 5 mg/kg dry sediment. The tPCB(Xc) concentrations from the Upstream
and River Bend sediments were consistently the lowest of the four Ashtabula River locations,
with the Upstream sediment tPCB(Xc) concentrations always less than the River Bend sediments
and similar to the Conneaut Creek Reference. Sediment tPCB(Xc) concentrations at the other
Ashtabula locations were moderate but variable, with the Turning Basin location having the
highest concentrations in most years.
Normalization of the tPCB(Xc) data to the sediment organic carbon content produced slightly
more consistent spatial and temporal patterns (Figure 3-56). The organic carbon-normalized
tPCB(Xc) highest-to-lowest ranking was the Turning Basin, Fields Brook, River Bend, and then
the Upstream location. The TOC normalization did not change the relative ranking of the 2011
Fields Brook surface sediment data point. Additional sampling is recommended to assure that
tPCB(Xc) concentrations at Fields Brook are not increasing over time. The spatial and temporal
patterns in the grain size-normalized (percent fines) tPCB(Zc) concentrations in sediment were
similar to the TOC-normalized results. Hence, only the organic carbon-normalized sediment
results are considered hereafter.
115
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The PCB congeners in the macrobenthos sediment locations were also evaluated by homolog
groups (Figure 3-57). In general, the post-dredging PCB homolog distribution was similar for
sediments from Fields Brook, River Bend, and the Turning Basin locations. A clear post-
dredging change in the above homolog distribution occurs at the River Bend and Turning Basin
locations, whereas there was no pre- to post-dredging change at the Fields Brook location. In
contrast, the Upstream location experienced a slight increase in the contribution of the hexa
homolog series after dredging but otherwise had a completely different homolog distribution
than determined for the other Ashtabula River locations. The Upstream homolog distribution
was also relatively similar to those from the Conneaut Creek Reference, and the distributions at
these two sites are likely influenced by low concentrations of the individual PCB congeners.
3.4.1.5 Co-located Surface Sediment PAHs
Surface sediment tPAH16 concentrations were relatively similar and constant across all of the
macrobenthos sampling locations (Figure 3-58). The highest concentration was measured at the
River Bend location in 2007; the concentration at this location had decreased by 2008 and
remained very consistent thereafter. The Total PAH concentrations at the Upstream and Turning
Basin locations were typically two to three times higher than those at the Fields Brook location.
tPAH16 concentrations at all of the Ashtabula River locations were very similar by 2011 (range
from 3.32 to 4.75 mg/kg dry weight), but were all greater than the Conneaut Creek Reference
concentration (0.319 mg/kg dry weight). tPAH16 concentrations at the Fields Brook location
were most similar to the Conneaut Creek Reference concentrations in 2009 and 2010.
The patterns and relative concentrations in the tPAH34 data were generally similar to the
tPAH16 data with the exception of the 2008 and 2010 Turning Basin samples and the 2007 River
Bend samples. The contribution of the alkylated PAHs was higher in these locations for these
years.
Normalization of the PAH data to percent fines and TOC was explored to better understand the
role of the observed differences and changes in the PAH data. In contrast to the un-normalized
data, the percent fines-normalized tPAH16 concentrations were relatively high at the Upstream
location in 2007. Elevated tPAH16 concentrations in the 2009 Upstream location were still
present in the percent fines-normalized data (Appendix F). In general, the patterns for tPAH34
and tPAH16 normalized to percent fines were very similar.
The organic carbon-normalized tPAH (16 and 34) data had the same spatial and temporal
patterns as the percent fines-normalized data (Figure 3-59). The most notable exception was
generally decreasing concentrations at the Upstream location over the study period. The lowest
Total PAHs were measured at the Conneaut Creek Reference, which were only slightly lower
than sediments from the Fields Brook location.
Comparatively, the organic carbon-normalized tPAH16 and tPAH34 sediment spatial and
temporal patterns were generally similar (Figure 3-59). The temporal data patterns from these
two locations were also similar for the 3 years for which the data could be compared.
116
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Table 3.11: Comparison of tPCB(Zc), tPAH16, and tPAH34 in Surface Sediments Collected during Deployment and Retrieval
at the Macrobenthos Sample Locations in the Ashtabula River (2007-2011).
Upstream
Fields Brook
Turning Basin
River Bend
Conneaut Creek Reference
Event
Collection
Date
tPCB{£c)
tPAH16
tPAH34
Collection
Date
tPCB{£c)
tPAH16
tPAH34
Collection
Date
tPCB{Ec)
tPAH16
tPAH34
Collection
Date
tPCB{Ec)
t PAH 16
tPAH34
Collection
Date
tPCB{X c)
tPAH16
t PAH 34
{mg/kg dry wt)
{mg/kg dry wt)
{mg/kg dry wt)
{mg/kg dry wt)
{mg/kg dry wt)
HD_DEPLOYMENT
7/24/2007
0.005
1.838
2.576
7/24/2007
0.697
1.076
1.478
7/24/2007
0.377
1.205
2.295
7/24/2007
0.185
13.200
22.718
N/A
N/A
N/A
N/A
HD_RETRIEVAL
8/20/2007
0.019
5.187
7.046
8/20/2007
6.095
1.079
1.927
8/20/2007
1.933
11.401
16.223
8/21/2007
0.055
32.961
53.453
N/A
N/A
N/A
N/A
H D_DEPLOYMENT
8/11/2008
0.015
5.368
6.963
8/12/2008
0.130
0.530
0.724
8/11/2008
4.397
5.411
12.447
8/11/2008
0.758
3.389
5.564
N/A
N/A
N/A
N/A
HD_RETRIEVAL
9/8/2008
0.008
5.321
7.073
9/8/2008
1.470
4.691
6.187
9/8/2008
5.331
4.918
16.320
9/8/2008
0.427
3.274
6.634
N/A
N/A
N/A
N/A
HD_DEPLOYMENT
7/22/2009
0.002
10.822
15.404
7/22/2009
0.788
1.678
2.468
7/22/2009
1.449
3.880
7.217
7/22/2009
0.204
2.845
4.817
7/22/2009
0.002
1.548
2.483
7/22/2009
2.684
4.340
8.921
HD_RETRIEVAL
8/17/2009
0.012
10.322
13.003
8/17/2009
2.777
3.267
4.818
8/17/2009
2.132
5.398
9.821
8/17/2009
0.242
3.315
5.926
8/17/2009
0.005
2.094
5.393
8/17/2009
2.210
5.394
9.939
H DDEPLOYMENT
7/28/2010
0.007
6.187
7.944
7/28/2010
0.007
0.019
0.034
7/28/2010
4.109
12.516
33.486
7/28/2010
0.632
2.746
5.456
7/28/2010
0.004
0.072
0.247
HD_RETRIEVAL
8/25/2010
0.009
2.235
3.198
8/25/2010
0.131
0.252
0.368
8/25/2010
1.891
3.894
7.678
8/25/2010
0.433
5.438
8.633
8/25/2010
0.004
0.055
0.260
2007
+
+
+
2007
++
0
+
2007
++
++
+
2007
-
+
++
2007
N/A
N/A
N/A
2008
-
0
0
2008
++
++
++
2008
+
-
++
2008
-
0
+
2008
N/A
N/A
N/A
2009
+
0
-
2009
++
+
+
2009
+
+
+
2009
+
+
+
2009
+
+
++
2010
+
-
-
2010
++
++
++
2010
-
-
-
2010
-
++
++
2010
0
-
+
+ = concentration increased substantially from deployment
++ = concentration increased greatly from deployment
- = concentration decreased substantially from deployment
0 = No apparent change
-------
12 i
Upstream
Fields Brook
Turning Basin
River Bend
Conneaut Creek Reference
PRE-DREDGE 2006 DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Figure 3-55. tPCB(^c) in Sediments by Macrobenthos Sample Location and Year.
350
300
250
200
150
100
50
-¦-Fields Brook
-^Turning Basin
-H-River Bend
-i Conneaut Creek Reference
Upstream
u
O
tuo
E
to
u
a.
PRE-DREDGE
2006
DURING-DREDGE POST-DREDGE
2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Figure 3-56. Organic Carbon-Normalized tPCB(^c) in Sediments by Macrobenthos
Sample Location and Year.
118
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2 60%
° 40%
2006 2007 2008 2009 2010 2011
D
2006 2007 2008 2009 2010 2011
S 60%
a 50%
S 40%
? 30%
2006 2007 2008 2009 2010 2011
2006 2007 2008 2009 2010 2011
100%
70%
50%
20%
0%
2006 2007 2008 2009 2010 2011
¦ Mono ¦ Di ¦ Tri ¦ Tetra ¦ Penta ^ Hexa ¦ Hepta ¦ Octa III! Nona ¦ Deca
Upstream; B), Fields Brook; C) Turning Basin; D) River Bend; E) Conneaut Creek Reference
2006 is Pre-Dredge; 2007 is During Dredge; 2008 - 2011 are Post-Dredge.
Figure 3-57. Percent of tPCB(Vc) as Contribution of PCB Homologs in Surface Sediment
Collected from the Ashtabula River Macrobenthos Sample Locations (2006-2011).
119
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A
-•-Upstream
-¦-Fields Brook
-~-Turning Basin
-x-River Bend
-*-Conneaut Creek Reference
-•-Upstream
-¦-Fields Brook
—~—Turning Basin
-~~-River Bend
-•-Conneaut Creek Reference
PRE-DREDGE DURING-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE
2006 2007 2008 2009 2010 2011
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Note: No PAHs were analyzed in 2006 samples.
Figure 3-58. tPAH16 (A) and tPAH34 (B) Concentrations (mg/kg dry wt.) in Surface Sediments from the Macrobenthos
Sample Locations in the Ashtabula River (2007-2011).
-------
o
2
¦—
QO
E
1,400 -
1,200
1,000
800
600
400
200
-•-Upstream
-¦-Fields Brook
-~-Turning Basin
-~~-River Bend
-*-Conneaut Creek Reference
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
1,400 -
1,200
1,000
800
600
400
200
-•-Upstream
-¦-Fields Brook
-~-Turning Basin
-*- River Bend
-~-Conneaut Creek Reference
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Figure 3-59. Organic Carbon-Normalized tPAH 16 (A) and tPAH34 (B) Concentrations (mg/kg OC) in Surface Sediments
from the Macrobenthos Sample Locations in the Ashtabula River (2007-2011).
-------
3.4.1.6 Co-Located Water Chemistry Associated with Macrobenthos Sampler (H-D) Locations
Water samples were collected during the 2006, 2007, 2008, 2009, and 2010 deployment and
retrieval of the macrobenthos samplers (H-Ds) (Table 3.12). Water samples were not collected
in 2011. PAH data were not measured in 2006.
Several analyte concentrations varied greatly between the two collection events. For example,
the 2008 Turning Basin tPCB(Xc) concentration was 13 ng/L at deployment and 158 ng/L at
retrieval. Note that concentrations shown below (Figure 3-60) represent averages the
deployment and retrieval data, to try to better represent the water column concentrations
experienced by the macrobenthos during this exposure period. There was a high variability
noted between the two sampling periods, and the average concentrations among the four
Ashtabula River locations were also highly variable for any given collection period (Figure
3-60).
Table 3.12: Number of Co-located Water Samples Collected at Macrobenthos Sampler
(H-D) Locations.
Conneaut
Fields
Turning
River
Creek
Year
Upstream
Brook
Basin
Bend
Reference
2006
1 (R)
1 (R)
1 (R)
1 (R)
0
2007
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
0
2008
2 (D, R)
2 (D, R)
2 (D, R)
2 (D, R)
0
2009
2 (D, R)
2 (D, R)
4 (D, R)
2 (D, R)
2 (D, R)
2010
3 (D, R)
2 (D, R)
2 (D, R)
1 (R)
2 (D, R)
2011
0
0
0
0
0
D = deployment; R = retrieval
3.4.1.7 Co-located tPCB(Zc) Water Concentrations
The 2006 pre-dredging tPCB(Xc) concentrations were similar at the Upstream and River Bend
locations. In contrast, the 2006 concentrations were approximately two and five times higher at
the Fields Brook and Turning Basin locations, respectively. tPCB(Xc) concentrations in the
water from three of the four Ashtabula macrobenthos deployment locations generally decreased
after 2006, but increased at the River Bend locations when dredging was active in 2007. Small
increases were measured at the Fields Brook and Turning Basin locations in 2010 relative to
2009. For example, the average tPCB(Xc) concentrations in the Fields Brook water samples
decreased four-fold from 2006 (109 ng/L) to 2009 (27 ng/L) and then increased slightly in 2010
(52 ng/L). Similarly, tPCB(Xc) concentrations in Turning Basin water samples decreased
approximately nine-fold from 2006 (242 ng/L) to 2009 (28 ng/L), increasing slightly in 2010 (39
ng/L). The tPCB(Xc) concentrations at the River Bend location increased from 2006 (51 ng/L)
to 2007 (121 ng/L), then decreased through 2010. In contrast, tPCB(Xc) concentrations in the
water column from the Upstream and Conneaut Creek Reference were consistently low (<20
ng/L) from 2007 through 2010. This is slightly lower than the 49 ng/L measured at the Upstream
122
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location in 2006. It is important to note that the water samples were unfiltered whole water
samples and may have been influenced by suspended solids.
Although the PCB concentrations in the waters of the Ashtabula River varied over time and
space, the PCB homolog patterns were similar within each site, particularly after the remedial
dredging of 2007 (Figure 3-61). The Fields Brook and Turning Basin homolog patterns were
generally similar after 2007. In contrast, the Upstream area was different than the other locations
through time. The PCB homolog distribution from the Upstream locations most closely
resembled those from the pre-remedial dredging samples from the River Bend and Turning Basin
locations. The pre-remediation Upstream location homolog series had higher contributions from
hexa and hepta homologs compared to post-remediation Fields Brook, Turning Basin, and River
Bend locations. The later three locations had higher concentrations of the tri and tetra homolog
series. The samples from the Reference Area were too variable in their homolog distributions to
make definitive observations. This was likely due to very low concentration of congeners and
MDLs impacting the composition.
i
GO
C
300
250
150
100
-H-River Bend
Turning Basin
Conneaut Creek Reference
Upstream
Fields Brook
u
ES
of
u
Cl
+¦"
<
~i r
DURING-DREDGE
2007
PRE-DREDGE
2006
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
Figure 3-60. Average tPCB(^c) in Water Macrobenthos Samples by Location and Year.
123
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A B
100%
2006 2007 2008 2009 2010 2006 2007 2008 2009 2010
E
2006 2007 2008 2009 2010
¦ Mono H Di ¦ Tri BTetra ¦ Penta 0 Hexa ¦ Hepta ¦ Octa nil Nona ¦ Deca
Upstream; B), Fields Brook; C) Turning Basin; D) River Bend; E) Conneaut Creek Reference
2006 is Pre-Dredge: 2007 is During Dredge; 2008 - 2011 are Post-Dredge.
Note: No water samples collected at the Coimeaut Creek Reference in 2006, 2007, and 2008.
Figure 3-61. Percent tPCBs as Contribution of PCB Homolog Data for Water Column
Samples from the Ashtabula River Macrobenthos Stations (2007-2010).
124
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3.4.1.8 Co-located Total PAH Water Concentrations
The tPAH16 concentrations obtained from the water samples between 2007 and 2010 appeared
to decrease consistently except for the Upstream location (Figure 3-62). This location had
variable concentrations over the 4 years sampled. The 2009 Upstream concentrations (344 ng/L)
were the highest measured during the project and remained elevated in 2010 (235 ng/L). The
2009 concentrations are comparable to the 2007 results at the Upstream (322 ng/L) and Fields
Brook (321 ng/L) locations. The 2008 Upstream concentration (95 ng/L) appears to be an
anomaly for this location.
Upstream
Fields Brook
Turning Basin
•River Bend
Conneaut Creek Reference
PRE-DREDGE
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
Upstream
Fields Brook
Turning Basin
River Bend
Conneaut Creek Reference
PRE-DREDGE
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
Figure 3-62. Average Water tPAH16 (A) and tPAH34 (B) Concentrations (ng/L) in Benthic
Water Samples by Location and Year.
125
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The tPAH16 concentrations at the other three locations trended downward between 2007 and
2010, with trends at the Fields Brook and River Bend locations the most systematic of the four
locations. The tPAH16 concentrations from the River Bend location were consistently the
lowest measured in the Ashtabula River, decreasing from 143 ng/L in 2007 to approximately 45
ng/L in 2009 and 2010. The Fields Brook concentrations decreased from a high of 321 ng/L in
2007 to a low of 58 ng/L in 2009. In contrast, the tPAH16 Turning Basin water column
concentrations increased from 2007 (113 ng/L) to 2008 (191 ng/L) before decreasing to -100
ng/L in 2009 (101 ng/L) and 2010 (109 ng/L). The patterns and trends in the tPAH34
concentrations were similar to those found for tPAH16.
3.4.2 Indigenous Brown Bullhead
Indigenous brown bullhead were collected from the Ashtabula River and Conneaut Creek from
2006 to 2011 (Table 3.13). Fish were not collected from Conneaut Creek in 2009. The fish
samples were analyzed for PCB congeners and tPAH16. In addition, some Ashtabula River
(2007 through 2011) and Reference (2010 and 2011) samples were analyzed for tPAH34. The
Ashtabula River samples were analyzed by Battelle. The Conneaut Creek Reference Area
samples were analyzed at a U.S. EPA laboratory. Chemical analysis results averaged by location
and year are presented below.
Table 3.13: Number of Indigenous Fish Samples Collected.
Year
Ashtabula River1
Conneaut
Creek
Reference2
Total
2006
10
1
11
2007
9
9
18
2008
10
10
20
2009
10
0
10
2010
10
10
20
2011
10
10
20
Total
59
40
99
1 Battelle
2EPA NERL
The average lipid (g lipid/g tissue) content of the fish samples from the Ashtabula River and
Conneaut Creek Reference Area was fairly consistent over the study period except at Conneaut
Creek in 2006 (which represents a single sample) (Figure 3-63). Average fish lipid content
ranged from 0.03 to 0.07 g/g (3% to 7 %) for the Ashtabula River and from 0.05 to 0.08 g/g (5%
to 8%) for the Conneaut Creek Reference. However, the lipid content of individual fish samples
within a single year varied greatly, especially at the Ashtabula River in 2011 (i.e., 0.03 to 0.14
g/g [3% to 14%]).
3.4.3 PCB Results in Indigenous Brown Bullhead Fish
The list of PCB congeners analyzed by Battelle and the U.S. EPA laboratory diverges because of
analytical method-based differences. A list of PCB congeners (n = 93) "common" to both
126
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laboratories' analyses was developed as a way to present and compare tPCB(Xc) results between
the two locations. Summary tables of results for tPCB(Zc) and percent lipids are provided in
Appendix G.
0.12 -,
Ashtabula River
Reference Area
0.10
,SP 0.08
HI
S
-se
¦o
0.06
Q.
01
<
0.02
0.00
PRE-DREDGE DURING-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE
2006 2007 2008 2009 2010 2011
Note: n=10 for all years except 2007 (n=9), 2006 Conneaut Creek Reference (n=l), and
2009 Conneaut Creek Reference (n=0)
Figure 3-63. Average Lipid Content with Error Estimates (Standard Deviations) in
Indigenous Brown Bullhead Collected from the Ashtabula River and Conneaut Creek.
The wet weight tPCB(Xc) concentration in brown bullhead from the Ashtabula River varied
similarly whether aggregated as the full PCB congener list or the "common" PCB congener list
(Figure 3-64; A). Moreover, the Conneaut Creek PCBs were significantly less than the
Ashtabula River samples regardless of aggregation method.
Temporally, the by-weight tPCB(Zc) concentrations in the brown bullheads peaked in 2007
(4.754 mg/kg wet wt) when remedial dredging was active. This 2007 peak was followed by
decreasing concentrations through 2009 (0.965 mg/kg wet wt). tPCB(Zc) concentrations
increased in 2010 and 2011 (an average of 1.44 mg/kg wet wt), a value that is approximately
50% higher than the 2009 low. The PCB concentrations in the brown bullhead from the
Conneaut Creek Reference ranged from 0.110 to 0.262 mg/kg wet wt from 2006 to 2011.
Lipid normalization changed the temporal pattern in the Ashtabula River, specifically the
tPCB(Zc) maximum concentration shifted from 2007 to 2008 (108 mg/kg lipid), although
uncertainty in the means measured as the standard deviation of the average suggest the shift was
127
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not significant (Figure 3-64; B). However, the 2009 minimum was consistent with un-
normalized results and was followed by a slight increase in 2010 and 2011 (39 mg/kg lipid). The
Conneaut Creek Reference brown bullhead lipid-normalized concentrations were low and
consistent with un-normalized PCB data, with the highest measured concentration occurring in
2006 (14.6 mg/kg lipid) and the lowest in 2011 (2.3 mg/kg lipid).
The brown bullhead PCB data from the Ashtabula River were dominated by tetra, penta, hexa,
and hepta homologs in all years (Figure 3-65). The relative homolog contributions were
strikingly consistent across the study period. In contrast, the Conneaut Creek Reference fish had
high relative concentrations of the di homolog in 2006, but hexa homologs were more prevalent
and relatively consistent in the 2007 through 2011 samples. These variations in the homolog
distribution in the Conneaut Creek Reference samples appears to be due to low concentrations
near the MDL that skew the composition distribution. The Ashtabula River samples also appear
to have a greater contribution of hepta homolog series than measured in the Conneaut Creek
Reference samples, clearly supporting different PCB source types.
3.4.4 PAH Results in Indigenous Brown Bullhead Fish
tPAH16 concentrations in the brown bullhead catfish were elevated in 2006 and 2007 (0.191 and
0.196 mg/kg wet wt, respectively), with decreased concentrations in 2008 through 2011 (ranging
from 0.056 to 0.111 mg/kg wet wt) (Figure 3-66). In contrast, the Conneaut Creek tPAH16
concentrations increased notably from 2006 to 2007. The apparently elevated 2007
concentrations decreased from 0.111 to 0.047 mg/kg wet wt. between 2008 and 2011.
The trends in the lipid-normalized tPAH16 concentrations in the Ashtabula River samples were
similar to those in the un-normalized data. The elevated concentrations in the Ashtabula River
samples in 2006 (4.9 mg/kg lipid) and 2007 (3.1 mg/kg lipid) decreased to 1.3 to 1.8 mg/kg lipid
in the 2008 through 2010 period. In contrast to the un-normalized concentrations, the lipid-
normalized concentrations increased in 2011 (3.0 mg/kg lipid) to concentrations similar to those
measured in 2007. The lipid-normalized tPAH16 concentrations in brown bullhead catfish from
the Conneaut Creek Reference (0.6 to 2.2 mg/kg lipid) were generally lower than or similar to
catfish from Ashtabula River.
tPAH34 concentrations were not available for catfish collected from the Ashtabula River in
2006. The highest concentrations measured were in 2007 (0.710 mg/kg wet wt.). tPAH34
concentrations were substantially lower and relatively consistent between 2008 and 2011
(ranging from 0.106 to 0.192 mg/kg wet wt). These concentrations were similar to those in
catfish collected from the Conneaut Creek Reference in 2010 and 2011 (0.152 and 0.083 mg/kg
wet wt, respectively).
The lipid-normalized tPAH34 data trend (Figure 3-66) was similar to the un-normalized results,
although slightly more variable. The highest lipid normalized tPAH34 lipid concentration was
reported for 2007 (11.2 mg/kg lipid) and ranged from 3 to 4.6 mg/kg lipid from 2008 to 2011.
The 2010 and 2011 Conneaut Creek Reference lipid-normalized tPAH34 concentrations (2.51
and 1.14 mg/kg lipid, respectively) were similar, possibly lower than those for the Ashtabula
River.
128
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A
^^Ashtabula River - full PCB list
Reference Area - full PCB list
i i
i i i i
IS 250
Ashtabula River - full PCB list
Reference Area - full PCB list
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006 2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006 2007 2008
POST-DREDGE
2009
POST-DREDGE POST-DREDGE
2010 2011
Note: n=10 for all years except 2007 (n=9) and 2006 Conneaut Creek Reference (n=l) and 2009 Conneaut Creek Reference (n=0)
Figure 3-64. tPCB(Zc) Concentrations (mg/kg wet wt [A], and mg/kg lipid-normalized [B]) with Error Estimates (Standard
Deviations) in Indigenous Brown Bullhead Collected from the Ashtabula River and Conneaut Creek.
-------
100%
2006 2007 2008 2009 2010 2011
2006 2007 2008 2009 2010 2011
¦ Mono ¦ Di ¦ Tri ¦ Tetra ¦ Penta ^ Hexa ¦ Hepta B Octa nil Nona ¦ Deca
2006 is Pre-Dredge; 2007 is During Dredge; 2008 - 2011 are Post-Dredge.
Note: No fish were collected from the Conneaut Creek Reference in 2009.
Figure 3-65. Percent of tPCB(Zc) as Homolog Contributions in Brown Bullhead Collected from the (A) Ashtabula River and
(B) Conneaut Creek Reference (2006-2011).
-------
Ashtabula River
Conneaut Creek Reference
Ashtabula River
Conneaut Creek Reference
ap 0.30
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006 2007 2008
POST-DREDGE POST-DREDGE
2009 2010
POST-DREDGE
2011
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006 2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Ashtabula River
Conneaut Creek Reference
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006* 2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
~ 12
D
-•-Ashtabula River
-•-Conneaut Creek Reference
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006* 2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
*Total tPAH34 concentrations were not available for fish from Ashtabula River in 2006.
Figure 3-66. tPAH 16 (wet wt [A] and Lipid-Normalized |B|); and tPAH34 (wet wt [C] and Lipid-Normalized |D|) Concentrations in
Indigenous Brown Bullhead with Error Estimates (Standard Deviation) Collected from the Ashtabula River and Conneaut Creek
Reference (2006-2001).
-------
3.5 Passive Samplers as Biological Surrogates
Hydrophobic chemicals, such as PCBs, are known to accumulate in lipophilic materials. This
principle is the basis for the design of most passive samplers for organic chemicals. Two
common passive samplers use solid phase microextraction (SPME) and semipermeable
membrane device (SPMD) materials to measure organics in water, porewater, and sediments. It
is well established that when the chemical nature and partitioning coefficients of these materials
are known, the hydrophobic chemical concentration measured in the passive sampler can be used
to calculate the time-weighted chemical concentration in the water in which the sampler is
placed. This assumes the chemical has attained equilibrium between the materials and water that
is sampled. The addition of performance reference compounds (PRCs) allows for estimation of
uptake even if equilibrium is not reached. The advantage in using these samplers compared to
analyzing water and sediment samples directly is that the concentration of contaminants in the
passive samplers represents a time-weighted average. Compared to collecting and analyzing
biological samples (e.g., indigenous fish), passive samplers provide sampling at a fixed location
and are easier to deploy and retrieve than collecting biological samples.
SPMDs (EST, St. Joseph, MO) were deployed at a series of water column locations in the
Ashtabula River in 2006, 2008, and 2011 (Figure 2-5 and Table 2.5). These locations are
primarily in the river reach that was remediated in 2006/2007. Perforated stainless steel carrier
canisters that housed the water column SPMDs were attached to a buoy that suspended the
SPMDs approximately 1 m above the sediment surface for 28 days (U.S. EPA, 201 lb). Five
SPMDs were deployed at each site and after recovery were composited at the laboratory for
chemical analysis. Duplicate sediment SPMD samples were recovered for chemical analysis
from Stations 3, 4, and 6 in 2006, from Stations 1, 3, 4, and 5 in 2008, and from Stations 23 and
24 in 2011 (see Table 2.5). Single samples were available from the remaining locations. Those
stations with duplicates were averaged together, and the tPCB(Zc) concentration was used in
analysis and graphing.
For sediment, five SPMDs were deployed in specially designed racks at a series of locations in
the Ashtabula River (Figure 2-4; U.S. EPA, 201 lb). These SPMDs were also deployed for 28
days. Each SPMD was lightly rinsed using site water on recovery to remove excess sediment
that adhered to the sampler. All five SPDMs from a location were transferred into a common
hexane-rinsed can for shipment to the laboratory. The five SPMDs from each site were
composited at the laboratory for chemical analysis.
The results from each matrix, as well as from co-located water and sediment samples, were
evaluated (U.S. EPA, 2006, 2007, 201 la). The south-to-north (upriver-to-down river) geospatial
relationship shown in Figures 2-4 and 2-5 was retained among the locations presented in the
figures. SPMD data were reported on the basis of individual SPMDs, which enabled the SPMD
data to be converted to water equivalent data using published USGS spreadsheet conversion
models (Alvarez, 2010a; Alvarez, 2010b):
The data presentation that follows is separated into subsections that summarize the SPMD water,
SPMD sediment, SPME water, and SPME sediment results. Summary tables of tPCB(Zc) and
tPCB(EH) for co-located sediments and waters are provided in Appendix H.
132
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3.5.1 Water Column SPMDs
Water column SPMDs were deployed in the Ashtabula River at up to 11 stations in 2006, 2008,
and 2011 (Table 3.14 and Table 2.5). One SPMD composite sample was collected per station,
except at Stations 23 and 24 in 2011, where duplicate composite samples were collected at these
two stations. Co-located whole (not filtered) water samples were also collected at most
locations, with one field duplicate sample collected in each sampling year.
The SPMD PCB data are evaluated in terms of two concentration units: 1) mass of chemical per
SPMD (SPMD volumes are uniform across the individual samplers); and 2) converted to
equivalent water column concentrations (pg/L). Two approaches for the conversion were used;
one was PRC-corrected, and the other was uncorrected. The conversions were accomplished
with published USGS spreadsheet models: Version 4.1 - Estimated Water Concentration
Calculator from SPMD Data When Not Using PRCs (Alvarez, 2010b) and USGS spreadsheet
Version 5.1 - Estimated Water Concentration Calculator from SPMD Data Using PRCs (Alvarez,
2010a). The conversion process for SPMD data spiked with PRCs used the following equation:
Cw= n (Equation 3-17)
{vsKsw[l-exp(v^)\)
where
N = the amount of chemical accumulated by the sample (typically in ng);
Vs = volume of the SPMD (in L or ml);
Ksw = SPMD-water partition coefficient
Rs = the sampling rate (L/d); and
t = the exposure time (d).
Regression models are used to estimate a chemical's site specific sampling rate (Rs) and SPMD-
water partition coefficient (Ksw) using the chemical's partitioning coefficient, loss rate of the
PRC from the SPMD during deployment, and the volume of the SPMD (Huckins et al., 2006).
The conversion process for SPMD data without added PRCs utilizes the same equation, but
experimentally-derived Rs values are used instead of a site-specific sampling rate calculated
when PRCs are added (Alvarez, 2001c). Alvarez (2010c) contains additional guidance regarding
the use of these equations and provides the experimentally derived R values.
Table 3.14: Number of Water Column SPMDs and Co-located Water Samples Collected.
Water Column
Co-located
Year
SPMDs
Water Samples
2006
11
10
2008
10
12
2011
13
12
In cases where PCB data were reported as ng/SAMPLE, the data were first converted to
ng/SPMD by dividing by the number of SPMDs in each composited sample (generally five
133
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SPMDs aggregated into one analytical sample). An average volume from the five composite
SPMDs was used as the SPMD volume for each analytical sample. Where measurements were
not available for specific SPMDs, an average volume of all the SPMDs deployed in that year was
used. Where available, SPMD dimensions and weights were used to estimate the SPMD volume
required for the water column concentration conversion.
The available PRCs varied depending on the survey year:
2006-PCB 38 and PCB 50
2008 - PCB 29, PCB 38, PCB 50, PCB 166
2011 - PCB 38 and PCB 186 (note: PCB 186 could not be used in the conversion
calculation because final mass was greater than initial mass for most SPMD samples).
3.5.1.1 PCB Trends in Water Column SPMD Concentrations
PCB concentrations per SPMD were comparatively similar across all stations within a given year
(Figure 3-67). The concentrations appeared most variable in 2006 and least variable (most
similar) in 2011. Water column PCB concentrations (ng/SPMD) measured by the SPMDs
decreased at five stations (15, 3, 25, 24, and 22) after the 2006/2007 dredging. Five other
stations (1, 4, 23, 8, and 5) showed slight increases from 2006 to 2008. The stations that
decreased were generally located in the dredged upriver reach (except for Station 22, which was
located just downstream of the area dredged); those that increased were in the dredged down-
river reach (except for Station 1 located in the Upstream area and Station 4 in the middle of the
area dredged). Every station appeared to have lower SPMD concentrations in 2011 compared to
2008, but with variable degrees of relative decrease.
The PCB concentrations of water samples collected within each year were similar across all
stations but changed dramatically after 2006. Specifically, the PCB concentrations decreased
about five-fold between 2006 and 2008. In contrast, PCB concentrations in the 2008 and 2011
water samples were similar (Figure 3-68), although 2011 concentrations were slightly lower than
in 2008.
An important consideration is the comparison of water sample COC concentrations with water
column SPMD COC concentrations. This comparison was accomplished by converting the
SPMD data to water concentration equivalent data. Figure 3-69 compares the 2006 PRC- and
non-PRC-corrected SPMD concentrations to the co-located water concentrations. Major
concentration differences are evident among the three approaches. Most glaring is the large
difference between the measured and calculated water concentrations. Specifically, the 2006
PRC-corrected PCB concentrations determined from the water column SPMDs are up to 20
times lower than the measured water column concentration (-100 ng/L), while the uncorrected
data are five to 10 times lower. However, it is important to note that the measured total PCBs in
the water column include both dissolved and particulate fractions, while the SPMD data reflect
only the "dissolved" or mobile PCB fraction. This artifact of the measurement likely accounts
for the elevated tPCB(Zc) concentrations observed in the co-located water samples compared to
the true 'dissolved' concentrations measured by the SPMDs. TSS concentrations (mg/L)
134
-------
measured in the co-located water samples in 2006 indicate that the water column concentrations
of tPCB(Xc) were likely elevated due to inclusion of the particulate bound PCBs present (Figure
3-69). Overall trends in concentrations between SPMD PCBs and water column PCBs were
similar.
4500
4000
3500
Q
| 3000
to
oo
c
X 2500
u
W
of
2000
u_
1500
1000
500
0
1 15 3 25 12 4 24 23 8 5 22
Upstream to Downstream
*Note: No water column SPMD was collected at Station 12 during retrieval.
Average of duplicate samples at Stations 3, 4, and 6 in 2006; from Stations 1, 3, 4, and 5 in 2008; and
from Stations 23 and 24 in 2011.
Figure 3-67. tPCB(Ec) Concentration per SPMD Suspended in the Water Column.
Contrary to the above observations, the 2008 and 2011 PCB concentrations in the water samples
were fairly similar to the equivalent water column concentrations calculated from the water
column SPMDs (Figures 3-70 and 3-71). More specifically, the measured PCBs were
approximately five times lower in both 2008 and 2011 than in 2006, and the PRC- and non-PRC-
corrected SPMD concentrations were on the order of two to four times less than the measured
concentrations. TSS concentrations were also two to five times lower in 2008 and 2011
compared to 2006. The relative order of concentrations among the three approaches did not
change among the 3 years (measured concentrations highest, followed by uncorrected equivalent
concentrations, followed by PCR-corrected equivalent concentrations as the lowest) although the
relative separation between the PRC- and non-PRC-corrected SPMD data appears to decrease
between 2008 and 2011. Moreover, less spatial variability is apparent among the stations in
2008 and 2011 (for both co-located water and equivalent water) relative to 2006.
I PRE-DREDGE 2006
I POST-DREDGE 2008
I POST-DREDGE 2011
r i iiill ii ii
Station Station Station Station Station Station Station Station Station Station Station
135
-------
¦ PRE-DREDGE 2006
¦ POST-DREDGE 2008
¦ POST-DREDGE 2011
- 1
1
(
1
IITI
Station Station Station Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
Upstream to Downstream
*Note: No water samples were collected at Station 1 and Station 25 in 2006.
Figure 3-68. tPCB(Ec) Concentrations in Co-located Whole Water Samples.
An additional comparison that the experimental design enabled was the ability to contrast
temporal responses for spatially averaged annual concentrations. Notably, the PRC-corrected
equivalent PCB concentrations (Figure 3-72) averaged across all sampling stations by year did
not reveal a clear temporal trend from 2006 to 2011. The non-PRC-corrected PCB
concentrations may have decreased slightly in 2011 relative to 2006 and 2008.
In contrast, the average water sample PCB and TSS concentrations markedly decreased from
2006 to 2008; a further slight decrease in PCB concentrations appears between 2008 and 2011.
However, it is important to note, as described above, that the measured total PCBs in the water
column include both dissolved and particulate fractions, while the SPMD data reflect only the
"dissolved" PCB fraction. The TSS data indicate that the particulates in the whole water samples
used for this comparison are greatly influencing the total PCB concentrations in the water
samples. Hence, future comparison of water column and passive sampler data must ensure that
the particulate fraction is removed from the sample before extraction.
136
-------
120 i
100
-Co-located Water Sample
-Equivalent Water Concentration - PRC
-Equivalent Water Concentration - no PRC
¦ TSS
a
U
Station Station Station Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
Upstream to Downstream
Figure 3-69. 2006 PRC- and Non-PRC-corrected Water Column SPMD tPCB(Ec)
Concentrations Compared to Co-located Whole Water tPCB(Zc) and TSS Concentrations.
120 -|
100
• Co-located Water Sample
M Equivalent Water Concentration - PRC
Equivalent Water Concentration - no PRC
TSS
aT
u
te-
station Station Station Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
Upstream to Downstream
Note: No water column SPMDs were collected at Station 12 during retrieval.
Figure 3-70. 2008 PRC- and Non-PRC-corrected Water Column SPMD tPCB(Ec)
Concentrations Compared to Co-located Whole Water tPCB(Ec) Concentrations.
137
-------
120
100
80
oa'
u
CL
60
40
• Co-located Water Sample
M Equivalent Water Concentration - PRC
t Equivalent Water Concentration - no PRC
TSS
/
~- -
- 30
25
20
15
o
IA
•a
-------
3.5.1.2 PCB Distribution in the Water Column SPMDs and Co-located Water Samples
The spatially averaged PCB distributions in the SPMD samples were comprised mainly of tetra,
tri, and penta homolog groups (Figure 3-73). Little change in the distribution of the homolog
groups measured in the water SPMDs was noted over time, except for a slight decrease in the
percentage of hexa and hepta homolog groups from 2006 to 2008 (and an associated increase in
tetra and tri homolog groups). The 2008 and 2011 PCB homolog distributions were very similar.
PCB homolog distribution in the whole water samples (Figure 3-73) was substantially different
than found in the SPMD samples. The 2006 water column PCB distribution consisted mainly of
heavier homologs, including hexa, penta, and octa homolog groups (Figure 3-73). Moreover,
there was a shift in the distribution between 2006 and 2008 with the lighter homologs making up
a larger percentage of tPCB(Zc)s in 2008. Specifically, the percentage of octa and nona
homologs decreased in 2008, whereas the percentages of tri and tetra homologs increased. There
was little change in the percentages of homolog groups between the 2008 and 2011 water
samples.
The comparability of the homolog distribution in the measured and SPMD samples may be
biased by the inclusion of organic particulates in the whole water samples. As mentioned earlier,
it is important to understand the role of the particulate fraction when comparing PCB
concentrations in SPMD samples with those in whole water samples as the SPMDs measure only
the dissolved fraction. Also, the SPMDs provide data as a time-weighted average concentration
of a chemical within the whole exposure period, which may account for some of the differences
seen in the PCB distribution of the two sampling methods.
139
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A
B
g 60%
£ 40%
PRE-DREDGE
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
g 60%
£ 40%
PRE-DREDGE
POST-DREDGE
2011
DURING-DREDGE POST-DREDGE
2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Mono ¦ Di ¦ Tri BTetra ¦ Penta ^ Hexa ¦ Hepta ¦ Octa llll Nona ¦ Deca
Figure 3-73. Percent of tPCB(Zc) as Homolog Distributions for (A) Water Column SPMD Samples and (B) Co-located Water
Column Samples from the Ashtabula River (2006, 2008, and 2011).
-------
3.5.2 Sediment SPMDs
Sediment SPMDs were deployed on the sediment surface in the Ashtabula River at 24, 22, and
11 stations in 2006, 2008, and 2011, respectively (U.S. EPA, 2006, 2007, 2011). One SPMD
composite sample was collected per station (Table 3.15), except at Stations 1, 3, 4, and 5 in 2008
and Stations 23 and 24 in 2010, where duplicate composite samples were collected at these
stations. Co-located sediment samples were also collected at 10 or 11 of these locations, with
one field duplicate sample collected in 2008 and 2010.
The sediment SPMD data were reported as ng/SAMPLE. Consistent with the water column
SPMD data, these concentrations were converted to ng/SPMD by dividing the sample
concentration by the number of SPMDs in each sample (five SPMDs per sample).
The PCB concentrations in the sediment SPMDs varied spatially in each of the 3 years they were
deployed (Figure 3-74). The sediment SPMD PCB concentrations also appeared to be more
variable in 2006 than in subsequent years. The highest sediment SPMD PCB concentrations
were measured during the pre-dredge sampling of 2006 (Figure 3-74). The only exception was
at Station 1, where the 2011 Total PCB concentrations were higher than in 2006 and 2008.
The PCB concentrations in the co-located sediment samples were typically less than 600 ng/g
dry wt and similar across stations and years (within a factor of two). Exceptions to this were at
Stations 1 and 24 in 2011, where concentrations of tPCB(Zc)s were greater than at any other
station or sampling year (Figure 3-75).
The average PCB concentrations in sediment SPMDs decreased from 1,622 ng/SPMD in 2006 to
564 ng/SPMD in 2008 (-65% decrease). Comparatively, the average PCBs in the co-located
sediment samples decreased by -12% (0.433 mg/kg dry in 2006 vs. 0.381 mg/kg dry in 2008)
(Figure 3-76). The PCB concentrations in both sediment SPMDs and co-located sediment
samples increased from 2008 to 2011 (581 ng/SPMD and 0.621 mg/kg dry). This increase
observed in 2011 may have been due to individual samples that had PCB concentrations notably
greater than at other stations (i.e., sediment SPMD at Station 25 [Figure 3- 74] and co-located
sediment at Stations 1 and 24 [Figure 3-75]). Some of these locations (Stations 24 and 25) were
not sampled prior to 2011 and were not included in the 2008 dataset.
Table 3.15: Number of Sediment SPMDs and Co-located Sediment Samples Collected.
Year
Sediment SPMDs
Co-located
Sediment
Samples
2006
24
10
2008
26
12
2010
13
12
141
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3.5.2.1 PCB Distribution in Sediment SPMDs and Co-located Sediment Samples
PCBs in sediment SPMDs were comprised mainly of tri, tetra, and penta homolog groups, with
very little presence of nona or deca homologs (Figure 3-77). Little change was in the homolog
groups measured in the sediment SPMDs over time, except for a decrease in the percentage of
hexa through nona homolog groups from pre-dredge 2006 to post-dredge 2008. The percentage
of hexa homologs increased in post-dredge 2011, while the tetra homolog percentage decreased.
PCBs in co-located sediment samples also consisted of mainly tri, tetra, and penta homolog
groups, but with a larger contribution of heavier congeners (hepta through deca homolog groups)
(Figure 3-77). From pre-dredge 2006 to post-dredge 2008, the percentage of tetra homologs in
co-located sediments increased, while the percentage of hexa, hepta, and octa homolog groups
decreased. There was little change in the percentages of homolog groups between post-dredge
2008 and post-dredge 2011 in co-located sediment. Overall, the spatial distribution of PCBs in
sediment SPMDs and co-located sediments was similar over time.
5000
4500
4000
2" 3500
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1500
1000
500
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*Note: No sediment SPMDs were deployed at Stations 2, 20, 16, 14, 13, 18, 11, 19, 10, 21, 9, 17, 7, 6 in
2011; none at Stations 25, 24, 23 in 2006 and 2008; and none at Station 22 in 2006.
Figure 3-74. tPCB(Ec) Concentration per SPMD Placed on Surface Sediments from the
Ashtabula River (2006 [n=21], 2008 [n=22], and 2011 [n=ll]).
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3.5.2.2 Estimation of Porewater Concentrations from Sediment SPMDs
The bioavailability of chemicals in sediments is often estimated using their concentrations in
sediment porewater. The sediment SPMD PCB data were converted to equivalent porewater
concentrations (pg/L) using the same models as those applied to the water column SPMDs
(Section 3.5.1). Two approaches for the conversion were employed: 1) PRC-corrected, and 2)
uncorrected. The conversions were accomplished with published USGS spreadsheet models:
Version 4.1 - Estimated Water Concentration Calculator from SPMD Data When Not Using
PRCs (Alvarez, 2010b) and USGS spreadsheet Version 5.1 - Estimated Water Concentration
Calculator from SPMD Data Using PRCs (Alvarez, 2010a). The conversion process is
summarized below.
In cases where PCB data were reported in the database as ng/sample, the data were first
converted to ng/SPMD by dividing by the number of SPMDs in each composited sample (five
SPMDs were aggregated into one analytical sample). An average volume from the five
composite SPMDs was used as the SPMD volume for each analytical sample. Where
measurements were not available for specific SPMDs, an average volume of all the SPMDs
deployed in that year was used. Where available, SPMD dimensions and weights were utilized
to estimate the SPMD volume required for the porewater concentration conversion.
The available PRCs ranged from one to four, depending on the survey year:
2006-PCB 38 and PCB 50
2008 - PCB 29, PCB 38, PCB 50, and PCB 166
2011 - PCB 38 and PCB 186
For some SPMDs in 2006, the PRCs could not be used in the conversion calculation because the
final mass of the PRC was greater than the initial mass. The stations affected in 2006 were the
following:
Stations 1, 10, 11, 13, 16, 18, 19, 2, 20, 3, 4 (both duplicates), 6 (both duplicates), 9
A comparison was made of the estimated porewater concentrations of PCBs to those in the
overlying water column (-30 cm from the water-sediment interface). Porewater concentrations
and surface waters represent different environmental compartments. It is often beneficial to
compare those data to determine potential flux into or out of the sediment. Figure 3-78A
compares the 2006 PRC- and non-PRC-corrected SPMD concentrations to the co-located water
concentrations. Major concentration differences are evident among the three approaches. The
co-located water concentrations were much higher than those estimated using the SPMDs
deployed at the sediment surface for all years. Specifically, the 2006 PRC-corrected tPCB(Zc)
concentrations calculated from SPMD data, were more than 20 times lower than the measured
concentration in the co-located water samples, while the uncorrected data is five to 10 times
lower. However, as described previously (Section 3.5.1.1), the measured tPCB(Zc) in the water
column include both dissolved and particulate fractions, while the SPMD data reflect only the
"dissolved" PCB fraction. TSS concentrations (mg/L) measured in the co-located water samples
144
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100% -
FR 60%
B
PRE-DREDGE DURING-DREDGE POST-DREDGE
2006 2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
E 50%
3RE-DREDGE
POST-DREDGE
2011
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Mono ¦ Di BTri ¦ Tetra ¦ Penta ^ Hexa ¦ Hepta ¦ Octa llll Nona ¦ Deca
Figure 3-77. Percent of tPCB(Zc) as Homolog Distributions for (A) SPMDs Placed on Surface Sediments, and (B) Co-located
Sediment Samples from the Ashtabula River (2006, 2008, and 2011).
-------
in 2006 indicate that the water column concentration of tPCB(Zc) was likely elevated due to
inclusion of the particulate-bound PCBs present (Figure 3-69).
In contrast, the 2008 and 2011 tPCB(Xc) concentrations in the water samples were more similar
to the equivalent porewater column concentrations calculated from the sediment SPMDs
(Figures 3-78B and 3-78C). More specifically, the measured tPCB(Zc)s were approximately five
times lower in both 2008 and 2011 than in 2006 and the PRC- and non-PRC-corrected SPMD
concentrations were approximately two times less than the 2006 concentrations. The relative
separation between the PRC- and non-PRC-corrected SPMD data appears to decrease from 2006
to 2011 for most stations. Overall, no evident trends were apparent in concentrations across
stations for either the SPMD or measured water concentrations for any year; however, there
appears to be less spatial variability among the stations in 2008 and 2011 (for both co-located
water and equivalent porewater) relative to 2006.
An additional comparison enabled by the experimental design was the ability to contrast
temporal responses for spatially-averaged annual concentrations. The PRC-corrected and non-
corrected equivalent PCB concentrations (Figure 3-79) averaged across all sampling stations by
year revealed a notable decrease in tPCB(Zc) concentrations from 2006 to 2008, with a slight
subsequent increase in 2011.
146
-------
80
40
A
• Co-located Water Concentration
¦ Equivalent Porewater Concentration - PRC
i Equivalent Porewater Concentration - no PRC
•
•
•
•
• •
X
¦
¦ ¦
m "
iii"
Station Station Station Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
CO
• Co-located Water Concentration
¦ Equivalent Porewater Concentration - PRC
Equivalent Porewater Concentration - no PRC
• •
•
• •
• ^
i t i
•
. . i
Station Station Station Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
100
• Co-located Water Concentration
« Equivalent Porewater Concentration - PRC
Equivalent Porewater Concentration - no PRC
Station StationStation Station Station Station Station Station Station Station Station
1 15 3 25 12 4 24 23 8 5 22
Note: A = 2006; B = 2008; C = 2011
Figure 3-78. Estimated Porewater Concentrations (PRC- and Non-PRC-corrected)
Compared to Co-located Water Concentrations for 2006, 2008, and 2011.
147
-------
120 i
< 40
l Co-located Water
l Equivalent Porewater Concentration - PRC
] Equivalent Porewater Concentration - no PRC
L
PRE-DREDGE DURING-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE POST-DREDGE
2006 2007 2008 2009 2010 2011
Figure 3-79. Inter-Annual Comparison of tPCB(Zc) Concentrations for Estimated
Porewater Concentrations (PRC- and Non-PRC-corrected) to Measured Whole Water
Column Concentrations.
3.5.3 Solid Phase Microextraction Devices
Similar to SPMDs, SPMEs can be used to sample hydrophobic contaminants, such as PCBs, in
various environmental media. However, SPMEs have a much shorter equilibrium time (on the
scale of hours or days for SPMEs), do not require solvent extraction for PCB analysis5, and, if
handled carefully, can be reused after analysis. SPMEs were deployed in both the surface
sediments and in the water column at locations corresponding to the SPMD deployments
(Figures 2-4 and 2-5). In 2006, water SPMEs were deployed at six SPMD/SPME stations for 28
days (Stations 15, 4, 23, 8, 5, and 22) (Figure 2-5). In 2008, water SPMEs were deployed at 10
stations (the same six stations as in 2006, as well as at Stations 1, 3, 25, and 24) (Figure 2-5). As
mentioned previously (Section 3.5.1), co-located water samples were also collected at these
SPMD/SPME stations.
In 2006 and 2008, sediment SPMEs were deployed for 28 days at 11 of the SPMD/SPME
stations (Stations 1, 15, 3, 12, 11, 4, 10, 8, 7, 6, and 5) (Figure 2-4). Duplicate sediment SPME
samples were recovered for chemical analysis from Stations 3, 4, and 5 in 2006 and from
Stations 1, 3, 4, and 5 in 2008 (see Table 2.6). Single samples were available from the remaining
5 SPMEs can be analyzed in one of two ways. They can be inserted directly into a gas chromatograph (GC) and
'extracted' directly into the column, or they can be solvent extracted and the extract then injected into the GC for
analysis. For this study, the SPMEs were solvent extracted and the extract was analyzed for PCBs (see Section
2.9.4).
148
-------
locations. Those stations with duplicates were averaged together and the total PCB
concentration was used in data analysis. Co-located sediment was collected at about half of the
sediment SPME stations in 2006 and 2008.
3.5.3.1 PCB Distribution in the Water Column SPMEs and Co-located Water Samples
Detected concentrations of PCBs in water column SPMEs in 2006 ranged from 11.18 ng/SPME
(Station 5) to 12.32 ng/SPME (Station 23) (Figure 3-80). All of the PCB congeners were below
the detection limit at Stations 15 and 22. There was little spatial variability in the water SPME
tPCB(Zc) concentrations measured across stations in 2006. tPCB(Zc) concentrations measured
in the 2008 water SPMEs increased, ranging from 13.03 ng/SPME (Station 1) to 23.12 ng/SPME
(Station 24) (Figure 3-80). Station 1 is located in the Upstream portion of the Ashtabula River
(south of Fields Brook and the Turning Basin locations).
¦ PRE-DREDGE 2006
¦ POST-DREDGE 2008
STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION
1 15 3 25 4 24 23 8 5 22
Upstream to Downstream
*Note: No water column SPMEs were deployed at Station 1 in 2006; no water column SPMEs were
retrieved in 2006 from Stations 3, 25, or 24.
Figure 3-80. tPCB(Ec) Concentration per SPME Suspended in the Water Column in the
Ashtabula River (2006 and 2008).
The PCB concentrations in the co-located water samples were also fairly consistent across
stations and ranged from 93.8 ng/L (Stations 8 and 15) to 104.8 ng/L (Station 22) in 2006
(Figure 3-81). The tPCB(Zc) concentrations in the co-located water samples, however,
decreased approximately five fold in 2008 ranging from 12.89 ng/L (Station 1) to 26.69 ng/L
(Station 8). There was no apparent correlation between the tPCB(Zc) concentrations measured
149
-------
with the SPMEs and the concentrations in the co-located water samples; water concentrations
decreased substantially from 2006 to 2008, while SPME concentrations exhibited a marginal
increase over the same time period. As mentioned earlier, TSS concentrations (mg/L) measured
in the co-located water samples in 2006 indicated that the water column concentration of
tPCB(Zc) was likely elevated due to inclusion of the particulate-bound PCBs present (Figure 3-
69).
The spatially-averaged PCB distribution in the SPME samples was comprised mainly of tetra,
penta, and hexa homolog groups (Figure 3-82A). The percentage of tetra and tri homolog groups
increased from 2006 to 2008, while an associated decrease occurred in the penta and hexa
homolog groups.
Comparatively, tPCB(Zc) homolog distribution in the water samples was somewhat different
than found in the SPME samples (Figures 3-82 B and 3-82A, respectively). The 2006 water
column PCB distribution consisted mainly of heavier homologs, including hexa, penta, and octa
homolog groups (Figure 3-82B). Moreover, there was a shift in the distribution between 2006
and 2008, with the lighter homologs making up a larger percentage of tPCB(Zc)s in 2008.
Specifically, the percentage of octa and nona homologs decreased in 2008, whereas the
percentages of tri and tetra homologs increased. Therefore, the PCB distribution in water
samples in 2008 became more similar to the distribution in the SPME samples.
120
100
80
~ 60
W
CO
u
a.
40
20
0
Figure 3-81. tPCB(Ec) Concentrations in Water Samples Co-located with SPMEs in the
Ashtabula River (2006 and 2008).
PRE-DREDGE 2006
¦ POST-DREDGE 2008
STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION
1 15 3 25 4 24 23 8 5 22
Upstream to Downstream
150
-------
3.5.3.2 PCB Distribution in the Sediment SPMEs and Co-located Sediment Samples
Detected tPCB(Xc) concentrations in sediment SPMEs ranged from 10.98 ng/SPME (Station 3)
to 12.43 ng/SPME (Station 7) in 2006 (Figure 3-83). All of the PCB congeners were below the
detection limit at Station 1 in both 2006 and 2008 (1/2 the detection limit was used to calculate
the tPCB(Zc) concentrations for this station). tPCB(Zc) concentrations measured in the sediment
SPMEs were greater and more variable in 2008, ranging from 13.2 ng/SPME (Station 7) to 19.72
ng/SPME (Station 12). Most PCB congeners in the sediment SPME samples were not detected.
Most of the congeners detected were either tetrachlorobiphenyls, trichlorobiphenyls, or
pentachlorobiphenyls, with more detections occurring in 2008 than in 2006.
tPCB(Zc) concentrations in the co-located sediment (top 10 cm) collected from six SPME
stations in 2006 ranged from 0.152 mg/kg dry wt (Station 3) to 0.563 mg/kg dry wt (Station 12)
(Figure 3-84). Co-located sediment was collected at seven of the SPME stations in 2008. tPCB
concentrations in the co-located sediment ranged from 0.043 mg/kg dry wt (Station 1) to 0.533
mg/kg dry wt (Station 5). There was no apparent correlation between the tPCB concentrations
measured with the SPMEs and the concentrations in the co-located sediment; tPCB
concentrations in surface sediments increased at some SPME stations from 2006 to 2008 and
decreased at other stations.
Comparatively, tPCB(Zc) homolog distribution measured in the sediment samples (Figure 3-82C
and 3-82D) was somewhat different than found in the SPME samples. The 2006 sediment PCB
distribution consisted mainly of tetra, penta, and tri homolog groups. Moreover, a shift occurred
in the distribution between 2006 and 2008, with the lighter homologs making up a majority of
tPCB(Zc) in 2008. Specifically, the percentage of penta, hexa, and octa homologs decreased in
2008, whereas the percentages of tri and tetra homologs increased.
151
-------
A
B
Hi
¦
J
PRE-DREDGE
2006
DURING-DREDGE POST-DREDGE
2007 2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
PRE-DREDGE
2006
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Figure 3-82. Percent of tPCB(Ec) as Homolog Distributions of the Water Column SPME Samples (A), Co-located Water
Samples (B), Sediment SPME Samples (C), and Co-located Sediment Samples (D) from the Ashtabula River (2006 and 2008).
-------
100% -
o. 60%
y 4U:-:
PRE-DREDGE
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
O- 60%
P 40%
PRE-DREDGE
DURING-DREDGE
2007
POST-DREDGE
2008
POST-DREDGE
2009
POST-DREDGE
2010
POST-DREDGE
2011
Mono ¦ Di iTri BTetra ¦ Penta ^ Hexa ¦ Hepta ¦ Octa llll Nona ¦ Deca
Figure 3-82 (continued). Percent of tPCB(Ec) as Homolog Distributions of the Water Column SPME Samples (A), Co-located
Water Samples (B), Sediment SPME Samples (C), and Co-located Sediment Samples (D) from the Ashtabula River (2006 and
2008).
-------
¦ PRE-DRE
¦ POST-DF
DGE2006
EDGE 2008
.
nnmin
STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION
1 15 3 12 11 4 10 8 7 6 5
Upstream to Downstream
*Note: tPCB(Sc) Concentrations at Station 1 in 2006 and 2008 were below the detection limit, and Vi the detection
limit was reported.
Figure 3-83. tPCB(Ec) Concentration per SPME Placed on Surface Sediments from the
Ashtabula River (2006 and 2008).
0.6 -i
¦ PRE-DREDGE 2006
¦ POST-DREDGE 2008
STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION STATION
1 15 3 12 11 4 10 8 7 6 5
Upstream to Downstream
Figure 3-84. tPCB(Ec) Concentrations in Surface Sediment Samples Co-located with
SPMEs from the Ashtabula River (2006 and 2008).
154
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4.0 DISCUSSION
An overall objective of this report was to evaluate selected methods to characterize pre-, during,
and post-dredging physical, chemical and biological conditions within the ORD study area at the
Ashtabula River.
The primary means to achieve this objective was to assess each method's ability to detect spatial
or temporal change or both. Three general questions were defined to focus the assessment
methods:
• How effective is the method for detection of significant changes at individual
locations and between reference locations and contaminated areas?
• How effective is the method for detecting changes in chemical distributions or
patterns in matrices?
• How do the methods compare to one another? In this study, however, a direct
comparison between methods was difficult because deployments were not always co-
located. A qualitative discussion of overall findings among methods is included.
The methods assessed included the use of passive samplers (SPMDs and SPMEs) developed to
complement or replace biota for chemical fate and transport studies associated with contaminants
in aquatic systems. The biota tested included macrobenthic organisms, caged organisms such as
fish and bivalves, and chemical concentrations in indigenous fish.
A substantial amount of the data generated and discussed in this report relate to the
macrobenthos samplers and the SPMD water and sediment samplers as well as their co-located
sediment and water samples. Comparison of PCB concentrations measured by the appropriate
method (i.e., the macrobenthos tissues and SPMDs) with the concentrations of their co-located
sediment and water samples (when available) was performed using linear correlation. Statistical
analyses were also used to assess whether change could be detected over time and space within
and among co-located matrices and to compare the changes in PCB congener patterns over space
and time. Limited data from the indigenous catfish study were also assessed.
Data Screening
Prior to statistical analyses, data generated for macrobenthos and fish tissues and sediment and
water samples were screened by plotting the naive observed data, and the averaged results were
used in subsequent analysis. These plots are provided in Appendix I.
4.1 Macrobenthos Tissue Concentrations using Artificial Substrate Samplers
The results from the macrobenthos sampling were evaluated with direct measures of PCB and
PAH concentrations in co-located sediment and water samples using a linear correlation and an
ANOVA model (Section 2.10). Table 4.1 summarizes the measurements by area, year, and
sample type that are presented separately in Section 2 and it also expands on the tables by
showing the actual sample numbers collected and available for statistical analysis. The locations
of the macrobenthos stations are shown in Figure 2-10.
155
-------
In addition, changes in chemical characteristics of the PCBs (i.e., congener patterns) were
evaluated using PCA to evaluate the methods ability to distinguish changes pre- and post-
dredging and to aid in assessing the usefulness of the methods in measuring the efficacy of
environmental dredging. The specific questions that guided this assessment were:
• Do the macrobenthos chemical data correlate with chemical composition in co-
located sediments and water?
• Do the macrobenthos chemical data correlate with changes in accumulated chemical
patterns in tissue and co-located sediment and water data?
• Do the macrobenthos chemical data correlate with passive sampler sampling methods
(i.e., SPMDs and SPMEs)?
Table 4.1: Summary of Macrobenthos Study Samples used in ANOVA.
Year
Area
Macrobenthos'3'
Macrobenthos
Water Samples'15'
Macrobenthos
Sediment
Samples'0'
Upstream
2
1
0
Fields Brook
2
1
0
2006
Turning Basin
2
1
0
River Bend
2
1
0
Reference
0
0
0
Upstream
2
2
2
Fields Brook
2
2
2
2007
Turning Basin
2
2
2
River Bend
2
2
2
Reference
0
0
0
Upstream
2
2
2
Fields Brook
2
2
2
2008
Turning Basin
2
2
2
River Bend
2
2
2
Reference
0
0
0
Upstream
2
2
2
Fields Brook
0
2
2
2009
Turning Basin
2
4
4
River Bend
2
2
2
Reference
2
2
2
Upstream
2
3
2
2010
Fields Brook
2
2
2
Turning Basin
2
2
2
River Bend
2
1
2
156
-------
Table 4.1 (continued): Summary of Macrobenthos Study Samples used in ANOVA.
Year
Area
Macrobenthos'3'
Macrobenthos
Water Samples'15'
Macrobenthos
Sediment
Samples'0'
Reference
2
2
2
Upstream
4
0
1
Fields Brook
2
0
1
2011
Turning Basin
2
0
1
River Bend
2
0
1
Reference
2
0
1
Values indicate the number of replicates included in the calculations of the average values used in the
statistical analysis.
Macrobenthos data were not collected at Fields Brook in 2009 and were collected in four samples
upstream in 2011.
Water samples were not collected in 2006 fortPAH16 and tPAH34 or at all in 2011.
Sediment samples (aside from PCB congeners) were not collected in 2006.
4.1.1 Macrobenthos ANOVA
To address the first question (change in time compared to reference), three ANOVA screening
models, one model for the macrobenthos, one model for the SPMDs, and one model for their co-
located sediments and waters, were developed (as described in Section 2.10). Graphic
representations of the observed data aggregated by year and area with averages are included in
Appendix C.
The ANOVA compared the contamination levels by year and area sampled as follows:
• Lipid-normalized tPCB(Ec), tPAH16, and tPAH34 in macrobenthos samples
• Contaminants in sediment associated with macrobenthos samples
• Contaminants in water associated with macrobenthos samples.
Overall, ANOVA model results for lipid-normalized macrobenthos samples are shown in Table
4.2 by chemical (tPCB(Zc), tPAH16, and tPAH34). The estimated MSE or variance, model r-
square, and the p-values for the area and year fixed effects are shown. In all cases, the
macrobenthos data were determined to be log-10 distributed and so the response for these models
is the log-10 transformed average. Both year and area were significant for all models. A high
tPAH value was noted in the Upstream macrobenthos data for 2009. Sensitivity analyses
indicate that without this outlier, both factors remain significant, with r-square values 8% to 10%
higher.
The effect of normalization was assessed by comparing the root MSEs in the above models with
those using the un-normalized results. In general, this assessment resulted in minimal change in
conclusions reached, as seen in Table 4.2.
157
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Table 4.2: ANOVA Model Results for Raw and Lipid-Normalized Macrobenthos Factors.
Factor
Root Mean
Square
Error
r-Square
p-Values
Year
Area
Raw (Wet Wt) Macrobenthos Factors
tPCB(Ec) (mg/kg)
0.281
0.750
0.100
0.024*
tPAH16 (mg/kg)
0.240
0.672
0.246
0.033*
tPAH34 (mg/kg)
0.240
0.788
0.025*
0.028*
Lipid-Normalized Macrobenthos Factors
tPCB(Ec) (mg/kg)
0.236
0.865
0.015*
0.003*
tPAH16 (mg/kg)
0.160
0.867
0.006*
0.005*
tPAH34 (mg/kg)
0.172
0.906
0.001*
0.010*
* Statistically significant at the 0.05 level.
The least square means for year and area are represented in Tables 4.3 and 4.4, respectively.
Note that p-values for yearly least square means are calculated after accounting for variance due
to area and have been Bonferroni adjusted for five multiple comparisons in Table 4.3 and for
three multiple comparisons in Table 4.4.
Table 4.3: Least Square Means and Confidence Intervals for Lipid-Normalized
Macrobenthos Factor Results with Significant Pairwise Comparisons by Year.
Factor
Year
Least Square
Mean
95%
Confidence
Interval
Pairwise Significant
Differences
tPCB(Ec) (mg/kg lipid)
2006
118.4
(58.2, 240.9)
2008 < 2006 (p=0.038)
2009 < 2006 (p=0.038)
2011 <2006 (p=0.038)
2007
115.1
(56.6, 234.3)
2008
26.1
(12.8, 53.1)
2009
23.5
(9.6, 57.4)
2010
40.3
(19.8, 82)
2011
26.7
(13.1, 54.4)
tPAH16 (mg/kg lipid)
2006
102.2
(63.2, 165.4)
2008 < 2006 (p=0.004)
2009 <2006 (p=0.011)
2010 <2006 (p=0.038)
2011 <2006 (p=0.007)
2007
60.8
(37.6, 98.3)
2008
23.1
(14.3, 37.4)
2009
27.0
(14.7, 49.3)
2010
43.5
(26.9, 70.4)
2011
27.6
(17.1, 44.7)
158
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Table 4.3 (continued): Least Square Means and Confidence Intervals for Lipid-
Normalized Macrobenthos Factor Results with Significant Pairwise Comparisons by Year.
Factor
Year
Least Square
Mean
95%
Confidence
Interval
Pairwise Significant
Differences
tPAH34 (mg/kg lipid)
2006
323.7
(192.6, 543.9)
2007 < 2006 (p=0.009)
2008 <2006 (p<0.001)
2009 <2006 (p=0.001)
2010 <2006 (p=0.002)
2011 <2006 (p<0.001)
2007
109.7
(65.3, 184.4)
2008
36.1
(21.5, 60.6)
2009
41.4
(21.6, 79.5)
2010
66.6
(39.6, 111.9)
2011
36.0
(21.5, 60.6)
The least square means estimates (i.e., variability of the data) and associated confidence intervals
for lipid-normalized tPCB(Ec), tPAH16, and tPAH34 data by year (Figure 4-1) demonstrate
graphically that concentrations of tPCB(Ec)s, tPAH16s, and tPAH34s decreased from 2006 to
2011. Moreover, the data set has less variability in the later years. The years that are statistically
different from each other are summarized in Table 4.3.
Likewise, p-values for area least square means calculated after accounting for the variance due to
year and a Bonferroni adjustment for a three multiple comparison indicate that Fields Brook had
higher concentrations of tPCB(Ec)s in the macrobenthos comparisons and the data were more
variable across the years.
Table 4.5 summarizes the mean measurement values for each of the response variables for the
Upstream and Conneaut Creek Reference locations by year; these values were not included in the
ANOVA models. Table 4.6 lists the overall mean measurements for the Upstream and Conneaut
Creek Reference locations for each of the response variables.
Table 4.4: Least Square Means and Confidence Intervals for Lipid-Normalized
Macrobenthos Factor Results with Significant Pairwise Comparisons by Area.
Factor
Area
Least
Square Mean
95%
Confidence
Interval
Pairwise Significant
Differences
tPCB(Sc)
(mg/kg lipid)
Turning Basin
45.1
(27.2, 74.7)
Turning Basin < Fields Brook
(p=0.034)
River Bend < Fields Brook
(p=0.002)
Fields Brook
122.5
(69.9, 214.7)
River Bend
22.6
(13.7, 37.5)
tPAH16
(mg/kg lipid)
Turning Basin
52.7
(37.5, 74.2)
River Bend < Turning Basin
(p=0.016)
River Bend < Fields Brook
(p=0.008)
Fields Brook
61.2
(41.8, 89.4)
River Bend
24.3
(17.2, 34.1)
159
-------
Table 4.4 (continued): Least Square Means and Confidence Intervals for Lipid-
Normalized Macrobenthos Factor Results with Significant Pairwise Comparisons by Area.
Factor
Area
Least
Square Mean
95%
Confidence
Interval
Pairwise Significant
Differences
tPAH34
(mg/kg lipid)
Turning Basin
96.3
(66.7, 139.2)
River Bend < Turning Basin
(p=0.021)
Fields Brook
98.7
(65.5, 148.7)
River Bend
43.4
(30, 62.7)
The least square mean value for the Fields Brook location was significantly greater than the least
square mean values for both the Turning Basin and River Bend locations for tPCB(Ec). The least
square mean value for the River Bend location was significantly less than the least square mean
values for both the Turning Basin and Fields Brook locations for tPAH16. For tPAH34, the least
square mean value was significantly less at the River Bend location when compared to the least
square mean for the Turning Basin location.
The least square means for each year for the combined Turning Basin, Fields Brook, and River
Bend areas for tPCB(Ec), tPAH16, and tPAH34, respectively, are shown in Figure 4-1. The least
square geometric means along with corresponding confidence intervals for each of the three
locations (all years) are displayed in Figure 4-2. Note that since a log transform was necessary
for the model, geometric means and confidence intervals are provided, which result in
confidence bounds that are not symmetric about the geometric mean.
160
-------
A
^ 250
2
!§ 200
txo
^ 150
wj
E
X ioo
w
8 50
Cl
" 0
[
] [
]
_ . I
4 4^4
2006
2007
2008* „ 2009"
Year
2010
2011*
: Tliis year was significantly different from 2006 at the 0.05 significance level.
B
200
3 150
txo
100
x 50
<
Q.
¦*->
0
r
i
4 i
1 1
1
1
1
1
2006
2007
2008* 2009*
Year
2010*
2011*
: This year was significantly different from 2006 at the 0.05 significance level.
600
•a 500
m 400
I1 300
S 200
x
< 100
_[
]_
I
* ffl ® ffl
1 1 1 1 1 1
2006
2007*
2008*
Year
2009*
2010*
2011*
: This year was significantly different from 2006 at the 0.05 significance level.
Figure 4-1. Least Square Means for Lipid-Normalized Macrobenthos tPCB(Ec) (A);
tPAH16 (B); and tPAH34 (C) (mg/kg Lipid) Measurements in Fields Brook, Turning
Basin, and River Bend Stations by Year with 95% Confidence Intervals.
161
-------
Table 4.5: Means for Lipid-Normalized Macrobenthos Chemical Measurements by Year
for Upstream and Conneaut Creek Reference.
Measurement
Location
2006
2007
2008
2009
2010
2011
tPCB(Ic) (mg/kg
lipid)
Upstream
21.702
12.400
7.013
11.213
0.140
0.659
tPCB(Ic) (mg/kg
lipid)
Conneaut Creek
Reference
NA
NA
NA
1.082
0.126
1.470
tPAH16 (mg/kg
lipid)
Upstream
251.128
230.472
63.181
514.668
53.341
9.877
tPAH16 (mg/kg
lipid)
Conneaut Creek
Reference
NA
NA
NA
10.255
14.715
7.820
tPAH34 (mg/kg
lipid)
Upstream
552.225
338.975
86.822
680.239
69.419
11.156
tPAH34 (mg/kg
lipid)
Conneaut Creek
Reference
NA
NA
NA
19.124
19.457
7.960
NA - No data were available for this year
* This year was significantly different from 2006 at the 0.05 significance level
Table 4.6: Means for Lipid-Normalized Macrobenthos by Measurement for Upstream and
Conneaut Creek References.
Measurement
Upstream
Conneaut Creek
Reference
tPCB(Ic)
8.855
0.893
tPAH16
187.111
10.930
tPAH34
289.806
15.513
4.1.2 Macrobenthos PCA
PCA was used to explore differences in congener compositions measured in macrobenthos
tissues across locations and years. The PCA analysis tested whether the PCBs in the study
region could be related to Aroclor compositions and whether the pre- and post-dredge samples
reflected substantial change after the remedial dredging. The analysis suggests the PCBs taken
up by the macrobenthos deployed at the Upstream and Conneaut Creek Reference cluster
together in the upper left side of the PCA graph for all years and tend to overlap with the Aroclor
1268 signature (Figure 4-3). In contrast, the 2007 through 2011 Turning Basin and River Bend
macrobenthos data cluster in the upper right side of the PCA graph near Aroclors 1248 and 1254.
All Fields Brook samples cluster tightly within the larger Aroclor 1248 and 1254 signature. The
2006 Turning Basin and River Bend samples are outliers and fall within the same general area as
Aroclors 1260 and 1262. The cumulative variance was 47%.
162
-------
A
250
£¦ 200
txo
1b 150
bJQ
E
~ 100
u
l/vl
S 50
Q.
+-»
0
[
]
J
I
1 1 1
Turning Basin
Fields Brook
Location
River Bend
B
200
~ 150
bjo
"SB
£_
ID
X
<
Q.
100
50
J
J
f *
1
,
0
1 1
Turning Basin
Fields Brook
Location
River Bend
600
I 500
_g> 400
1? 300
3 200
x
< 100
*
I
Turning Basin
Fields Brook
Location
River Bend
Figure 4-2. Least Square Means for Lipid-Normalized Macrobenthos tPCB(Ec) (A),
tPAH16 (B), and tPAH34 (C) (mg/kg lipid) Measurements in Turning Basin, Fields Brook,
and River Bend Stations by Area with 95% Confidence Intervals.
163
-------
o —
UPT1
Uf^Pltl^06
m _
CM
O
CL
WfWs
/
Aroclor 1268
RF09
A1248
A123^1QfH^42 RB97 R5077
t FB°sFB^Ki
UP07"
A1221
Aroclors 1232.1016. and 1242
Aroclor 1254
A1254
TB08
A13^160
/
Aroclors 1260 and 1262
RB11
RB11 TBC
RB06
RB06
TB11
0
RB08
-6
-4
TB06
TB06
T
-2
T
0
T
2
T
4
T
6
PC1
Cumulative Variance Explained: PC1 29 %; PC2 47 %
Area
Symbol
Color
Upstream
UPxx
Purple
Fields Brook
FBxx
Blue
Turning Basin
TBxx
Yellow
River Bend
RBxx
Red
Conneaut Creek Reference
RFxx
Green
"xx" represents year
Figure 4-3. PCA for Macrobenthos tPCB(Ec) (All Stations, All Years).
4.1.3 ANOVA Analysis of Surface Sediment for Macrobenthos Stations
The raw (dry wt), TOC-normalized, and percent fines-normalized sediment tPCB(Zc), tPAH16,
and tPAH34 ANOVA screening model results are shown in Table 4.7. This table summarizes
estimated MSE, model r-square, and p-values for the year and area fixed effects. Area was
significant for tPCB(Ec) congeners normalized to TOC. The effect of normalization was
assessed by comparing the root MSEs in the models to one another. Normalizing to TOC
produced a large benefit for both tPAH16 and tPAH34 (MSE difference = 5216.7 and 9385.4,
164
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respectively) and a somewhat smaller benefit for tPCB(Ec) (MSE difference = 0.152).
Normalizing to percent fines demonstrated an additional benefit for tPAH16 and tPAH34 (MSE
difference = 186.6 and 347, respectively), but no benefit for tPCB(Ec) (MSE difference =
-0.331). The effect of area was only significant for tPCB(Ec); the corresponding results are
presented in Table 4.8.
Table 4.7: Screening ANOVA Model Results for Sediment Samples Associated with
Macrobenthos Sample Factors.
Factor
Root Mean Square Error
r-Square
p-Values
Year
Area
Raw(Dry Weight) Macrobenthos Factors
tPCB(Ic) (mg/kg Dry)(a)
0.560
0.522
0.697
0.055
tPAH16 (mg/kg Dry)
5.410
0.415
0.547
0.351
tPAH34 (mg/kg Dry)
9.745
0.390
0.700
0.291
TOC-Normalized Factors
tPCB(Ic) (mg/kg Dry)
0.408
0.655
0.528
0.027*
tPAH16 (mg/kg Dry)
0.194
0.403
0.745
0.239
tPAH34 (mg/kg Dry)
0.360
0.395
0.869
0.197
Percent Fines-Normalized Factors
tPCB(Ic) (mg/kg Dry)(a)
0.739
0.468
0.780
0.131
tPAH16 (mg/kg Dry)
0.007
0.392
0.382
0.832
tPAH34 (mg/kg Dry)
0.013
0.320
0.609
0.643
(a) tPCB(Ic) congeners normalized to both TOC and percent fines were found to be Iog-base10 distributed.
* Statistically significant at the 0.05 level.
Further examination of the least squares means for area for tPCB(Ec) congeners normalized to
TOC are presented in Table 4.8. Note that p-values for area least square means are calculated
after accounting for variance due to year and after they have been Bonferroni adjusted for three
multiple comparisons. Figure 4-4 presents the least square mean estimates and associated
confidence intervals for these factors by area.
165
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Table 4.8: Least Square tPCB(Ec) Means and Confidence Intervals for Sediment Sample
Measurements Associated with Macrobenthos Samples with Significant Pairwise
Comparisons by Year.
Factor
Area
Least
Square
Mean
95% Confidence
Interval
Pairwise Significant
Differences
tPCB(Ic)
(mg/kg OC)
Turning Basin
0.011
(0.004, 0.028)
River Run < Turning
Basin (p=.033)
Fields Brook
0.007
(0.003, 0.018)
River Bend
0.002
(0.001, 0.004)
^ °-03
u
o 0.025
W)
^ 0.02
W)
S, 0.015
g °'01
g 0.005
Q.
0
[
]
[
]
1
1 1
Turning Basin
Fields Brook
Location
River Bend
Figure 4-4. Least Square Means for tPCB(Ec) Normalized to TOC (mg/kg Dry) Sediment
Sample Measurements Associated with Macrobenthos Samples by Area with 95%
Confidence Intervals.
4.1.4 Surface Sediment PCA
PC A was used to explore differences in congener compositions measured in surface sediment co-
located with the macrobenthos stations across locations and years. The PCA graph for
macrobenthos surface sediment is similar to the macrobenthos PCA graph with the Upstream and
Conneaut Creek Reference samples clustering in the upper left side of the PCA graph in the
general vicinity of Aroclor 1268 (Figure 4-5). The Turning Basin, River Bend, and Fields Brook
samples all cluster together in the upper right side of the PCA graph near Aroclor 1248. The
2006 Turning Basin and 2006 River Bend sediment samples again are the outliers, similar to the
macrobenthos samples. The 2007 River Bend and 2010 Fields Brook samples also do not appear
where expected based on the other data and cluster around Aroclor 1254. The cumulative
variance was 57%.
166
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-10 -5 0 5
PC1
Cumulative Variance Explained: PC1 45 %; PC2 57 %
Area
Symbol
Color
Upstream
UPxx
Purple
Fields Brook
FBxx
Blue
Turning Basin
TBxx
Yellow
River Bend
RBxx
Red
Conneaut Creek Reference
RFxx
Green
Figure 4-5. PCA Showing PCB Congeners in Surface Sediment Co-located with
Macrobenthos.
4.1.5 Macrobenthos Water ANOVA
ANOVA model results for water sample measurements associated with macrobenthos samples
are shown in Table 4.9 by contaminant, including estimated MSE, model r-square, and the p-
values for the area and year fixed effects. Year values were significant for tPCB(Zc).
Further examination of the least square means for year and area for PCB congeners are described
in Table 4.10. Note that p-values for year least square means are calculated after accounting for
variance due to area and have been Bonferroni adjusted for five multiple comparisons; p-values
for area least square means are calculated after accounting for variance due to year and have
been Bonferroni adjusted for 10 multiple comparisons. Figure 4-6 displays the least square mean
estimates and associated confidence intervals for PCB congeners by year.
UP06
upUPH
UP08
P10
A1268
UP07
R^®®SjP07
UP08
\
Aroclors 1260 and 1262
TB06
FB1° Aroclors 1248. 2426.1101. and 1232
RB07
A1221
RB07
A1254
RB06
A1248
406
'08
167
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Table 4.9: ANOVA Model Results for Water Sample Measurements Associated with
Macrobenthos Sample Factors.
Factor
MSE
r-Square
p-Values
Year
Area
tPCB(Ic) (ng/L Liquid)
39.362
0.760
0.026*
0.162
tPAH16 (ng/L Liquid)
55.805
0.703
0.078
0.216
tPAH34 (ng/L Liquid)
78.160
0.713
0.063
0.294
* Significant at the 0.05 level of significance.
Table 4.10: Least Squares Means and Confidence Intervals for Water Sample
Measurements Associated wil
th Macrobenthos Sam
pies Factors.
Factor
Year
Least Squares
Mean
95% Confidence
Interval
Pairwise Significant
Differences
tPCB(Sc) (ng/L
Liquid)
2006
132.404
(80.288, 184.52)
2009 < 2006 (p=0.028)
2010 <2006 (p=0.031)
2007
114.906
(62.789, 167.022)
2008
55.595
(3.478, 107.711)
2009
24.827
(-20.723, 70.377)
2010
32.380
(-13.17, 77.93)
200.00
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W
150.00
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100.00
£
50.00
w
0.00
CO
u
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-50.00
[
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r
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[
] [
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I I I I I
2006 2007 2008 2009* 2010*
Year
* This year was significantly different from 2006 at the 0.05 significance level.
Figure 4-6. Least Squares Means for tPCB(Ec) (ng/L Liquid) Sample Measurements
Associated with Macrobenthos Samples by Year with 95% Confidence Intervals.
168
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Table 4.11 lists the mean tPCB(Ec) (ng/L liquid) measurements by year for the Upstream and
Conneaut Creek Reference locations, which were not included in the ANOVA model.
Table 4.11: Means for tPCB(Ec) (ng/L Liquid) Sample Measurements by Year for
Upstream and Conneaut Creek Reference
Location
2006
2007
2008
2009
2010
2011
Upstream
49.450
17.088
15.885
17.455
11.570
49.450
Conneaut Creek
Reference
NA
NA
NA
17.578
15.638
NA
NA: No data were available for this year.
4.1.6 PC A for Waters from Macrobenthos Stations
PCA was used to explore differences in congener compositions measured in water samples co-
located with the macrobenthos tissue samplers across locations and years. The PCA graph for
macrobenthos water samples is very different from the macrobenthos PCA graph and the surface
sediment PCA graph (Figure 4-7). In the water PCA analysis, the 2007 Fields Brook, River
Bend, and Turning Basin samples all clustered in the upper left corner of the PCA graph. The
2008, 2009, and 2010 data are in close proximity to one another, but not in the same tight cluster
as before and closer to Aroclor 1254 and Aroclor 1268. The cumulative variance was 48%.
4.1.7 Comparison of Macrobenthos Tissue and Co-located Sediment and Water
PCBs
4.1.7.1 Correlation Analysis
A simple linear correlation was performed using tPCB(Zc) data (normalized to lipids) from all
stations and all years and tPCB(Zc) in co-located sediment (normalized to TOC) and co-located
surface water (Figure 4-8). Little correlation was observed between the tissue and sediment data.
A somewhat stronger correlation was observed between tPCB(Zc) in macrobenthos tissue and
co-located tPCB(Zc) in the surface water. Uptake of PCBs by the macrobenthos occurs
primarily through contact with the water column, so this observation is not unexpected.
169
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FB07FB07
CM
o
CL
FB
6®07 '7
RB07
TB08
^~Aroclors 1248.1242.1232. and 1016
A1221
FB10
A1254
RBO^609 T?S®§ RB08
s
Aroclor 1268
A"1 ^09
/M8S&
FB08
TB09
-10
0
PC1
Cumulative Variance Explained: PC1 35 %; PC2 48 %
Area
Symbol
Color
Upstream
UPxx
Purple
Fields Brook
FBxx
Blue
Turning Basin
TBxx
Yellow
River Bend
RBxx
Red
Conneaut Creek Reference
RFxx
Green
Figure 4-7. PCA Showing PCB Congeners in Waters with Macrobenthos Samples.
4.1.7.2 PCA Comparing Macrobenthos Tissues, Sediment, and Water
PCA showing PCB congener distribution of macrobenthos tissues, and co-located sediments and
waters is presented in Figure 4-9. Interestingly, the sediment along with tissue patterns for all
locations except the upstream sites in all years and the River Bend and Turning Basin in 2006
cluster near to each other, between A1248 and A1254. This would seem to indicate that the PCB
patterns observed in the tissues during this time frame reflect what is observed in the sediments.
The upstream sites for sediment and tissue appear to cluster around the heavier Aroclor
1260/1268, indicating that the composition of PCBs in this location is different from the
downriver sites. This should be interpreted that the PCB congeners are of a similar make-up
regardless of whether they are derived from water, sediment, or tissue samples.
170
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4S0
P 400
350
300
250
= 200
g 150
c 100
50
450
0
50
100
150
200
250
300
350
400
Total PCB in Macroinvertebrate Samples (ng/g lipid)
160
J 140
120
100
40
20
0
40
60
120
20
80
100
140
160
180
200
Total PCB in Macroinvertebrate Samples (|ig/g lipid)
Figure 4-8. Correlation Plot between tPCBs(Sc) in Macrobenthos Tissues and Co-located
Sediments (TOC Normalized) and Waters.
171
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CM
O
CL
Macroi invertebrate
Sediment
Water
Aroclor
TB06
TB06
RB06
RB06
RB06
Aroclor 1254
TB06
Aroclor 1260 & 1262
UP0BP07 |
ft!
hm up(^09
UP11
TB^06
UP07
RB07
UP1QM268
RB08
rn^r,RB08
FB10
FB09
A1221
A1248
dQPCfc
RF09
UP07
Aroclor 1248 A1016
0
PC1
Cumulative Variance Explained: PC1 24 %; PC2 37 %
Code: XX##, where XX = station and ## = year
Figure 4-9. PCA Showing PCB Congeners in Macrobenthos Tissue and Co-located Surface
Sediments and Waters.
4.2 SPMDs
SPMDs were used to measure integrated in situ PCB concentrations from either the water
column or porewater. Water concentrations were calculated from the water column SPMDs to
compare with the PCBs directly measured in the water column both with and without the PRC
recoveries. Sediment SPMD results were used to calculate porewater concentrations; however,
direct porewater measurements were not determined during this study. Comparisons were made
between the concentrations found in both types of SPMDs to the co-located water and sediment
measurements using a linear correlation model.
ANOVA was performed to determine significance differences over time and space of the PCBs
in the water column SPMD and co-located water samples only.
172
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4.2.1 Correlation between SPMDs and Co-Located Sediments and Waters
PCBs measured in the water column were made on whole water samples, not filtered samples,
and SPMDs would not accumulate particle-associated chemicals. As such, no correlation was
observed between the concentrations found in the water column SPMDs compared to the actual
measured water concentrations either on a ng/SPMD or ng/L basis. It is interesting to note,
however, that the water column concentrations in 2006 were much higher than those measured in
subsequent years (post-dredging); however, this difference was not observed in the water column
SPMDs (Figures 4-10 and 4-11). However, as noted in Section 3.5.1.1, these higher water
column concentrations are likely a result of high particulates in the water column.
4500
4000
Q 3500
5
&¦ 3000
£ 2000
8 1500
Cl
q 1000
5
S; soo
S o H 1 1 1 1 1
0 20 40 60 30 100 120
Co-located Water tPCB(EC) (ng/L)
Figure 4-10. Correlation between Water Column SPMD and Co-located Whole Water
Sample tPCB(ZC) Concentrations.
173
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£ 8
u =
g 3
20 4-0 60 30
Co-located Water tPCB(EC) (ng/L)
100
120
Figure 4-11. Correlation between Water Column SPMD Estimated Water and Co-located
Whole Water Sample tPCB(EC) Concentrations.
Additional evaluation of the data sets (2006 and 2008/2011) across sampling stations within a
single year did not reveal any localized correlation between water column measurements and
SPMD measurements (Figures 4-12 and 4-13).
4500
0 4000
s
5; 3500
"35
£¦ 3000
C
H 2500
CG
q! 2000
1 1500
CL
£ 1000
^ 500
0
70 75 30 35 90 95 100 105 110
Co-located Water tPCB(EC) (ng/L)
A -a
2006
~ 15
* 24
R1 = 0.004S
~ 22
~ 12
~ 5
~ 23
t 1 1 1 1 r
Note: Chart symbols are labelled with the Station ID.
Figure 4-12. Correlation between 2006 Water Column SPMD (ng/SPMD) and Co-located
Whole Water Sample tPCB(EC) Concentrations by Stations.
174
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3000
a
2 2500
Q.
%
•S 2000
(J
-------
2006
2008 & 'Oil
Co-located Water tPCB(ZC) (ng/L)
Figure 4-15. Correlation between Sediment SPMD (ng/L) and Co-located Whole Water
Sample tPCB(ZC) Concentrations.
4.2.2 Water Column SPMD ANOVA
A two-way ANOVA was performed to evaluate the effects of year and station on PCBs in the
water column SPMDs. The average level is modeled to be a constant average plus an offset for
additive effects for year and station, where the constant (intercept) is the estimated level for
Station 3 of year 2006. For SPMD concentrations, some 2006 observations were notably higher
than the rest of the data, making the range in the values wider in 2006 than the following years.
However, the PCB levels are generally of the same order of magnitude, and the residuals from
each of the ANOVA models are approximately normally distributed with a zero mean.
Therefore, the ANOVA was performed on the natural scale.
Visual inspection of quantile-quantile plots (not shown) indicated that the residuals were
approximately normally distributed for each response; in all cases, the Shapiro statistic for non-
normality was not significant. The year effect was significant for all responses except the
equivalent water concentrations using PRC when the analysis includes Station 25 2006 values.
If Station 25 2006 is excluded from the latter analysis, the effect of year is significant. The effect
of station is only significant for the co-located water concentrations. Table 4.12 summarizes the
model results. ANOVA indicates that the effect of year is significant (p= 0.013) and the effect of
station is not statistically significant (p=0.299). Neither year nor station has significant effect on
water concentrations estimated from the PRC when all the data are included. Year effect is
significant for the co-located water concentrations (p<0.0001). For this response, the effect of
station is also significant (p=0.0003).
The 2006 observation for Station 25 has a very large residual that contributes to a high estimate
of mean square error. If the analysis is repeated with this observation excluded, the effect of
176
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year is significant (p=0.033). All subsequent evaluations of these data excluded Station 25
results from 2006.
Table 4.12: Results of the Two Way ANOVA for Water Column SPMDs and Co-located
Water Samples.
Factor
Root Mean
Square Error
r-Square
p-Values
Year
Station
tPCB(Ic)s (ng/SPMD)
617.0
0.576
0.013*
0.299
Estimated Water Concentration (ng/L) using
PRCs (Including Station 25 2006)
1.5
0.517
0.101
0.249
Estimated Water Concentration (ng/L) using
PRCs (Excluding Station 25 2006)
1.1
0.621
0.033*
0.113
Estimated Water Concentration (ng/L) NOT
using PRCs
4.3
0.576
0.013*
0.299
Co-located Water Concentration (ng/L)
(Excluding Station 25 2006)
4.2
0.992
<0.0001*
<0.0001*
* Statistically significant at the 0.05 level.
SPMD tPCB(Zc) (ng/SPMD). The post-hoc pairwise analysis of SPMD (ng/g SPMD) results by
year (pooling all station locations) indicates that the decrease in average PCB levels across
stations from 2006 to 2011 is statistically significant (Table 4.13). However, no significant
change was observed between other years.
Table 4.13: Least Squares Means and Confidence Intervals for SPMD tPCB(Ec)
(ng/SPMD).
95% Confidence
Interval
Pairwise
Factor
Year
Least Squares Mean
Significant
Differences
tPCB(Zc)
(ng/SPMD)
2006
2140.64
(1691.4, 2589.8)
2011<2006
(p=0.0141)
2008
1844.60
(1373.5, 2315.7)
2011
1329.36
(880.2, 1778.6)
Estimated Water Concentrations using PRCs. The post-hoc pairwise analysis of estimated
water concentration (ng/L) results by year (pooling all station locations) indicate that the change
in average PCB levels across stations from 2008 to 2011 is statistically significant (Table 4.14).
Unlike the other responses, the change in concentrations actually increased from 2008 to 2011.
However, no significant change was observed between other years. The analysis indicates a
significant year-to-year variability, but this variability is not indicative of a trend for this
response.
Co-located Water Concentrations. The post-hoc pairwise analysis of co-located water
concentration (ng/L) results by year (pooling all station locations) indicate that the decreases in
average PCB levels across stations from 2006 to 2008 and from 2006 to 2011 are statistically
significant (Table 4.15).
177
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Table 4.14: Least Squares Means and Confiden<
Concentrations usin
:e Intervals for Estimated Water
g PRCs.
Factor
Year
Least Squares Mean
95% Confidence
Interval
Pairwise
Significant
Differences
Estimated Water
Concentration
using PRCs (ng/L)
2006
3.8
(2.9, 4.8)
2011>2008
(p=0.037)
2008
3.4
(2.5, 4.3)
2011
4.7
(3.9, 5.6)
Table 4.15: Least Squares Means and Confidence Intervals for Co-located Water
Concentrations.
Factor
Year
Least Squares Mean
95% Confidence
Interval
Pairwise
Significant
Differences
Co-located Water
Concentration
(ng/L)
2006
95.6
(92.8, 98.5)
2006>2008
(p<0.0001)
2006>2011
(p<0.0001)
2008
18.9
(16.2, 21.6)
2011
17.7
(15.1, 20.3)
4.3 Indigenous Fish
Brown bullheads were sampled to evaluate remedy effectiveness and relate the remedy to the BUIs.
Table 4.16 shows the number and location of brown bullhead samples collected from 2006 through
2011.
Table 4.16: Brown Bullhead Samples Collected from 2006 through 2011 in the Ashtabula
River and the Conneaut Creek Reference Location.
Year
Station
Fish Samples'3'
2006
Ashtabula River
10
Conneaut Creek Reference
1
2007
Ashtabula River
9
Conneaut Creek Reference
9
2008
Ashtabula River
10
Conneaut Creek Reference
10
2009
Ashtabula River
10
Conneaut Creek Reference
0
2010
Ashtabula River
10
Conneaut Creek Reference
10
2011
Ashtabula River
10
Conneaut Creek Reference
10
(a)Fish samples were not collected at individual areas within the Ashtabula
River; tPAH34s were not analyzed in 2006, and reference tPAH34s were
not analyzed prior to 2010; reference samples were not measured in
2009.
178
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4.3.1 ANOVA for Fish Tissue Chemistry
ANOVA was conducted on the fish data for tPCB(Zc), "Common" tPCB(Ec) (see Section
3.4.2.1 for discussion of tPCB(Ec) determination for fish samples), tPAH16, and tPAH34 (Table
4.17). There was not a significant difference among concentrations measured in fish at either the
Reference location or within the Ashtabula River by area or over time (Table 4-17).
The effect of lipid normalization of the contaminant tissue data was assessed by comparing the
root mean square errors in the above models with those using the non-normalized results. In
general, there is little change in conclusions as is seen in Table 4.17, but area was significant for
the concentrations of tPCB(Xc) and the "Common" tPCB(Xc) in fish caught within the Ashtabula
River. These concentrations were significantly different than those for fish collected at the
Conneaut Creek Reference location. In addition, the root mean square errors were much smaller
using non-normalized factors.
Further examination of the least squares means of tPCB(Xc)s calculated using all available
congeners as well as the "common" PCB list by area is provided in Table 4.18. This analysis
shows that using the 'common list' of PCB congeners did not have an effect on the evaluation of
the fish results. Note that p-values for year least squares means were calculated after accounting
for variance due to year. Figures 4-16 and 4-17 display the least squares means estimates and
associated confidence intervals for tPCB(Zc) by area using both calculation methods.
In Table 4.18, the least square means for Conneaut Creek Reference for each Factor is set to 0
since the ANOVA model gives a negative value for each measurement. Additionally, the lower
bound of the 95% confidence interval is also truncated at 0 since a negative concentration does
not make sense in the context of the problem.
179
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Table 4.17: ANOVA Model Results for Fish Factors.
Factor
Root Mean
Square Error
r-Square
p-Values
Year
Area
Wet Weight Fish Factors
tPAH16 (mg/kg)
0.046
0.616
0.469
0.648
tPAH34 (mg/kg)
0.068
0.984
0.201
0.655
tPCB(Ic) (mg/kg)
1.077
0.805
0.461
0.056
Common tPCB(Ic) List (mg/kg)
0.888
0.793
0.475
0.063
Lipid-Normalized Fish Factors
tPAH16 (mg/kg lipid)
0.946
0.497
0.866
0.261
tPAH34 (mg/kg lipid)
1.383
0.970
0.301
0.403
tPCB(Ic) (mg/kg lipid)
22.806
0.860
0.373
0.031*
Common tPCB(Ic) List (mg/kg lipid)
18.940
0.847
0.395
0.036*
* Statistically significant at the 0.05 level.
Table 4.18: Least Squares Means and Confidence Intervals for Fish Sample Measurements
Factor
Area
Least Square
Mean
95% Confidence
Interval
tPCB(Ec) (mg/kg)
Ashtabula River
60.935
(27.684, 94.185)
Conneaut Creek
Reference
0.000*
(0.000*, 36.062)
Common tPCB(Ec)
List (mg/kg)
Ashtabula River
47.684
(20.071, 75.298)
Conneaut Creek
Reference
0.000*
(0.000*, 30.32)
The value is set to zero since the model returns a negative value.
180
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100
#J3
~£fc
S
u
W
CO
u
Q.
80
60
40
20
r
1
1 E
3 1
Ashtabula River
Conneaut Creek Reference
Figure 4-16. Least Squares Means for tPCB(Ec) Normalized to Lipids (mg/kg Lipid)
Calculated using tPCB(Ec) Fish Sample Measurements by Area with 95% Confidence
Intervals.
if
S
u
W
CO
u
Q.
o
E
E
o
u
80
70
60
^ 50
S"
:§•40
~ 30
20
10
0
[
J
1 E
3 1
Ashtabula River
Conneaut Creek Reference
Figure 4-17. Least Squares Means for tPCB(Ec) Normalized to Lipids (mg/kg Lipid)
Calculated using Common Congener Fish Sample Measurements by Area with 95%
Confidence Intervals.
4.3.2 PCA for Fish
The fish collected from the Ashtabula River each year (2006 to 2011) were evaluated using PCA.
Some of the years had much less variability within the samples compared to the other years
(Figure 4-18). The 2006 Ashtabula River samples clustered together in the middle to lower left
of the graph. The 2007 data clustered together in the bottom of the graph. The 2008, 2009,
2010, and 2011 samples are all clustered together in the upper left corner of the plot. The
cumulative variance was 51%. Fish were collected throughout the river in each year and direct
exposure in any given year may have been different; however, these results appear to show that
fish from 2006 (pre-dredging) and 2007 (during dredging) appear to have accumulated different
PCB compositions than fish collected from post-dredging conditions from 2008 through 2010.
181
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Note: Code =
Pink = 2006
Gray = 2007
Blue = 2008
Purple = 2009
Green = 2010
Yellow = 2011
Black = Aroclor
Figure 4-18. PCA using tPCB(Zc) for Brown Bullheads from the Ashtabula River from
2006 through 2011.
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Fish #.Year
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5.0 CONCLUSIONS
This research project was designed to develop and evaluate methods and metrics based on
physical, chemical, and biological lines of evidence (LOEs) for characterizing sediment and
ecosystem response to remediation, and more specifically to environmental dredging. A further
objective was to develop methods and define an approach for measuring and characterizing
sediment residuals formation during environmental dredging. The methods generated for
evaluating dredge residuals were thoroughly discussed in "Field Study on Environmental
Dredging Residuals: Ashtabula River, Volume 1. Final Report" (EPA, 2010). This report
incorporates the 2010 findings with additional focus placed on the fate and transport of
sediments and contaminants during dredging operations, biological response to remediation, and
surface sediment chemistry as of the last comprehensive survey conducted in 2011. The
conclusions below summarize observations noted during the data interpretation process and
recommendations on the utility of the methods employed to obtain field measurements for
evaluating remedy effectiveness.
5.1 Water Sampling during Dredging - Turbidity Measurements
Environmental dredging, by design, seeks to minimize off-site migration of sediment suspended
and chemicals of concern (COCs) during operations. With a goal of rapidly identifying
mechanisms and minimizing their contributions to dredge residuals, field instrumentation can be
used to monitor suspended sediment in dredge plumes permitting real-time or near real-time
measurements during remediation activities. In contrast, collection of field samples followed by
laboratory analyses results in a significant time lag from dredging implementation to
measurement and documentation of residuals. This delay does not allow for field operations
changes in a timely manner to minimize generation of residuals and off-site migration of COCs.
Given the decreasing cost, greater availability of field monitoring instrumentation, and improved
user interface and data processing, it is strongly recommended that real-time turbidity
measurements be employed whenever possible to permit rapid feedback of dredge plume
information to the on-site project management team during field operations.
Advantages and disadvantages of using optical and acoustical backscatter methods exist for the
measurement of TSS. Optical and acoustical backscatter signals are both proxies for particle
concentration. Both techniques require careful calibration of backscatter against field samples of
TSS, the success of which is highly dependent on field sampling protocols and spatial and
temporal correlation of measurements. Optical and acoustic backscatter are also both sensitive to
particle shape (theories for both assume spherical particles) as well as particle size.
Optical techniques for estimating TSS can lead to overestimates of particle concentrations for
smaller particle size distributions. Errors associated with optical derivations of TSS are small for
well-sorted sediments. Errors are greatest when only a small amount of fine material is present
because this material dominates the optical backscatter response. As such, errors in
concentration estimates are smallest when the size distribution of the calibration sediment closely
matches the size distribution of the measured suspended sediments.
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Optical backscatter is also sensitive to density and composition of the particles and the ratio of
optical backscattering to total scattering increases with the bulk index of refraction of particles;
hence, denser particles will result in higher backscatter ratios regardless of concentration or size.
Another limitation of optical turbidity sensors is that they provide measurements at only one
location per sensor, although multi-sensor arrays are becoming increasingly feasible as
technology and affordability improve. Importantly, optical sensors are also highly susceptible to
biofouling, particularly in productive inland waters and, therefore, require a comprehensive
operations and maintenance plan to provide high quality data. These reductions in data quality
for optical sensors are not correctable, i.e., optical data from biofouled sensors are not useful for
determination of TSS.
Compared to optical turbidity sensors that rely on backscatter of optical signals, acoustic
methods rely on the backscatter response of acoustic Doppler current profiler (ADCP) sensors.
These ADCPs provide simultaneous measurements at multiple depths throughout the water
column with a single sensor. However, it has been reported that the sensitivity of the acoustic
response to a particle can increase with the radius of the particle to the fourth power. In other
words, the acoustic response can increase with particle size and not necessarily particle
concentration. Additionally, the acoustic detection limit of particles is dependent on the
relationship between acoustic frequency and particle size. As such, the ADCP should detect,
with good sensitivity, silt-sized particles greater than 20 |im in diameter. Finer particles (< 20
|im) are detected, but with less sensitivity. Additionally, acoustic response is generally well
correlated with a change in particle concentration for particles between 25 and 400 |im in
diameter, regardless of variable particle size distribution or composition.
A primary advantage of acoustic methods is that a single acoustic current profiler can provide
continuous estimates of TSS at multiple depths, as well as measurements of current velocity and
direction at the same locations. These velocity and direction measurements are essential for
computing estimates of suspended sediment flux. Further, ADCPs are not particularly
susceptible to the effects of biofouling.
A summary of the primary advantages and disadvantages between optical and acoustic methods
for the derivation of TSS is provided below:
• A single ADCP provides depth-resolved TSS data, whereas one optical turbidity
sensor provides a TSS estimate at only one depth in the water column. However,
multi-array optical turbidity sensors are becoming more feasible as technology and
costs improve.
• An ADCP provides data for derivation of TSS and current velocities and directions.
These parameters are required to calculate suspended sediment flux and system
hydrodynamics, which has been shown to affect TSS variability.
• Optical turbidity sensors are sensitive to changes in particle composition.
Additionally, both acoustic and optical systems are susceptible to errors induced by
variable particle size distributions.
• The amount of data collected per measurement is much greater for an ADCP as
compared to an optical turbidity sensor. Also, with a ADCP, the conversion of
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measured echo intensity to backscatter to calculate TSS is significantly more
computationally intensive than for optical-based data.
• Optical sensors are impacted by biofouling, and data corrections are not possible once
fouling has impacted data quality. Conversely, acoustic systems are not susceptible
to reductions in data quality from biofouling.
• Acoustic current profilers are more costly than optical turbidity sensors. At the time
of this report, acoustic current profilers are about four times the cost of optical
turbidity sensors.
• Both acoustic and optical systems are produced by a number of manufacturers, each
of which has its own set of operations and maintenance protocols. These widely
different protocols and their user interfaces can make it challenging to compare data
or switch between sensors. Ease of use is dependent on the interference of a specific
sensor and not necessarily on the type of system (acoustic vs. optical).
In addition to assessing optical and acoustic methodologies, Laser In-Situ Scattering and
Transmissometry (LISST) technology was evaluated for characterizing suspended sediment. As
mentioned previously, the disadvantages of using a LISST instrument to derive TSS
concentrations for this project were the assumptions that all particles are in the size range
measured by LISST and that the bulk density of particles was constant with depth. However, in
spite of these limitations, the relationship between turbidity measures using a LISST instrument
and optical turbidity sensors was at times pronounced; this observation suggests promise for the
LISST instrument's ability to directly measure TSS. Future LISST monitoring methods for TSS
derivations would require multiple LISSTs to cover a wider range of particle sizes as well as
concurrent and co-located collection of TSS samples to develop a site-specific correlation.
Further research would be required to investigate the effects of variable bulk particle densities on
estimates of particle concentration.
5.2 Water Sampling during Dredging- Resuspended Sediment Mass
Measurements
A critical aspect of environmental dredging operations is managing the resuspension of
contaminated sediment and limiting the generation of dredge residuals. Monitoring suspended
sediment near the dredge is important for making operating decisions to maximize dredge
production and minimize environmental impacts from residuals or off-site migration of
resuspended sediment. This research project demonstrated that measurements of suspended
sediment using turbidity sensors mounted at multiple depths (e.g., the multi-depth water sampler)
or an ADCP (in unidirectional or low frequency directional flow conditions) together with
concurrent water sample collection for TSS can be an effective method for real-time or near real-
time monitoring of suspended sediment. These measurements conducted in real-time would
permit a project manager to quickly evaluate and optimize remedial operations.
Identification and mapping of the dredge plume and determination of the relative strength of the
dredge plume were evaluated through calculation of TSS gradients. This approach proved robust
for all sets of progressive transects when accounting for background TSS variations due to
natural and seiche-effected flow. Averaged over a transect, normalized plume strength (NPS)
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was always larger for transects that were closer to the dredge as compared to those farther
upstream or downstream of the dredge. The total volume of the dredge plume was estimated by
measuring TSS in progressive transects until background TSS levels were encountered and the
boundary of the plume was fully identified. As the dredge operations progressed, the furthest
reach of the plume migrated further into the undredged zone and the volume of water in the
dredged area increased due to the continuous deepening of the channel. A more useful
measurement than plume volume is the flux of sediment or contaminant crossing the project
boundary as described in the next section.
Co-located measurements of current velocity/direction and TSS enabled direct computation of
sediment flux (g/s) at specific locations relative to dredge operations. Flux measurements
collected at fixed locations over a relatively long period of time (i.e., hours) were useful for
establishing an empirical relationship between total mass of sediment suspended by the dredge
per hour of operation as a function of distance from the dredge. This empirical relationship can
be used to estimate the generation of TSS during dredging at specific time periods as well as
total TSS over the entire remediation project. Additionally, an estimate can be made of the
residual solids mass generated due to resuspension.
5.3 Water Sampling during Dredging - Link to Contaminant Distributions
Understanding the generation of suspended sediment during dredging operations and its impact
on dredge residuals is primarily driven by concerns regarding the mobilization or redistribution
of contaminants associated with the suspended sediment. Therefore, this part of the research
focused on characterizing suspended sediment and contaminant flux during dredge operations.
Whole water samples analyzed for TSS and PCB concentrations were used to develop a
relationship to enable estimation of the PCB concentration or mass in the water column derived
from TSS measurements, noting that TSS was also estimated using an empirical relationship
between TSS and turbidity. Although the correlation between PCB and TSS was very good (r2 =
0.7) and between TSS and turbidity was excellent (r2 > 0.9), it is important to note that these
strong correlations may be site, contaminant, and project specific. These relationships depend on
site-specific conditions such as sediment type, contaminant type and concentration, water flow,
dredge type, dredge operations, etc.
As with most field data collection activities and programs, the quality of project data and results
and the derived conclusions could be improved by establishing standard operating procedures
and quality assurance protocols for collecting data. The strategy for placement and timing for
conducting measurements along specific transects relative to dredge operations is described in
detail in Sections 2 and 3. Our research findings lead to the following suggestions:
• Collect whole water samples for analysis of PCB and TSS concentrations at several
depths repeatedly during monitoring periods.
• Measure particle size distribution as a function of depth during whole water sampling
to investigate the relationship (if any) between PCB concentration and particle size.
• Select transects in locations not affected by the remediation activities to determine
spatially-resolved (horizontal and vertical) background conditions for comparison to
background conditions determined from fixed platforms.
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• Collect data on transects simultaneously upstream and downstream of the dredge (i.e.,
using two monitoring vessels) to account for background or flow direction changes
caused by seiche or tidal effects.
• Collect data under various conditions to understand how environmental factors (e.g.,
river flows, sediment characteristics, and contaminant concentrations) and dredge
operations (e.g., a change in dredge operators, dredge speed and position, production
rates, and occurrence of debris) impact contaminant flux
• Select and maintain transects at pre-determined distances from the dredge, such as
along the critical project boundary or sensitive areas, as dictated by project objectives.
• The ability to provide real-time or near-real-time information on resuspension of
contaminants and the generation of dredge residuals provides significant
opportunities to minimize the environmental impact of dredging and reduce project
costs by optimizing dredge operations.
5.4 Contaminants in Surface Sediment
The interaction of receptors with contaminants generally occurs in the surface sediment and is
critical to long-term recovery of the ecosystem. Consequently, characterizing contaminants in
surface sediment is a crucial LOE for assessing remedy effectiveness. Though substantial
research is ongoing to establish the complex exposure relationship between contaminant
concentrations in the pore water of sediments and the biota living therein, surface sediment bulk
chemistry continues to be a critical measure in managing contaminated sediments. The typical
sediment layer interval sampled for this study was generally within the top 0.15 m of the surface
to correlate chemical concentrations in the sediment with uptake in the benthic community.
However, the surface sample interval derived from core samples did vary to some degree in
2006, 2007, and 2011 to focus on residuals characterization. Though these varied intervals were
necessary to characterize short-term measures of dredge residuals, they made it more difficult to
use these same data for long-term evaluation of the recovery of surface sediments over time.
Based on the above findings, it is recommended that using a specified depth interval to define
surface sediment would aid in comparing concentration data over time and provide a uniform
interval for characterizing benthic exposure. In addition, a fixed interval provides consistency to
evaluate and distinguish historic vs. recent contamination. The ability to detect short-term
temporal changes in the sediment surface, especially when evaluating the potential for
recontamination from a continuing source or non-point contribution, was best accomplished by
analyzing the top 0.02 m of the sediment surface rather than the top 0.15 m or more. It is
recommended, therefore, that to evaluate recontamination or potential on-going sources, a very
small surface interval (e.g., 0.02 m) consistent with projected or measured sedimentation rates be
isolated for analysis. Smaller depth intervals can be combined or depth-averaged to provide data
for a larger depth increment. However, it should be recognized that the depth interval will be
specific to site and project conditions (e.g., depth of contamination or deposition rates). Further,
it should be noted, the smaller the depth intervals, the greater the number of samples requiring
collection, which will increase field and analytical chemistry costs. These costs vs. the value of
the information obtained need to be considered and optimized to meet project objectives. A
thorough understanding of the site conceptual site model (CSM) that identifies the critical
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mechanisms impacting the site and complete familiarity with the selected remedy operations are
required to design the appropriate depth interval and sampling strategy.
5.5 Macrobenthos
As a lower part of the food web, macrobenthos are a critical linkage between contaminants found
in the sediment and the resulting exposure of ecological receptors and eventually humans. This
biological LOE is important as a short-term indicator of remedy effectiveness and a long-term
measure of benthic impairment and recovery. Macrobenthos was collected by colonizing benthic
invertebrates on artificial substrates deployed for a prescribed time period. In this research
project, Hester-Dendy (H-D) artificial substrate samplers were used to collect
macroinvertebrates: 1) to measure the bioaccumulation of chemical contaminants, 2) as an
indicator of ecological health, and 3) as a measure of uptake within the food web. Reduced
availability of the COCs results in lower macrobenthos contaminant levels and decreases the
loading of the COCs to the food web. These macrobenthos samplers were evaluated at a limited
number of stations pre-, during, and post-dredging for a standard period of exposure.
This report evaluated the use of the H-D sampling method to measure uptake of PCBs and PAHs
in macrobenthos. Ecological impacts (ecological condition, population impacts, community
impacts, etc.) were not reported or discussed herein. Changes in tissue concentrations were
evaluated spatially (between stations) and temporally (by deployment year) to determine if this
approach detected significant changes. Due to the limited replication at each station, comparison
of chemical concentrations by station could only be determined by pooling all data over all years.
A significant difference was detected between the Reference Site location (Conneaut River) and
the remediation project area (Ashtabula River), and a difference also was detected between the
original source of the contaminants and the upstream reference location in the Ashtabula River.
The experimental design used in this study exhibited limited ability to detect significant changes
spatially within the project area due to lack of spatial coverage and also as a result of background
COC concentration changes throughout the study area. Conversely, changes over time were
significantly different following dredging. A substantial reduction in macrobenthos contaminant
concentrations was observed when comparing pre-remediation values in 2006 with post
remediation values in 2007 and 2011. The concentration changes detected in the tissues were not
as significant as those found comparing co-located sediments and water samples in terms of
spatial and temporal trends. Again, additional replication at each station would have aided in
defining more detailed changes in the system. A critical finding for this portion of the research
was that the LOE approach was effective in detecting changes in contaminant concentrations in
macrobenthos when comparing pre-remedy conditions to post-remedy conditions. In fact, a
statistically significant reduction in concentration was observed within the first year following
remediation, which indicates this approach can provide a short-term LOE that the remedy is
progressing as designed.
5.6 Indigenous Fish Tissue Contaminant Concentrations - Brown Bullhead
Contaminants in fish and adverse impacts to fish and fish populations are often a common metric
to indicate exposure to and ensuing effects from contaminated sediments. Fish consumption is a
common route of contaminant exposure for humans and aquatic and terrestrial receptors. As
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such, fish consumption advisories are often a long lasting Best Use Impairment (BUI) for AOCs.
Also, increased incidences of deformities, erosion of fins/barbels, lesions, and tumors (DELTs)
in specific target fish species are commonly used as endpoints to document impacts to wildlife.
The goal of removing fish and wildlife consumption advisories, as well as other BUIs such as
fish and wildlife degradation and loss of habitat within a reasonable time frame is a major
consideration in the selection of technologies for remediation and restoration at contaminated
sediment sites.
At the Ashtabula River AOC, brown bullhead catfish were chosen as an indicator or metric for
adverse impacts to wildlife. In addition to GLNPO and State monitoring for DELTs in brown
bullheads, ORD developed methods to correlate responses to environmental dredging in: 1) the
tissue concentrations of the indigenous fish, and 2) genotoxic endpoints related to exposure (e.g.,
the Comet assay). Brown bullheads are particularly susceptible to contaminants in sediments as
a bottom dwelling species that feeds by foraging in the sediment. There are also documented
incidence rates of DELTs resulting specifically from exposure to PAHs and related
contaminants. ORD, therefore, monitored tissue concentrations of PCBs and PAHs and the
anticipated reductions in those concentrations over the duration of the Ashtabula River AOC
project. Brown bullheads were also collected from an uncontaminated Reference Site (Conneaut
Creek) approximately 14 miles east of the Ashtabula River. Fish samples from the Ashtabula
River were collected and analyzed for 6 consecutive years from 2006-2011 and from the
Reference Site for the same years except 2009. Fish were analyzed for PCBs and PAHs and
reported in both wet wt. and lipid normalized concentrations. The Comet assay was conducted
on subsets of these fish throughout the project, and the DELTs were documented in Meier et al.,
2015.
Wet wt. and lipid normalized PCB concentrations measured -2.3 mg/kg wet wt. and -60 mg/kg
lipid, respectively, in the Ashtabula River in 2006, prior to dredging. The wet wt. concentration
doubled in 2007 during dredging, and the lipid normalized value increased over the next 2 years
peaking in 2008 (the year immediately after completion of dredging) also at approximately
double the pre-dredge concentration. PCBs levels dropped substantially in 2009 (2 years after
the completion of dredging) to 20%-25% of their earlier maximum values. Both wet wt. and
lipid normalized concentrations leveled off in 2010 and 2011 at slightly higher values than their
2009 minimum concentrations. The final measured post-dredge concentrations in 2011 were
-40% (wet. wt.) and -35% (lipid normalized) less than the 2006 pre-dredge concentrations. As
expected for an uncontaminated Reference Site, PCB concentrations were consistently low over
the entire 6-year project period for the Conneaut River at 10%-15% of the final post-dredge
values measured for the Ashtabula River.
PAHs concentrations in fish tissue were analyzed for tPAH16 (priority pollutant PAHs) as well
as £tPAH34 (priority pollutant plus alkylated PAHs) and reported as both wet weight and lipid-
normalized values. Concentrations for both PAH groups were significantly reduced from pre-
dredging levels following remediation. For brevity, only the £tPAH34 data are discussed
below. By the end of the project period, PAH fish tissue concentrations were reduced 59%-73%
from baseline values. PAH levels decreased from 0.71 mg/kg wet wt. in 2007 to 0.192 mg/kg
wet wt. (73%) reduction) in 2011. A 59% decrease in lipid normalized PAH concentrations from
11.2 to 4.6 mg/kg lipid was observed over the same time span. Throughout the entire 6-year
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period, PAH concentrations in the Conneaut Creek Reference Site remained at or below the
lowest levels reported for the Ashtabula River.
The reductions in PCB and PAH concentrations measured in indigenous Ashtabula River brown
bullheads over the life of this project were encouraging and attested to the removal of the bulk of
the contaminated sediment from the AOC. The lowering of contaminant levels in indigenous
fish tissue was less than the estimated 95%+ mass removal of contaminated sediment and
associated COCs achieved via dredging. Due to the life expectancy and required time to
introduce new cohorts of this fish species, a slower response time was expected. Over time as
older fish are replaced by new cohorts, further reductions in indigenous fish tissue contaminant
concentrations are anticipated. The decreases in fish tissue concentrations observed over the life
of this project have contributed to the removal of three fish-related Best Use Impairments (BUIs)
from the Ashtabula River AOC.
5.7 Semipermeable Membrane Device (SPMD)
Passive samplers were used to measure PCB water concentrations and to estimate
bioaccumulation from both the water column and sediment. SPMDs were evaluated for
characterizing aqueous and surface sediment pore water concentrations. Overall, SPMD-derived
concentrations did not correlate well with either whole water or sediment concentrations.
SPMD-derived water concentrations based on laboratory-estimated partitioning constants and
field-measured Performance Reference Compounds (PRCs) were within a factor of 2 to 5 of the
measured whole water concentrations in grab samples in post-dredge years (2008 and 2011);
however, the pre-dredge (2006) SPMD-calculated water concentrations were lower by a factor of
more than 10 compared to the measured whole water concentrations. This was likely due to the
fact that SPMDs measure a time-weighted, dissolved concentration over a long equilibration
period, and, in the Ashtabula River, the primary contaminant, PCBs, was highly non-polar and
partitions to suspended sediments. In addition, the passive sampler-derived concentrations were
compared to whole water grab samples that may not have been representative of the time-
averaged concentration in the water column during the exposure period. This discrepancy in
how samples are collected (i.e., long exposure vs. instantaneous grab) often makes comparisons
between passive samplers and grab samples difficult to correlate.
As discussed in Section 3.5, SPMD-calculated dissolved concentrations did not vary
significantly in the water column, especially in post-dredging years (2008 and 2011).
Conversely, TSS concentrations decreased markedly from pre-dredge (2006) to post-dredge
years (2008 and 2011). However, the post-dredge 2011 concentrations were found to be
significantly greater than those measured from pre-dredge 2006, contrary to what was found for
the other measurements in the water column.
SPMDs were also deployed on the surface of the sediments. Again, because the SPMDs
measure organic contaminants present in the dissolved phase, sediment SPMDs were targeting
PCBs from the surface pore water. Traditional pore water measurements were not made, so no
direct comparison was possible. However, a simple correlation was performed between the
sediment SPMDs and the surrounding sediment concentrations and the co-located whole water
samples to determine if any correlations were observed. As with the water SPMDs, minimal
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correlation was observed between the sediment SPMD and the co-located sediment or water
sample PCB concentrations. Further research is needed to understand the limitations of the
SPMDs used in this study as well as investigating alternative passive samplers, such as
polyethylene devices (PEDs), for characterizing contaminated sediment sites.
5.8 Summary
The remediation of contaminated sediments is necessary to minimize and manage: 1) the risk of
exposure of the contaminants to human and wildlife receptors, and 2) impairment to ecosystem.
This report describes various field sampling and measurement methods, data collection
techniques, and laboratory analysis procedures applied across multiple LOEs (physical,
chemical, and biological) to estimate contaminated sediment dredge residuals and evaluates these
methods and metrics for their use in assessing remedy effectiveness. Generally, with few
exceptions, the methodologies employed were consistent among themselves for characterization
of dredging residuals and measurement of pre-, during, and post-dredging conditions.
The development and demonstration of the methods and metrics described in this research report
and used on the Ashtabula River provided valuable information and lessons learned. For
example, methods used to measure dredge residuals (e.g., high resolution bathymetry paired with
incremental sediment coring, forensics, and sediment profiling imagery) were developed and
indicated that sediment and PCB mass removals were in excess of 95% of targeted goals with
environmental dredging. This combined survey approach used in conjunction with real-time
suspended sediment monitoring was vital in estimating the mass of sediment and PCBs inventory
removed by dredging in our study area. Approaches were developed and demonstrated to
estimate the mass of resuspended sediment and associated COCs contributing to the residuals
after dredging. Finally, innovative biological metrics exhibited significant reductions in
contaminant levels in both macroinvertebrate and fish tissue following dredging at this river.
These reductions in fish tissue concentrations correlated with the reductions observed in
genotoxicity.
The diversity, comprehensiveness, and ease of use of the metrics and approaches developed with
this research greatly enhances their potential utility for conducting weight of evidence (WOE)-
based remedy effectiveness assessments (REAs) for various sediment remediation technology
projects such as engineered capping, monitored natural recovery, and active treatment, as well as
environmental dredging. Through examination and evaluation of the comprehensive dataset
generated on this project, improvements for future use of these methodologies and techniques
have been proposed and recommendations for additional research have been made.
As indicated previously, the primary objective of this specific research approach was to develop
and demonstrate selected biological, chemical, and physical monitoring methods and metrics that
can be integrated and applied on future remediation projects for conducting WOE-based REAs.
The data generated on the Ashtabula River research project along with other relevant data from
this site and other remediation projects in the Great Lakes and Superfund Programs are currently
being developed into a comprehensive REA approach. As the initial product of this new
integrated approach, an REA is currently being prepared for the Ashtabula River project by
GLNPO and ORD and will be reported separately.
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6.0 REFERENCES
Agrawal, Y.C. and H.C. Pottsmith. 1994. "Laser Diffraction Particle Sizing in STRESS," Cont.
Shelf Res., 14, 1101-1121.
Alvarez, D A. 2010a. Estimated Water Concentration Calculator from SPMD Data Using
Multiple PRCs: Version 5.1. Microsoft Excel spreadsheet.
hiip://www.cere.uses.gov/Branches.aspx?Branchld=8. Accessed March 12, 2012.
Alvarez, D A. 2010b. Estimated Water Concentration Calculator from SPMD Data When Not
Using PRCs: Version 4.1. Microsoft Excel spreadsheet.
htty://www.cerc.uses.gov/Branches.asm?BranchId=8. Accessed March 12, 2012.
ASTM. 1993. American Society for Testing and Materials (ASTM) Procedure D2488-93.
D2488-09a Standard Practice for Description and Identification of Soils (Visual-Manual
Procedure).
Battelle, GeoChem Metrix, U.S. Navy SPAWAR, and U.S. EPA (G. Durell, G. Johnson, J.
Leather, and M. Mills). 2012. A Handbook for Determining the Sources of PCB
Contamination in Sediment. U.S. Navy Technical; Report TR-NAVFAC EXWC-EV-
1302. U.S. Navy, NAVFAC, PortHueneme, CA, October.
Bohren, C.J. and D.R. Huffman. 1983. Absorption and Scattering of Light by Small Particles,
John Wiley, New York, NY.
Burgess, R.M., R. Lohmann, J.P. Schubauer-Berigan, M.M. Perron, L. Lefkovitz and M.G.
Cantwell. 2015. Application of Passive Sampling for Measuring Dissolved
Concentrations of Organic Contaminants in the Water Column at Three Marine
Superfund Sites. Environ. Tox. andChem. 34(8)1720-1733. D01:10.1002/etc.2995.
Downing, A., P.D. Thorne, and C.E. Vincent. 1995. "Backscattering from a Suspension in the
Near-field of a Piston Transducer," J. Acoustic. Soc. Amer., 97, 1614.
Emery, W.J. and R.E. Thomson. 1997. Data Analysis Methods in Physical Oceanography,
Elsevier Science Inc., New York, NY.
Gartner, J.W. 2004. "Estimating Suspended Solids Concentrations from Backscatter Intensity
Measured by Acoustic Doppler Current Profiler in San Francisco Bay, California," Mar.
Geol., 211, 169-187.
Hayes, D.F., T.R. Crockett, T.J. Ward, andD. Averett. 2000. "Sediment Resuspension during
Cutterhead Dredging Operations," J. Waterway, Port, Coast., and Ocean Engr., 126,
153-161.
Hayes, D. and P.-Y. Wu. 2001. "Simple Approach to TSS Source Strength Estimates,"
Proceedings of the WEDA XXI Conference, Houston, TX, June 25-27.
192
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Lazorchak, J., M. Griffith, M. Mills, J.P. Schubauer-Berigan, F. McCormick, R. Brenner, and C.
Zeller. 2015. Proof of Concept for the Use of Macroinvertebrates as Indicators of PCB
Contamination in Lake Hartwell. Environ. Tox. and Chem. 34(6), 1277-1282.
D01:10.1002/etc.2918.
Lynch, J.F., J.D. Irish, C.R. Sherwood, and Y.C. Agrawal. 1994. "Determining Suspended
Sediment Particle Size Information from Acoustical and Optical Backscatter
Measurements," Cont. Shelf Res., 14, 1139-1165.
Meier, J.R., J.M. Lazorchak, M. Mills, P. Wernsing, and P.C. Baumann. 2015. "Monitoring
Exposure of Brown Bullheads and Benthic Macroinvertebrates to Sediment
Contaminants in the Ashtabula River Before, During, and After Remediation," Environ.
Tox., and Chem., 34(6), 1267-1276. D01:10.1002/etc.2877.
Schubauer-Berigan, Joseph P., Eric A. Foote, and Victor S. Magar. 2012. "Using SPMDs to
Assess Natural Recovery of PCB-contaminated Sediments in Lake Hartwell, SC: I. A
Field Test of New In-Situ Deployment Methods," Soil and Sediment Contamination: An
International Journal, 21:1, 82-100. http://dx.doi.org/10.1080/15320383.2012.636777
Sea Engineering Incorporated (SEI). 2007. Draft Data Report. Joint USEPA NRMRL/NERL
Project for Evaluation of Environmental Dredging for Remediating Contaminated
Sediments in the Ashtabula River. Available through Corresponding Investigator.
Shulkin, M. and H.W. Marsh. 1962. "Sound Absorption in Sea Water," J. Acoustic. Soc. Amer.,
34, 864-865.
StataCorp LP. 1996-2016. Stata Data Analysis and Statistical Software.
http://www.stata.com/features/. STATA Version 13.0.
Thorne, P.D., C.E. Vincent, P.J. Hardcastle, S. Rehman, andN. Pearson. 1991. "Measuring
Suspended Sediment Concentrations Using Acoustic Backscatter Devices," Mar. Geol.,
98, 7-16.
Twardowski, M.S., E. Boss, J.B. Macdonald, W.S. Pegau, A.H. Barnard, and J.R.V. Zaneveld.
2001. "A Model for Estimating Bulk Refractive Index from the Optical Backscattering
Ratio and the Implications for Understanding Particle Composition in Case I and Case II
Waters,"./. Geophys. Res., 106, 14,129-14,142.
U.S. Environmental Protection Agency (U.S. EPA). 2006. Final Quality Assurance Project Plan
(QAPP) for Joint U.S. EPA GLNPO/NRMRL/NERL Project for Evaluation of
Environmental Dredging for Remediating Contaminated Sediments in the Ashtabula
River; ORD Applied Research QAPP. QAPP ID # 163-Q14-0., Cincinnati, OH. July.
Available through Corresponding Investigator.
193
-------
U.S. Environmental Protection Agency (U.S. EPA). 2007. Final Quality Assurance Project Plan
(QAPP) for Joint U.S. EPA GLNPO/NRMRL/NERL Project for Evaluation of
Environmental Dredging for Remediating Contaminated Sediments in the Ashtabula
River - Phases 2 and 3; ORD Applied Research QAPP. QAPP ID # 163-Q16-0.
Cincinnati, OH. May. Available through Corresponding Investigator.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Field Study on Environmental
Dredging Residuals: Ashtabula River, Volume 1. Final Report. EPA/600/R-10/126.
September.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. Final Quality Assurance Project Plan
(QAPP); Joint U.S. EPA ORD/GLNPO Evaluation of Remedy Effectiveness and
Development of Site Delisting Lines-of-Evidence for the Ashtabula River Environmental
Dredging Project. Addendum 3 to QAPP QAID# 163-Q16-0. Cincinnati, OH. July.
Available through Corresponding Investigator.
U.S. Environmental Protection Agency (U.S. EPA). 201 lb. Data Report on Ecosystem
Monitoring for the Ashtabula River Environmental Dredging Project. EPA/600/R-
11/102. National Risk Management Research Laboratory, Office of Research and
Development, U.S. EPA, Cincinnati, OH. September.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Draft Report. Summary of Field Site
Assessment Activities to Support Source Identification and Tracking. EPA Contract No.
EP-W-09-024, Work Assignment 2-13. Cincinnati, OH. June. Available through
Corresponding Investigator.
U.S. Environmental Protection Agency (U.S. EPA). 2016a. Sustainable and Healthy
Communities Strategic Action Plan. 2016-2019. Contaminated Sites Project 3.61.
https://www.epa.gov/research/siistainable-and-healthy-commimities-strateeic-research-
U.S. Environmental Protection Agency (U.S. EPA). 2016b. EPA's Superfund Program, Fields
Brook, Ashtabula, OH.
https://cumulis.epa.gov/supercpad/cursites/csitinfo.cfm?id=0504723. Last updated
December 15, 2016.
194
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