PHASE 2 REPORT - REVIEW COPY
FURTHER SITE CHARACTERIZATION AND ANALYSIS
VOLUME 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
MAY 1999
For
U.S. Environmental Protection Agency
Region 2
and
U.S. Army Corps of Engineers
Kansas City District
Volume 2D - Book 3 of 4
Bioaccumulation Models
Limno-Tech, Inc.
Menzie-Cura & Associates, Inc.
Tetra Tech, Inc.

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PHASE 2 REPORT - REVIEW COPY
FURTHER SITE CHARACTERIZATION AND ANALYSIS
VOLUME 2D - BASELINE MODELING REPORT
HUDSON RTVER PCBs REASSESSMENT RI/FS
MAY 1999
For
U.S. Environmental Protection Agency
Region 2
and
U.S. Army Corps of Engineers
Kansas City District
Volume 2D - Book 3 of 4
Bioaccumulation Models
Limno-Tech, Inc.
Menzie-Cura & Associates, Inc.
Tetra Tech, Inc.

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PHASE 2 REPORT - REVIEW COPY
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HUDSON RIVER PCBs REASSESSMENT RI/FS
CONTENTS
BOOK 3 OF 4	Pages
TABLE OF CONTENTS
LIST OF APPENDICES	 iv
LIST OF TABLES 	v
LIST OF FIGURES	 vii
LIST OF ACRONYMS 	x
I. INTRODUCTION
1.	INTRODUCTION	1
1.1	Background 	1
1.2	Purpose of Report	1
1.3	Report Format and Organization	2
2.	GENERAL BACKGROUND ON PCB UPTAKE	5
2.1	PCB Compounds	5
2.2	PCB Accumulation Routes	6
2.2.1	Direct Uptake from Water 	6
2.2.2	Uptake via Food	6
2.2.3	Uptake from Sediments 	7
2.3	Food Web Models from the Literature and their Sensitivity to Input Parameters
	'	8
3.	MODELING APPROACH: FISH BODY BURDENS	11
3.1	Modeling Goals and Objectives	11
3.2	Conceptual Basis for Hudson River Bioaccumuiation Models 	13
3.3	Bivariate BAF Analysis for Fish Body Burdens 	18
3.3.1	Rationale and Limitations for Bivariate BAF Analysis	18
3.3.2	Theory for Bivariate BAF Analysis of PCB Bioaccumuiation	19
3.4	Probabilistic Bioaccumuiation Food Chain Model	20
3.4.1	Rationale and Limitations	20
3.4.2	Model Structure	22
3.4.3	Spatial Scale for Model Application	24
3.4.4	Temporal Scales for Estimating Exposure to Fish	24
3.4.5	Characterizing Model Compartments 	24
3.4.5.1	Sediment to Benthic Invertebrate Compartment	24
3.4.5.2	Water column: Water Column Invertebrate Compartment .. 25
3.4.5.3	Forage Fish Compartment 	25
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CONTENTS
BOOK 3 OF 4	Pages
3.4.5.4	Piscivorous Fish Compartments	26
3.4.5.5	Demersal Fish 	27
3.5 FISHPATH and FISHRAND Mechanistic Modeling Framework	27
3.5.1	Rationale and Limitations	27
3.5.2	Model Structure	28
3.5.2.1 Rate Constants	29
3.5.3	Spatial Scale for Model Application	31
3.5.4	Temporal Scales for Estimating Exposure to Fish	31
3.5.5	Application Framework	32
3.5.5.1	Comparison with Gobas (1993) Lake Ontario Data:
The Steady-State Case	32
3.5.5.2	Comparison with Gobas (1995) Lake Ontario Data:
The Time-Varying Case	32
4.	BIVARIATE BAF ANALYSIS OF FISH BODY BURDENS	33
4.1	Data Used for Development of Bivariate BAF Analyses	33
4.1.1	Fish Data 	33
4.1.1.1	Locations and Species Analyzed	33
4.1.1.2	Lipid Normalization 	34
4.1.1.3	Season. Age, and Sex 	34
4.1.1.4	Laboratories and Methods for PCB Analysis	35
4.1.1.5	Standardization of PCB Analytical Results	36
4.1.1.6	Theoretical "What if?" Analysis 	37
4.1.1.7	Split Sample Comparisons 	40
4.1.1.8	Interlaboratorv Comparisons	41
4.1.1.9	Translation Methods 	42
4.1.2	Water column Data	43
4.1.3	Sediment Data	46
4.1.4	Functional Grouping of Sample Locations for Analysis 	48
4.2	Results of Bivariate BAF Analysis	48
4.3	Discussion of Bivariate BAF Results 	50
4.3.1	Comparison to Published BAF Values 	50
4.3.2	Fit of Bivariate Models to Observations 	51
4.3.3	Relative Importance of Sediment and Water Pathways	53
4.4	Summary 	54
5.	CALIBRATION OF PROBABILISTIC BIOACCUMULATION FOOD CHAIN
MODEL 	55
5.1 Overview of Data Used to Derive BAFs	55
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CONTENTS
BOOK 3 OF 4	Pages
5.1.1	Benthic Invertebrates	55
5.1.2	Water Column Invertebrates	55
5.1.3	Fish	56
5.1.4	Literature Values 	56
5.2	Benthic Invertebrate: Sediment Accumulation Factors (BSAF)	57
5.2.1	Sediment Concentrations 	57
5.2.2	Approach 	57
5.2.3	Calculations of BSAF Values for Benthic Invertebrates 	58
5.3	Water Column Invertebrate: Water Accumulation Factors (BAFs) 	59
5.3.1	Approach 	59
5.3.2	Calculation of BAF water for Water Column Invertebrates 	61
5.4	Forage Fish: Diet Accumulation Factors (FFBAFs)	61
5.4.1	Approach	61
5.4.2	Forage Fish Body Burdens Used to Derive FFBAF Values	62
5.4.3	Calculation of FFBAF Values for Forage Fish	62
5.5	Piscivorous Fish: Diet Accumulation Factors (PFBAF):
Largemouth Bass	63
5.5.1 Largemouth Bass to Pumpkinseed BAF for Total PCBs 	63
5.6	Validation of Probabilistic Model Using Fate and Transport Model
Output as Input 	64
5.7	Discussion of Results 	64
6.	FISHPATH AND FISHRAND: TIME-VARYING MECHANISTIC MODELS
BASED ON A GOBAS APPROACH	67
6.1	Model Input Data	67
6.1.1	Non Species-Specific Parameters	67
6.1.1.1	Sediment and Water Concentrations 	67
6.1.1.2	Temperature 	68
6.1.1.3	Total Organic Carbon in Sediment	69
6.1.1.4	Log Octanol-Water Partition Coefficient (KoJ 	69
6.1.2	Species-Specific Data 	69
6.1.2.1	Lipid Content	70
6.1.2.2	Fish Weight	71
6.1.2.3	Dietary Composition	71
6.2	Results of the Calibration Exercise 	72
7.	PRELIMINARY BIOACCUMULATION MODEL PREDICTIONS 	73
7.1 Probabilistic Empirical Model	73
7.1.1 Sediment and Water Concentration Inputs	73
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BOOK 3 OF 4	Pages
7.1.2	Preliminary Predicted Largemouth Bass Body Burdens under
Zero Upstream Boundary Conditions 	73
7.1.3	Preliminary Predicted Largemouth Bass Body Burdens under
Constant Upstream Boundary Conditions	74
7.2	FISHRAND Results	74
7.2.1	Sediment and Water Concentration Inputs 	74
7.2.2	Predicted Preliminary PCB Concentrations in Fish under Constant
Upstream Boundary Conditions	74
7.3	Discussion of Preliminary Predictions	75
8.	DISCUSSION OF UNCERTAINTY 	77
8.1	Model Uncertainty	77
8.1.1	Model Uncertainties in the Fate and Transport Models	77
8.1.2	Model Uncertainties in the Bioaccumulatin Models	77
8.1.2.1	Probabilistic Empirical Model and Bivariate Statistical
Model 	78
8.1.2.2	FISHRAND and FISHPATH 	78
8.2	Parameter Uncertainty 	79
8.2.1	Sensitivity Analysis	80
8.2.2	Lipid Content	80
9.	SUMMARY AND CONCLUSIONS	83
9.1	Summary of Food Web Models	84
9.2	Principal Report Findings 	86
REFERENCES 	89
LIST OF APPENDICES
Appendix A FISH PROFILES	 A-l
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BOOK 4 OF 4
LIST OF TABLES
2-1 A Comparison of the BAF Range Predicted by Bobas and Thomann Models
4-1 Count of NYSDEC Hudson River Fish Samples for PCB Aroclor Quantitation
Collected Between River Miles 142 and 193 by Laboratory and Year
4-2 Aroclor Standards and NYSDEC Rules for Calculating Total PCBs from
Analyses Reported by Hazleton and Hale Creek for Upper Hudson River
Samples
4-3 Packed-Column Peaks Used by NYSDEC Contract Laboratory "'Hazleton"
and Associated PCB Congeners for Upper Hudson Fish Sample Aroclor
Quantitation
4-4 Weight Percents of Congeners in Packed-Column Peaks Used for "Hazleton"
Aroclor Quantitation Schemes, based on Capillary Column Analyses of
Aroclor Standards
4-5 NYSDEC Upper Hudson Fish Concentrations as mg/kg-lipid Converted to
Tri+PCBs for Bivariate BAF
4-6 Assignment of Water column Concentrations to Fish Sampling Locations in
the Upper Hudson River
4-7 Summer Average Water Column Concentrations of Tri-PCBs (ng/1) Used
for Bivariate BAF Analysis
4-8 Annual Sediment Tri-PCB Concentrations used in Bivariate BAF Analysis
4-9 Models of Mean Tri-PCB Concentrations in NYSDEC Hudson River Fish
Samples Based on Water Column Concentration Only (mg/kg-Lipid)
4-10 Models of Mean Tri-PCB Concentration in NYSDEC Upper Hudson Fish
Samples Regressed on Water Column and Sediment Concentration
4-11 Models of Base-10 Log of Mean Tri-rPCB Concentration in NYSDEC
Hudson River Fish Samples Based on Log Water Column Concentration
Only (mg/kg-Lipid)
4-12 Models of Base 10 Log of Mean Tri+PCB Concentration in NYSDEC Upper
Hudson Fish Samples Regressed on Log Water Column and Log Cohesive
Sediment Concentration
4-13	Normalized Beta Coefficients and Elasticities for the Bivariate .Arithmetic
Model
5-1	Final Distributions Used in Probabilistic Empirical Model
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BOOK 4 OF 4
6-1 Distributions Used in FISHRAND
6-2	Summary of Relative Percent Difference Between Modelled and Observed
7-1	Year by which Selected Target Levels are Achieved under Current
Assumptions using FISHRAND
8-1	Results of Sensitivity Analysis for Spearman Rank Correlation - Lipid
Normalized
8-2 Results of Sensitivity Analysis for Partial Rank Correlation - Lipd Normalized
8-3 Results of Sensitivity Analysis for Spearman Rank Correlation - Wet Weight
8-4 Results of Sensitivity Analysis for Partial Rank Correlation - Wet Weight
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BOOK 4 OF 4
LIST OF FIGURES
3-1 Conceptual Framework for Empirical Probabilistic Model
3-2 Conceptual Schematic of FISHPATH, FISHRAND, and Gobas Field Measurement for
Lake Ontario
3-3 Comparison Between FISHPATH, FISHRAND and Published Gobas:
Steady-State
3-4	Comparison Between FISHPATH, FISHRAND and Published Gobas:
Time-Varying
4-1	Comparison of Hazleton PCB Quantitations and Sum of Tri — Congeners
4-2 Summer Average Water Column Concentration, Sum Tri PCBS
4-3 Scatterplot Matrices for Fish, Sediment, and Water Tri+ PCB Concentrations
in the Upper Hudson River 1977-1997
4-4 Relation of Mean Concentration in Pumpkinseed to Summer Average Water
Column Concentration
4-5 Observed versus Predicted Concentrations of Tri-f-PCBs for Brown Bullhead
from Bivariate BAF Model
4-6 Observed versus Predicted Concentrations of Tri 4-PCBs for Largemouth Bass
from Bivariate BAF Model
4-7 Observed versus Predicted Concentrations of Tri-PCBs for Pumpkinseed
from Bivariate BAF Model
4-8 Comparison of Arithmetic and Log-Log Bivariate BAF Models for Tri + PCBs
in Pumpkinseed
4-9 Comparison of Bivariate BAF Model Predictions and Observations of Mean
Summer Body Burden of Tri -f PCBs in Brown Bullhead
4-10 Comparison of Bivarite BAF Model Predictions and Observations of Mean
Summer Body Burden of Tri + PCBs in Pumpkinseed
4-11	Comparison of Bivariate BAF Model Predictions and Observations of Mean
Summer Body Burden of Tri -i-PCBs in Largemouth Bass
5-1	TOC-Normalized PCB Concentrations in the Hudson River Based on
Phase 2 1993 Data
5-2 BSAF Results
5-3 Cumulative Distribution Function for BSAF
5-4 Water Column to Water Column Invertebrate BAF Results
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BOOK 4 OF 4
5-5 Forage Fish Concentrations and FFBAF Results
5-6 Summary of Largemouth Bass to Pumpkinseed Ratios
5-7 Hindcasting Concentrations from Fate and Transport Modeling for Water and
Sediment
5-8 Probabilistic Empirical Model Calibration Results using Output from Fate and
Transport Model (for Pumpkinseed)
5-9 Probabilistic Empirical Model Calibration Results using Output from Fate and
Transport Model (for Largemouth Bass)
5-10	Probabilistic Empirical Model Calibration Results on a Wet Weight Basis
using Output from Fate and Transport Model (for Largemouth Bass)
6-1	Hindcasting Concentrations from Fate and Transport Modeling for Water and
Sediment used in FISHRAND
6-2 Lipid Distributions for FISHRAND
6-3 Weight Distributions used in FISHRAND
6-4 Calibration Results for FISHRAND using Fate and Transport Model Output
as Input
6-5	Seasonal Differences in White and Yellow Perch
7-1	Predicted Concentrations from Fate and Transport Model under Zero Upstream
Boundary Conditions
7-2 Predicted Concentrations from Fate and Transport Model under Constant
Upstream Boundary Conditions
7-3 Predicted Lipid-Normalized Concentrations under Zero Upstream Boundary
Condition from Empirical Probabilistic Model
7-4 Predicted Wet Weight Concentrations under Zero Upstream Boundary
Condition from Empirical Probabilistic Model
7-5 Average Contribution to Variance under Zero Upstream Boundary Condition for
Empirical Probabilistic Model
7-6 Predicted Lipid-Normalized Concentrations under Constant Upstream
Boundary Condition from Empirical Probabilistic Model
7-7 Predicted Wet Weight Concentrations under Constant Upstream Boundary
Condition from Empirical Probabilistic Model
7-8 Average contribution to Variance under Constant Upstream Boundary Condition for
Empirical Probabilistic Model
7-9 Predicted Dissolved Water and Dry Weight Sediment Concentrations under
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Zero Boundary Conditions
7-10 Predicted Dissolved Water and Dry Weight Sediment Concentrations under
Constant Upstream Boundary Conditions
7-11 Predicted Lipid-Normalized Concentrations from FISHRAND for River Mile 189
7-12 Predicted Wet Weight Concentrations from FISHRAND for River Mile 189
7-13 Predicted Lipid-Normalized Concentrations from FISHRAND for River Mile 168
7-14 Predicted Wet Weight Concentrations from FISHRAND for River Mile 168
7-15 Predicted Lipid-Normalized Concentrations from FISHRAND for River Mile 157
7-16 Predicted Wet Weight Concentrations from FISHRAND for River Mile 157
7-17 Predicted Lipid-Normalized Concentrations from FISHRAND for River Mile 154
7-18 Predicted Wet W7eight Concentrations from FISHRAND for River Mile 154
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
ACRONYMS
BAF	Bioaccumulation Factor
BCF	Bioconcentration Factor
BSAF	Benthic Invertebrate: Sediment Accumulation Factor
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)
FFBAF	Forage Fish: Diet Accumulation Factors
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
HOC	Hydrophobic Organic Chemicals
HUDTOX	Hudson River Mass Balance Model
i.e.	That is
kg	Kilogram
m/s	Meters per second
mg/1	Milligrams per liter
mi"	Square miles
MT	Metric Ton
MVUE	Minimum Variance Unbiased Estimator
NAPL	Non-aqueous Phase Liquid
ng/rrr	Nanograms per cubic meter
ng/L	Nanograms per liter
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
PCBs	Polychlorinated Biphenyls
PFBAF	Piscivorous Fish: Diet Accumulation Factors
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
ACRONYMS
RI/FS
Remedial Investigation/Feasibility Study
RMA-2V
Thompson Island Pool Hydrodynamic Model
ROD
Record of Decision
RPI
Rensselaer Polytechnic Institute
TSS
Total Suspended Solids
ug/g (ppm)
Micrograms per gram (parts per million)
|ig/L
Micrograms per liter
US EPA
United States Environmental Protection Agency
USGS
United States Geological Survey
WASP4
I'SEPA. Water Quality Analysis Simulation Program, Version 4
TOC
Total organic carbon
TOXI4
Toxic Chemical Module in WASP4
WASTOX
USEPA toxic chemical modeling framework
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Chapter 1

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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. The Lower Hudson refers to the
portion of the river downstream of Federal Dam to the Battery.
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) immediately downstream of the old Fort
Edward dam site.
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 for striped bass in the Lower Hudson River. This ban remains in effect.
In 1984 the U.S. Environmental Protection Agency (L'SEPA) 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.
See Book 2 for figures of the river.
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:
Phase 1 - Interim Characterization and Evaluation
Phase 2 - Further Site Characterization and Analysis
Phase 3 - Feasibility Study.
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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 confirmatory 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 re-issued in August 1998 provides the validated
data for the Phase 2 investigation. The Data Evaluation and Interpretation Report
(TAMS/CADMUS/Gradient, 1997) 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 Baseline Modeling Report is Volume 2D 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. The scope of the Preliminary Model
Calibration Report was limited to documentation of the conceptual approaches, databases and
preliminary calibration results for the fate and transport and bioaccumulation models. The
Bivariate and Empirical Probabilistic models were included in the Preliminary Model Calibration
Report.
1.3 Report Format and Organization
Section 2 of this report contains background information on the theory of PCB uptake
into fish, a general summary of the preliminary modeling results and initial conclusions drawn
from this work. Section 3 contains a description of the specific approaches taken for each of the
fish body burden models as well as mathematical descriptions of the individual models. Section 4
contains the results from the bivariate BAF analyses. Section 5 contains initial calibration and
validation results for the probabilistic empirical model using the hindcasting sediment and water
results from the fate and transport models. Section 6 contains initial calibration and validation
results for FISHRAND (mechanistic time-varying model incorporating distributions and based
on a Gobas approach) using the hindcasting sediment and water results from the fate and
transport models. Section 7 provides preliminary predictive results for 1998 - 2018 based on
inputs from the fate and transport models for the constant upstream boundary condition. Section
8 contains a discussion of the uncertainties in the modeling analysis as well as a sensitivity
analysis. Section 9 presents the summary and conclusions for Books 3 and 4 of the Baseline
Modeling Report.
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The material in this report has been divided into four separate books. Book 1 contains the
report text, a list of references, and a glossary of abbreviations and acronyms for the fate and
transport modeling. Book 2 contains all tables, figures, plates and appendices for Book 1. Book
3 contains the report text, a list of references, and a glossary of abbreviations and acronyms for
the food chain modeling. Book 4 contains all tables, figures, plates, and appendices for Book 3.
Within Book 4, Appendix A contains ecological profiles for fish species represented in the fish
body burden models and the derivation of feeding preference distributions for the individual fish
species.
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Chapter 2

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2. GENERAL BACKGROUND ON PCB UPTAKE
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.
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: and. excretion rates.
3.	Environmental factors, including temperature, pH, light, current, suspended particles, and
dissolved organic compounds.
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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 predator}' 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.
2.2.1	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.
2.2.2	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.
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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.
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, 1995. 1998) 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
pharmacokinetic module. The mechanistic model presented here (FISHRAND) is based on these
approaches (1993, 1995, 1998).
As part of this modeling effort, Menzie-Cura & Associates have evaluated a number of
fish gut contents from the NYS DEC sampling effort. Similarly. Exponent, Inc. on behalf of
General Electric conducted a study on fish gut contents and identified specific invertebrates
down to the lowest practical taxonomic level in the diets of fish. This information, together with
historical data from the Hudson River power plant studies, have been used to more precisely
define food web relationships in the Hudson. The results of this effort are discussed in Appendix
A.
2.2.3 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
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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 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.
2.3 Food Web Models from the Literature and their Sensitivity- to Input Parameters
All bioaccumulation models use a set of parameters to predict the body burdens of
organic contaminants in higher organisms. The uncertainty associated with these parameters
contributes to the uncertainty of the risk estimate. Burkhard (1998) compared the sensitivity of
the Gobas (1993) and Thomann (1989) model outputs to changes in their input parameters.
Sensitivity of the models to changes in input parameters was determined by running each model
once with nominal input values, and then changing one input value by 10%. and running the
model with the altered input value. A sensitivity index of 1.0 means that a 10% change in the
input parameter resulted in a 10% change in the model output. In this case, the model output
examined was the Bioaccumulation Factor, which is equal to the ratio of the lipid-normalized
concentration of chemical in fish to the concentration of freely dissolved chemical in water.
For both models, the input parameters with the largest influences were:
•	lipid contents of the organisms;
•	Kow of the chemical;
•	ratio of the concentration of chemical in sediment organic carbon to the concentration
in overlying water (nsocw); and,
•	feeding preferences of the organisms (only for chemicals with log Kow exceeding 6).
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The Sensitivity Index ranged up to about -20 (indicating a decrease in BCF) for the
feeding preference of a benthic invertebrate on phvtoplankton in the Thomann model. The
models were less sensitive to changes in organism weight, temperature (input to Gobas model
only) and sediment organic carbon (input to Gobas model only).
The approach described above is limited because it does not take into account uncertainty
in input modeling parameters. For example, an input parameter with low sensitivity (i.e.
sensitivity index is close to 1) adds considerable uncertainty to estimates of model outputs if the
measurement uncertainty distribution of this input parameter is relatively large. Uncertainty
associated with the input parameters may result from analytical errors in the measurement of the
parameter, sampling that is not representative of the population, or lack of sufficient information
about the parameter. Moreover, many input parameters are variable in nature (fish body weight,
lipid content, etc.)
The dual influences of variability and uncertainty in the input parameters on model
outputs must be considered when evaluating the overall model uncertainty. Monte Carlo
simulations should be performed for each input parameter, using a plausible range of values or
distribution for each input parameter. Burkhard (1998) compared the ratios of the 90,n and the
10th percentiles of the model output derived from the simulations among input parameters. For
both models, nsocw, Kow, and feeding preferences resulted in the largest range of simulated
output values. Table 2-1 summarizes results from Burkhard (1998).
Note, however, that the findings of Burkhard (1998) are based on the analysis of a Great
Lakes food web in which benthic organisms are an important food source for higher trophic level
organisms. In food webs where the benthic component is less important, the importance of the
sediment-related input parameters on the uncertainties associated with predicted model outputs
may be different.
The model used by Iannuzzi et al. (1996) is based on a Monte Carlo version of the
equations developed by Thomann et al. (1992), and Gobas (1993). They developed probabilistic
distributions for several parameters that are typically used in mechanistic bioaccumulation
models to predict the uptake of organic contaminants in aquatic food webs. The ranges, central
tendencies, and distributions of key parameters of the models were derived from a critical
evaluation of the literature on the physiology and ecology of three common estuarine organisms
rather than from site-specific experimental data. Distributions of the physical/chemical
characteristics (i.e. the octanol-water partition coefficient, Kovv) for several congeners of PCBs
were also compiled from the literature.
This model was used to estimate the concentrations of five coplanar PCB congeners in
adult mummichog fish, blue crab, and striped bass, using distributions of available data on PCB
and total organic carbon (TOC) concentrations that were measured in surface sediments from the
Passaic River in northern New Jersey. A model sensitivity analysis was performed to rank input
parameters according to their contribution to model predictions.
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Results of the sensitivity analysis suggest that the input parameters that most influence
the model (not listed in order of importance) are:
BSAF (biota-sediment accumulation factor) for infaunal organisms;
lipid content;
chemical concentrations in sediment;
total organic carbon (TOC) content of sediments;
the chemical assimilation efficiency (CAE);
residence time in the river for striped bass; and,
log Kow.
In summary, both Burkhard (1998) and Ianuzzi et al. (1996) concluded that the lipid
content of the exposed organisms and the Kow of the contaminant influence estimates of tissue
concentrations more than other parameters. The ability of organisms to metabolize specific PCB
congeners is also an important factor in the quantitative evaluation of uncertainty.
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Chapter 3

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3. MODELING APPROACH: FISH BODY BURDENS
3.1 Modeling Goals and Objectives
The goal of this component of the modeling effort is to develop a framework for relating
body burdens of PCBs 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 the 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:
•	relate historical body burden data (originally reported as PCB Aroclors, Aroclor
totals, and, individual congeners for a limited subset of the historical data) to
exposure concentrations in water and sediments;
•	relate current and future body burdens (as PCB Aroclors, totals, and individual
congeners) to exposure concentrations in water and sediments;
•	provide estimates in a form that can be used for human health risk assessments;
•	provide estimates in a form that can be used for ecological risk assessments; and.
•	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 and the impact of No
Action and/or potential remedial alternatives.
To achieve these objectives, three modeling approaches have been developed to relate
PCB exposure concentrations in water and sediment to body burdens. Each of these approaches
organizes the data in different ways to provide complementary views of PCB uptake. These
approaches are introduced next.
Bivarictte BAF Analysis'. This analysis uses available time series data to develop statistical
relationships between concentrations in water and sediments and those in fish based on
observations from the historical New York State Department of Environmental Conservation
(NYS DEC) yearly monitoring. This analysis represents an empirical perspective of the
statistical relationship between fish body burdens and sediment and water exposures in a tiered
approach to food chain modeling.
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Empirical Probabilistic Food Chain Model. This model relies on knowledge of feeding
relationships to link body burdens to water and/or sediments through a series of empirical
transfer coefficients using a combination of the historical NYS DEC data. New York State
Department of Health (NYS DOH) data, and the US EPA Phase II data. This model provides
ground-truth information on observed relationships between food-web compartments.
FISHRAND and FISHPATH: Gobas Time-Varying Mechanistic Models: These mechanistic,
time-varying models are based on the modeling approach presented in Gobas (1993 and 1995).
The models rely on solutions of differential equations to describe the uptake of PCBs over time,
and incorporate both sediment and water sources to predict the uptake of PCBs based on prey
consumption and food web dynamics. Two models are presented: FISHPATH, a deterministic
version, and FISHRAND, a fully probabilistic version.
These approaches complement one another and represent a logical progression in the
evaluation of PCB uptake. Both the bivariate analysis and the empirical probabilistic model
utilize derived Bioaccumulation Factors (BAFs) and rely on organizing observed data into
meaningful relationships, while FISHPATH and FISHRAND are mechanistic and based on
mass-balance of PCBs rather than direct observations. The agreement between these and the
resultant estimates of body burdens provide a check on the three approaches. The bivariate
analysis indicates the relative importance of water and sediment pathways from a statistical, data-
based point of view irrespective of the underlying biology. The probabilistic bioaccumulation
model represents a slight refinement and limited mechanistic consideration by explicit
incorporation of feeding preference data and uncertainty and variability information.
FISHPATH describes the mass-balance of PCB uptake and elimination on a deterministic basis,
while FISHRAND will predict probability distributions of expected concentrations in fish based
on mechanistic mass-balance principles, an understanding of PCB uptake and elimination, and
information on the feeding preferences of the fish species of interest.
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:
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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
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 BAF Analysis uses pumpkinseed, brown bullhead, largemouth bass, white
perch, and yellow perch. Sufficient historical data were not available for spottail shiner;
however, goldfish 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 would be
desirable to have a model for the shortnose 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 evaluate the shortnose
sturgeon, based on physiological, feeding and habitat selection characteristics.
3.2 Conceptual Basis for Hudson River Bioaccumulation Models
The food chain models developed here share a common conceptual 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;
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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 for the bivariate BAF analysis and the probabilistic empirical model;
4.	Fish body burdens are in gwasz-steady-state with the water and/or sediment at time scales
on the order of one or more years under both the bivariate BAF analysis and the
probabilistic empirical model.
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. "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 particulate-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 ol
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 chain both through the dissolved phase and ingestion
of particulate matter. As Di Toro et al., state, "biological effects (to invertebrates) appear tc
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 oi
exposure. In fact, neither of these conclusions necessarily follow from these data."
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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 BAP 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
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/fugacitv 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 BAF
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
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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.
Very little information is available on how often contaminants in the environment reach
equilibrium among phases. If equilibrium conditions are not reached, time-variant models are
more appropriate for predicting contaminant concentrations. The distributions of contaminant
concentrations might differ from predicted concentrations if the system is not in equilibrium
because there is high temporal variability or because biological processes maintain
disequilibrium conditions. Many ecosystem and physical processes are variable over time. The
input of a contaminant into an estuary, for example, can occur during episodic events, such as
large storms or periodic disposal of dredged sediments.
The FISHRAND and FISHPATH models are designed to evaluate the time-varying
effects of PCB uptake on predicted PCB fish tissue concentrations based on sediment and water
exposure concentrations predicted from fate and transport models as inputs. Both these models
and the empirical probabilistic model rely on information regarding feeding preferences of the
fish species. To more precisely define food web dynamics in the Hudson River. Menzie-Cura
undertook the following analysis.
The invertebrate component in the fish diet can consist of invertebrate species that are
themselves exposed to PCBs in surface water, pore water, and through their food. The food items
of invertebrate species are may, in turn, be exposed to different levels and types of PCBs.
Understanding this component of the food web is not simple. Food habits of fish species are
described in Appendix A. Invertebrates eaten by Hudson River fish occupy a range of habitats
and eat a range of organic materials. The habitat and feeding preference for individual
invertebrate species influences the extent to which they are exposed directly and indirectly to
PCBs in sediments and in the water column. In our opinion these influences can only be
approximated based on available information and there are uncertainties associated with these
estimates. A qualitative conceptual framework for considering how invertebrates can be exposed
to PCBs in water and sediment is given below. It shows that invertebrate species probably
experience a gradient of exposure conditions ranging from predominantly sediment exposure to
predominantly surface water exposure. However, we believe that there are many species that will
fall between these extremes and which will experience both sediment and water exposures. We
have considered this when ascribing feeding preferences for fish that rely on invertebrates for
food. However, we acknowledge that there is little quantitative information for determining the
extent to which many of invertebrate species - primarily those that live on the surface of
sediments - are influenced by sediment and water exposures.
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Conceptual Framework for Considering The Influence
Of Sediment and Water as Exposure Media
For Invertebrates in the Diet of Fish


Source of Food
General

Phytoplankton
Periphyton
Surface
Organic
Deposits
Deeper Organic
Deposits
Physical
Water column
or Phytophilous
Zooplankton
Phytophilous
invertebrates
Phytophilous
invertebrates

Flabitat
At sediment
surface
Meroplankton,
Epibenthic
invertebrates
living in littoral
zone
Epibenthic
invertebrates


Below sediment

Infaunal
Infaunal

surface

invertebrates
invertebrates
The simplified conceptual framework indicates how habitat location and food type could
influence the relative degree of influence of water and sediment on PCB exposure for
invertebrates. The increasing influence of sediments is illustrated qualitatively with an increasing
gray scale. Habitat affects the availability of different food types as well as the water exposures
experienced by invertebrates. For example, infaunal invertebrates are exposed primarily to pore
water while zooplankton are exposed primarily to surface water. Epibenthic invertebrates may be
exposed to some mix of pore water and surface water.
Examples of invertebrate species that may occupy the matrix of physical habitat and food
type are given below.
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Source of Food
General
Physical
Habitat
Phytoplankton
Periphvton
Water column
or Phytophilous
Bosmina
(Cladocera);
Copepods,
Gastropods
Surface
Organic
Deposits
Dicrotendipes
spp.
(Chironomidae)
At sediment
surface
Below sediment
surface
Gammarus spp.
(Amphipoda),
Ostracods
Gastropods,
Caecidotea
(Isopoda)
Deeper Organic
Deposits
Chironomus ; Limnodrilus
(Chironomidae) spp.
(Oligochaetea)
As the conceptual framework suggests. PCB exposure for invertebrate species can be
complex, involving aspects of their feeding and physical ecology. Some species occur in a
variety of habitat types. Examples include the amphipod Gammarus and the chironomid insect
larvae of the genera Polypedilum and Dicrotendipes. Some invertebrates - planktonic rotifers,
copepods, and cladocerans - are carried with water masses and experience exposures associated
with "parcels" of water that are transported downstream. Other invertebrates live on the surface
of plants and experience water exposures that vary over time as water passes a particular
location. Still others are meroplanktonic (Chaoborus, Gammarus) and may be carried with the
currents diurnally the remainder of the time spent in the sediments. Therefore while we simplify'
the characterization of food webs for modeling purposes, it should be evident that the system is
complex and that representations of relationships between water, sediment, invertebrates, and
fish should be viewed as uncertain estimates. We have made an effort to represent this
uncertainty in the models by expressing feeding preferences as triangular distributions in
FISHRAND and as uniform distributions in the empirical probabilistic model. However, our
ability' to do this is limited by the available knowledge about the system and the species within it.
We do not think that this uncertainty can be easily reduced.
3.3 Bivariate BAF Analysis for Fish Body Burdens
3.3.1 Rationale and Limitations for Bivariate BAF Analysis
The Bivariate BAF Analysis provides an empirical summary of historical data on fish
body burden in the Hudson River. The analysis relies on the available time series of
environmental and fish concentration 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
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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. For this reason, the
Bivariate BAF Analysis has not been used to predict future concentration trends. The Bivariate
BAF Analysis, however, is an important first step for the development of more complex, food
web models, for which the database is limited. By summarizing historical relationships between
fish body burdens and environmental concentrations, the Bivariate BAF Analysis provides
important constraints on the form and parameterization of the food web bioaccumulation model.
3.3.2 Theory for Bivariate BAF Analysis of PCB Bioaccumulation
The general theoretical framework for deriving Bivariate Statistical Models was
introduced in Section 3.3. 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. Correlating fish body
burdens to both water and sediment removes the difficulty of disequilibrium between the
sediment and water compartments.
The Bivariate BAF .Analysis 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.
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 model of Thomann et al., (1992) for
Lake Ontario, which avoids the need for a detailed study of population dynamics through steady-
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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, and for providing a statistical perspective on the more
sophisticated, biologically-based food chain models.
As discussed in Section 3.3, 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:
CL
fl,
Cs
Bw... '
focw
focs
(3-1)
in which, for species i:
Cf = PCB concentration in fish (wet-weight basis)
fl = Lipid fraction in fish
Bw = Partial BAF relating fish concentration to water-column
concentration
Csw = PCB concentrations on suspended solids
focw = Organic carbon fraction of suspended solids
Bs ~ 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, foc%v 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.
3.4 Probabilistic Bioaccumulation Food Chain Model
3.4.1 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 BAF Analyses
that predict single population statistics such as the average values of PCBs. The conceptual
approach is presented in Figure 3-1. The Probabilistic Models have been developed to provide:
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1.	information on the fractions of the fish populations that are at or above particular PCB
levels; and
2.	an empirical framework for constructing biologicallv-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 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 mean seasonal 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
by a distribution of transfer or bioaccumulation factors. 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
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role of direct water uptake versus food was examined, and quantitatively evaluated using
FISHRAND and FISHPATH, the mechanistic models.
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. As discussed
earlier, identification of the specific life histories of the invertebrates that fish tend to consume
plays an important role in identifying predominant exposure pathways.
3.4.2 Model Structure
The conceptual framework for the probabilistic PCB food chain models is illustrated in
Figure 3-1. A separate model is developed for each fish species reflecting the particular 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, only the results for total
PCBs, expressed as ITri+, will be discussed. The models are designed to evaluate quasi steady-
state conditions on an annual basis. The features of the models include:
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
whole water column concentrations. These invertebrates are presumed to be exposed to
PCBs associated with organic particulate material in the form of detritus or algae as well
as through direction partitioning of the dissolved phase. 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 mariner are presumed to be in steady state with temporally
averaged whole water column concentrations of PCBs as described by whole water
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 have been derived for the calibration congeners, Aroclors,
and total PCBs (TAMS/Gradient, PMCR. 1996). Results presented here are for total
PCBs, expressed as STri+.
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 total concentration of
PCBs in the water (including both particulate and dissolved). Since feeding occurs
primarily in the warmer months, the probabilistic model has been developed using
summer averages. The fate and transport model results are averaged to provide summer
water concentrations and annual sediment concentrations.
Based on the above, the following media and biological compartments are identified: 1)
water, 2) sediment, 3) water invertebrates, 4) sediment invertebrates, 5) forage fish, and 6) the
individual fish species.
The food chain models are currently implemented as a Monte Carlo spreadsheet model.
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/Crystal 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
method 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. In this case, both variability and uncertainty are
represented in the distributions. For example, observed variability in the relationship between
sediment concentrations and benthic invertebrates is attributable to both true population
heterogeneity (variability) as well as measurement error (uncertainty). It is operationally difficult
to truly separate these two sources. Consequently, the model can be viewed as predicting
population profiles of PCB concentrations rather than the uncertainty associated with predictions
for any given percentile of variability.
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3.4.3	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
larger stretches of the river.
The HUDTOX model provides daily estimates of sediment and water concentrations for
segments in the upper river (see Books 1 and 2). For water concentrations, there are both spatial
and temporal gradients in concentration that are appropriately averaged to provide estimates
representative of how fish integrate exposures. Fish exposures will van" around this mean value.
Calibration results for fish body burdens are presented for two river miles: 189 (Thompson Island
Pool), and 168 (Stillwater). These locations represent the bulk of fish concentration data for the
upper river. Fate and transport modeling segments 10 through 29 in the TIP are averaged, and 37
through 41 for Stillwater.
Predictions are provided at four river reaches: 189 (TIP), 168 (Stillwater), 157, and 154
(just above the Federal Dam).
3.4.4	Temporal Scales for Estimating Exposure to Fish
Exposure concentrations for water are estimated as summer averages (May through
September). This averaging period is coincident with the time that fish are at their summer
foraging areas. Sediment concentrations show very little variation on an annual basis, thus
sediment concentrations are averaged annually.
3.4.5	Characterizing Model Compartments
3.4.5.1 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 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:
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where,
BSAF =
Cbenthic
Csediment
BSAF =
Cbenthic
Csediment
(3-2)
biota - sediment accumulation factor
= the concentration of PCB in an individual organism as p.g/g lipid
= mean PCB concentration in sediments as (j.g/g organic carbon
3.4.5.2 Water Columrt: Water Column In vertebrate Compartment
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, 1998) provides estimated partition coefficients for a number of key
congeners. These data show 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.
Combining both the dissolved and particulate concentrations in a whole water
concentration, we considered the role of total water using a BAF approach between water and
fish:
where.
PWBAF - Cmver(/Cwater
PWBAF = The bioaccumulation factor between water column
invertebrates and total water PCB concentrations
Cinvert	= m§ PCB per Kg lipid in invertebrate tissue
Cwater	- mg PCB per L water
(3-3)
3.4.5.3 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
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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:
Cff
FFBAF = —^	(3-4)
^diet
where,
forage fish bioaccumulation factor
concentration in individual forage fish (jag per g lipid)
weighted average of diet concentration (|j.g per g lipid - species-specific
benthic and water column invertebrate fractions)
FFBAF--
cff :
Cdiet
3.4.5.4 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 - ^	(3-5)
c
diet
where,
BAF = piscivorous fish bioaccumulation factor relative to diet
Cfish = concentration in piscivorous fish (ug per g lipid)
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Cdiet = weighted average of diet concentration (ug 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.
3.4,5.5 Demersal Fish
The final category of fish to be considered are the demersal or bottom-feeding fish. The
best species to consider for this compartment is the brown bullhead, which feeds primarily from
sediment sources, although it is properly considered an omnivorous fish. Brown bullhead Iipid-
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:
BSAF =	(3-6)
^ Sid
where,
BSAF = brown bullhead bioaccumulation factor
CgB = concentration in brown bullhead (ug per g lipid)
Csec/ = concentration in the sediment (fig per g carbon).
The dietary bioaccumulation factor is defined as;
BAF =	(3-7)
^' mrert
where,
BAF = brown bullhead bioaccumulation factor
CjjSh = concentration in brown bullhead (jig per g lipid)
Cinvert= concentration in benthic invertebrate (jig per g lipid).
3.5 FISHPATH and FISHRAND Mechanistic Modeling Framework
3.5,1 Rationale and Limitations
FISHPATH and FISHRAND incorporate time-varying information on water and
sediment concentrations to mechanistically describe the uptake of PCBs into fish tissues. The
models are based on the peer-reviewed time-varying Gobas model (Gobas, 1993; Gobas et al.,
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1995; 1999). FISHPATH is a deterministic model programmed in Stella™ while FISHRAND is
designed to incorporate probability distributions and is programmed in Fortran-90 and Delphi-3.
Figure 3-2 shows the conceptual model for the Hudson River food web. The numbers
show in the Figure 3-2 represent the mean dietary percentage from particular compartments for
each species. Development of the distributions for each of the parameters described in this
section is presented in section 6.
3.5.2 Model Structure
The model consists of a series of compartments as in the empirical probabilistic model.
Pelagic invertebrates are assumed to be in equilibrium with truly dissolved water column
concentrations, and benthic invertebrates are assumed to be in equilibrium with sediment
concentrations. Forage fish feed on these two compartments in accordance with their species-
specific foraging strategies. Piscivorous fish consume some amount from each compartment in
the same proportions as in the probabilistic model.
Biota can gain PCBs via uptake from the water column or through consumption of
contaminated prey (both sediment and water based), and lose PCBs via fecal excretion or
respiration.
The general form of the differential equation describing the change in concentration of
PCBs in biota with respect to time is given by:
dt
(3-8)
where:
k;	=	gill uptake rate (L/Kg/d)
Cwd	=	truly dissolved concentration in water
kd	=	dietary uptake rate (d'1)
Cdie.	=	concentration in the diet {gig)
k2	=	gill elimination rate (d"1)
k.	=	fecal egestion rate (d")
k^	=	metabolic rate (d"1) (assumed to be zero)
kg	=	growth rate (d"1) (takes the place of explicit age-class consideration)
Cfl5h	=	concentration in fish
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3.5.2.1 Rate Constants
Direct Uptake from Water
The rate at which fish take up chemicals from water depends upon the gill
ventilation rate and the rate of diffusion of the chemical across the gills. The Gobas (1993) model
uses experimental data to derive uptake rates based on:
Kow of the compound,
weight of the fish (VF, in kg ),
rate of chemical transport in the aqueous phase of the gill (Qw. in units of L/dav).
rate of chemical transport in the lipid phase of the gill (QL, in units of L/day).
where:
k, = gill uptake rate (d"1)
Qw = transport rate in the aqueous phase
Q, = transport rate in the lipid phase
Vf= fish weight in kg (described by a distribution in FISHRAND)
The transport rates in the aqueous and lipid phases are given by:
k\ =
V// +Vf/
/£?» /£?i *
(3-9)
0H = 88J*t}06
(3-10)
0\ = ^-
~ 100
(3-11)
The gill elimination rate is then given by:
L/ * Ko
(3-12)
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Uptake from Consumption of Prey Items
The rate at which fish take up chemicals from food depends upon the food ingestion rate,
the rate of diffusion of the chemical across the intestinal wall, and the fecal egestion rate. The
Gobas model (1993) assumes that the efficiency with which chemicals are taken up from food is
related to the transport of chemical across aqueous and lipid phases of the gut:
=	(vi 3)
Vf
where:
kd = dietary uptake rate constant (d"1)
Ed = uptake efficiency (unitless)
Fd = food ingestion rate (kg food/day)
Vf = fish weight (kg)
The uptake efficiency, Ed, is given by:
Ed =				(3-14)
5.3e - 8 * A'o. - 2.3
And the food ingestion rate. Frf. in [kg food/day], is given by:
Fd = 0.022 * V/0M *e006r	(3-15)
where:
Fd food ingestion rate (kg food/day)
Vf = fish weight (kg) (described by a distribution in FISHRAND)
T = monthly mean water temperature (deg C)
Fecal egestion rate constant
The fecal egestion rate is given by:
ke = 0.02*kd	(3-16)
ke = fecal egestion rate (d"1)
kd = dietary uptake rate constant (d"1)
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Growth rate constant
The growth rate constant presented in the original Gobas model is given by the following
equations.
For temperatures greater than 10°C (T>10°C), the growth rate constant, kg. is given by:
For temperatures less than or equal to 10°C (T<10°C), the growth rate constant, kg, is given by:
3.5.3	Spatial Scale for Model Application
The model takes as starting concentrations the predicted sediment and water
concentrations from the fate and transport model. Concentrations are averaged across individual
sampling grids to represent the integrating effects of fish foraging and habitat strategies. In the
Thompson Island Pool, 29 segments are averaged for water and 5 segments (both cohesive and
noncohesive) for sediments. Sediment concentrations represent a weighted average of cohesive
and non-cohesive sediments based on area and an assumption that fish, on average, spend 75% of
their time over cohesive sediments (except for white perch, which tend to range throughout the
river, including main channel areas). Water column concentrations are also area-weighted, under
the assumption that fish will spend preferentially more time in near-shore areas but are likely to
be found throughout the range.
3.5.4	Temporal Scales for Estimating Exposure to Fish
The model uses dissolved water concentrations averaged on a monthly basis, and annual
average sediment concentrations. Sediment concentrations show significant spatial
heterogeneity, but little variation over time. Very little is gained by specifying monthly average
sediment concentrations versus annual averages. Dissolved water concentrations, by contrast,
show significant temporal variability. Consequent}-, the mechanistic models use monthly-
average dissolved water concentrations as inputs.
The expected value for spatially and temporally averaged exposures is obtained under the
assumption that concentrations follow a lognormal distribution. Under this assumption, the
expected value is given as:
kg = 0.01 * Vj~°2
(3-17)
kg = 0.002* V/~02
(3-18)
E[x] = expln(*w
(3-19)
And the variance as:
F[.x] = (£[jc]):
(3-20)
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3.5.5 Application Framework
Two versions of this model have been developed. The first, FISHPATH, was developed
using Stella™, a dynamic simulation software package. This version of FISHPATH provides
time-varying, but deterministic, results. This model was first developed based on the Gobas
published version (1993). The model was run in steady-state mode using the Lake Ontario data
presented in Gobas (1993) to demonstrate and verify model functionality and reliability. The
model was then coded in Fortran-90 with user interface developed in Delphi-3 and again run in a
steady-state, deterministic manner to demonstrate and verify concordance with the Stella version
and with the Gobas (1993) published results. The Fortran version, however, has the ability to
incorporate probabilistic information and is referred to as FISHRAND.
3.5.5.1	Comparison with Gobas (1993) Lake Ontario Data: The Steady-State Case
The steady-state solution is given by:
k\ * Cw + kd* Cdiei
Cfish =		(-?"21)
kl + ke + km + kg
Figure 3-3 shows the comparison between FISHPATH, FISHRAND. and published data
from Gobas (1993). Pages 1 and 2 of this figure present the variables used in the model. Page 3
describes bioavailability in the water column and bioaccumulation in phytoplankton and
zooplankton. This page also shows the predicted results from the Gobas model as published
("Predicted" in the table), observed results from field observations, and the results from
FISHRAND run in steady-state (final column). The final box shows the result from FISHPATH.
Page 3 shows that FISHRAND, FISHPATH, and the original Gobas predictions show good
agreement.
Page 4 of Figure 3-3 shows the comparison for benthic invertebrates. FISHRAND and
the Gobas model as published show identical predictions. Pages 5 and 6 present the equations
used for fish uptake , while page 7 presents the final comparisons between the Gobas model as
published (1993), field observations, and FISHRAND and FISHPATH. FISHRAND and
FISHPATH predict virtually identically to published Gobas results, indicating that the models
are performing as published.
3.5.5.2	Comparison with Gobas (1995) Lake Ontario Data: The Time-Varying Case
Figure 3-4 shows the comparison between FISHPATH, FISHRAND, and published data
from Gobas (1995). FISHRAND and FISHPATH were run using inputs specified in Gobas
(1995) and compared to results published in that article. Model results showed concordance with
the published data, indicating that the models were correctly coded and ready to be modified for
use in the Hudson River modeling application.
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Chapter 4

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4. BIVARIATE BAF ANALYSIS OF FISH BODY BURDENS
4.1 Data Used for Development of Bivariate BAF Analyses
Equation 3-1 presents an idealized formulation for developing bivariate BAFs. 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 quantitation methods have
changed over time. Establishing the spatial/temporal history of sediment concentrations also
presents difficulties.
Initial attempts to develop bivariate BAFs for the Hudson River were presented in the
PMCR (TAMS/Gradient, 1996), using data through 1992. Since that time, additional fish, water
column, and sediment data have become available, running through 1997. Additional evidence
has also been developed on the proper interpretation of historical Aroclor PCB quantitations.
Finally, the approach used for bivariate BAFs has been refined based on comments generated in
EPA's Peer Review of the PMCR. Data and methods used for development of the BAFs are
described below.
4.1.1 Fish Data
4.1.1.1 Locations and Species Analyzed
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. As in the PMCR, the bivariate BAF
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 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 (if the lower
environmental concentrations in this reach are accounted for), thus providing a larger database
for analysis. Samples collected further downstream within the freshwater portions of the Hudson
were not included due to lack of contemporaneous measurements of water column and sediment
concentrations.
The longest-running and most extensive sample data in the Upper Hudson come from
NYSDEC collections at River Miles 168-176 (near Stillwater) and at River Miles 142 and 152
(below Federal Dam). A good representation over time is also available for River Miles 189-190
(lower 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 (Micropterus salmoides), and brown bullhead
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(Ictalurus nebulosus). Lesser, but still extensive, data are available for goldfish/carp (Cyprio
carpinus), white perch (Morone americana), 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 within the river. Pumpkinseed occupy
a lower trophic level: they feed primarily on invertebrates and are an important food source for
larger fish. Goldfish also occupy a lower trophic level, feed primarily on invertebrates in the
water column, and consume detrital algae. Brown bullhead are omnivorous bottom feeders, with
diet including offal, waste, small fish, mollusks, invertebrates, and plants. Feeding preferences
may vary with the age and size of the individual. Thus, a range of trophic positions and forage
preferences are available for analysis in the historic data. Appendix A provides more detailed
information on the foraging strategies of each of these species (except goldfish).
Data summaries for the NYSDEC fish analyses through 1988 were provided in the Phase
1 report, while the PMCR provided a summary'' through the 1992 sampling, with a total of 10,311
fish analyses available, of which 3,412 were collected between River Miles 142 and 194.
Additional data are now available for 1993 through 1997, including 994 NYSDEC samples
collected between River Miles 142 and 194. and some corrections have been made to the
database supplied by NYSDEC. Analyses presented in this chapter are based on a release of the
NYSDEC database provided on November 17, 1998, which contains some minor additions and
updates subsequent to the release of TAMS/'Gradient Database Release 4.1.
4.1.1.2	Lipid Normalization
As described in Section 3, PCBs accumulate primarily in fish lipid tissue, and it is
appropriate to normalize fish body burdens to concentration on a lipid basis. This helps remove
variability in concentrations due to variability in individual lipid content. Nearly all the
NYSDEC fish analyses report percent lipid, so lipid-normalized concentrations are readily
calculated. It should be noted, however, that extraction and determination of lipid content is also
subject to uncertainty. This does not, however, present a major problem. Laboratory analyses
for PCBs are based on a lipid extract; thus the lipid normalized concentration should be
consistent (except for round-off error) as long as the extraction procedures used for PCB and
lipid analysis are consistent, even though results are reported on a wet-weight basis. Error in
lipid determination primarily introduces error into reported wet-weight concentrations, which are
not used in the BAF analysis.
4.1.1.3	Season, Age, and Sex
PCB body burdens in fish may vary in accordance with seasonal growth and spawning
cycles. These bioenergetic factors are not included in the simple BAF approach; however, their
importance as potential confounding factors should be recognized. To helD minimize these
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effects, only data from summer collections (May to September) were used. Within this time
period, collections for individual species have tended to be even more focused. Most summer
samples are in the May-June period for brown bullhead (95%), goldfish (100%), largemouth bass
(97%), white perch (100%), and yellow perch (100%). Pumpkinseed samples are predominantly
from August-September (90%). The empirical models which result will be specific to these
collection times.
Age of individuals also affects PCB body burden, as various PCB congeners tend to
bioaccumulate over time. Sex differences in PCB concentrations have also been noted in the
Hudson and elsewhere, perhaps due in part to loss of PCBs from females when eggs are expelled
(see Sloan et al. 1995). Within the historical database, age is usually not given, and weight or
length are uncertain surrogates. Sex determination is also missing for many samples. Therefore,
the BAF analysis has not accounted for age and sex effects, although these undoubtedly
contribute to the variability among individual samples.
4.1.1.4 Laboratories and Methods for PCB Analysis
An important conclusion of the PMCR (see also Butcher et al., 1997) is that 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. The historical fish analyses in the NYSDEC database report
primarily packed-column Aroclor quantitations. 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.
Shifts in laboratories may also influence results. A summary of samples between River
Miles 142 and 193 by laboratory and year is provided in Table 4-1. As will be seen from this
table, a majority of the Upper Hudson samples from 1977 on were analyzed by the same contract
laboratory (referred to for convenience as "Hazleton"), although this laboratory has undergone a
number of changes in name and/or ownership (see also Sloan et al., 1985). The major
exceptions are samples from 1991 to 1992, analyzed by NYSDEC s Hale Creek Field Station
("Hale Creek"). As described below, it has been possible to develop analyses of what was
actually measured (in terms of PCB congeners) by the various Aroclor quantitation methods used
by Hazleton and Hale Creek. This has not been possible for the six laboratories represented in
the "Other" category. Therefore, the analysis has been restricted to Hazleton and Hale Creek
results, 1977 to 1997.
Aroclor standards used by these two laboratories for quantitation, and NYSDEC
conventions for estimating total PCBs from Aroclor data, are summarized in Table 4-2.
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Quantitations by Hazleton for 1977 through 1990 are consistently based on analysis against
Aroclor 1016 and Aroclor 1254 standards on packed column GC; 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, and the vast majority
of samples collected between River Miles 142 and 193 were reported with values above
quantitation limits for both Aroclor 1016 and Aroclor 1254. Total PCB concentrations in fish
through 1990 have were 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 nondetects (versus less than 1 percent nondetects 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.
Hazleton analyses through 1990 are discussed in detail in the PMCR and in Butcher et al.
(1997). These analyses against Aroclor standards on an OV-1 stationary phase were based on
only a few packed-column peaks, and are sensitive to the quantitation method used, which has
changed over time. Estimating an Aroclor concentration from a few peaks can introduce
significant error in estimates if the environmental distribution of PCB congeners differs from that
of the unaltered Aroclor standard. After commencing in 1977, quantitation peaks were changed
in 1979 and in 1983; the 1983 quantitation scheme was used consistently through 1990 (see
Sloan and Jock, 1990; Armstrong and Sloan, 1988). Hazleton analyses from 1992 on substituted
an .Aroclor 1248 or 1242 standard for Aroclor 1016, and added Aroclor 1260. Quantitation
peaks for the 1992~ Aroclor 1248 method were tentatively identified from area reports and
sample calculation sheets provided by EnChem, successor to Hazleton, coupled with
interpretation of sample chromatograms to identify peaks identified on absolute retention time
(RT) in terms of retention time relative to p.pr-DDE (RRT), as used by Webb and McCall (1973)
and others. Packed-column GC peaks and associated congeners are summarized in Table 4-3.
For 1991-1993, the database contains many fish analyses for Aroclors performed using
capillar,- column GC at NYSDEC's Hale Creek field station. The approach is documented in
"Analytical and Laboratory Procedures at Hale Creek Field Station", which contains the method
documentation for "OC1.103. Organochlorine Residues", dated 9/27/1990. The Hale Creek
analyses were performed on a Perkin-Elmer Sigma 115 with SPB-1 methyl silicone bonded
phase capillarv- column. The Control inputs attached to this method appear to show that Aroclor
1016 was analyzed via 7 capillary column peaks (with retention times relative to p,p'-DDE
ranging from 0.73 to 0.87), and .Aroclor 1254-1260 (combined) by 14 peaks (with retention times
relative to p,p'-DDE ranging from 0.96 to 1.31). A specific identification of congeners
associated with these SPB-1 peaks has not been made.
4.1.1.5 Standardization of PCB Analytical Results
The "Hazleton"' and Hale Creek results in the NYSDEC database include Aroclor
quantitations by five different sets of methods/quantitation peaks. As demonstrated in Butcher et
al. (1997), these shifts in quantitation can introduce spurious apparent changes in reported
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Aroclor and total PCB concentrations in fish. For instance, the change in quantitation peaks
between 1977 and 1979 is estimated to result in an apparent decline in Aroclor 1016
concentration of approximately 40 percent, regardless of actual environmental trends.
It is thus essential to establish a consistent quantitation basis, or "translation" procedure,
to develop an empirical analysis of trends in fish concentrations and correlations between fish
body burdens and environmental concentrations. Development of translations for historical data
has relied on a weight of evidence approach. Three separate lines of evidence have been
pursued:
•	Split Sample Analyses, in which one sample is split and analyzed by different
methods. This is the most direct approach, but is available for only a limited number
of methods and samples.
•	Interlaboratorv Comparisons, designed to evaluate contract laboratory performance.
The interlaboratorv comparisons are similar to split samples, in that they provide
direct comparison between methods, but do not provide detailed documentation on
methods used.
•	Theoretical "What If?" Analysis, in which the performance of historical Aroclor
quantitation methods is evaluated in terms of PCB congeners based on a theoretical
analysis.
The baseline or reference condition for the development of translation procedures is taken
as the sum of PCB congeners as quantitated by Aquatec for the TAMS/Gradient Phase 2
sampling. Translations have been developed for two targets: total PCBs (i.e., sum of quantitated
congeners, consisting of 90 target and 36 non-target congeners and representing more than 90
percent of the total concentration of Aroclors 1016. 1242, and 1254. as described in the DEIR,
Appendix A), and the sum of trichloro- through decachlorobiphenvls (denoted STri-). The latter
target was selected for the BAF analysis because most of the historical monitoring of PCB
concentrations in water and sediment is most readily interpreted in terms of STri-, as described
in the Baseline Modeling Report. Because fish tend not to accumulate significant amounts of
mono- and dichlorobiphenvls, translations of historical quantitations to either total PCBs or
STri-r- are expected to be similar.
4.1.1.6 Theoretical "What if? " Analysis
The theoretical analysis is presented first, because it can be developed for all the
"Hazleton" methods and provides some insights for interpreting the limited data available from
split samples and interlaboratorv comparisons.
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. According to Sloan et al. (1984):
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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
[Aroclor] = | y area f.
\j=i
¦RF.
(4-1)
where.
areaj
RFc
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.
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:
I
)='
area
[congener, ]
1=1
RF„.
(4-2)
where.
[congener;]
RF.,
number of congeners associated with selected packed
column peaks,
concentration of an individual PCB congener i associated
with the selected packed column peaks, and
the response factor for congener i, defined as the
concentration of congener / in the Aroclor standard
divided by the peak area contributed by this congener.
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Where the congener response factors within the peaks are relatively consistent, this may
also be approximated as
n
^congener;]
RF		(4"3)
j=l
where
RF = area-weighted mean response factor for the selected packed
P
column peaks or their constituent congeners in a capillary-
column analvsis. RF is defined as the concentration of the
p
Aroclor standard times the weight percent of PCB congeners
contained in the selected peaks divided by the peak area, or:
m	n
X wt % peak^	wt % congene^
RFp = [ Aroclorstd ] • 	= [ Aroclorstd ] • 			
^Tarea,	Xareak
j=I '	k = l
Substituting Equation (4-3) into Equation (4-1) yields
"	RF
[Aroclor] « congener,]			(4-4)
/=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:
n
[congene^]
[Aroclor] * 		(4-5)
y] wt%peakj
j=i
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. Calibrated
response factors for the congeners that are (1) included within peaks used for quantitation of a
specific Aroclor and (2) regularly detected in Hudson River biota were, however, found to vary
over a small range, and, in most cases, estimated response factors relative to BZ #52 for these
congeners are within 15% of unity. Thus, the simple approximation of (4-5) is judged to provide
an adequate basis for comparing historical packed-column GC analyses with more recent
capillary column results.
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As indicated by Equation (4-5), translating between congener data and historical Aroclor
quantitations also requires the total weight percent of the quantitated peaks in the Aroclor
standards. These values were obtained by summing the weight percentages of congeners
associated with packed column peaks in Aroclor standards (see Table 4-3) as developed from
analyses of Aroclor standards in the Phase 2 laboratory effort. The weight percentages are given
in Table 4-4. It should be noted that weight percentages reported for individual congeners in
Aroclor standards vary considerably (e.g., Albro and Parker, 1979; Schulz et al., 1989: Draper et
al., 1989 for Aroclor 1016). Some of this variability is likely due to batch differences in Aroclor
standards, and some to analytical methods. For purposes of this study, it is most important to use
consistent results for Aroclor standards analyzed by the same methods and laboratory as the
reference biological data.
The congener data ETri+ may be regressed against Hazleton reported results for total
PCBs to yield a translator. Regression results are summarized below and in Figure 4-1.
Standard errors for the dependent variable estimates and for each coefficient are shown in
parentheses below the equation.
iTri-r = -200.7 + 0.8720 x 1977 Sum (1016-1254)	R2 = 99.4%
(862.7) (97.2) (0.0065)
STri+ = -62.5 + 1.224 x 1979 Sum (1016+1254)
(881.6) (98.7) (0.0093)
R- = 99.3%
!Tri+ = -216.5 - 1.320 x 1983 Sum (1016+1254)
(961.8) (108.4) (0.0109)
R- = 99.2%
iTri- =-111.0 + 0.8798 x 1992 Sum (1248+1254^1260)
(1762) (198.4) (0.0135)
R- = 97.3%
4.1.1.7 Split Sample Comparisons
The NYSDEC database (11/17/98 update) contains a limited number of fish samples
analyzed for PCBs by multiple laboratories. Most relevant for the "Hazleton" analyses are splits
of 1995 samples from the Hudson analyzed by both Hazleton (using the 1992 method) and
NOAA (using capillary column GC analysis comparable to the Aquatec results). There are two
other series of splits between Hazletcn and Hale Creek (1987 Smith Pond; 1996 Queensberry
area), but for these samples Hazleton reports against Aroclor 1016 and 1254/60 standards.
Hazleton thus apparently used a version of the Hale Creek method, and not their own "1992"
method for these analyses. There are also 1993 split samples between Aquatec and Hale Creek
for pumpkinseed in the upper Hudson. These samples may be matched on tag number to identify
true split samples.
The 1995 Hazleton-NOAA splits consist of 20 largemouth bass (collected between river
miles 113 and 189) and 35 striped bass (collected between river miles 27 and 152), quantified for
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107 target congeners. In 54 out of the 55 samples the total calculated by Hazleton was greater
than the total calculated by NOAA (the one exception is the most highly contaminated sample).
The slope of a regression of the NOAA results against the Hazleton results is 0.87, and is not
significantly different from the theoretical relationship obtained between sum of congeners and
the Hazleton 1992 method using the "what if?" analysis presented in above. The split samples
thus appear to confirm the theoretical analysis.
The 1993 Hale Creek-Aquatec splits consist of 15 pumpkinseed samples, including three
highly contaminated specimens from Griffin Island. For 13 of the 15 samples, the total reported
by Aquatec using capillary column GC is higher than the Hale Creek Aroclor sum. The two
exceptions are very lightly-contaminated specimens. The slope of a regression of the Aquatec
results against the Hale Creek results is 1.46, with an R: of 94%. This result is consistent with an
interpretation that Hale Creek analyses are approximately equivalent to Hazleton analyses by the
1983 method.
The results of 1997 split samples between EnChem (successor to Hazleton) and GE's
contractor NEA (identified to peak/'congener basis by capillary column GC) are not yet ready to
be released or reported in detail, but results of 56 samples were made available for preliminary
inspection by NYSDEC. The theoretical "What if?" analysis suggested that the 1992-1997
Hazleton/EnChem Aroclor method should result in substantially higher results than the 1983
Hazleton method, and should yield a slight overprediction of the sum of congeners, with a slope
of about 0.90 for congener sum versus Hazleton Aroclor sum. The provisional data suggest that
this is indeed the case, as the EnChem Aroclor sum appears to be consistently higher than the
NEA sum of congeners. The average ratio between NEA and EnChem results is approximately
equal to the theoretical slope of 0.90. Regression analysis suggests that the over-prediction could
be even greater. However, it should be noted that the NEA congener analysis is not necessarily
fully equivalent to the Aquatec congener analysis which serves as a baseline for our comparison.
Thus, the provisional 1997 data also appear to confirm the theoretical analysis.
4.1.1.8 Interlaboratory Comparisons
NYSDEC has conducted several rounds of interlaboratory comparison for contract
laboratory evaluation. Results for 1989, 1992, and 1995 comparisons were provided by
NYSDEC. For the 1989 study, eight laboratories participated, analyzing four samples. These
samples are not identified, but three of the four appear to have had significant PCB
contamination. The 1992 study included twelve laboratories and analysis of five samples (two
Lake Ontario coho salmon, clean largemouth bass composite, Hudson River striped bass, and
great horned owl tissue). The 1995 study involved four laboratories and three samples. One of
the samples was a composite of previously analyzed fish with no detectable PCBs. Samples 2
and 3 were splits of the same sample, which was a composite of striped bass fillets collected
from New York City Harbor with less than 1 ppm PCBs. Hazleton and Hale Creek participated
in each of these interlaboratory comparisons. The quantitations were to Aroclor standards of the
individual laboratory's choosing, and separate reference analyses for PCB congeners by capillary-
column were not included.
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No clear trend among laboratories is evident in the 1989 comparisons. Comparison of
Hazleton results is difficult, however, because Hazleton used Aroclor 1248, 1254, and 1260
standards, while Hale Creek results, using Aroclor 1016 and 1254/60 standards, predate their
1990 methods documentation. Hazleton results were lower than Hale Creek on the two more
contaminated samples (total PCB concentration of about 10 ppm), and higher than Hale Creek on
the two lightly contaminated samples (less than 1 ppm). Comparison is also hampered by not
knowing which (if any) samples are Hudson River fish. Samples which represent
congener/Aroclor mixtures significantly different from those found in the Hudson River would
likely provide different results on a comparison of Hazleton and other methods.
In the 1992 interlaboratory comparisons, Hazleton Environmental Services (HES) used
Aroclor 1242, 1254, and 1260 standards, which differs from the methods used by Hazleton for
Upper Hudson River fish samples in the 1990s. 1992 Hale Creek analyses were apparently done
using their capillary column method OC1.103, as discussed above. Hazleton and Hale Creek
were in relatively close agreement for four of the five samples, including all the fish samples.
The major discrepancy is in the analysis of the owl tissue, for which Hazleton reported 4.5 ppm
total PCBs, versus 1.5 for Hale Creek. One reason for the discrepancy is that Hazleton
quantitated this sample as Aroclor 1260 only. Hazleton's "1992'" method for Aroclor 1260 uses
only three peaks, which represent the more chlorinated end of the 1260 spectrum, accounting for
only about 8 percent of the total mass of .Aroclor 1260. Scaling up to total PCBs from a few-
peaks at one end of the spectrum is likely to result in significant potential for mis-estimation. In
all the fish samples, Hazleton's results were slightly less than those reported by Hale Creek, with
an average difference of -13%. The discrepancy is greatest (-21%) for the Hudson River striped
bass sample.
In the 1995 comparisons, Hazleton used their standard "1992'" approach of quantitating to
Aroclor 1248. 1254 and 1260 standards. For the two contaminated 1995 samples, results from
Hazleton were approximately 1.4 times those from Hale Creek. The report transmitting the 1995
results (memorandum from Larry Skinner to Robert Bauer, January 17, 1996, Comparison Study
of Contract Labs for Total PCB and % Lipids states "All laboratories were in the acceptance
limits of ±3 standard deviations of the mean, with laboratory 2 [Hazleton] being consistently
higher than the rest. The ratio of Hazleton to Hale Creek in 1995 is consistent with predictions
from the theoretical analysis of'Hazleton" methods, assuming that Hale Creek results are similar
to Hazleton 1983 method results."
4.1.1.9 Translation Methods
The available evidence suggests that the "what if?" analyses provide a reasonable basis
for translating "Hazleton" Aroclor results to a basis consistent with congener analyses.
Approximate translation of the Hale Creek Aroclor data can be based on the analyses of split
samples described above.
Regression relationships between Aroclor sum and congener total can be performed with
our without a constant. In most cases, it was found that the constant was not significantly
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different from zero. In addition, a zero-intercept regression is attractive because (1) samples
detected as near-clean by packed column are best interpreted as likely to be near-clean on
capillary column analysis as well, and (2) a zero-intercept regression will prevent prediction of
any negative concentrations on transformation. Therefore, zero-intercept results are presented
below.
Resulting zero-intercept translation methods for the state variable ITrH- are presented
below. Applicable laboratory codes from the database are also are indicated. Note that the
proposed translation factors are only applicable to the laboratories for which they were
developed.
Time
Period
Equation
Applicable
Laboratory Codes
1977-
1978
0.8642 • (Aro 1016 - Aro 1254)
WI. RAL
1979-
1982
1.2210 «(Aro 1016-Aro 1254)
RAL, HAZ
1983-
1990
1.3070 • (Aro 1016 + Aro 1254)
HAZ. RAL
1990- | 1.4157 • (Aro 1016+ Aro 1254/60)
1993 |
HC
1992-
1997
0.8754 • (Aro 1248 + Aro 1254 + Aro 1260)
HAZ, HES, EC
The annual averages of ITri+ PCB concentrations (as mg/kg-lipid) for summer-collected
fish samples, arranged by species and a "group"' designating location, are shown in Table 4-5.
The original NYSDEC data, contained in the TAMS/Gradient database, have been corrected to a
consistent ITri-i- basis using the relationships described above.
4.1.2 Water Column Data
As noted in the PMCR (TAMS/Gradient 1996) and earlier by Brown et al. (1985), a good
predictor of annual average fish PCB body burden appears to be the summer average water
column concentration. Therefore, the BAF analyses use summer averages of water column data,
based on observations for May through September for consistency with the averaging period
used for fish. For fish collected in May or June this means that the water column average
includes samples from after the time of fish collection. Given the relative sparsity of water
column observations, however, it appears likely that including all water column data for May
through September will provide a better statistical estimate of concentrations in a given season
than restricting the estimate to May-June observations only.
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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 the Phase I Report (TAMS/Gradient 1991). For the
Phase 2 analysis, USGS data have been obtained through the end of Water Year 1995.
Significant corrections and updates to the USGS data have occurred since the release of the
PMCR, and are reflected in Database Release 4.1.
There are three major sources available for the USGS water column PCB data:
WATSTORJE, USGS/Albany NWIS database, and printed USGS Water Resources Data, New
York. For some years there are significant discrepancies between these data sources, requiring a
retrospective reconciliation. Data used in the PMCR were obtained primarily from
WATSTORE, but WATSTORE is a secondary source, which is periodically updated from the
USGS/New York NWIS electronic database system. Where discrepancies exist, WATSTORE is
less reliable than the other two sources. We noted major differences between sources for the
period prior to October 1986, primarily related to (1) failure to reflect actual PCB detection limit
of 0.01 (j.g/1 for many observations, which was lower than the default detection limit of 0.1 ug/1
expected by WATSTORE for the relevant parameter codes, and (2) failure to report Aidentified
Aroclors shown in the printed reports. Almost all USGS PCB data from the Hudson from
October 1983 on was quantitated at an 0.01 jag /I detection limit, but WATSTORE generally
does not show this until 10/86. In addition, a significant fraction of the data prior to October
1983 was also quantitated at the 0.01 ug /I detection limit.
USGS PCB data were revised using both NUTS and the printed Water Resources Data.
For October 1983 through September 1986, data at the lower detection limit of 0.01 ug/1 are
primarily given only in the printed data, which is also the source for Aroclor identification. For
1978-1982, the printed data show total PCBs at a detection limit of 0.1 ^g/1 and do not report
identified Aroclors: however. NWIS for these years shows that some samples were quantitated at
the 0.01 |ag/l level and does show Aroclors.
USGS analyses prior to 1986 were obtained using packed-column GC; those from 1988
on used a capillary column methodology (personal communication from Ken Pearsall,
USGS/Troy, to Jonathan Butcher, Tetra Tech. based on letter received from Brooke Connor in
USGS Denver laboratory). It was previously believed that all analyses prior to November 1987
used packed column GC; however, QEA has obtained original chromatograms and sample
analysis sheets indicating use of a capillary column method as early as fall of 1986 (personal
communication from Jim Rhea, QEA, to Jonathan Butcher, Tetra Tech, 10/30/1998).
The USGS packed column methodology is described in general in Wershaw et al. (1983).
A clearer description of exactly what was done is given in Schroeder and Barnes (1983). The
analysis was a two-step procedure: (1) Determine an appropriate Aroclor standard, based on
requirements that at least 60 percent of the peaks in the standard are present in the sample and
"both relative peak ratios and column detention time must match." If a single Aroclor standard
cannot be found which matches these criteria, use a standard containing a mixture of two or more
Aroclors. (2) Calculate concentrations Abv dividing the area of a sample=s identified PCB peaks
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by the area of all peaks for an Aroclor standard, then multiplying this ratio by the concentration
of the Aroclor standard.
Step 2 indicates that this is not a Webb and McCall (1973) procedure with peak-by-peak
quantitation. Instead, the observed peaks in a sample are scaled-up to estimate a complete
Aroclor concentration. No compensation is made for differing response factors, only the sum of
peak areas is used. It is not certain exactly which packed-column peaks were observed by
USGS, although it appears likely that the mono- and dichlorobiphyenyls were not represented.
The first peak used is thought to be either RRT .21 or RRT .28. For quantitations against an
Aroclor 1221 or 1232 standard (where there is substantial unobserved concentration in peaks
below RRT .21) this approach is equivalent to assuming that the earlv-eluting (unobserved)
congeners in the sample are present in the same fraction as in the Aroclor standard. In reality,
concentrations of these congeners (e.g., BZ#4) are likely to be higher in the environment due to
dechlorination. In addition, USGS used a dual column method, and always selected the lower of
the two values obtained. Finally, no corrections were made for incomplete extraction.
Extraction efficiency, it is estimated, probably exceeds 80 percent in nearly all samples.
Because of these factors, it is difficult to predict exactly what was measured in USGS
packed column analyses. For GE, NEA conducted split sample experiments to compare the
USGS packed column method (based on the description in Schroeder and Barnes) to capillary
column analyses, using individual or mixed standards composed of Aroclor 1242, 1254, and
1221 (O'Brien & Gere, 1993). Updated results of these analyses are contained in
TAMS/Gradient Database Release 4.1. Regression analysis of the split samples reveals that a
linear relationship exists between USGS-method total PCBs and capillary column ITri-. with an
intercept not significantly different from zero and a slope not significantly different from one.
Thus, the USGS packed-column data can be used as a direct measure of ITri-.
The interpretation of USGS capillary column analyses is less clear at present (although
QEA is currently engaged in examining original chromatograms and sample analysis reports).
During the period 1988 to Sept. 1991 USGS continued the approach of selecting a single or
mixed Aroclor standard for quantitation. In contrast to earlier years, however, only Aroclors
1242, 1248 and 1254 are reported as identified Aroclors: Aroclor 1221, 1232 and 1016 standards
were not used. From October 1991 on. USGS consistently reported quantitations against Aroclor
1242 and 1254 standards, presumably based on a specified rule for peak separation. Because
Aroclor 1242 was the lightest Aroclor standard used, it is suspected, pending further
investigation, that USGS data for this period should also approximate ITri-;-.
Most of the historical USGS results are available only as whole water quantitations. Few
USGS samples distinguish dissolved and particulate PCB fractions, and almost no organic
carbon data were collected. Therefore, the preferred formulation of normalizing the particulate
fraction corrected to an organic carbon basis, cannot be employed. Instead, all regressions were
based on whole water, unfiltered PCBs. The BAFs for fish concentrations are thus relative to
whole water rather than organic carbon-normalized particulate PCBs.
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Starting in 1991, capillary-column determinations of PCBs in the water column are
available on a homologue and congener basis from GE. These high-resolution data are presumed
more accurate than USGS results, and may be used to directly estimate ZTri-K The same may be
done with TAMS/Gradient Phase 2 water column results from 1993.
Summer average water column concentrations were estimated at four locations,
corresponding to reaches with available fish sampling. Assignment of sources for water column
concentrations is shown in Table 4-6. For the period from 1991 on, capillary column PCB
analysis by EPA and GE is used where available; however, during 1994-1996 GE did not sample
below Thompson Island Dam, so USGS data are used. For the Thompson Island Pool, upstream
USGS data at Rt. 197, Fort Edward is judged of limited value for determining exposure
concentrations, due to the gain in PCB concentrations within the pool. Therefore, Thompson
Island Pool concentrations are estimated from downstream measurements, scaled by a drainage
ratio where appropriate. Prior to 1987, scaled USGS Stillwater data have been used in preference
to Schuvlerville data to estimate Thompson Island Pool concentrations because averages at the
two stations are generally similar, but greater sampling density is available at Schuvlerville.
USGS Fort Miller data, commencing in 1987, are assumed representative of the Thompson
Island Pool for 1987-1990. For 1991 on, GE Thompson Island Dam-West data are used for the
Thompson Island Pool, with application of a bias correction factor. This bias correction factor
(discussed in the Baseline Modeling Report) is necessary to make these nearshore concentration
measurements approximate center-channel concentrations, consistent with estimation from
downstream USGS data in earlier years.
1993 concentrations below Thompson Island Dam are estimated from TAMS/Gradient
Phase 2 monitoring. Flow-averaged samples are available at Waterford, while instantaneous
transect samples are used at Stillwater and Green Island. Except for 1993, direct water column
monitoring results are not available below Federal Dam (except for a limited number of early
USGS data, all non-detects). Concentrations in this reach are therefore estimated by drainage
area scaling from Waterford or other upstream stations. This scaling is equivalent to assuming
that incremental flow from the Mohawk River contributes insignificant PCB concentration.
Summer average concentrations used for BAF estimation are summarized in Table 4-7 and
Figure 4-2.
4.1.3 Sediment Data
The second forcing function for the bivariate BAFs is sediment concentration. Fish may
accumulate PCBs from the sediment directly through the consumption of benthic organisms or
direct ingestion in the case of deposit feeders, or indirectly through the consumption of other
organisms which consume benthos. Surface sediment concentrations are anticipated to be
correlated to water column concentrations; however, full equilibrium with the water column is
likely to exist only at the interface, and not through the entire bioactive depth. In depositional
areas, sediment concentrations will resemble water column concentrations, but with a "memory"
integrating across several years. Further, because most of the movement of sediment occurs
during spring floods, sediment concentrations should be more closely tied to spring high flow-
concentrations than to summer low flow concentrations. Thus, sediment concentration data
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provides a separate, semi-independent exposure data series to the bivariate BAF. The Pearson
correlation coefficient between average water column and sediment concentrations used in this
analysis is 0.56.
Areally-averaged annual observations of sediment concentrations for reaches in which
fish collections occurred do not exist. Indeed, the sediment database covers only a few points in
time, including the 1976/78 NYSDEC survey of the Upper Hudson, the 1984 NYSDEC survey
of the Thompson Island Pool, the 1991 GE survey of the Upper Hudson, and targeted sampling
of hotspot locations in the 1994 EPA Low Resolution Coring program. As with the fish data,
there are significant analytical differences between these sampling campaigns. Finally, sediment
concentrations in the Hudson are known to exhibit a high degree of spatial heterogeneity, so that
inference from small samples may not be representative of a reach-average exposure
concentration.
Because of these limitations, observed sediment data are not used directly in the Bivariate
BAF analysis. Instead, predicted sediment concentrations, averaged over 0 to 4 cm depth, from
the HUDTOX model were used. For the HUDTOX hindcast run, all the available sediment data
were processed to provide a consistent estimate of ITri+ PCBs and the model was calibrated to
provide a reasonable fit to available observations in time and space. The HUDTOX predictions
thus provide a best-estimate, process-based interpolation of the available sediment data.
HUDTOX results are a smoothed estimate of observed data in space and time, which helps
minimize the effects of sparse data and analytical uncertainty on BAF estimates which depend on
spatially averaged exposure concentrations.
The calibrated HUDTOX model provides reach-by-reach estimates of XTri-i- for the
Hudson River between Fort Edward and Federal Dam. with separate estimates for cohesive and
non-cohesive sediments. We assumed that cohesive (fine-grained) sediment concentrations are
most relevant to fish exposure pathways from sediment independent of water column
concentrations. Two different approaches were used to process sediment data (Table 4-8).
Method 1 uses an arithmetic weighted average of model predictions of cohesive and non-
cohesive sediment concentrations, based on the relative area in each sediment type for a reach
and the assumption that fish show a preference for organic sediments, spending 75% of their time
in such sediments. These results have been normalized to organic carbon (OC) concentrations.
A similar approach was used for the probabilistic bioaccumulation model (section 5) and
FISHRAND (section 6); however, for the bivariate BAF analysis weighted model predictions are
used directly, without any attempt to derive corrected moment estimates from a log-normal
distribution assumption. Method 2 uses model predictions of dry-weight sediment
concentrations from the cohesive sediment area only. These estimates are reported in Table 4-8
as base-10 logarithms, as they were used in a log-log regression model, as described below.
For the area from River Mile 142 to 153, below Federal Dam, no HUDTOX model
predictions of sediment concentration are available. This reach has also not been covered by
NYSDEC sediment surveys. For this reach, sediment concentration trends over time were
estimated based on analysis of TAMS/'Gradient High Resolution Core 11, from the Albany
Turning Basin at River Mile 143.5. This location accumulated steady sediment deposition
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following dredging in 1971 (see TAMS/Gradient 1997). In dated cores with steady deposition
rates, a core layer provides an indication of the PCB content o sediment deposited from the water
column at the core location in a given year. As there are no significant local sources of PCBs in
this reach, surface cohesive sediment concentrations in this reach are assumed to be equal to the
concentration in the corresponding dated core layer. Core 11 was collected in August 1992.
Prior to about 1984, concentrations of STri+ in dated layers of this core appear to be less than
concentrations in cohesive sediment above Federal Dam near Waterford accounting for flow-
dilution from the Mohawk. This early period likely represents residual effects of mass
movement of highly contaminated organic sediment downstream to Waterford following removal
of the Fort Edward Dam. After 1984, concentrations in Core 11 appear to follow a trend similar
to concentrations in cohesive sediment near Waterford, diluted by incremental flow from the
Mohawk. Sediment concentrations at this station were therefore extended for 1993-1997 based
on relationship to concentrations in cohesive sediment near Waterford. For Method 1, the
average ratio between Group 3 sediment and Core 11 concentrations was used to extend the
record. For Method 2, post-1992 concentration estimates are based on simple flow dilution by
the Mohawk (factor of 0.585).
4.1.4 Functional Grouping of Sample Locations for Analysis
Four functional groupings of available data were formed for the purposes of analysis.
These represent the major fish sampling locations and associated environmental data. The
groups are:
Group 1: River Mile 188 to 193, the lower Thompson Island Pool from Griffin Island to
Thompson Island Dam.
Group 2: River Mile 168 to 176, the NYSDEC fish collection station near Stillwater.
Prior to 1997. samples are from River Mile 168.
Group 3: River Mile 155 to 157, Waterford area above Federal Dam (limited NYSDEC
sample collection only). Most of these samples are from River Mile 157, several miles above the
confluence with the Mohawk River.
Group 4: River Mile 142 to 152, the upper part of the Lower Hudson, below Federal
Dam. These stations are influenced by dilution from the Mohawk River. Most samples are from
River Mile 142 (Albany Turning Basin) and River Mile 152 (Green Island).
4.2 Results of Bivariate BAF Analysis
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 given as the inverse of
the standard error of the mean (Theil, 1971), giving relatively less weight to smaller or less
consistent samples. As expected, models on means have much stronger predictive ability than
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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.
In contrast to the PMCR (TAMS/Gradient 1996), all analyses presented here are in terms
of ETrH- PCBs. Quantitations of individual Aroclors potentially provide information on
bioaccumulation of lighter versus heavier Aroclors. as presented in the PMCR. However, the
changes in quantitation methods for fish (Section 4.1.1) make it difficult to draw inferences
regarding individual Aroclor quantitations over time.
Regression models were created by species for the four individual sample groups
described above and across all groups based on (1) a standard BAF approach with regression to
water-column concentration only, and (2) bivariate BAF regression on water column and
sediment concentrations. Results were generally consistent among groups, implying that cross-
sectional models across groups are appropriate.
For a given species, plots of mean fish body burden versus water column concentration
show a general positive correlation, but with variability which appears to increase with water
column concentration. Figure 4-3 displays scatterplot matrices for lipid-normalized fish
concentration versus water and organic carbon-normalized sediment concentrations for all six
fish species under consideration. In all species, except perhaps goldfish, there appears to be a
positive correlation between fish body burden and both water and sediment concentrations.
However, the strength of the relationships vary by species. For instance, brown bullhead have a
stronger linear relationship to sediment, while pumpkinseed have a stronger linear relationship to
water concentrations. In all cases, the variance or spread of the distribution increases with
concentration. This condition of scale-dependent variability (heteroscedasticitv) can present
problems for regression analysis and suggests use of a log-log transformation (see Figure 4-4 for
pumpkinseed). A log-log transformation results in a stabilization of variance; however, the
transformation implies that the relationship of fish body burden to water and sediment
concentration is non-linear (multiplicative rather than additive model) and that BAFs are also
scale-dependent, rather than constant.
Two sets of regressions are presented. The first set uses arithmetic average
concentrations in fish and water, coupled with Method 1 estimates for sediment. The second set
uses log-log-regressions, with sediment estimates given by Method 2 (see Table 4-8). For each
set, regressions were conducted against water concentration only (standard univariate BAF
approach), and against water and sediment concentrations simultaneously (bivariate BAF
approach).
Table 4-9 shows results of regression analysis of arithmetic average fish concentrations
versus water concentrations. The percentage of total variability explained by the regressions is
fairly low (R: ranging from 26 to 72 percent); however, the coefficient on water column
concentration is in all cases statistically significant at the 95 percent confidence level.
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Table 4-10 shows a bivariate regression on arithmetic average water concentrations and
Method 1 sediment concentrations. The bivariate approach increases R2 for all species, with all
but goldfish having adjusted multivariate R2 values greater than 67 percent. Large improvement,
however, is seen only for brown bullhead and largemouth bass, species which presumably have a
larger sediment-originated food chain pathway of PCB bioaccumulation.
Better statistical results are obtained with a log-log transformation to remove
heteroscedasticity. Table 4-10 displays the results of a univariate log-log BAF analysis of fish
lipid concentration against water concentration. The explanatory power of water-only models is
again relatively low, with R2 values ranging from 38 to 77 percent. Much of the unexplained
variability is due to differences among sample location groups, with the regression line for Group
1 (Thompson Island Pool) generally lying parallel to, but above the regression line for
downstream groups (see, for instance, Figure 4-4). This suggests that differences in sediment
concentrations among locations may increase explanatory power.
Table 4-11 displays the results of the log-log bivariate BAF analysis. Including sediment
concentration as an independent variable brings all R2 estimates above 74%, with a large increase
for brown bullhead, goldfish, and largemouth bass. A large increase in predictive ability for
these species occurs because the coefficient on sediment is large, presumably reflecting a
significant sediment food chain contribution to body burden. For pumpkinseed and white and
yellow perch the coefficient on sediment concentration is smaller, and a correspondingly smaller
increase in R~ occurs.
Figures 4-5 through 4-7 show observed versus predicted average concentrations from the
log-log bivariate BAF model for brown bullhead, largemouth bass, and pumpkinseed. In each
case a strong positive correlation is evident, although there is also clearly variability which is
unexplained by the simple BAF model.
4.3 Discussion of Bivariate BAF Results
4.3.1 Comparison to Published BAF Values
For comparison to published BAF results, Tables 4-9 through 4-12 contain estimates of a
univariate log ^ q BAF for total PCBs in units of liters of water per kg of fish lipid. The BAF may
be obtained directly from the coefficient on water concentration (with appropriate units
correction) from the arithmetic univariate model. A BAF estimate may also be obtained from the
coefficient on water in the bivariate model, but the result may not be fully comparable to a
univariate BAF. For the log-log models, the BAF is scale-dependent, and values reported in the
tables are based on typical Upper Hudson concentrations of 100 ng/i and surface sediment
concentrations of 10 mg/kg ITri-.
The calculated logJ0 BAFs for the univariate arithmetic model range from 6.28 for
pumpkinseed to 6.66 for largemouth bass on a L/kg basis. Estimates are slightly lower for the
bivariate arithmetic model. These univariate BAFs, relating lipid-normalized body burden in
fish to total PCB concentrations in water, are sometimes denoted as BAF^ (U.S. EPA, 1994).
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BAFs are also frequently reported on the basis of the freely-dissolved fraction of a chemical in
the water column, BAFjfd. The two forms of the univariate BAF can be related as
RAF'
BAF,fd =	(4-6)
'd
wherefy is the freely dissolved fraction of the chemical. Under average conditions in the
Upper Hudson, the freely dissolved fraction of Tri- is estimated, based on analysis of three-
phase partitioning in the DEIR for representative congeners, to be about 50 percent for £Tri+
PCBs. Using Equation (4-6), base-10 logarithms of BAFj^s would thus be equal to the
calculated BAF^s plus about 0.3 log units.
U.S. EPA (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 U.S. EPA (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 Gobas model predicts a BAFj^ 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 6.21 to 6.28 + 0.3 presented here for Pumpkinseed
BAFjfd. The Gobas model prediction for BZ#28 and BZ#31 in piscivorous fish is 6.68, which
compares well with the Hudson River Largemouth Bass estimate of BAF^ of 6.44 to 6.66 -
0.3.
4.3.2 Fit of Bivariate Models to Observations
A bivariate BAF approach, including both water and sediment as independent variables,
improves on the ability of a simple univariate BAF approach to fit observations of fish body
burdens of ITri- PCBs. Section 4.2 presents the bivariate model in two forms: as an arithmetic
and as a log-log regression. The log-log model has advantages in terms of both predictive ability
and residual error structure. A disadvantage is that the log-log model introduces a non-linearity
into the BAF relationships. This non-linearity is not large, however, in the range of
concentrations observed in the Hudson River. For example, Figure 4-8 compares the arithmetic
and log-log univariate (water-only) models for pumpkinseed, showing the close similarity
between the regression lines. (The univariate rather than the better-performing bivariate model is
shown here for clarity of display in two dimensions).
While the overall model fit is good, many individual data points are not accurately
predicted by the bivariate model. Performance of the model can best be visualized by examining
long runs of data at specific locations. The most extensive fish time-series data are for brown
bullhead, pumpkinseed, and largemouth bass in Group 2 (River Miles 168-176), and for
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pumpkinseed and brown bullhead in Group 4 (River Miles 142-152). Observations and model
predictions for these series are shown in Figures 4-9 through 4-11. In examining these figures, it
should be recalled that individual observations have been weighted by the inverse of their
standard error. Thus, some apparent outliers represent small sample sizes with high uncertainty.
For brown bullhead (Figure 4-9), both models do a reasonable job of capturing trends in
concentration in Group 2 (although underestimating observations for 1992 and 1997). while in
Group 4 the arithmetic model provides a closer fit to observations. Both models underpredict
concentrations in brown bullhead in Group 4 from 1993 on, probably reflecting an error in
sediment concentrations which are based on high resolution core data through 1992, but
estimated thereafter.
For pumpkinseed (Figure 4-10), model fit is quite close in Group 4. This species is less
sensitive to sediment concentrations that brown bullhead (as described in Section 4.3.3), and
predictions are apparently unaffected by estimated sediment concentrations after 1992. In Group
2, the general trend in PCB body burden is captured, but some individual observations lie well
off the regression line. For instance, high body burdens in 1989 and 1992 are not captured by the
model. This is a period in which the upstream Bakers Falls source was active, and exposure
concentrations may have been higher than captured in limited water column monitoring. Finally,
the model does not match the strong drop in PCB body burdens in the most recent. 1997
samples.
Finally, for largemouth bass (Figure 4-11), the model does an adequate job of capturing
trends over time, except that average body burdens in small samples in the earliest years are
under-estimated.
Variability in observations which is unexplained by the bivariate BAF may have a
number of sources. These can generally be divided into data uncertainty and model uncertainty.
Data uncertainty addresses the fact that exposure concentrations in water and sediment are not
precisely known. Water column concentrations are in many cases estimated from only a few
samples, and the estimates have considerable uncertainty relative to actual summer average
concentrations. Sediment concentrations are derived from the output of the HUDTOX model,
which has been calibrated to sediment observations at a limited number of points in time. As
with water, sediment concentration estimates may mis-represent actual exposure concentrations
in a given year. Data uncertainty has two effects: it may cause individual observations to be mis-
estimated, and it may bias the regression coefficients. Use of the full data set, including
observations over 21 years at multiple sample locations, provides a robust model which should
minimize the regression coefficient. The major source of unrepresented variability is likely to be
uncertainty in the estimates of water column exposure concentrations. This would explain why
model fit is better for brown bullhead, which depends strongly on sediment concentrations, than
for pumpkinseed, which is most strongly driven by water column concentrations. In addition,
errors in model fit appear to increase upstream, with the highest error variance for estimates
within the Thompson Island Pool, particularly for observations prior to 1991. Water column
concentrations in the Thompson Island Pool are expected to be more variable than those
downstream, as this is near the source area; further, water column concentration estimates prior
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to the start of GE sampling in 1991 are based on downstream USGS measurements and should
be regarded as highly uncertain.
The second component of unexplained variation, model uncertainty, reflects the fact that
the simple bivariate BAF model does not provide a complete representation of the factors
controlling PCB bioaccumulation in fish. Most notably, the BAF model does not take into
account age, weight, size-related foraging strategies, and sex of individuals, all of which may be
important to PCB bioaccumulation and could result in systematic differences between individual
samples. The simple BAF approach also does not take into account the differences in PCB
congener patterns present in water, sediment, and biota, nor differences in congener patterns
among locations. Unlike data uncertainty, model uncertainty' can be addressed through use of
more sophisticated models, such as those presented in Sections 5 and 6.
4.3.3 Relative Importance of Sediment and Water Pathways
As discussed in Section 3, PCBs may enter the food chain from environmental
concentrations in either water or sediment. The relative importance of these two environmental
sources will depend on food preferences and behavior of a given species, among other factors.
The bivariate model gives a qualitative indication of the importance of water versus sediment
which is useful in developing more complex bioaccumulation models. The two sources cannot
be fully separated by statistical analysis, however, as water and sediment concentrations are
correlated, as are coefficient estimates in the bivariate model.
Two methods which can be used to make statements about the relative importance of the
independent variables in a multiple regression model are normalized beta coefficients and
elasticities (Pindyck and Rubinfeld, 1981). Normalized beta coefficients are the coefficients
obtained from a linear regression in which each variable is normalized by subtracting its mean
and dividing by its standard deviation. For two independent variables. X, and X2. the normalized
regression model has the following form:
^^ = f3:X['~Xy +p:x"~Xl (4-7)
5,1	^2
where the values indicate standard deviations and an overbar indicates the mean value.
The normalization corrects for scale differences among the independent and dependent variables.
A normalized beta coefficient of 0.7 can be interpreted to mean that a 1 standard deviation
change in the independent variable will lead to an 0.7 standard deviation change in the dependent
variable. Elasticities interpret the effect of a percentage change in the independent variable on
the dependent variable, and also represent a normalization of the regression. The elasticity for a
coefficient j is calculated at the point of the means of each of the independent variables as
(4-8)
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Normalized beta coefficients and elasticities for the bivariate arithmetic model are given
in Table 4-13. For pumpkinseed, which forage primarily in the water column, and for white and
yellow perch, which migrate small distances, water column concentrations appear to be the most
important variable in determining body burden of ITri+ PCBs. In contrast, brown bullhead,
resident fish which forage on the bottom, are more sensitive to sediment concentrations. At the
highest trophic level, largemouth bass, which are primarily piscivorous, appear to respond about
equally to water and sediment pathways, suggesting that the bass integrate food web
contributions from both water column and sediment/detrital feeders.
4.4 Summary
A bivariate BAF analysis, relating lipid-based XTri-r PCB concentrations in fish to PCB
concentrations in both the water column and sediment, provides good explanatory power in
predicting annual mean body burden in six fish species throughout the Upper Hudson River,
based on analysis of NYSDEC monitoring data for 1975 through 1997. Water-column and
sediment PCB concentrations are clearly not in complete equilibrium in most of the Upper
Hudson, and inclusion of sediment concentration as an independent variable results in a
significant increase in explanatory power.
The increase in explanatory power provided by the bivariate approach is greatest for those
species which have a larger sediment-derived component of food-chain pathways. PCBs in
brown bullhead appear to be most strongly determined by sediment concentrations, while PCBs
in pumpkinseed and white and yellow perch are more strongly related to water column
concentrations. Largemouth bass, occupying the highest trophic level in the water column,
appear to integrate both sediment and water pathways.
The BAF analysis summarizes the historic data on PCB concentrations in fish, water, and
sediment. It is not intended to be a predictive tool, as the coefficients which have been derived
are potentially biased by uncertainty in exposure concentration data, and the simple BAF
representation makes no attempt to account for causal relationships between exposure and body
burden. While the BAF approach appears adequate to estimate annual average concentrations, it
does not represent 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. The bivariate BAF analysis does, however, provide an important foundation for more
sophisticated analyses, as presented in sections 5 and 6.
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Chapter 5

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5.
CALIBRATION OF PROBABILISTIC BIO ACCUMULATION FOOD
CHAIN MODEL
The components of the food chain model and general model structure are described in
Section 3.5. The model takes as exposure concentrations the summer-averaged total (STri+)
water concentration for PCBs and the annual average sediment concentration for PCBs
normalized to fraction organic carbon. As discussed in Section 3.5, 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. The derivation of the BAFs is presented in the Preliminary Model
Calibration Report (1996). Analyses presented here are based on Release 4.1 of the
TAMS/Gradient database. Results presented here are draft and subject to change based on
ongoing model refinement.
Each compartment in the model is briefly described. The relationship between each of
the compartments is described by a distribution of accumulation factors for total PCBs 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 the Preliminary
Model Calibration Report (TAMS/Gradient. 1996) 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.
5.1 Overview of Data Used to Derive BAFs
5.1.1	Benthic Invertebrates
The EPA team collected 20 (including background) colocated 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, 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.
5.1.2	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 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
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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.
5.1.3	Fish
The EPA 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 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.
5.1.4	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 that may experience conditions unlike those in the
Hudson River.
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5.2 Benthic Invertebrate: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 mean sediment concentration at a sampling location; and,
3.	Using the final distribution representative of the relationship between benthic
invertebrates and sediment within the overall model to predict the historical fish data in a
validation exercise.
5.2.1	Sediment Concentrations
An assessment of the range of sediment concentrations by river mile and congener
provides information on the variability inherent in these data. Figure 5-1 shows mean TOC-
normalized sediment concentrations (|ig/g) and associated 95% confidence intervals for the
upper and lower portions of the Hudson River. This figure shows that sediment concentrations,
even normalized, show significant spatial variability.
5.2.2	Approach
BSAF for benthic invertebrates were calculated from the Phase 2 dataset using colocated
sediment and benthic samples. The sampling rationale will be presented as part of the ecological
risk assessment (work in progress). PCB concentration 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 BSAF are individually collected samples of sediment and
benthic invertebrates. In the absence of this ideal condition, we used individual benthic
invertebrate samples and mean sediment concentrations for a given co-located sampling location.
However, in the areas that display highly variable PCB concentrations in sediments, it may be
that the mean does not adequately represent the exposure level for benthic invertebrates. The
heterogeneity in sediment concentrations over small spatial scales contributes to higher
variability in the BSAF calculated from data collected in these areas. Thompson Island Pool is
an area in which such variability in calculated BSAF occurs. Matching individual invertebrate
concentrations to the 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.
Species identified as epibenthic showed BSAF that were not significantly different from
species identified as benthic based on t-tests. In addition, the sampling program did not
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specifically sample for epibenthic species (Chernoff, 1995, personal communication) and were
only identified as such as a function of sampling rather than species identification. 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 BSAF
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 such 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. The final BSAF distribution is characterized by a geometric mean and geometric
standard deviation. 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.
The BSAF by river mile charts were developed using the data for the combined benthic
species as reported in database release 4.1. The charts for BSAF by river mile and the BSAF by
species show the mean BSAF and the associated 95% confidence interval. These plots provide
information on the variability of BSAF by river mile, and the species that contribute most to the
observed variability. Those species showing the highest variability also have the lowest number
of samples, indicating the sensitivity of statistical analyses to artifacts of undersampling.
5.2.3 Calculations of BSAF Values for Benthic Invertebrates
Figure 5-2 shows the BSAF for XTri+ 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 5-2 also shows the BSAF ITri+ 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.
The model was run by applying the distribution derived above to each mean sediment
concentration by river mile. The 10th, 25th, 50'\ 75th, 90th percentiles and maximum were
calculated. These percentiles were compared to the output from the frequency analysis on the
benthic invertebrate data using the SPSS™ software package. After log-transforming the results,
the observed benthic invertebrate concentrations were plotted against the percentiles predicted
from the model. The results of this exercise were presented in the Preliminary Model Calibration
Report (LTI et al., 1996). Figure 5-3 presents the cumulative distribution for BSAF estimated
for £Tri+ PCBs.
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The modeled ITri- PCB distributions in benthic invertebrates compared favorably to the
observed distributions of ITri-^- PCB concentrations as presented in the PMCR (LTI et al., 1996).
The BSAF 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 maximum observed concentrations.
5.3 Water Column Invertebrate:Water Accumulation Factors (BAFs)
5.3.1 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 presented in the Preliminary Model Calibration Report (1996) was
based on relating 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.
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/Tetra Tech/Gradient, 1998 - 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.
This report presents an alternative approach which also relates water concentrations to
observed water-column macroinvertebrate concentrations using a BAF approach, but rather than
incorporating the POC-normalized water column concentration, this approach relies on a total
water concentration (i.e., uptake from both the dissolved and particulate phases). This alternative
approach was explored because the historical data only measured total PCBs. In the PMCR (LTI
et al., 1996), assumptions were made about the relationship of total suspended solids (measured
by the USGS) and total water concentrations based on observed relationships from the Phase II
dataset. To estimate particulate organic carbon from a whole water concentration, it was
necessary to assume a fraction organic carbon of the total suspended sediments. The BAF
approach presented here was chosen to avoid making these assumptions.
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These BAF derivations rely 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 short-term 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, Oligochaetes, 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.
This study showed that the PCB congener pattern in the chironomid tissue differed
significantly from the congener pattern observed in the water (Novak. 1988; TAiMS/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 10'
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 the 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 potential relationships between water column invertebrates and water column
concentrations. 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.
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In this approach, total water column concentrations are related to macroinvertebrates by:
BAFwater = Crave/Cw3ter	(5-1)
where,
= The bioaccumulation factor between water column invertebrates
and particulate bound PCB in mg/Kg / mg/L
Cinvm = mg PCB per Kg lipid in invertebrate tissue
Cwater = mg PCB per L total water
5.3.2 Calculation of BAFwater for Water Column Invertebrates
Figure 5-4 presents the results of BAF calculations for water column invertebrates.
Values shown are the mean with 95% confidence intervals. The mean log-transformed BAF is
approximately 6.1. The bottom section of Figure 5-4 shows the cumulative distribution function
for whole water to water column invertebrates.
5.4 Forage Fish:Diet Accumulation Factors (FFBAFs)
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 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 in order to develop
the accumulation factors. The approach used to develop FFBAF for forage fish is described
below.
5.4.1 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. Measured benthic
invertebrate concentrations were used to estimate the benthic component combined with water
column invertebrate concentrations estimated from the water column BAF discussed previously.
FFBAF values were derived by:
1. Evaluating the available data for forage fish <10 cm for each river mile. The dietary
concentration was estimated based on life history and foraging information (see
Appendix A).
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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 water column invertebrates for total PCBs
using the distribution described earlier in this section and combining these estimates with
measured benthic invertebrate concentrations.
4.	Deriving a river-wide distribution of FFBAF by taking the ratio of a measured individual
forage fish concentration to the arithmetic 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 specifically for small pumpkinseed sunfish that
may be eaten by other fish species. Other approaches for pumpkinseed are discussed in
subsequent sections.
5.4.2	Forage Fish Body Burdens Used to Derive FFBAF Values
Bar charts were developed to show lipid-normalized concentrations in forage fish by river
mile. Mean concentrations and 95% confidence intervals are shown for the upper and lower
Hudson River for total PCBs in Figure 5-5.
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).
Figure 5-5 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. This figure shows that
forage fish total PCB concentrations at most of the river miles ranged from just above 0 to about
300 ug/g. River miles 189.5, 191.5, and 194.5 show significantly higher concentrations than at
other locations in the river. Concentrations are highest at 189.5, lower but still much higher than
river-wide averages at 191.5, and then increasing again at 194.1 to nearly the level at 189.5.
5.4.3	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
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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 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 model calculated 10th, 25th, 50th, 75th, and 90th percentiles and the maximum.
Percentiles were calculated from the observed forage fish body burden 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. These results were presented in the Preliminary Model
Calibration Report (LTI et al., 1996). The lower portion of Figure 5-5 shows the cumulative
distribution function for total PCB forage fish:diet accumulation factor.
5.5 Piscivorous Fish:Diet Accumulation Factors (PFBAF): Largemouth Bass
The Phase 2 dataset imposes limitations on these analyses. 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, an analysis was made relating largemouth bass lipid-normalized
concentrations to pumpkinseed lipid-normalized concentrations for measurements reported as
Aroclors 1016 and 1254 (representative of LTri+, which, in turn, is representative of total PCBs).
5.5.1 Largemouth Bass to Pumpkinseed BAF for Total PCBs
Figure 5-6 shows the ratio of largemouth bass greater than 25 cm to pumpkinseed less
than 10 cm for total PCBs by river mile and year. The lower portion of this figure shows the
cumulative distribution function for largemouth bass to pumpkinseed ratios. The largemouth
bass samples were collected in the spring, and the pumpkinseed samples in the fall. The
following spring individual largemouth bass concentrations were divided by the arithmetic mean
pumpkinseed concentration for the previous fall.
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5.6	Validation of Probabilistic Model Using Fate and Transport Model Output as Input
Table 5-1 presents the final distributions used in the empirical probabilistic model. Full
details on distribution development were presented in the Preliminary Model Calibration Report
(1996). The sediment and water concentrations used to generate pumpkinseed and largemouth
bass concentrations were obtained from the hindcasting results from the fate and transport model
(see Books 1 and 2). Figure 5-7 shows the TOC-normalized sediment concentrations and whole
water summer concentrations used in the empirical probabilistic model.
The model was run for river miles 168 (Stillwater) and 189 (TIP) as these are the two
locations with the most fish data. Figure 5-8 shows the results of the calibration on lipid-
normalized basis for each of the river miles, Figure 5-9 presents the results on a wet-weight basis
assuming the same percent lipid as FISHRAND and F1SHPATH (average of all lipids across
river miles or 0.15 for largemouth bass). Wet weight concentrations were not estimated for
pumpkinseed.
5.7	Discussion of Results
On a lipid-normalized basis, average concentrations predicted by the model show-
excellent agreement with observed averages. Similarly, the predicted 90th percentile
concentrations predicted by the model show good agreement with maximum observed
concentrations, except for particular years showing temporary increases in body burdens. The
inability of the model to capture 1991 and 1992 observed concentrations for largemouth bass is
related to two factors: the first is that the model is an empirical model. To the extent that the
BAF relationships constructed between compartments represent a variety of conditions in the
river, these will be represented in the output. The model is not designed to predict short-term
fluctuations in concentrations, or short-term responses in the system. The second is that the
predictions in fish will mirror concentrations in sediment and water. The largemouth bass, as a
top piscivore and large fish, integrates both sediment and water exposure sources but to some
extent mirrors water. Consequently, these concentrations are sensitive to the specified water
concentrations. While 1992 shows a transient increase in whole water concentrations (see Figure
5-7), it is not high enough to be reflected in fish concentrations.
The assumption of percent lipid plays an important role in estimating wet weight
concentrations from the predicted lipid-normalized concentrations. On a lipid-normalized basis,
model predictions and observed data show excellent agreement. The agreement is less robust
when evaluated on a wet weight basis. This may be attributable to uncertainties in lipid
quantitation, as well as year-to-year changes and fluctuations in lipid composition in the fish
population of interest.
Largemouth bass percent lipid values, evaluated over all years and sampling locations,
show significant variability, although for most years the lipid fractions are between one and
three. The lipid content of a fish can make a tremendous difference in predicted concentrations.
To represent the potential population of largemouth bass, we chose to use the median lipid value
for the upper river as a whole, under the assumption that a fish in one location could equally well
have been found at a different location. Under this assumption, predicted wet weight
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concentrations show less of a good fit with the historical data than using the observed lipid
content from year-to-year. However, for future predictions, unless a mechanistic explanation
could be hypothesized, there is very little basis upon which to vary lipid content from year-to-
year. Consequently, we prefer to use a median lipid content to translate lipid-normalized results
to wet weight results.
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Chapter 6

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6. FISHPATH AND FISHRAND: TIME-VARYING MECHANISTIC
MODELS BASED ON A GOBAS APPROACH
6.1 Model Input Data
Both the historical NYS DEC and US EPA Phase II datasets were used in the
development and validation of the FISHPATH and FISHRAND models. Distributions of
species-specific fish weight, lipid content (expressed as a percentage), organic carbon content of
sediment (expressed as a percentage), and feeding range preferences for the individual fish
species were developed for use in FISHRAND. Sediment and truly dissolved water
concentrations from the 21-year hindcasting of the fate and transport model were used to
generate fish body burdens to compare to the historically observed NYS DEC data set. Further
distributions incorporated include a distribution for Kow and for starting sediment and water
concentrations as predicted by the fate and transport models.
6.1.1 Non Species-Specific Parameters
A number of environmental parameters specific to either the location or form of PCBs
being modeled were described by distributions, including:
•	Annual sediment concentrations (location specific);
•	Monthly water concentrations (location specific);
•	Monthly temperature (location specific);
•	Log octanol-water partition coefficient (Kow) (STri+); and,
•	Total organic carbon in sediment (inside TIP versus outside TIP).
6.1.1.1 Sediment and Water Concentrations
The sediment and water concentrations used in calibrating and validating the FISHRAND
model were generated from the fate and transport model (Books 1 and 2). Figure 6-1 presents
the dry weight sediment concentrations and dissolved water concentrations predicted by the
hindcasting calibration. The probabilistic empirical model uses TOC-normalized sediment
concentrations and whole water concentrations, while FISHRAND and FISHPATH use truly
dissolved water concentrations and dry weight sediment concentrations (jig PCB / g solid).
The model requires monthly dissolved water column concentrations and annual sediment
concentrations (sediment concentrations vary only slightly within a given year, allowing for the
use of an annual concentration). HUDTOX generates daily water column and sediment
concentrations for the hindcasting period and every other day for the prediction period. These
results are averaged by month for water and by year for sediment, characterized by a mean and
standard deviation (equations 3-19 and 3-20). Sediment concentrations represent an area-
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weighted average of cohesive and non-cohesive sediments and assume that fish preferentially
spend 75% of their time in cohesive sediment areas.
Initial concentrations are not available for each of the FISHRAND compartments in 1977
for all river miles. Since the model requires initial PCB concentrations to be specified and these
were all set to zero, the model requires several years to reach a quasi-equilibrium state in which
PCB concentrations (in fish, in particular) are not sensitive to initial concentrations. Therefore,
all modeling results are considered robust beginning in the early 1980 "s.
6.1.1.2 Temperature
Growth rate is modeled as a temperature dependent relationship, thus, monthly average
temperature is required for FISHRAND. Temperature data for all upper Hudson river locations
was compiled from the General Electric and EPA datasets. Together, these datasets provided
nearly 2,200 datapoints over the course of several years. Temperature data were grouped by
month and year of collection and river mile and statistically evaluated across locations. Monthly
temperatures are characterized by distributions that are the same for each location.
Actual monthly averages were used for the periods for which there were measurements
available. For time periods for which there were no measurements, the following approach was
taken: temperature was regressed day of the month for each month, and then the monthly
average was obtained as the square of the area under the regression line, divided by the number
of days in that month.
Additional averaging was made over all years of observations to obtain "annually
averaged monthly average" model for temperature (in which average temperatures for a fixed
month in different years are equal). This model was applied to the actual monthly average
temperature measured to obtain residuals from the annually averaged model. The residuals are
well described by a normal distribution with a mean of zero and a standard deviation of 2.21.
Thus in time periods for which there were no measurements (1977-1990, and 1998-2018),
FISHRAND uses a normal distribution of the temperature with the mean equal to the annually
averaged monthly average described above (dependent on month number only) and a standard
deviation of 2.21 (not dependent on time).
During the summer months, when temperatures are highest and fish are consuming the
most dietary items, some fish species are likely to spend proportionally more of their time in
shallower, nearshore areas which may not have been captured in the monitoring program. The
approach taken was to adjust the distribution upward 20% in the sensitivity analysis to evaluate
the potential effect of temporary increases in temperature during the summer months.
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6.1.1.3 Total Organic Carbon in Sediment
The distribution for total organic carbon content is shown in Table 6-1. Based on 1993
data, mean TOC does differ significantly within the Thompsons Island Pool as compared to other
upper Hudson locations outside the pool. Consequently, two distributions were used in this
analysis. Section 8.0, Uncertainty Analysis, discusses the sensitivity of the model to TOC
assumptions.
6.1.1.4 Log Octanol-Water Partition Coefficient (K0J
The Kow used in this analysis is representative of the distribution of Kows that might be
expected in the XTri- PCB mixture. Several approaches for characterizing Kow were evaluated.
Individual PCB congeners contained in the ZTri-^- mixture will be taken up by fish to varying
degrees as expressed by the Kow. One approach was to evaluate an average congener profile in
water and fish in the upper Hudson and weight the Kow values according to the weighting of that
particular congener in the mixture. This approach proved infeasible, however, and another
approach was taken.
In the approach taken, Kow is described by a triangular distribution according to the
cumulative distribution of Kows in the mixture. This distribution ranges from 5.12 to 8.3 with a
mode of 6.6. Individual Kow values were obtained from the Great Lakes Initiative Technical
Support Document for the Procedure to Determine Bioaccumulation Factors (EPA. 1995).
6.1.2 Species-Specific Data
Data from the historical NYS DEC fish monitoring results. US EPA Phase II data and the
NYS DOH macroinvertebrate data collection effort were used to develop species-specific
distributions for:
•	Lipid content for fish, benthic invertebrates, water column invertebrates, and
phytoplankton
•	Fish weight
•	Dietary composition of fish diet
These distributions represent typical values found in the population of interest based on
observed data. Using distributions for particular parameters instead of point estimates in effect
follows a population over time in which fish enter and leave the compartment in equal rates.
Triangular distributions were derived for the dietary composition for each fish species based on
the proportion of the diet represented by benthic invertebrates, water column invertebrates,
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phytoplankton, and/or forage fish based on the indicator species gut contents analysis presented
in Appendix A. Table 6-1 presents a summary of the distributions used in this analysis.
6.1.2.1 Lipid Content
Lipid Content for Fish
Figure 6-2 presents the cumulative distribution functions for lipid content in each of the
fish species. Lipid data were combined across years and locations based on a series of analyses
described next. Only those lipid data were used for the fish of appropriate size (i.e., only
largemouth bass > 25 cm; pumpkinseed < 10 cm; white perch > 17 cm; yellow perch > 15 cm).
This resulted in keeping all of the historical NYS DEC largemouth bass data (no exclusions as all
fish were greater than 25 cm) and none of the EPA Phase II data (fish were all very small). The
Phase II data was also not suitable for pumpkinseed, which were all very large fish (larger than
the largemouth bass). For yellow perch, the historical NYS DEC dataset showed a lipid
distribution that was very low when compared to the literature (Great Lakes technical support
documents) and to the Phase II dataset. Thus, only Phase II yellow perch lipid data were used in
developing a lipid content distribution. White perch and brown bullhead lipid were obtained
from the historical NYS DEC dataset. None of the data points were excluded for brown bullhead
and approximately 100 small fish were excluded for white perch.
Individual percent lipid measurements were regressed against both weight and length for
each species and location to determine if there was a correlation between lipid content and either
weight or length which should be accounted for in the model. In a few cases, this analysis
showed a weak correlation but overall there was no relationship between lipid and weight or
length. Thus, the model assumes no correlation between the two but rather samples randomly
from the assigned lipid distribution for each species.
Lipid content in fish will depend on a number of factors, including temperature, prey
availability, and foraging success. Year-to-year differences in lipid content are difficult to
predict, so the ideal situation is one in which species-specific lipid distributions can be developed
irrespective of location or time. The first step in developing species-specific lipid distributions
was to statistically evaluate lipid data across years and locations to determine if there were clear
differences. Comparisons of means (using the Bonferroni correction to account for multiple
comparisons) was carried out to determine significant differences. If there were clear
differences, an effort was made to discern the origin of the differences.
There was no pattern to differences in lipid content within a species by location or year.
Typically, differences were observed across years and locations, for example, between river mile
168 in 1993 and river mile 189 in 1995. There were no observable consistent differences such
as, for example, 1995 lipid content was lower at all locations, or river mile 189 was consistently
lower than 168. As there were no observable patterns to differences in lipid, and no clear basis
upon which to predict a lipid distribution for any given year, lipid data across all years and
locations were combined within a species.
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All derived lipid distributions were compared to the literature (EPA, 1994 and 1995) to
determine whether they were within the range observed for these species in other systems. The
yellow perch distribution based on the NYS DEC data was significantly lower than a) values
from the literature, and b) the measured lipid from the EPA Phase II program. As initial model
calibration runs showed yellow perch concentrations were significantly overpredicted, the Phase
II data only were used to derive the lipid distribution. Subsequent model calibration results for
yellow perch were then within the range of observed data.
Lipid content represents an important parameter in all the bioaccumulation models.
Further analysis on lipid content is currently being carried out and a more detailed rationale will
be provided.
Lipid Content for Benthic and Water Column Invertebrates
The US EPA Phase II data were used to develop a lipid distribution for benthic
invertebrates presented in Table 6-1. The NYS DOH dataset was used to develop a lipid
distribution for water column invertebrates from the multiplate sampling effort. These
distributions were compared to the literature.
Literature values were used to construct a phvtoplankton distribution (Gobas. 1993).
Only the spottail shiner consumes a small amount of phytoplankton.
6.1.2.2	Fish Weight
Figure 6-3 present the cumulative distribution functions for fish weight for each of the
fish species. As described previously, an effort was made to determine if there were observable
relationships between weight and lipid content which should be accounted for in the model
structure. The same data were used to develop both the lipid content and weight distributions.
6.1.2.3	Dietary Composition
Dietary composition is based on the results of the analysis presented in Appendix A for
each individual fish species and summarized in Table 6-1. As noted in Section 3, it is very
difficult to quantitatively describe feeding preferences based on snapshots of information.
Further, despite the extensive gut content analyses that have been conducted by Menzie-Cura and
Associates, Inc. and Exponent, Inc., soft-bodied organisms that may have been consumed
typically will have been digested, thus, it is virtually impossible to specifically identify all the
prey organisms in the diet of fish. The results presented in Table 6-1 represent professional
judgment and a careful analysis of all the available data.
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6.2 Results of the Calibration Exercise
Using the predicted hindcasting for sediment and water from the fate and transport
models, Figure 6-4 shows the results of the calibration for each of the fish species for both lipid-
normalized and wet weight concentrations. In general, the model is better at capturing Iipid-
normalized concentrations versus wet weight concentrations. Table 6-2 presents the relative
percent difference between the mean FISHRAND predicted concentration and the observed
concentrations for lipid-normalized and wet weight results across all species and locations. The
lowest upstream modeling location (river mile 154, just above the Federal Dam) is compared to
monitoring results from river mile 152, just below the Federal Dam. Thus, these results may not
be directly comparable.
The model predicts a monthly fish body burden, which can be further averaged to
represent a seasonal or annual concentration. To evaluate whether the model was capturing
observed seasonal differences, results from river mile 154 were compared to the NOAA 1995
dataset for river mile 152. These results are shown in Figure 6-5. Again, these two locations
may not be directly comparable. The model shows that concentrations tend to increase in the late
summer, when feeding is maximized. This is also the time that lipid content is likely to be
highest; thus, lipid-normalized concentrations would be at their lowest. FISHRAND does not
currently adjust the lipid distribution seasonally insofar as lipid increases and decreases
throughout the year will balance each other out. A t-test using the NOAA 1995 dataset of lipid
content between spring and fall was highly insignificant in the case of white perch and highly
significant in the case of yellow perch.
Historical data for spottail shiner and benthic invertebrates is only available for 1993.
The mean benthic invertebrate concentration within the TIP was 13.9 ppm wet weight, as
compared to 14.0 predicted by FISHRAND.
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Chapter 7

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7. INITIAL BIOACCUMULATION MODEL PREDICTIONS
This section describes the initial modeling results from the probabilistic empirical model
as well as FISHRAND. Sediment and water concentration inputs are taken from the fate and
transport model (Books 1 and 2). These results may be modified in response to additional
refinements in the transport and fate modeling results. Two modeling predictions were provided
from the fate and transport modelers: a zero upstream boundary" condition (cessation of the
source at Ft. Edward) and a constant upstream boundary condition (assuming a small but
constant upstream source). A comparison of these two results shows that they are very close.
Thus, only the constant upstream boundary condition was evaluated.
7.1 Probabilistic Empirical Model
The model takes TOC-normalized annual average sediment concentrations and summer-
averaged whole water concentrations as inputs. Based on the distributional relationships
between compartments presented in Section 5. predictions were made for the zero and constant
upstream boundary conditions for pumpkinseed and largemouth bass.
7.1.1	Sediment and Water Concentration Inputs
Figure 7-1 shows the sediment and water concentrations used for the zero upstream
boundary condition, while Figure 7-2 presents the sediment and water concentrations predicted
from the fate and transport model under the constant upstream boundary condition. These
figures show that sediment concentrations decline exponentially between 1998 and 2018 under
both scenarios and also show very similar concentrations. Whole water concentrations show
significant variability over time.
7.1.2	Predicted Largemouth Bass Body Burdens under Zero Upstream Boundary
Conditions
Figure 7-3 shows the empirical probabilistic model lipid-normalized predictions for
largemouth bass at river miles 189 and 168. while Figure 7-4 presents the same results on a wet
weight basis. Under the assumptions presented here, largemouth bass only barely achieve 2 ppm
on an average basis for river mile 189 by the end of the modeling period. For river mile 168.
largemouth bass achieve 2 ppm wet weight PCB concentration on an average basis by 2010.
Average wet weight concentrations hover above and below 2 ppm for several years. The upper-
bound concentrations (represented by the 90!h or 95th percentiles) do not achieve 2 ppm by the
end of the modeling period for these river miles.
At river mile 157. 2 ppm is predicted for 2005 on a mean basis, and as soon as 2003 for
river mile 154. The 90th percentile predicted concentration for river mile 157 achieves 2 ppm by
2011, and 2017 for the 95th percentile. In the calibration, the 90:h percentile concentrations
typically occurred at or above the maximum observed concentration, except for 1991 and 1992
for largemouth bass at river mile 168. Thus, it is likely that in the absence of predicted short
term fluxes or spikes in water column concentrations, the 90th percentile predicted concentration
will be protective of the population at this level. The predicted 90th percentile concentration
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achieves 2 ppm by 2007, and the 95th percentile predicted concentration achieves 2 ppm by 2008
for river mile 154.
7.1.3 Predicted Largemouth Bass Body Burdens under Constant Upstream Boundary
Conditions
Figure 7-6 shows the empirical probabilistic model lipid-normalized predictions for
largemouth bass at river miles 189 and 168, while Figure 7-7 presents the same results on a wet
weight basis. Under the assumptions presented here, largemouth bass only barely achieve 2 ppm
on an average basis for river mile 189 by the end of the modeling period. For river mile 168.
largemouth bass achieve 2 ppm wet weight PCB concentration on an average basis by 2010.
Average wet weight concentrations hover above and below 2 ppm for several years. The upper-
bound concentrations (represented by the 90,h or 95tn percentiles) do not achieve 2 ppm by the
end of the modeling period.
7.2 FISHRAND Results
This model uses truly dissolved water concentrations averaged monthly and annual
average sediment concentrations as inputs. The model mechanistically describes PCB uptake
over time and results are presented here for largemouth bass, yellow perch, pumpkinseed. brown
bullhead and white perch under the constant upstream boundary condition. As discussed next,
the fate and transport modeling results for the zero and constant upstream boundary conditions
were similar enough that only the constant upstream boundary condition was modeled using
FISHRAND.
7.2.1	Sediment and Water Concentration Inputs
Figure 7-9 shows the sediment and water concentrations used for the zero upstream
boundary condition, while Figure 7-10 presents the starting sediment and water concentrations
predicted from the fate and transport model under the constant upstream boundary condition.
These figures show that sediment concentrations decline exponentially between 1998 and 2018
under both scenarios and also show very similar concentrations. Dissolved water concentrations
show significant variability over time. Monthly average concentrations are used in FISHRAND
for water and annual average sediment concentrations.
7.2.2	Predicted PCB Concentrations in Fish under Constant Upstream Boundary
Conditions
Figures 7-11 through 7-18 present the results of the predictive modeling for river miles
189, 168, 157 and 154. The odd-numbered plots are lipid-normalized. the even-numbered plots
show the wet weight results. The mean predicted concentration is depicted by a square line,
while the 95th percentile is depicted by the triangular line.
Table 7-1 presents a range of years that particular target levels that have been used in
other contexts will be achieved. These target levels should not be viewed as endorsement of
particular goals, but rather merely provide perspective on the range and trend of predicted
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concentrations. The selected target levels include: the FDA Action Level of 2 ppm wet weight,
and several ranges of values designed for the protection of human consumption of fish from the
Great Lakes Uniform Sportfish Advisory Task Force. None of the fish for any of the locations
achieve concentrations less than 0.5 ppm within the modeling period (up to 2018). Generally,
there are approximately ten years between the mean and 95lh percentile in achieving any
particular target level, suggesting that ten years is an appropriate interval for capturing the
observed range in population concentrations.
The best estimate is the center of the ranges shown in Table 7-1. A final quantitative
uncertainty is currently underway, but initial results suggest that based on the relative percent
difference between predicted and observed, as well as an initial evaluation of parameter
uncertainty, the error bounds on the mean estimate of time to achieve any given target is
approximately plus or minus three years.
Concentrations in largemouth bass in the TIP achieve 2 ppm on a mean basis several
years before river mile 168. As shown in Figures 7-9 and 7-10, predicted dissolved water
concentrations are typically higher at river mile 168 than in the TIP. Consequently, those fish
who derive much of their body burden from water column sources (such as the largemouth bass),
will reflect this exposure through higher body burdens. The brown bullhead, by contrast,
exposed to higher sediment concentrations within the TIP and primarily a bottom feeder, does
not achieve 2 ppm within the modeling period in the TIP, but does so at river mile 168.
7.3 Discussion of Results
The empirical probabilistic model results show increasing spread in the concentrations at
higher percentiles. That is, the difference between the 90th and 95th percentiles is greater than the
difference between the median and the average, for example. This is an attribute of lognormal
distributions.
Results of a sensitivity analysis conducted for FISHRAND are presented in section 8.
This analysis shows that lipid content is an important variable in predicting fish body burdens.
The models were designed to predict the observed variability in fish tissue measurements
taken since 1977. Some of the variability that has been observed over time is attributable to
uncertainty, but this is likely to be small relative to the actual population heterogeneity in the
environment. The parameter-specific distributions developed here were designed to capture
variability rather than uncertainty. It can be argued that the dietary composition distributions, for
example, represent uncertainty, but in fact they were derived based on observations of what fish
have consumed in the environment. Similarly the lipid distribution, which contains measurement
error, is primarily a distribution reflecting the differences in lipid content among individual fish.
Presenting predicted fish body burdens probabilistically provides important information
for decision makers and for other aspects of the analysis. The ecological and human health risk
assessments require predicted body burdens to evaluate the potential risk from PCB exposure
under specific conditions. Any probabilistic analyses that might be planned for the human health
and/or ecological risk assessments will benefit from these results.
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The modeling results can be used directly in the context of specific numerical target
levels. It is straightforward to obtain specific modeling results, that is, if risk managers
determine a particular percentile of population should achieve a target level (say, the 75:h or 90ch
need to achieve 0.2 ppm wet weight, or 2.0 ppm wet weight), these results can be explicitly
predicted. For example, under current assumptions and based on the results of the fate and
transport modeling, both FISHRAND and the empirical probabilistic model predict that
concentrations in largemouth bass will achieve 2 ppm at Waterford within the prediction period
under the constant upstream boundary condition.
FISHRAND modeling results suggest that PCB concentrations in largemouth bass
decline more quickly within the TIP than at river mile 168. As discussed above, this is partially
attributable to the higher predicted dissolved water concentrations at river mile 168 as compared
to 189 from the fate and transport models. This higher dissolved water concentration will be
reflected in the organisms with proportionally greater direct exposure to water column sources.
As discussed in the sensitivity analysis portion of section 8, the percent lipid distributions for
individual fish species and for water column invertebrates play an important role in the model.
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Chapter 8

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8. DISCUSSION OF UNCERTAINTY
This section provides a discussion of uncertainties in the bioaccumulation model
approach and assumptions. These uncertainties can be broadly categorized as model uncertainty
and parameter uncertainty. Model uncertainty is the error associated with how well a model
approximates the true relationships between environmental components. For example, these
would include terms representing functional aspects of the environment that were not included in
the analysis. Model error includes: inappropriate selection or aggregation of variables, incorrect
functional forms, and incorrect boundaries. Parameter uncertainty refers to the uncertainty in
' ₯ »
estimating specific values of parameters and forcing functions in the models (e.g., sediment and
water concentrations, etc.) as well as inherent variability (e.g., lipid content, fish weight). Most
modeling parameters will exhibit both variability and uncertainty. Variability, which typically
cannot be reduced but can be better characterized by collecting additional data, represents known
variations in parameters based on observed heterogeneity in the environment. True uncertainty
in parameter estimates could be reduced by collecting more data.
8.1 Model Uncertainty
8.1.1	Model Uncertainties in the Fate and Transport Models
Since the bioaccumulation models rely on the sediment and water concentrations from the
fate and transport models, it is important to identify potential sources of uncertainty in these
models to be able to understand the effect on predicted fish body burdens. By necessity, the fate
and transport models are not able to capture every single mechanism contributing to transport
processes. The most important of these have been selected for explicit modeling, based on
professional judgment, prior experience and existing models. See Book 1 for a further discussion
of uncertainties in the fate and transport models.
A qualitative analysis of the uncertainties in the models suggests that uncertainties in fish
body burdens is approximately a factor of two. That is, given adjustments in parameters and
changes in sediment and water exposure concentrations from modifications to the fate and
transport models, resulting fish body burdens will not change by more than a factor of two.
However, a factor of two correspondingly increases the 95th percentile, such that the interval of
years within which a particular target level might be achieved grows larger (i.e., from 10 years to
20 years).
8.1.2	Model Uncertainties in the Bioaccumulation Models
By necessity', the bioaccumulation models also contain a number of simplifications in
uptake processes. In addition, the two statistical approaches presented here contain inherent
limitations as compared to the mechanistic approaches. These two aspects of model uncertainty
in the bioaccumulation models are discussed next.
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8.1.2.1	Probabilistic Empirical Model and Bivariate Statistical Model
These two models use observed data to construct relationships between compartments.
One limitation of these kinds of statistical approaches lies in their predictive power. Models of
this sort cannot reliably be used in terms of prediction as they do not necessarily capture the
mechanistic basis for responses to changes in the system. They can be used to extrapolate
beyond the range of observed data to evaluate trends based on current conditions, but they cannot
be used to evaluate changes in the system and expected responses to those changes.
8.1.2.2	FISHRAND and FISHPATH
FISHRAND and FISHPATH are based on the modeling approach developed by Gobas
(1993). This approach has been used in the Great Lakes as well as in a number of other
modeling contexts. Further refinements on the original model have been presented in the
literature (Gobas et al., 1995; Morrison et al., 1997). These later approaches involve the
following modifications:
•	Explicit consideration of benthic invertebrate feeding preferences (e.g.. burrowers
versus epibenthic species etc.) resulting in a biomagnification mechanism rather than
the equilibrium partitioning (BSAF) approach taken here;
•	An age-class model for each year of a fish's life rather than the growth dilution
approach presented here; and.
•	An explicit pharmacokinetic model to consider the role of metabolism.
Benthic feeding: FISHRAND and FISHPATH do not explicitly consider benthic feeding
strategies but rather rely on the original equilibrium partitioning approach for several reasons.
First, distributions are used in FISHRAND for a) sediment concentrations, b) total organic
carbon in sediment, and c) benthic invertebrate percent lipid. The sediment concentration
distributions are described as lognormal, while the TOC and lipid distributions are described as
triangular. Given these distributional shapes and the nature of the relationship between sediment
concentrations and invertebrate concentrations, the use of these distributions in the BSAF
equation adequately describe the observed variability in benthic invertebrate concentrations as
compared to empirical data. This observed variability may be attributable to biomagnification
but insofar as the model adequately describes observed data and the equilibrium partitioning
equation has been widely used and accepted, it was decided to take this approach for
FISHRAND.
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As shown in Figure 5-2, observed biotarsediment accumulation factors from the EPA
Phase II database average one. exactly what equilibrium partitioning would predict. The species
categorized as benthic versus epibenthic from the Phase II dataset did not show statistically
significant different BSAFs (t-tests).
Age-Class Modeling: The body weight, lipid content and dietary preferences change
significantly over the lifespan of individual fish and the latest Gobas model is developed for
individual generations of age classes of organisms (Gobas et al., 1995). In this study, we have
categorized fish into species-specific age classes. For example, in the case of largemouth bass,
yellow perch, white perch and brown bullhead, the adults in the population are of primary
concern. It is the adult fish in the population that will be consumed by humans and some
ecological receptors. Forage fish (pumpkinseed and spottail shiner) serve as primary prev base
for the larger fish (that are piscivorous) and also other ecological receptors (such as mink and
kingfisher, as examples). Juvenile fish of all species are assumed to have feeding habits more
similar to the forage fish. Two classes of forage fish are considered: one that obtains its
predominant food source from the water column (pumpkinseed) and the other equally from water
and sediment (spottail shiner). These two categories are representative of the kinds of feeding
strategies forage fish and juvenile fish will utilize.
These discreet fish populations are represented by distributions for fish weight and lipid
concentrations. Each individual fish in the population is assumed to grow. i.e. to increase its
individual volume and weight. Such volume increase can lead to decrease in concentration in
this fish if uptake is too slow to compensate for the reduction in chemical mass per volume. The
volume of the population is assumed to be equilibrated by the processes of fish death and
reaching the minimal size to be included in the population.
Pharmacokinetics: The metabolism of PCBs likely plays an important role in the ability
of fish to retain PCBs (Niimi, 1997; Gobas, 1999). Experimental data suggest that PCBs can
biomagnifv in the food chain due to pharmacokinetic processes in fish (Gobas. 1999, Connolly,
1988, Gobas. 1993). Specifically, food digestion and absorption in gastrointestinal tract is
hypothesized to increase PCB fugacitv. Even though these processes have been recently
incorporated in the fish bioaccumulation model by Gobas (Gobas et al.. 1999) we believe that the
experimental database and theoretical foundation of this model have to be developed further to
provide better estimates for the required parameters and associated uncertainties. The model has
to be validated in different settings before attempting to use it for regulator)' decisions.
Therefore, FISHRAND model does not directly account for these processes and uses as the
prototype an earlier version of Gobas model that was tested and applied for several sites and in
different environmental settings (Morrison et al., 1997. Buckhard, 1998).
8.2 Parameter Uncertainty
All of the parameters used in FISHRAND have some uncertainty associated with them.
For example, even though there is an extensive database of percent lipid for specific fish species
across locations and times, there is laboratory uncertainty associated with these measurements.
The full extent of that uncertainty is not known. Fish feeding preferences are highly uncertain.
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Stomach content analyses provide only limited information as the soft-bodied organisms are the
first to be digested and cannot typically be observed, even if a fish is caught immediately after
consuming such organisms. Biomass data, which are required to translate numbers of organisms
observed in the stomach contents to meaningful percent mass or volume estimates, are often
unavailable. Further it is typically not known whether a fish will selectively feed on particular
organisms or whether the fish is strictly an opportunistic feeder, in which case feeding will in
large measure depend on the biomass of prey items in the environment.
8.2.1	Sensitivity Analysis
Our literature review and experimental data collected for the Hudson River has shown
that: 1) river ecosystem characteristics vary significantly from one location to another depending
on flow rate, depth, sediment structure, etc.; and 2) certain parameters in the model (such as
feeding preferences) are only imprecisely known. Moreover, most of the measurements are not
easily related to the FISHRAND generic input parameters because, by their own nature,
experimental measurements are taken at a specific time and space while the FISHRAND model
parameters are, in contrast, values corresponding to averages over time, space and species.
The effect of variation of all input parameters on all model outputs were evaluated in a
sensitivity analysis using the Monte-Carlo methodology. This is a powerful tool to analyze
uncertainties in model predictions. In this method, combinations of values for the input
parameters are generated randomly. Each parameter appears with the frequency suggested by its
probability distribution. For each combination of input parameters, the output of the model is
recorded. The combination of all possible outputs generated in this manner is used to construct
the distribution of model outputs, which reflect the influence of the undetermined parameters on
the output values.
The partial rank and Spearman rank regression techniques (Morgan and Henrion, 1990)
are used as a formal method to find the most important parameters for the model performance. If
the Spearman or partial rank regression coefficient (PRRC or SRRC) is close to 1 or -1 for a
specific input model parameter, this parameter significantly influences model output. Table 8-1
shows that the correlation coefficients estimated for the percent lipid in water column
invertebrates are above 0.5 for most species and location for the lipid normalized results. The
percent lipid in fish is strongly negatively correlated with PCB body burden expressed on a lipid-
normalized basis. This is because increases in lipid increase the PCB storage capacity of the fish,
reducing the apparent concentration. As expected, the percent lipid in fish is positively
associated for the wet weight results, but less so. This confirms that particularly on a lipid-
normalized basis, the percent lipid distribution is very important. Kow and benthic percent lipid
are also important for some species on a wet weight basis. Feeding preferences are only weakly
correlated with body burdens in terms of sensitivity to this parameter.
8.2.2	Lipid Content
Lipid content of organisms play an important role in the model. Uncertainty in the
interpretation of observed data is attributable to differences in laboratory determination of lipid
content of fish tissue. PCBs are lipophilic, stored primarily in fatty tissue, and it is generally
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agreed that lipid normalization (i.e., expressing PCB body burden on a lipid basis) provides a
more consistent basis for evaluating bioaccumulation than wet-weight PCB concentrations.
Lipid-normalized PCB body burden is calculated as the reported wet-weight PCB concentration
divided by the lipid concentration. The model, too, first estimates a wet-weight concentration
and then lipid-normalizes these results. Unfortunately, any imprecision in the determination of
lipid concentration will also result in imprecision in the calculation of lipid-normalized PCB
body burden. Further, the propagation of uncertainty will be non-linear, as the lipid-normalized
concentration involves division by the lipid content. Therefore, estimation of the uncertainty in
lipid-based PCB concentrations must also include an analysis of the uncertainty in determination
of lipid concentration. Inter-laboratory comparisons conducted by NYSDEC in September 1992
showed an average variability between laboratories of ten percent in determining lipid content of
biological specimens, with results from some pairs of laboratories showing a consistent relative
bias.
Based on the results of NOAA's mussel method detection limit (MDL) study (see
TAMS/Gradient. February 1993 for details), the percent lipid determination for benthic
invertebrates was considered to be estimated. Therefore, the percent lipid of benthic
invertebrates was based on the mean of all invertebrates analyzed in the Phase 2 study. The
variability seen in the percent lipid composition was associated with the small sample mass
associated with some of the samples (1 gram wet weight). The confidence of percent lipids was
higher for fish samples, which had more material available for analysis.
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Chapter 9

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9. SUMMARY AND CONCLUSIONS
Three food chain models were developed to describe the uptake of PCBs, expressed as
STri-^-, which is representative of total PCBs in fish tissues. These models include:
Bivariate BAF Analysis
The Bivariate BAF Analysis relates measured PCB levels in water and sediments (two
variables, or "bivariate7') to measured PCB levels in fish. This analysis was applied to the Upper
Hudson River and to a segment of the Lower Hudson River near Albany. The Bivariate BAF
Analysis was developed using the historical PCB Aroclor database. Results presented in this
report build upon the earlier analysis presented in the Preliminary Model Calibration Report
(1996).
Empirical Probabilistic Food Chain Model
The Empirical Probabilistic Food Chain Model is contructed by linking 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 invertebrates in the water
column and sediments. The Probabilistic Model was developed using both historical and 1993
field data, and was applied to the Upper Hudson River down the Federal Dam at Troy. In
contrast to the Bivariate BAF Analysis, which provides average body burden estimates, the
Probabilistic Model provides information on the expected range of uncertainty and variability
around these average estimates.
Mechanistic Time-Varying Models (FISHPATH and FISH RAND) Based on Gobas (1993)
As a result of the peer review process conducted for the Preliminary Model Calibration
Report, it was determined that a time-varying, mechanistic model should be included in the suite
of models being used to evaluate the potential for PCB uptake into fish tissue. Consequently,
two additional mechanistic models were developed to describe the uptake, absorption, and
elimination of PCBs in fish over time. These models are based on the peer-reviewed uptake
model developed by Gobas (1993 and 1995). This is the same form of the model that was used
to develop criteria under the Great Lakes Initiative (EPA. 1995). Two versions of the model were
developed for the sake of quality assurance and convenience:
•	FISHPATH: a deterministic version programmed in Stella-4™ Software
•	FISHRAND: a probabilistic version programmed in Fortran-90 and Delphi-3 using the
LSODE (the Livermore Solver for Ordinary Differential Equations (Radhakrishnan and
Hindmarsh, 1993))
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Food Web Biology
As part of the development of the food web models, species-specific profiles (i.e.,
descriptions of feeding behavior, habitat preferences, 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: information on species-
specific characteristics influencing bioaccumulation potential of PCBs; as well as the details of
specific gut analyses conducted by Menzie-Cura & Associates, Inc., Exponent, Inc.; and
information in the literature from the Hudson River power plant studies. These profiles helped
develop dietary composition distributions for each of the fish species.
Applicable and Relevant and Appropriate Requirement (ARARj
Appropriate target levels for fish body burdens have not yet been established. Several target
levels used in other contexts are presented in this report to provide perspective on predicted fish
concentrations. These include the 2 ppm wet weight United States Food and Drug
Administration (FDA) Action Level, and the Great Lakes Uniform Sportfish Advisory Task
Force PCB concentrations in fish for human consumption. These are as follows:
•	Greater than 1.9 ppm wet weight - no fish consumption is recommended
•	1.1 - 1.9 ppm wet weight: 6 meals per year
•	0.1-1.0 ppm wet weight: 1 meal/'month
These values should not be construed as endorsement of particular target levels, but rather
are designed to provide perspective on predicted fish body burdens relative to environmentally-
protective concentrations that have been developed for other purposes.
9.1 Summary of Food Web Models
•	The Bivariate BAF Analysis represents PCBs in terms of the sum of trichloro- through
decachlorbiphenyls (denoted ITri-). Historical Aroclor quantitation schemes are not
consistent with one another, but can be translated to a consistent estimate of ITri-.
Information on mono- and dichlorobiphenyl concentrations is not available in most of the
historical PCB monitoring data. The Probabilistic Bioaccumulation Food Chain Model
and FISHRAND and FISHPATH also represent ZTri- (equivalent to total PCBs).
•	The Bivariate BAF Analysis 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 a statistical perspective on the empirical relationships
between water, sediment, and fish body burdens. The statistical model relies on a
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bivariate regression approach which relates fish body burdens 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.
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.
Statistical distributions of bioaccumulation factors have been derived for:
•	sediments to benthic invertebrates;
•	whole water PCB concentrations to water column invertebrates;
•	expected dietary concentrations to composite forage fish; and
•	pumpkinseed to largemouth bass.
FISHPATH and FISHRAND were developed based on Gobas (1993) and compared to
published modeling results for Lake Ontario to verify model functionality. These models
were then modified for the Hudson River by eliminating Lake Ontario species and
including Hudson River species: largemouth bass, spottail shiner, pumpkinseed, yellow-
perch. white perch, and brown bullhead. FISHPATH is the deterministic version, while
FISHRAND incorporates frequency distributions for most of the parameters.
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 brevirostrumj and striped bass (Morone
saxcitilis). These profiles describe foraging strategies, home-ranges, habitat preferences
and information on reproduction for each of these species.
The foraging strategies of the invertebrate prey base for the fish species is viewed as a
key component to evaluating relative sediment versus water influences on fish body
burdens. An analysis is presented here that uses an indicator species approach based on
identified macroinvertebrates from the gut contents of Hudson River fish in order to
differentiate sediment versus water exposure pathways via the food chain.
Using the hindcasting results from the fate and tranport models, both the probabilistic
empirical model and FISHRAND/FISHPATH accurately capture observed historical
PCB concentrations in fish. Comparisons are available for largemouth bass and
pumpkinseed at river miles 168 and 189 for the empirical probabilistic model, while
comparisons are available for largemouth bass, pumpkinseed, yellow perch and brown
bullhead for FISHRAND.
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•	The probabilistic empirical model predicts particular percentiles (50th or median, average.
75th, 90th, and 95th are presented here). FISHRAND also has the ability to predict
particular percentiles but only the median (50th) and 95th are presented here.
•	FISHRAND captures largemouth bass lipid-normalized and wet weight concentrations
within the Thompson Island Pool to within a factor of 1.5. lipid-normalized
concentrations at river mile 168 to within a factor of 1.2. and wet weight concentrations
at river mile 168 to within a factor of two. In general, wet weight concentrations for all
species and locations are within approximately a factor of two of observed
concentrations, although there are exceptions. Lipid-normalized results are generally
within a factor of 1.5, again, with some exceptions. Comparisons to observed data for the
lowest upstream location (154) show less good agreement with observed data as the
observations were collected from below the Federal Dam and FISHRAND models just
above the dam.
•	Individual congeners will also be modeled but these results are not presented here.
9.2 Principal Report Findings
This report does not present definitive answers to the principal Reassessment questions
since this will require the completion of all of the Phase 2 and Phase 3 reports. However, a
number of conclusions have been drawn based on the work presented here, including:
•	The Bivariate BAF Analysis for fish body burdens explains about 80 percent of the
observed variability in summer average concentrations of tri- through deca-chlorinated
PCBs in fish from the freshwater portion of the Hudson River. Much of the remaining,
unexplained variability is due to uncertainty in historic water column concentrations. The
BAF analysis suggests a need to consider both the water column and local sediments as
sources for bioaccumulation of PCBs in Upper Hudson River fish. The relative
importance of water and sediment sources determined in the Bivariate BAF Analysis is
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 PCBs from both water column and sediment pathways.
•	The Probabilistic Bioaccumulation Food Chain Model captures the historical New York
State Department of Environmental Conservation (NYS DEC) mean observed fish
concentrations using the fate and transport sediment and water concentrations as inputs.
The model predicted that 90,l: percentiles typically occur at maximum observed
concentrations, suggesting that the model is protective of fish populations at this level.
•	FISHPATH (deterministic Gobas mechanistic time-varying model) and FISHRAND
(probabilistic Gobas mechanistic time-varying model) accurately reproduce both steady-
state and dynamic published results for Lake Ontario, indicating the models are
functional as originally intended. These models were developed in response to the peer
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review comments which specified the development of a mechanistic model to be included
in the suite of models used to evaluate PCB uptake in fish from the Hudson River.
FISHRAND predicts expected body burdens in fish on a population-level basis. The
model assumes a cycling of the population in which older fish are replaced by younger
fish within a particular size range. For this modeling application, we are interested in the
adult of the species for piscivorous, semi-piscivorous and omnivorous fish while for the
forage fish we are interested in the young-of-year (or yearlings).
Both the probabilistic and mechanistic models were run using predicted hindcasting water
and sediment concentration results from the fate and transport models as inputs in a
validation exercise. The models were used to predict observed fish concentrations (from
NYS DEC) for the period 1977 - 1996 for several locations above the Federal Dam at
Troy. The fate and transport models assumed a) a constant upstream boundary condition,
and b) a zero upstream boundary condition (see Books 1 and 2). As these results were
very similar, only the constant upstream boundary condition was run for FISHRAND.
Predictions from the probabilistic empirical model for largemouth bass compare
favorably to the results for FISHRAND. The probablistic empirical model provides a
useful check on the FISHRAND results.
Forecasts for the FISHRAND model suggest that largemouth bass will achieve 2.0 ppm
on an average wet weight basis between 2008 and 2014, with the best estimate of 2011
for river mile 189 (within the Thompson Island Pool), and between 2011 and 2019 (best
estimate 2015) for river mile 168 (Stillwater) under constant upstream boundary
conditions. Largemouth bass average values will not achieve target levels of 1.1 ppm or
0.2 ppm within the 21-year forecast period at these locations. In addition, the 95th
percentile value will not achieve any of the target levels in the forecast period. Note that
the target levels are for comparison purposes only, and that appropriate levels will be
determined in the Feasibility Study.
Forecasts suggest that for river mile 189, average values for yellow perch will achieve 2.0
ppm between 2007 and 2014 (best estimate 2010), and 1.1 ppm between 2015 and 2021.
95" percentile values would not reach any of the targets within the forecast period.
Average yellow perch values will achieve 2.0 ppm between 2008 and 2014 (best estimate
2011) for river milel68, but the lower target values and the 95'k percentile values will be
not reached within the forecast period.
For brown bullhead, the average fish body burden is forecasted to reach 2.0 ppm between
2014 and 2020 (best estimate 2017) at river mile 168. Within the 21-year forecast period,
no other target levels will be achieved for average brown bullhead at river mile 168, and
none of the target levels are achieved at river mile 189.
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•	At river miles 157 and 154, forecasts for all species modeled achieved the FDA action
level of 2 ppm by 2021. even at the 95th percentile value.
•	For all locations and species modeled, predicted average body burdens did not fall below
0.5 ppm within the 21-year forecast period.
•	The sensitivity analysis showed that the percent lipid distribution in individual fish
species is the most important parameter in FISHRAND, followed by Kou and percent
lipid in prey items. Based on the ability of the model to capture historical observed
concentrations by a factor of 2, the uncertainty bounds on the time to achieve particular
target levels is estimated to be plus or minus three years.
•	Additional modeling work is currently underway. This additional effort includes
modeling specific individual congeners for several locations to document the ability of
the model to capture the biomagnification dynamics. The modeling analysis will also be
updated to reflect revisions to the fate and transport model results.
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