PHASE 2 REPORT - REVIEW COPY
FURTHER SITE CHARACTERIZATION AND ANALYSIS
VOLUME 2B - PRELIMINARY MODEL CALIBRATION REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
OCTOBER 1996
for
U.S. Environmental Protection Agency
Region II
Volume 2B
Book 1 of 2
Limno-Tech, Inc.
and
Menzie Cura & Associates, Inc.
and
The CADMUS Group, Inc.
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
REGION 2
290 BROADWAY
NEW YORK, NY 10007-1866
OCT I 1 1396
To All Interested Parties:
The U.S. Environmental Protection Agency (EPA) is pleased to release the Preliminary
Model Calibration Report for the Hudson River PCBs Superfund site. This report, the
second in a series of six reports that will make up the Phase 2 Report, presents preliminary
or interim findings about the modeling effort that is being conducted as part of the
Reassessment RI/FS for the Hudson River PCBs Superfund site. EPA decided, based on
extensive public comment, that it was important to share with those interested in the
Reassessment the assumptions and data sets that would be used in the models, prior to
using the models, even though this meant issuing a report on a work in progress. In order
to determine the most appropriate data sets and assumptions for the models selected, EPA
needed to do a significant amount of the modeling effort before this report could be issued.
Much of the data used in the Preliminary Model Calibration Report is from EPA's Phase 2
investigation, although data from numerous other sources have also been used. Although
some of the data used in this report had not been validated, EPA believes it is important to
provide the findings of the models in this preliminary report so that interested parties can
fully evaluate the implications of the assumptions used within the models. Since the time
the work for this report was conducted, the validation of the Phase 2 data has been
completed, and the validated data are contained in the database that EPA released in March
1996. Any remedial decision for the site will be based upon validated data.
EPA would appreciate receiving comments on the Preliminary Model Calibration Report by
November 22, * 396, so that any changes in the scope of the modeling effort can be made
without delay to the Reassessment. It should be recognized that several significant
assumptions in this report are based on findings which will be reported on in the Data
Evaluation and Interpretation Report. That report, the third in the series of six Phase 2
reports, will be issued within the next several months. As such, EPA will accept comments
on the Preliminary Model Calibration Report that are related to the Data Evaluation and
Interpretation Report during the comment period for the latter report. Please refer to the
report section and page number in each comment. Comments should be sent to:
A joint liaison group meeting will be held to discuss the Preliminary Model Calibration
Report on Monday, October 28, 1996, at 7:30 p.m. at the Best Western Hotel at
200 Wolf Road in Albany.
Douglas Tomchuk
US EPA - Region 2
290 Broadway - 20th Floor
New York, NY 10007-1866
Attn: PMCR Comments
OAAi>AU«i/Q«o»AitkiA . ...hk /~\;i ui/A mno/ .~t--j ni»r\o/ r>-.
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2
We look forward to your involvement on the Preliminary Model Calibration Report and
throughout the Reassessment. If you have any questions, please contact Ann Rychlenski,
the Community Relations Coordinator for the Hudson River PCBs site Reassessment at
(212) 637-3672.
c
Richard L. Caspe, Director
Emergency and Remedial Response Division
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CONTENTS
EXECUTIVE SUMMARY E-1
1. INTRODUCTION 1-1
1.1 Background 1-1
1.2 Purpose of Report 1-1
1.3 Report Format and Organization 1-3
2. SUMMARY AND PRELIMINARY CONCLUSIONS 2-1
2.1 Summary 2-1
2.1.1 Overall Approach 2-1
2.1.2 Water Column and Sediment Models 2-1
2.1.3 Fish Body Burden Models 2-2
2.2 Preliminary Conclusions 2-3
2.2.1 Upper Hudson River PCB Mass Balance 2-3
2.2.2 Thompson Island Pool Hydrodynamics and Sediment Erosion 2-6
2.2.3 Upper Hudson River Fish Body Burdens 2-7
2.2.4 Lower Hudson PCB Mass Balance and Striped Bass Bioaccumuiation 2-9
3. MODELING APPROACH: TRANSPORT AND FATE 3-1
3.1 Introduction 3-1
3.2 Modeling Goals and Objectives 3-1
3.3 Conceptual Approach 3-2
3.4 Hudson River Database 3-3
3.5 Upper Hudson River Mass Balance Model 3-4
3.5.1 Introduction 3-4
3.5.2 State Variables and Process Kinetics 3-5
3.5.3 Spatial-Temporal Scales 3-7
3.5.4 Application Framework 3-9
3.6 Thompson Island Pool Hydrodynamic Model 3-9
3.6.1 Introduction 3-9
3.6.2 State Variables and Process Mechanisms 3-10
3.6.3 Spatial- Temporal Scales 3-12
3.6.4 Application Framework 3-12
3.7 Thompson Island Pool Depth of Scour Model 3-13
3.7.1 Introduction 3-13
3. 7.2 Process Representation 3-14
3. 7.3 Spatial Temporal Scales 3-16
3. 7.4 Applications Framework 3-16
3.8 Lower Hudson River PCB Transport and Fate Model 3-16
3.8.1 Introduction 3-16
3.8.2 State Variables and Process Kinetics 3-17
3.8.3 Spatial-Temporal Scales 3-21
3.8.4 Applications Framework 3-21
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4. CALIBRATION OF UPPER HUDSON RIVER PCB MODEL 4-1
4.1 Introduction 4-1
4.2 Historical Trends in Water Quality Observations 4-1
4.3 Overview of Preliminary Calibration Dataset 4-2
4.4 Model Input Data 4-4
4.4.1 System-Specific Physical Data 4-4
4.4.2 Externa! Loadings 4-5
4.4.3 Forcing Functions 4-10
4.4.4 Boundary Conditions 4-11
4.4.5 Initial Conditions 4-12
4.5 Internal Model Parameters 4-14
4.5.1 Solids Model Parameters 4-14
4.5.2 PCB Model Parameters 4-14
4.6 Calibration Approach 4-14
4.6.1 Transport Model (Water Balance) Specification 4-15
4.6.2 Solids Model 4-15
4.6.3 PCB Model 4-16
4.7 Calibration Results 4-18
4. 7.1 Solids Model 4-18
4. 7.2 PCB Model 4-20
4.8 Mass Balance Component Analysis 4-23
4.9 PCB Model Calibration Sensitivity Analysis 4-27
5. CALIBRATION OF THOMPSON ISLAND POOL HYDRODYNAMIC MODEL 5-1
5.1 Introduction 5-1
5.2 Model Input Data 5-1
5.2.1 System-Specific Physical Data 5-1
5.2.2 Forcing Functions 5-2
5.2.3 Boundary Conditions 5-3
5.3 Internal Model Parameters 5-3
5.4 Calibration Approach 5-4
5.5 Calibration Results 5-4
5.6 Model Validation 5-5
5.6.1 Rating Curve Velocity Measurements 5-5
5.6.2 FEMA Flood Studies 5-5
5.7 100 Year Flood Model Results 5-6
5.8 Sensitivity Analyses 5-6
5.8.1 Manning's 'n' 5-6
5.8.2 Turbulent Exchange Coefficient 5-7
5.9 Conversion of Flow Velocity to Shear Stress 5-7
5.9.1 Results 5-9
5.10 Discussion 5-9
6. APPLICATION OF THOMPSON ISLAND POOL DEPTH OF SCOUR MODEL 6-1
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6.1 Introduction 6-1
6.2 Available Data 6-1
6.2.1 Bottom Sediment Distribution 6-2
6.2.2 Resuspension Experiments 6-2
6.3 Model Parameterization and Uncertainty 6-3
6.3.1 Rearrangement of Erosion Equation 6-3
6.3.2 Parameter Estimation 6-4
6.3.3 Prediction Limits 6-5
6.4 Depth of Scour Predictions at Selected Locations
in Cohesive Sediment Areas 6-5
6.5 Global Results for Cohesive Sediment Areas 6-7
7. APPLICATION OF LOWER HUDSON RIVER PCB TRANSPORT AND
FATE MODEL 7-1
7.1 Introduction 7-1
7.2 Model Input Data 7-1
7.2.1 System-Specific Physical Data 7-1
7.2.2 Externa! Loadings 7-2
7.2.3 Forcing Functions 7-3
7.2.4 Boundary Conditions 7-4
7.2.5 Initial Conditions 7-4
7.3 Internal Model Parameters 7-4
7.4 Application Approach 7-6
7.5 Application Results 7-7
7.6 Diagnostic Analyses 7-8
7.6.1 Component Analysis 7-8
7.6.2 Sensitivity Analysis 7-9
7.7 Discussion 7-10
8. MODELING APPROACH: FISH BODY BURDENS 8-1
8.1 Modeling Goals and Objectives 8-1
8.2 Background 8-3
8.2.1 PCB Compounds 8-3
8.2.2 PCB Accumulation Routes 8-4
8.3 Theory for Models of PCB Bioaccumulation 8-7
8.4 Bivariate Statistical Model for Fish Body Burdens 8-10
8.4.1 Rationale and Limitations for Bivariate Statistical Model 8-10
8.4.2 Theory for Bivariate Statistical Models of PCB Bioaccumulation 8-10
8.5 Probabilistic Bioaccumulation Food Chain Model 8-12
8.5.1 Rationale and Limitations 8-12
8.5.2 Model Structure 8-14
8.5.3 Spatial Scale for Model Application 8-16
8.5.4 Temporal Scales for Estimating Exposure to Fish 8-16
8.5.5 Characterizing Model Compartments 8-16
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9. CALIBRATION OF BIVARIATE STATISTICAL MODEL FOR
FISH BODY BURDENS 9-1
9.1 Data Used for Development of Bivariate BAF Models 9-1
9.1.1 Fish Data 9-1
9.1.2 Standardization of PCB Results for NYSDEC Fish Analyses 9-3
9.1.3 Water Column Data 9-7
9.1.4 Sediment Data 9-8
9.1.5 Functional Grouping of Sample Locations for Analysis 9-10
9.2 Results of Bivariate BAF Analysis 9-11
9.3 Discussion of Bivariate BAF Results 9-13
9.4 Summary 9-15
10. CALIBRATION OF PROBABILISTIC BIOACCUMULATION FOOD
CHAIN MODEL 10-1
10.1 Overview of Data Used to Derive BAFs 10-1
10.1.1 Benthic Invertebrates 10-1
10.1.2 Water Column Invertebrates 10-2
10.1.3 Fish 10-2
10.1.4 Literature Values 10-3
10.2 Benthic Invertebrate:Sediment Accumulation Factors (BSAF) 10-3
10.2.1 Sediment Concentrations 10-3
10.2.2 Approach 10-4
10.2.3 Calculations of BSAF Values for Benthic Invertebrates 10-6
10.3 Water Column Invertebrate:Water Accumulation Factors (BAFs) 10-12
10.3.1 Approach 10-12
10.3.2 Calculation of BAFwater for Water Column Invertebrates 10-15
10.3.3 Alternative Approaches 10-16
10.4 Forage Fish:Diet Accumulation Factors (FFBAFs) 10-18
10.4.1 Approach 10-19
10.4.2 Water Column Concentrations Used to Derive FFBAF Values 10-20
10.4.3 Forage Fish Body Burdens Used to Derive FFBAF Values 10-20
10.4.4 Calculation of FFBAF Values for Forage Fish 10-23
10.4.5 Calculation of FFBAFs for Small Pumpkinseed Sunfish 10-24
10.5 Piscivorous Fish:Diet Accumulation Factors (PFBAF) 10-25
10.5.1 Approach Used for Yellow Perch 10-25
10.5.2 Approach Used for Largemouth Bass 10-25
10.5.3 Approach Used for White Perch 10-26
10.6 Demersal Fish:Sediment Relationships 10-27
10.6.1 Approach and Calculations of BAF Values 10-27
10.7 Summary of Probabilistic Food Chain Models 10-27
10.8 Illustration of Food Chain Model Application 10-28
10.9 Comparison of Bivariate Statistical and Food Chain Models 10-28
REFERENCES R-1
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EXECUTIVE SUMMARY
The U.S. Environmental Protection Agency (USEPA) is currently conducting a study
of the Hudson River PCB Superfund Site, reassessing the interim No Action
decision that the Agency made in 1984. The purpose of the Reassessment is to
determine an appropriate course of action for the PCB-contaminated sediments in
the Upper Hudson River in order to protect human health and the environment.
PCBs (polychlorinated biphenyls) were discharged into the Upper Hudson River from
two capacitor plants in Hudson Falls and Fort Edward, New York. The Superfund
Site extends from Hudson Falls to the Battery in New York City, a distance of
approximately 200 river miles. Unacceptable levels of PCBs in fish tissue have
resulted in fishing bans and fishing restrictions throughout the river.
This report provides an update on the mathematical modeling efforts being
conducted as part of the Reassessment. It is meant as a preliminary or interim
report, in that the purpose of the report is to provide interested parties with
information about the data and assumptions that are being used in the models, prior
to completion of the actual modeling work. Therefore, many of the conclusions are
preliminary and may change as the models are further refined and calibrated. When
the models are completed, the modeling results will be presented in the Baseline
Modeling Report and model predictions for various remedial alternatives will be
included in the Phase 3 Report (Feasibility Study).
Study Objectives
The models described in this Preliminary Model Calibration Report were designed to
answer the following questions:
1. When will PCB levels in the fish population recover to levels meeting human
health and ecological risk criteria under continued No Action?
2. Can remedies other than No Action significantly shorten the time required to
achieve acceptable risk levels?
3. Are there contaminated sediments now buried and effectively sequestered from
the food chain which are likely to become "reactivated" following a major flood,
resulting in an increase in contamination of the fish population?
The overall goal of the modeling analysis is to develop and field validate useful and
scientifically credible mass balance models in order to answer these questions. The
modeling approach is based on the principle of conservation of mass, that is, the
quantity of material that enters a section of the river must be equal to the quantity
of material that leaves the section, plus any internal sources or minus any
environmental losses.
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A large body of information from site-specific field measurements, laboratory
experiments and a search of the scientific literature was synthesized within models
for the Upper Hudson River and the tidal freshwater portion of the Lower Hudson
River. Models were developed for the transport and fate of PCBs in the water
column and sediments, and for PCB body burdens in fish. The integration of these
different models allows for the simulation of transport and fate of PCBs that enter
the river from the upstream boundary at Fort Edward, from various tributaries,
across the air-water interface and across the sediment-water interface.
Transport and Fate Model Development
The overall concept involved the development and application of a set of individual
models to describe hydrology, solids dynamics and PCB dynamics in the river water
and sediments. The principal time frame of interest is from 1983 through 1994.
Diverse and extensive data from numerous sources were used in developing and
calibrating the models.
The Reassessment database contains information from: USEPA, New York State
Department of Environmental Conservation (NYSDEC), U.S. Geological Survey
(USGS), General Electric (GE) and private and academic research investigators. The
most intensive datasets available are from the USEPA Phase 2 investigations
conducted in 1993 and 1994. The USEPA database for the Reassessment is
described more fully in the Database Report which was issued in October, 1995.
The database itself was issued in March, 1996, and is available on CD-ROM.
Upper Hudson River PCB Mode/
The Upper Hudson River PCB Model (HUDTOX) is a mass balance model that
includes hydrology, solids and PCBs in river water and sediments. HUDTOX was
applied to the Upper Hudson River from the northern tip of Rogers Island (upper end
of Thompson Island Pool) to Federal Dam at Troy. HUDTOX provides the ability to
simulate total PCBs, as well as specific PCB congeners (BZ#4, BZ#28, BZ#52,
BZ#[90+101] and BZ#138), based on their particular physical and chemical
properties. To date, HUDTOX has been calibrated to field data for the period
January 1 through September 30, 1993, coinciding with the USEPA Phase 2
monitoring program.
Thompson Island Poo! Hydrodynamic ModeI
This model computes localized water velocities corresponding to different river
flows for areas within Thompson Island Pool (where the contaminated sediments
are most concentrated). Results from this model are used as input to the
Thompson Island Pool Depth of Scour Model.
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Thompson Island Poo/ Depth of Scour Mode!
The quantity of sediments likely to become "reactivated" {scoured) following a
major flood depends on the velocity of the river flow. River velocities are computed
using the Thompson Island Pool Hydrodynamic Model. The Thompson Island Pool
Depth of Scour Model is used to determine the range of scour depths and quantities
of resuspended (scoured) solids and PCBs during high flow events. The maximum
flow simulated using this model corresponds to a 100-year flood.
Lower Hudson River PCB Model
An existing mass balance model developed by Thomann et al., (1989) was used for
hydrology, solids and PCBs in Lower Hudson River water and sediments. The
Thomann model was applied to the portion of the Hudson River below Federal Dam
at Troy. The model represents total PCBs in terms of the sum of individual PCB
homologues. The model was validated using revised PCB loads over Federal Dam
without the need for re-calibration of the original model parameters.
[Note: EPA understands that as of September 1 996, the Thomann model is being
updated under a grant from the Hudson River Foundation, and that certain
modifications have been made to the published model. EPA is evaluating whether
the updated model will be available or appropriate for use in the Reassessment.]
Development of Fish Body Burden Models
The overall concept involved development and application of a set of models for
relating body burdens of PCBs (expressed as Aroclor equivalents, individual
congeners or total PCBs) in fish to exposure concentrations in Hudson River water
and sediments.
Bivariate Statistical Model
The Bivariate Statistical Model relates measured PCB levels in water and sediments
(two variables, or "bivariate") to measured PCB levels in fish. This model was
applied to the Upper Hudson River and to a segment of the Lower Hudson River
near Albany. The Bivariate Statistical Model was developed using the historical
PCB Aroclor database.
Probabilistic Bioaccumulation Food Chain Model
The Probabilistic Bioaccumulation Food Chain Model relies upon feeding
relationships to link fish body burdens to PCB exposure concentrations in water and
sediments. The model combines information from available PCB exposure
measurements with knowledge about the ecology of different fish species and the
relationships among larger fish, smaller fish, and smaller animals in the water
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column and sediments. The Probabilistic Model was developed using both historical
and current field data, and was applied to the Upper Hudson River and to a
segment of the Lower Hudson River near Albany. In contrast to the Bivariate
Statistical Model, which provides average body burden estimates, the Probabilistic
Model provides information on uncertainty and variability around these average
estimates.
As part of the development of the Probabilistic Model, species-specific profiles (i.e.,
descriptions of feeding behavior, range and movement) were developed for Yellow
Perch, Largemouth Bass, Pumpkinseed Sunfish, Brown Bullhead, White Perch,
Spottail Shiner, Shortnose Sturgeon and Striped Bass. These profiles include
characteristics that could potentially affect bioaccumulation of PCBs.
Thomann Food Chain Mode/
The Thomann Food Chain Model is part of the PCB transport and fate model for the
Lower Hudson River. The model links PCB exposure concentrations to PCB body
burdens in White Perch and Striped Bass. The Thomann model does not explicitly
consider the effect of contaminated sediments on accumulation of PCBs in the food
chain.
Principal Report Findings
Since this is a preliminary calibration report, it does not present definitive answers
to the principal Reassessment questions. However, a number of preliminary
conclusions have been drawn based on the work presented here, including:
The PCB mass balance model for the Upper Hudson River (HUDTOX)
provides a reasonable representation of hydraulics, solids dynamics and PCB
dynamics for the period of simulation corresponding to the USEPA Phase 2
monitoring program.
The principal external loadings of total PCBs to the Upper Hudson River
during the period of the HUDTOX simulation were across the upstream
boundary at Fort Edward (74 percent).
During the period of the HUDTOX simulation, the model computed a large
gain in total PCB mass across Thompson Island Pool between Fort Edward
and Thompson Island Dam.
Large increases in water column concentrations of dissolved phase PCBs,
especially for lower-chlorinated congeners, are observed to occur across
Thompson Island Pool. These increases appear to be caused by an internal
source within Thompson Island Pool.
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The processes controlling PCB dynamics in Thompson Island Pool are not
fully understood at the present time. One hypothesis that could explain the
large increases in PCB concentrations across the pool is sediment-water
advective flux of pore water PCBs due to groundwater inflow. Such a pore
water advective flux would be relatively more important for lower-chlorinated
PCB congeners due to their greater water solubilities.
The Thompson Island Pool Hydrodynamic Model produced results that were
in good agreement with available information for river flow velocities and
water elevations.
For a 100-year flood event, the Thompson Island Pool Depth of Scour Model
predicts that 1,838,600 pounds of solids and 55 pounds of total PCBs will
be scoured from the cohesive sediment areas. This mass of PCBs represents
less than 1 percent of the total reservoir of PCBs in the cohesive sediment
areas of Thompson Island Pool, based on measurements of the in-place
reservoir of PCBs from the 1984 NYSDEC survey.
For a 100-year flood event, the Thompson Island Pool Depth of Scour Model
predicts a median depth of scour of 0.41 inches in the cohesive sediment
areas. Considering the uncertainty in model predictions, the average depth
of scour for this event could range from 0.08 to 2.46 inches.
The Bivariate Statistical Model for fish body burdens indicates the relative
importance of both water column and local sediments as pathways for
bioaccumulation of PCBs in Upper Hudson River fish. Reported Aroclor 1016
burdens are mainly attributed to water column concentrations for all species.
Reported Aroclor 1254 burdens, which include more highly-chlorinated PCB
congeners that tend to accumulate in fat, show a wide range in the relative
importance of water column and sediment pathways among different
species. Results for Aroclor 1254 are consistent with species feeding
behavior: for species that feed in the water column, the water column
pathway tends to dominate, while for bottom-feeders, the sediment pathway
tends to be dominant. Fish-eating species at higher levels in the food chain
appear to accumulate Aroclor 1 254 from both water column and sediment
pathways.
The Probabilistic Bioaccumulation Food Chain Model indicates that water
pathways contribute significantly to PCB body burdens in forage fish
(including Pumpkinseed Sunfish) and Yellow Perch. Water and sediment are
both important for Largemouth Bass, and sediment is the main exposure
pathway for the bottom-feeding Brown Bullhead. These results compare
favorably with results from the Bivariate Statistical Model.
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Results from the original Lower Hudson River modeling effort by Thomann et
al., (1989) were successfully reproduced.
Future Baseline Modeling Efforts
The conclusions presented in this Preliminary Model Calibration Report indicate that
significant new understanding has been gained about PCB transport, fate and
bioaccumulation in the Hudson River. The modeling work for this Reassessment is
continuing and more definitive conclusions will be presented in the Baseline
Modeling Report. The purpose of this future modeling work is to reduce
uncertainties contained in the preliminary models. Future plans include continued
development of both the transport and fate mass balance models, and the fish body
burden models. They also include applications of these models to additional field
data that became available after completion of this preliminary model calibration
work.
Future work with the HUDTOX model will include development of a more finely-
resolved spatial segmentation grid for Thompson Island Pool, application to daily
suspended solids data collected during the Spring 1994 high-flow period and re-
calibration using the complete, validated, Phase 2 field data for 1993. Finally, a
long-term (1984-1993) hindcasting calibration will be conducted to confirm the
predictive capability of the model over a decadal time scale.
Future work with the Thompson Island Pool Depth of Scour Model will include
extension of the modeling framework to include both cohesive and non-cohesive
sediment areas, and application to the complete, validated, Phase 2 field data for
1993.
Future work with the fish body burden models will include application to NYSDEC
1995 fish data, further analysis of exposure pathways involving water column
invertebrates and exploration of patterns of congener uptake between and among
different fish species. In addition, use of a model based on fugacity (Gobas, 1993),
or chemical potential, will be explored.
The final version of HUDTOX will be used to simulate PCB concentrations in the
water column and sediments due to No Action and various flood events. For these
simulations, the output of the HUDTOX model for the Upper Hudson River will be
linked to the Thomann model for the Lower Hudson River. In turn, the PCB outputs
from these Upper and Lower Hudson River models will be linked to the fish body
burden models. Finally, predictive modeling simulations to evaluate the effects of
various remedial scenarios will be presented as part of the Phase 3 Report
(Feasibility Study).
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1. INTRODUCTION
1.1 Background
The Hudson River watershed encompasses an area of 13,390 square miles,
principally in the eastern portion of New York State (Figure 1-1). The Hudson River
PCB Superfund Site extends from Hudson Falls, New York, to the Battery in New
York Harbor (River Mile 0), a stretch of almost 200 river miles (Figure 1-2). The
Upper Hudson refers to the 40-mile stretch of river upstream of Federal Dam at
Troy to Hudson Falls (Figure 1-3). The Lower Hudson refers to the portion of the
river downstream of Federal Dam to the Battery (Figure 1-4).
For approximately 30 years, two General Electric (GE) facilities, one in Fort
Edward and the other in Hudson Falls (Figure 1-5), used polychlorinated biphenyls
(PCBs) to make electrical capacitors. GE discontinued use of PCBs in 1977 when
they ceased to be manufactured and sold in the United States. From 1957 through
1975, between 209,000 and 1.3 million pounds of PCBs were discharged from
these facilities into the Upper Hudson River. Migration of PCBs downstream was
greatly enhanced in 1973 with the removal of Fort Edward Dam and the
subsequent release of PCB-contaminated sediments. A region of special concern is
the highly-contaminated sediments in Thompson Island Pool (TIP) which is located
immediately downstream of the old Fort Edward dam site (Figure 1-5).
In 1976, the New York State Department of Environmental Conservation
(NYSDEC) imposed a ban on fishing in the Upper Hudson River due to the potential
risk posed by consumption of PCB-contaminated fish. In August 1995, the Upper
Hudson was re-opened to fishing, but only on a catch-and-release basis. NYSDEC
also imposed a ban on commercial fishing for striped bass in the Lower Hudson
River. This ban remains in effect.
In 1984 the U.S. Environmental Protection Agency (USEPA) completed a
Feasibility Study on the site that investigated remedial alternatives and issued a
Record of Decision (ROD) later that year. The ROD called for: (1) an interim No
Action decision concerning river sediments; (2) in-place capping, containment and
monitoring of remnant deposit (formerly impounded) sediments; and, (3) a
treatability study to evaluate the effectiveness of the Waterford Treatment Plant in
removing PCBs from Hudson River water.
1.2 Purpose of Report
In December 1990, USEPA issued a Scope of Work for reassessing the No
Action decision for the Hudson River PCB site. The scope of work identified three
phases:
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Phase 1 - Interim Characterization and Evaluation
Phase 2 - Further Site Characterization and Analysis
Phase 3 - Feasibility Study.
The Phase 1 Report (TAMS/Gradient, 1991) is Volume 1 of the Reassessment
documentation and was issued by USEPA in August 1991. It contains a
compendium of background material, discussion of findings and preliminary
assessment of risks.
The Final Phase 2 Work Plan and Sampling Plan (TAMS/Gradient, 1992)
detailed the following main data collection tasks to be completed during Phase 2:
High- and low-resolution sediment coring
Geophysical surveying and confrmatory sampling
Water column sampling (including transects and flow-averaged
composites)
Ecological field program.
The Database Report (Volume 2A in the Phase 2 series of reports;
TAMS/Gradient, 1995) and accompanying CD-ROM database issued in March 1996
provides the validated data for the Phase 2 investigation. The Data Evaluation and
Interpretation Report (Volume 2C in the Phase 2 series of reports;
TAMS/CADMUS/Gradient, 1996 - pending publication) presents results and findings
of water column sampling, high-resolution sediment coring, geophysical surveying
and confirmatory sampling, geostatistical analysis of 1984 sediment data and PCB
fate and transport dynamics.
This Preliminary Model Calibration Report is Volume 2B in the Phase 2 series
of reports. It includes descriptions of the transport and fate mass balance models,
and the fish body burden models that are being used for this PCB Reassessment
RI/FS. All of the work described herein was conducted as part of Task 4 -
Preliminary Model Calibration Report. The scope of Task 4 was limited to
documentation of the conceptual approaches, databases and preliminary calibration
results for each model. With the exception of the Thompson Island Pool Depth of
Scour Model, no results are presented for use of the calibrated models as predictive
tools. All results in this report are preliminary results from ongoing investigations
and should be considered strictly provisional in nature.
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The modeling work for this PCB Reassessment RI/FS is continuing as part of
the Baseline Modeling phase of the overall study. To provide a more complete
context for the modeling results in the present report, work plans for this Baseline
Modeling phase are presented in Appendix B. These plans include continuing
development with both the transport and fate mass balance models, and the fish
body burden models. They also include applications of these models to additional
Phase 2 field data that became available after completion of this preliminary model
calibration work.
1.3 Report Format and Organization
Section 2 of this report contains a general summary of the preliminary model
calibration work and preliminary conclusions drawn from this work. Section 3
contains a description of the overall approach for the transport and fate models,
and descriptions of the individual models for the Upper Hudson River, Thompson
Island Pool and the Lower Hudson River. Section 4 contains preliminary model
calibration results for the Upper Hudson River PCB Model. Section 5 contains
preliminary model calibration results for the Thompson Island Pool Hydrodynamic
Model. Section 6 contains preliminary model calibration results for the Thompson
Island Pool Depth of Scour Model and predictions from this model for a range of
flood events, including the 100-year flood. Section 7 contains application results
for the Lower Hudson River PCB Model.
Section 8 of this report contains a description of the overall approach for the
fish body burden models and descriptions of the individual models for the Upper
and Lower portions of the Hudson River. Section 9 contains preliminary model
calibration results for the Bivariate Statistical Model. Section 10 contains
preliminary model calibration results for the Probabilistic Bioaccumulation Food
Chain Model.
The material in this report has been divided into two separate books. Book 1
contains the report text, a list of references, and a glossary of abbreviations and
acronyms. Book 2 contains all tables, figures, plates and appendices. Within Book
2, Appendix A contains ecological profiles for fish species represented in the fish
body burden models. Appendix B contains the plans for future modeling work.
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2. SUMMARY AND PRELIMINARY CONCLUSIONS
2.1 Summary
The following is a general summary of the modeling work conducted to date
under Task 4 of this Hudson River PCB Reassessment RI/FS.
2.1.1 Overall Approach
The overall modeling approach is based on the principle of conservation of
mass. A large body of information from site-specific field measurements,
laboratory experiments and the scientific literature was synthesized within
quantitative models for the Upper Hudson River and the tidal freshwater portion of
the Lower Hudson River. Models were developed for the transport and fate of PCBs
in the water column and bedded sediments, and for PCB body burdens in fish.
The contents of this report are limited to documentation of the conceptual
approaches, databases and preliminary calibration results for each model. With the
exception of the Thompson Island Pool Depth of Scour Model, no results are
presented for use of the calibrated models as predictive tools. All results in this
report are preliminary results from ongoing investigations and should be considered
strictly provisional in nature.
2.1.2 Water Column and Sediment Models
1. Three separate mass balances are being conducted for the Hudson River: (a)
a water balance; (b) a solids balance; and (c) a PCB balance. Each balance
includes specification of external inputs, internal sources and sinks, and
system outputs.
2. The PCB mass balance in the Upper Hudson River is being conducted using
the HUDTOX model developed as part of this RI/FS. This mass balance
includes total PCBs and five separate congeners, or groups of co-eluting
congeners, corresponding to BZ#4 (2,2'-dichlorobiphenyl), BZ#28 (2,4,4'-
trichlorobiphenyl), BZ#52 (2,2',5,5'-tetrachlorobiphenyl), BZ#[90 + 101]
(2,2',3,4',5-Pentachlorobiphenyl and 2,2',4,5,5'-pentachlorobiphenyl) and
BZ#138 (2,2',3,4,4',5'-hexachlorobiphenyl).
3. The PCB mass balance in the Lower Hudson River is being conducted using
an existing transport and fate model developed by Thomann et al., (1989).
This mass balance includes total PCBs represented as the sum of individual
homologues.
4. Bathymetry, delineation of cohesive (fine-grained) and non-cohesive (coarse-
grained) sediment areas, and an inventory of sediment PCBs were discretized
within a fine-scale, grid-based Geographical Information System (GIS) for
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Thompson Island Pool in the Upper Hudson River. The cohesive sediment
areas in Thompson Island Pool are considered to encompass most of the
known PCB "hotspots".
5. Separate Hydrodynamic and Depth of Scour Models were applied to
Thompson Island Pool to estimate the range of scour depths and quantities
of solids and PCBs eroded from cohesive sediment areas due to large flood
events. The maximum design flow in this investigation was 47,330 cfs at
Rogers Island, corresponding to a flow return period of 100 years.
2.1.3 Fish Body Burden Models
1. Three approaches for fish body burdens are being used in this study: (a) a
Bivariate Statistical Model; (2) a Probabilistic Bioaccumulation Food Chain
Model; and (3) the Thomann food chain model. The Thomann food chain
model is part of the transport and fate model for the Lower Hudson River.
Each of these approaches provides a different perspective on the question of
PCB bioaccumulation in fish.
2. The Bivariate Statistical Model represents PCBs in terms of total PCBs and
selected Aroclors. The Probabilistic Bioaccumulation Food Chain Model
represents total PCBs, selected Aroclors, and the five congeners used in
calibration of the HUDTOX model. The Thomann food chain model
represents PCB homologues.
3. The Bivariate Statistical Model for fish body burden in a given species is
based on the historical dataset of Aroclor measurements, with corrections for
changing quantitation methods. It is designed to provide an empirical,
preliminary scoping of the causal relationships described in the Probabilistic
Bioaccumulation Food Chain Model. The statistical model relies on a
bivariate regression approach which relates fish body burden to
concentrations in both water and sediment. This allows for the possibility
that water and sediment concentrations are not in equilibrium, as is
frequently observed in the Upper Hudson River.
4. The Probabilistic Bioaccumulation Food Chain Model consists of the following
biotic compartments: (a) benthic invertebrates; (b) water column
invertebrates; (c) forage fish; (d) piscivorous fish; (e) demersal fish; and (f)
omnivorous fish. PCB concentrations are expressed as lipid-normalized in
biota, total organic carbon normalized in sediments and fraction organic
carbon normalized in the particulate phase in the water column.
Relationships among compartments are expressed as bioaccumulation factors
between the concentration in a given compartment and the expected dietary
exposure for that compartment. The dietary exposure is based on a
weighted concentration in the diet.
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5. Species-specific profiles are presented for Yellow Perch (Perca flavescens),
Largemouth Bass (Micropterus salmoides), Pumpkinseed (Lepomis gibbosus),
Brown Bullhead (Ictalurus nebulosus), White Perch (Morone americana),
Spottail Shiner (Notropis hudsonius), Shortnose Sturgeon (Acipenser
brevirostrum) and Striped Bass (Morone saxati/isj. These profiles describe
foraging strategies, home-ranges and information on reproduction for each of
these species.
6. Several sample look-up tables are provided for the predicted 25th, mean,
75th and 95th percentiles for the yellow perch model. Note that the values
in these look-up tables are based on unvalidated data and are subject to
change. They are provided in this report strictly for illustrative purposes.
7. Statistical distributions of bioaccumulation factors have been derived to date
for:
(a) sediments to benthic invertebrates (calibration congeners, Aroclors
1016 and 1254, and total PCBs);
(b) particulate phase in the water column to water column invertebrates
(total PCBs and Aroclors 1016 and 1254);
(c) expected dietary concentrations to composite forage fish (total PCBs
and Aroclors 1016 and 1254)
(d) expected dietary concentrations to yellow perch (total PCBs);
(e) pumpkinseed to largemouth bass (total PCBs and Aroclors 1016 and
1254);
(f) sediment to brown bullhead (calibration congeners and total PCBs);
and
(g) benthic invertebrates to brown bullhead (calibration congeners and
total PCBs).
2.2 Preliminary Conclusions
The following preliminary conclusions can be drawn from the modeling work
conducted to date under Task 4 of this Hudson River PCB Reassessment RI/FS.
2.2.1 Upper Hudson River PCB Mass Balance
1. The PCB mass balance model for the Upper Hudson River (HUDTOX)
provides a reasonable representation of hydraulics, solids dynamics and PCB
dynamics during a period of simulation corresponding to the Phase 2 water
column monitoring program, January 1 through September 30, 1993.
2. The principal hydraulic inputs to the Upper Hudson River during the period of
simulation were inflow across the upstream boundary at Fort Edward (34
percent) and tributary inflow from the Mohawk River near the downstream
boundary at Federal Dam (43 percent). The remaining inputs were from
smaller tributaries and direct runoff.
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3. The principal external solids loadings to the Upper Hudson River during the
period of simulation were from the Mohawk River (58 percent) and the
Hoosic River (20 percent). Solids loadings across the upstream boundary at
Fort Edward represented only 8.5 percent of the total external solids
loadings.
4. The principal external loadings of total PCBs to the Upper Hudson River
during the period of simulation were across the upstream boundary at Fort
Edward (74 percent) and from the Mohawk River (18 percent).
5. Hydraulic inputs and external loadings of solids and total PCBs to the Upper
Hudson River during the period of simulation showed a strong seasonal
dependence. The spring high flow period (March 26 - May 10), which
represents 17 percent of the total period of simulation, was responsible for
56 percent of the hydraulic inputs, 87 percent of the solids loadings and 68
percent of the total PCB loadings to the Upper Hudson River during the total
period of simulation.
6. The calibrated HUDTOX model represents the average behavior of water
column total PCBs and the five congener groups reasonably well during the
simulation period. There were 24 combinations of PCB types and model
spatial segments for which field data were available for model calibration.
Segment-average values for model output were significantly different (p <
0.05) than segment-average observed values in only three of these 24 cases.
7. The calibrated HUDTOX model was also successful in representing the more
highly resolved day-to-day variability across all model segments. Regression
analyses of model output vs. observations were conducted using paired daily
values for total PCBs and each of the five congener groups. Results
indicated that the HUDTOX model explained an average of 70 percent of the
overall spatial-temporal variability in these more highly resolved field data.
8. During the total period of simulation, there was an 8.1 percent gain in water
column solids mass across Thompson Island Pool between the upstream
boundary at Fort Edward and Thompson Island Dam. The gain in water
column solids mass was 7.8 percent during the spring high flow period and
10 percent during the remaining lower flow period.
9. Over the period of simulation, the total mass of solids input across the
upstream boundary at Fort Edward was equal to 92 percent of the solids
mass transported across Thompson Island Dam. The corresponding
quantities during the spring high flow period and the remaining lower flow
period were 93 percent and 91 percent, respectively. The total mass of
solids due to gross resuspension from the surface sediment layer in
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Thompson Island Pool was equal to 20 percent of the solids mass
transported across Thompson Island Dam. The corresponding quantity
during the spring high flow period was 16 percent. During the remaining
lower flow period, solids in Thompson Island Pool were lost from the water
column due to net sedimentation.
10. During the total period of simulation, there was a 102 percent gain in total
PCB mass across Thompson Island Pool between the upstream boundary at
Fort Edward and Thompson Island Dam. The gain in total PCB mass was
104 percent during the spring high flow period and 100 percent during the
remaining lower flow period.
11. During the total period of simulation, the total mass of total PCBs input
across the upstream boundary at Fort Edward was equal to 49 percent of the
total PCB mass transported across Thompson Island Dam. The
corresponding quantities during the spring high flow period and the remaining
lower flow period were 49 percent and 50 percent, respectively. The total
mass of total PCBs due to gross rcsLispension from the surface sediment
layer in Thompson Island Pool was ec,_ percent of the total PCB mass
transported across Thompson Island Dam. The corresponding quantities
during the spring high flow period and the remaining lower flow period were
57 percent and 58 percent, respectively.
12. There were significant differences between the dynamics of total PCBs and
the dynamics of lower-chlorinated PCB congeners. For example, during the
total period of simulation, there was a 585 percent gain in BZ#4 mass across
Thompson Island Pool between the upstream boundary at Fort Edward and
Thompson Island Dam. The gain in BZ#4 mass was 1435 percent during the
spring high flow period and 278 percent during the remaining lower flow
period.
13. During the total period of simulation, the total mass of BZ#4 input across the
upstream boundary at Fort Edward was equal to only 1 5 percent of the total
BZ#4 mass transported across Thompson Island Dam. The corresponding
quantities during the spring high flow period and the remaining lower flow
period were 6.5 and 27 percent, respectively. The total mass of BZ#4 due
to gross resuspension from the surface sediment layer in Thompson Island
Pool was equal to 80 percent of the total BZ#4 mass transported across
Thompson Island Dam. The corresponding quantities during the spring high
flow period and the remaining lower flow period were 94 percent and 59
percent, respectively. The principal factor responsible for differences
between total PCBs and lower-chlorinated congeners appears to be that
sediments in Thompson Island Pool are relatively more contaminated with
lower-chlorinated congeners than with total PCBs.
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14. Large increases in water column concentrations of apparent dissolved phase
(i.e. truly dissolved plus dissolved organic carbon-bound) PCBs, especially for
lower-chlorinated congeners, are observed to occur across Thompson Island
Pool. These increases appear to be caused by an internal source within
Thompson Island Pool. It is not clear whether these increases in PCB
concentrations originate from historical sediment sources or from more
recent discharges.
15. The physical, chemical and biological processes controlling PCB dynamics in
Thompson Island Pool are not fully understood at the present time. One
hypothesis that could explain the large increases in PCB concentrations
across Thompson Island Pool is sediment-water advective flux of pore water
PCBs due to groundwater inflow. Such a pore water advective flux would be
relatively more important for lower-chlorinated PCB congeners due to their
greater water phase solubilities.
16. Sensitivity analyses were conducted with the calibrated HUDTOX model in
which total PCB loadings across the upstream boundary at Fort Edward were
varied by plus/minus 30 percent. In response to these variations, the total
PCB mass transported across Thompson Island Dam varied by plus/minus 14
percent and total PCB loadings across Federal Dam to the Lower Hudson
River varied by plus/minus 7 percent.
17. Sensitivity analyses were conducted with the calibrated HUDTOX model in
which initial total PCB concentrations in the sediments were varied by
plus/minus 30 percent. In response to these variations, the total PCB mass
transported across Thompson Island Dam varied by plus/minus 16 percent
and total PCB loadings across Federal Dam to the Lower Hudson River varied
by plus/minus 20 percent.
2.2.2 Thompson Island Pool Hydrodynamics and Sediment Erosion
1. The Thompson Island Pool hydrodynamic model (RMA-2V) is a two-
dimensional, vertically-averaged, time-variable model. This model was used
to predict steady-state velocity distributions in Thompson Island Pool for a
range of design flows. Results from the RMA-2V model are in good
agreement with available measurements for river flow velocities and water
elevations. Results from the model for a 100-year flow were consistent with
independent results from a FEMA flood modeling study.
2. For a 100-year flood event (47,330 cfs at Rogers Island), the RMA-2V model
predicts a mean river flow velocity of 0.945 fps in Thompson Island Pool.
Based on the predicted two-dimensional flow velocity distribution, mean
applied shear stress in the cohesive sediment areas of Thompson Island Pool
was estimated to be 19.5 dynes/cm2 for this event.
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3. For a 100-year flood event, the Thompson Island Pool Depth of Scour Model
predicts that 834,000 kg of solids and 25 kg of total PCBs will be eroded
from the cohesive sediment areas. This mass of PCBs represents less than
1 percent of the total reservoir of PCBs in the cohesive sediment areas of
Thompson Island Pool, based on measurements of the in-place reservoir of
PCBs from the 1 984 NYSDEC survey.
4. For a 100-year flood event, the Thompson Island Pool Depth of Scour Model
predicts a median depth of scour of 0.16 cm in the cohesive sediment areas.
Considering the uncertainty in model predictions, the average depth of scour
for this event could range from 0.03 cm (5th percentile) to 0.97 cm (95th
percentile).
5. As part of the Phase 2 high-resolution sediment coring effort, detailed
vertical profiles are available at five locations in Thompson Island Pool. At all
of these locations, depths of observed PCB concentration peaks were greater
than predicted median depths of scour for a 100-year flood event.
6. Analysis of uncertainties in the Thompson Island Pool Depth of Scour Model
was conducted at the locations of the five high-resolution sediment cores.
At four of these five locations, depths of observed PCB concentration peaks
were outside the middle 90 percent certainty ranges around predicted median
depths of scour.
7. Results from the Thompson Island Pool Depth of Scour Model represent
erosion of solids and PCBs from only the cohesive sediment areas, which are
considered to encompass mo^t of the known PCB "hotspots". This model is
based on assumptions and governing equations that were developed and
validated exclusively for cohesive sediments.
2.2.3 Upper Hudson River Fish Body Burdens
1. The Bivariate Statistical Model provides good explanatory power in predicting
annual mean total PCB and Aroclor body burden in fish, based on analysis of
NYSDEC samples collected from River Mile 142 to River Mile 193 between
1975 and 1992. This scoping exercise indicates that a steady-state food
web model of fish body burden, driven by water column and sediment PCB
concentrations, is feasible.
2. The Bivariate Statistical Model for fish body burdens indicates the relative
importance of water column and local sediment-derived pathways for
bioaccumulation of PCBs in five species of Upper Hudson River fish,
measured as Aroclor equivalents. Reported Aroclor 1016 burdens are
dominantly attributed to water column inputs in all species. Reported Aroclor
1254 burdens, which include more lipophilic and more highly-chlorinated PCB
congeners in the quantitation, show a wide range in the relative importance
of sediment and water column pathways among different species. The
results for Aroclor 1 254 are consistent with species foraging behavior and
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trophic position: for species at lower trophic levels which forage in the water
column, the water column pathway is dominant, while for bottom-foraging
species the sediment pathway is dominant. Piscivorous species at higher
trophic levels appear to integrate Aroclor 1254 accumulation from both
water column and sediment pathways.
3. Statistical models for fish body burdens based on historical monitoring data
are dependent on the manner in which Aroclors were quantified by NYSDEC.
Reliability of parameter estimates for the statistical models is also limited by
the data on water column concentrations, which are generally available only
as total PCBs for the period prior to 1991, and the lack of adequate data on
time trends in sediment exposure concentrations. The statistical models also
do not attempt to address variability in body burden resulting from age and
variations in foraging with size, nor seasonal patterns related to temperature
and spawning cycles.
4. Biota body burdens in the Probabilistic Bioaccumulation Food Chain Model
are expressed as lognormal distributions in which 90 percent of the predicted
concentrations fall within the observed range for the five calibration
congeners, Aroclors 1016 and 1254, and total PCBs.
5. The Probabilistic Bioaccumulation Food Chain Model indicates that water
pathways contribute significantly to PCB body burdens in forage fish
(including pumpkinseed sunfish) and yellow perch. Water and sediment are
important for largemouth bass and sediment is the main exposure pathway
for brown bullhead. These results compare favorably with the results from
the Bivariate Statistical Model.
6. Results from the Probabilistic Bioaccumulation Food Chain Model are
sensitive to initial concentrations. Although the relationships among each of
the compartments have been well established, the model predictions reflect
the underlying variability and uncertainty in the water column and sediment
PCB concentrations. Model predictions also reflect uncertainties in inter-
compartmental relationships. The model defines the percentage of a fish
species population expected to be at or below a given concentration (i.e. at
the 90th percentile concentration, 90 percent of that species population will
experience PCB body burdens at or below the 90th percentile concentration).
7. The sample look-up tables provide an indication of the expected biota body
burdens under different sediment-water concentration combinations. The
Probabilistic Bioaccumulation Food Chain Model can be used as a tool to
evaluate the change in the ratio between water and sediment concentrations.
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2.2.4 Lower Hudson PCB Mass Balance and Striped Bass Bioaccumulation
1. Results from the original Lower Hudson River modeling effort by Thomann et
al., (1989) were successfully reproduced in this Hudson River PCB
Reassessment RI/FS. The Thomann et al., (1989) model is presently the
best available tool for quantifying: (a) PCB transport and fate; and (b)
bioaccumulation in striped bass in the Lower Hudson River.
2. Subsequent to the original Thomann et al., (1989) modeling effort, revised
estimates were made for PCB loadings across Federal Dam to the Lower
Hudson River. Preliminary simulations with the Thomann model using these
revised PCB loadings indicate that results are still consistent with the original
calibration, due in part to large uncertainties in available field observations in
the Lower Hudson River.
3. Results from the Thomann model indicate that under recent historical
conditions, the tidal freshwater portion of the Lower Hudson River is
influenced primarily by PCB loadings across Federal Dam, and that the
estuarine portion of the Lower Hudson :s influenced primarily by direct
external loadings and loadings from the vicinity of New York City.
4. Results from the Thomann model indicate that net uptake of PCBs by striped
bass occurs primarily in the mid-lower Hudson River between River Miles
18.5 and 78.5, and that net loss of PCBs from striped bass occurs in all
spatial segments downstream of this area.
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3. MODELING APPROACH: TRANSPORT AND FATE
3.1 Introduction
This section contains a description of the overall approach for the transport
and fate models, and descriptions of the individual models for the Upper Hudson
River, Thompson Island Pool and the Lower Hudson River. Section 3.2 contains the
goals and objectives of the overall modeling work in this Hudson River PCB RI/FS.
Section 3.3 contains a discussion of the conceptual approach for the transport and
fate, and fish body burden models.
Section 3.4 contains a brief summary of the Hudson River database created
to support this RI/FS. Section 3.5 contains a description of the Upper Hudson River
Mass Balance Model. Section 3.6 contains a description of the Thompson Island
Pool Hydrodynamic Model. Section 3.7 contains a description of the Thompson
Island Pool Depth of Scour Model. Section 3.8 contains a description of the Lower
Hudson River PCB Transport and Fate Model.
Detailed descriptions of the Bivariate Statistical Model and the Probabilistic
Bioaccumulation Food Chain Model are contained in Section 8.
3.2 Modeling Goals and Objectives
The goals and objectives of the modeling work described herein were
designed to answer the following principal RI/FS questions:
1. When will PCB levels in fish populations recover to levels meeting human
health and ecological risk criteria under continued No Action?
2. Can remedies other than No Action significantly shorten the time required to
achieve acceptable risk levels?
3. Are there contaminated sediments now buried and effectively sequestered
from the food chain that are likely to become "reactivated" following a major
flood, possibly resulting in an increase in contamination of the fish
population?
The overall goal of the modeling analysis in the reassessment effort is to
develop and field validate scientifically credible mass balance models for evaluating
and comparing the impacts of continued No Action, various remedial scenarios and
hydrometeorological events in terms of PCB concentrations in the water column
and sediment, and PCB body burdens in fish.
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The specific objectives of the modeling analysis are the following:
1. Develop and apply a predictive model for PCB levels in water and sediments
over long-term (decadal) time scales in the Upper Hudson River;
2. Evaluate the impacts of PCB loadings from the Upper Hudson River on PCB
levels in water and sediments in the freshwater portion of the Lower Hudson
River;
3. Estimate the risk of resuspension of PCBs from the deeply buried sediments of
Thompson Island Pool in response to a "catastrophic" flood event;
4. Estimate the impacts of potential resuspension from a "catastrophic" event in
Thompson Island Pool on downstream PCB concentrations in water and
sediments;
5. Evaluate and apply quantitative models of the relationships between PCB water
and sediment concentrations and fish Dody burdens in the Upper and Lower
Hudson River; and,
6. Apply the Hudson River models to evaluate and compare predicted responses to
continued No Action, various remedial scenarios and hydrometeorological events
in terms of PCB concentrations in water, sediments and fish.
The principal study areas are the Upper Hudson River from Fort Edward to
Federal Dam at Troy (Figure 1-3) and the freshwater portion of the Lower Hudson
River from Federal Dam to River Mile 55 (Figure 1-4). More detailed analyses are
being conducted in Thompson Island Pool (TIP), a 6-mile portion of the Upper
Hudson River between Fort Edward and Thompson Island Dam (Figure 1-5).
3.3 Conceptual Approach
The overall modeling approach in this RI/FS reassessment is based on the
principle of conservation of mass. Models are being developed for the transport
and fate of PCBs in the water column and bedded sediments, and for PCB body
burdens in fish. The principal modeling endpoints in this study are the following:
PCB concentrations in water, bedded sediments and fish
Mass of PCBs eroded in Thompson Island pool due to a "catastrophic"
flood event
Mass loading rates of PCBs at Thompson Island Dam due to a
"catastrophic" flood event
Mass loading rates of PCBs at Federal Dam
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Mass loadings rates of PCBs from the freshwater portion of the Lower
Hudson River to the estuarine portion.
Three separate mass balances are being conducted for the Hudson River: (1)
a water balance; (2) a solids balance; and (3) a PCB mass balance. A water
balance is necessary because PCB transport is influenced by river flow rates and
mixing rates. A solids balance is necessary because PCB fate is influenced by the
tendency of PCBs to sorb, or attach, to both suspended and bedded solids in the
river. Finally, a PCB mass balance is necessary to estimate PCB water column and
sediment concentrations as a function of external loadings, sediment-water
exchanges and air-water exchanges.
Two separate models are being applied to Thompson Island Pool to estimate
the mass of PCBs eroded due to large flood events: a hydrodynamic model and a
depth of scour model for solids and associated PCBs. The hydrodynamic model is
being used to determine flow velocities and shear stresses at the sediment-water
interface. The depth of scour model is being used to determine the range of scour
depths and quantities of resuspended solids and PCBs in cohesive sediment areas.
Three approaches for fish body burdens are being used in this study: (1) a
Bivariate Statistical Model; (2) a Probabilistic Bioaccumulation Food Chain Model;
and (3) the Thomann food chain model. Each of these approaches provides a
different perspective on the question of PCB bioaccumulation in fish. The Bivariate
Statistical and Probabilistic Bioaccumulation Models are presented in Section 8.
The Thomann food chain model is part of the transport and fate model for the
Lower Hudson River and is presented in Section 3.8.
3.4 Hudson River Database
All modeling work in the present report utilized the extensive database that
was created to support this Hudson River PCB RI/FS (TAMS/Gradient, 1995). This
database contains information from a large variety of different sources:
New York State Department of Environmental Conservation (NYSDEC)
New York State Department of Health (NYSDOH)
New York State Department of Transportation (NYSDOT)
General Electric Company (GE)
Lamont-Doherty Earth Observatory (LDEO)
United States Geological Survey (USGS)
National Oceanic and Atmospheric Administration (NOAA)
United States Environmental Protection Agency (USEPA).
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The database contains measurements for sediments, fish and aquatic biota, surface
water flow and surface water quality. The database includes a total of
approximately 750,000 records. Almost 350,000 of these records contain data
acquired as part of the USEPA Phase 2 Work Plan and Sampling Plan
(TAMS/Gradient, 1992). The remaining records contain data from a large number
of historical and ongoing monitoring efforts in the Hudson River.
All transport and fate modeling work in the present report was conducted
using field data contained in Release 2.3 of the TAMS/Gradient Phase 2 database.
Release 2.3 was an earlier version of the database that contained much unvalidated
data and did not contain results from the Phase 2 low-resolution sediment coring
effort or from high-frequency measurements conducted during the Spring 1994
high-flow period. Release 3.1, issued in March 1996, contains the final, validated
Phase 2 field data.
3.5 Upper Hudson River Mass Balance Model
3.5.1 Introduction
The mass balance model being used for the Upper Hudson River (HUDTOX)
is a modified version of the EPA-supported WASP4 toxic chemical model (Ambrose
et al., 1988). Many of the modifications to WASP4 were developed as part of the
Green Bay Mass Balance Study (Bierman et al., 1992). The HUDTOX model is
designed to accomplish the following modeling objectives:
1. Predict PCB concentrations in the water column and sediments over long-term
(decadal) time scales in the Upper Hudson River;
2. Estimate the impacts of potential resuspension from a "catastrophic" event in
TIP on downstream PCB concentrations in the water column and sediments;
and,
3. Evaluate and compare predicted responses to continued No Action, various
remedial scenarios and hydrometeorological events in the Upper Hudson River.
The HUDTOX model includes both water column and sediment compartments, and
simultaneous mass balances for water, solids and PCBs. The model is three-
dimensional in space and variable in time.
Figure 3-1 contains conceptual representations of the water, solids and PCB
mass balances in the HUDTOX model. Mass is balanced in space in terms of a
finite number of control volumes, or spatial segments. These spatial segments are
linked, as specified by the user, to allow inter-segment transport of water, solids
and PCBs through mechanisms such as advective flow, dispersive mixing, particle
settling and sediment resuspension. Physical-chemical mechanisms are included to
describe the transformation and fate of PCBs within model segments. These
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mechanisms include equilibrium partitioning between PCBs and solids, and
sediment-water and air-water exchanges.
The HUDTOX model requires a large amount of input data in the form of
system-specific physical characteristics, external loadings, forcing functions,
boundary conditions and initial conditions. The principal model inputs include the
following:
Geometry for model spatial segmentation grid in water column and
bedded sediments
Advective flow rates and dispersive mixing rates
External mass loading rates for all model state variables
Particle gross settling, resuspension and net burial velocities
Equilibrium partition coefficients for PCBs
Sediment-water and air-water exchange rates for PCBs
Atmospheric gas phase PCB concentration
Initial conditions for all model state variables.
Many of these model inputs, such as geometry and water flow rates, are specified
using direct measurements for the Upper Hudson River. Some model inputs, such
as sediment-water and air-water exchange rates, are specified using available
information from the scientific literature. Finally, model inputs such as gross
settling and resuspension velocities are determined through calibration of model
output to Upper Hudson River field data.
3.5.2 State Variables and Process Kinetics
The general HUDTOX mass balance equations are fully documented in
Ambrose et al., (1988). This reference includes model theory, a user manual and a
programmer's guide. Apart from the water balance equations, the HUDTOX model
consists of two simultaneous, coupled mass balances for solids and PCBs. These
two mass balances can be viewed as submodels within the overall HUDTOX
modeling framework.
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Solids Submodel
Particle dynamics are important in controlling the transport, transformation
and fate of PCBs in aquatic systems due to the tendency of PCBs to sorb, or
attach, to both suspended and bedded solids (e.g. Eadie and Robbins, 1987).
Karickhoff (1979; 1984) has shown that organic carbon is the principal sorbent
compartment for hydrophobic organic chemicals, such as PCBs, in aquatic systems.
In addition to organic carbon in particulate form, dissolved organic carbon (DOC)
can also be important in the sorption and fate of PCBs (e.g. Eadie et at., 1990;
Bierman et al., 1992).
The HUDTOX solids submodel consists of two state variables: total
suspended solids (TSS), and DOC. These two state variables represent the sorbent
compartments for toxic chemicals within the HUDTOX model framework. Figure 3-
2 displays the relationships among these solids state variables in HUDTOX.
All particulate matter, both biotic and abiotic, is represented as TSS in
HUDTOX because neither the Phase 2 nor the historical data include sufficient
parameter measurements to allow simulation of multiple solids types. To represent
particulate organic carbon, a constant organic carbon fraction is assigned to TSS
(Thomann and Mueller, 1987). Internal loadings of biotic solids due to primary
production are represented in the solids model. These loadings were externally
specified using estimates based on field measurements of primary productivity in
the freshwater portion of the Lower Hudson River (Cole et al., 1992).
Toxic Chemical Submodel
The principal state variable in the HUDTOX toxic chemical submodel is total
chemical concentration. HUDTOX represents the components of total chemical in
three phases through the use of equilibrium partitioning relationships. Figure 3-3
illustrates these phases which include: truly dissolved chemical, TSS-sorbed
chemical, and DOC-sorbed chemical. The PCB measurements in the Phase 2
database do not distinguish DOC-bound PCBs from truly dissolved PCBs, but
measure these phases together as "apparent" dissolved phase PCBs. Nonetheless,
it is important to distinguish truly dissolved phase concentrations from DOC-bound
concentrations because bioconcentration of PCBs, due to direct uptake from
ambient water, is driven by only the truly dissolved phase component (e.g. DiToro
et al., 1991).
A schematic diagram of the HUDTOX toxic chemical submodel components
and process mechanisms is shown in Figure 3-4. Since the solids and chemical
submodels in HUDTOX are fully integrated, they are structurally the same. The
toxic chemical submodel includes additional process mechanisms to simulate
equilibrium phase partitioning between unbound (or truly dissolved), TSS-sorbed,
and DOC-sorbed chemical. In addition, air-water exchange of dissolved chemical,
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arid sediment-water exchanges of truly dissolved and DOC-sorbed chemical are
included.
Two important differences between the HUDTOX and WASP4 toxic chemical
kinetics should be noted. First, HUDTOX includes temperature-corrected Henry's
Law constants (H) as described in Achman et al., (1993) and shown in Equation
3-1.
log Ht = log H29g * (7.91 - 3414.0 / T) / (7.91 -3414.0/298.15) (3-1)
This correction affects air-water exchange to a significant degree for PCBs,
since HT can vary by an order of magnitude for the range of water temperatures
(approximately 0 to 30 deg. C) observed in the Upper Hudson River. A second
kinetic modification employed in HUDTOX is a temperature correction for PCB
partition coefficients (Kp) as proposed by TAMS/CADMUS/Gradient (1996 - pending
publication). The form of this empirical relationship is shown in Equation 3-2.
log KpJ = log Kp29315oK + TSF * ( 1/T - 1/293.15) (3-2)
where,
Kp = partition coefficient (L/kg)
T = water temperature (°K)
TSF = temperature slope factor (°K).
Other enhancements which simplify application of the toxic chemical model
have also been made and are described by Bierman et al., (1992) for the model
application to Green Bay, Lake Michigan.
3.5.3 Spatial-Temporal Scales
The HUDTOX water column geometry was developed with 13 spatial
segments to represent the Upper Hudson River. The model segments run from the
northern tip of Rogers Island (River Mile 194.6) to Federal Dam (River Mile 153.9)
as displayed in Figure 3-5. The resolution of this spatial segmentation grid was
determined primarily by the available field data in Release 2.3 of the TAMS/Gradient
Phase 2 database. As part of the future modeling work (Appendix B), a more
finely-resolved spatial segmentation grid will be developed for TIP. This grid will be
two-dimensional in the horizontal and will consist of 10-20 spatial segments.
The period of simulation for the HUDTOX model calibration in the present
report was January 1 to September 30, 1 993, coinciding with the Phase 2 water
column monitoring program. The characteristic time scale for this HUDTOX
calibration was monthly to seasonal. As part of the future modeling work
(Appendix B), the HUDTOX model will also be calibrated to high-frequency data
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collected during the Spring 1994 high-flow period. In addition, a long-term
hindcasting calibration will be conducted over a decadal time scale (1984-1993).
The selection criteria for specifying the HUDTOX water column segmentation
include the following:
1. The location of major tributaries to the Upper Hudson River;
2. The location of lock and dam structures along the river;
3. The location of any significant sources of direct PCB loading to the river;
4. The location of Phase 2 and historic sampling stations;
5. The location of USGS gaging stations; and,
6. Sediment PCB "hot spot" locations along the river.
The specific water column geometry was determined based upon data
ollected by the TAMS/Gradier t team and General Electric
(TAMS/CADMUS/Gradient, 1996 - pending publication). Surficial areas of
HUDTOX segments were determined within the ARC/INFO Geographic Information
System (GIS) based upon Upper Hudson River shoreline coordinates. Table 3-1
provides a comparison of the HUDTOX segment surface areas with river geometry
developed during the 1984 Feasibility Study (NUS, 1984).
Hydrographic survey data collected by General Electric during 1991 (O'Brien
and Gere, 1993b) were used to estimate HUDTOX model segment cross-sections.
The TAMS/Gradient team also has extensive hydrographic measurements of a
portion cf the Upper Hudson River, but the General Electric data provide a more
complete coverage. No significant differences were found between the two
datasets in regions covered by both surveys, including TIP, so the General Electric
data were used exclusively in determining cross-section geometry for HUDTOX.
The General Electric bathymetric elevation data were processed into distinct river
cross-sections. Figure 3-6 displays the approximate locations of the bathymetric
data along the length of the river. Water surface elevations representative of
average flow conditions in the river were assigned to each bathymetric cross-
section to determine average HUDTOX segment cross-sectional areas and depths.
Thirteen water column segments (numbered 1 through 13) were constructed to
represent the Upper Hudson River. Segments 1, 2 and 3 are used to represent
Thompson Island Pool.
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The HUDTOX sediment geometry, underlying the water column
segmentation, was based upon the only recent, comprehensive sediment
information available at the time of this preliminary model calibration work (O'Brien
and Gere, 1993a). The General Electric sediment cores are represented in layers of
0-5 cm, 5-10 cm, and 10-25 cm, so HUDTOX was developed with directly
corresponding sediment segment layers, plus two additional deep layers
representing 25-50 cm and 50-100 cm. Altogether, 65 segments (numbered 14-
78) are used to represent the Upper Hudson River sediment within the HUDTOX
model framework as shown by Figure 3-7. A non-interacting sediment boundary
layer segment (Segment 79) was also used to simplify the tracking of any deep
burial of toxic chemical out of the spatial segmentation grid.
The upper 5 cm active sediment layer thickness is consistent with
applications of WASP4-based PCB models at other sites. A surficial sediment mixed
layer depth of 4 cm was determined for Green Bay, Lake Michigan, based on 210Pb
sediment profiles (Bierman et al., 1992), while a 10 cm surficial sediment layer was
used by Velleux and Endicott (1994) to model PCBs in the Lower Fox River,
Wisconsin. The deeper sediment layers of 25 and 50 cm in thickness were
incorporated in HUDTOX to ensure that the total sediment PCB reservoir will be
represented in future long-term historical hindcasting and decadal projection
applications.
3.5.4 Application Framework
The HUDTOX model was developed within the EPA-WASP4 computer coding
framework maintained and distributed by the EPA Center for Exposure Assessment
Modeling, Athens, Georgia (Ambrose et al., 1988). The model was compiled and
run using the FTN77/486 FORTRAN 77 software (Version 2.51) developed by the
University of Salford and distributed by OTG Systems, Inc. Model development,
testing and applications were conducted on IBM-PC compatible computers with 32
bit, 80486- and Pentium-based microprocessors.
3.6 Thompson Island Pool Hydrodynamic Model
3.6.1 Introduction
The TIP Hydrodynamic Model was developed to provide necessary input
information for the TIP Depth of Scour Model. The Depth of Scour Model requires
information on shear stresses exerted at the sediment-water interface for a given
river flow rate. The TIP Hydrodynamic Model computes river flow fields in terms of
water depths and velocities. In turn, these river flow fields are used to compute
shear stresses at the sediment-water interface.
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The TIP Hydrodynamic Model consists of the RMA-2V finite element model.
This hydrodynamic model, developed and used by the U.S. Army Corps of
Engineers, is a well-known model which has been applied to many different rivers
and estuaries in the United States. In these applications, RMA-2V has been shown
to accurately model various flow fields. Also, RMA-2V is the only hydrodynamic
model for which commercially available software has been developed for pre- and
post-processing the model input and output.
A short summation of the hydrodynamic model is as follows. A finite
element grid is first constructed for the TIP section of the river. The RMA-2V finite
element model solves for the river's flow field at specified nodes of the elements.
The flow field is hydraulically determined by the specified upstream flow, the river's
boundary conditions and the river's resistance to flow. The downstream boundary
was obtained from a rating curve developed for the stage-discharge gage near the
Thompson Island Dam, the downstream boundary. The river's resistance is
quantified by the river channel's Manning's 'n'.
This next sections describe the solution variables and equations used to
compute the values of these variables, the temporal and spatial scales of the model
and the framework in which the model was applied.
3.6.2 State Variables and Process Mechanisms
The model state variables are the velocities of flow in the x and y directions
(horizontally), u and v, and the depth of flow, h. To solve for these three variables,
three equations are needed. These are as follows:
1. Continuity
3h + d(uh) d(vh) _
(3-3)
Gt dx dy
2. Momentum
a. x-direction (longitudinal) momentum
(3-4)
b. y-direction (transverse) momentum
(3-5)
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where,
h = water depth [L]
u = depth - integrated flow velocity in the x-direction
(longitudinal) [L/T]
v = depth - integrated flow velocity in the y-direction (lateral)
[L/T]
x = distance in the longitudinal direction [L]
y = distance in the lateral direction [l_]
t = time [L]
g = acceleration due to gravity [L/T2]
a0 = bottom elevation [L]
Cf = flow roughness coefficient [dimensionless]
n = Manning's n channel roughness coefficient
[dimensionless]
= normal turbulent exchange coefficient in the x direction
Ejj. = tangential turbulent exchange coefficient in the x
direction
Eyy = normal turbulent exchange coefficient in the y direction
Eyx = tangential turbulent exchange coefficient in the y
direction
p = water density [M/L3]
q = resultant velocity = (u2+v2)1/2 [L/T].
Consistent units are used in the above equations, all spatial dimensions in
feet, the time dimension in seconds, etc. The flow roughness coefficient Cf is
\f=n ~ -2^i/3
related to the Manning's coefficient by the relation Cf=(2.22 » g . n2)/h1/3
Because the RMA-2V model was run for steady state conditions, which is
explained in the next section, the model actually solved the above equations with
the time derivatives equal to zero. Also, two forces that are sometimes included in
these equations, the Coriolis force and the wind stress force, have been neglected
here. These forces are small compared to the other forces for rivers and can be
neglected.
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3.6.3 Spatial-Temporal Scales
The RMA-2V model was applied to TIP, the 6-mile portion of the Upper
Hudson River between Fort Edward (River Mile 195) and Thompson Island Dam
(River Mile 189) (Figure 3-8). The model computes the velocity flow field and
depths at nodes of the finite element grid. Figure 3-9 shows the finite element grid
used for the TIP. This grid is composed of approximately 3000 elements and 6000
nodes.
The finite element grid in the TIP channels was developed from the extensive
river bathymetry measurements conducted by GE in 1991. These measurements
included many more data points than actually needed to construct the grid. Only
the data points approximately every 50 feet transversely, and every 300 feet
longitudinally, were used. The finite element grid in the floodplain was constructed
from elevations taken from the USGS topographic maps. As s*en in Figure 3-9, the
grid in the floodplain is much coarser than in the TIP channels. This is justified
because velocities in the floodplain are much smaller than in the TIP channels and
do not vary as much.
The spatial scale of the model was largely determined by the resolution
needed to adequately define the flow field variations and hence shear stress
variation. The shear stresses exerted on the river bottom depend on the magnitude
of the vertically averaged velocity and the depth of flow above the bottom.
Because both of these quantities can vary significantly across the flow field
(transversely), the shear stress will also vary across the river. This variation must
be determined because sediment PCB concentrations are not uniformly distributed
across the flow field. Therefore the bottom shear stresses must be determined for
each point in the river. For this reason primarily, a two dimension model must be
used since a one dimensional model does not account for the transverse variation
of the velocity and depth of flow and therefore the transverse variation of shear
stresses can not be computed.
The hydrodynamic model was applied assuming that the flow through the
TIP was at steady state, i.e., the flow did not vary over time. This assumption is
justified given that the historical flow record at Fort Edward shows that the Hudson
River high flows events occur over an extended time (several days) and for the
purposes of computing the velocity field and shear stresses, this time is long
enough to establish steady state conditions.
3.6.4 Application Framework
The RMA-2V model was first calibrated to the known hydrodynamic data of
the river. The Manning's n for the river is the primary calibration parameter for the
model. River data, such as river stage-discharge relations for the upstream (Lock 7)
gaging station was used to calibrate the model. Other data, such as velocity
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measurements made by the USGS during high flow events, were used to validate
the model results.
The RMA-2V model was then applied to the TIP to compute the two
dimensional flow field and ultimately the bottom shear stresses which occur during
high flow events. The specific steps used in this process are as follows:
1. The flow field velocity and depth was determined using RMA-2V;
2. The river bottom shear velocity for each node was determined from
calculated velocity and depth at each node.
3. The bottom shear stresses were then calculated from the bottom shear
velocities using the relation r= p . u z.
The commercial software, FastTabs by the Boss Corp., was used as a pre-
and post-processor for the RMA-2V model. This software enables the user to
quickly construct a finite element grid and allows for quick and easy evaluation of
the model results.
3.7 Thompson Island Pool Depth of Scour Model
3.7.1 Introduction
The Depth of Scour Model was designed to accomplish the modeling
objective of estimating the risk of resuspension of PCBs from the deeply buried
sediments of TIP in response to a "catastrophic" flood event. The model provides
quantitative and qualitative information to estimate this risk. The Depth of Scour
model estimates the total mass of solids and PCBs eroded from cohesive sediments
for each high flow event at specified spatial and temporal scales. In addition, more
detailed estimates of local scour at selected locations in TIP were conducted. These
analyses included an explicit consideration of the uncertainty in the estimates.
It is important to note here that the model has not been designed to simulate
the subsequent transport and redistribution of these eroded sediments. The
entrainment, deposition, and post-deposition consolidation of sediments is a
complex phenomenon and only partially understood at the current time. The
evaluation of the dynamic characteristics of the scouring phenomenon is beyond
the scope of the current framework. Hence, the model as currently designed
evaluates only the mass remobilized for each design high-flow event.
In addition to estimating the mass of solids and PCBs eroded from cohesive
sediments in TIP, more detailed estimates of the local scour were conducted at
selected locations. As part of the Phase 2 monitoring program, sediment cores
were taken at six locations in the TIP area and analyzed at a high vertical
resolution. Some of these sediment cores show peak PCB concentrations in excess
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of 2,000 p.g/g (ppm). The vertical resolution of PCB data at these locations allowed
a more detailed investigation of the potential risk of scour in response to large
events.
The conceptual approach used for the TIP Depth of Scour model is shown in
Figure 3-10. To compute the masses of solids and PCBs eroded at a fine spatial
scale an ARC/INFO-based Geographical Information System (GIS) was utilized. The
site was discretized into a regularly spaced grid of dimensions 10x10 feet.
Computations were conducted locally at the nodal locations where flow field
information was available from the TIP Hydrodynamic Model. Subsequently results
were interpolated to generate individual grids. The sediments were spatially
differentiated into cohesive and non-cohesive areas, with analyses restricted to only
cohesive sediment areas for this preliminary model calibration effort.
3.7.2 Process Representation
To compute the depth of erosion and total mass of solids eroded from
cohesive sediments for a high-flow event two categories of information are
necessary. First, the hydrodynamic conditions at the sediment-water interface need
to be specified. The primary forcing function for entrainment is the shear stress
exerted at the sediment-water interface by flowing water. The TIP Hydrodynamic
Model yields estimates of velocities (and bottom shear stress) at a fine spatial
resolution. Secondly, the physico-chemical properties of the bedded sediments
greatly influence the magnitude (and rate) of entrainment of sediments for a given
event, and the resulting depth of scour.
Entrainment mechanisms can be classified into two distinct categories based
on sediment bed properties. The main parameters affecting the entrainment of non-
cohesive sediments include grain size and shape (and their distributions), the
applied shear stress, bed roughness, and specific weight. Bed sediments which are
primarily fine grained and/or possess a high clay content exhibit interparticle effects
which are cohesive in nature. The resultant entrainment properties are very
different from non-cohesive sediment beds (no interparticle interactions). Since the
toxic contaminants of interest (PCBs) are associated primarily with fine grained
sediments, this distinction is of considerable importance in the TIP area.
The TIP Depth of Scour Model in the present report was developed for only
cohesive sediments. As part of the future modeling work (Appendix B), the Depth
of Scour Model will be extended to include both cohesive and non-cohesive
sediments in TIP.
Cohesive Sediment Erosion
The influence of particle diameter has a significantly lower influence on the
entrainment characteristics of cohesive sediments in comparison to electrochemical
influences. Relatively small amounts of clay in the sediment-water mixture can
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result in critical shear stresses far larger than those in non-cohesive materials of
similar size distribution (Raudkivi, 1990).
All previous studies on the entrainment of cohesive sediments hypothesize
that the scour magnitude (and rate) is primarily influenced by the excess applied
shear stress (i.e. the difference between the applied shear stress and the critical
shear stress of the surficial sediments) and the state of consolidation (or age after
deposition) of the bed sediments (Partheniades, 1965; Mehta et al., 1989; Xu,
1991). The mass of material resuspended (or rate of entrainment) can be
expressed in the following functional form:
M = f(x -xc; age;other sediment properties)
where, M is the mass (or rate) of material resuspended, and x is the applied shear
stress, and xc is the bed critical shear stress. The function f has been expressed in
a variety of different forms ranging from linear (e.g. Partheniades, 1965),
exponential (e.g. Parchure and Mehta, 1 985), and the power relationship developed
by Lick and co-workers (e.g. Gailani et al., 1991).
Based on statistical analysis of laboratory and field data Lick et al (1995)
proposed an erosion equation of the following form which approximated his
experimental data:
P c \m
t } (3-6)
ld L c
where, e is the total amount of material resuspended (g/cm2); td is the time after
deposition; and a0,n, and m are empirical constants.
The depth of scour can be calculated as :
Zscour - (3-7)
C bulk
where, Cbutk is bulk sediment density (g/cm3). This equation has been applied and
results validated to several rivers (e.g. Fox River, Detroit River, and Buffalo River).
The above empirical formulation (Equation 3-6) is not appropriate when the
applied shear stresses are greater than about 20 dynes/cm2. The erosion rates,
however, still exhibit a power relationship. The applied shear stresses rarely exceed
20 dynes/cm2 over fine-grained sediments in TIP, even for major storms, thus the
above functional form can be utilized. It should be pointed out here that Equation
3-6 has been derived from laboratory experiments and needs to be calibrated for
specific sites. A truly fundamental and generic model to characterize event-driven
resuspension is beyond the current research state-of-the-art.
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3.7.3 Spatial Temporal Scales
The selection of the appropriate spatial scales for the TIP Depth of Scour
Model was primarily driven by the extent of variability in the river bed sediment
properties and PCB concentrations. Since PCB concentrations can vary by several
orders of magnitude across distances as small as a few hundred yards
(TAMS/CADMUS/Gradient, 1996 - pending publication), a fine scale Geographical
Information System (GIS) based approach has been adopted. The site has been
discretized into a uniformly sized grid with cell spacing of 10 feet. This level of
spatial resolution should be adequate in capturing the spatial variability in the depth
of scour and mass of solids and PCBs eroded from the cohesive areas in TIP.
The cohesive computations result in a mass estimate for the entire event
assuming that the event peak shear stress is established instantaneously.
Experiments by Lick et al., (1995) indicate that this mass is eroded over the time
scale of approximately one hour.
3.7.4 Applications Framework
The TIP Depth of Scour Model is a GIS-based computational framework
designed to yield estimates of mass of solids and PCBs eroded for specific design
events. All computations were carried out on a grid with ten by ten foot cells. The
GIS utilized in the model was ARC/INFO. All computations and processing were
carried out on a SUN SPARC-20 workstation. The model as currently designed is
operational only on this hardware-software platform.
3.8 Lower Hudson River PCB Transport and Fate Model
3.8.1 Introduction
The modeling approach taken for the Lower Hudson River differs from that
for the Upper Hudson in that existing PCB fate/transport and food chain model
applications were used. Thomann et al., (1989,1991) developed both a physico-
chemical and a food chain model to describe PCB concentrations in Lower Hudson
River water, sediments and fish. This existing model was used essentially
unchanged for this RI/FS reassessment. The use of the Thomann model is intended
to accomplish the following modeling objectives:
1. Evaluate the impacts of PCB loadings from the Upper Hudson River on PCB
levels in water and sediments in the freshwater portion of the Lower Hudson
River;
2. Provide quantitative relationships between PCB water and sediment
concentrations and fish body burdens in the freshwater portion of the Lower
Hudson River; and,
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3. Evaluate and compare predicted responses to continued No Action, various
remedial scenarios and hydrometeorological events in the freshwater portion of
the Lower Hudson River.
The remainder of this section provides a summary description of the model
framework as described in Thomann et al., (1989, 1991). It is divided into
sections describing:
State Variables and Process Kinetics
Spatial-Temporal Scales
Applications Framework
3.8.2 State Variables and Process Kinetics
The Lower Hudson River PCB model consists of linked submodels:
1. Physico-chemical Model: Predicting PCB Concentrations in Water and
Sediment; and,
2. Food Chain Model: Predicting PCB Concentrations in White Perch and
Striped Bass.
Each submodel is designed to consider a single PCB homologue; results for
total PCB concentrations are obtained by summing model results from simulations
for each individual homologue. State variable and process kinetics for the two
modules will be discussed separately.
Physico-chemical Model
The physico-chemical model used for model calibration contains two state
variables: (1) total homologue concentration in the water column; and (2) total
homologue concentration in sediments.
Solids concentration in the water column was treated as a forcing function
for model calibration. The equation for total (i.e., dissolved plus particulate) PCB
homologue concentration in the water column segment i is given in explicit finite
difference form as:
|7C ^
~Kl.iVl.iCH.i k,i j As
v He y
(3-8)
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where,
cT2,i
fdl.i
K,.i
CTl.i
v,i
volume of water column segment i (L ) -
Note: subscript 1 denotes water column
total homologue concentration in water column segment i (M/L3)
flow from segment i to segment j
-------
where,
K = porosity-corrected partition coefficient (L3/M).
The mass balance equation for total PCB homologue in the active bed
sediment is
dCfo;
V2i =~Kf A
^d2.iCT2,i
I Ul.l 11,1
J
VuAs^p2,iCT2,i ~~ Vd,i,^'sfp2,iCT2j + Kf,iA
^dl.iCT!,i ^-2i^2iCT2,i + Vsi ^s^pf, j0!!, i
f c- r*
d3,iCT3,i d2.iCT2,i
K
(3-11)
2,i /
where,
K2 i = decay rate in active sediment segment I (1/T)
fp2 = PCB particulate fraction in active sediment segment I
(dimensionless)
fd3i = PCB dissolved fraction in deep sediment segment I
(dimensionless)
cX3i = total homologue concentration in deep sediment segment I
(M/L3)
3 i = porosity of deep sediment segment I (dimensionless).
The terms in Equation 3-11 correspond respectively to: diffusive exchange
with the water column, decay, settling, resuspension, net sedimentation, and
diffusive exchange with deep sediments.
Food Chain Model
The state variables for the food chain model are the organism weight and
whole body burden for each food web compartment. The model does not explicitly
consider the effect of contaminated sediments on food chain bioaccumulation. The
equation for individual organism weight is:
^¦ = K<3-12)
where,
wk = organism weight in compartment k (M/L3)
akj = mass assimilation efficiency of organism j in compartment k
(M(predator)/M(prey))
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weigh specific consumption of organism j in compartment k
(M(predator)/M(prey)/T)
respiratory weight loss in compartment k (1/T).
Equation 3-12 states that net weight gain is equal to the difference between
food assimilated and respiration losses.
The mass balance equation for whole body burden is:
pkj = food preference of compartment k on compartment j
akj = homologue assimilation efficiency.
Equation 3-13 states that an organism can gain toxicant via uptake from the
water column, lose toxicant via excretion, and/or gain toxicant via consumption of
contaminated prey.
Equations 3-12 and 3-13 were applied over 27 food chain compartments,
comprised of:
(3-13)
where,
c
whole body burden in compartment k (M)
organism PCB concentration in compartment k (M/L3)
contaminant uptake frcn the water compartment k
(L3/T/M)
dissolved PCB concentration (M/L3)
excretion rate for compartment k (T1)
Phytoplankton
Zooplankton
Small fish
White perch: 7 year classes
Striped bass: 17 year classes.
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As seen in Figure 3-11, the first two year classes of striped bass are
assumed to feed solely on zooplankton. Intermediate age striped bass feed on
small fish and young white perch, while older striped bass feed primarily on
intermediate age white perch.
3.8.3 Spatial-Temporal Scales
The spatial and temporal scales represented in the Lower Hudson model were
selected to represent the long-term time scale and a broad geographic spatial scale.
According to Thomann et al., (1989) the reasons for these coarse scales were as
follows:
Many significant components of the problem are associated with long
time and broad space scales, i.e., decadal loading of PCBs, long term
"memory" of sediment contamination, long life span and geographic
distribution of striped bass
Construction of a model with more detailed resolution was not feasible
due to data availability, computational requirements, and time and funding
limits.
The Lower Hudson model operates with a daily time step, with the intended
temporal resolution of discerning year to year and decade to decade changes.
The spatial domain of the model extends from Federal Dam as an upstream
boundary to the New York Bight and Long Island Sound as a downstream boundary
(Figure 3-12). Also shown in Figure 3-12 is the model segmentation. Fifteen
segments represent the Hudson River water column from Federal Dam to the
Battery. An additional 1 5 segments are used to represent areas below the Battery,
including six segments for the New York Bight and four segments for Long Island
Sound. Each water column segment is underlain by from two to fourteen vertical
sediment segments. The model contains a total of 1 20 sediment segments.
3.8.4 Applications Framework
The Lower Hudson River modeling was conducted using the computer
program WASTOX (Part 1, Exposure Concentration and Part 2, Food Chain). The
WASTOX program provides a framework for modeling the fate of toxic chemicals in
aquatic environments. It was developed at Manhattan College, based on a version
of the WASP model used there, under cooperative agreements with the
Environmental Research Laboratory, Gulf Breeze, Florida, and the Large Lakes and
Rivers Research Station of the Environmental Research Laboratory, Duluth,
Minnesota.
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WASTOX Part 1 was used to generate exposure concentrations. The
exposure concentrations were processed using task-specific computer programs to
provide inputs for WASTOX Part 2. WASTOX Part 2 was used to generate food
chain concentrations.
The specific WASTOX executables used were WTXSS3.EXE (1-26-88) for
Part 1 and FCHN2-C.EXE (11-4-88) for Part 2. These files were provided to LTI by
Dr. Robert V. Thomann, who had primary responsibility for the Lower Hudson
model application. These were run on IBM PC-compatible computers.
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4. CALIBRATION OF UPPER HUDSON RIVER PCB MODEL
4.1 Introduction
A complete description of the Upper Hudson River PCB Model is contained in
Section 3.5. The present section contains preliminary calibration results for this
model for a period of simulation from January 1 to September 30, 1993. The
calibration was conducted for total PCBs and five separate PCB congeners, or
groups of co-eluting congeners, corresponding to BZ#4, BZ#28, BZ#52,
BZ#90 +101 and BZ#138.
Consideration of total PCBs is necessary in order to represent a complete
mass balance for all of the individual PCB congeners. In addition, the USEPA
currently uses total PCBs as the exposure concentration for estimating human
cancer risk. The five calibration congeners, or groups of co-eluting congeners,
represent a wide range of physical-chsrr.cal properties that influence PCB
environmental transport and fate. These congeners were used for model calibration
in order to establish the technical credibility of the model over a range of different
conditions.
Section 4.2 contains a summary of historical trends in flow, TSS and total
PCBs in the Upper Hudson River. Section 4.3 contains an overview of Release 2.3
of the TAMS/Gradient database that was used for this preliminary model
calibration. Section 4.4 contains descriptions of model input data. Section 4.5
contains descriptions of internal model parameters. Section 4.6 discusses the
calibration approach used for water, solids and PCBs. Section 4.7 contains model
calibration results. Section 4.8 contains results from a diagnostic and components
analysis conducted with the calibrated model. Section 4.9 contains results from a
limited set of sensitivity analyses conducted with the calibrated model.
4.2 Historical Trends in Water Quality Observations
From 1957 through 1975, between 209,000 and 1.3 million pounds of PCBs
were discharged to the Upper Hudson River from two GE facilities, one in Fort
Edward and the other in Hudson Falls (Figure 1-5). GE discontinued use of PCBs in
1977 when they ceased to be manufactured and sold in the United States.
Migration of PCBs downstream was greatly enhanced in 1973 with the removal of
Fort Edward Dam and the subsequent release of PCB-contaminated sediments.
Figure 4-1 illustrates historical trends in USGS field data for flow, TSS and
total PCBs at Fort Edward from 1977 through 1992. In general, peak annual flows
tend to occur in spring, accompanied by large increases in TSS concentrations.
Trends in total PCB concentrations are confounded, in part, by changes in analytical
methods and detection limits over this historical period. Nonetheless, it appears
that total PCB concentrations were at maximum values during the late-1970s and
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early-1980s and then declined substantially in the mid- to late-1980s. Note that
these PCB data are plotted on a log scale.
Figure 4-2 illustrates trends in these same parameters at both Fort Edward
and Thompson Island Dam from 1991 through 1993. Although the magnitudes of
peak annual flows differ among years, they still tend to occur in spring,
accompanied by large increases in TSS concentrations. An exception to this
association between high flows and increases in TSS concentrations appears to
occur in fall of 1991. At this time large increases in TSS concentrations do not
appear to be associated with high flows.
In an apparent reversal of an earlier trend, total PCB concentrations in fall of
1991 increased beyond 1,000 ng/l for the first time since 1983. Total PCB
concentrations continued to remain at relatively high levels during 1992 and 1993.
TAMS/CADMUS/Gradient (1996 - pending publication) contains a discussion of
possible sources of PCBs that could be responsible for these observations.
A curious phenomenon is that there appear to be substantial differences in
total PCB concentrations between Fort Edward and Thompson Island Dam, a
distance of only 6 miles. The reason for these differences is not clear because
there are no large tributary inputs to TIP between these two locations. In
particular, it is not clear whether this apparent increase in PCB load originates from
historical sediment sources or from more recent discharges.
It is important to recognize that the physical, chemical and biological
processes controlling PCB dynamics in the Upper Hudson River, especially in TIP,
are not fully understood at the present time. Furthermore, the HUDTOX model
calibration in the present report is limited to a period of simulation from January 1
to September 30, 1993. It is not yet clear whether the PCB dynamics operative
during this simulation period are fully representative of historical PCB dynamics, or
whether they will be representative of PCB dynamics under future conditions. The
long-term (1984-1993) hindcasting calibration to be conducted as part of the future
modeling work (Appendix B) will provide more information on historical PCB
dynamics.
4.3 Overview of Preliminary Calibration Dataset
Daily USGS flow data were available at four mainstem Upper Hudson River
stations: Fort Edward, Stillwater, Waterford, and Green Island (Figure 1-3). Other
estimates of daily flow, at Schuylerville, Stillwater and Waterford, were also
developed as part of the Phase 2 monitoring effort (TAMS/CADMUS/Gradient,
1996 - pending publication). These estimates were developed prior to release of
Water Year (WY) 1993 data from USGS, and are based on least-squares fit
regression models of historical NYSDOT staff-gauge height and USGS flow records.
The 1993 USGS daily flows were chosen over the Phase 2 estimates for use in the
HUDTOX calibration for two reasons: first, a consistent source of data for
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upstream, mainstem, and tributaries could be used for developing the flow fields;
and second, the USGS data are the official flow records for the river.
There are four major tributaries flowing into the Upper Hudson River: Batten
Kill, Fish Creek, Hoosic River, and Mohawk River. Daily flows were recorded by the
USGS on Hoosic and Mohawk Rivers. These gaged flow data formed the basis in
estimating ungaged flows for the Upper Hudson River. Figure 4-3 shows the
variation of daily flows at the upstream boundary, Hoosic River, and Mohawk River
for the preliminary model calibration period. Figure 4-4 shows daily flows and
available USGS TSS data at Fort Edward.
The Phase 2 water column monitoring program included 12 PCB samples
from the Hudson River at Fort Edward, 6 samples from the Hoosic River, and 6
samples from the Mohawk River during the preliminary model calibration period.
Six of the 12 PCB measurements at Fort Edward were instantaneous (transect)
samples and 6 were flow averaged samples, each composited over a period of
about 2 weeks. The GE 1993 water column dataset included larger numbers of
samples, taken at both Fort Edward and Thompson Island Dam. Figure 4-5 shows
otal PCB concentrations at Fort Edwarc for both the Phase 2 and GE datasets,
along with daily river flows.
The Phase 2 database contains values for total PCBs and congener
concentrations in two different formats (TAMS/CADMUS/Gradient, 1996 - pending
publication). Value 1 reports the quantitation limits for non-detected PCB
congeners; however, zero values are used for summing these congeners when
deriving total PCB concentrations. Value 2 contains non-detected congener
concentrations that are set to zero or one-half the quantitation limits, depending on
the frequency of non-detected results within a sample grouping. In most cases,
differences between these two values were minimal and could not be distinguished
graphically. Therefore, only Value 2 was used for model inputs and for comparison
with model output during this preliminary model calibration.
The GE PCB database included measurements of individual capillary column
peak values instead of PCB congener concentrations (O'Brien and Gere, 1993a,
1993c, 1993d, 1994). The TAMS/Gradient Team investigated the relationship
between these data and the PCB congener measurements in the Phase 2 database
for the Rogers Island sampling station. This analysis determined that total PCBs,
Peak#24, Peak#31 and Peak#82 in the GE database correspond well to total PCBs,
BZ#28, BZ#52 and BZ#138, respectively, in the Phase 2 database. The
correspondence between the GE capillary column peak measurements is
documented in Release 3.1 of the TAMS/Gradient Phase 2 database
(TAMS/Gradient, 1995). On the basis of direct data comparisons, however, there
were significant discrepancies between the two datasets for BZ#138.
Consequently, it was decided that both GE and Phase 2 data would be used in
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computing loadings for total PCBs, BZ#28 and BZ#52. Only Phase 2 data were
used for congeners BZ#4, BZ#101 +90 and BZ#138.
The only available sediment data contained in Release 2.3 of the
TAMS/Gradient Phase 2 database for the calibration period are for nine high
resolution sediment cores. These data are not sufficient to provide representative
estimates of average sediment concentrations for the HUDTOX sediment segments.
These high resolution cores are limited in number and are specifically located in
depositional areas of the river. The GE 1991 sediment survey data (O'Brien and
Gere, 1993a) provide a more extensive coverage of bottom sediments and were
used to specify HUDTOX sediment conditions in the present calibration. At the
time of this preliminary model calibration work, this was the only recent dataset
that provided a comprehensive picture of sediment conditions, including both solids
and congener-specific PCB characterizations.
4.4 Model Input Data
Three distinct types of model inputs are necessary to apply the HUDTOX
mass balance model:
1. System-specific physical data;
2. External loadings, forcing functions, boundary and initial conditions; and,
3. Process-related parameters.
The following subsections describe these model inputs for the present
HUDTOX calibration. Two transient events complicated the model calibration for
this period: first, 100-year floods in the Mohawk and Hoosic Rivers from spring
snowmelt in 1993; and second, large sediment solids releases from spring
construction activities on Hudson River Lock No. 1 just upstream from Waterford
(Figure 1-3). An additional source of uncertainty is the unknown amounts of
upstream loadings due to migration of PCBs from the overburden and bedrock at
the GE facilities in Hudson Falls and Fort Edward (Figure 1-5)
(TAMS/CADMUS/Gradient, 1996 - pending publication).
4.4.1 System-Specific Physical Data
HUDTOX employs the water column and sediment segmentation described in
Section 3.5.3. Figures 3-5 and 3-7 provide a map of the water column
segmentation, and a schematic of the water and sediment segmentation,
respectively. The specific geometry of the model segmentation is presented in
Table 4-1. The water column is represented by a single vertically-mixed layer, while
five different layers represent the vertical profile of the Upper Hudson River
sediment physical and chemical properties. Thus, a total of 13 water column, and
65 sediment segments represent the river from the northern tip of Rogers Island to
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Federal Dam. As discussed in Section 3.5.3, a non-interacting sediment segment is
used to accumulate any chemical burial out of the system, so that a total of 79
segments is simulated within the HUDTOX model framework.
4.4.2 External Loadings
Upstream, Tributary and Ungaged Flows
To develop a better understanding of load-response dynamics, a spring high-
flow period was operationally defined to encompass the period from March 26
through May 10, 1993. Flood conditions occurred throughout the Upper Hudson
River Basin between these dates. USGS daily flow measurements at Green Island
during this period were always above 15,000 cfs, and were generally above 7,000
cfs at the Fort Edward gauging station.
Figure 4-6(a) summarizes external water inputs and shows the dominance of
upstream and Mohawk River flows in the Upper Hudson River water balance.
Upstream flow into Segment 1 was specified from USGS daily records at Fort
Edward. Similarly, USGS flow records were used to specify Hoosic River (Segment
9) and Mohawk River (Segment 13) inflows on a daily basis. Appropriate
corrections were applied to account for any drainage area increases between gaging
stations, and for inflows from smaller tributaries.
Ungaged flows were estimated on a monthly basis using Water Year (WY)
1993 USGS daily flow records at Hudson River mainstem stations (Fort Edward,
Stillwater, and Waterford) and available information on ungaged tributary drainage
areas. As discussed in Section 4.3, the NYSDOT staff-gauge vs. flow relationships
developed by TAMS were not used to specify Hudson River flows because
WY1993 USGS data for Stillwater and Waterford stations became available in time
for this model calibration.
When using the USGS flow measurements and the estimated flow from
Batten Kill and Fish Creek to conduct a water balance for the Upper Hudson River,
there was a residual amount of unbalanced water. This extra flow was presumed
to result from other ungaged minor tributaries, direct runoff, and non-point sources.
The amount of residual flow each model segment receives was assumed to be
proportional to the longitudinal length of that segment. Estimation of daily flows
using this method was not appropriate due to reasons such as time lags in
hydrographs between upstream and downstream stations. Therefore, these minor
ungaged flows were estimated on a monthly average basis.
Batten Kill and Fish Creek discharge into the same HUDTOX model segment.
Therefore, the flow from these two tributaries was estimated as a single ungaged
point source based on the increase in flow magnitude and drainage area from Fort
Edward to Stillwater USGS stations. The ratio of these two values represents the
flow yield per unit drainage area. This yield was assumed to be applicable to
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Batten Kill and Fish Creek. Thus the total discharge from Batten Kill and Fish Creek
was estimated by applying this yield to their total drainage areas.
Initial solids modeling efforts indicated that ungaged tributary solids loads
between Fort Edward and the Hoosic River were substantially underestimated. The
combined drainage areas of the ungaged Batten Kill (394 mi2) and Fish Creek (90
mi2) tributaries constitute approximately 94 percent of the Hoosic River drainage
area (510 mi2), so daily flow and TSS loads were estimated based upon Hoosic
River data and the limited available data for Batten Kill.
Table 4-2 is a summary of average flows over the calibration period from
January 1 to September 30, 1993. It can be seen that ungaged minor tributary or
non-point flows account for less than 10 percent of all inflows to the Upper Hudson
River for the 272-day preliminary model calibration period.
Solids Loads
Tributary loading estimation methods usually take advantage of the
correlation between constituent concentrations and flow so that a complete
concentration time series can be constructed from the more readily available flow
data. Various methods of loading estimation were examined by Preston et al.,
(1989), including averaging estimators, ratio estimators, and regression methods.
Regression methods usually exploit the correlation between log-transformed
constituent concentration and flow. A bias is introduced when constituent
concentrations are estimated from this type of log-transformed correlations. The
minimum variance unbiased estimator (MVUE) developed by Cohn et al., (1989)
employs corrections to eliminate this bias.
Daily TSS loads were estimated for Fort Edward, Hoosic River, and Mohawk
River using the MVUE method. Statistical distributions of TSS and flows, as well
as correlations between them, were examined for the USGS, Phase 2 water column
monitoring program, and GE datasets. Flow data collected by the USGS
approximated log-normal distributions. With TSS concentrations, USGS data
resembled log-normal distributions, while the GE data did not. There were
insufficient data points in the Phase 2 water column monitoring program to draw a
definitive conclusion in this regard.
Good correlations between TSS and flow were generally observed with the
USGS and Phase 2 data, while poor correlations were found with the GE data. To
avoid the complications of a possible underlying, long-term trend in TSS
concentrations, only 1993 data were considered for use estimating upstream
boundary TSS loading. Since there were only six instantaneous TSS measurements
at the upstream boundary in the Phase 2 database, they were excluded from
regression analysis for the sake of internal consistency. As a result, only the 1993
USGS measurements were used in the final regression analysis to define the
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upstream boundary condition for TSS during the preliminary model calibration
period.
For loadings from the Hoosic and Mohawk Rivers, 1993 USGS TSS data
were not available at the time of this work and historical records were used to
conduct regressions. Consequently, while the 1 993 USGS data were used in the
MVUE regressions for computing daily TSS load estimates at the HUDTOX model
upstream boundary (i.e. Fort Edward), the historic USGS data were used for the
Hoosic and Mohawk Rivers. This approach improved the reliability of estimated
loads because it eliminated the potential incompatibilities among different TSS
datasets, and resulted in improved log-normal distributions and better correlations
between TSS and flow.
Estimated daily loads for the upstream boundary, Hoosic River, and Mohawk
River are shown in Figure 4-7. It should be noted that total TSS loads during the
model calibration period and TSS loads during high flow events from the Hoosic
River were greater than those from the upstream boundary, even though the
magnitude of flow in the Hoosic is typically lower (Figure 4-3).
The Hoosic River discharge measured at the Eagle Bridge USGS gage station
corresponds to a drainage area of 512 mi2, while the total drainage area of Hoosic
River is 710 mi2 (TAMS/Gradient, 1992). To correct for this difference, all Hoosic
flow data were increased by the same percentage of the drainage area increase,
while TSS concentrations remained the same. This constituted a 39 percent
increase in flow and TSS loadings from the Hoosic River between the gage and the
confluence with the Upper Hudson River.
The Phase 2 water column monitoring program collected six TSS
measurements from Batten Kill during 1993. These limited measurements were
insufficient to define a time series of TSS concentrations for the modeling period
and therefore were averaged to yield a median TSS of 5.0 mg/l. This value was
adopted for Batten Kill, Fish Creek, and all other ungaged sources. These ungaged
sources did not contribute significantly to the overall mass balance of water or TSS
as shown by Tables 4-2 and 4-3.
Initial modeling of TSS as a tracer during low flow conditions revealed that
Batten Kill and Fish Creek were probably contributing significant solids loads to the
Upper Hudson River. Subsequently, daily flow and TSS loading estimates were
developed for these tributaries based on available, but limited, information. Daily
combined Batten Kill and Fish Creek solids loading was estimated based on an
average ratio of Batten Kill to Hoosic River TSS concentrations (64 percent for
combined Phase 2 and GE data) and tributary drainage areas (94 percent).
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Figure 4-6(b) illustrates that the principal external solids loadings to the
Upper Hudson River during the calibration period were from the Mohawk River (58
percent) and the Hoosic River (20 percent). Solids loadings across the upstream
boundary at Fort Edward represented only a small fraction (8.5 percent) of total
external solids loadings. Also, approximately 56 percent of the upstream and
tributary hydraulic inputs and 87 percent of the external solids loading occurred
during the spring high flow period which represents just 17 percent of the total
period of simulation.
Internal solids loading due to primary production is often a significant source
of solids in aquatic systems (e.g., Bierman et al., 1992). The Phase 2 monitoring
program did not include measurements of primary production or measurements of
the water quality constituents needed to apply a primary production model. To
represent solids dynamics as accurately as possible in the HUDTOX model, an
estimated primary production rate equivalent to 1.2 g TSS/m2-day (at 20°C) was
used, based on field measurements by Cole et al., (1992) in the tidal freshwater
portion of the Lower Hudson River. An Arrhenius temperature correction factor of
1.066 (Ambrose et al., 1988) was used to correct the rate due to the lower
temperatures of the Upper Hudson River and to represent the variation in primary
production due to seasonal (and daily) changes in ambient river temperatures. At
20°C, this rate is equivalent to 175 g carbon/m2-year, assuming that the organic
carbon content of phytoplankton solids (measured by dry weight) is 40 percent.
The total solids load contribution of this internal source is presented in Table 4-3.
The Phase 2 database contains insufficient field measurements to reliably
specify external loadings for calibration of DOC in the HUDTOX model. The
available data indicate that DOC levels are relatively stable throughout the Upper
Hudson River. Consequently, DOC dynamics in HUDTOX were represented within
the solids submodel in a simplified fashion. DOC was represented as a model state
variable, but it was not used as a calibration target. Instead, constant DOC
concentrations for external inflows were specified to maintain water column
concentrations close to 4.83 mg/l, the average level measured during the Phase 2
sampling program (TAMS/CADMUS/Gradient, 1996 - pending publication). The
dominance of upstream and Mohawk River DOC loads is seen in Figure 4-6(c),
while Figure 4-6(b) indicates the relatively greater significance of TSS loads from
other tributaries.
PCB Loads
To evaluate the feasibility of using a regression method to estimate PCB
loading time series, correlations between PCB and flow, as well as between PCB
and TSS were examined. It was found from the Phase 2 database that PCBs are
generally better correlated with TSS than with flow, and that higher chlorinated
congeners correlated with TSS better than lower chlorinated congeners. No
correlation between PCB and flow or between PCB and TSS was observed in the
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more frequently measured GE 1993 data. Therefore, only the Phase 2 dataset
could possibly be used in MVUE or other regression methods. However, the
quantity of Phase 2 data was insufficient to construct reliable correlations for
estimating the time series of external PCB loads to the Upper Hudson River.
As a result, linear interpolation of PCB concentrations was used to compute
the HUDTOX upstream boundary PCB loads for this preliminary model calibration
period. To illustrate temporal coverage of available PCB data, Figure 4-5 shows
total PCB concentrations at the upstream boundary from both Phase 2 and GE
datasets along with daily Upper Hudson River flows at Fort Edward. As seen from
Figure 4-5, there was an apparent outlier in the GE 1 993 PCB dataset collected in
January. Consistent with recommendations by TAMS/CADMUS/Gradient (1996 -
pending publication), this data point was excluded from all PCB loading estimations.
Only 6 instantaneous water column PCB samples from the 1993 Phase 2
water column monitoring program were available for estimating loads from the
Hoosic River, Mohawk River and Batten Kill This was an insufficient quantity for
generating a reliable time series to represent dynamic PCB loading conditions for
the modeling period. Therefore, these data were simply averaged to yield constant
PCB concentrations for these three tributaries. The average PCB concentration in
Batten Kill was applied to both Batten Kill and Fish Creek. A small PCB
concentration of 10 ng/L was assumed for other direct minor tributaries and direct
runoff since field data were not available to better quantify these loads, and also
because water quality monitoring in the Upper Hudson River does not indicate a
presence of any other significant incremental PCB loads (TAMS/CADMUS/Gradient,
1996b - pending publication). PCB congener fractions for these minor sources
were estimated based on the data collected for Batten Kill.
Loads from these minor sources constitute only a small fraction of the overall
PCB loads and do not significantly affect the HUDTOX model results. Table 4-4
summarizes the accumulated total PCB loads for the Upper Hudson River
preliminary model calibration period. Estimated daily total PCB loads entering the
HUDTOX upstream boundary at Fort Edward, and from the Hoosic and Mohawk
Rivers, are shown in Figure 4-8.
Table 4-5 summarizes all of the constituent mass loadings from the different
external sources for the 1993 HUDTOX calibration. The upstream PCB loads are
generally dominant, with 74 percent of the total external PCB load passing by Fort
Edward as indicated by Figure 4-9(a). Figures 4-9(b-f) summarize the external
loadings for the remainder of the selected PCB calibration congeners. The effect of
high spring flows in the Upper Hudson River on the external PCB loads is also
illustrated by these figures. With the exception of BZ#4, greater than 70 percent of
the upstream PCB loads occur during the spring high flow period. The BZ#4 load
shows the opposite behavior with just 27 percent of the upstream load occurring
during the spring high flow period, reflecting this congener's relative low affinity for
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sorbing onto solids. Overall, 68 percent of the total PCB external loading to the
Upper Hudson River occurs during the spring high flow period.
The Mohawk River is the second largest external source of PCBs to the river
(17 percent) and even surpasses upstream boundary loads for PCB congener
BZ#138 (58 percent for Mohawk River versus 29 percent across the upstream
boundary (Figure 4-9) during both spring high flow and lower flow conditions.
However, there is much uncertainty in the tributary PCB loadings for the HUDTOX
calibration period because they are based on the average of just 6 sample
collections over the 9-month 1993 Phase 2 water column monitoring program. For
example, 100 year floods occurred during 1993 spring runoff in the Mohawk River,
while the Hudson River above Schuylerville experienced at most once in 5 year
flooding. Therefore, the relative magnitudes of the external PCB and TSS loads
during the calibration period may not represent long-term average conditions.
An additional source of uncertainty is the unknown amounts of upstream
loadings due to migration of PCBs from the overburden and bedrock at the GE
facilities in Hudson Falls and Fort Edward (TAMS/CADMUS/Gradient, 1996 -
pending publication). These sources are upstream of the HUDTOX model boundary
at Fort Edward. It is impossible to determine what portion of the actual upstream
PCB loadings were captured by the Phase 2 water column monitoring program.
Direct atmospheric PCB loads to the Upper Hudson River are assumed to be
negligible for the 1993 HUDTOX calibration period. This assumption is reasonable
since the water surface area available for direct deposition is negligible in
comparison to the drainage area of the watershed.
4.4.3 Forcing Functions
Ambient environmental conditions can significantly affect the kinetic
processes which determine the fate of PCBs in the Upper Hudson River. The
HUDTOX model framework includes time variable forcing functions for sediment,
water, and air temperatures. In the water column, temperature affects the
partitioning of PCBs and air-water gas exchange which may lead to volatilization of
PCBs. Time series forcing functions for water temperature, measured at three
stations during the Phase 2 monitoring program, were constructed and assigned to
represent conditions in nearby HUDTOX model segments. Table 4-6 presents the
water temperature time series developed from field measurements at Phase 2 Upper
Hudson River water quality sampling stations 4, 5, and 8.
Sediment temperatures are not typically measured, so active sediment layer
segment temperatures were assumed to be the same as the overlying surface
waters. A constant temperature of 7 °C was used in the HUDTOX calibration for all
lower sediment layers. The air temperature time series was developed based on
1993 mean monthly measurements at Albany, New York available from the NOAA
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National Climatic Data Center. Volatilization losses of PCB through air-water
exchange in the WASP-based HUDTOX model are controlled by the shear-induced
turbulence of the river hydraulics, so air temperature has no effect on the present
model formulation.
Two other environmental forcing functions are also incorporated in the
HUDTOX framework, but these are not significant to the present PCB calibration.
Wind speed and ice cover can both affect air-water gas exchange. In a highly
advective river system such as the Upper Hudson River, hydraulic considerations
(i.e. water velocity and depth) and temperature typically drive atmospheric
exchange more than wind. Relationships describing gas exchange due to wind-
driven shear are usually applied to open waters such as lakes, embayments and
large estuarine systems.
4.4.4 Boundary Conditions
Upstream Hudson River and major tributary boundary conditions were
incorporated in the model calibration through use of external loading functions
Jescribed in Section 4.4.2. Boundary conditions were used in HUDTOX to specify
state-variable constituent concentrations for the smaller ungaged tributary and non-
point source inflows along the length of the river. Figure 4-6, Figure 4-9 and Table
4-5 summarize external loads for the calibration period. Non-point source loads
were not significant for either solids (0.6 percent) or any PCB types (0.4 percent to
2.3 percent) represented in the HUDTOX model.
HUDTOX also requires that the atmospheric boundary gas phase
concentration of toxic chemical state variables be specified in order to predict
atmospheric exchange at the air-water interface. No recent site-specific
atmospheric PCB measurements in the Hudson River vicinity were available for this
HUDTOX calibration. Historical atmospheric PCB data in the Upper Hudson River
indicate a large variation in concentration, with higher levels localized to landfill and
remnant areas (TAMS/Gradient, 1991). GE 1989 air sampling near remnant areas
show PCB concentrations up to 230 ng/m3. However, the detection limits were
high (50 ng/m3) and the vast majority of the air PCB measurements were below
detection (246 out of 252 samples at three locations). For lack of more
representative, site-specific atmospheric data, a gas phase concentration of 0.77
ng/m3 for total PCBs was employed based on measurements from the Green Bay
Mass Balance Study (Bierman, et al., 1992). Gas phase concentrations for
individual PCB congeners were estimated based upon the ratio of water column
congener to total PCB concentrations measured at Upper Hudson River tributary
stations during the 1993 Phase 2 monitoring program. The resulting atmospheric
concentrations used in the HUDTOX calibration for PCB congener BZ#s 4, 28, 52,
101+90, and 138 were: 0.0952, 0.0195, 0.0357, 0.0205, and 0.0113 ng/m3,
respectively.
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4.4.5 Initial Conditions
Bottom sediment properties and PCB concentrations are needed by HUDTOX
to simulate sediment-water interactions. The only sediment data in Release 2.3 of
the TAMS/Gradient Phase 2 database were for nine high resolution sediment cores.
These data were not sufficient to provide representative estimates of average
concentrations for HUDTOX sediment segments. The GE 1991 sediment survey
data (O'Brien and Gere, 1993a) provided a more extensive coverage of bottom
sediments and were therefore used instead to represent initial sediment conditions
for this preliminary HUDTOX model calibration.
The GE 1991 sediment survey data are composites of individual samples
taken from different locations within a river stretch. The composite measurements
represent the average of all these individual samples within a vertical sediment
layer. An approximate river mile was assigned to each composite sample. To
estimate average concentrations for model segments, the GE composite samples
were assigned to each HUDTOX segment based on their river mile locations.
Composite samples within the same model segment and same sediment depth
range were processed to estimate mean sediment solids and PCB concentrations.
This processing involved assigning the GE composites to either a coarse or fine
sediment category, based on the physical characterizations of individual samples
within each composite group. Weighted solids and PCB concentrations were then
computed based upon the areal distribution of sediment solids types determined for
the TIP (approximately 20 percent fine and 80 percent coarse - see Section 6).
A solids mass density (rs) of 2.67 g/cm3 was chosen for model calibration as
a median value based on an analysis of solids from the High Resolution Coring
Program (TAMS/CADMUS/Gradient, 1996 - pending publication). The mean initial
3 6 3
sediment solids concentration (or bulk density) was 1.1 g/cm (or 1.1x10 g/m )
across all sediment segments, based on the 1991 GE sediment survey data. This
value corresponds reasonably well with the median value of 1.3 g/cm3 determined
from the Phase 2 high resolution sediment cores (TAMS/CADMUS/Gradient, 1996 -
pending publication). Table 4-7 presents the specific initial sediment solids
concentrations used for the HUDTOX solids model calibration.
The GE 1991 sediment data include PCBs sorbed to sediment and PCBs
dissolved in pore water (apparent dissolved concentrations). While the number of
sediment bound PCB samples was significantly greater than that of pore water
samples, more than 80 pairs of sediment and pore water samples were matched
from the database. Total PCB concentrations were estimated by combining the
solid sorbed and pore water dissolved fractions from these pairs. It was found that
the solid sorbed PCBs account for more than 98 percent of total PCB
concentrations in all cases. Therefore, the pore water fraction was neglected and
only the solid-bound fraction was included in estimating total PCB concentrations in
the sediments for the HUDTOX model initial conditions.
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Another complication was that the GE dataset contains reported PCB
capillary column peak measurements instead of the specific PCB congener
concentrations reported in the Phase 2 database. These peak values were
converted to congener concentrations in accordance with a TAMS/Gradient Team
investigation of the comparability between the GE and Phase 2 datasets. A
memorandum by Cook (1994) describes factors for estimating individual PCB
congener values from the GE PCB peak measurements. More specifically, GE
Peak#24, Peak#31, Peak#82 and total PCB measurements were found to be
directly comparable to the Phase 2 BZ#28, BZ#52, BZ#1 38 and total PCB data,
and required no conversions. The analysis found that comparable BZ#4+ 10 values
were consistently estimated by multiplying GE Peak#5 measurements by a factor of
five. PCB congener BZ#101 +90 values, comparable to those in the Phase 2 data,
were estimated by doubling the GE Peak#53 measurements.
Because PCB congener BZ#4 was a model calibration target, the
TAMS/Gradient Team extracted concentration values for this congener from
BZ#4+10 water column measurements us.7,y results from a statistical regression
analysis applied to the Phase 2 high resolution core data. From more than 440
pairs of BZ#4 and BZ#10 data, the ratio of BZ#4 to BZ#4+10 had an average
value of 0.7842 and a standard deviation of 0.227. This indicated that the relative
compositions of BZ#4 and BZ#10 were stable in the bottom sediments. Thus,
BZ#4 was initially computed from the BZ#4+ 10 values using this ratio.
It was later found that BZ#4 concentrations estimated using this method
were greater than total PCBs concentrations in some instances, thus necessitating
a different approach. The percentage of BZ#4 in total PCBs was computed using
Phase 2 high resolution core data for each model segment where data were
available. BZ#4 concentrations in each individual model segment were then
estimated using this percentage value or a value interpolated based on adjacent
upstream and downstream segments. These percentages ranged from 0.07 to
0.12 derived from a total number of 148 samples. Estimated initial PCB
concentrations in the sediments for each upper layer HUDTOX model segment (0-5
cm) are listed in Table 4-8.
The GE composited sediment core samples were analyzed for 3 distinct
vertical sediment layers: 0-5 cm, 5-10 cm, and 10-25 cm deep. These layers
correspond directly to the top 3 sediment layers in HUDTOX sediment
segmentation grid (Figure 3-7). There are two additional sediment layers in the
model: 25-50 cm and 50-100 cm. Sediment properties and PCB concentrations
need to be specified for these model segments as well. Because data were not
available and these layers will not have any significant impact on the model results
for the 9-month calibration period, data for the 10-25 cm layer were assumed to
also be representative of the corresponding deeper sediment layers.
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4.5 Internal Model Parameters
The conceptualized mass transport and kinetic structure of HUDTOX
contains a number of internal model parameters. Figures 3-2 and 3-4, respectively,
illustrate the HUDTOX solids and toxic chemical model structures. Beyond
advective flow, longitudinal dispersion (DL) is common to both the HUDTOX solids
and PCB models. However, DL is not significant to the mass transport processes in
the model since the Upper Hudson River is highly advective. Also, many of the
interfaces between model segments are located at lock and dam structures along
the river (see Figure 3-5), effectively preventing large-scale mixing between these
segments.
4.5.1 Solids Model Parameters
The process parameters affecting solids transport and transformation within
the HUDTOX framework (Figure 3-2) are presented in Table 4-9. The solids mass
balance was conducted for both TSS and DOC. A small degree of solids
degradation in the sediment, with DOC as a by-product, was required to maintain
an approximately constant DOC concentration gradient between the sediment and
water column. This degradation approximates the mineralization of particulate
detrital carbon in the sediment.
4.5.2 PCB Model Parameters
The toxic chemical fate and transport mechanisms within the HUDTOX
model framework are depicted by Figure 3-4. The required process parameters used
to describe these PCB dynamics are presented in Table 4-10. Each of the model
parameters is defined in the table along with the appropriate units for use in
HUDTOX. The sources used to derive each parameter are also listed in Table 4-10.
Note that all of the parameters used in calibrating HUDTOX are congener-specific
and are based upon either an a priori analysis of Phase 2 water column monitoring
data (TAMS/CADMUS/Gradient, 1996 - pending publication) or on values from the
scientific literature. Parameters for total PCBs were estimated by using median
values from the ranges of values for individual congeners.
4.6 Calibration Approach
In mass balance modeling, spatial averaging of measured water quality
constituents is commonly employed to estimate constituent concentrations that
are representative of individual model segments. Model simulations can then be
compared with these average concentration values and their variability to evaluate
model performance. In this preliminary model calibration, the Upper Hudson River
water column was divided into 13 longitudinal segments (Figure 3-5), but there
were only 6 water column sampling stations in the Phase 2 water column
monitoring program. Also, the sampling stations were located far apart, so that no
more than one station could be assigned within a single model segment. Therefore,
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it was not possible to actually estimate average water quality parameter
concentrations for individual model segments. Instead, water quality parameters
simulated by the model were compared directly with the time series of data
measurements from single sampling stations located within the appropriate model
segments.
The Phase 2 water column monitoring program included 6 stations over the
section of the Upper Hudson River being modeled, as shown in Table 4-11. For the
TSS calibration, additional daily measurements collected by the Rensselaer
Polytechnic Institute (RPI) at stations located in HUDTOX Segments 10 and 11,
and the USGS data were also used.
4.6.1 Transport Model (Water Balance) Specification
The first step in developing a calibration of HUDTOX for the 1993 simulation
period involved specifying external advective flows and their respective routing
schemes through the model segmentation to form a mass balance for water. As
discussed in Section 4.4.2, these include upstream, tributary, and ungaged
including nonpoint) flows. Only surface ;vater flows were included in the present
calibration of HUDTOX. This calibration of HUDTOX does not include potential
groundwater inflows to the Upper Hudson River, since the information needed to
spatially and temporally estimate these sources does not exist at this time. The
potential effect of groundwater inflow (also termed pore water advection) on water
column PCB levels in TIP is examined, however, as part of the HUDTOX model
calibration diagnostics in Section 4.8.
The bulk mixing among surface water segments in HUDTOX must also be
specified as part of the transport conditions in the HUDTOX calibration. Since
HUDTOX is one-dimensional in this respect, a longitudinal dispersion (DL) coefficient
was used. The limited number of mainstem water quality stations in the Phase 2
monitoring program, and the lack of a good natural tracer prevent calibration of DL
within HUDTOX. Instead, the value of DL used for model calibration was estimated
from data generated during a 1967 USGS dye study conducted in the Upper
Hudson River near Fort Edward (Shindel, 1969). Also note that DL is set to zero for
7 of the 12 HUDTOX segment interfaces because they are located at dams along
the river.
4.6.2 Solids Model
The HUDTOX solids model is specified in terms of two constituent state
variables: particulate solids (total suspended solids in the water column and bedded
solids in the sediment), and dissolved organic carbon (DOC). As discussed in
Section 4.4.2, DOC is not a calibration target for this preliminary model application.
Instead, DOC is simulated as a state variable to distinctly represent the transport
and transformation of DOC-bound components within the PCB mass balance. The
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approach used for the particulate solids calibration of HUDTOX for the calibration
period was the following:
1. As a screening-level check on the consistency between external loadings and
observed water column concentrations, TSS was first simulated as a
conservative tracer;
2. Internal solids loadings due to primary production were added to the model;
3. A constant gross settling velocity for water column TSS was specified,
based on ranges of values used in similar model applications to other
systems;
4. Solids resuspension velocities during non-event conditions were determined
by calibrating model output to observed TSS values;
5. Solids resuspension velocities during high flow events were increased so that
model output represented observed increases in TSS during these events;
6. Final calibration of solids resuspension velocities was conducted by ensuring
that model output for cumulative solids transport fluxes matched observed
solids fluxes at Stillwater and Waterford. These observed fluxes were
derived using the MVUE loading method and available USGS field
measurements for river flow and TSS at these two locations; and,
7. Solids kinetic processes in HUDTOX were adjusted to maintain an
approximately constant DOC concentration gradient between the sediment
and water column. To accomplish this, a small degree of sediment solids
mineralization to DOC was required.
4.6.3 PCB Model
The PCB calibration was built upon the above hydraulic and solids mass
balances. Emphasis was placed on specification of site-specific external PCB
loadings, and independent specification of PCB process-related parameters using
site-specific measurements or values from the scientific literature. The PCB
calibration was not arbitrarily "tuned" to match model output with observed
concentration values. This calibration approach served as a good test of the
underlying hydraulic and solids mass balances. The specific steps in the HUDTOX
calibration for total PCBs and the five calibration congeners were the following:
1. Using Phase 2 sampling program data (TAMS/CADMUS/Gradient, 1996 -
pending publication), a constant fraction of organic carbon (foc) on TSS was
determined and specified for the water column. GE data from 1991 were
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used to estimate constant foc values in bedded sediments on a segment-
specific basis;
2. Values for the partition coefficients, and Kdoc were fixed based upon a
three-phase partitioning analysis of the Phase 2 water column monitoring
data (TAMS/CADMUS/Gradient, 1996 - pending publication). Median values
(at 20°C) determined from this data analysis were specified for each of the
five selected PCB congeners. Values for total PCBs were estimated based
upon the distributions of partition coefficients for the individual congeners;
3. Congener-specific temperature slope factors (tsf) were used to represent
seasonal variation of PCB partition coefficients. This effect is described by
Equation 3-2. Estimates of tsf values for each calibration congener were
generated as part of the Phase 2 data analysis (TAMS/CADMUS/Gradient,
1996 - pending publication);
4. The chemical diffusion coefficient (D,;) between sediment layers was fixed at
the level of molecular diffusion. Dsi was then adjusted on a PCB congener-
specific basis for differences in molecular weight (O'Connor, 1985);
5. The chemical diffusion coefficient (Dswj) between the active sediment layer
and the water column was increased to 10 times the molecular diffusion rate
to account for the influences of bioturbation and water currents (DiToro and
Fitzpatrick, 1993);
6. Congener-specific Henry's Law constants (H at 25 °C) were specified based
on a literature compilation developed as part of the Green Bay Mass Balance
Study (Bierman et al., 1992). The correction to ambient river temperature for
H is shown by Equation 3-1 in Section 3.5.2; and,
7. Air-water gas exchange (volatilization) of dissolved phase PCBs was
internally calculated based upon hydraulic conditions and chemical-specific
characteristics as implemented in the standard WASP4 toxic chemical model
(Ambrose et al., 1988). A standard reaeration temperature correction factor
(9) of 1.024 was employed. Note that enhanced gas exchange over dams
was not included in HUDTOX for this preliminary model calibration. This
phenomenon has little effect on the PCB modeling for the January-September
1993 preliminary model calibration period, but it may be a significant factor
for long-term predictions.
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4.7 Calibration Results
4.7.1 Solids Model
The values of the model parameters used to calibrate the HUDTOX solids
model are presented in Table 4-12. The basis for the selection of the calibration
values and the solids modeling results are discussed in the following paragraphs.
The HUDTOX solids model calibration strategy required first that an
assessment of any potentially significant unmeasured sources of solids loads to the
Upper Hudson River be evaluated. Initial simulations using TSS as a conservative
tracer indicated that significant external solids loads were likely entering the river
between Fort Edward and the confluence with the Hoosic River, and that primary
production might be a significant internal source of solids during summer low-flow
conditions. Section 4.4.2 discusses how these solids loads were estimated for
inclusion in the HUDTOX solids model calibration. Calibration of the solids settling
and resuspension rates were undertaken once these unmeasured loads were
estimated.
A constant solids gross settling velocity (Vs) of 2.0 m/day was used in the
model calibration. This value is consistent with the range of gross solids settling
velocities used in other relevant mass balance modeling studies. Thomann et al.,
(1989, 1991) used a value of 3.05 m/day in a model of PCB homologues in the
Lower Hudson River. LTI (1992) used a value of 2.0 m/day in a model for TCDD
(dioxin) in the Columbia River Basin. Bierman et al., (1992) used a value of 2.5
m/day in a model of total PCBs and PCB congeners in Green Bay, Lake Michigan.
USEPA (1984) used values ranging from 0.25 to 0.80 m/day, depending on river
flow, in a model of four heavy metals in the Flint River, Michigan.
Much of the Phase 2 water column monitoring program during 1993
occurred during lower-flow, non-event conditions. The TSS concentrations
measured during these periods provided a set of data for calibrating a baseline
solids resuspension velocity for Upper Hudson River. A spatially variable Vr was
used, with higher values specified at downstream model segments to better
represent observed water column TSS concentrations. The required increase in Vr
may be due in part to an increase in the actual proportion of sediment area subject
to resuspension in downstream segments. During high-flow events, a multiplying
factor was applied to Vr to empirically represent the influence of increased sediment
scour on water column TSS concentrations. A value of 60 for this factor was
determined by calibrating model output for cumulative solids flux to observed
cumulative solids fluxes at Stillwater and Waterford. The factor was applied when
the mean ambient velocity exceeded 3.0 ft/sec.
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Analysis of the Phase 2 high resolution sediment cores indicated that long-
term solids deposition rates ranged between 0.5 and 5.0 cm/year
(TAMS/CADMUS/Gradient, 1996 - pending publication). Also, some of the cores
could not be dated, suggesting that settling and resuspension may be at near-
equilibrium in portions of river. The results could not be used to specify solids
burial velocity (Vb) in the HUDTOX model because the sediment cores were taken in
depositional areas of the river and do not necessarily represent the segment-
average spatial scale of the HUDTOX model. For solids in this preliminary model
calibration, a constant burial velocity of 6.0x10*6 m/day (or 0.22 cm/yr) was
assigned on the basis of best professional judgment.
Model results for the preliminary model calibration period are not sensitive to
changes in this solids burial velocity; however, decadal-scale predictive results for
PCBs are expected to be very sensitive to specification of the solids burial velocity.
Consequently, prior to use of the HUDTOX model for such predictive simulations, a
long-term (1984-1993) hindcasting calibration will be conducted (Appendix B) to
ensure that the model accurately represents observations of solids and PCB
dynamics in the Upper Hudson River.
Figure 4-10 presents the solids model calibration results for the period of
simulation. Phase 2 data from transect and flow averaged sampling events are
plotted against computed TSS for model Segments 3, 6, 8 and 12. USGS data are
plotted for model Segments 8 and 12, while the nearly-daily TSS measurements
taken by Bopp at two stations in the Upper Hudson River are plotted in Segments
10 and 11. The ability of the model to capture some of the TSS variation over the
course of the spring snowmelt event is evident during April 1993 (Julian days 90 to
120). TSS levels in the river are generally low and flat during non-event conditions,
providing data for calibrating baseline resuspension rates in the river.
A comparison of the computed cumulative TSS flux with that generated
through MVUE regression of the USGS TSS data is shown in Figures 4-11 and 4-
12. The cumulative TSS fluxes at Stillwater are comparable. Differences in
cumulative fluxes occur at Waterford, probably due in part to springtime
construction activities at Lock 1. It is possible that large amounts of solids were
released from the lock during this period, and these would not be accounted for in
the HUDTOX solids model.
It should be noted that much of the dynamic variation in computed TSS
concentrations is driven by external solids loadings from upstream and from
tributaries. Consequently, further adjustments in settling and resuspension rates in
the HUDTOX model result in only marginal improvements in the overall TSS
calibration.
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Two simple goodness-of-fit tests were conducted to quantitatively evaluate
the TSS calibration. Figure 4-13 plots the computed vs. observed TSS
concentrations for the preliminary model calibration period. A large degree of
scatter is evident, but the regression line falls along a near one-to-one slope, and
the correlation coefficient (R2) of 0.69 is relatively high. The results from a set of
Student's t-tests comparing computed versus observed mean TSS concentrations
for individual model segments are shown in Table 4-13. Segment-mean values for
model output were significantly different (p < 0.05) than segment-mean observed
values in just one of six cases. Failure to pass the t-test in downstream model
segment 11 indicates that uncertainties remain in the present model calibration.
Some of these uncertainties are due to insufficient data for specification of external
solids loadings from downstream tributaries and sediment solids concentrations.
Some uncertainty is also due to the fact that the preliminary HUDTOX model is not
an "event" model designed to represent day-to-day variability, but instead is
designed to represent variability on weekly to monthly time scales, depending on
the time scales of the external forcing functions.
A final aspect of the solids model calibration involved the selection of a
sediment solids degradation rate to maintain near-constant dissolved organic carbon
(DOC) levels in the sediments. Since organic carbon is not modeled as a state
variable in HUDTOX, this degradation represents the mineralization of particulate
detrital carbon (PDC) in the sediments. DOC is a by-product of this degradation
process, which allows a significant DOC concentration gradient to exist from the
sediments to the water column. The selected solids degradation rate of 1.1x10 6
day"1 maintains an appropriate DOC concentration gradient and has a negligible
effect on sediment solids levels.
4.7.2 PCB Model
The chemical-specific HUDTOX parameters for total PCBs and the five
calibration congeners are presented in Table 4-14. Figure 4-14 presents the
calibration results for water column total PCBs (particulate plus dissolved) in
selected model segments. GE PCB data are shown in Segment 3 (at Thompson
Island Dam), while the Phase 2 data (Transect and Flow-Average sampling events)
are shown in Segments 3, 6, 8, and 1 2. Note that an apparent increase in PCB
levels from approximately Julian Day 160 to 180 (June 9-29, 1993) is due to the
upstream boundary conditions at Rogers Island.
Results in Figures 4-14 through 4-19 present the preliminary HUDTOX model
calibration (solid lines) for total PCBs, and total concentrations of the five
congeners, respectively. In general, the model output provides a good
representation of the temporal structure of the PCB data. Model output is lower
than field observations in TIP; however, a significant increase in PCB concentration
between the upstream boundary shown in the Segment 1 plot and Segment 3 is
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demonstrated. This is consistent with the observed net gain of PCBs in the water
column across TIP (TAMS/CADMUS/Gradient 1996 - pending publication).
The actual causal mechanism(s) for the observed PCB gain across TIP is not
yet fully understood. Some of this effect is probably due to higher concentrations
of lower chlorinated PCB congeners (e.g. BZ#4) in TIP sediments, relative to
concentrations of higher chlorinated congeners. Because lower chlorinated
congeners tend to have lower partition coefficients, TIP sediments are relatively
enriched with both particulate and dissolved phases of these lower chlorinated
congeners. Consequently, sediment-water PCB fluxes due to pore water advection,
resuspension or diffusion will tend to be relatively enriched with lower chlorinated
congeners. A comparison between BZ#4 (Figure 4-15) and BZ#138 (Figure 4-19)
illustrates this behavior when upstream boundary conditions are compared to
HUDTOX results (solid lines) for Segments 1 and 3, which are located in TIP.
This preliminary HUDTOX model was used to test the hypothesis that PCB
gains across TIP might be consistent with dJvective flux of pore water PCBs due to
groundwater inflow. This evaluation was conducted for total PCBs and BZ#4, the
calibration target with the lowest partition coefficient (Table 4-14). If this
sediment-water exchange mechanism is important, then the flux for BZ#4 would be
expected to be larger than fluxes for total PCBs and any of the other target
congeners.
Potential groundwater inflow to TIP was estimated using two independent
methods. First, Darcy's law (Equation 4-1) was applied to estimate groundwater
flow velocity using estimates of hydraulic conductivity (k) and the groundwater
hydraulic gradient (/):
u = k *j (4-1)
where,
u = Darcy velocity [L/T]
k = hydraulic conductivity [L/T]
j = hydraulic gradient [L/L].
Groundwater flow into TIP was estimated by multiplying the Darcy velocity
by the bottom area of the pool. The pool is approximately 6 miles long and 800
feet wide, a bottom surface area of 2.53x107 ft2 ( = 2.35x106 m2).
The hydraulic gradient was estimated from land surface topography using
USGS quadrangle maps. The measured land surface slope ranges between 0.01
and 0.03, averaging approximately 0.02 along the length of the TIP river reach. A
relatively low hydraulic conductivity of 6x10"4 cm/sec was applied to represent an
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average silty-sand content in TIP sediments (Freeze and Cherry, 1979). This results
in an estimated 10 cfs of groundwater inflow to TIP. Note that hydraulic
conductivity for primarily sandy sediments in on the order of 1x10~2 cm/sec. A
hydraulic conductivity of 2x10"3 cm/sec, representative of a more heterogeneous
(e.g. gravel, sand, and silt) sediment, produces an estimated groundwater inflow to
TIP of 30 cfs.
Second, an analysis of gains in Hudson River flow between USGS gages at
Hadley and Fort Edward was conducted. Average annual flow increases by
approximately 120 cfs over this 30 mile section of river. No major tributaries enter
the river between these two stations, while the drainage basin area increases by
approximately 100 mi2. If all of the increase in flow was attributed to
groundwater, then the maximum likely groundwater inflow over the 6 mile reach of
TIP would be 24 cfs. Accounting for surface runoff over the reach reduces this
estimate, but it still provides a reasonable approximation of groundwater inflow to
TIP. In addition, evaluation of the flow differences between Hadley and Fort Edward
on an annual basis may underestimate the actual groundwater inflow during
summer low flow periods if river levels fall in relation to nearby groundwater levels.
It should also be noted that observed increases in PCBs across TIP are at a
maximum during summer low flow periods.
Based on the above analyses, a value of 30 cfs for total groundwater inflow
to TIP was chosen for evaluating the effect of potential pore water advection.
Dashed lines in Figures 4-14 and 4-15 show the results for the HUDTOX calibration
parameterization for total PCBs and BZ#4, but include an assumed pore water
inflow of 30 cfs across TIP. Results indicate that porewater advection is potentially
of sufficient magnitude to influence total PCB concentrations in TIP; however,
information is not available to quantify the temporal and spatial variability of
potential groundwater inflows to TIP. Furthermore, the present analyses are based
on sediment PCB concentrations as reported in the GE 1991 sediment survey data.
The surficial (0-5 cm) sediment PCB concentration measured during 1991 may not
fully represent 1993 conditions.
More detailed calibration results for total PCBs and the five calibration
congeners are presented in Figures 4-20 through 4-31. These temporal profiles
provide a comparison of model results with available data for both apparent
dissolved and particulate phase PCBs in the water column. In general, the
HUDTOX model represents the mean behavior of water column total PCBs and the
five congener groups reasonably well. Variability in the relative influence of
porewater inflows across different PCB congeners is evident by the dashed lines in
the plots of total PCBs and BZ#4, which represent the computed PCB
concentrations with constant porewater advection into TIP model segments. Total
PCBs and BZ#4 water column concentrations increase significantly when porewater
advection is included in the HUDTOX model. The other four PCB congeners have
higher partition coefficients than BZ#4, consequently, porewater advection would
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be expected to be less important for these calibration targets than for BZ#4.
Porewater advection was not evaluated for any of these other four congeners.
A set of t-tests was also conducted to provide a quantitative evaluation of
the PCB model calibration (without porewater advection). These tests
demonstrated the ability of the model to represent the mean behavior of PCBs
among the individual model segments. Tables 4-15 through 4-17 contain results
from these tests for total, dissolved, and particulate phase PCBs. For 88 percent of
the comparisons across all calibration targets and model spatial segments, there
were no significant differences (p < 0.05) between segment-mean values for
model output and segment-mean observed values for total PCBs. Corresponding
results for dissolved and particulate phase PCBs were 79 percent and 71 percent,
respectively.
Regression analyses were conducted to provide additional quantitative
evaluation of the HUDTOX model calibration. Model output is plotted versus
observed data for each of the PCB calibration targets in Figures 4-32 through 4-34
for total, dissolved and particulate phase PCBs, respectively. Only Phase 2 transect
data are included in these plots, since the flow-averaged PCB data cannot be
compared on a point-to-point basis with the model output values.
The calibrated HUDTOX model was successful in representing day-to-day
variability across all model spatial segments. Although scatter is evident in these
comparisons and there are several apparent outlying data points, the calibrated
model explained an average of 70 percent of the overall spatial-temporal variability
in these day-to-day field data. HUDTOX was also successful in representing the
average behavior of water column total PCBs and congener groups within each
model spatial segment.
4.8 Mass Balance Component Analysis
As part of the HUDTOX modeling effort, a mass balance component analysis
was developed for the solids and PCB calibrations. This type of evaluation focuses
on the significance of the various sources, sinks and mass reservoirs for each state
variable within the modeling framework. The entire Upper Hudson River model
segmentation and the TIP were analyzed for the 9-month preliminary model
calibration period. In addition, the 1993 spring high flow period was examined
separately from the rest of the model simulation period. This high flow period was
defined for the mass balance analysis as extending from March 26 through May 10,
1 993 in order to capture spring flooding conditions occurring throughout the entire
Upper Hudson River.
For the entire model calibration period, Figures 4-35 and 4-36 present the
mass balance component diagrams for the solids calibration in TIP and the Upper
Hudson River, respectively. Upstream Hudson River solids loads dominate both
sediment resuspension and primary production sources in TIP, but solids loads from
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downstream tributaries and sediment solids dynamics are more dominant for the
Upper Hudson River as a whole. The principal external solids loadings to the Upper
Hudson River during the simulation period were from the Mohawk River (59
percent) and the Hoosic River (20 percent). Solids loadings from the upstream
boundary at Fort Edward represented only 8.5 percent of the total external solids
loadings. The minimal effect of the solids degradation (i.e. mineralization) required
to maintain near-constant sediment DOC concentrations is also evident from the
solids mass balance component diagrams.
The effect of the 1993 spring high flow period on the components of the
solids mass balance is shown in Figure 4-37. Approximately 85 percent of the
solids load across the upstream boundary, and 87 percent of the tributary solids
load, enters the Upper Hudson River during the spring high flow period. Also, the
bulk of the computed sediment solids resuspension, in both TIP (70 percent) and
the entire Upper Hudson (78 percent), occurs during the spring high flow period.
However, it should be noted that the TSS gain across TIP is just 8 percent during
the spring high flow period because the upstream boundary condition dominates the
solids load to this section of the river. Only 5 percent of the solids mass passing
over Thompson Island Dam is due to net resuspension within TIP over the entire
1993 calibration period. During the lower flow periods, the gross settling flux of
TSS exceeds sediment solids resuspension, and a net settling of TSS occurs
throughout the Upper Hudson, including Thompson Island Pool. A net 10 percent
TSS gain across TIP occurs during the lower flow period, primarily due to internal
solids load generation through primary production.
Mass balance diagrams for the HUDTOX model calibration of total PCBs are
shown in Figure 4-38 and 4-39. These diagrams summarize results for the entire 9-
month preliminary model calibration period. Note that these diagrams are not a
representation of the mass balance for a complete year, and should not be
construed to indicate river characteristics outside of the period simulated.
The principal sources of total PCBs to the water column of the Upper Hudson
River during the entire 9-month period of simulation were internal loadings due to
resuspension from the surface sediment layer (859 kg) and external loadings across
the upstream boundary at Fort Edward (352 kg). The principal losses of total PCBs
from the water column were outflow at Federal Dam (985 kg) and gross settling of
particulate phase PCBs (311 kg). Volatilization losses (53 kg) for total PCBs were
nearly an order-of-magnitude lower than other loss processes.
The principal source of total PCBs to the surface sediment layer of the Upper
Hudson River during the period of simulation was gross settling of particulate phase
PCBs (311 kg). The principal losses of total PCBs from the surface sediment layer
were resuspension to the water column (859 kg) and net burial to deeper sediment
layers (418 kg).
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The principal sources of total PCBs to the water column of TIP during the
period of simulation were internal loadings due to resuspension from the surface
sediment layer (406 kg) and external loadings across the upstream boundary at Fort
Edward (352 kg). The principal losses of total PCBs from the water column were
outflow at Thompson Island Dam (712 kg) and gross settling of particulate phase
PCBs (46 kg). Overall, net resuspension in TIP (i.e. resuspension minus gross
settling) represent 51 percent (360 kg) of the total PCB mass transported across
Thompson Island Dam.
The principal source of total PCBs to the surface sediment layer in TIP during
the period of simulation was gross settling of particulate phase PCBs (46 kg). The
principal losses of total PCBs from the surface sediment layer were resuspension to
the water column (406 kg) and net burial to deeper sediment layers (194 kg). In
TIP (Figure 4-38) the resuspension of total PCBs is relatively more important than in
the Upper Hudson River as a whole. The PCB mass flux from resuspension was
nearly 10 times greater than PCB gross settling in TIP, while it was less than a
factor of 3 greater over the entire Upper Hud^n River.
Figure 4-40 displays the PCB mass balance components for the 9-month
1993 calibration period for both (a) Upper Hudson River; and (b) TIP. Stacked bars
are used to show the effect of the 1993 spring high flow period relative to the total
simulation period. The 45-day spring high flow period dominates both the external
and internal (i.e. resuspension) loading sources of PCBs to the water column for the
simulation period. Approximately 70 percent (247 kg) of the total PCB loading
across the upstream boundary occurs during the spring high flow period. Also,
more than 70 percent of the computed resuspension of total PCBs from the
sediment, in both TIP (73 percent or 285 kg) and the entire Upper Hudson River
(70 percent or 628 kg), occurs during the spring high-flow period.
Figure 4-40b also shows the greater than two-fold net gain of total PCBs
across TIP during both spring high flow (104 percent or 256 kg) and lower flow
(100 percent or 104 kg) conditions. This is in contrast to the small 8 percent net
gain of TSS across the pool during the spring high flow period, and a 10 percent
gain during the remaining lower flow period (see Figure 4-37). These results point
out that relatively high PCB concentrations in the sediments of TIP have a
significant effect on Upper Hudson River PCB dynamics. Even low rates of solids
exchange between the sediment and water column can transfer significant
quantities of PCBs into the water column. In fact, the model results indicate that
TIP contributes 52 percent (121 kg) of the total PCB (231 kg) resuspension load to
the Upper Hudson River during the lower flow period of the 1993 calibration.
The importance of TIP as a source of PCBs to the Upper Hudson River is
illustrated by Figures 4-41 through 4-45 which show the mass balance over the
HUDTOX calibration period for congeners BZ#4, BZ#28, BZ#52, BZ#101 +90 and
BZ#138, respectively. The differences in the dynamics of total PCBs and BZ#4 are
4-25
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demonstrated by the 585 percent (79 kg) gain in BZ#4 mass across TIP during the
calibration period versus the 102 percent (361 kg) gain for total PCBs. During the
spring high flow period, BZ#4 shows a 1435 percent (51 kg) gain across TIP and a
gain of only 278 percent (27 kg) during the lower flow periods of the model
calibration period.
These results illustrate the variations in relative importance of different fate
and transport mechanisms across the range of PCB congeners simulated. For
example, dissolved and DOC-bound diffusive transport from the sediment transfers
a greater proportion of BZ#4 into the water column than it does for the other
congeners. Also, the mass balance results show that the relative loss of BZ#4 due
to volatilization is significantly greater than for other congeners. These findings are
not unexpected because BZ#4 has the lowest partition coefficient (log Kpoc = 5.108,
see Table 6-9) among the PCB congeners simulated. These factors, along with the
relatively high percentage of lower-chlorinated congeners in TIP sediments, are the
principal reasons responsible for the large computed differences in PCB dynamics
across the range of PCB congeners included in this preliminary model calibration.
The influence of potential porewater PCB fluxes was examined in the
component mass balances for TIP for BZ#4 (Figures 4-46 and 4-47). For the entire
preliminary model calibration period, an assumed, constant 30 cfs of porewater
advection into TIP caused a 34 percent increase (92 kg to 124 kg) in the outflow of
PCB congener BZ#4 at Thompson Island Dam. Figure 4-48 shows the mass
balance for the BZ#4 simulation with the inclusion of pore water advection.
Approximately 30 percent of the sediment BZ#4 load from TIP was due to pore
water advection for this particular simulation. The corresponding increase in total
PCB mass outflow from TIP was less than 6 percent (712 kg to 753 kg) as shown
in Figures 4-49 and 4-50. Higher-chlorinated PCB congeners show minimal water
column increases due to porewater advection, a modeling result which is consistent
with field observations.
It should be noted that all results from these component analyses are
premised on the HUDTOX preliminary model calibration for the period of January 1
through September 30, 1993. This calibration contains many sources of
uncertainty. The principal uncertainties in the HUDTOX calibration at the present
time are the following:
1. Suspended solids loads from tributaries downstream of Thompson Island Dam
(91 percent of total external solids loadings) are uncertain due to the limited
number of field measurements, for both TSS and flow, in the tributaries;
2. PCB loads across the upstream boundary of the model at Fort Edward, the
principal source, are uncertain due to unknown amounts of PCB loadings from
the GE facilities at Hudson Falls and Fort Edward, and the limited number of field
measurements;
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3. Large uncertainties in specification of the three-dimensional sediment physical
properties and sediment PCB concentrations;
4. Uncertainties in specification of solids settling and resuspension velocities, and
the potential dependence of these rates on river flow; and
5. Uncertainties due to an incomplete understanding of the causative mechanism(s)
for observed increases in water column concentrations of lower-chlorinated
congeners across TIP.
Plans for future modeling work (Appendix B) contain elements that will
address some of these uncertainties. The more finely-resolved spatial segmentation
grid for TIP will better represent horizontal differences in sediment-water
interactions. In conjunction with use of data from the Phase 2 low-resolution
sediment coring effort, this grid will allow more accurate specification of sediment
physical properties and sediment PCB initial concentrations. Calibration of
HUDTOX to high-frequency TSS data for Spring 1994 will reduce uncertainties in
solids settling and resuspension velocities. Additional hypothesis testing and
oensitivity analyses will lead to better unaerstanding of the causative mechanism(s)
for observed increases in PCB concentrations across TIP.
4.9 PCB Model Calibration Sensitivity Analysis
To provide additional insight into the parameterization of this preliminary
HUDTOX calibration, a limited sensitivity analysis was conducted. Two of the
HUDTOX model inputs have been identified as having a large degree of uncertainty:
sediment PCB initial conditions and upstream PCB boundary conditions. To
evaluate responses of the model to changes in these conditions, sensitivity
analyses were conducted with the calibrated HUDTOX model in which sediment
initial conditions and upstream PCB loadings were varied by plus/minus 30 percent.
A total of four independent simulations was conducted to produce sensitivity
results for total PCBs and the five selected PCB congeners used for the HUDTOX
calibration.
The sensitivity results for the 30 percent variation in sediment PCB initial
conditions are presented in Figures 4-51 through 4-56 for total PCBs, and
congeners BZ#4, BZ#28, BZ#52, BZ#101+90 and BZ#138, respectively. These
time series plots illustrate that water column PCB levels are very sensitive to the
sediment conditions during transient flood events when a greater exchange of
solids between sediment and water is occurring. Figure 4-52 shows that computed
BZ#4 concentrations exhibit greater sensitivity to sediment conditions than other
congeners during the lower flow period in summer of 1993 (after May 10th or Day
130).
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The model results for a 30 percent variation in upstream boundary PCB loads
were less sensitive than to a 30 percent change in sediment initial conditions.
Model results are presented in Figures 4-57 through 4-62 for total PCBs, and
congeners BZ#4, BZ#28, BZ#52, BZ#101 +90 and BZ#138, respectively. As
expected, the results show a trend of decreasing sensitivity to the upstream
boundary moving downstream, as sediment-water interactions and other external
sources affect PCB levels in the water column. The sensitivity of computed PCB
concentrations to transient spikes of upstream PCBs during lower flow periods is
also very apparent during June 1993 (Julian Days 1 53 to 181).
The results of the sensitivity simulations are summarized by the total PCBs
mass balances shown in Figures 4-63 and 4-64. For analyses in which initial total
PCB concentrations in the sediments were varied by plus/minus 30 percent, total
PCB mass transported across Thompson Island Dam varied by plus/minus 16
percent, and total PCB loadings across Federal Dam to the Lower Hudson River
varied by plus/minus 20 percent. For analyses in which total PCB loadings across
the upstream boundary at Fort Edward were varied by plus/minus 30 percent, total
PCB mass transported across Thompson Island Dam varied by plus/minus 14
percent, and total PCB loadings across Federal Dam varied by plus/minus 7 percent.
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5. CALIBRATION OF THOMPSON ISLAND POOL HYDRODYNAMIC
MODEL
5.1 Introduction
Estimation of resuspension of bottom sediments containing PCBs in the
Thompson Island Pool (TIP) as a result of high river flows requires a hydrodynamic
model to represent the flow conditions of interest. The calibration of the
hydrodynamic model, RMA-2V, as applied to the TIP, is described in this section.
RMA-2V is a finite element model, primarily developed for and maintained by the U.
S. Army Corps of Engineers. The purpose of the hydrodynamic modeling is to
calculate a two-dimensional (longitudinally and laterally), vertically-averaged,
velocity field in TIP for various flows of interest. By knowing the two-dimensional
flow field, the two-dimensional shear stresses exerted on the bottom of the river
can be calculated. From the two-dimensional shear stresses, the mass of river bed
sediment eroded during the various flows of interest can be calculated with the TIP
Depth of Scour Model (Section 6).
The description of the TjP hydrodynamic modeling effort is divided into 10
sections. Section 5.2 describes the input data required by the model to simulate
TIP. Section 5.3 describes the internal model parameters used in the calibration.
Section 5.4 describes the calibration approach. Section 5.5 provides the calibration
results. Section 5.6 describes additional, separate sources of information used to
validate the model calibration results. Section 5.7 describes predictive results for
the 100-year flood event. Section 5.8 describes model sensitivity in response to
changes in various model inputs. Section 5.9 describes the conversion of the
vertically-averaged velocities computed by the model to the corresponding bed
shear stresses. Finally, Section 5.10 contains a discussion of the model results.
5.2 Model Input Data
A hydrodynamic model requires specific input data describing the hydraulic
conditions of the system chosen for simulation. These input data consist of the
system specific physical data, the forcing functions or upstream boundary
conditions, and the downstream and side channel boundary conditions. These are
described below.
5.2.1 System-Specific Physical Data
The system specific physical data consists of the river dimensions used to
develop the model's segmentation and the river's resistance to flow, which is
expressed in terms of the Manning's 'n'. Manning's 'n' is a calibration parameter
derived from comparing the model output to river observations for a range of flows.
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Model Segmentation
The RMA-2V model uses a six-node triangular element scheme to describe
the physiography of the target system. The model segmentation consists of
approximately 6000 nodes defining 3000 elements. Each node is defined by an x-y
coordinate and its corresponding elevation. The vertically averaged velocity vector
is calculated at each of these nodes for a given flow condition. Figure 5-1 shows
the river segmentation used in the model calibration.
The model segmentation or model grid for the main channel is based on the
bathymetric survey performed by General Electric in 1991 (O'Brien and Gere,
1993b). The model grid for the adjacent floodplain is based on the USGS
topographic maps. Smaller elements are used in the main channel where changes
in velocity can be large and larger elements were used in the floodplain where the
velocity and its changes are relatively small. The nodes of the finite element grid in
the main channel are located approximately every 50 feet across the river and
approximately 300 feet along the channel.
Manning's 'n'
The input parameter, Manning's 'n', expresses the river's hydraulic resistance
to flow. Conceptually, resistance to flow reflects the character of the sediments
and the nature of the flow pathways. This parameter is commonly a calibration
parameter since its value cannot be predicted accurately from a measurement of
the physical dimensions of the river or from a description of the sediment type.
Two site specific flow modeling r/tudies, Zirnmie (1985) and FEMA (1982) had
been conducted previously and the Manning's 'n' values can be expected to be near
the values used in these studies. Table 5-1 contains the Manning 'n' values used in
these two studies.
For this study, the values of Zimmie were used initially and then
subsequently calibrated to best fit the recorded observations of the river, especially
those at high flow. The sensitivity of the model to changes in this parameter is
discussed below in Section 5.8.
5.2.2 Forcing Functions
The principal forcing function of the model consists of the upstream
boundary condition, which is the specified flow. The model was run for a total of
eight different flows (Table 5-2). The first four flows are of interest because the
concentration of suspended sediment in the river was sampled when they occurred.
The suspended sediment concentration data taken during these flows will be used
to help calibrate the TIP Depth of Scour Model. Recall that the Depth of Scour
Model requires the shear stresses computed from the velocities calculated in the
hydrodynamic model calibrated here. The fifth flow is of interest because it is the
highest flow recorded in TIP after the Fort Edward dam was removed in 1973. The
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final three flows are of interest because they represent high flow events with a
specified return period.
The model results for these eight design flows will be used in the TIP Depth
of Scour Model to evaluate the risk of resuspension of PCBs from the deeply buried
sediments. These design flows were specified at the most upstream transect of
the model grid. This transect is approximately 500 feet upstream of Rogers Island.
5.2.3 Boundary Conditions
The boundary conditions of the model consist of the side channel boundary
condition and the downstream boundary condition. The side channel boundary
condition is the requirement that the velocity normal to the sides of the channel is
zero. This is implicitly performed in the RMA-2V model. The downstream
boundary condition consists of specifying the water surface elevation at the most
downstream transect, which is the Thompson Island Dam. This water surface
elevation was taken from the rating curve for Gauge 118, which is located just
above Thompson Island Dam. The rating curve was developed from a regression
analysis performed on the discharge-water level data accumulated during the 11
year period of 1983 to 1993 (TAMS/CADMUS/Gradient, 1996 - pending
publication). Examination of this rating curve showed that the regression is good
for flows up to 30,000 cfs; however, the third-order polynomial developed in the
regression fails to accurately predict increasing river elevations for flows above
30,000 cfs. Refined extrapolation using engineering best judgment and a
theoretical rating curve (Zimmie, 1985) was used to determine the water levels at
Thompson Island Dam above these flows.
The downstream boundary must be specified as an elevation since if a flow
is specified, there would not be a way to incorporate the backwater effects of the
dam into the model.
5.3 Internal Model Parameters
There are two internal model parameters, the Manning's 'n' for the river and
the turbulent exchange coefficients. Only the Manning's 'n', one for the main
channel and one for the floodplain, were used as calibration parameters. The other
main input parameter, the turbulent exchange coefficient, is not a true physical
parameter since it reflects the flow field, the model grid, and the numerical solution
technique of RMA-2V. Therefore, values were assigned for the turbulent exchange
coefficients based on guidelines in the literature (Thomas and McNally, 1 990) and
not changed in the calibration procedure. Moreover, changes in this parameter do
not significantly affect the model's results and model sensitivity to changes in this
parameter is discussed in Section 5.8.
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5.4 Calibration Approach
The calibration approach consists of determining an appropriate value for the
turbulent exchange coefficients and then varying the Manning's 'n' so that the river
levels computed by the model agree well with the river levels predicted by the
upstream rating curve for each flow input at the upstream transect of the grid.
Note that only one value of Manning's 'n' was used for the entire length of the
main channel since there is no physical data on which to base a variation.
The upstream rating curve used for comparing to model output during
calibration was Gauge 119, near Lock Number 7, which is near the southern tip of
Rogers Island (Figure 5-2). The Gauge 119 rating curve is similar to the Gauge 118
in that they are both third-order polynomial regressions on data from 1983 to 1993
and these regressions are only fully valid for flows less than 30,000 cfs. As with
Gauge 118, the Gauge 119 water levels for flows above 30,000 cfs were
determined using best engineering judgment.
Because this component of the study is primarily interested in higher Hudson
River flows, those conditions above 30,000 for the rating curves for both
Gauge 119 (upstream) and Gauge 118 lauwnstream) are unsubstantiated.
Therefore, the calibration first focused on the flow of 30,000 cfs. The Manning's
'n' values were calibrated for 30,000 cfs and were then used in the model to
predict water elevations for lesser flows. These predicted water elevations were
then compared with the elevations from the Gauge 119 elevations.
The turbulent exchange coefficients were determined to be well-represented
by 100 lb-sec/ft2. This determination is based on the guidelines given in the RMA-
2V manual (Thomas and McNally, 1990). Specifically, the guidelines given in the
manual suggest a range of values from 50 to 200 lb-sec/ft2. and the model results
proved to be relatively insensitive within this range of values.
5.5 Calibration Results
As described above, the model was primarily calibrated for the flow of
30,000 cfs. The Manning's 'n' for the final calibration were 0.020 for the main
channel and 0.060 for the floodplain. The model computed the same river water
surface elevation as observed at Gauge 119 using these calibration values. Table
5-3 shows this result along with the comparison of model output vs. rating curve
water levels for lesser flows. Although the calibrated Manning's 'n' appears to be
somewhat low, it was judged that a higher value could not be justified given the
model's results especially those at low flows.
As seen when comparing the last two columns in Table 5-3, the model's
results are slightly higher than the rating curve for the smaller flows. However, for
the calibration flow of 30,000 cfs, the model result for river water elevation at
Gauge 11 9, was the same as the rating curve. This observation of excellent model
5-4
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fit at the upper limit of the river observations is helpful for examining the critical
flows above 30,000 cfs.
5.6 Model Validation
There were two additional and separate sources of information used to
validate the calibration results. The first source is Hudson River velocity
measurements made in the TIP by the USGS. The second source is the flood study
conducted by FEMA. A comparison between model results with these sources of
information are discussed below.
5.6.1 Rating Curve Velocity Measurements
The USGS periodically measures the flow in the Hudson River in TIP to
develop and update the river's rating curves. For the rating curve located at Scott's
Paper upstream of Rogers Island, the flow is measured by measuring the depth and
velocity at numerous points over the cross-section of the river at Rogers Island.
These data are taken at the bridges over the Hudson River on both sides of Rogers
Island. Figure 5-3 shows the location in the Hudson River where the velocities
were taken. Using these data, the model's simulated velocities can be compared to
the measured velocities as a check on the accuracy of the model.
The model was run for the same discharge (29,800 cfs) as measured on 18
April 1993. The model computed velocities approximately the same or in places
slightly lower than measured. For example, the river velocities measured in the
middle of the channel by the USGS were approximately 4.3 feet per second (fps)
while the model computed velocities of approximately 4.1 fps. Even though these
values are sufficiently close for validation, it should be noted that these measured
velocities should be slightly higher since the bridges from which the velocity
measurements are taken constrict the flow, causing localized higher velocities. The
model does not include the localized effect of the bridges and, therefore, no
constriction is accounted for in the model.
5.6.2 FEMA Flood Studies
The Federal Emergency Management Agency regularly conducts studies on
rivers to predict the flood elevations in rivers for various frequencies of flows. The
results of the study conducted by FEMA in 1984 were used as an additional check
on the reasonableness of the model. The 100 year flow used by FEMA (52,400
cfs) is greater than the 100 year flow used in this study (47,330 cfs) so that a
direct comparison of 100 year flood elevations was not initially possible. However,
the model was eventually run for the 100 year FEMA flow of 52,400 cfs, and the
model predicted a river elevation at Fort Edward of 130.4 ft. NGVD (National
Geodetic Vertical Datum, formerly Sea Level Datum of 1929). The FEMA flood
study using the HEC-2 program (with the higher Manning 'n' values) predicted a
river elevation of 1 30.7 ft. NGVD. These results are very comparable and each
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model reflects a slightly different representation of the river hydraulics. The RMA-
2V model developed here was also run for 52,400 cfs with a Manning's 'n' of
0.030 for the main channel and 0.075 for the floodplain (approximately the same
as the FEMA study). This resulted in a predicted river elevation of 131.7. More
importantly, the river velocities do not vary appreciably for the various
representations. Given this comparison, the model results are judged to be
comparable to the FEMA flood studies.
5.7 100 Year Flood Model Results
The model was run for the 100 year flood of 47,330 cfs and the predicted
river elevation at the downstream tip of Rogers Island was 1 28.6 ft. This elevation
is slightly lower than the extrapolated rating curve's elevation of 129.1. Again, the
model's predicted velocities would not be appreciably effected by the difference
observed between the model results and the extrapolated rating curve. This model
run was used as the baseline run for testing the model sensitivity which is
discussed in the next section. Figure 5-4 shows the model grid along with the
computed velocity vectors.
5.8 Sensitivity Analyses
The sensitivity of the model to the principal inputs was evaluated by varying
the finite element grid size, the Manning's 'n', and the turbulent exchange
coefficient. The model's sensitivity to the grid size was checked by running the
model with a finite element grid with approximately two times the number of
elements as the finite element grid i«sed. The results obtained with the larger grid
resolution were the same as the smaller grid and, therefore, it was concluded that
the finite element grid used here was of sufficient resolution to simulate the river
flow.
The sensitivity of the model to the Manning's 'n' and the turbulent exchange
coefficient was measured by the effect on the predicted water elevations for the
100 year flood at the downstream tip of Rogers Island (Gauge 119). The
sensitivity results are presented in the following discussion.
5.8.1 Manning's 'n'
The Manning's 'n' was varied over a reasonable range for the main channel
and the floodplain. The model was run for the 100-year flood of 47,330 cfs and
the results are contained in Table 5-4. These results indicate that changes in
Manning's 'n' do not significantly affect results from the calibrated model. It is also
evident that the main channel Manning's 'n' generally affects the results much
more than the floodplain's Manning's 'n', as would be expected since higher
velocities and most of the flow occur in the main channel.
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5.8.2 Turbulent Exchange Coefficient
There are four turbulent exchange coefficients (E) and all four were set to
100 Ib-sec/ft^ in the baseline run. The TABS-2 (RMA-2V) manual provides
guidelines in choosing values for these coefficients. These guidelines are: (1) in
general, there is a tendency for these coefficients to be assigned at values that are
too high rather than too low; and (2) most rivers without flow reversal will have
coefficients in the range of 10 to 100 Ib-sec/ft^. Table 5-5 shows the effects of
varying these turbulent exchange coefficient values in the calibrated model.
It can be concluded that the high values of E do not affect the river elevation
dramatically, especially evidenced by the small increase in the river elevation for
doubling the coefficients. Also, the model predicts higher elevations for higher
turbulent exchange coefficients. This means that if higher turbulent exchange
coefficients were used in the calibration, then a lower Manning's 'n' would have to
be used to obtain equally good agreement with the observed rating curve. Given
these results, it was judged that a turbulent exchange coefficient of 100 was
indeed reasonable and that further calibration was not required.
5.9 Conversion of Flow Velocity to Shear Stress
The conversion of the vertically-averaged river velocities obtained from the
RMA-2V model to shear stresses is required to compute the resuspension of bed
sediments in the TIP. Several candidate conversion formulations were investigated.
The four methods, with a short description of each, are presented below.
1) Smooth wall log velocity profile
This conversion method (Thomas and McNally, 1990, Schlichting, 1979)
derives from the assumption that the vertical velocity profile at any point in
the river follows the smooth wall log velocity profile. The following equation
describes this velocity profile.
-^ = 2.5ln(3.32wd/v)
(5-1)
where,
d
n
u
v
*
vertically averaged velocity
shear velocity
depth of flow
kinematic viscosity.
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The applicability of this relation to the Hudson River is suspect since it is
known that the bottom of the river is not hydraulically smooth and,
therefore, it is doubtful whether this expression is applicable to describe the
velocity distribution in the channel.
2) Method used by Gailani (Gailani et al., 1991), on the Fox River
xh - 0.03 u2 (5-2)
where,
Tb - bottom shear stress.
This relation is based on empirical evidence obtained in laboratory flumes
(personal communication with Gailani, 1994). This relation is somewhat
theoretically based since the shear velocity can be approximated by a fixed
fraction of the vertically averaged velocity.
3) Rough wall log velocity profile
= 6.25 + 2.51n(d/k) (5-3)
u *
where,
u = vertically averaged velocity
u * = shear velocity
d -- depth of flow
k = equivalent Nikuradse roughness.
This relation (Thomas and McNally, 1990) describes the velocity profile for a
rough wall river flow, which is typically the condition for all river flows. The
only parameter for this equation is k, the roughness factor. This parameter
can be estimated from the Manning's roughness (Chow, 1960), and for 'n'
= 0.02, k was determined to be 0.04 feet.
4) Manning shear stress equation
3.81 - u-n
15-4.
This shear stress conversion (Thomas and McNally, 1990) is based on
equating the one-dimensional Manning equation with the definition of the
cross-sectional average shear stress, which is
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u* = (gdS)h2
(5-5)
where,
g
d
S
acceleration due to gravity
the average depth of flow
the slope of the river.
Note both equations are only valid for the whole cross-section of the river,
the depth and velocity in these equations are the cross-sectional averages.
Therefore, these equations are not strictly applicable for a vertically averaged
point in the cross-section.
5.9.1 Results
Figure 5-5 gives the variation of shear stress with the average vertical
velocity for the four different methods. The depth used to calculate the conversion
for methods 1,2 and 4 was 10 feet. As seen in Figure 5-5, Method 1, the smooth
wall velocity profile, yields the smallest shear stress, while Method 4, the Manning
shear stress equation, yields the highest, while Methods 2 and 3 yield similar shear
stresses. Based on theoretical considerations and site specific characteristics,
Method 3, the rough wall velocity profile, was selected as the most representative
conversion method to use.
5.10 Discussion
The calibrated RMA-2V model is a reasonable representation of TIP
hydraulics for various flow regimes. This conclusion is based on the good
agreement found between model output for water levels and rating curve results at
Lock 7, and the good agreement between model output for velocities and those
measured by the USGS. The model is able to simulate flows well above the
calibration flow, 30,000 cfs, based on the reasonable agreement between the 100-
year flow predictions by this model and the FEMA model, and the lack of sensitivity
of high flow results to changes in internal model parameters.
The sensitivity analyses show that the RMA-2V model is not appreciably
sensitive to changes in the calibration parameters. However, the analysis on the
conversion of the flow field output (vertically averaged velocity and depth) to the
river bed shear stress shows that the shear stress can vary significantly depending
on the conversion method used. The method chosen in this analysis was judged to
be the most firmly-grounded in a theoretical sense, and it gives similar results to a
conversion method based on detailed laboratory data.
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6. APPLICATION OF THOMPSON ISLAND POOL DEPTH OF SCOUR
MODEL
6.1 Introduction
This section describes application of the Depth of Scour Model for the
Thompson Island Pool (TIP). The model is based on a statistical fit of observed TIP
erosion data to a modified form of the Lick equation discussed in Section 3.7. The
model is applied to address the following questions at flood flow conditions:
1. What is the range of expected scour depths at each of five Phase 2 high
resolution coring sites in TIP?;
2. How do these depth of scour ranges compare to observed depth profiles of PCB
concentrations at these sites?; and
3. What is the expected range of total PCB and solids mass eroded from cohesive
sediments throughout TIP?
Section 6.2 describes the data available to support the depth of scour model.
Section 6.3 describes how data from resuspension studies of Hudson River
sediments were used to define parameter values and characterize uncertainty in the
scour predictions. Section 6.4 provides predicted ranges for depth of scour at each
of the five Phase 2 high resolution coring sites, and compares these ranges to
observed PCB concentration profiles. Section 6.5 provides global computations for
total mass of PCBs and solids remobilized from cohesive sediments throughout TIP.
This analysis focuses strictly on cohesive sediment areas because: (1) cohesive
sediment areas are considered to encompass most of the known PCB "hotspots";
and (2) available Hudson River resuspension experiments (conducted specifically to
allow parameterization of Lick's erosion equation for cohesive sediments) allow
greater confidence to be placed in resuspension estimates from cohesive areas than
from non-cohesive areas.
6.2 Available Data
The construction and application of the TIP Depth of Scour Model requires a
wide variety of system-specific data. Table 6-1 contains a detailed description of
the data and information requirements for the hydrodynamic and Depth of Scour
models for TIP. For each sub-model, Table 6-1 outlines the data requirements, its
purpose, origin, form, and availability. For purposes of discussion in this section,
the data will be divided into categories of: (1) bottom sediment distribution; and (2)
resuspension experiments. It should be noted that Release 2.3 of the
TAMS/Gradient database does not include the low resolution core data from TIP or
the high-frequency water column TSS data from Spring of 1994.
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6.2.1 Bottom Sediment Distribution
The bedded sediments in TIP were delineated as cohesive and non-cohesive
based on the side-scan sonar profiles of fine and coarse sediments
(TAMS/CADMUS/Gradient, 1996 - pending publication). The area of non-cohesive
sediments in TIP is approximately five times that of cohesive sediments. The PCB
distributions were obtained from kriging analysis of the 1984 NYSDEC sediment
survey data (TAMS/CADMUS/Gradient, 1996 - pending publication). Results of
these analyses are available as a surficial coverage and a vertically-integrated
coverage. Inventories of PCBs in the cohesive and non-cohesive areas were
computed from the vertically integrated coverages. The surficial coverage indicates
the average concentration in the top 30 centimeters of the sediments. Values fcr
this surficial coverage were used to specify sediment total PCBs in the TIP depth of
scour model.
Based on the vertically-integrated coverage, the inventory of PCBs in the
cohesive areas was 3208 kg, as compared to 7974 kg in the non-cohesive areas.
These areas represent a total inventory of 11.2 metric tons (MT) of total PCBs in
TIP. The total inventory of PCBs in the entire TIP area is approximately 14.5 MT.
.lie difference is due to the fact that the kriging analysis interpolates over the
entire TIP area, i.e. areas in addition to that designated as cohesive and non-
cohesive. Those areas, primarily rocky and/or unmapped by the side scan sonar,
are not planned to be simulated. In addition there are minor differences in the
procedures employed in truncating the GIS coverages to the TIP shore line. A
discussion of the total PCB inventory of the Thompson Island Pool can be found in
the Data Evaluation and Interpretation Report (TAMS/CADMUS/Gradient, 1996 -
pending publication).
6.2.2 Resuspension Experiments
The data used to parameterize the Depth of Scour Model for TIP sediments
were obtained from resuspension experiments described in HydroQual (1995). This
report contained two different sets of experimental data. The first dataset came
from an annular flume study, where sediments from three different locations in TIP
were transported to a laboratory at the University of California at Santa Barbara and
subjected to two types of experiments involving shear stress. Multiple shear stress
tests were conducted by filling the flume with sediment, allowing it to compact for
1, 3, or 14 days with the flume at rest, and running (i.e., rotating) the flume at
successively higher levels of shear stress, with steady state suspended sediment
concentrations achieved (as indicated by concentration measurements at 30 minute
intervals) before each shear stress increase. A continuous flow test was conducted
by filling the flume with sediment and running it continuously for 47 days at a shear
stress of about one dyne/cm2, except that on several days the shear stress was
increased to 5 dynes/cm2 for two hours, and one multiple shear stress test similar
to those described above was conducted.
6-2
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The purpose of these experiments was to investigate the effects of bed
compaction and to estimate the value of the critical shear stress. Based upon these
laboratory flume experiments, HydroQual (1995) concluded that: the critical shear
stress was approximately 1 dyne/cm2, the maximum time since deposition (td) was
7 days, and the exponent, n, for td was 0.5. These parameter values were directly
used in the analysis described below.
The second set of sediment resuspension measurements described in
HydroQual (1995) consisted of field studies using a portable resuspension device,
commonly called a shaker. Surficial sediment cores were collected and brought to
shore at 20 locations in TIP and 8 locations downstream; each location had one
(TIP) or two (downstream) sets of three cores each. Each core was subjected to a
shear stress in the shaker and the resulting resuspension potential was determined.
The field study produced 107 resuspension potential-shear stress data pairs for the
Hudson River, with 60 measurements specific to TIP. The shear stresses used in
the field study ranged from 5 to 11 dynes/cm2. Observed sediment erosion rates
in TIP ranged from 0.06 to 28.84 mg/cm2.
From the TIP-specific data, HydroQual (1995) assumed a TIP-wide constant
value of 3 for m, and back-calculated core-specific values for a0 necessary to
produce the observed erosion. The methodology used to determine the value for m
was not provided. They reported a mean value and standard deviation for a0 of
0.071 (in units of mg- day1/2/cm2) and 0.062 respectively, not including some
excluded values.
6.3 Model Parameterization and Uncertainty
This section describes how data from resuspension studies of Hudson River
sediments were used to define parameter values for the scour equations presented
in Section 3.7, and characterize total uncertainty in the scour predictions. It begins
with a description of rearrangement of the erosion equation to allow parameter
estimation, discusses the parameter values obtained, and concludes with a
discussion of prediction uncertainty.
6.3.1 Rearrangement of Erosion Equation
As discussed in Section 3.7.2, a formulation known as Lick's equation
(Gailani, et al., 1991) has been used to predict erosion as a function of shear stress
for fine-grained cohesive sediments:
£ = -r( -) (6-1)
fd T c
where s is the total amount of material resuspended (g/cm2); td is time after
deposition; and Oq, n, and m are empirical constants.
6-3
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If the value of rc is known or assumed, while the other parameters are
unknown, then Lick's equation can be reduced from five parameters to two using a
dimensionless shear stress parameter r':
s = A(x (6-2)
where
r' = (r-rc) / zc,
A = Vtd"
Equation 6-2 can be linearized as follows:
ln(e) = ln(A) + (m)ln(z') (6-3)
Therefore, a linear regression may be performed to fit a straight line to data
for erosion vs. dimensionless shear stress in log-log space. The slope obtained
from this regression will correspond to the exponent m from Lick's equation, while
the intercept will correspond to the log of the lumped term a(/td". Characterization
of the distribution of errors around this regression will also provide an estimate of
the uncertainty in erosion predictions.
6.3.2 Parameter Estimation
All statistical analyses were conducted using SYSTAT Version 6.0 for
Windows, and only data from TIP were considered. A linear regression of natural
log erosion (in mg/cm2) vs. natural log r' produced a constant (i.e. intercept) value
of -3.829 and a slope value of 2.906. Of 60 TIP data points, two outliers were
deleted; 58 data points were used. The outliers were identified solely on the basis
that their studentized residuals were too large (absolute value greater than 3.0).
The outliers were: (1) erosion = 0.06 at shear stress = 5; and (2) erosion = 0.47
at shear stress = 11. The regression R-squared value was 0.541. p-values for
both the regression constant and the slope were <0.00001. An analysis of the
residuals strongly indicated that they could be assumed to be normally distributed.
It was concluded on the basis of these and other statistical indications that the use
of linear regression was supported by the data.
The value of 2.906 obtained for m is similar to the value of 3 reported by
HydroQual (1995). Assuming from the flume studies that the maximum time since
deposition (td) was 7 days, and the exponent, n, for td was 0.5, the lumped term
corresponds to a value of a0 of 0.0575. This value is within the uncertainty of the
value shown above as reported by HydroQual.
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6.3.3 Prediction Limits
Given a regression line with normally distributed residuals, prediction limits
for new observations (for a given value of the independent variable) fall on a
Students t-distribution (Neter et al., 1985). For large sample sizes, the Students t-
distribution is approximately normal. Predicted values for new observations were
therefore calculated as percentiles of normal distributions, in log-log space. The
resulting predicted distribution in ordinary space (again, for given values of shear
stress) is log-normal, and is easily calculated. The final step was to divide the
erosion (in mg/cm2) by the bulk density (in mg/cm3) to get the depth of scour in
cm. A value of 1462 mg/cm3 was used for the bulk density, based upon observed
site data (TAMS/CADMUS/Gradient, 1996 - pending publication).
Predictions based on transformations can be subject to transformation bias
when a single number is used to characterize the distribution of values; for
example, the mean of logarithmically transformed data is usually not equal to the
log of the mean of the data. This model avoids bias by reporting percentiles of the
distribution of predicted observations. There is a one-to-one correspondence
between the percentiles of a log-normal distribution in normal space and the
percentiles of its log-space normal distribution.
There are several assumptions inherent to this analysis. Some of these
include:
The value for critical shear stress and, to a lesser extent, the values for
time since deposition and the exponent on time since deposition)
observed from the annular flume studies apply throughout TIP
The statistical model is valid for extrapolation to higher values of shear
stress than were used experimentally
The bulk density, at a specific location, used for converting erosion to
depth of scour can be represented as a single number.
In reality, these assumptions are likely to be violated to some extent. They
are unavoidable, however, if predictions based upon the data are to be made,
regardless of method.
6.4 Depth of Scour Predictions at Selected Locations in Cohesive Sediment Areas
As part of the Phase 2 high resolution sediment coring study, the
TAMS/Gradient team collected five sediment cores in TIP. The availability of
detailed measurements of sediment physical-chemical properties at these five
locations created the opportunity for a finely resolved analysis of resuspension
potential in TIP.
6-5
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Prediction of the expected range of scour depth at each core required a
mixture of pool-wide and location-specific data, On a pool-wide basis, the depth of
scour model described in Section 6.3 was assumed to apply equally to all five
sediment cores. Location-specific inputs consisted of predicted shear stress at
each coring location, and sediment bulk density measured for each core. Table 6-2
lists all location-specific input values for each of the five cores.
Table 6-3 contains summary results for each of the five sediment core
locations. Results indicate that Core HR-25 is the most likely of the five locations
to erode significantly. Cores HR-26 and HR-20 are also susceptible to some
erosion, while cores HR-23 and HR-19 are much less susceptible. The predicted
median depths of scour for the for the five locations range from less than 0.03 (HR-
23) to approximately 2 cm (HR-25). The third and fourth columns in Table 6-3
show the range of predicted scour depths encompassing the middle 90 percent of
expected values (i.e. 5th to 95th percentile) for each core location.
Predicted median depth of scour provides information on quantities of solids
that can potentially resuspend during an event; however, it provides incomplete
information on quantities of PCBs that can potentially resuspend. The last column
i Table 6-3 contains the observed depth of the total PCB peak at each of the five
core locations. By comparing predicted median depths of scour and observed
depths of PCB peaks, a more complete picture emerges of potential PCB erodability.
For example, results indicate that Core HR-25 is likely to experience scour of
sufficient magnitude to substantially erode the total PCB peak at that location.
Total PCB peaks at the other four locations are predicted to be unscoured; i.e. the
total PCB peaks are likely to stay intact after a 100-year flood event.
Figures 6-1 through 6-5 show the observed total PCB profiles with depth for
each of the five sediment cores. Three different scour horizons are also depicted
on the figures, corresponding to the 5th percentile, median, and 95th percentile.
Core HR-25 is the only core location at which the total PCB peak is predicted to be
significantly eroded by the 100-year event. Based on the nature of the core profile
at this location, this area appears to be a high energy area subject to strong
sediment-water interactions. This interpretation is consistent with the incomplete
and fractured nature of the core profile. The sediments in this area represent
transient rather than old bedded sediments and may contain PCBs from more recent
upstream sources. In summary, the above analysis suggests that cohesive
sediment areas in TIP are most likely to be depositional areas that remain relatively
less disturbed during major flood events.
The above probabilistic analyses are specific to the location of the five Phase
2 high resolution sediment cores, and the 100-year flood event. Results from these
analyses do not constitute estimates of uncertainties for other cohesive sediment
areas in TIP, or for flood events with different return periods. Figure 6-6 shows the
generic envelope of scour predictions based on the Lick resuspension sub-model for
6-6
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a wide range of applied shear stresses. Figure 6-6 indicates that the 90%
prediction interval (i.e. 5th to 95th percentile) provides an envelope spanning
somewhat less than two orders of magnitude. Figure 6-6 can be used as a
nomograph in interpreting large scale scour projections based on the GIS
visualization studies.
Finally, results in Figure 6-7 represent the application of the generic scour
predictions in Figure 6-6 to the specific locations of the five Phase 2 high resolution
sediment cores for the 100-year flood event. Results in Figures 6-1 to 6-5
correspond to the predicted depth of scour at the 5th, 50th and 95th percentiles
for each of the five core locations. Results in Figure 6-7 illustrate the predicted
chances of scour over the entire probability range for each of the five cores for the
100-year flood event. Consistent with the above results, Core HR-25 is predicted
to be the most susceptible to event-driven erosion, and Core HR-23 is predicted to
be the least susceptible.
6.5 Global Results for Cohesive Sediment Areas
Plate 6-1 shows the site map of Thompson Island Pool and is the base map
of reference for the GIS-based erodability maps which follow. Plate 6-2 shows the
delineation of TIP sediments into cohesive and non-cohesive areas. The potential
for erosion of cohesive sediment-associated PCBs in TIP was investigated for a set
of five design flows ranging from 8,000 cfs to a maximum of 47,330 cfs, the 100-
year flood event. These event flows are based upon the Log Pearson flood
frequency analysis for the Fort Edward Gauge conducted by Butcher (1993).
Table 6-4 contains design flows and the mean values of corresponding
velocities and shear stresses (cohesive sediments only) predicted by the TIP
hydrodynamic model.
Plate 6-3 shows the distribution of steady-state velocities in TIP as predicted
by the TIP hydrodynamic model for the 100-year flood event (Section 5.7). Plate
6-3 and subsequent plates depict only the normal river channels, i.e. flood plain
conditions are not represented. The nodal values representing the output from the
hydrodynamic model were interpolated using a triangulated irregular network (TIN)
to yield smoothed estimates of the velocities and shear stresses for display. All
computations and intermediate grid calculations were, however, performed at the
nodal locations of the hydrodynamic model. This is necessary as the nonlinear
nature of the computations result in different mass estimates if the averaging is
conducted prior to or subsequent to the computations.
Most of the flow around Rogers Island occurs in the western channel and
consequently high velocities are depicted. For the 100-year event, velocities in the
eastern channel are less than 2 fps indicating (qualitatively) that the potential for
scour is considerably smaller on that side of the island. Velocities are found to be
higher in the region spanning Rogers Island to the confluence with Snook Kill as
6-7
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compared to areas further downstream. Velocities immediately downstream of the
Snook Kill confluence show a region with elevated velocities between 3 and 4 fps.
Part of the sediments (Plate 6-2) in this region are cohesive and are thus potential
regions of high scour. Further downstream the channel east of Billings Island and
to the south also shows high velocities ranging up to a maximum of 5 fps. Plate 6-
4 shows the corresponding shear stresses (dynes/cm2) for the 100-year event.
For the 100-year event, Plate 6-5 shows the mass of solids eroded from the
cohesive sediments in TIP. The numbers represent a total scour for each grid cell
(kg/event), normalized to kg/m2. Significant scour can be discerned along the
shores of the river about a half mile downstream of the southern tip of Rogers
Island. Another large scour pocket can be discerned just north of Billings Island on
the eastern side of the river channel. Moderately high scour is also visible on the
eastern shore of Billings Island near the south end.
Plate 6-6 shows the depth of scour (cm) corresponding to the mass of
cohesive solids eroded shown in Plate 6-5. The largest scour depths are
approximately 2.5 cm. The influence of tributary flows on sediment water
interactions in the main channel in the vicinity of the confluence cannot be
estimated since tributary flows were not included in the hydrodynamic model
calibration.
For the same 100-year event, Plate 6-7 shows the mass of PCBs eroded
from the cohesive sediments in TIP. The numbers represent a total scour for each
grid cell (kg/event), normalized to gm/m2. Table 6-5 contains results for total
masses of solids and PCBs eroded from cohesive sediment areas in TIP. The total
reservoir of PCBs in the cohesive areas of TIP was estimated as 3208 kg, based on
a kriging analysis of the 1984 NYSDEC sediment survey (TAMS/CADMUS/Gradient,
1996 - pending publication). Table 6-5 indicates that only approximately one
percent of this reservoir is predicted to erode for the 100-year event. The predicted
median depth of scour for this event is only 0.16 cm. Results in Table 6-5 indicate
that the cohesive sediment areas of TIP will experience little, if any, scour even for
large events. Since the cohesive sediment areas encompass most known "hot
spots" (as defined by 1978 NYSDEC survey), the TIP resuspension model predicts
that these localized areas of high contamination will not be affected significantly by
large flood events.
It is significant to note that the predicted mass of total PCBs eroded from the
cohesive sediment areas of TIP during a 100-year event (25 kg) is less than the
total external PCB loading across the upstream boundary of the HUDTOX model at
Fort Edward during the entire period of simulation from January 1 to September 30,
1993 (354 kg).
6-8
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Plates 6-8 to 6-27 show predicted results for the four design flow events
other than the 100-year event. For these events, the TIP resuspension model
predicts that between 0.001 and 0.3 percent of the total PCB reservoir in the
cohesive sediment areas of TIP will be remobilized (Table 6-5).
The mass of PCBs eroded from non-cohesive sediment areas of TIP was not
estimated in this preliminary calibration effort for two principal reasons: first, lack
of a current spatial inventory of PCBs in the non-cohesive areas; and second, the
Lick erosion equation is only applicable to cohesive sediments. At the present time,
the most spatially-resolved data for sediment PCBs in TIP are from the 1984
NYSDEC sediment survey. These data were used to specify sediment PCB
distributions in both the cohesive and non-cohesive sediment areas. Consequently,
all predictions of PCB resuspension in TIP in this report are premised on the
assumption that these 1984 sediment PCB distributions are representative of
present-day conditions. This assumption is probably more valid for cohesive
sediment areas than for non-cohesive sediment areas because, as the above results
indicate, lower shear stresses tend to occur in cohesive areas and higher shear
stresses tend to occur in non-cohesive areas. Thus it is likely that PCBs associated
with non-cohesive sediments in TIP have been more disturbed by high-flow events
during the past 10 years than PCBs associated with cohesive sediments.
As part of the future modeling work (Appendix B), the TIP Depth of Scour
Model will be expanded to include non-cohesive sediment areas. This task will
begin with a detailed characterization of TIP sediments in terms of particle type,
particle size distribution, clay content, porosity and total PCB concentration.
Results from this characterization will be used to develop a finer-scale horizontal
segmentation grid for both cohesive and non-cohesive sediment areas. Each of the
sediment segments in this grid will be characterized by a unique set of values for a
suite of physical-chemical parameters, including the proportional distribution of total
solids mass into multiple particle size classes.
Using the best available information from the scientific literature, critical
shear stresses will be estimated as a function of the physical characteristics and
particle size classes in each segment. Given a set of segment-specific physical-
chemical properties and applied shear stresses, total masses of eroded solids and
PCBs, and depths of scour, will be estimated for cohesive and non-cohesive
sediment types in each segment. These results will be summed to form cumulative
gross erosion estimates for TIP, or they will be used to characterize different
sediment areas within TIP with respect to erodability.
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7. APPLICATION OF LOWER HUDSON RIVER PCB TRANSPORT AND
FATE MODEL
7.1 Introduction
Lower Hudson River modeling was conducted using the existing model
application of Thomann et al., (1989, 1991). This section contains a summary
description of the model application; a more detailed description is provided in
Thomann et al., (1 989). The section is divided into sub-sections discussing:
Model Input Data
Internal Model Parameters
Applications Approach
Diagnostic Analyses
Sensitivity Analyses
Discussion.
7.2 Model Input Data
The Lower Hudson River modeling application required model input data
describing many characteristics of the site. These consisted of:
System-specific Physical Data
External Loadings
Forcing Functions
Boundary Conditions
Initial Conditions.
7.2.1 System-Specific Physical Data
The primary inputs concerning system-specific physical data were model
segmentation and geometry. Model segmentation for the physico-chemical and
food chain models were discussed previously in Section 3.8.3, with segment maps
provided in Figures 3-11 and 3-12. Model segment geometry for physico-chemical
model segments one through ten, as well as geometry for the New York Bight and
Long Island Sound, was determined from National Ocean Survey charts (Thomann
et al., 1989). Geometry for the remaining segments was calculated by aggregating
segment geometry for the NYC 208 Model (Hydroscience, Inc. 1978b).
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7.2.2 External Loadings
The application of the Lower Hudson model required loading estimates for
several parameters, including: cesium, solids, and PCB homologues. Cesium was
used during model calibration as a tracer variable to help calibrate solids settling
and resuspension velocities. Three sources of cesium loads were considered in the
original Lower Hudson application: (1) atmospheric deposition; (2) Indian Point; and
(3) river inputs from the Upper Hudson. Atmospheric loads were estimated from
90 137 90
Sr areal loading rates from Bopp and Simpson (1984), and a Cs: Sr ratio of
1.59. Indian Point loads were derived from the estimates of Wrenn et al., (1972)
and Jinks and Wrenn (date not given). River inputs from the Upper Hudson were
estimated from sediment core cesium data from the Albany Turning Basin (to
provide an annual estimate of each year's particulate cesium concentration) and a
correlation between annual stream flow and solids loading.
Solids loads were estimated from numerous sources. Riverine inputs from
Connecticut were taken from Farrow et a'., (1986). New York/New Jersey riverine
and runoff inputs were based upon a flow-solids correlation for the Hudson River at
Waterford. This resulted in an assumed concentration of 150 mg/l. The remaining
solids loading estimates were based upon published reports as follows:
New York/New Jersey Treated Wastewater (Mueller et al., 1982)
Connecticut Treated Wastewater (Farrow et al., 1986)
Barged Solids (Mueller et al., 1976)
Dredge Spoils (Olsen et al., 1984).
PCB loadings were required by the model for each homologue. These were
determined by first calculating total PCB loads for each source on an annual basis,
then estimating the fraction of the total load comprised by each homologue. Total
PCB loads from the Upper Hudson were calculated in three different ways,
depending upon the time period of concern: (1) 1946-1959; (2) 1959-1975; and
(3) 1976-1987. Loadings for the period 1946 through 1959 were calculated by
linearly interpolating between an assumed zero load for 1945 and the loading
calculated for 1959. PCB load estimation for 1959 through 1975 followed the
same procedure used for cesium. Sediment core data were analyzed to estimate
particulate phase PCB concentrations for each year, and total PCB loading was
estimated from observed solids loading. PCB loads for the period 1976-1987 were
based upon USGS data obtained through NYSDEC.
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7.2.3 Forcing Functions
Forcing functions included in the Lower Hudson model application consisted
of:
Advective flows
Horizontal dispersion coefficients
Vertical diffusion coefficient
Fish migration patterns.
Two types of advective flow patterns were used in the Lower Hudson model
application: (1) a constant hydrology model using a single long term average
circulation pattern; and (2) a variable hydrology model that considers yearly
variation in advective transport.
The constant hydrology model used average annual river flows for all
tributaries entering the Lower Hudson. A 4"-' 3,348 cfs enters the Hudson
River and exits into the Bight, with primary sources being the Upper Hudson,
Mohawk, Raritan, and Passaic Rivers. A total of 19,459 cfs enters (and exits) Long
Island Sound, of which the primary sources are the Connecticut and Housatonic
Rivers. Additional, but smaller, sources of water to the system include urban and
rural runoff, sewage treatment plant discharges, and discharges of raw sewage.
Reported current measurements are used to define a circulation pattern for the
Bight.
Horizontal dispersion coefficients for the Lower Hudson water column were
taken from previous model efforts (Hydroscience, Inc. 1975; Hydroscience, Inc.,
1978a; Hydroscience, Inc., 1978b). Vertical diffusion of interstitial dissolved PCB
concentrations were set to represent assumed molecular diffusivity levels.
The time variable hydrology model categorized the variability of flow
conditions as low, average, or high, at any point in time. The transport parameters
for the average flow condition were identical to those described above for the
constant hydrology model. The low flow condition corresponded to the lowest
quartile annual average flow (73 percent of the long term average), while the high
flow condition corresponded to the highest quartile. Horizontal dispersion
coefficients were adjusted by +/-30 percent in the high and low flow years,
respectively.
Since the detailed model segmentation used in the physicochemical model
was found to result in excessive computational times, the study area was divided
into five regions for the food chain modeling. These were based upon the assumed
migration patterns of the striped bass. Region 1 corresponded to the Upper Hudson
Estuary from River Miles 153.5 to 78.5, (physicochemical model Segments #1-8).
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Region 2 covered River Miles 78.5 to 18.5 (physicochemical Segments #9-14) and
was termed the Mid-Lower Estuary. Region 3 corresponded to New York Harbor
(Segments 15, 16, 24, 25, 26). Region 4 corresponded to the remaining study
area of New York Bight and Long Island Sound. Migration patterns were based
upon the work of Waldman (1988a, 1988b). A fifth region was defined to cover
the open ocean. PCB concentration was assumed to be zero in this region. The
youngest striped bass year classes (0-5) spend the majority of their lives in the Mid-
Lower Estuary. Older striped bass (>6 yrs.) reside primarily in the open ocean, with
spring time migrations into the Mid-Lower Estuary
7.2.4 Boundary Conditions
The Lower Hudson physicochemical model requires specification of several
constituents at the downstream boundary of the model domain. These include
salinity, cesium, solids, and PCB homologues. Salinity concentrations were
determined from annual mean surface and vertical contours from Bowman (1977).
A boundary concentration of zero was used for cesium. Solids boundary conditions
were set at 0.5 mg/l in New York Bight (based upon Young and Hillard, 1984;
Biscayne and Olsen, 1976) and 1.0 mg/l in Long Island Sound (from Riley and
Schurr, 1959). A boundary concentration of zero was used for all PCB
homologues.
7.2.5 Initial Conditions
The two primary parameters for which time variable simulations were
conducted, cesium and PCBs, assumed initial conditions of zero throughout the
study area.
7.3 Internal Model Parameters
The Lower Hudson model required specification of numerous model
parameters. These included:
Solids settling velocity
Solids sedimentation velocity
Solids resuspension velocity
Cesium partition coefficient
Cesium decay rate
PCB partition coefficient
PCB volatilization rate
7-4
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PCB decay rate
Growth and respiration rates
Bioconcentration feature
Uptake and excretion rates.
The solids settling velocity was assumed as 10 ft/day throughout the study
area. Solids sedimentation velocities were estimated by an areal weighting of the
sedimentation regions given in Bopp (1979), and ranged from 0.025-0.5 cm/yr.
Net suspension rates were calculated for each segment using the above velocities
and the assumption of conservation of mass for the upper sediment layer.
A salinity dependent cesium partition coefficient was used to account for the
saturation of sorption sites by potassium in sea water. The cesium partition
3 5
coefficient was also made solids-dependent, and ranged from 10 to 10 in the
water column and 10° to 103 in the surface sediments. A cesium decay rate of
0.2295/year was taken from the model of Bopp and Simpson (1984).
PCB partition coefficients in the water column were estimated for each
homologue as a function of octanol-water partition coefficients, fraction organic
carbon of suspended solids, and solids concentrations using the Particle Interaction
Model of DiToro (1985), following Thomann and Salas (1986). PCB partitioning in
the sediments include a third phase representing dissolved organic carbon (DOC),
such that the partition coefficient can be calculated as foc/DOC.
Volatilization of PCBs was assumed to be controlled by transfer across the
liquid film; such that a constant volatilization rate could be used across all
homologues. This volatilization rate of 0.6 m/day was based upon an assumed
oxygen transfer rate of 1.0 m/day and consideration of the ratio of the molecular
weight of PCBs to that of oxygen.
The PCB decay rate was conservatively set to zero for both the water
column and sediments, in light of conflicting data on expected decay. Sensitivity
analyses were conducted to determine model response to different assumptions
regarding PCB decay rates in sediments. The results of these analyses are
discussed in Section 7.6.2.
The food chain model requires specification of growth and respiration rates
for each compartment, including: zooplankton, small fish, white perch, and striped
bass. Zooplankton growth and respiration rates were based upon published data
for Gammarus, and correspond to a growth rate of 0.1/day and respiration rate of
0.06/day. The small fish compartment (representing fish of approximately 10g
weight, e.g., smelt and pumpkinseed) were based upon generalized weight-
7-5
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dependent relationships. A value of 0.00631/day was used as the growth rate and
0.0227 for the respiration rate. Age and weight-dependent growth rates were
calculated for white perch based upon the work of Mansueti (1957) and Bath and
O'Connor (1982). A weight-dependent respiration rate was calculated following
the work of Thomann and Connolly (1984) and Connolly and Tonelli (1985).
Age-dependent growth rates for striped bass were calculated from the
observed change in average weight class, using the data of Setzler et al., (1980)
and Young (1988). Respiration rates were calculated from the equations of
Thomann and Connolly (1983) and Connolly and Tonelli (1985).
Bioconcentration factors (BCFs) were required for all of the food chain model
compartments discussed above, as well as for phytoplankton. A constant (across
homologues) BCF of 30 l/g(w) was used for phytoplankton, based upon the work of
Oliver and Niimi (1988). For the remaining compartments, BCFs were calculated
based upon the assumption that the lipid-normalized BCF was equal to the octanol-
water partition coefficient for each homologue.
Chemical uptake from the water column is calculated as a function of the
respiration rate, oxygen concentration, and efficiency of transfer across the gill
surface. Homologue-specific transfer efficiencies were based upon the work of
Thomann (1989). Excretion rates were calculated as the quotient of the uptake
rate from the water column and the BCF.
Chemical assimilation efficiency from food was determined on a homologue-
specific basis, following the work of Thomann (1989). These assimilation
efficiencies were held constant across all trophic levels.
7.4 Application Approach
The Lower Hudson modeling effort followed a multiphase application
approach:
Salinity Calibration
Suspended Solids Calibration
Cesium Model Calibration
Physicochemical Model Calibration
Food Chain Model Calibration.
The salinity model application was used to validate model transport
processes, including the horizontal dispersion coefficient. The suspended solids
model calibration was used to validate solids settling, resuspension, and deposition
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velocities. Cesium was used as an additional calibration variable to test the
model's ability to simulate the behavior of a particle-based contaminant with a well
defined loading history to the Lower Hudson. The physicochemical model
calibration was used to test the model's ability to simulate total PCB concentrations
in water and sediment. The food chain model calibration was used to test the
model's predictive capability of total PCB concentrations in white perch and striped
bass.
7.5 Application Results
This section provides a summary description of the results of the applications
described above. A more complete description of results is given in Thomann et
al., (1989). The transport model was run to steady state using long-term average
hydraulic conditions and compared to data from two sources: Hydroscience
(1978b) and Olsen (1979). As seen in Figure 7-1, the model compares well to the
Olsen (1979) data, but predicts a downstream displacement compared to the
Hydroscience (1978b) data. Similar comparisons were also made to the
Hydroscience (1978b) data collected in Long Island Sound and the Newark Bay-
Arthur Kill-Raritan Bay-Apex transact. ~r' .del calibration was judged
satisfactory given the paucity of long term data. The suspended solids calibration
(Figure 7-2) generally overpredicted the observed data; this was attributed to the
fact that sampling data was biased towards exclusion of high flow (and
consequently high solids) periods.
The cesium calibration was conducted for the period 1954 to 1983. Model
results were compared to annual average data for dissolved cesium in Segment 12
(on a temporal basis) and to the spatial distribution of particulate cesium for the
mid- to late-1980s. The model approximately reproduces temporal and spatial
trends in the observed data.
The PCB calibration consisted of summing the results of computed
concentrations for each homologue and comparing this total to observed total PCB
data. Figure 7-3 shows the spatial distribution of model results for 1978 compared
to observed data for the period 1977-1979. The model typically falls within the
range of the observed data and generally reproduces the observed spatial trends,
although the variability in observed data is large. The PCB calibration for sediments
consisted of comparison to both surficial and sediment core data. The comparison
for spatial sediments in shown in Figure 7-4, and is similar to the water column
comparison in that model results fall within a wide range of observed data. Figure
7-4 shows sediment core data for Segments 1-5, compared to two sets of model
results which assumed high and low flow hydrology. Model predicted profiles
generally fall below the observed core data, although core data were taken from
discrete locations that may not represent segment-average conditions.
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The food chain model results were compared to white perch and striped bass
data collected over time in model Region 2. As seen in Figure 7-5, white perch
results for Region 2 were consistent with the limited observed data. A more robust
temporal dataset was available for striped bass (Figure 7-6). The model comparison
to this data shows the annual average and range of model results, with the model
accurately describing temporal trends in data.
7.6 Diagnostic Analyses
Limno-Tech conducted component and sensitivity analyses for the Lower
Hudson River model. There are two main purposes for running component and
sensitivity analyses on the model. First, the analyses provide increased
understanding of model behavior by characterizing the importance of individual
model mechanisms. Second, with this understanding, it will be possible to estimate
changes in Lower Hudson River water, sediment, and fish PCB concentrations in
response to changes in model inputs, without performing additional model
simulations.
7.6.1 Component Analysis
An analysis was conducted in which mass balance components for the
exposure model and the bioaccumulation model were evaluated in terms of rates of
change of total homologues PCB concentration. The exposure model components
that were investigated are loading, net advection, net water column dispersion, net
settling, and volatilization. Degradation was assumed not to occur, and water-
sediment dispersion was assumed to be negligible. Segments 2, 15, 17, and 28
were examined as representative water column segments. Bioaccumulation model
components are uptake (water column only), consumption (food only), loss (actual
release of PCBs), and total loss (loss including growth dilution). Food chain model
Regions 2, 3, 4, and 5 are examined.
The component analysis for the exposure model shows the net settling and
volatilization are consistently important components, while loading, net advection,
and net dispersion vary widely in importance in different segments. The
component analysis for the bioaccumulation model shows that consumption is the
dominant component in model Region 2 (upstream) while loss is dominant in
Regions 3, 4, and 5 (downstream).
Table 7-1 summarizes the results of the component analysis of the exposure
model as the relative magnitudes of the processes of loading, net advection, net
dispersion, net settling, and volatilization.
Table 7-2 summarizes the results of the component analysis of the
bioaccumulation model as the magnitudes of the processes of uptake,
consumption, loss, and total loss. Four example striped bass year classes (0, 2, 6,
and 17) are used for each food chain model Region from 1 to 5.
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Consumption is the dominant component in Region 2 while loss is dominant
in Regions 3, 4, and 5. Uptake is small in all regions. Total loss, which includes
growth dilution, is appreciably larger than loss for earlier year classes but not for
the later ones.
7.6.2 Sensitivity Analysis
Exposure model sensitivities were computed separately for both dissolved
and total PCBs for net settling, biodegradation, external loading, upstream load, and
volatilization. All parameters were set to +/- 50 percent of baseline values, except
for biodegradation. Because biodegradation is zero at baseline, a "high" value and a
one-tenth high value were compared to baseline. Segments 2, 15, 17, and 28
were examined as representative water column segments.
Table 7-3 summarizes the results of the exposure model sensitivity analysis.
This table shows that the sensitivities of both dissolved and total PCBs are
identical. The sensitivity analysis for the exposure model shows that PCB
concentrations are not sensitive to settling. Additionally, Segments 2 and 1 5 were
found to be only slightly sensitive to loading, while Segments 17 and 28 were
found to be slightly sensitive to the upstream load. PCB concentrations were very
sensitive to biodegradation, loadings, upstream load, and volatilization, excepting
the segments identified above.,
Bioaccumulation model sensitivities were computed for bioconcentration
factors, respiration rates, growth rates, PCB assimilation efficiencies, and dissolved
concentrations. Each of these parameters weie set to +/- 50 percent of baseline
value, except for growth rates and assimilation efficiencies. Growth rates were set
to + /- 10 percent of the baseline value since + /- 50 percent would be physically
unrealistic, and assimilation efficiency was set to +/- 0.2 from the base fraction.
Food chain model Regions 2, 3, 4, and 5 were examined and the results are
summarized in Table 7-4.
The sensitivity analysis for the bioaccumulation model shows that
bioaccumulated PCB concentrations in Regions 2 through 5 are very sensitive to
bioconcentration factors and PCB assimilation efficiency, are quite sensitive to
respiration and dissolved concentrations. However, PCB concentrations are shown
to be only slightly to moderately sensitive to growth rates.
Based on the results of these sensitivity analyses, it appears that in general,
the model results will be reliable if accurate data are used for the following inputs:
biodegradation, loadings, upstream load, volatilization, bioconcentration factors,
PCB assimilation efficiency, respiration and dissolved concentrations. Additionally,
since the model is not sensitive to settling and growth rates, it will likely produce
reliable results even if these data are uncertain.
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7.7 Discussion
The Lower Hudson modeling application was unique in that an existing model
application was used in place of new model development for purposes of this
study. Subsequent to the original model application, some of the PCB loading
estimates to the Lower Hudson were called into question. For example,
(TAMS/Gradient, 1991) states that the original modeling likely overestimated the
PCB loadings from the Upper to the Lower Hudson. This was due to a claimed high
flow bias in using PCB concentration and river discharge measurements taken at
the USGS monitoring station in Waterford, New York. The TAMS/Gradient report
implies that an estimate of about 13,000 kg transported past Waterford from the
Upper to the Lower Hudson for the period 1977-83 is superior to an estimate of
about 19,000 kg derived from the Thomann et al., (1989) report. Using the
Waterford corrected mean loads for 1977 through 1983 in Table B.4-4 of the
TAMS/Gradient report gives a total of 12,400 kg, which is 35 percent below the
19,000 kg estimate. The report also implies that load estimates for years before
the 1977-1983 period were overestimated in the Thomann et al., (1989) report,
but it does not provide an alternative figure.
Limno-Tech investigated the effect that an upstream loading discrepancy of
the magnitude suggested by the TAMS/Gradient report would have on predicted
concentration profiles and the validity of the original model calibration. The Lower
Hudson model was run with a 35 percent reduction in upstream loading across the
board from 1946 to 1983. Figure 7-7 shows the original (Thomann et al., 1989)
model results along with model results using the revised loading from the Upper
Hudson. Although the revised loading estimates result in a significant change in
computed results, both sets of computed results fall within the range of the
observed data. Thus, this loading discrepancy does not necessarily invalidate the
model calibration.
Other sources of data collected subsequent to the development of the
existing Lower Hudson model conflict with some of the assumptions contained in
the model. Additionally, regular collections of striped bass by NYSDEC between
River Miles 88 and 1 53 demonstrate that both adults and juveniles migrate into
Region 1. Incorporation of these new data into the Lower Hudson modeling
framework may improve its predictive ability.
EPA understands that as of September 1996, the Thomann model is being
updated under a grant from the Hudson River Foundation, and that certain
modifications have been made to the previously-published model. EPA is evaluating
whether the updated model will be available or appropriate for use in this Hudson
River RI/FS.
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8. MODELING APPROACH: FISH BODY BURDENS
8.1 Modeling Goals and Objectives
The goal of this component of the modeling effort is to develop a framework
for relating body burdens of PCBs (expressed as Aroclor equivalents, individual
congeners or total PCBs) in fish to exposure concentrations in Hudson River water
and sediments. This framework is used to understand historical and current
relationships as well as to predict fish body burdens for future conditions. Estimates
of PCB body burdens in fish are intended to be used for human health and
ecological risk assessments and aid in decision making regarding options for
addressing PCB-contaminated sediments in the upper Hudson.
The objectives of the body burden modeling effort are based on discussions
with the investigators responsible for human health and ecological risk assessments
and with the fate and transport modeling team. Because PCB analytical protocols
have varied over time, the framework needs to account for historical as well as
current data to the extent possible. Accordingly, the framework is structured to
meet the following objectives:
1. relate historical body burden data (as PCB Aroclors and Aroclor totals) to
exposure concentrations in water and sediments;
2. relate current and future body burdens (as PCB Aroclors, totals, and
individual congeners) to exposure concentrations in water and sediments;
3. provide estimates in a form that can be used for human health risk
assessments;
4. provide estimates in a form that can be used for ecological risk assessments;
and,
5. provide a set of modeling tools that can be coupled with the output from the
PCB fate and transport models to evaluate future management goals.
To meet these objectives, two modeling approaches have been developed to
relate body burdens to water and sediment concentrations. One - used with the
historical PCB Aroclor database - is referred to as the Bivariate Statistical Model.
The other - derived using historical and current data - is referred to as the
Probabilistic Bioaccumulation Food Chain Model. In each case, the model relates
PCB exposure concentrations in water and sediments to body burdens. The major
difference between the two approaches is that the Bivariate Statistical Model uses
available time series data to develop statistical relationships between
concentrations in water and sediments and those in fish while the Probabilistic
Bioaccumulation Food Chain Model relies upon feeding relationships to link body
burdens to water and/or sediments.
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The two approaches complement one another. Each utilizes derived
Bioaccumulation Factors (BAFs). The agreement between these and the resultant
estimates of body burdens provide a check on the two approaches. It is anticipated
that there will be some modeling applications for which the Bivariate Statistical
Model is the better tool and other applications where the Bioaccumulation Model
will provide the desired information. The Bivariate Model provides mean body
burden estimates while the Probabilistic Model explicitly incorporates feeding
preference data and uncertainty and variability information around the mean
estimate. By incorporating observed variability in the underlying data, the
Probabilistic Model provides a context for the results of the Bivariate Model. While
the Bivariate Model provides mean estimates, the Probabilistic Model provides
population statistics, such as what percent of a fish species population is expected
to experience concentrations at or below a specified level.
In addition, a third approach is being explored which is not described in this
report but will be part of the next task. This approach involves a modification of
the Gobas (1993) food web model. This model relies on the theory of fugacity, or
chemical potential, and is focused specifically on food digestion and absorption in
the gastrointestinal tract. The model incorporates both sediment and water
sources, but, similar to the probabilistic model presented here, relies on prey
consumption and food web dynamics to describe the uptake of PCBs. The Gobas
model also incorporates uncertainty and variability information.
Selection of fish species for modeling body burdens was based on several
criteria including: 1) importance for fishing, 2) abundance, 3) importance in diet of
other fish, 4) representative of particular habitats or trophic levels, and 5)
representative of other fish species. Upon discussion with NYSDEC, USEPA, and
NOAA the following species were selected for bioaccumulation modeling:
Fish Species
Characteristics
Spottail Shiner
Forage Fish, Feeds on invertebrates in water column and sediments
Pumpkinseed
Forage Fish, Feeds on invertebrates in water column (on aquatic plants)
and to a limited degree sediments; popular recreational fish but seldom
eaten
Brown
Bullhead
Lives in contact with sediment and feeds on a variety of animal life on or
in the sediments; can be fished recreationally and is eaten occasionally
Yellow Perch
Inhabits water column and feeds on invertebrates and small fish; popular
recreational fish and is commonly eaten
Largemouth
Bass
Larger individuals feed primarily on fish but will also eat other
vertebrates and invertebrates; popular recreational fish and is commonly
eaten
White Perch
Feeds on invertebrates and small fish; lives in the tidal portion of the
Hudson; undergoes migrations within the river
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Ecological profiles for the selected fish species are provided in Appendix A
and are used to discern behavioral and trophic characteristics that could affect
accumulation of PCBs.
The Bivariate Statistical Model uses pumpkinseed, brown bullhead,
largemouth bass and white perch. Sufficient historical data were not available for
the other species on this list; however, cyprinids were added to the statistical
analysis.
In addition to the fish species listed above, the striped bass is included in the
evaluation. However, no new models have been developed for this species. A major
confounding factor is that the striped bass are a migratory species that are resident
in the river for only a portion of the year. As such, it is inappropriate to assume
that all PCB exposure occurs within the Hudson River, and under the current
modeling framework, this is a key assumption. The modeling program relies upon
the work of Thomann to derive estimates for striped bass. It wculd be desirable to
have a model for the short nose sturgeon, an endangered fish species in the tidal
portion of the Hudson. However, data are insufficient to develop a model for this
species. It is anticipated that a species-to-species extrapolation will be employed to
_.aluate the short nose sturgeon, based on physiological, feeding and habitat
selection characteristics.
8.2 Background
8.2.1 PCB Compounds
This report examines bioaccumulation of Aroclors for the historical datasets
and selected congeners for the Phase 2 dataset. A challenge to developing a
modeling framework for PCB bioaccumulation is that PCBs consist of 209 individual
congeners, each of which exhibit varying degrees of bioaccumulation potential,
depending on the degree and substitution of chlorination. The more highly-
chlorinated congeners tend to accumulate in fish tissues. This effect may be a
function not of increased uptake, but rather decreased elimination efficiency from
the fish.
Until recently most environmental studies of PCB contamination measured
only complex mixtures or total PCBs. Much of the historical PCB data are reported
as Aroclors, mixtures comprised of various congeners, some of which are
accumulated more effectively than others. While Aroclors accurately describe
commercial PCB mixtures, they may be poor descriptors for PCB mixtures in fish
and environmental media. This can pose limitations on model development, as
discussed in subsequent sections.
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Studies that have measured PCBs as individual congeners have provided
insights into the bioaccumulation processes for water column- and sediment-based
communities. Several researchers have noted that whether or not total PCB levels
increase with position in the food chain, chlorine content of PCB body burdens
tends to increase (Smith et al., 1985; Oliver and Niimi, 1988; Van der Oost et al.,
1988; MacDonald et al., 1993). Congener patterns of caged fathead minnows and
feral brown bullhead from the area around Thompson Island Pool in the Hudson
River were generally similar, sharing 60 percent of their 20 most abundant peaks,
but the bullhead had higher concentrations of hexa- and heptachlorobiphenyls
(Jones et al., 1989). The fish contained 17 peaks that were not detectable in
water samples. It has been noted that when young bluefish enter the Hudson River
from offshore, heavier, more chlorinated congeners were accumulated to a greater
level than lighter, less chlorinated congeners (LeBlanc and Brownawell, 1994).
A variety of factors control accumulation of PCB congeners (Shaw and
Connell, 1984; Jones et al., 1989; Kadlec and Bush, 1994; Ankley et al., 1992;
LeBlanc and Brownawell, 1994):
1. Individual PCB congener characteristics, including solubility and partition
coefficients, degree of chlorination, and stereochemistry. Shaw and Connell
(1984) found that more planar molecules are more strongly absorbed that
those with more regular shapes.
2. Characteristics of the fish, including lipid content of gills, blood, and tissue,
cardiac output, ventilation volume, gill surface area, epithelium layer of gill,
aqueous stagnant layer of gill, ability to biotransform PCBs, excretion rates.
3. Environmental factors, including temperature, pH, light, current, suspended
particles, dissolved organic compounds.
8.2.2 PCB Accumulation Routes
Fish and other aquatic animals are exposed to PCBs through direct contact
with water (bioconcentration), and sediment, as well as through dietary sources
(bioaccumulation). Due to their hydrophobicity, PCBs tend to accumulate in the
lipid portion of organisms. PCBs have also been found to accumulate in predatory
fish tissues at higher concentrations than the concentrations in the surrounding
water would predict (Thomann and Connolly, 1984), a process known as
biomagnification. Depending upon the position of an aquatic organism within the
aquatic food web, exposure may be intensified through food sources as organisms
consume other organisms that have bioaccumulated PCBs in the lipid portion of
their tissues. Because of the important role of food as an exposure pathway, the
feeding ecology of a fish species is a key aspect in distinguishing between the
relative contribution of the water column and sediments to body burdens of PCBs.
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Direct Uptake from Water
For fish, direct uptake of PCBs from water occurs primarily across the gills.
No significant evidence exists for absorption through the epidermis (Shaw and
Connell, 1984).
The significance of direct uptake from water of PCBs has been debated.
Based upon laboratory studies, Shaw and Connell (1984) argued that uptake via the
gills was the major route or accumulation of PCBs. Some field studies have
indicated that water column uptake could account for PCB concentrations observed
in biota, if PCB concentrations were normalized for lipid content of the organism
(e.g., Clayton et al., 1977).
Other researchers have continued to examine the potential for
bioconcentration through the gills to account for PCB concentrations. Caged
rainbow trout that were fed clean, commercial food appeared to accumulate PCBs
directly from contaminated waters of the St. Lawrence River (Kadlec, 1994; Kadlec
and Bush, 1994). Barron (1990) noted that simple evaluations of uptake directly
from the water column have assumed that bioconcentration is controlled by the
hydrophobicity of the compound, as measured by its octanol-water partition
coefficient. He argued that bioconcentration appears to be independent of octanol-
water partition coefficients when the coefficient is small or when the molecule to
be accumulated is large. He summarized other factors that affect bioconcentration:
molecular shape, degree to which the compound is bound to dissolved organic
matter, lipid content of the gills, size of the organism, blood flow, variations in
enzyme content and activity, and exposure temperature and ionic content.
Uptake via Food
Field studies and modeling efforts have indicated that biomagnification
through the food chain is an important component for bioaccumulation. Sloan et
al., (1984), for example, suggested that the presence of higher chlorinated Aroclor
mixtures in fish of the Lower Hudson River might reflect a food chain component to
bioaccumulation. Using existing field data, Thomann (1981, 1989) derived steady-
state food chain models, considering uptake of contaminants from both water and
food sources through several trophic levels. The models indicated that food
assimilation, excretion, and net weight gain were important characteristics that
determined bioaccumulation levels. They also demonstrated that for top predators,
such as Hudson River striped bass, almost all the observed PCB body burden could
be attributed to a food source. In Lake Michigan lake trout, only 2 to 3 percent of
the PCB accumulation could be predicted from water column concentrations using
an age-dependent model (Thomann and Connolly, 1984), while transfer through the
food chain accounted for up to 99 percent of the body burden of PCBs in Lake
Michigan lake trout.
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Many researchers have tested, refined, or elaborated upon Thomann's food
chain models. One test of the approach examined PCB accumulation in young-of-
the-year bluefish which enter the Hudson River Estuary from relatively
uncontaminated offshore waters and grow quickly (LeBlanc and Brownawell,
1994). Connolly et al., (1985) considered growth rates, respiration rates, food
assimilation efficiency, predator-prey relationships, PCB assimilation efficiency, and
bioconcentration factors for PCBs when they applied a model to existing data from
the Hudson River system. They predicted PCB levels in Hudson River striped bass,
assuming various reductions in concentrations of PCBs in the water column. They
also began efforts to incorporate lipid- and non-lipid components of the striped bass
into the model. Pizza and O'Connor (1983) conducted laboratory experiments to
determine rates of PCB accumulation from the gut and elimination from the body in
young-of-the-year striped bass from the Hudson River. An EPA model, Food and
Gill Exchange of Toxic Substances, or FGETS, has been used to predict average
concentrations of contaminants in the food web over time (e.g., Woolfolk et al.,
1994). This model incorporates bioconcentration of contaminants from the water
column and biomagnification in the food chain.
Gobas et al., (1993) examined the roles of food digestion, food absorption,
and rates of gill elimination and metabolic transformation upon bioaccumulation.
This model has recently been updated to include exposure from both water and
sediment sources, and a Monte Carlo-based uncertainty analysis. A further aspect
to the work presented here will be to develop the Gobas model for use on the
Hudson River. This will provide a check on the models presented here and may
provide further insight into the role of water versus sediment in forage fish and
piscivorous fish exposures.
Uptake from Sediments
Equilibrium partitioning has been suggested to be the major factor controlling
bioaccumulation in sediment-based benthic communities. Bierman (1990) used field
data from the Great Lakes to determine that for animals at the lower and middle
parts of the food chain, including oligochaetes, chironomids, amphipods, sculpin,
small smelt, and large smelt, predicted bioconcentration factors based upon
equilibrium partitioning coefficients accounted for concentrations of hydrophobic
organic compounds. Comparing laboratory and field data, Ankley et al., (1992)
confirmed that for oligochaetes, concentrations of PCBs in the sediments could be
used to predict concentrations of PCBs in organisms, but that for other species,
food or possibly ingestion of contaminated particles could affect concentrations.
Ingestion of contaminated food also seemed to be a factor in accumulation of PCBs
in a freshwater lake (Van der Oost et al., 1988).
A steady-state food chain model with a benthic invertebrate component was
developed to account for both water column and sediment sources of contaminants
(Thomann et al., 1992). This model considered four exposure routes for ingestion
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of particulate contaminants: sediment organic carbon, overlying plankton, interstitial
water, and overlying water. Applying the model to an amphipod-sculpin food web
in Lake Ontario (Oliver and Niimi, 1988), Thomann and his co-workers (1992)
found that accumulation was based primarily upon a benthic food web rather than
upon direct uptake from the water column. They noted however, that including the
overlying water and phytoplankton as a food source were necessary to explain the
field data. Considering only interstitial water and sediment particles as contaminant
sources was not satisfactory.
8.3 Theory for Models of PCB Bioaccumulation
The Bivariate Statistical and Probabilistic Food Chain Models share a common
theoretical basis including:
1. PCB body burdens in fish are related ultimately to exposure concentrations in
water and/or sediments;
2. PCBs in the water column and sediments are not necessarily in equilibrium
with each other;
3. Within the water and sediment compartments, an equilibrium or quasi -
steady-state condition exists at temporal scales on the order of a year and
spatial scales on the order of a river segment;
4. Fish body burdens are in gaas/'-steady-state with the water and/or sediment
at time scales on the order of one or more years.
PCB concentrations measured in biota are assumed to be in steady state with
PCBs in the environment for the development of bioaccumulation factors (BAFs),
and thus can be related by linear coefficients or bioaccumulation factors similar to
partitioning coefficients. A steady-state condition is usually considered to hold
within a given year; thus the BAF approach represents temporal changes only
annually. The simplest approach considers that biota and all environmental
compartments are in equilibrium with one another, in which case the concentration
in any medium can be predicted from the concentration in any other medium. The
BAF method is readily modified to address situations in which a disequilibrium
exists at steady state between different environmental compartments.
Consider first a completely equilibrated system: Fish may accumulate PCBs
through partitioning from the water column, through ingestion of sediment, or
through the food chain, while organisms at lower trophic levels may also
accumulate PCBs from both water column and sediments. Describing exact
accumulation pathways is the task of food web models, but concentrations in any
medium or "compartment" in a fully equilibrated system can be predicted from
those in any other compartment. As PCBs partition strongly to organic matter and
have low solubility, the major environmental reservoir is typically the sediment.
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"Partitioning" from sediment to biota is conceptually similar to equilibrium
partitioning from sediment and pore water as well as from sediment to the water
column. Thus, for an equilibrated system, dissolved concentrations in sediment
pore water might provide a good index of the bioavailable component. Typically,
analytically resolving truly dissolved and DOC-complexed fractions is a very difficult
task for pore water samples, but, for lipophilic compounds in sediments with typical
organic carbon contents, partition coefficients are such that the mass present in
dissolved and DOC-complexed forms is relatively insignificant compared to the total
particulato-sorbed mass. This implies that the dissolved portion can be quite well
predicted from the sediment-water partition coefficient, regardless of DOC levels.
On the other hand, pore water concentrations vary significantly in response to
sediment organic carbon fraction (foe). Therefore, sediment concentration
normalized to foe is the best readily available predictor of dissolved concentrations
in an equilibrated system (Di Toro et al., 1991). This approach is being used by
EPA's Office of Water for establishing sediment quality criteria (USEPA, 1991).
Of course, PCBs may enter the food c^.ain both through the dissolved phase
and ingestion of particulate matter. As Di Toro et al., state, "biological effects (to
invertebrates) appear to correlate to the interstitial water concentration. This has
been interpreted to mean that exposure is primarily via pore water. However, the
data correlate equally well with the organic carbon-normalized sediment
concentration. This suggests that the sediment organic carbon is the route of
exposure. In fact, neither of these conclusions necessarily follow from these data."
The reason for this surprising conclusion is contained in fugacity, or chemical
potential theory, which holds that the biological activity of a contaminant is
controlled by its chemical potential (Mackay, 1979). As discussed by Di Toro et
al., if pore water and organic carbon phases of the contaminant are in equilibrium
then the chemical potentials exhibited by the two phases are equal. "Hence, so
long as the sediment is in equilibrium with the pore water, the route of exposure is
immaterial. Equilibrium experiments cannot distinguish between different routes of
exposure." Thus, in the simplified equilibrium case, it is necessary to estimate the
chemical potential in only one phase. The question then becomes determining
which phase is easiest to measure. Where DOC complexing occurs, sediment
concentration normalized to foe is clearly the most directly measurable index of
chemical potential.
Fish may accumulate PCBs via pathways which arise in the water column as
well as from the sediment. The simple equilibrium BAF approach works if sediment
and water-column concentrations are in equilibrium with one another, or if all PCB
accumulation in fish derives from pathways commencing in the local sediment. On
the other hand, if fish accumulate PCBs from both water-column and sediment
pathways, and water-column concentrations are not in equilibrium with pore water
in the same locale, the full-equilibrium assumptions are not valid. In the Hudson
and other flowing rivers, it is likely that the upper sediment layer and the water
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column are generally not in equilibrium with one another for hydrophobic toxicants.
Further, the upper, bioactive sediment zone is typically not in equilibrium with
deeper, buried sediments. However, the sediment-sorbed concentrations and pore-
water concentrations within the bioactive zone should be very close to equilibrium,
while, in the water column, the dissolved and sorbed fractions should also be close
to equilibrium, except during transient events.
The equilibrium partitioning/fugacity arguments set forth by Di Toro et al.,
(1991) state that the best readily measurable index of chemical potential should be
the sediment sorbed fraction normalized to foe. This argument applies to both
sediments and water column. Both should be compared to the lipid-normalized
burden in the organism (Chiou, 1985), as 3AF estimates are best expressed on a
lipid-normalized basis (USEPA, 1994). BAF factors are expected to vary from
species to species with trophic level and foraging preferences. Variability may also
reflect differing lipid compositions, with correspondingly different rates of uptake of
lipophilic compounds, between fish species (Ewald and Larsson, 1994).
Preliminary analysis suggested that both water and sediment pathways may
be important for the accumulation of PCBs in Hudson River fish, and that water
column and sediment concentrations are not in equilibrium with one another.
TAMS/Gradient (1991) Phase 1 RI/FS analyses revealed that summer average
water-column concentrations appear to provide a good predictor of average PCB
burden in fish species, confirming earlier observations of Brown et al., (1985). This
could reflect a dominant role for water-column pathways, or simply an equilibrium
between water-column and pore-water PCB concentrations. A role for sediment
pathways is suggested by the observation that concentrations in fish in the
Thompson Island Pool appear to be elevated above those collected downstream at
River Mile 175 by a factor greater that the observed change in water-column
concentration. Water-column PCB concentrations in the Upper Hudson below
Thompson Island Dam do not appear to be in equilibrium with the upper level of the
sediment; for instance, TAMS/Gradient 1993 flow-averaged sampling indicated that
total PCB concentrations decline by about 40 percent between Thompson Island
Dam and River Mile 156.6 (Waterford), largely representing dilution. The decline in
surface sediment concentrations appears to be much more substantial: The GE
Sediment Sampling and Analysis Program (O'Brien & Gere, 1993a) revealed a
decline in average total PCB concentrations in the top 5 cm of sediment of 90
percent between Thompson Island Pool (River Mile 188.3 to 193) and the reach
from River Mile 155 and River Mile 170. In summary, below Thompson Island Dam
the water column is not in equilibrium with local sediments. Thus, models for
bioaccumulation need to consider both water and sediment pathways, rather than
relying on a BAF based on concentrations in a single medium.
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8.4 Bivariate Statistical Model for Fish Body Burdens
8.4.1 Rationale and Limitations for Bivariate Statistical Model
The Bivariate Statistical Model relies on the available time series of
environmental and fish data in the Upper Hudson to relate observed PCB
concentrations in fish to PCB levels in the water and sediment. If water and
sediment concentrations are not in equilibrium, a single BAF is not adequate;
instead bioaccumulation is controlled by the simultaneous effects of both water and
sediment concentrations. Thus, a statistical model with two independent variables
(water and sediment concentrations) is appropriate.
The development of statistical relationships is enhanced by the availability of
extensive historical monitoring data that enable comparison of PCB levels in fish
and the environment over time. The nature of these data, which consist primarily
of Aroclor-equivalent quantitations in the fish and total PCB estimates by packed-
column gas chromatography in the water column, however, constrains the
statistical approach. Although more recent studies by TAMS/Gradient, NOAA, and
GE provide congener-specific PCB measurements in all media, these data are limited
in that they (1) are available only for the 1990s, (2) represent only a small number
of individual samples for a given fish species, and (3) do not provide a time-series
perspective on the relationship between fish body burdens and environmental
concentrations.
Statistical relationships do not, of course, prove physical causality.
Statistical models that capture historic conditions are not guaranteed to predict
accurately future conditions, particularly if the characteristics of the PCB source
change over time. The Bivariate Statistical Model, however, is an important first
step for the development of more complex, food web models, for which the
database is limited. By reflecting historical trends, the Bivariate Statistical Model
provides important constraints on the form and parameterization of the food web
bioaccumulation model. The mean estimates provided by the Bivariate Model are
complemented by the explicit incorporation of the uncertainty around the means, as
provided by the Probabilistic Model.
8.4.2 Theory for Bivariate Statistical Models of PCB Bioaccumulation
The general theoretical framework for deriving Bivariate Statistical Models
was introduced in Section 8.3. The fact that the water and sediment
compartments are not in equilibrium with each other, but are approximately
internally equilibrated, suggests that bivariate BAFs that relate body burden to both
sediment and water-column chemical potential could account for bioaccumulation
pathways from both water and sediment. These bivariate BAFs could then be used
to predict fish body burdens for various combinations of water and sediment
exposure levels subject to the limitations described above. Correlating fish body
8-10
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burdens to both water and sediment removes the difficulty of disequilibrium
between the sediment and water compartments.
The Bivariate Statistical Model is essentially a 'black box' approach wherein
the details of exposure pathways and physiological processes are not specified but
the net effect is captured. The actual PCB concentration found in a given fish
depends on the cumulative effects of dietary/food chain accumulation, plus direct
accumulation from the water (and perhaps sediment), all balanced by species-
specific rates of depuration or metabolism. Net accumulation in a fish species thus
depend on all lower trophic levels. There are, however, only two main external
forcing functions, water and sediment PCB concentrations, which enable a 'black
box' model to be developed through statistical analyses with water and sediment
concentrations as input and fish burden as output. Detail on specific food chain
relationships is provided in Section 8 and Appendix A.
For steady-state concentrations in the environment, the net result of the
unspecified processes contained within the 'black box' is functionally equivalent to
a steady-state food web model. For instance, the simplified steady-state food web
^odel of Thomann et al., (1992) for Lake Ontario, which avoids the need for a
detailed study of population dynamics through steady-state assumptions, is
externally forced by water and sediment concentrations alone. It is thus equivalent
to a bivariate BAF relating fish body burden to water and sediment concentrations,
where the food web interactions determine the values of the two BAF factors.
Therefore, a bivariate regression relating average PCB body burden in a given
species (by location and year) to concentrations in local water and sediment
provides a useful tool for assessing bioaccumulation of PCBs by fish, for aiding in
the development of the Food Chain Model described in Section 8.5, and for
evaluating the output of that model.
As discussed in Section 8.3, fugacity theory indicates that chemical potential
is best estimated by the sorbed fraction in both sediments and water column,
normalized to foe. This suggests a regression analysis to predict fish PCB burdens
from environmental concentrations through species-specific relationships should
take the following form:
ft,
Bw, CS'
ft>cv
+
f°Cs
(8-1)
in which, for species
Cf - PCB concentration in fish (wet-weight basis)
fl = Lipid fraction in fish
Bw = Partial BAF relating fish concentration to water-column
concentration
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Csw
focw
Bs
PCB concentrations on suspended solids
Organic carbon fraction of suspended solids
Partial BAF relating fish concentration to upper-zone sediment
concentration
Css - PCB concentration in upper zone sediments (dry-weight basis)
focs = Organic carbon fraction of the sediments.
While this formulation is theoretically optimal, focw is not available in the historic
database for the Hudson River; as a result, Bw must be expressed on a whole-water
basis as a matter of practical necessity.
8.5 Probabilistic Bioaccumulation Food Chain Model
8.5.1 Rationale and Limitations
The Probabilistic Food Chain Models are developed to predict distributions of
PCB body burdens within the selected fish species. These models compliment the
Bivariate Statistical Models that predict single population statistics such as the
average values of PCBs. The Probabilistic Models have been developed to provide:
1. information on the fractions of the fish populations that are at or above
particular PCB levels; and
2. an empirical framework for constructing biologically-based food chain
relationships that explicitly incorporate variability and uncertainty inherent in
the underlying data.
PCB body burdens in Hudson River fish vary among individuals within a
species for any given reach of the river. This intra-species variability in
concentrations can be described as a distribution. The characteristics or shapes of
these distributions can be important for evaluating human health and ecological
risks. For example, two distributions may have the same average value but may
differ in spread, one having values distributed closely around the average, the other
including much higher as well as much lower values. The distribution with a greater
fraction of high values may pose a greater risk than the tighter distribution.
Probabilistic models that predict the characteristics of distributions provide risk
assessors with the information needed for making these evaluations. Probabilistic
models also provide a tool for quantifying the uncertainties associated with
estimating body burdens of PCBs.
The distribution of concentrations of PCBs within a species reflects a number
of factors that are also variable. These include the composition of PCBs, spatial and
temporal exposure field of PCBs in water and sediments, the uptake and depuration
8-12
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rates of PCBs within and among trophic levels, and the feeding behavior and history
of the fish. Many of these factors are unknown or poorly known for the selected
Hudson River species. The approach taken in building the Probabilistic Food Chain
Models is to combine information from available measurements for the river with
knowledge concerning the ecology of fish species and the trophic relationships
among fish and invertebrates.
The models presume quasi steady-state conditions for which average annual
exposure concentrations in water and surface sediments change slowly relative to
the species uptake and depuration kinetics. The models are constructed by
identifying the major pathways linking individual fish species with sediment and
water components. These pathways include direct exposure as well as trophic
relationships. Within the models, each major pathway is represented as a transfer
or bioaccumulation factor. Using information on species' ecology, statistical
distributions for PCB transfer or bioaccumulation factors are developed among
media and biological components. These factors are derived from measurements of
PCB concentrations in various compartments and do not require assumptions about
kinetic processes, although it is assumed that fish will be in a quasi steady-state
with the environment. The transfer and bioaccumulation factors reflect the sum of
the underlying processes and are specific to Hudson River fish and environmental
conditions. The derived factors are compared to those in the literature for
reasonableness.
The models are designed to identify the relative contributions of PCBs in
Hudson River sediments and water to body burdens of the six selected fish species.
Because exposure to PCBs may occur via water column and sediments, it is
important to distinguish between these two media. Food is expected to be the
primary route of exposure for fish but direct uptake from water may also be
important depending on the specific chemical. In developing the models, the role of
direct water uptake versus food was examined.
Because of the important role of food as an exposure pathway, what and
where a fish eats are viewed as key aspects of distinguishing between the relative
contribution of the water column and sediments to a species' body burden of PCBs.
Some species feed predominantly on benthic invertebrates, others on water column
invertebrates, and still others on forage fish. Some species, such as the
largemouth bass, feed on all three components to varying degrees.
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8.5.2 Model Structure
The conceptual framework for the probabilistic PCB food chain models is
illustrated in Figure 8-1. Variables are identified in Table 8-1. A separate model is
developed for each fish species reflecting that species biology and available
information on PCB BAFs. These models can be developed for individual
congeners, homologue groups, Aroclors, or total PCBs. In this report, results for
the calibration congeners, Aroclors 1016 and 1254 and total PCBs are presented.
The calibration congeners were selected to represent a range of physical and
chemical parameters that drive fate and transport and uptake in the Hudson River.
However, the parameters of interest to risk assessors and site regulators are
generally Aroclors and total PCBs. The models are designed to evaluate quasi
steady-state conditions on an annual basis. The features of the models are:
1. Two groups of invertebrates are described: a) invertebrates that live within
sediments and feed primarily on sedimentary material (primarily deposit
feeders) and, b) invertebrates that feed primarily on organic particulate
matter transported in the water column (zooplankton, many epiphytic
invertebrates, and some filter feeding invertebrates).
2. Invertebrates in group "a" are presumed to reflect localized sediment
concentrations and to be in steady state with the sediments as described by
lipid and organic carbon normalized BAFs.
3. Invertebrates in group "b" are presumed to reflect PCB concentrations
associated with particulate material in the water column on an organic
carbon normalized basis. These invertebrates are presumed to be exposed to
PCBs associated with organic particulate material in the form of detritus or
algae. In the Hudson, it is presumed that both forms of organic material will
be important in the diets of invertebrates. The invertebrates that feed in this
manner are presumed to be in steady state with temporally averaged water
column concentrations of PCBs on an organic solids basis as described by
organic carbon normalized BAFs.
4. In most cases, the models are designed to estimate body burdens in adult
fish. These larger fish are the ones important for human health risk
assessment. In addition, because the primary population-level risk of PCBs
to fish is reproductive impairment, body burdens in adults can be used in the
ecological evaluation. Because young fish of some species (e.g.,
Pumpkinseed sunfish) are important as forage fish, body burdens are
estimated for these juveniles. Fish fall into one of several types depending
on their foraging strategies. The species-specific models incorporate such
information and recognize the variability that exists among and within
species.
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5. The lipid normalized BAF factors between invertebrates and fish, and fish and
fish are represented by distributions derived from Phase 1 and 2 studies
carried out in the Hudson and from the literature. Values are derived for the
calibration congeners, Aroclors, and total PCBs.
6. The food chain models are designed to take as input the water and sediment
concentrations predicted by the fate and transport models described in earlier
sections. The key input parameter for sediments is the PCB concentration
normalized to sediment organic carbon. The key input parameter for the
water column is the PCB concentration in the particulate organic carbon
phase. These exposure concentrations can be provided as summer or annual
averages. Since feeding occurs primarily in the warmer months, the
probabilistic model has been developed using summer averages. It is
anticipated that the fate and transport models will provide input parameters
on a summer-averaged basis.
Based on the above, the following media and biological compartments are
identified: 1) water, 2) sediment, 3) water invertebrates, 4) sediment invertebrates,
r~) forage fish, and 6) the individual fish species. The relationships between fish
species and compartments are shown in Table 8-2.
The food chain models are designed to be implemented in one of three forms,
a) a Monte Carlo Spreadsheet Model, b) equations combining individual distributions
into cumulative distributions, and c) a nomograph or look-up table.
For the Monte Carlo Spreadsheet Model, the relationships among
compartments and the distributions for BAFs are incorporated into an Excel
spreadsheet with a Crystal Ball software add-in. Excel is a standard spreadsheet
and provides the basic computational framework. Crystal Ball software permits the
input data to be represented as distributions rather than single point values; the
software also enables Monte Carlo analyses to be performed. The species-specific
Excel/Crystaj Ball spreadsheet incorporates uncertainties in exposure
concentrations, food chain transfers, foraging behavior, and lipid content. Monte
Carlo operations yield cumulative distributions of body burdens on a lipid normalized
and whole fish basis for each species. Key variables in the Probabilistic Model are
represented by a distribution of values rather than a single point estimate (such as a
mean or upper-bound value). Monte Carlo simulation is a means of sampling from
these distributions within a computational framework. Generally, the greater the
number of simulations, the lower the standard error associated with the mean. In
developing the Probabilistic Model, Monte Carlo simulations were run a minimum of
10,000 trials.
The distributions are representative of variability in the data as described in
subsequent sections. The distributions can also represent uncertainty, for example,
by providing a range of feeding proportions rather than single values. However, in
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the analyses presented here, the derived distributions are representative of data
variability.
8.5.3 Spatial Scale for Model Application
The river segments used to assess exposure to fish are the same as those
used in the HUDTOX fate and transport model. For most fish species, these model
segments are expected to encompass the exposure zones for fish that may be
caught in a particular segment of the river. The primary zone of exposure for most
fish species is presumed to be the summer foraging areas. Fish are expected to
obtain most of their PCB body burden via food. Profiles for the species (Appendix
A) indicate most of the feeding occurs during the warmer periods of the year. On a
relative basis, little feeding occurs in the winter. Therefore, the summer foraging
areas are where most of the fish species' exposure occurs. Because most of the
selected fish species exhibit limited spatial movements during the summer, foraging
areas and exposure zones can be highly localized. A notable exception is the white
perch, a semi-anadromous species that migrates over stretches of the river.
The HUDTOX model provides single mean values for sediment and water for
each of the segments. In some segments, there may be little spatial variability
around this mean. This is probably the case in the lower Hudson. However, for
other segments - including Thompson Island Pool - there are strong spatial gradients
in sediment concentrations (and perhaps water) that reflect differences in sediment
type as well as locations of "hot spots". Thus actual exposures may vary greatly
around the mean. Different fish species will also tend to forage over particular
sediment types further complicating the ability to represent the exposure field.
These factors probably contribute to the observed variability in fish body burdens in
Thompson Island Pool and will be a source of uncertainty in predicting the
distribution of fish body burdens when model estimates of water and sediment are
available only for mean conditions.
8.5.4 Temporal Scales for Estimating Exposure to Fish
Exposure concentrations for water and sediments are estimated as summer
averages (May through September). This averaging period is coincident with the
time that fish are at their summer foraging areas.
8.5.5 Characterizing Model Compartments
Sediment to Benthic Invertebrate Compartment
This compartment of the model relates the concentrations of PCB in benthic
invertebrates to sediment concentrations of PCB. It assumes that the PCB levels in
the invertebrates are related directly to levels in the surrounding sediments. This
relationship is represented by an empirically-derived biota sediment accumulation
factor (BSAF) that reflects the combination of passive and/or active
8-16
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bioaccumulation mechanisms occurring in the sediments. PCB uptake into benthic
invertebrates appears to be the result of partitioning between the organic carbon of
the sediments and the lipid of the invertebrate species (Bierman, 1990). This
relationship is a simple ratio:
BSAF = (8-2)
C^sedimenl
where,
BSAF = biota - sediment accumulation factor
Cbenthic - the concentration of PCB in an organism as ug/g lipid
Csediment = the concentration of PCB in sediments as ug/g organic carbon
Particulate Water Column:Water Column Invertebrate Compartment
The particulate water column to water column invertebrate exposure
pathway is important because water column invertebrates represent the single
largest dietary contribution to the forage fish and several larger fish species,
including white and yellow perch. This exposure route also has direct implications
for the exposure of higher order piscivores, such as largemouth bass, through the
invertebrates to forage fish to piscivore pathway.
The particulate phase in the water column represents the primary dietary
contribution to water column invertebrates (zooplankton and invertebrates that live
on the surfaces of rocks or aquatic plants). Because these invertebrates comprise a
significant portion of the diet of forage fish and some game fish, the food chain
model is sensitive to the BAF values used to represent this compartment. Other
studies have indicated the importance of this food chain transfer step (Oliver and
Niimi, 1988; Skoglund, 1996).
Individual PCB congeners can be strongly associated with either the truly
dissolved phase in the water column or the particulate phase. These differences
average out to some extent when considering total PCBs. The Data Evaluation and
Interpretation Report (TAMS/CADMUS/Gradient, 1996 - pending publication)
provides estimated partition coefficients for a number of key congeners. These
results are summarized in Table 8-3 for the calibration congeners. Clearly, the
fraction of PCB concentrations associated with the particulate phase increases with
increasing chlorination. For the lighter chlorinated congeners, bioaccumulation is
driven primarily by direct uptake from the dissolved phase in the water. For the
higher chlorinated congeners, consumption of particulate matter represents the
route of greatest bioaccumulation.
8-17
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Under the assumption that the majority of water column PCBs are associated
with particulate organic carbon, we evaluated the relationship of water column
invertebrates to the particulate phase in the water column as:
PWBAF = Cmver/Coc
(8-3)
where,
PWBAF = The bioaccumulation factor between water column
invertebrates and particulate bound PCB
Cimert - m9 PCB per Kg lipid in invertebrate tissue
= mg PCB per Kg organic carbon in suspended particulates.
Forage Fish Compartment
Several of the fish species selected for modeling consume other, smaller
forage fish of which there are numerous species in the Hudson. Rather than
quantify PCB concentrations in individual forage fish species, the model assumes
that piscivorous fish will consume any species less than 10 cm. This assumption is
supported by forage fish abundance data for the Hudson River from the literature as
well as piscivorous fish gut analyses (MPI, 1984). A composite forage fish
compartment has been developed that reflects the composition of forage fish in the
Hudson and the feeding habits of these fish. The details of how the forage fish
compartment was derived are presented in Appendix A. The analysis indicated that
Hudson River forage fish are composed of species that feed to varying degrees on
invertebrates in the water column and in the sediments. When the relative
abundance and feeding behavior of the species are taken into account, the
composite forage fish diet is comprised of approximately 67% water column
invertebrates and 33% sediment invertebrates. All piscivorous fish that feed on
Hudson River forage fish are assumed to be preying on species that - on average -
feed on water column and sediment invertebrates in these percentages.
The forage fish bioaccumulation factor (FFBAF) is defined as:
C
FFBAF = -/
(8-4)
where,
FFBAF = forage fish bioaccumulation factor
concentration in composite forage fish (ug per g lipid)
weighted average of diet concentration (^g per g lipid)
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Piscivorous Fish Compartments
Adult piscivorous fish eat a combination of forage fish and invertebrates.
Since forage fish concentrations are derived primarily from water column
invertebrate concentrations, it is assumed that direct ingestion of water column
invertebrates by piscivorous fish is encompassed in this step. In the model,
therefore, piscivorous fish PCB body burdens are quantitatively related (in varying
degrees, depending on the fish species) to the benthic invertebrate and forage fish
boxes.
The piscivorous fish under consideration include largemouth bass, white
perch and yellow perch. These species also feed upon invertebrates, which can
represent from 10% of the diet in adult largemouth bass to 85% of the diet in the
case of yellow perch. The piscivorous fish bioaccumulation factor (BAF) is defined
as:
BAF = piscivorous fish bioaccumulation factor relative to diet
£fish = concentration in piscivorous fish (^g per g lipid)
Cdiet = weighted average of diet concentration (^g per g lipid).
In the case of yellow perch, the weighted average in the diet is expressed as
15 percent forage fish, 20 percent benthic invertebrates and 65 percent water
column invertebrates. The largemouth bass diet is 90 percent forage fish and 10
percent benthic invertebrates.
Demersal Fish
The final category of fish to be considered are the demersal or bottom-
feeding fish. The best species to consider for this box is the brown bullhead, which
feeds primarily from the bottom. Brown bullhead lipid-normalized concentrations
were compared to benthic invertebrate lipid-normalized concentrations as well as
sediment TOC-normalized concentrations.
The BSAF for brown bullhead is defined as:
BAF =
C,,
diet
(8-5)
/here,
BSAF = ^
(8-6)
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where,
BSAF = brown bullhead bioaccumulation factor
CBB = concentration in brown bullhead per g lipid)
CSed ~ concentration in the sediment (/jg per g carbon).
The dietary bioaccumulation factor is defined as:
BAF =
C
invert
where,
brown bullhead bioaccumulation factor
concentration in brown bullhead (fjg per g lipid)
concentration in Lenthic invertebrate (,ug per g lipid).
BAF =
Cfish
r -
^ invert
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9. CALIBRATION OF BIVARIATE STATISTICAL MODEL FOR FISH
BODY BURDENS
As described in Section 8, two parallel tracks were pursued for modeling
bioaccumulation of PCBs in fish in the Hudson River: a statistical model, based
entirely on evaluation of observed data from the Hudson; and a food web model,
which incorporates toxicokinetic, physiologic, and trophic level constructs. The
two efforts are complementary: The statistical model calibration, presented in this
section, aids in interpretation of historic data and provides a foundation for
calibrating the food web model,
9.1 Data Used for Development of Bivariate BAF Models
Equation 8-1 presents an idealized formulation for developing bivariate BAF
models. Actual implementation is constrained by data availability. Among other
issues, quantitation methods used for fish are not directly equivalent to those used
for water, and water column organic carbon fraction has not regularly been
monitored. Establishing the spatial/temporal history of sediment concentrations
also presents difficulties.
Statistical development of a bivariate BAF requires a sufficiently large range
of data (over differing environmental conditions in space and/or time) to distinguish
accumulation originating from water column and sediment pathways. While there
is evidence for disequilibrium, sediment and water concentrations are still correlated
with one another. As a result, the impacts of each individual source on fish
become more difficult to distinguish, and a larger database is required to determine
bivariate BAF factors than would be required for a single BAF. Data and methods
used for development of the BAF models are described below.
9.1.1 Fish Data
The analysis is based on NYSDEC fish data from the Upper Hudson River
below Fort Edward coupled with NYSDEC data from the uppermost part of the
Lower Hudson River (above River Mile 142). Samples collected between River Mile
142 and 153 are from the freshwater portion of the Lower Hudson. The species
collected in this area are, however, largely the same as those collected in the Upper
Hudson, and PCBs in this reach are derived primarily from the Upper Hudson. It is
therefore appropriate to include samples between River Mile 142 and 153, thus
providing a larger database for analysis. Table 9-1 summarizes the present count of
samples available in the database.
The longest-running and most extensive sample data in the Upper Hudson
come from NYSDEC collections at River Mile 175 (between Schuylerville and
Stillwater) and at River Mile 153 (just below Federal Dam, and thus technically in
the Lower Hudson). A good representation over time is also available for River
9-1
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Miles 189-190 (Thompson Island Pool), and smaller amounts of data are available
at River Mile 160 (Waterford, above Federal Dam). The species for which the most
data are available are pumpkinseed (Lepomis gibbosus), largemouth bass (Microptes
salmoides), and Brown bullhead (Ictalurus nebu/osus). Lesser, but still extensive,
data are available for cyprinids or carp (Cyprio carpinus) and yellow perch (Perca
flavescens).
These species represent a range of trophic levels, habitat preference, and
foraging behavior: Largemouth bass are piscivorous, with adults occupying the top
of the aquatic food chain. Yellow perch represent an intermediate trophic level,
foraging on invertebrates and small fish. Unlike largemouth bass, yellow perch are
migratory. Pumpkinseed are at a lower trophic level: they feed primarily on
invertebrates and are an important food source for larger fish. Cyprinids are also at
a lower trophic level, feed primarily on invertebrates in the water column, and also
consume detrital algae. Brown bullhead are omnivorous bottom feeders, with diet
including offal, waste, small fish, mollusks, invertebrates, and plants. Feeding
preferences also vary with the age and size of the individual. The profiles of
selected species are addressed in greater detail in Appendix A. Thus, a range of
trophic positions and forage preferences are available for analysis in the historic
data.
Raw data for the NYSDEC Hudson River fish analyses through 1988 were
summarized in the Phase 1 report. Through the 1992 sampling, there are a total of
10,599 fish analyses available in the TAMS/Gradient database, of which 3,432
were collected between River Miles 142 and 194. Table 9-2 summarizes available
lipid-normalized PCB data for the most frequently sampled species in the database.
Tables 9-3 and 9-4 summarize data by year and location for pumpkinseed,
largemouth bass, brown bullhead, cyprinids and yellow perch. For these tables,
Aroclor concentrations have been corrected to a basis consistent with the
quantitation method in use from 1983 on, as described in Section 9.1.2.
The bivariate statistical model development used all data for these species
collected between River Miles 142 and 194. Most of these samples were collected
in late spring (April - June), but some samples were collected in different seasons.
Sampling time for individual species as well as target size and age groups have also
varied somewhat from year to year. These differences likely contribute to
variability in observed PCB body burden, but are not addressed in the statistical
analyses, except for correction to concentration on a lipid basis. The more complex
probabilistic bioaccumulation model (Section 10) provides a more sophisticated
treatment of these and other factors which affect observed PCB body burden in
Hudson River fish.
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9.1.2 Standardization of PCB Results for NYSDEC Fish Analyses
Valid interpretation of historical trends in PCB concentrations cannot be made
without consideration of the changes in analytical methods which have occurred
over time. That is, a comparison is valid only when there is consistency in what is
being measured. The most dramatic change in analytical methods is that between
the Phase 2 TAMS/Gradient data, using state-of-the-art, capillary-column, PCB
congener analyses, and older analyses based on packed-column quantitation of
Aroclor equivalents. Because an Aroclor is a complex mixture of many individual
congeners, interpretation of the historic Aroclor data raises difficult technical
issues. In addition, Aroclor quantitation methods have changed over time, and
these changes have significant implications for the interpretation of historical trends
in the data and the development of valid statistical relationships.
The NYSDEC fish analyses report packed-column Aroclor quantitations and
percent lipid, so lipid-normalized Aroclor values may be calculated. Congener-
specific data are generally not available. Quantitations have consistently used
Aroclor 1016 and Aroclor 1254 standards; an Aroclor 1221 standard was used
through 1990, but not thereafter. Reported detection limits range from 0.01 to 1.0
ppm wet weight for each Aroclor, with detection limits for most samples at 0.1
ppm. An Aroclor 1242 standard was not used, despite the fact that most GE
releases to the river were Aroclor 1242. Aroclor 1242 is, however, similar in
composition to Aroclor 1016, although relative weight percents of individual
congeners differ. Total PCBs have generally been calculated by NYSDEC as the
sum of Aroclor 1016 plus Aroclor 1254, because (1) 68 percent of the total
Aroclor 1221 results, and 55 percent of those between River Mile 142 and 196 are
reported as non-detects (versus less than 1 percent non-detects for Aroclor 1016
and Aroclor 1254 in this section of the river); (2) Aroclor 1221 quantitations are
not available for later data; and (3) when Aroclor 1221 is detected, substantial
double-counting may occur between quantitations to Aroclor 1016 and Aroclor
1221 standards.
The NYSDEC quantitations to Aroclor standards were based on only a few
packed-column peaks, and are sensitive to the quantitation method used, which has
changed over time. Further, the mix of PCB congeners present in the environment
is not exactly equivalent to any fresh Aroclor or mixture of Aroclors: In particular,
there are dechlorination product congeners present in the system, and natural
partitioning or fractionation effects have also altered the mixture, with the more
strongly-sorbing congeners tending to remain in the particulate phase, while other
congeners move more readily into the water column and air. In biota, congener-
specific rates of accumulation and depuration further alter the mixture.
An interpretation of what was actually measured in historical packed-column
analyses can be made by converting the TAMS/Gradient Phase 2 fish congener data
to equivalent Aroclor measurements as if analyzed by NYSDEC methods. In this
9-3
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approach the congener concentrations are analyzed to deduce the packed column
peak areas which would have been measured by NYSDEC methods, followed be
estimation of the corresponding NYSDEC estimate of Aroclor concentrations.
According to the description of the NYSDEC method given by Sloan et al.,
(1984):
Quantitation was done by comparing several peak heights or areas to
those produced by the respective Aroclors. The principal peaks used
for quantitation include a single one for Aroclor 1221 representing a
monochlorobiphenyl; two for Aroclor 1016 reflecting mixtures of
trichlorobiphenyl; and three peaks for Aroclor 1254 primarily
composed of tetra-, penta- and hexachlorobiphenyl congeners.
While the NYSDEC method employs several peaks for Aroclor quantitation,
these are evaluated via a single composite response factor. Given selection of m
packed-column peaks for quantitation, the reported Aroclor value is obtained as
The area within the selected packed-column peak is related to the sum of the
concentrations of individual PCB congeners associated with those peaks by
congener peak response factors:
[Aroclor}= ^area; ¦ RFS
V7-=i )
(9-1)
where,
area.
[Aroclor]
the reported concentration of the PCB Aroclor,
the area associated with packed-column peak j, and
a composite or net response factor defined as the
concentration of standard Aroclor injected divided by the
sum of the peak areas of the selected packed-column
peaks.
[tUIlgCLl
area, = >
J < p c
j=i i = i K-V,
^ [congene^]
(9-2)
where,
n
number of congeners associated with selected packed
column peaks,
9-4
-------
[congener^ = concentration of an individual PCB congener /' associated
with the selected packed column peaks, and
RFci = the response factor for congener /', defined as the
concentration of congener i in the Aroclor standard
divided by the peak area contributed by this congener.
Where the congener response factors within the peaks are relatively
consistent, this may also be approximated as
n
X [congener, ]
Xareaj * M (9-3)
j=i
where
RFp = area-weighted mean response factor for the selected packed
column peaks or their constituent congeners in a capillary
column analysis. RFp is defined as the concentration of the
Aroclor standard times the weight percent of PCB congeners
contained in the selected peaks divided by the peak area, or:
n
wt % peak j y wt % congener^
RFp = [Aroclorstd ] = [Aroclorstd ] ^
^ areak
Zareaj
j=l k = l
Substituting Equation (9-3) into Equation (9-1) yields
RF
[Aroclor]« 2^[congener;] - (9-4)
i=i RFp
Because the ratio of the response factors on the right-hand side of this
equation is equivalent to the inverse of the weight percent of total PCBs contained
in the selected packed column peaks, this simplifies to:
X[congenerj
[Aroclor] * ^ (9-5)
^wt%peakj
9-5
-------
where the denominator represents the total weight percent of the Aroclor contained
in the congeners making up the packed column peaks used for quantitation. The
relationship is only approximate, because the response factors of individual
congeners are not equal. Analyses of response factors in congener calibration
indicates, however, that there is a relatively small range of response factors among
the congeners which are included in peaks used by NYSDEC for quantitation of
each Aroclor and which are found at relatively significant concentrations in the
Hudson River. Thus, the simple approximation of (9-5) is judged to provide an
adequate basis for comparison.
The NYSDEC fish Aroclor quantitations used three different sets of packed-
column peaks for each Aroclor, with changes in 1979 and 1983 (John F. Brown,
Jr., personal communication to T.D. Gauthier, 1994). Beginning in 1983, a
consistent method has been employed. The peaks and associated congeners for
quantitation of Aroclor 1016 and Aroclor 1254 are shown in Table 9-5.
Translating between congener data and NYSDEC Aroclor quantitations also
requires the total weight percent of the quantitated peaks in the Aroclor standard,
which is obtained by summing the weight percents of relevant congeners obtained
in the April 1994 analyses of Aroclor stanc.^, . . Jucted for the TAMS/Gradient
team (shown in Table 9-6).
For each TAMS/Gradient Phase 2 fish sample, Aroclor quantitation
equivalents were estimated by the three NYSDEC methods and their total compared
to the sum of congeners. Results clearly indicate that the NYSDEC sum by the
1977 method consistently overestimates the wet-weight total PCB concentration in
fish (Figures 9-1 to 9-3). The 1979 and. 1983 methods consistently underestimate
total PCBs. The average percent difference between NYSDEC-method estimates
and the sum of congeners estimates is +25.5 percent for the 1977 method, and
-13.1 percent and -14.6 percent for the 1979 and 1983 methods, respectively.
These observations have important implications for analysis of the older fish
data. Specifically, the Phase 1 report noted (see Figure B.3-14) that an apparent
order of magnitude decrease in Aroclor 1016 in fish occurred between 1978 and
1980 at all stations. It now appears that a significant portion of this decline (i.e., a
decline of about 40 percent) may be due solely to the change in quantitation peaks
that occurred in 1979. This "artificial" decline is imposed on a genuine, but smaller
than previously estimated, decline in actual PCB concentrations in fish over this
time period.
Why does the change in quantitation methods produce these results? The
TAMS/Gradient Phase 2 fish data can be used to compare the consistency of
NYSDEC Aroclor quantitations over time, as shown in Figures 9-4 through 9-7. In
these figures, the congener concentrations in each Phase 2 sample were used to
estimate Arocior quantitations "as if" analyzed by each of the historical NYSDEC
9-6
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packed-column quantitation schemes. For Aroclor 1016, the 1977 method
produces substantially higher estimates than the 1983 method. The 1979 and
1983 Aroclor 1016 methods provide nearly consistent results. In contrast, there is
little difference between the Aroclor 1254 results calculated by the different
methods. The 1977 methodology for Aroclor 1016, which used packed-column
peak 47, includes a number of congeners (BZ #47, BZ #49, and BZ #52) that
consistently contribute a significantly higher percentage to fish concentration than
to the Aroclor standard. In addition, BZ #52 is an important contributor to Aroclor
1254 (5.2 percent by weight), and its use as a quantitation peak for Aroclor 1016
results in double-counting with Aroclor 1254. Using these congeners in the
packed-column quantitation scheme overestimates the total amount of "Aroclor" in
a fish sample. In contrast, the congeners used in the 1983 method consistently
have a greater percent contribution in the Aroclor 1016 standard than in the fish
samples, resulting in a smaller Aroclor 1016 estimate than is needed to reconstruct
a total PCB estimate when summed with Aroclor 1254.
Results indicate that the NYSDEC fish database (without correction) should
be internally consistent for Aroclor 1254, but will be approximately consistent for
Aroclor 1016 only from 1979 on. To use the entire dataset, corrections must be
introduced to account for the changes in quantitation schemes (including minor
corrections for Aroclor 1254). The present analysis is based on NYSDEC Aroclor
quantitations by, or corrected to, the 1 983 scheme. Because the relationships are
nearly linear, the correction is accomplished through regression relationships. The
resulting correction schemes are summarized in Table 9-7. Application of the
corrections place the entire series of historical data on a consistent basis,
appropriate for statistical analysis.
9.1.3 Water Column Data
For most of the period of fish sampling, the only data available on water-
column concentrations are the USGS monitoring. These data commence in 1977
for most locations in the Upper Hudson, with 6 to 58 samples per station per year.
Sampling locations and methodology were described in detail in TAMS/Gradient
(1991). For this analysis, USGS data coincident with the fish data were utilized
from 1977 through the end of calendar year 1992.
Most of the historical USGS results are available as total PCB (whole water)
quantitations only. USGS also quantitated Aroclors from 1986 on. The method
used, however, consisted of obtaining a visual match of the sample chromatogram
to either a single Aroclor standard or a mixture of Aroclor standards, followed by
quantitation based on total peak area. The USGS method thus does not provide a
direct link to specific congeners, unlike the fish analysis, and individual Aroclor
quantitations are subjective. Therefore, only the total PCB estimate reported by
USGS was used for this analysis. When a reported total was missing from the
database, a total was calculated as the sum of detected Aroclor concentrations.
9-7
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Few USGS samples distinguish dissolved and particulate PCB fractions, and almost
no organic carbon data were collected. Therefore, the idealized formulation given in
Equation 8-1, involving the particulate fraction corrected to an organic carbon basis,
cannot be employed. Instead, all regressions were based on total, unfiltered PCBs.
Predictive equations for fish concentrations of Aroclors and total PCBs can be
developed based on total water concentration; the resulting coefficients, however,
will only be true BAFs for total PCBs.
As noted in the Phase 1 report (TAMS/Gradient, 1991) and by Brown et al.,
(1985), a good predictor of yearly average fish PCB burden appears to be the
summer average water-column concentration. Therefore, analyses use summer
averages of water-column data, based on observations from June through
September (Table 9-8). USGS reported a detection limit of 0.1 pg/L for samples
collected prior to November 1986, and 0.01 //g/L thereafter. Total PCB non-
detects were set to one-half the detection limit in the calculation of averages.
Particularly for the period prior to October 1986, many observations are at or near
the detection limit, and sample size is low in some years at some stations. Thus,
the relative accuracy of the water column-data is low, which decreases predictive
ability.
9.1.4 Sediment Data
Sediment data are the most problematic for establishing a bivariate BAF,
because no detailed time series/cross-sectional coverage exists. Having yearly
averages of surface and subsurface sediment concentrations at the locations where
fish samples were collected would be ideal, but these data do not exist.
Additionally, sediment concentrations in the Hudson are known to exhibit a high
degree of heterogeneity, so that means from small samples are likely to be
unrepresentative. The most intensive sediment work is the 1984 sediment survey,
but this covers only the Thompson Island Pool. The 1977/78 sediment survey
covers the whole contaminated portion of the upper river; the quantitation
methods, however, differ from those used in other studies, and, because of
subjective interpretation procedures used in the Aroclor quantitation scheme, are
not readily comparable to other data. Finally, recent studies by GE (O'Brien & Gere,
1993a) and the TAMS/Gradient team provide congener-specific coverage of recent
sediment conditions.
The sediment component in a bivariate statistical model of bioaccumulation
includes a variety of exposure pathways, which are not necessarily well-
represented by a single concentration value. During summer low flow conditions,
concentrations in sediment at the sediment-water interface are likely near
equilibrium with the water column; sediment a few cm beneath the surface,
however, may not be in equilibrium with the water column. If quasi-equilibrium
exists, concentrations at the sediment water interface may not provide much
additional information on exposure beyond that available from water column
9-8
-------
concentrations. On the other hand, PCBs from sediment stores which are not in
equilibrium with the water column may also contribute to exposure pathways. For
instance, benthic organisms may serve as a "pump" to bring PCBs from somewhat
deeper sediments into the water column food chain, and localized hotspots or seeps
of sediment pore water may provide exposure concentrations significantly higher
than the average of surface sediment concentrations.
Two indirect approaches were investigated to compensate for the lack of
detailed time-series data for sediments, one assuming changes with time in
sediment exposure concentration based on interpretation of dated sediment cores
and the other assuming relatively constant average sediment concentration at a
given location over time:
Approach 1: The first approach utilizes time-varying concentrations in newly
deposited sediment and is based on use of dated high-resolution sediment cores in
the TAMS/Gradient database. In cores reflecting steady deposition, a dated core
layer provides an indication of the PCB content of sediment deposited from the
water column at the core location in a given year. The congener data from the core
analyses can be normalized to organic carbon (OC), and/or converted to Aroclor
quantitations on an "as if" basis comparable to fish quantitation methods. It is not
clear, however, to what extent the concentrations pleasured in the cores reflect the
exposure concentration of PCBs entering the food chain from the sediment
pathway. In-channel depositional areas suitable for the production of a dateable
depositional record are limited, and may better reflect a Spring total water-column
concentration than a sediment exposure concentration. PCB levels from the dated
cores were used in two ways in the regressions. First, models were investigated
based on year-by-year interpretations of dates and associated concentrations in the
cores. As these data appear to show significant random noise, and date
attributions are uncertain, models were also evaluated using statistically smoothed
versions of the core profiles. A more detailed analysis of the high-resolution core
data is presented in TAMS/CADMUS/Gradient (1996 - pending publication).
Approach 2: The second approach assumes that the sediment exposure
concentration of relevance to modeling biotic accumulation pathways is not the
concentration in newly deposited sediment, but the average near surface
concentration or store of PCB mass. Note that what is sought for the statistical
model is the sediment exposure concentration which contributes to total exposure
separate from, or not in equilibrium with the water column PCB concentration. The
total store of near surface PCBs is relatively constant over time at a given location:
Most of the Upper Hudson is near a dynamic equilibrium in the sediment bed, i.e.,
neither much deep burial or massive channel scour seems to occur in most years.
Evidence from the Thompson Island Pool coupled with geochemical evidence on
degradation potential (TAMS/CADMUS/Gradient, 1996 - pending publication)
suggests that the inventory of in-place PCBs changes only slowly with time.
Further, during the period analyzed (1977-1992), no significant flood-scour events
9-9
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have occurred on the Upper Hudson comparable to the approximately 75- to
100-year event of 1976, which caused a major redistribution of contaminated
sediments. Therefore, Approach 2 assumes that the bioavailable stores of PCBs in
sediment at a given location have been approximately constant since the late-
1970s. Under this assumption, a model can hold sediment concentration constant
through time at a given location. The GE sediment sampling program data (O'Brien
& Gere, 1993a) provide an internally consistent picture of PCBs throughout the
Upper Hudson and can be normalized to organic carbon content for use in Approach
2.
9.1.5 Functional Grouping of Sample Locations for Analysis
Four functional groupings of available data by location were formed for the
purposes of analysis. These represent the major fish sampling locations and
associated environmental data. The groups are identified below, along with data
assumptions:
Group 1: River Mile 189 to 193, representing the Thompson Island Pool.
USGS has not monitored water column concentrations at the Thompson Island Dam
(downstream end of this reach), and only l: ;a are available from the USGS
monitoring station at the next dam at Fort Miller. On the other hand, analyses
discussed in the Data Evaluation and Interpretation Report
(TAMS/CADMUS/Gradient, 1996 - pending publication) suggest that water-column
concentrations in the upper Hudson River are approximately constant, on average,
between the Thompson Island Dam and the confluence with the Hoosic River
during low-flow conditions, as there is little tributary inflow in this reach.
Therefore, summer average water-column concentration is represented by the
USGS monitoring at Stillwater (taken just above the Hoosic confluence at River Mile
168). (USGS Fort Edward at Rogers Island water-column data are not
representative of water-column concentrations downstream in the Thompson Island
Pool, due to loading of PCBs from sediments within the pool, and the Rogers Island
water-column data are not strongly correlated with fish concentrations between
River Miles 189 and 193.) Dated core data (for sediment Approach 7) were taken
from TAMS/Gradient Core 19. Core 23 (also dated) was omitted as
unrepresentative: it is in the Moses Kill Delta and PCB concentrations appear to be
much lower than in other Thompson Island Pool cores. GE near surface sediment
data (Approach 2) for River Mile 188.5 to 193.5 (their Reach 8) provided average
PCB concentrations of 42.55 ppm, or 2358.75 mg PCB/kg-OC.
Group 2: River Mile 175, the NYSDEC fish collection station between
Schuylerville and Stillwater. Water-column concentrations are represented by the
USGS Stillwater data at River Mile 168. Thus, Group 2 is assigned the same
water-column concentration as Group 1, but differs in sediment concentrations.
Sediment concentrations (Approach 1) used high-resolution data from
TAMS/Gradient Cores 21 and 22, both from River Mile 177.4, with interpolation
9-10
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across the two profiles. GE total PCB sediment averages (Approach 2) for River
Mile 173.5 to 177.5 (Reaches 5GH and 5IJ) were 16.52 ppm, or 1257.04 mg
PCB/kg-OC.
Group 3: River Mile 160, above Federal Dam (recent NYSDEC collection
only). Water-column concentrations are represented by the USGS Waterford USGS
station at River Mile 156.5. No dateable cores were retrieved in this reach, so this
station was omitted from regressions using Approach 1. For Approach 2, GE
sediment concentration averages for River Miles 159.7 to 163.6 (Reaches 2AB and
2CD) were 6.48 ppm, or 669.69 mg PCB/kg-OC.
Group 4: River Mile 142 to 155, representing collections in the upper part of
the Lower Hudson, below Federal Dam. These stations are influenced by the
Mohawk River, especially below River Mile 154.5. Water-column concentrations
were represented by the Waterford station diluted by the increased flow from the
Mohawk River. Based on Phase 2 investigations, PCB contributions in the Mohawk
River are assumed negligible compared to loads from the Upper Hudson. Dated
core data from TAMS/Gradient Core 11 at River Mile 143.5 (Albany Turning Basin)
were used for Approach 1. GE sediment averages (Approach 2) for River Miles
1 54.3-1 55.7 (Reaches 1E and 1 F) were 1.1 25 ppm, or 77.20 mg PCB/kg-OC.
9.2 Results of Bivariate BAF Analysis
Regression models were created for the four individual sample groups
described above and across all groups based on (1) correlation to water-column
concentration only, (2) water column concentration with sediment Approach 1, and
(3) water-column concentration with sediment Approach 2. Results were generally
consistent among groups, implying that cross-sectional models across groups are
appropriate; therefore, the cross-sectional model results are presented.
For a given location and year, the PCB analyses of individual samples for a
given species exhibit a high degree of variability, reflecting individual characteristics
and intra-year environmental effects that cannot be addressed in the simple
regression approach described here. In contrast, the central tendency or mean of
species-location-year observations shows much less variability. Analysis of means
used a weighted regression, with weights determined as the square root of the
sample size. As expected, models on means have much stronger predictive ability
than models on individual observations. As the intention of the bivariate BAF
analysis is to provide initial information on the central tendency of fish body burden
response, models on the means are reported here. Tables 9-9 and 9-10 display the
results of the analysis.
9-11
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In developing final models, the following key points emerged:
' Summer average water-column concentration alone is a good predictor for
average Aroclor 1016 burden (as quantitated by NYSDEC) in most species of
fish. Water-column concentration is not as good a predictor for Aroclor 1254
burden. When combined with the relationship to water-column concentration, a
time trend versus year was usually significant for Aroclor 1254, but not Aroclor
1016, in the regressions. This suggests that other time-variable factors beside
water-column concentration are significant for Aroclor 1254.
Incorporation of estimates of time-dependent changes in depositional sediment
PCB concentration did not improve model predictive ability. The high-resolution
core data (sediment Approach 1) were almost never strong predictors of fish
PCB burden, either alone or in combination with water column concentrations.
The relationship to the core data was often not statistically significant when
water was included. Some statistically significant relationships to the core data
were negative, e.g., between fish body burden Aroclor 1254 and Aroclor 1254
(normalized to OC) in the sediment. These data do not appear to be useful for
estimating sediment-pathway PCB exposure.
Assuming constant sediment exposure concentrations by location (sediment
Approach 2) provides much stronger predictive ability. Water and OC-
normalized sediment concentration together provided a satisfactory set of
explanatory variables for Aroclor 1016. In stepwise multiple regression tests,
other variables were statistically significant only occasionally and water was
always the single most significant variable. For Aroclor 1254, a negative trend
with time (in addition to water and sediment concentrations) was still found
statistically significant for largemouth bass, but was only marginally significant
at the 5 percent level and contributed a minor portion of total explanatory
power. Regressing on water and sediment alone provided only a small increase
in standard error of the estimates obtained with the time variable included
(maximum 7 percent increase for the largemouth bass model). The statistical
correlation to time may reflect a real trend, such as a slow decline in bioavailable
sediment concentration; however, the apparent trend may also be an artifact of
the data because (1) water concentrations have tended to decline over time
toward USGS detection limits, resulting in less "power" in recent observations;
(2) assumed constant sediment values are likely not entirely accurate; (3)
largemouth bass, which are at the top of the food web, may integrate exposures
over several years; and (4) the spatial distribution and collection time of fish
samples has varied from year to year.
9-12
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9.3 Discussion of Bivariate BAF Results
For comparison to published results, Tables 9-9 and 9-10 contain estimates
of a univariate logjo BAF for total PCBs in units of liters of water per kg of fish
lipid. This univariate BAF is based on the fitted regression coefficients on water
column concentration, Bw. Because the water column concentrations are reported
as ppb (ywg/L) and fish concentrations as ppm (mg/kg-lipid), the univariate BAF is
estimated from the regression coefficient as:
log10(^F) = log10(5H.xl03) (9-6)
where,
rtAr_(kg-pCB) (kg~uPid)
(kg - PCB) (L - water)
The calculated log BAFs for total PCBs presented in Table 9-10 (as measured
by the sum of NYSDEC Aroclor 1016 and Aroclor 1254 quantitations) range from
6.14 for pumpkinseed to 6.79 for goldfish when expressed on a L/kg basis. These
univariate BAFs, relating lipid-normalized body burden in fish to total PCB
t
concentrations in water, are sometimes denoted as BAF! (USEPA, 1994). BAFs are
also frequently reported on the basis of the freely-dissolved fraction of a chemical in
fd
the water column, BAF! The two forms of the univariate BAF can be related as
BAF,fd = (9-7)
d
where f is the freely dissolved fraction of the chemical. Estimates of the average
dissolved fraction of key PCB congeners in the Hudson are presented in the Data
Evaluation and Interpretation Report (TAMS/CADMUS/Gradient, 1996 - pending
publication). Under average conditions in the Upper Hudson, the dissolved fraction
appears to be about 50 percent for congeners in the range of BZ#25 through
BZ#53 used to quantitate Aroclor 1016, and about 33 percent or less for the
congeners used to quantitate Aroclor 1254. Using Equation (9-7), base-10
fd t
logarithms of BAFf s would thus be equal to the calculated BAF! s plus about 0.3 to
0.52 units.
fd
USEPA (1994) summarizes estimated BAF! s for PCB congeners by trophic
level based on the food web/fugacity model of Gobas (1993) for conditions in Lake
Ontario. Results calculated here compare favorably to results presented by USEPA
(1994) for BZ #28 and BZ #31. These congeners are both included in the
quantitation scheme used by NYSDEC for Aroclor 1016, and constitute about 14
percent of the total weight of raw Aroclor 1242. For BZ#28 and BZ#31, the
9-13
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Gobas model predicts a BA?! of 6.51 for alewives. Similar to pumpkinseed, this
species feeds on invertebrates that accumulate PCBs from the water column
(assumed alewife diet of 60 percent zooplankton and 40 percent Diporeia sp.) The
Gobas model estimate compares well to the estimate of 6.14 + 0.3 presented here
for pumpkinseed BAF( . Similarly, the Gobas model result for BZ#28 and BZ#31 in
piscivorous fish is 6.68, which compares well with the Hudson River largemouth
bass estimate of BAF[ = 6.51 + 0.3.
Figures 9-8 through 9-13 display the ability of the final regression equations
to predict observed mean concentrations over all stations for Aroclor 1016 and
Aroclor 1254 lipid-normalized averages in pumpkinseed, largemouth bass, and
brown bullhead, the three species for which the most data are available. Each
yearly observation is keyed to location. It should be recalled that the regression
was weighted to the square root of sample size; thus, some points that lie away
from the match line represent small samples which had little weight in the
regression. As indicated by the R2 values presented in Table 9-10, the fit is
generally better for Aroclor 1016 than for Aroclor 1254. The difference in
goodness-of-fit in part reflects limited knowledge of the time course of PCB
concentrations in the sediments and ch^..^ congener composition in the
sediments between stations, but may also represent greater sample-to-sample
variability in the accumulation of more highly chlorinated congeners. On the plot
for Aroclor 1254 in largemouth bass (Figure 9-12), the models appear generally to
underestimate burdens at River Mile 175, but overestimate those downstream at
River Miles 142-155. This may reflect inaccurate sediment averages at one or both
stations. Also, water-column concentrations near the detection limit may be over-
estimated downstream resulting in a slight bias in the regression relationship.
The most complete fish time-series data in the Upper Hudson are those
collected at River Mile 175. Figures 9-14 through 9-19 compare predictions to
observations across time at this station for pumpkinseed, largemouth bass, and
brown bullhead. The Aroclor 1016 results generally track well, while the Aroclor
1254 results show greater variability. Some discrepancies are attributable to small
sample size: most sample sizes were between 20 and 30 individuals or
composites, but some were as small as a single individual. Also, systematic
changes in collection probably affected results. For instance, from 1979 to 1989
largemouth bass and brown bullhead were collected at River Mile 175 in June,
while pumpkinseed were collected in September. In 1991 and 1992, collections of
all three species shifted to May, with a few December samples. Shifting to earlier
in the year likely affects the observed PCB burden. Finally, the size and weight of
fish collected varies from sample to sample. For instance, Pumpkinseed collected
at River Mile 175 between 1981 and 1988 were primarily yearlings, with mean
weights in the range of 16 to 50 g, whereas those collected in 1991 and 1992 had
mean weights of about 250 g. Size has important implications for feeding
preferences and is also correlated with age, which was not measured.
9-14
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Nonetheless, no clear relationship could be established between PCB burden
and either weight or length. The relationships presented here represent broad
averages across a variety of factors, including season and size, in keeping with the
goal of establishing a scoping tool preparatory to the physically-based food web
analysis.
All regression equations in Table 9-10 are calculated with the same
independent variables and therefore provide a consistent basis for examining
estimated relative contributions of sediment and water. The regression partial sums
of squares associated with the two independent variables (water-column
concentration and sediment concentration normalized to organic carbon) can be
used to calculate the proportion of total explained variability attributed to water and
sediment sources (Table 9-11), with the caveat that the sediment exposure
pathway in the statistical model represents only those sediment exposure pathways
not explained by water column concentrations. For Aroclor 1016, between 61 and
99.7 percent of the explained variability is estimated to be due to water-column
inputs. The estimated water-column contribution for Aroclor 1016 is high even for
bottom-feeding brown bullhead. As quantified by NYSDEC, Aroclor 1016 results
represent primarily trichlorinated congeners below BZ #45, which are generally
expected to be strongly driven by the water column, as opposed to sediment
pathways.
A different picture emerges for more highly chlorinated congeners
represented in Aroclor 1254 quantitations, with considerable range in the
importance of the sediment pathway, which appears to reflect the trophic level and
forage preference of the species. The water-column pathway remains dominant for
some species, including pumpkinseed and cyprinids, which forage primarily in the
water column, and yellow perch, which are migratory. In contrast, brown bullhead,
which forage primarily in the sediment, have an estimated 86 percent contribution
from the sediment pathway. At the highest trophic level, largemouth bass, which
are primarily piscivorous, are estimated to obtain about 42 percent of their
measured Aroclor 1 254 burden from the water column and about 58 percent from
sediment pathways. These intermediate numbers suggest that the bass integrate,
or average out, food web contributions from both water- column and
sediment/detrital feeders.
9.4 Summary
Bivariate BAF models, relating lipid-based PCB burden in fish to PCB
concentrations in both the water column and sediment, provide good explanatory
power in predicting annual mean totai PCB and Aroclor body burden in five fish
species throughout the Upper Hudson River, based on analysis of NYSDEC
monitoring data for 1975 through 1992. Water-column and sediment PCB
concentrations are clearly not in complete equilibrium in most of the Upper Hudson,
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and inclusion of sediment concentration as an independent variable results in a
significant increase in explanatory power.
The analysis indicates that a steady-state food web model, functionally
equivalent to the bivariate BAF in terms of input and output, is feasible. It should,
however, be emphasized that the specific values of coefficients developed in the
analysis of the NYSDEC data are highly dependent on the nature of Aroclor
quantitations in fish and the water column, which do not represent the complete
congener pattern of true Aroclors and additionally were not obtained by consistent
methods. Finally, scoping models are adequate to estimate annual means, but do
not reflect individual and within-year variability expected to result from age and
variations in foraging with size, nor seasonal patterns related to temperature and
the spawning cycle. These issues are addressed through the development of the
Probabilistic Bioaccumulation Food Chain Model in Section 10.
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10. CALIBRATION OF PROBABILISTIC BIOACCUMULATION FOOD
CHAIN MODEL
The components of the food chain model and general model structure are
described in Section 8.5. The model takes as exposure concentrations the summer-
averaged water concentration for PCBs normalized to particulate organic carbon and
the annual average sediment concentration for PCBs normalized to fraction of
organic carbon. As discussed in Section 8.5, these exposure concentrations are
converted to body burdens of PCBs through a number of bioaccumulation factors
(BAFs) that link media and food chain components. These BAF values and the
uncertainty or variability around them are derived from the available data for the
Hudson and from data for other systems. This section of the report describes how
these BAFs were derived for each food chain component, examines the goodness-
of-fit between modeled body burden data and observations in the river, and
provides an illustration of how the model is anticipated to be used in a predictive
mode for one of the selected fish species - the yellow perch.
Analyses presented here are based on Release 3.1 of the TAMS/Gradient
database, except for the yellow perch example, which is based on unvalidated data
from Release 2.3. Results presented here are draft and subject to change based on
ongoing data validation. Results are presented primarily for illustrative purposes
and to demonstrate the methodology, rationale, and limitations.
Each compartment in the model is described separately for each of the
calibration congeners, Aroclors 1016 and 1254, and total PCBs. The relationship
between each of the compartments is described by a distribution of accumulation
factors based on field data. These BAFs relate the body burden of one
compartment to the expected dietary exposure of that compartment. The dietary
exposure is assumed to implicitly incorporate actual exposures from all sources
(i.e., direct water uptake). Distributions presented in this report are derived for the
calibration congeners, Aroclors 1016 and 1254, and for total PCBs to describe the
range of expected bioaccumulation factors between two compartments.
10.1 Overview of Data Used to Derive BAFs
Table 10-1 shows the ecological sampling locations by river mile and the
corresponding water column stations.
10.1.1 Benthic Invertebrates
The TAMS/Gradient team collected 20 (including background) collocated
benthic invertebrate and sediment samples during the Phase 2 field collection
program. Five sediment samples and three to five benthic invertebrate samples
were taken at each location. Benthic invertebrates were identified to the taxonomic
group level for PCB analyses. PCB results were provided for individual congeners,
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homologue sums, total PCBs, and Aroclor equivalents. In addition, percent lipid
data are also provided. These data were used to characterize the relationship
between sediment PCB concentrations and resulting benthic invertebrate body
burdens.
10.1.2 Water Column Invertebrates
Phase 2 activities did not include data collection related to water column
invertebrates. The data on water column invertebrates is obtained from the
NYSDOH studies done as part of the Hudson River PCB Reclamation Demonstration
Project (Simpson et al., 1986). NYSDOH conducted long-and short-term
biomonitoring studies from 1976 to 1985 using caddisfly larvae, multiplate
samples, and chironomid larvae. NYSDOH placed artificial substrate samplers
(multiplates) along 1 7 sites for five weeks in the Hudson river from Hudson Falls to
Nyack, New York (Novak et al., 1988). Samplers remained in place for five weeks
during July through September collecting a composite of sediment, algae, plankton
and various macroinvertebrates. After collection, the samplers were analyzed for
Aroclors 1016 and 1254. Total PCB values are obtained by summing the individual
values for Aroclors 1016 and 1254. Percent lipid values are also provided. These
data, combined with information from the Phase 2 dataset, provide an indication of
the relationship between water column invertebrates and water column sources.
The short-term biomonitoring study conducted by NYSDOH involved the
chironomid larvae, Chironomus tentans. Twenty-five laboratory-raised chironomid
larvae in nylon mesh packets were placed, in groups of ten, in steel mesh baskets
at four Hudson River locations (one at Bakers Falls, two at Thompson Island Pool,
and one at Fish Creek). One set of packets was exposed to the sediment at a
collection site on the eastern shore of Thompson Island Pool. The remainder were
placed in the water column. These short-term data are available for selected
congeners and provide some information related to the time-frame and magnitude
of the short-term relationship between water column invertebrates and water
column sources.
10.1.3 Fish
The TAMS/Gradient team collected fish data from the same 20 benthic
invertebrate and sediment locations. Between three to five of the selected fish
species were collected at each location (i.e., not all species were collected from all
locations, for further detail, refer to the TAMS/Gradient SAP/QAPP, 1992). Data
are provided for individual congeners, homologue sums, total PCBs, and Aroclor
equivalents. Percent lipid, length and weights of individual fish as well as
composited samples are also provided.
NYSDEC has been collecting fish data for over 30 species in the Upper
Hudson since 1975. From 1975 to 1988, fish data were collected every year. In
1988, fish sampling frequency changed from yearly to every other year. The bulk
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of the sampling (75 percent) has been conducted for striped bass, largemouth bass,
brown bullhead, pumpkinseed, American shad, and American eel.
For the NYSDEC samples, chemical analyses for Aroclors 1016, 1254 and in
some years, 1221 and 1242, are provided in the database as well as weight,
length, percent lipid, and, for some years, sex and age. Generally, 30 fish were
collected for each species at several locations.
10.1.4 Literature Values
There are studies from the literature which provide additional information on
the relationship between sediment, benthic invertebrates, water and water column
invertebrates, (e.g. Whittle et al., 1983; Bierman, 1990; Bierman, 1994; Wood et
al., 1987; Larsson, 1984; Lake et al., 1990; Oliver, 1987; Oliver & Niimi, 1988;
Thomann, 1981; van der Oost et al., 1988; Thomann, 1989; Thomann & Connolly,
1984; Bush et al., 1994; Thomann et al., 1992; Harkey et al., 1994; Endicott et
al., 1994; and others). These studies are primarily useful for comparative
purposes, as they refer to systems which may experience conditions unlike those
in the Hudson River.
10.2 Benthic lnvertebrate:Sediment Accumulation Factors (BSAF)
Distributions of BSAFs between sediment concentrations and benthic
invertebrate concentrations were derived by:
1. Evaluating the sediment data to determine which river miles display
significant heterogeneity and variability in concentrations;
2. Calculating the BSAF by dividing a measured individual benthic invertebrate
concentration by the geometric mean sediment concentration at a sampling
location; and,
3. Conducting a statistical analysis to identify outliers and extreme values and
presenting goodness-of-fit results for the final distribution representative of
the relationship between benthic invertebrates and sediment.
10.2.1 Sediment Concentrations
An assessment of the range of sediment concentrations by river mile and
congener provides information on the variability inherent in these data
(TAMS/CADMUS/Gradient, 1996 - pending publication). Figures 10-1 through 10-8
provide box-plots of sediment concentrations by river mile. The box contains the
middle 50 percent of values, called the interquartile range, and the lines extending
from the ends of the box show the extreme values not considered outliers. Outliers
are identified by an "o" and extreme outliers identified by an asterisk. An outlier is
defined as a value that falls 1.5 times outside the interquartile range, and an
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extreme value is more than 3 times outside the interquartile range. These values
were not eliminated from the analyses, but rather provide important information on
the variability of concentrations at a given river mile. Plots are provided for BZ#4,
BZ#28, BZ#52, BZ#101 with BZ#90, BZ#138, Aroclor 1016, Aroclor 1254 and
total PCBs for each river mile expressed as ug/g on a TOC-normalized basis.
Figures 10-1 through 10-8 provide information on the distribution of
sediment concentrations at each river mile. The lower river, (miles 25.8 through
143.5) show significantly lower and less variable PCB concentrations than the
upper river. Note that the Aroclor 1016 concentrations (Figure 10-6) are similar to
the Total PCB concentrations (Figure 10-8), indicating the dominance of the lower
chlorinated congeners in the upper river. BZ#4 (Figure 10-1) shows the highest
concentrations of the individual congeners plotted.
10.2.2 Approach
BSAFs for benthic invertebrates were calculated from the Phase 2 dataset
using collocated sediment and benthic samples. The sampling rationale will be
presented as part of the ecological risk assessment (work in progress). PCB
oncentration and lipid data were available for Amphipods, Bivalves, Chironomid,
Gastropods, Isopods, Odonata, Oligochaetes, Unsorted Total (everything in a
sample), Sorted Total (unidentified remaining after sorting), and Epibenthic species.
The ideal data pairs to calculate BSAFs are individually collected samples of
sediment and benthic invertebrates. In the absence of this ideal condition, we used
individual benthic invertebrate samples and the geometric mean sediment
concentration for a given co-located sampling location.
However, in the areas which display highly variable PCB concentrations in
sediments, it is unlikely that the geometric mean adequately represents the
exposure levels for benthic invertebrates, particularly for the lower chlorinated
congeners or mixtures such as Aroclor 1016. The heterogeneity in sediment
concentrations over small spatial scales contributes to higher variability in the
BSAFs calculated from data collected in these areas. Thompson Island Pool is an
area in which such variability in calculated BSAFs occurs. Matching individual
invertebrate concentrations to the geometric mean sediment exposure in this area
results in more variable ratios. Also, the ratios for Thompson Island Pool are higher
in magnitude than for the upper river generally and significantly higher than the
lower river.
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Species identified as epibenthic showed BSAF that were not significantly
different from any other species. In addition, the sampling program did not
specifically sample for epibenthic species (Chernoff, 1995, personal
communication) and were only identified as such during subsequent analyses. The
BSAF calculated for each river mile were combined to represent the range of
accumulation factors in river generally. The implications for the food chain model
are that this distribution of BSAFs represent the range among the prey species of
fish feeding off the bottom. This is a reasonable approximation if the fish feed on
benthic invertebrates indiscriminately so that the probability of preying on a
particular species is proportional to that species' abundance.
For those sampling locations at which there were enough data to run
normality tests, it was determined that the benthic invertebrate data follow a
lognormal distribution. This was verified by log-transforming benthic invertebrate
PCB concentrations and running standard normality tests. Given lognormally
distributed invertebrate concentrations, the appropriate statistic for use in the BSAF
calculations is a geometric mean sediment concentration. The variability in the
sediment and benthic invertebrate concentrations has a significant impact on
calculated BSAF, because widely divergent individual benthic invertebrate
concentrations are normalized to one sediment concentration considered to be
indicative of exposures over time.
Bar charts were developed to show calculated BSAF by river mile and
invertebrate species, and scatter plots were developed to show species BSAF by
sediment concentrations. Finally, charts showing the goodness-of-fit between
modeled output and observed concentrations are presented. A set of charts was
prepared for the calibration congeners, Aroclors 1016 and 1254, and total PCBs.
The BSAF by river mile charts were developed using the data for the
combined benthic species (no epibenthic species). The charts for BSAF by river
mile and the BSAF by species show the mean BSAF within an error bar indicating
plus and minus one standard error of the mean. These plots provide information on
the variability of BSAF by river mile, and the species that contribute most to the
observed variability. Identifying the species showing the greatest variability may
indicate that the primary exposure is not sediment, but rather overlying water. This
hypothesis will be examined in greater detail in the next phase of work. The scatter
plots show BSAF for each of the species by the TOC-normalized geometric mean
sediment concentrations. These plots show whether there is a relationship
between sediment concentration and BSAF.
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10.2.3 Calculations of BSAF Values for Benthic Invertebrates
BSAF for Congener BZ#4: Biota to Sediment Calculations
Figure 10-9 shows the BSAFs for BZ#4 (all species combined) by river mile.
River miles 189 and 189.5, within the Thompson Island Dam area, have higher
mean BSAF than the other river miles (over 10 and 4, respectively), and greater
uncertainty in the estimate (wider error bars). Mean BSAF range from 0 to 1 for
the remaining river miles, with narrower error bars.
Figure 10-10 shows the BSAF for BZ#4 (all river miles combined) by
species. The BSAF for most species are less than 1.0; the narrow error bars
indicate relatively little uncertainty in the mean BSAF. Three species have distinctly
higher mean BSAF, accompanied by wider error bars. The BSAF for Chironomids,
about 9, is the highest ratio, with the widest error bars, due at least partially to the
small sample size (3). The chironomid samples are primarily from the Thompson
Island Pool area, and show very high concentrations relative to both other
invertebrates as well as the mean sediment concentration. The BSAF for the
Isopods and the aggregate Sorted Total, about 4, are two to three times higher
than the majority of the remaining species, with greater uncertainty in the estimates
as represented by wider error bars.
Figure 10-11 provides the scatter plot of the BSAF for BZ#4 for each species
by the sediment concentrations. Most points on the plot show BSAF from 0 to less
than 10, regardless of sediment concentrations. The Chironomids have a high
BSAF at a relatively low sediment concentration and the Isopods and the Sorted
Total have high BSAF at the highest sediment concentration.
The variability in the BSAF for BZ#4 may be affected by its relatively high
solubility. For BZ#4, the direct exposure of benthic invertebrates to pore water
could be significant. The predicted phase distribution of BZ#4 in pore water
relative to total sediment concentrations varies from 8 to 45 percent, depending on
the method used (TAMS/CADMUS/Gradient, 1996 - pending publication). A
fraction of this is DOC-bound. The bioavailability of the DOC-bound fraction is
considered low (DiToro et al., 1991). The estimated concentration factor (ng g1
dry weight) (Novak et al., 1990) for BZ#4 in chironomid is 5,830, and it required
only 0.2 days to reach 90 percent equilibrium. This indicates that chironomid
respond quickly to changes in water concentrations and may be showing more of a
response to water concentrations than to direct sediment exposure.
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BSAF for Congener BZ#4: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. There were not enough
invertebrate data to characterize the measured frequency distribution (high numbers
of non-detects which skewed the distribution), so the goodness-of-fit was done by
comparing individual benthic invertebrate concentrations to modeled output (Figure
10-12). The line identified as "measured" represents individual data points. The
model 50th percentile output follows the data most closely, although individual
elevated observations were captured by the maximum from the model.
BSAF for BZ#28: Biota to Sediment Calculations
Figure 10-13 shows the BSAF for BZ#28 (all species combined) by river mile.
Most of the means are from approximately 0.5 to 1.5, with narrow error bars. The
BSAF for river miles 189 and 189.5 show wider errors bars, indicating greater
uncertainty in the estimates of the mean. The widest error bar is around the river
mile 100 mean estimate.
Figure 10-14 shows the BSAF for BZ#28 (all river miles combined) by
species. The BSAF for Chironomids is about three times greater, and has wider
error bars, than the BSAF of the other species. The BSAF for the Gastropods,
about 1, has wider error bars than the other species. The samples sizes of the
Chironomids and Gastropods are small (3 and 4, respectively).
Figure 10-15 provides the scatter plot of the BSAF BZ#28 for each species
by the sediment concentrations. Most of the BSAF are between 0 and 1.5, for all
sediment concentrations. Higher BSAF for Chironomids (about 6 and 3) are shown
for sediment concentrations of approximately 21 ug/g. At this sediment
concentration, a high BSAF, about 3.5, is also shown for the Unsorted Total. A
high BSAF, roughly 2.5, is shown for Gastropods at 18 ug/g. At lower sediment
concentrations, high BSAF are shown for the Unsorted Total (about 4 at 1 ug/g),
for the Sorted Total and Isopods (about 3 and 2.5, respectively, at 8 ug/g). No
trend by sediment concentration is observed.
The pore water contribution to BZ#28 is small, less than 10 percent
(TAMS/CADMUS/Gradient, 1996 - pending publication). However, the
concentrations may be high enough to contribute significantly to the benthic
invertebrate body burdens. It may also be that chironomid and other benthic
invertebrates may be responding to temporal water column PCB concentration
changes.
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BSAF for BZ#28: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. A frequency distribution
was estimated from the observed data for each sampling location in order to
compare observed results to the modeled output. Both the observed and modeled
percentiles were log-transformed and the observed benthic invertebrate
concentrations plotted against the percentiles predicted from the model. Figure 10-
16 shows the results of this analysis. The center line is the estimated regression
line, and 95 percent confidence interval. The observed and modeled 50th and 90th
percentiles compared favorably, although the shape of the observed distribution
differed slightly from the modeled output.
BSAF for BZ#52: Biota to Sediment Calculations
Figure 10-17 shows the BSAF BZ#52 (all species combined) by river mile.
Higher and more uncertain BSAF are shown for river mile 189. River mile 100
shows high uncertainty around the estimate of the mean BSAF. The other river
,i!es have BSAF between 1 and 2, with 'ess uncertainty in the mean estimate as
evidenced by smaller error bars.
Figure 10-18 shows the BSAF BZ#52 (all river miles combined) by species.
BSAF for most of the species are between 2 and 4, with fairly wide error bars,
indicating relative uncertainty in the mean estimate. Three species have lower
BSAF, between 0.5 and 1.5, with narrow error bars: amphipods, bivalves, and
odonata. Chironomid, isopods and gastropods shows the highest BSAF and the
greatest uncertainty around the BSAF estimate.
Figure 10-19 provides the scatter plot of the BSAF BZ#52 for each species
by the sediment concentrations. Most of the BSAF are less than 5. There are
three high BSAFs: for the Unsorted Total, about 15, at the lowest concentration,
and for the sorted Total and Isopods, about 16 and 14, respectively, at
approximately 9 ug/g.
BSAF for BZ#52: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-20 shows these results. The
modeled and observed percentiles compare favorably.
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BSAF for BZ#101 (with BZ#90): Biota to Sediment Calculations
Figure 10-21 shows the BSAF BZ#101 with BZ#90 (all species combined) by
river mile. River mile 100 has the highest BSAF, about 4, with very wide error
bars, indicating significant uncertainty in the mean estimate. River miles 189 and
189.5 also have slightly higher BSAF than the remaining river miles.
Figure 10-22 shows the BSAF BZ#101 with BZ#90 (all river miles combined)
by species. Chironomids and Gastropods have the highest BSAF (about 4), with
the widest error bars. The other river miles have BSAF between 0.5 and 2.5, with
narrower error bars.
Figure 10-23 provides the scatter plot of the BSAF BZ#101 with BZ#90 for
each species by the sediment concentrations. There is a wide range in BSAF from
just above 0 to 6.5 for all sediment concentrations. There are two distinct points:
BSAF near 10 for the Unsorted Total at the lowest concentration, and at 2 ug/g for
the Sorted Total.
BSAF for BZ#101 (with BZ#90): Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-24 shows the results of this
analysis. The modeled and observed percentiles compare favorably.
BSAF for BZ#138: Biota to Sediment Calculations
Figure 10-25 shows the BSAF for BZ#138 (all species combined) by river
mile. River mile 189 has the highest mean BSAF, about 4, with the widest error
bars. River miles 25.8, 100, and 189.5 all show BSAF slightly above 2, while the
remaining river miles show BSAF around 1.
Figure 10-26 shows the BSAF BZ#138 (all river miles combined) by species.
Chironomid, gastropods and isopods show the highest BSAF, with wide error bars
for chironomid and isopods. BSAF for the remaining species range from about 1 to
2, with narrower error bars.
Figure 10-27 provides the scatter plot of the BSAF BZ#138 for each species
by the sediment concentrations. With one exception, the BSAF for all sediment
concentrations cluster from 0 to about 6. The BSAFs for Isopods at about 1.4 ug/g
is twice as great, approximately 12.
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BSAF for BZ#138: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-28 shows the results of this
analysis. The modeled and observed percentiles compare favorably.
BSAF for Aroclor 1016: Biota to Sediment Calculations
Figure 10-29 shows the BSAF for Aroclor 1016 by river mile (across all
species). The BSAF are all less than 1 with narrow error bars for all river miles
except 100, 189, and 189.5. The BSAF for river mile 189 shows the greatest
variability.
Figure 10-30 shows the BSAF by species (across all river miles). The BSAF
for chironomid and isopods are higher (approximately 4) with wider error bars than
for the remaining species. Gastropods, which have shown variable BSAF for
individual congeners, show no significant difference from other species for Aroclor
1016.
Figure 10-31 provides the scatter plot of the BSAF for Aroclor 1016 for each
species by the sediment concentrations. The sorted total and isopods show high
BSAF (16 - 18) at 350 ug/g geometric mean sediment concentration. The highest
sediment concentrations show tightly clustered and fairly low BSAF.
BSAF for Aroclor 1016: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-32 shows the results of this
analysis. The modeled and observed percentiles compare favorably.
BSAF for Aroclor 1254: Biota to Sediment Calculations
Figure 10-33 shows the BSAF for Aroclor 1254 (all species combined) by
river mile. As has been observed in previous figures, the BSAF for river miles 100,
189 and 189.5 are highest and show the greatest variability. The BSAF for the
remaining river miles are approximately 1 with fairly narrow error bars.
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Figure 10-34- shows the BSAF for Aroclor 1254 by species (across all river
miles). Unlike for Aroclor 1016, Aroclor 1254 BSAF by species are highest and
most variable for chironomid, gastropods and isopods.
Figure 10-35 provides the scatter plot of the BSAF for Aroclor 1054 for each
species by the sediment concentrations. The highest BSAF are observed for a
geometric mean sediment concentration of 90 ug/g. BSAF for the highest sediment
concentrations (above 200 ug/g) are all between 0 and 4.
BSAF for Aroclor 1254: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-36 shows the results of this
analysis. The modeled and observed percentiles compare favorably.
BSAF for Total PCBs: Biota to Sediment Calculations
Figure 10-37 shows the BSAF for Total PCBs (all species combined) by river
mile. The mean BSAF for river miles 100, 189 and 189.5 are higher and have
wider error bars than the other river miles. The BSAF for river mile 189 is about 6;
the BSAF for river miles 100 and 189.5 are about 3. The BSAF for the other river
miles are about 1, with very narrow error bars.
Figure 10-38 shows the BSAF Total PCBs (all river miles combined) by
species. The BSAF for chironomids, about 4, is higher and has wider error bars
than the other river miles. The BSAF for Isopods, about 3, also has wide error
bars. BSAF for the remaining river miles range between 0 and 2, with narrower
error bars.
Figure 10-39 provides the scatter plot of the BSAF Total PCBs for each
species by the sediment concentrations. The BSAF for most species and sediment
concentrations range between 0 and 5. The highest BSAF are for the Sorted Total
and Isopods, about 15 and 13, respectively, at about 450 ug/g. A high BSAF is
also shown for Chironomids, about 9 at about 300 ug/g.
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BSAF for Total PCBs: Biota to Sediment Goodness-of-Fit
The model was run by applying the distribution derived above to each
geometric mean sediment concentration by river mile. The 10th, 25th, 50th, 75th,
90th percentiles were calculated as well as a maximum. These percentiles were
compared to the output from the frequency analysis on the benthic invertebrate
data done using the SPSS software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the
percentiles predicted from the model. Figure 10-40 shows the results of this
analysis. The modeled and observed percentiles compare favorably.
Summary of Biota-Sediment Accumulation Factors
The modeled PCB distributions compare favorably to the observed
distributions of PCB concentrations for individual calibration congeners, Aroclors
1016 and 1254, and total PCBs. The model for benthic invertebrates captures the
observed variability in the underlying data. In areas where the sediment
concentrations display heterogeneity (such as Thompson Island Pool), the model
accurately captures the maximum observed concentrations. However, in the Lower
iudson River, where sediment (and biota) concentrations display far less
heterogeneity, the model tends to overpredict the maximum observed
concentrations. In this case, the 75th percentiles capture the maximum observed
concentrations, while the 90th percentiles overpredict by a factor of 2 or more. It
may be more appropriate to use only Lower Hudson River distributions at those
locations at which sediment concentrations (and corresponding benthic invertebrate
concentrations) do not show much variability.
10.3 Water Column lnvertebrate:Water Accumulation Factors (BAFs)
10.3.1 Approach
Water column invertebrates are defined as those that receive most of their
exposure to PCBs via the water column. As defined, this group includes
zooplankton as well as invertebrates living on substrates such as plants or rock
surfaces but are not in direct contact with the sediments. The approach taken
relates body burdens in water column invertebrates (on a lipid-normalized basis) to
water concentrations (normalized to particulate organic carbon). This was done for
the following reasons:
1. It is assumed that PCBs in the particulate phase in the water column and
PCBs in the dissolved phase in the water column are in quasi steady-state
over time scales of months during the Summer as discussed in Section 8.
Thus by establishing relationships between invertebrates and a particular
phase (particulate organic carbon in this case), overall accumulation from the
water column will be taken into account.
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2. The relationship to PCBs normalized to particulate organic carbon was
selected because, while water column invertebrates will accumulate PCBs
directly from the dissolved phase, the higher chlorinated congeners are
predominantly associated with the particulate phase which form the food
base for the invertebrates. Partition coefficients derived in the Data
Evaluation and Interpretation Report (TAMS/CADMUS/Gradient, 1996 -
pending publication) show that as much as 60 percent of PCBs in the water
column are associated with the particulate phase for tetra- and higher
chlorinated congeners.
Because there are no Phase 2 TAMS/Gradient samples for water column
invertebrates, and only a few collocated water column sampling stations and
ecological survey stations, several approaches were explored to derive relationships
that could be used in the food chain model. The approach described below and
alternative approaches (Section 10.3.3) are subject to various data limitations and
extrapolation problems. As a result, there is considerable uncertainty in the BAFs
that relate water column invertebrate body burdens to particulate water column
concentrations of PCBs.
The approach selected for deriving BAF values for water column
invertebrates relies upon historical data from the New York State Department of
Health studies for the Hudson River PCB Reclamation Demonstration Project
(Simpson et al., 1986). NYSDOH conducted long- and shortTterm biomonitoring
studies from 1976 to 1985 using caddisfly larvae, multiplate samples and
chironomid larvae.
NYSDOH placed artificial substrate samplers (multiplates) along 17 sites for
five weeks in the Hudson river from Hudson Falls to Nyack, New York (Novak et
al., 1988). Samplers remained in place for five weeks during July through
September collecting a composite of sediment, algae, plankton and various
macroinvertebrates. After collection, the samplers were analyzed for Aroclors
1016 and 1254. Invertebrates collected on the samplers included: Chironomidae,
ONgochaetes, Trichoptera, Ephemeroptera, Amphipoda and Elimidae. Chironomid
larvae and pupae were the most abundant invertebrate component from Fort
Edward to Saugerties. In addition, caddisfly larvae were hand-picked from rocks at
five designated sites: Hudson Falls, Fort Edward, Fort Miller, Stillwater and
Waterford.
The short-term biomonitoring study conducted by NYSDOH involved the
chironomid larvae, Chironomus tentans. Twenty-five laboratory-raised chironomid
larvae in nylon mesh packets were placed, in groups of ten, in steel mesh baskets
at four Hudson River locations (one at Bakers Falls, two at Thompson Island Pool,
and one at Fish Creek). One set of packets was exposed to the sediment at a
collection site on the eastern shore of the Thompson Island Pool. The remainder
were placed in the water column.
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This study showed that the PCB congener pattern in the chironomid tissue
differed significantly from the congener pattern observed in the water
(TAMS/Gradient, 1991). Other studies have also found this to be the case (Kadlec
and Bush 1994). Water column invertebrates respond on the order of days to
changes in water column concentrations of PCBs. Novak (1984) found that
chironomids exposed to the water column show concentrations 105 times higher
than water concentrations within 96 hours. The data show that concentrations in
water column invertebrates represent the first important link in the biomagnification
of PCBs along the aquatic food chain.
Other studies have shown that kinetic processes are significant even before
this stage of the food web (Skoglund et al., 1996). In a model developed for thd
Great Lakes, Skoglund found that phytoplankton accumulate more PCB than would
be predicted by equilibrium partitioning alone. Under low growth conditions, the
kinetic model and the equilibrium model results were similar. However, during
periods of intense growth, the equilibrium model did not fit the observed data as
well as the kinetic model.
The NYSDOH multiplate samples represent the only Hudson River specific
information available on the relationship between water column invertebrates and
water column concentrations. Under the assumption that the majority of water
column PCBs are associated with organic rich particles, we evaluated the
relationship of water column invertebrates to the particulate phase in the water
column as:
RAF = C /r
water ^invert' ^oc
where,
BAFwater = The bioaccumulation factor between water column invertebrates
and particulate bound PCB
Cinvert = m9 PCB per Kg lipid in invertebrate tissue
Coc = mg PCB per Kg organic carbon in suspended particulates.
The equation describes the relationship between individual multiplate
biological species and the water column, providing an indication as to how much
PCB associated with the organic fraction of the particulate in the water column is
likely to partition into the lipid of individual species. To define the relationship
between PCB and organic carbon associated with the particulate matter in the
water column, the following equation was used:
Coc= Csolid * TSS/POC
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mg PCB per Kg organic carbon in suspended particulates
Csoiid = mg PCB per Kg solid on multiplate sampler from NYSDOH
TSS = Total Suspended Solids in Kg/I (from TAMS/Gradient Phase 2)
POC = particulate organic carbon in Kg/I (from TAMS/Gradient Phase 2).
Note that TSS and POC were not synopticaily measured with Csolid. The
derivation of a BAF described above, assumes that the relationship between TSS
and POC is relatively consistent over time for a given river segment. The average
summer TSS and POC measurements were taken from the TAMS/Gradient Phase 2
dataset and paired by location to the Csond found on the multiplate samples from the
NYSDOH study.
The NYSDOH data are not available on a congener basis. The long-term
monitoring data only provide information on Aroclor 1016 and Aroclor 1254, and
total PCBs. The values derived for total PCBs can be used in the model for totals
and Aroclors but do not represent individual congeners. Further analysis is required
to obtain values of individual congeners. The short-term studies address uptake of
specific congeners, but cannot be used in this analysis, as they reflect uptake
responses on the order of 48-96 hours, rather than quasi-steady state conditions.
10.3.2 Calculation of BAFwater for Water Column Invertebrates
The BAF^ate,. between PCB concentrations in individual species from NYSDOH
multiplate samples and the mg PCB per kg organic carbon associated with
particulate matter in the water column is shown in Table 10-2 for Aroclor 1016,
Table 10-3 for Aroclor 1254 and Table 10-4 for total PCBs. Figures 10-41 through
10-43 show the distributional analysis conducted for these data. Distribution
fitting was done using Crystal Ball 4.0 for Excel. The data for each Aroclor and
total PCBs are matched against a number of known distributions and goodness-of-
fit tests conducted using appropriate statistical techniques. The most commonly
used tests include Chi-square, Kolmogorov-Smirnov, and Anderson-Darling, but
each test may not be appropriate for all distributions. In this case, the results from
the Kolmogorov-Smirnov and Anderson-Darling tests are more important to
consider, since these tests are more appropriate for data that are asymptotically
sensitive, or require a close fit at the tails (Madansky, 1988). Results were
compared for all tests as described next.
The Chi-square test breaks the observed distribution down into areas of equal
probability and compares the individual data points within each area to the number
of expected data points. A p-value of 0.5 or greater generally indicates a close fit
when using this test. The Kolmogorov-Smirnov and Anderson-Darling tests weight
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the observed and theoretical distributions greater at the tails than at the midranges.
A value of less than 0.03 for Kolmogorov-Smirnov and a value of less than 1.5 for
Anderson-Darling generally indicate a close fit for the particular distribution.
Figure 10-41 presents the results of the distributional analysis for Aroclor
1016. Values range from slightly above zero to 56. Page 2 of Figure 10-41 shows
the calculated percentiles. The widest range of BAF are found between the 90th
and 100th percentiles, indicating that only 10 percent of the modeled population
experience these higher BAFs. The best fit for a distribution for these data,
although they appear lognormal, is actually an extreme value distribution. The Chi-
square for the extreme value fit was 10.2 with a p-vafue of 0.68, Kolmogorov-
Smirnov was 0.04 and Anderson-Darling was 1.29.
Figure 10-42 presents the results of the distributional analysis conducted for
Aroclor 1254. The percentiles calculated for Aroclor 1254 are similar to those for
Aroclor 1016 except that the maximum observed BAF for Aroclor 1254 is 70. The
best distributional fit for Aroclor 1254 is the Weibull distribution, showing a Chi-
square of 20.14 and a p-value of 0.13, Kolmogorov-Smirnov is 0.06 and Anderson-
Darling was 0.71.
Figure 10-43 presents the results of the distributional analysis conducted for
total PCBs. The calculated percentiles ranges from slightly above zero to 58, with
the greatest range of BAF between the 90th and 100th percentiles. Goodness-of-fit
tests showed that the best distributional fit was the beta distribution with a Chi-
square of 15.3 and a p-value of 0.429, Kolmogorov-Smirnov was 0.05 and
Anderson-Darling was 1.01.
The distributions of bioaccumulation factors for the accumulation of
particulate organic carbon normalized PCB water concentrations to water column
invertebr?tes all show similarly elongated right tails, with the greatest spread in
accumulation factors between the 90th and 100th percentiles. Only 10% of the
population is expected to experience the range of accumulation factors between the
90th and 100th percentiles. The maximum observed BAF was used to truncate each
of the distributions described above so that no water column invertebrates
accumulated PCBs at greater than the observed maximum. These BAFs are used to
model the accumulation of particulate organic carbon normalized PCB water
concentrations to water column invertebrates for Aroclors 1016 and 1254, and
total PCBs.
10.3.3 Alternative Approaches
Several alternative approaches are being considered to evaluate the BAFs
developed in this study. These will be explored further in the next phase but are
summarized briefly below. Because there are no data suitable for model validation,
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it will be important to evaluate other approaches to quantifying this compartment in
the model.
Alternative Approach 1: Oliver and Niimi (1988)
Oliver and Niimi (1988) conducted bioaccumulation field studies in Lake
Michigan. They evaluated field results for an aquatic food web on a congener-
specific basis, a portion of which may provide useful information for this model
compartment. They provide BAFs for whole water to zooplankton and zooplankton
to a common water column invertebrate, Mysis re/icta. They also provide measured
suspended sediment values, although they do not provide an indication of the
fraction organic carbon. However, "...material in the water column is mainly
resuspended bottom sediment...", so it may be possible to use the measured TOC
in bottom sediments as a surrogate value.
Oliver & Niimi (1988) estimated BAFs ranging from 2 to greater than 14 on a
lipid-normalized basis for individual congeners from total water to plankton.
Derived ratios from plankton to mysids ranged from 1 to 10 on a lipid-normalized
basis for individual congeners.
The whole water zooplankton BAF may provide enough information on the
expected concentration in water column invertebrates. However, the data in Oliver
and Niimi (1988) are presented as arithmetic averages and standard deviations,
rather than log-space statistics required by the probabilistic model. Given the
lognormal distribution of the underlying data, the BAFs predicted by Oliver and
Niimi (1988) would tend to overpredict the geometric means utilized in the
probabilistic model.
Alternative Approach 2: Great Lake Initiative (GLI) BAF
The Great Lake Water Quality Initiative Technical Support Document provides
a procedure for determining bioaccumulation factors in four trophic levels:
phytoplankton, zooplankton, small fish and top predator fish. The approach relies
on the BAF equation provided in Oliver and Niimi (1988) divided by a laboratory-
measured BCF for each trophic level. The result is a food chain multiplier based on
the Log Kow of the contaminant in question. For Level 2, zooplankton, food chain
multipliers for contaminants with Log Kow greater than 6.5 can range from 0.1 to
19.
Alternative Approach 3: Bivariate Statistical Analyses (Section 9)
Section 9 provides the results of statistical analyses conducted for the
NYSDEC fish monitoring results on an Aroclor basis. This section provides BAFs
for several fish species, including pumpkinseed, a common forage fish. Embedded
in this value is the BAF from water to water column invertebrates, and from water
column invertebrates to fish. By combining the information from these analyses
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with information derived by comparing pumpkinseed concentrations to those found
on the NYSDOH multiplate samplers, it may be possible to disaggregate the relative
contribution of the water column to water column invertebrates.
Alternative Approach 4: Flow-Averaged Summer Concentrations to Benthic
Chironomids
As discussed in Section 10.2, benthos-associated chironomid show
significantly higher PCB concentrations, particularly for the lower chlorinated
congeners, than do the remaining benthic invertebrates. It is likely that some, if not
all, benthic invertebrates are experiencing potential exposure from water as well as
sediment, especially in areas such as Thompson Island Pool. One approach
involves considering the BAF from summer flow-averaged total water
concentrations to certain benthic invertebrate species, particularly chironomid. The
dataset for this approach is limited, and is therefore restricted by small sample
sizes. However, an exploratory analysis is included as part of future modeling work
(Appendix B).
Alternative Approach 5: Sediment Pore Water to Benthos as a Surrogate for Whole
Water to Water Column Invertebrates
This approach involves evaluating the relationship between estimated pore
water and benthic invertebrates. It may be that this relationship is indicative of the
relationship between whole water column concentrations and invertebrates. This
approach requires equilibrium assumptions between PCBs and sediments as
generally derived for nonionic organic compounds (e.g. Shea, 1988).
Alternative Approach 6: Evaluating Other Modeling Approaches
Skoglund (1996) recently developed a kinetic accumulation model for PCBs
based on data collected from Green Bay, Lake Michigan. Although data from other
systems may not be indicative of conditions in the Hudson River, the general
dynamics that have been observed in these datasets may provide useful insight into
mechanistic processes in the Hudson. One of the most important aspects revealed
by the work of Skoglund et al., is that an equilibrium model significantly
underestimates observed accumulation. This provides further evidence that
significant accumulation occurs at the low end of the food web. The data used in
the development of the Skoglund model have been made available and exploratory
analyses are included in the plans for future modeling work (Appendix B).
10.4 Forage FishrDiet Accumulation Factors (FFBAFs)
As discussed in Section 8 and Appendix A, forage fish are treated as a single
compartment that reflects the composition and feeding habits of species in the
Hudson. As a group, forage fish are expected to have a diet that varies depending
on the data available for that given river mile. Individual forage fish will vary from
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this percentage. For example, spottail shiners are expected to feed evenly on water
column and benthic invertebrates, while pumpkinseed favor water column food
sources. An appropriate weighted mean was used in the model depending on the
specific species caught at a sampling location. The approach used to develop
FFBAFs for forage fish is described below.
Note that there is an important distinction between model development and
model implementation. In model development, the full BSAF and BAF distributions
are used to estimate the range of expected PCB concentrations in the forage fish
diet. The ratio of individual measured forage fish concentrations to the mean
expected concentration in the diet (by sampling location) represents the distribution
of forage fish bioaccumulation factors. However, in model implementation, the
mean and associated standard error are used to represent the distributions derived
through model development.
10.4.1 Approach
Forage fish consume both water column and benthic invertebrates. As a
result, their dietary exposure to PCBs is represented as a weighted average of the
PCB concentration in the diet. Distributions in the FFBAF are derived from
measured concentrations of PCBs in forage fish at a river mile divided by the
estimated concentrations in their diet. The distributions for the benthic invertebrate
and water column invertebrate compartments discussed earlier were used to
estimate concentrations in those compartments. Due to the lack of information
regarding congener-specific uptake into the water column invertebrate
compartment, which comprises a significant portion of forage fish food, only
distributions for Aroclors 1016 and 1254 and total PCBs could be derived.
FFBAF values were derived by:
1. Evaluating the available data for forage fish <10 cm for each river mile.
Determining feeding preferences for use in the model based on typical
species composition at a given river mile combined with abundance data for
the Hudson River (Appendix A).
2. Plotting concentrations to identify a) which species contribute most to data
variability and b) which river miles show the greatest uncertainty and
variability in observed concentrations.
3. Estimating the expected PCB concentrations in benthic invertebrates and
water column invertebrates for Aroclors 1016 and 1254 and total PCBs
using the distributions described earlier in this section. A congener-specific
analysis is still pending based on the results from the water column
invertebrate box.
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4. Deriving a river-wide distribution of FFBAF by taking the ratio of a measured
individual forage fish concentration to the geometric mean dietary
concentration. The mean diet is represented by the weighted average of the
benthic invertebrate (measured) and water column invertebrate (estimated)
compartments.
The method provides a basis for deriving FFBAF values for forage fish as a
group as well as for the selected fish species, spottail shiner and adult pumpkinseed
sunfish. The Phase 2 data were not adequate for estimating FFBAF values for
small pumpkinseed sunfish that may be eaten by other fish species. Other
approaches for pumpkinseed are discussed in subsequent sections.
10.4.2 Water Column Concentrations Used to Derive FFBAF Values
Because forage fish feed on water column invertebrates and because there
are no synoptic data for these invertebrates in the Phase 2 dataset, body burdens
for the invertebrates were estimated from water column measurements and the
BAFwater distribution relating invertebrates to water as discussed in Section 10.3. A
summer average particulate water concentration was used for a given river reach,
jrmalized to fraction organic carbon.
The fraction organic carbon associated with the particulate matter in the
water column is described as:
FOC = 0.611 x WLOI@375°
where,
FOC = fraction organic carbon
0.611 = constant from the Data Evaluation and Interpretation Report
(TAMS/CADMUS/Gradient, 1996 - pending publication)
WLOl = weight-loss-on-ignition from TAMS/Gradient Phase 2 dataset
10.4.3 Forage Fish Body Burdens Used to Derive FFBAF Values
Bar charts were developed to show lipid-normalized concentrations in forage
fish by river mile. Two charts were prepared for each of the individual congeners,
Aroclors 1016 and 1254, and for total PCBs. The boxplots present the average
and 50% of observed values contained within each box. The lines extending from
the boxes show the upper and lower values with extreme values shown as
asterisks and identified by species. Extreme values are those values more than 1.5
times outside the interquartile range represented by the box. The mean forage fish
concentration by river mile charts show mean concentrations (all species combined)
within an error bar indicating plus and minus one standard error of the mean. The
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standard error provides information on how confident one is about the mean
estimate.
In general, concentrations show far less variability in the lower river than in
the upper river. As a trend, concentrations relatively steadily decline from river mile
169.5 down to 88.9. At river mile 58.7, a slight increase is seen. Within the
upper river, concentrations are highest at river mile 189.5. River mile 191.5 shows
lower concentrations than river miles 194.1 or 189.5, probably as a result of the
specific location chosen for sampling. However, these data show that PCB body
burdens in forage fish are highly variable in the Thompson Island Pool area and
areas close to sources of PCBs. Forage fish body burdens may also reflect the
sediment type of the habitat (i.e. fine-grain sediments tend to accumulate higher
levels of PCBs).
Forage Fish Body Burden Data for BZ#4
Figure 10-44 shows that the concentrations of BZ#4 in forage fish do not
display much variability from river mile 169.5 on down the river. However, sample
sizes were smaller than for the upper river. The upper river, by contrast, displays
greater variability in BZ#4 concentrations. Maximum observed concentrations at
river miles 189.5 and 194.1 are in the tessellated darter. Figure 10-45 shows that
the concentrations of BZ#4 at most river miles ranged from just above 0 to about 5
ug/g. Three river miles had considerably higher means, with wider error bars: River
mile 194.1, with a mean just over 20 ug/g, river mile 189.5, with a mean of
approximately 18 ug/g, and river mile 191.5, with a mean of 8 ug/g.
Forage Fish Body Burden Data for BZ#28
Figure 10-46 shows that the concentrations of BZ#28 in forage fish are most
variable between river miles 189.5 and 194.1. Tessellated darters and spottail
shiners represent the species with the highest observed concentrations. Figure 10-
47 shows that concentrations at most river miles ranged from just above 0 to just
under 20, with narrow error bars. Concentrations at river miles 194.1 and 189.5
were considerably higher, about 80 and 100 ug/g, respectively, with wide error
bars, indicating uncertainty in the mean estimate.
Forage Fish Body Burden Data for BZ#52
Figure 10-48 shows that the concentrations of BZ#52 are most variable
between river miles 189.5 and 194.1. Tessellated darters and spottail shiners
represent the species with the highest observed concentrations. BZ#52
concentrations for most of the river miles range from just above 0 to 20 ug/g.
Higher concentrations (around 80 ug/g) and wider error bars are shown for river
miles 194.1 and 189.5. A somewhat higher mean concentration (about 40 ug/g)
was also shown for river mile 1 91.5.
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Forage Fish Body Burden Data for BZ#101 with BZ#90
Figure 10-50 shows that river miles 189.5 through 194.1 display the
greatest variability in forage fish body burdens, with one spottail shiner at river mile
194.1 exceeding the mean by almost a factor of 5. Figure 10-51 shows that
forage fish concentrations at most of the river miles ranged from just above 0 to
about 15, with narrow error bars. Again, Thompson Island Pool and the area
closest to PCB sources, river miles 189.5 through 194.1, show much higher
concentrations, with wider error bars.
Forage Fish Body Burden Data for BZ#138
Figure 10-52 shows that PCB concentrations display the greatest variability
within the Thompson Island Pool. Figure 10-53 shows that forage fish
concentrations at most of the river miles ranged from just above 0 to about 10
ug/g. Thompson Island Pool shows much higher concentrations, with wider error
bars.
Forage Fish Body Burden Data for Aroclor 1016
Figure 10-54 presents the boxplots for Aroclor 1016. Individual tessellated
darters and spottail shiners show higher concentrations than the remaining fish
within the Thompson Island Pool. From river mile 169.5 on down the river,
concentrations are tight and steadily decreasing. Figure 10-55 shows that the
mean estimates from river mile 169.5 on down the river show narrow error bars,
but there is less confidence in the mean estimates for river miles 189.5 and 194.1.
Forage Fish Body Burden Data for Aroclor 1254
Figure 10-56 provides boxplots for Aroclor 1254. High concentrations are
observed in individual tessellated darters and spottail shiners. These high
concentrations contribute to the wide error bars on the means calculated for river
miles 189.5 and 194.1, as presented in Figure 10-57.
Forage Fish Body Burden Data for Total PCBs
Figure 10-58 shows that mean concentrations are similar for river miles
189.5 and 194.1, and significantly higher at these locations than elsewhere in the
river. Figure 10-59 shows that forage fish Total PCB concentrations at most of the
river miles ranged from just above 0 to about 300 ug/g.
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10.4.4 Calculation of FFBAF Values for Forage Fish
The body burden data provide important information on the expected
variability in forage fish concentrations. The data show that the greatest variability
in fish concentrations exists within the Thompson Island Pool and areas closest to
the source of PCBs. This is also the area showing greatest sediment concentration
heterogeneity, and an analysis of the water column data show that water column
concentrations vary significantly depending on the time of year. Fish in this area
experience transient exposures and integrate both "hot spots" and less
contaminated area exposures.
The forage fish model was run for Aroclors 1016, 1254, and total PCBs to
evaluate the goodness-of-fit between observed and modeled fish body burdens. As
described in Appendix A, the expected contribution of benthic and water column
invertebrates was estimated based on the forage fish data available for each river
mile. For example, there are a number of river miles for which forage fish
concentrations are represented by spottail shiners. Data show that spottail shiners
consume relatively equal amounts of benthic and water column invertebrates.
Other river miles have a number of forage fish species represented, and accordingly
a weighted mean was used to estimate an overall feeding preference by river mile.
The next phase of work will focus on model verification through a comparative
analysis with the Gobas model.
The model calculated 10th, 25th, 50th, 75th, and 90th percentiles as well as a
maximum. Percentiles were calculated from the observed forage fish distribution at
each river mile using the SPSS software package. The modeled concentrations of
PCBs in forage fish follow a lognormal distribution, characterized by long right tails.
After log-transforming the fish concentration percentiles (both observed and
modeled), the observed percentiles were plotted against the model-generated
percentiles. This plot is shown in Figure 10-60 for Aroclor 1016, Figure 10-61 for
Aroclor 1254, and Figure 10-62 for total PCBs. The center line represents the
regression equation with 95% confidence limits. A second set of figures presents
individual forage fish observations with modeled output superimposed. These data
are presented by river mile, and note that each river mile has anywhere from 3 to
15 individual data points (see Figures 10-54 through 10-59 for the n at each river
mile).
Figure 10-63 shows individual observed forage fish concentrations at each
river mile with the 50th and 90th calculated percentile values from the model. At
river mile 194.1, the model 90th percentile estimate exceeds the maximum
observed concentration. At river mile 169.5, the 90th percentile calculated from
the model falls within the range of the maximum observed forage fish
concentrations. In the lower river, the 50th percentile concentrations match the
observed values best, while the 90th percentile estimates exceed the maximum
observed forage fish concentrations.
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Figure 10-64 presents the individual observed forage fish concentrations by
river mile ("measured" line) with model outputs superimposed. In the lower river,
the modeled 50th percentile represents the most accurate descriptor of observed
forage fish concentrations. Due to the variability in the upper river, the 90th
percentile modeled output captures most of the observed variability, and the
maximum modeled output is high (i.e., 100% of observations are significantly less
than predicted).
Figure 10-65 presents the results for total PCBs. Individual observed forage
fish concentrations are represented by a dashed line, with modeled outputs
superimposed. Again, the modeled maximum exceeds the observed maximum in
every instance except one observation at river mile 189.5. The modeled 50th
percentile represents the closest fit in most portions of the river.
One of the goals of the probabilistic model is to predict a high-end exposure
[i.e., 90% or 100% of the population will experience PCB body burdens at this
level). It is important to capture information about the variability of fish
concentrations, particularly in the Thompson Island Pool area, in order to more
effectively reach management decisions. In areas where variability dominates, the
oility to make predictions is confoundei. The next phase of this analysis will
focus on validating the model through hindcasting and by using recently-collected
NYSDEC data that were not used in model development.
10.4.5 Calculation of FFBAFs for Small Pumpkinseed Sunfish
Knowing that pumpkinseed, for example, consume primarily water column
invertebrates (Appendix A), the TAMS/Gradient team explored this relationship
further. The Phase 2 dataset did not contain many data for pumpkinseed around
the 10cm size range, so the NYSDEC data were explored in more detail. Individual
pumpkinseed (less than 10 cm) concentrations were compared to the NYSDOH
lipid-normalized multiplate data. Multiplate data are available for July and August
of a given year while the pumpkinseed were sampled in September. The average
multiplate concentration was used as a dietary concentration for pumpkinseed.
River Mile 175 (Stillwater) provided the best available dataset. These data are
shown in Table 10-5 for Aroclor 1016, Table 10-6 for Aroclor 1254 and 10-7 for
total PCBs. These FFBAF values are significantly less than the FFBAF values
estimated from the model. This may indicate that the BAFwater used to estimate
water column invertebrate concentrations in the model is too low and that the bulk
of the bioaccumulation occurs already in the water column to water column
invertebrate step.
The next phase of this analysis will be to evaluate the distribution between
pumpkinseed and the multiplate samplers in more detail.
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10.5 Piscivorous Fish:Diet Accumulation Factors (PFBAF)
The Phase 2 dataset imposes limitations on these analyses. There are very
few data available for large (greater than 150 cm) piscivorous fish, notably
largemouth bass. In fact, yellow perch is one of the only semi-piscivorous fish in
the correct size range. In order to demonstrate the method, PFBAF derived through
the Phase 2 dataset have been explored using yellow perch. Largemouth bass are
discussed in Section 10.5.2.
10.5.1 Approach Used for Yellow Perch
Only larger yellow perch were selected for analysis (greater than 150 cm).
This species consumes a small percentage of forage fish (between 10 and 15
percent of its diet), the balance comprised of invertebrates. The PFBAFs for yellow
perch were derived as follows:
1. Determine weighted average dietary contribution to yellow perch. This is
estimated to be 15 percent forage fish, 20 percent benthic invertebrates, and
65 percent water column invertebrates. These proportions are being
evaluated in a sensitivity analysis to determine the impact of changing
feeding preferences.
2. Estimate the expected yellow perch accumulation factors by dividing the
measured individual yellow perch concentrations by the mean dietary
concentrations. The mean dietary concentration is calculated using the
percentages shown in step 1 as applied to the measured geometric-mean
concentration for each compartment (except for water column invertebrates,
for which there are no measured data).
Figure 10-66 shows the distribution of PFBAF values for yellow perch for
total PCBs. Figure 10-67 shows the predicted distribution of yellow perch
concentrations based on these bioaccumulation factors. The results are presented
here primarily as a demonstration of the method.
10.5.2 Approach Used for Largemouth Bass
In the TAMS/Gradient Phase 2 dataset, there were no data available for
largemouth bass of the correct size (all samples were for largemouth bass less than
16 cm). Largemouth bass do not become piscivorous until at least 20 cm. At the
small sizes of the largemouth bass in the Phase 2 dataset, the largemouth bass
display feeding patterns equivalent to a typical forage fish, such as pumpkinseed.
Therefore, analysis for largemouth bass has to rely on the data from the Phase I
NYSDEC dataset. In the absence of suitable Phase 2 data, a preliminary analysis
was made relating largemouth bass lipid-normalized concentrations to pumpkinseed
lipid-normalized concentrations for measurements reported as Aroclors 1016 and
1254.
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Largemouth Bass to Pumpkinseed BAF for Aroclor 1016
Figure 10-68 shows the ratio of largemouth bass greater than 25 cm to
pumpkinseed less than 10 cm for Aroclor 1016 by river mile and year. The ratios
range from less than one to nearly four, showing a fairly consistent and tight
relationship. Pumpkinseed derive between 80 and 90 percent of their PCB body
burden from water column sources. Largemouth bass are also closely tied to the
water column, and the bivariate statistical analysis of the same dataset showed
that largemouth bass are 91 percent explained by the water column.
Largemouth Bass to Pumpkinseed BAF for Aroclor 1254
Figure 10-69 shows the ratio of largemouth bass greater than 25 cm to
pumpkinseed less than 10 cm for Aroclor 1254 by river mile and year. These ratios
display greater variability than do the ratios for Aroclor 1016. They range from 1
to almost 15, with outliers up to 22. Generally, however, the ratios are near 5,
consistent with data from other studies.
Largemouth Bass to Pumpkinseed BAF for Total PCBs
Figure 10-70 shows the ratio of largemouth bass greater than 25 cm to
pumpkinseed less than 10 cm for total PCBs by river mile and year. These ratios
are similar to those derived for Aroclor 1254, but show lower standard errors and
fewer outliers. The range is generally from 1 to 5, except for River Mile 190 during
1990.
Additional Analyses for Largemouth Bass
Additional work is underway to define the relationships between largemouth
bass body burdens and their diet. The distributions derived above will be explored
in greater detail, and the model will be used to "predict" 1995 data collected by
NYSDEC. The use of the Gobas (1993) model is also being explored, as it has been
shown that the dynamics of digestion and gastrointestinal absorption may play the
most important role in determining PCB body burdens in piscivorous fish.
10.5.3 Approach Used for White Perch
The available white perch data are only for river miles where there are no
corresponding water column data. White perch, as described in the fish profiles,
tend to exhibit a diet that is 50 percent sediment sources and 50 percent water
column sources. Therefore, the necessity for initial water column concentrations
precluded a detailed analysis of white perch using the Phase 2 dataset. Work is
still underway on the white perch bioaccumulation model and no results are
presented in this report.
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10.6 Demersal Fish:Sediment Relationships
10.6.1 Approach and Calculations of BAF Values
Brown bullhead accumulation factors were calculated by two methods:
1. Individual brown bullhead PCB concentrations were compared to the
geometric mean sediment concentrations at a given river mile;
2. Individual brown bullhead concentrations were compared to the geometric
mean benthic invertebrate concentrations at a given river mile.
Table 10-8 shows these results. There are only four individual brown
bullhead samples available from the Phase 2 dataset, making detailed statistical
analysis of the distribution difficult. The next phase of work will focus on
incorporating data from other sources in more accurately defining the distribution,
and using NYSDEC data for validation.
10.7 Summary of Probabilistic Food Chair. Models
Probabilistic food chain models have or are being developed for six fish
species. This work is still in progress. In addition, the models that have been
developed are being reviewed and modified on an on-going basis. This report
provides an overview of the general structure of the models but should not be
considered to reflect the final structure. The models are being used to explore the
relationships within the food web in the Hudson River and to evaluate data
variability. The status of the food chain models at this writing (August, 1996) is as
follows:
Fish Species
Status of Model/Future Work
Spottail Shiner
Model complete for total PCBs and Aroclors 1016 and 1254; next steps involve
comparing model outputs to historical data for total PCBs; these results may be used to
further tune the model; additional work is required to use the model for individual
congeners
Pumpkinseed
Model development is continuing; data issues still need to be resolved
Brown Bullhead
Model complete for total PCBs and congeners; next steps involve comparing model
outputs to historical data for total PCBs; these results may be used to further tune the
model
Yellow Perch
Model complete for total PCBs; a sensitivity analysis is being performed with regard to
effects of different dietary assumptions; next steps include comparing model outputs to
historical data for total PCBs; these results may be used to further tune the model;
additional work is required to use the model for specific Aroclors and congeners
Largemouth Bass
Preliminary model has been developed for Aroclors and total PCBs; next steps involve
comparing model outputs to historical data; these results may be used to further tune the
model; data are not sufficient for constructing a model for specific congeners
White Perch
Work has begun on this model but there are a number of data issues still to be resolved
10-27
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10.8 Illustration of Food Chain Model Application
The yellow perch model has been run under a set of assumptions to illustrate
one form in which output would be provided. It should be noted that the model
output is for illustrative purposes and that the model is not in final form. All results
are based on unvalidated data.
An example of a model run is given in Figure 10-71, the report generated
from a Monte Carlo run of the model in Crystal Ball. This model run has taken as
input an average water and sediment concentration. The report illustrates the
various transfer steps in the process as distributions. Note that because forage fish
and yellow perch body burdens will reflect the result of an "average" diet, mean
BAF values and associated standard errors of the means are used to represent
transfers among the food chain components. Resulting PCB body burdens (on a
lipid normalized basis) are represented as a full distribution. The model can also
provide output on a whole body or fillet basis but these are not included with the
example run.
One way in which the model can be used is to generate look-up tables or
lomographs for various combinations cf water and sediment PCB levels. The
model is run for combinations of water and sediment concentrations (yielding
output similar to that shown in Figure 10-71 for each run) and the percentiles
extracted from the model output. The result is a look-up table such as presented in
Tables 10-9 through 10-12. These tables provide look-up tables for the 15th
percentile, the average, the 75th percentile, and the 95th percentile. These look up
tables can then be linked with the output of the HUDTOX model. Other percentiles
of interest to human health and ecological risk assessors or of regulatory interest
could also be specified. The model can be used to identify the fraction of the
population expected to fall above or below a selected concentration.
The look-up tables will provide information on how different sediment:water
exposure concentrations impact fish body burdens under a specific set of feeding
assumptions. Sensitivity analyses are included as part of the future modeling work
(Appendix B). These analyses will evaluate the relative contributions of sediment
and water exposure pathways.
10.9 Comparison of Bivariate Statistical and Food Chain Models
The Bivariate Statistical Model has been applied to the historical dataset of
Aroclors 1016 and 1254 while the food chain models have been applied primarily
to the TAMS Phase 2 data. While it is planned to run the food chain models for the
historical dataset, this has not yet been done. It is possible to compare the models
in terms of the degree to which PCB body burdens are related to water and
sediment exposures as well as the general magnitudes of total uptake.
10-28
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The models have been applied, in common, to three species: yellow perch,
largemouth bass, and brown bullhead. The relative contributions of water and
sediments to the body burdens of these species is summarized below. It should be
noted that sediments and water concentrations are related and that the
comparisons reflect the predominance of particular exposure pathways rather than
the importance of particular sources.
jj Fish Species
Bivariate Statistical Model
Probabilistic Food Chain Model
Pumpkinseed
Sunfish
Aroclor 1016
61 % water
39% sediment
Aroclor 1 254
72% water
28% sediment
Modeling has not been completed.
Dietary analysis (Appendix A) indicates
the species feeds 80% in water column
and 20% from sediment
Yellow Perch
Aroclor 1016
84% water
16% sediment
Aroclor 1254
81 % water
19% sediment
Water contributes 18 to 40% under one
set of assumptions and 67 to 90% under
another set of assumptions; model is
being evaluated to determine which
combination is more likely
Largemouth Bass
Aroclor 1016
88% water
1 2% sediment
Aroclor 1254
42% water
58% sediment
Water contributes 49 to 80%
Brown Bullhead
Aroclor 1016
73% water
27% sediment
Aroclor 1254
14% water
86% sediment
Sediment is considered to represent
100% of the source
A comparison of the two models reveals some similarities and some
differences. Both models indicate the importance of water exposure pathways for
the pumpkinseed and the yellow perch (under a specific set of assumptions about
feeding). Both species rely upon water column invertebrates as a large fraction of
their diets.
In the case of largemouth bass, the Bivariate Statistical Model suggests that
water was more important for Aroclor 1016 (91%) and sediment was more
important for Aroclor 1254 (56%). In comparison, the Probabilistic Food Chain
Model suggests that water contributes 49 to 80% of the total PCB body burden.
The importance of a sediment component in both models - as compared to yellow
perch and pumpkinseed - indicates that they may be reflecting a common exposure
pathway. Based on the food chain model, this reflects a higher percentage of
forage fish in the diet of largemouth bass combined with a high percentage of
benthic invertebrates.
10-29
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The two models show similarities and differences for the brown bullhead.
The Bivariate Model indicates that water is the major source for Aroclor 1016 and
sediment the major source for Aroclor 1254. The food chain model is based on a
direct relationship between body burdens and either sediments or sediment
invertebrates. As a result, brown bullhead body burdens are 100% related to
sediments. Because of the differences between the two models, this relationship
will be examined further by applying the food chain model to the dataset used for
the Bivariate Model.
The results of the Bivariate Model help to define the dietary contribution from
water and sediment pathways. The mean estimates from the Bivariate Model are
complemented by the distributional analysis provided by the Probabilistic Model.
The Probabilistic Model presents a range of expected concentrations. The mean
estimate does not address the likelihood that the majority (or what percentage) of
fish will experience that concentration. The Probabilistic Model incorporates the
observed variability in the extensive Hudson River dataset to better define the
percentage of the population that will be at or below a particular PCB
concentration.
10-30
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GLOSSARY
Abbreviations and Acronyms
BAF Bioaccumulation Factor
BCF Bioconcentration Factor
BSAF Benthic Invertebrate: Sediment Accumulation Factors
CD-ROM Compact Disc - Read Only Memory
cfs Cubic feet per second
cm Centimeter
Corp. Corporation
deg. C Degree Celsius
DOC Dissolved Organic Carbon
e.g. For example
EPA Environmental Protection Agency
et al. and others
FA Flow Average (Phase 2 Water Column Monitoring Program)
FEMA Federal Emergency Management Agency
FFBAF Forage Fish: Diet Accumule"'-- rs
FGET Food and Gill Exchange of Toxic Substances Model
foe Fraction organic carbon
fps Feet per second
g Gram
GBTOX Green Bay Mass Balance Model
GE General Electric
GIS Geographic Information System
GLI Great Lake Initiative
HEC-2 US Army Corps of Engineers, Hydraulic Engineering Center,
Surface Water Profile Model
HOC Hydrophobic Organic Chemicals
HUDTOX Hudson River Mass Balance Model
i.e. That is
kg Kilogram
m/s Meters per second
mg/l Milligrams per liter
mi2 Square miles
MT Metric Ton
MVUE Minimum Variance Unbiased Estimator
NAPL Non-aqueous Phase Liquid
ng/m3 Nanograms per cubic meter
ng/L Nanograms per liter
NGVD National Geodetic Vertical Datum
NOAA National Oceanic and Atmospheric Administration
NYSDEC New York State Department of Environmental Conservation
NYSDOH New York State Department of Health
NYSDOT New York State Department of Transportation
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oc
Organic Carbon
PCBs
Polychlorinated Biphenyls
PFBAF
Piscivorous Fish: Diet Accumulation Factors
RI/FS
Remedial Investigation/Feasibility Study
RMA-2V
Thompson Island Pool Hydrodynamic Model
ROD
Record of Decision
RPI
Rensselaer Polytechnic Institute
TIN
Triangulated Irregular Network
TIP
Thompson Island Pool
TSF (tsf)
Temperature slope factor
TSS
Total Suspended Solids
ug/g (ppm)
Micrograms per gram (parts per million)
fiq/L
Micrograms per liter
USEPA
United States Environmental Protection Agency
USGS
United States Geological Survey
WASP4
USEPA, Water Quality Analysis Simulation Program, Version 4
TOXI4
Toxic Chemical Module in WASP4
WASTOX
USEPA toxic chemical modeling framework
WY
Water year
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