United States
Environmental Protection
Agency
Results of the Lake Michigan
Mass Balance Project:
Polychlorinated Biphenyls
Modeling Report

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                                                   EPA-600/R-04/167
                                                    December 2006
Results of the Lake Michigan Mass
               Balance Project:
       Polychlorinated Biphenyls
               Modeling  Report
                        Prepared for

               U.S. Environmental Protection Agency
                Great Lakes National Program Office
                   77 West Jackson Boulevard
                    Chicago, Illinois 60604
                        Prepared by

               U.S. Environmental Protection Agency
               Office of Research and Development
      National Health and Environmental Effects Research Laboratory
                 Mid-Continent Ecology Division
          Large Lakes and Rivers Forecasting Research Branch
                  Large Lakes Research Station
                      9311 Groh Road
                  Grosse lie, Michigan 48138
                   Ronald Rossmann, Editor
                                           /T~y Recycled/Recyclable
                                               Printed with vegetable-based ink on
                                               paper that contains a minimum ot
                                               50% post-consumer fiber content
                                               processed chlorine Iree

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                                          Notice
The information in  this  document has been  obtained primarily through funding by the United States
Environmental Protection Agency (USEPA) under the auspices of the Office of Research and Development
(ORD) and by the Great Lakes National Program Office (GLNPO).  The report has been subjected to the
Agency's peer and administrative review and it has been approved for publication as a USEPA document.
Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

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                                         Foreword
The Lake Michigan Mass Balance Project (LMMBP) was initiated by the United States Environmental
Protection Agency (USEPA), Great Lakes National Program Office (GLNPO) to determine strategies for
managing and remediating toxic chemicals in the lake basin.  Within the ecosystem approach, the mass
balance framework is considered the best means of accomplishing this objective, and GLNPO requested the
assistance of the USEPA Office of Research and Development (ORD) to facilitate and produce mathematical
models that account for the sources, sinks, transport,  fate, and  food chain bioaccumulation of certain
chemicals. This approach has been used in the past and builds upon the modeling efforts that have occurred
in the Assessment and Remediation of Contaminated  Sediments (ARCS) Program and the lower Fox
River/Green Bay Mass Balance Project. The feasibility of such studies and resultant alternative management
options for contaminants in large rivers and a large embayment were demonstrated, and a logical extension
to the entire Lake Michigan receiving water body and major tributaries was warranted. There were a large
number of cooperators in this project, and by focusing Federal, State, local, private, and academic efforts and
resources on a common goal, much more was accomplished than if these entities acted independently.

The project was conducted in conjunction with the Enhanced Monitoring Program and the approach required
that all monitoring and field research be coordinated and common methodologies used.  Mathematical
modelers were consulted during planning for sample design, parameters, and temporal and spatial sampling
considerations.  The product was then a consistent and reliable database of information that was accessible
by project participants and the public. Data for the LMMBP were collected primarily during 1994 and 1995 and
have been compiled according to specified quality assurance/quality control (QA/QC) requirements, and other
data assessments have been made for modeling purposes.

The need  to consider the environmental benefits and consequences of alternative remediation choices to
protect and improve our environment continues to intensify as: 1)  environmental problems become more
complex; 2) the means to address and investigate problems become more technical, time-consuming, and
expensive; and 3) the actual costs to implement action strategies has escalated.  The integrated PCBs mass
balance modeling results are presented in this document and can aid managers in establishing priorities for
both lake-wide and local improvements. The forecasting of PCB concentrations in top predator fish is one of
the primary endpoints of this investigation as it relates to both ecosystem and human health. The capability
of forecast modeling presented here is a  salient feature of this approach directed toward providing multiple
alternatives, which then can be examined through benefit-cost analyses.

This report presents the current status and results of the  PCB modeling effort through the summer of 2006.
Within this document some recommendations have been provided for potential future work with the models.
Of course, a model and modeling applications are never complete, and it is expected that further efforts will
change some results, insights, and our understanding of Lake Michigan. These efforts require an investment
of resources and time, and improvements with additional model run executions are measured in years. In the
larger picture, the need for Agency modeling technologies continues to intensify and the requirement for

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reduced uncertainty will lead to future improved generations of models.  We have put great ernp'       .
following  guidance provided by the USEPA and other agencies in assuring that the scientlTIC     *
implemented accurately and completely by model computer code and that best modeling practices nave ueen
instituted.  We also submitted this to scientific peer review using an interdisciplinary panel of scientists and
experts that reviewed model theory and application which evolve on a continuing basis. The purpose is to
ensure that decisions based on the modeling efforts are reliable and scientifically credible.

This document is not intended to include all of the details and background required to understand the entire
LMMBP. Rather the reader should refer to the LMMBP Work Plan and other materials on the GLNPO web
site and the Lake Michigan Mass Balance Modeling Quality Assurance Plan on the ORD-Grosse lie web site
for further information.

This document includes replies to peer reviewer comments made during a peer review conducted 27-28 July
2004 in Romulus, Michigan.  These replies and the original peer review comments are found in Part 7.
                                              IV

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                                          Abstract
The Lake Michigan Mass Balance Project (LMMBP) was conducted to measure and model nutrients, atrazine,
polychlorinated biphenyls (PCBs), frans-nonachlor, and mercury to gain a better understanding of the sources,
sinks,  transport, fate, and effects of these substances within  the  system and to aid managers in the
environmental decision-making process for the Lake Michigan  Basin. The United States Environmental
Protection Agency (USEPA) Office of Research and Development (ORD) was requested to conduct and
facilitate modeling in cooperation with the USEPA Region V; the USEPA Great Lakes National Program Office
(GLNPO); other Federal agencies; the States of Michigan, Wisconsin, Illinois, and Indiana; the Tribes; and the
public  and private sectors.  The effort was supported  by  intensive sampling of  the  atmosphere, major
tributaries, sediments, water column, and biota during the 1994-1995 field years as well as by extensive quality
assurance and database development.  Multimedia, mass balance modeling frameworks  were applied to
examine primary source and loss categories and make various model forecasts  for a variety of loading
scenarios.  This report focuses on the modeling practices applied and results for PCBs from the MICHTOX
screening-level model and the higher-resolution LM2-Toxic and LM  Food Chain models. A unique aspect of
this work is the modeling of PCBs on a congener-level basis to make predictions of total PCBs in the system.

Results of the system mass balance show that the greatest, external gross input of PCBs  to the system is
atmospheric vapor phase absorption followed by tributary inputs and atmospheric deposition, respectively.
The greatest gross losses from the system are volatilization and deep burial in sediments. Internal PCBs
loading from sediment resuspension is substantial. Gross PCBs inputs to, losses from, and cycling processes
within the system each typically exceed 1000 kg/year.  Tributary  inputs and atmospheric deposition are
approximately 381 and 980 kg/year, respectively.  Results indicate that during the mass balance field collection
years of 1994-1995, the Fox, Grand, Calumet, and Kalamazoo Rivers had the largest tributary loads of PCBs
to Lake Michigan. When all gross input and output fluxes are summed, the system exhibits a net  loss of
approximately 3,229 kg/year of PCBs. The mass balance results demonstrate the importance of contaminant
cycling and the dynamic interactions among air, water, and sediments. These interactions, with present PCS
inventories already in the lake, will continue to control PCS concentrations in the system.

LM Food Chain, linked to LM2-Toxic, and MICHTOX were used to forecast future concentrations of PCBs in
lake trout at two sites for various loading scenarios. Scenarios  included constant 1994-1995 conditions, fast
continued recovery with an atmospheric  load  half-life  of  6.0  years, slow continued recovery with an
atmospheric load  half-life of 20.0 years, and  various combinations of reduced atmospheric and tributary
loadings.  Forecasts indicate that PCBs concentrations in lake trout will continue to decrease. For the fast
continued recovery scenario, the target level for the unrestricted consumption of fish (0.075 ppm) was
forecasted to be achieved for five to six year-old lake trout between the years 2030 and 2036. The  narrow
forecast  range for scenarios,  past actions, the long-term  decrease in loads,  and decreasing  PCB
concentrations in the  system indicate  that PCBs are presently controlled by dynamic interactions  among
media,  as well as air and sediment cycling.  Model results from the present two models are compared. In the
future, these results will be compared  to those from a greater-resolution model under development (LM3-

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Toxic).  It is anticipated that the higher-resolution model will better delineate the nearshore and sediment
zones, define lake interactions with tributary inputs, and describe PCBs in lake trout populations.

This synthetic lake-wide perspective is anticipated to aid managers in moving forward on pollution prevention,
remedial actions, and legislative priorities associated with the Lake Michigan Lake-wide Management Plans.
It will also help describe expected local improvements associated  with Remedial Action Plans in Areas of
Concern.  This abstract does not necessarily reflect USEPA policy.
                                               VI

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                                    Table of Contents
Notice	     ii
Foreword	     jjj
Abstract  	     v
Table of Contents	    vii
List of Figures	   xviii
List of Tables	   xxx
Abbreviations	   xxxvi
Acknowledgments	xxxviii
Executive Summary	   xxxix

Part 1  Introduction	     1

       Chapter 1 Project Overview	     1

       1.1.1   Background	     1
       1.1.2   Description	     2
       1.1.3   Scope	     3
               1.1.3.1   Modeled Pollutants	     3
                       1.1.3.1.1   PCBs	     3
                       1.1.3.1.2   Isomer frans-Nonachlor	     5
                       1.1.3.1.3   Atrazine	     5
                       1.1.3.1.4   Mercury	     5
               1.1.3.2  Other Measured Parameters	     6
               1.1.3.3  Measured Compartments	     6
       1.1.4   Objectives	     6
       1.1.5   Design	     7
               1.1.5.1   Organization	     7
               1.1.5.2  Study Participants	     9
               1.1.5.3  Workgroups  	     9
               1.1.5.4  Information Management	     9
                       1.1.5.4.1   Data Reporting	    10
                       1.1.5.4.2   Great Lakes Environmental Monitoring Database	    11
                       1.1.5.4.3   Public Access to LMMBP Data	    11
               1.1.5.5  Quality Assurance Program	    11
       1.1.6   Project Documents and Products	    13
                                              VII

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 Chapter 2  PCBs Modeling Overview
 1 .2. 1   Background [[[
 1 .2.2   Modeling Objectives ..................................................    '«
 1 .2.3   Historical Modeling [[[    1?
        1.2.3.1   Lake-1  [[[    17
        1 .2.3.2   Completely-Mixed Model .......................................    18
        1 .2.3.3   General Mass Balance Framework for Toxic Chemicals in the
                Great Lakes .................................................    18
        1 .2.3.4   Food Web Bioaccumulation Model  ...............................    18
        1 .2.3.5   MICHTOX ..................................................    18
        1 .2.3.6   Green Bay Mass Balance Project ................................    19
        1 .2.3.7   SEDZL  [[[    19
 1 .2.4   Model Resolution [[[    19
 1 .2.5   Models Developed and Applied .........................................   21
        1 .2.5.1   Lake Process Models .........................................   21
        1 .2.5.2   Hydrodynamics (POM) ........................................   21
        1 .2.5.3   Eutrophication/Sorbent Dynamics (LM3-Eutro) ......................   22
        1 .2.5.4   Contaminant Transport and Fate (LM2-Toxic) .......................   22
        1 .2.5.5   Food Web Bioaccumulation (LM Food Chain) .......................   23
 1 .2.6   Model Quality Assurance ..............................................   23
 1 .2.7   Model Application and Computational Aspects .............................   23
        1 .2.7. 1   Annual Simulations ...........................................   23
        1 .2.7.2   Long-Term Simulations ........................................   23

 Chapter 3  Information Management ...........................................   26

 1 .3. 1   Overview of Information Management at the LLRS ..........................   26
 1 .3.2   Calculation of Total PCBs  .............................................   28
 1 .3.3   Regression Analysis of Measured Congener, Total PCB Data ..................   28
 1 .3.4   Summary [[[   30

 Appendix 1 .3.1   List of Parameters Analyzed and Principal Investigators for
                the LMMBP .................................................   31
 Appendix 1 .3.2   Example of Data Verification Checklist Used for the LMMBP ............   35
 Appendix 1 .3.3   Printout of Information Stored in the LMMBP Tracking Database
                (R:/Access2000/lmmb/lmtrack.mdb) ..............................   39
 Appendix 1 .3.4   Generalized Format for the LMMBP Water Data to be Analyzed
                With IDL Programs ...........................................   44
 Appendix 1 .3.5   Generalized Format for the LMMBP Sediment Data to be Analyzed
                With IDL Programs  ...........................................   46
 Appendix 1 .3.6   Generalized Format for the LMMBP Fish Data to be Analyzed With
                IDL Programs ...............................................   48

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Chapter 4 Representativeness of the LMMBP Years Relative to Lake Michigan's
          Historic Record	   55

1.4.1    Introduction	   55
1.4.2    Ice Cover	   55
1.4.3    Water and Air Temperatures	   56
1.4.4    Lake Water Levels	   59
1.4.5    Precipitation	   59
        1.4.5.1   Annual Comparisons	   59
        1.4.5.2   Monthly Comparisons	   59
1.4.6    Tributary Flows 	   59
1.4.7    Wave Heights	   59
1.4.8    Summary	   63

Chapter 5 PCBs in the Lake Michigan Ecosystem	     66

1.5.1    Introduction	   66
1.5.2    Atmospheric	   66
        1.5.2.1   Vapor Phase	   66
        1.5.2.2   Precipitation	   69
        1.5.2.3   Paniculate	   71
        1.5.2.4   Dry Deposition  	   71
1.5.3    Lake Water  	   71
        1.5.3.1   Total PCBs	   71
        1.5.3.2   Dissolved PCBs  	   71
        1.5.3.3   Paniculate PCBs	   78
1.5.4    Tributaries	   78
1.5.5    Sediment	   83
1.5.6    Biota 	   86
1.5.7    Summary	   86

Chapter 6 Congener Pattern Matching of Data Collected for the Lake Michigan
          Mass Balance Project (LMMBP)	   93

1.6.1    Introduction	   93
1.6.2    Analytical Approach	   94
1.6.3    Methodology	   94
1.6.4    Results  	   94
        1.6.4.1   Comparison of Modeled Congener Patterns to All Analyzed Congener
                Patterns  	   94
        1.6.4.2   Comparison of Median to Mean Data	   97
        1.6.4.3   Comparison of Congener Patterns in Different Media in Segment 21
                Saugatuck Biota Box	   97
        1.6.4.4   Comparison of Atmospheric Congener Data	  101
        1.6.4.5   Comparison of Tributary  Congener Patterns	  103
        1.6.4.6   Comparison of Ages 5 and 6 Lake Trout Congener Patterns in All
                Biota Boxes	  105
        1.6.4.7   Comparison of Different Lake Trout Age Class Congener Patterns
                in Saugatuck Biota Box	  105
1.6.5    Conclusions	  105
                                       IX

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       Chapter 7 Hindcasting and Forecasting Functions for PCBs in the Lake Michigan
                 Ecosystem	     "

       1.7.1    Introduction	   11°
       1.7.2    Forecast Functions 	   110
               1.7.2.1  Tributary Loads	   111
               1.7.2.2  Atmospheric Loads	   112
       1.7.3    Hindcast Functions 	   112
       1.7.4    Estimated PCB Storage	   117

Part 2  LM2-Eutro	   120

       Chapter 1 Conclusions (Executive Summary)  	   120

       Chapter 2 Recommendations	   123

       Chapter 3 Model Description	   125

       2.3.1   Transport Scheme for Lake Michigan  	   125
       2.3.2   Sediments	   126
       2.3.3   Formulation of Eutrophication Equations	   126

       Appendix 2.3.1  Development of LMS-Eutro Equations	   130

       A2.3.1.1   Phytoplankton Growth	   130
       A2.3.1.2   Zooplankton Kinetics	   133
       A2.3.1.3   Carbon Interactions	   133
       A2.3.1.4   Phosphorus	   135
       A2.3.1.5   Nitrogen	   137
       A2.3.1.6   Silica	   138

       Chapter 4 Model Input and Field Data	   140

       2.4.1   Loading and Sediment-Water Interactions	   140
              2.4.1.1  Atmospheric Loads	   140
              2.4.1.2  Tributary Loads	  140
              2.4.1.3  Shoreline Erosion 	  141
              2.4.1.4  Sediment	  142
       2.4.2   Field Data  	  142
              2.4.2.1  Open Lake Nutrient and Carbon Data	  142
                       2.4.2.1.1  Total Phosphorus	  142
                       2.4.2.1.2 Dissolved Phosphorus	  144
                       2.4.2.1.3 Soluble Reactive Phosphorus 	  144
                       2.4.2.1.4 Nitrate  	  145
                       2.4.2.1.5  Ammonia	  145
                       2.4.2.1.6  Total Kjeldahl Nitrogen	  145
                       2.4.2.1.7  Dissolved Silica 	  145
                       2.4.2.1.8  Dissolved Organic Carbon	  145

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                2.4.2.1.9  Particulate Organic Carbon  	   145
                2.4.2.1.10 Green Bay Nutrient Data	   145
        2.4.2.2  Plankton	   146
                2.4.2.2.1  Phytoplankton 	   146
                2.4.2.2.2  Chlorophyll a	   146
                2.4.2.2.3  Phytoplankton Carbon	   147
                2.4.2.2.4  Zooplankton	   149
                2.4.2.2.5  Zooplankton Carbon	   150
2.4.3   Initial Conditions  	   150
2.4.4   Parameter Estimation  	   152
        2.4.4.1  Physical Measurements 	   152
                2.4.4.1.1  Secchi Disk	   152
                2.4.4.1.2  Solar Radiation and Temperature 	   152
        2.4.4.2  Primary Production Estimates	   152

Appendix 2.4.1  Modeled Versus Measured Variables	        157

Chapter 5 Calibration	   159

2.5.1    Description of Process	   159
2.5.2   Selection of Best Calibration	   160
        2.5.2.1  Phytoplankton	   160
        2.5.2.2  Particulate Organic Carbon	   167
        2.5.2.3  Total Phosphorus	   167
        2.5.2.4  Dissolved Silica	     167

Chapter 6 Model Confirmation	   168

2.6.1    Additional Field Data	   168
2.6.2   MICH1 Model	   168
2.6.3   Comparison of LM3-Eutro to the MICH1 Model and Field Data	   168

Chapter 7 Results - Application of Model	   171

2.7.1    Scenario 1  - Constant Conditions	   171
        2.7.1.1  Description of Assumptions	   171
        2.7.1.2  Results and Discussion	   171
2.7.2    Scenario 2 - Virtual Elimination (Lower Bound)	   171
        2.7.2.1  Description of Assumptions	   171
        2.7.2.2  Results and Discussion	   171
2.73    Scenario 3 - Best Estimate of Current Trends Resulting From Previous
        Actions	   173
        2.7.3.1  Description of Assumptions	   173
        2.7.3.2  Results and Discussion	   173
2.7.4    Scenario 4 - Scenario 1 With Instantaneous Reduction of Tributary Loads to
        Zero	   173
        2.7.4.1   Description of Assumptions	   173
        2.7.4.2  Results and Discussion	   174
                                        XI

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       275   Scenario 5 - Scenario 1 With Instantaneous Reduction of Atmospheric Loads
              to Zero  	  }74
              2.7.5.1  Description of Assumptions	  ' '4
              2.7.5.2  Results and Discussion	  174
       2.7.6   Scenario 6 - Scenario 1 With Tributary and Atmospheric Loads Increased 20% ....  174
              2.7.6.1  Description of Assumptions	  174
              2.7.6.2  Results and Discussion	  176
       2.7.7   Scenario 7 - Application of Great Lakes Water Quality Agreement Loads to
              Model	  176
              2.7.7.1  Description of Assumptions	  176
              2.7.7.2  Results and Discussion	  177
       2.7.8   Scenario 8 - Estimate of Total Maximum Daily Loads to Reach International
              Joint Commission's Target Total Phosphorus Concentration	  177
              2.7.8.1  Description of Assumptions	  177
              2.7.8.2  Results and Discussion	  177
       2.7.9   Scenario Comparison and Discussion	  177
       2.7.10 Mass Budget	  177

       Chapter 8 Results Provided for LM2-Toxic	  182

       2.8.1   Description	  182
       2.8.2   Manipulation of Results	  182

Part 3  Level 1 Models	  183

       Chapter 1 MICHTOX PCB Model Executive Summary  	  183

       Chapter 2 MICHTOX Recommendations	  185

       Chapter 3 MICHTOX PCB Fate and Transport Modeling  	  186

       3.3.1   Description	   186
       3.3.2   Description of Data Used in MICHTOX	  189
              3.3.2.1  Water Column PCB Concentrations   	  189
              3.3.2.2  Surficial Sediment PCB Concentrations 	  189
              3.3.2.3  Atmospheric and Tributary Loads	  189
       3.3.3   Model Confirmation	  191
              3.3.3.1  Description of Hindcast Process	  191
              3.3.3.2  Hindcast Results	  192
              3.3.3.3  Comparison to the LMMBP Data	  196
       3.3.4   Model Uncertainty	  198

       Chapter 4 MICHTOX Food Chain  Modeling	  201

       3.4.1   Model Development	  201
       3.4.2   Description of the Data Used in MICHTOX Food Chain	  202
              3.4.2.1  Description of Data	           202
              3.4.2.2  Sources and Choice of Constants	'.'.'.'.'.  203
                                             XII

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       3.4.3   Model Confirmation	  203
       3.4.4   Results - Forecast Scenarios 	  207
              3.4.4.1   Conditions Remain the Same as 1994-1995 (Constant Conditions) ..    ..  207
              3.4.4.2   Continued Recovery - Fast	  208
              3.4.4.3   Continued Recovery - Slow 	  209
       3.4.5   Model Sensitivity	  209
              3.4.5.1   No Atmospheric Wet and Dry Deposition Loadings	  209
              3.4.5.2   No Tributary Loadings 	  210
              3.4.5.3   No Atmospheric Deposition and No Tributary Loadings  	  210
              3.4.5.4   Sediment Total PCB Concentration Initial Conditions Set to Zero	  210

       Appendix 3.4.1   Derivation of a Hypothetical Lake Michigan Lake Trout Fish
                       Consumption Criteria for PCBs	  212

Part 4  LM2-Toxic	  216

       Chapter 1  Executive Summary	  216

       Chapter 2 Recommendations	  221

       Chapter 3 Model Description	  223

       4.3.1   Model Framework 	  223
       4.3.2   Model Configuration	  224
              4.3.2.1   Spatial Resolution - Segmentation	  224
              4.3.2.2   Temporal Resolution	  228
       4.3.3   Water Balance	  228
       4.3.4   Solid Balance	  231
              4.3.4.1   Solid Kinetics	  232
              4.3.4.2   Sediment Transport	  233
                       4.3.4.2.1   Steady-State Resuspension Calibration	  234
                       4.3.4.2.2  Empirical Wave-Induced Resuspension Calculation	  235
                       4.3.4.2.3  The Sediment Bed - Semi-Lagrangian Option	  237
       4.3.5   Chemical Balance	  238
              4.3.5.1   PCB  Partitioning 	  239
              4.3.5.2   PCB  Air-Water Exchange	  240
              4.3.5.3   PCB-Specific Parameterization	  242
       4.3.6   Modification	  242

       Appendix 4.3.1   Lake Michigan Resuspension Field Data Set 	  246
       Appendix 4.3.2   Notes From Nathan Hawley on the Data Set in Appendix 4.3.1  	  247

       Chapter 4 Model and Field Data	  248

       4.4.1   Water Transport	  248
              4.4.1.1   Circulation	    248
              4.4.1.2   Vertical Dispersion	  250
              4.4.1.3   Verification of  Water Transport Fields	  250
                                              XIII

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 4.4.2   Organic Carbon	   257
        4.4.2.1   Loads  	   257
        4.4.2.2   Field Data and Initial Conditions	   258
        4.4.2.3   Parameterization	   266
 4.4.3   PCBs	   271
        4.4.3.1   Loading	   272
        4.4.3.2   Field Data, Initial Conditions, and Boundary Conditions	   273
        4.4.3.3   Parameterization	   273
        4.4.3.4   Kinetic Time Functions  	   281

 Appendix 4.4.1   Sample Data Interpolation for the LMMBP	   286

 A.4.4.1.1   The Distance Square Inverse Method	   287
 A.4.4.1.2  The Natural-Neighborhood Method	   287
 A.4.4.1.3  Application	   288
           A4.4.1.3.1    Contouring Plots 	   288
           A.4.4.1.3.2   Volume-Weighted Averaging With Formulations	   288
 A.4.4.1.4  Discussion	   289
 A.4.4.1.5  Steps to Run nngridr	   289

 Chapter 5 LM-2 Toxic Calibration and Confirmation	   291

 4.5.1   Vertical Dispersion Coefficients Calibration	   291
 4.5.2   Organic Carbon Dynamics Calibration	   293
        4.5.2.1   Calibration Process/Procedure	   293
        4.5.2.2   Results and  Discussion	  293
 4.5.3   PCB Dynamics Calibration	  294
        4.5.3.1   Calibration Procedures  	  304
        4.5.3.2   Results and  Discussion	  305
 4.5.4   The LM2-Toxic Confirmation	  313
        4.5.4.1   Mass Balance Checking 	  313
        4.5.4.2   Chloride Model 	  313
        4.5.4.3   137Cs and 239>24°pu Simulation and Results	  313
        4.5.4.4   Long-Term Organic Carbon Simulations	  317
        4.5.4.5   PCB Hindcast	  319
                 4.5.4.5.1   Data and Procedure for the PCB Hindcast	  319
                 4.5.4.5.2   Results From the LM2-Toxic PCB  Hindcast	  328

 Appendix 4.5.1   Results From Thermal Balance Model	  337
 Appendix 4.5.2  Calibrated Results for Organic Carbons  	  349
 Appendix 4.5.3  Calibration Results for PCB28+3, and ZPCBs	  365
 Appendix  4.5.4  Simulation Results From Chloride	  418
 Appendix  4.5.5  Primary Production for the LM2-Toxic  	  426

 Chapter 6 The LM2-Toxic Application and Interpretation	  430

4.6.1    Conversion of PCB Congener Results to Total PCBs 	  430
4.6.2    Mass Budget Diagnosis of the  LM2-Toxic for the LMMBP Period	  430
                                       XIV

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       4.6.3   LM2-Toxic Application for Long-Term Forecast and Sensitivity Scenarios	  436
       4.6.4   Results of the Forecast and Sensitivity Scenarios and Discussion  .       	  437
       4.6.5   Results Provided for the LM Food Chain Model  	  442

       Chapter 7 LM2-Toxic Sensitivity Analysis  	  444

       4.7.1   Primary Production Sensitivity	  444
       4.7.2   PCB Loads Sensitivity   	  450

Part 5  LM Food  Chain	  453

       Chapter 1 Executive Summary	  453

       Chapter 2 Recommendations	  455

       5.2.1   Additional Model Validations	  455
       5.2.2   Model Applications  	  455
       5.2.3   Future Improvements	  456

       Chapter 3 Model Description	  457

       5.3.1   Chemical Bioaccumulation  in  Fish	  457
              5.3.1.1   Chemical Uptake  From Water	  458
              5.3.1.2  Chemical Uptake  From Prey	  459
              5.3.1.3  Chemical Elimination Via Gills	  459
              5.3.1.4  Chemical Dilution  by Growth	  460
       5.3.2   Chemical Bioaccumulation  in  the Base of Food Webs   	  460
              5.3.2.1   Chemical Bioaccumulation in Zooplankton  	  460
              5.3.2.2  Chemical Bioaccumulation in Diporeia 	  461
       5.3.3   Model Description of Exposure Environment	  462

       Chapter 4 Description of Data, Constants, and Other Information Necessary
                 to Run Model	  465

       5.4.1   Chemical Properties of PCB Contaminants  	    465
       5.4.2   Site-Specific Data 	  465
              5.4.2.1   Fish Food Web Structures	  465
                       5.4.2.1.1  Lake Trout Food Web	  465
                       5.4.2.1.2  Coho Salmon Food Web	  468
              5.4.2.2  Fish Growth Rates	  475
              5.4.2.3  Energy Density of Food Web Components	  476
              5.4.2.4  Exposure Conditions	  476
                       5.4.2.4.1  PCB Concentrations in Water  	  481
                       5.4.2.4.2  PCB Concentrations in Sediment  	  481
                       5.4.2.4.3  Exposure Temperature	  483
                       5.4.2.4.4  Oxygen Concentration in Water	  483
                                              xv

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       5.4.3   Physiological Data of Fish and Other Organisms	  483
               5.4.3.1  Species-Specific Respiration Rates	  483
               5.4.3.2  Respiration Rates Adjusted for Specific Dynamic Action (SDA)  	  486
       5.4.4   Calibrated Model Parameters  	  487

       Chapter 5 Calibration	  492

       5.5.1   Introduction 	  492
       5.5.2   Description of Process	  492
       5.5.3   Calibration Results  	  493
       5.5.4   Field Data for PCBs in Fish and Their Comparisons to Calibrated Model
               Outputs	  495

       Appendix 5.5.1   PCB Concentrations	  502
       Appendix 5.5.2   Agreement Between Modeled and Observed PCB Concentrations  	  516

       Chapter 6 Model Verification  	  526

       5.6.1   Introduction	  526
       5.6.2   Model Applicability to  Other Sites  	  526

       Chapter 7 Model Sensitivity and Uncertainty	  528

       5.7.1   Introduction	  528
       5.7.2   Sensitivity Analysis 	  528
               5.7.2.1   Chemical Assimilation Efficiency (a)	  529
               5.7.2.2   Food Assimilation Efficiency (P)  	  529
               5.7.2.3   Chemical Relative Gill Transfer Coefficient (EJE0) 	  531
               5.7.2.4   The Fraction of Ingested Energy for Specific Dynamic Action (SDA) 	  531
               5.7.2.5   Fish Growth  Rate 	  532
               5.7.2.6   Octanol-Water Partition Coefficient Kow	  533
               5.7.2.7   Fish Diet	  533

       Chapter 8 Model Application  	  536

       5.8.1   Introduction	  536
       5.8.2   Simulation of Fish PCB Levels Based on Hypothetical Exposure Inputs	  536
               5.8.2.1   Exposure Concentration Inputs Used for Model Simulations	  536
               5.8.2.2   Responses of Fish Models to Different Exposure Inputs	  537
               5.8.2.3  Discussion	  542

Part 6  Comparison of Model Results	  544

       6.1     Summary	  544
       6.2     Comparison of Models	  545
               6.2.1    Model Similarities  	             545
               6.2.2    Model Differences  	           545
       6.3     Comparison of Model Results	         547
               6.3.1    Comparison of Mass Budget Analyses	           547
               6.3.2    Comparison of Model Forecast Scenarios  	         547
                                              XVI

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Part 7  Appendices	  552

       Appendix 7.1 Lake Michigan Mass Balance Project (LMMBP) PCB Peer
       Review Report  	  552

       7.1.1   Executive Summary	  552
       7.1.2   LMMBP Peer Review Panel  	  553
       7.1.3   LMMBP PCB Charge to Peer Reviewers	  553
              7.1.3.1   Overall Multimedia Ecosystem Modeling Approach  	  555
              7.1.3.2  Overall Model Performance	  555
              7.1.3.3  Suitability for Management	  555
       7.1.4   Modelers' Responses to Peer Review Comments	  555
       7.1.5   Modelers' Responses to Specific Comments Made by Peer Review
              Panel Member - James Martin  	  563

       Appendix 7.2 Comments as Received From Dr. James Martin Peer Review
                  Summary: Lake Michigan Mass Balance Project	  573

       7.2.1   General Comments	  573
              7.2.1.1   Overall Multi-Media Ecosystem Modeling Approach	  573
              7.2.1.2  Overall Model Performance	  573
              7.2.1.3  Suitability for Management	  574
       7.2.2   Specific Recommendations	  574
              7.2.2.1   POM and Linkages	       574
              7.2.2.2  LM2-Eutro and LM3-Eutro	  574
              7.2.2.3  Level 1 Model	  575
              7.2.2.4  LM2-Toxic	  575
              7.2.2.5  LM Food Chain	  575
              7.2.2.6  LM3-Toxic	  575
       7.2.3   Specific Comments	  575
              7.2.3.1   Hydrodynamics and POM Linkage 	    575
              7.2.3.2  LM2-Eutro and LM3-Eutro	  577
              7.2.3.3  Level 1 Model 	  577
              7.2.3.4  LM2-Toxic	  578
              7.2.3.5  LM Food Chain	  579
                                            XVII

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                                      List of Figures
 1.1.1   Simplified mass balance approach	   2

 1.1.2   The LMMBP sampling locations	   8

 1.1.3   Flow of information in the LMMBP	  ig

 1.2.1   Surface water segmentation for alternative Lake Michigan mass balance model levels	  20

 1.2.2   Model construct used for the LMMBP to model PCBs	  22

 1.4.1   Location of the NOAA's buoys in Lake Michigan  	  55

 1.4.2   Monthly mean water temperatures in southern Lake Michigan	  58

 1.4.3   Monthly mean water temperatures in northern Lake Michigan	  58

 1.4.4   Mean June water temperatures in southern Lake Michigan 	  58

 1.4.5   Mean June water temperatures in northern Lake Michigan	  58

 1.4.6   Monthly mean air temperatures in southern Lake Michigan 	  60

 1.4.7   Monthly mean air temperatures in northern Lake Michigan	  60

 1.4.8   Mean June air temperatures in southern Lake Michigan	  60

 1.4.9   Mean June air temperatures in northern Lake Michigan	  60

 1.4.10  Record of mean monthly water levels for Lake Michigan 	  60

 1.4.11   Annual precipitation to Lake Michigan between 1949 and 1998	  60

1.4.12  Comparison of 1982, 1983, 1994, and 1995 monthly mean precipitation to the means
        for the period of 1949 through 1998	  61

1.4.13   Comparison  of tributary flow for hydrodynamic model calibration (1982-1983) to the
        historic means	  61
                                            XVIII

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1.4.14  Comparison of tributary flow for the study period (1994-1995) to the historic means	   62

1.5.1   Median concentration of vapor phase PCBs in the atmosphere during 1994 and 1995
       for all seasons of both years  	   68

1.5.2   Time variation of vapor phase PCBs in Lake Michigan	   70

1.5.3   Time variation of vapor phase PCBs in Lake Michigan at Sleeping Bear Dunes based
       on IADN data	   70

1.5.4   Median concentration of wet (precipitation) PCBs in the atmosphere during 1994 and
       1995 for all seasons of both years	   72

1.5.5   Time variation of precipitation PCBs in Lake Michigan at Sleeping Bear Dunes based
       on IADN data	   73

1.5.6   Median concentration of particulate PCBs in the atmosphere during 1994 and 1995 for
       all seasons of both years  	   74

1.5.7   Time variation of atmospheric particulate PCBs in  Lake Michigan at Sleeping Bear
       Dunes based on IADN data 	   75

1.5.8   Distribution of total PCBs (ng/L) in 1994-1995 Lake Michigan water	   76

1.5.9   Time variation of total PCBs in Lake Michigan water ....     	     77

1.5.10  Time variation of total PCBs in Lake Michigan water since 1986 	   78

1.5.11  Distribution of dissolved PCBs (ng/L) in 1994-1995 Lake Michigan water	   79

1.5.12  Distribution of dissolved PCBs (ng/L) in 1994-1995 summer hypolimnetic Lake Michigan
       water	   80

1.5.13  Time variation of dissolved PCBs in Lake Michigan water  	   81

1.5.14  Distribution of particulate PCBs (ng/L) in 1994-1995 Lake Michigan water    	   81

1.5.15  Time variation of particulate PCBs in Lake Michigan water  	   82

1.5.16  Relative loads of PCBs to Lake Michigan from tributaries  	   83

1.5.17  Total PCBs in 1994-1995 Lake Michigan surficial sediments (ng/g)	   85

1.5.18  Vertical variation of PCBs in dated sediment cores collected for the LMMBP	   85

1.5.19  Vertical variation of PCBs in dated sediment cores reported by Swackhamer and
       Armstrong (1988)	   87

1.5.20  Vertical variation of PCBs in dated sediment cores reported by Hermanson ef al. (1991)  .  . .   87
                                              XIX

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1.5.21  Vertical variation of PCBs in dated sediment cores reported by Golden et al. (1993)  	  88

1.5.22  Vertical variation of PCBs in dated sediment cores reported by Schneider et al. (2001)	  88

1.5.23  PCB concentrations in various members of the lake trout food web during the LMMBP	  89

1.5.24  PCB concentrations in various age classes of lake trout during the LMMBP 	  90

1.5.25  Time variation of PCB concentration in five to six year-old lake trout from Lake
       Michigan	  90

 1.5.26  Time variation of PCB concentrations in bloater from Lake Michigan	  91

 1.6.1   LM2 surface water segmentation and LMMBP biota boxes  	  94

 1.6.2  Cumulative frequency distribution - PCB congeners in segment 2 vapor phase	  98

 1.6.3  Cumulative frequency distribution - PCB congeners in segment 2 dry deposition	  98

 1.6.4  Cumulative frequency distribution - PCB congeners in segment 2 wet deposition	  98

 1.6.5  Cumulative frequency distribution - dissolved PCB congeners in segment 2 water	  98

 1.6.6  Cumulative frequency distribution - particulate PCB congeners in segment 2 water	  98

 1.6.7  Cumulative frequency distribution - PCB congeners in segment 2 surficial sediment	  98

 1.6.8  Cumulative frequency distribution - dissolved PCB congeners in Kalamazoo River water ....  99

 1.6.9  Cumulative frequency distribution - particulate PCB congeners in Kalamazoo River water ...  99

 1.6.10 Cumulative frequency distribution - age 5-6  Saugatuck lake trout	  99

 1.6.11  Cumulative frequency distribution - PCB congeners in segment 2 vapor phase	  99

 1.6.12 Cumulative frequency distribution - PCB congeners in segment 2 dry deposition	  99

 1.6.13 Cumulative frequency distribution - dissolved PCB congeners in Kalamazoo River water	  99

 1.6.14 Cumulative frequency distribution - particulate PCB congeners in Kalamazoo River water ...  100

 1.6.15  Cumulative frequency distribution - PCB congeners in segment 2 wet deposition	100

 1.6.16  Cumulative frequency distribution - dissolved PCB congeners in Saugatuck water	100

1.6.17  Cumulative frequency distribution - particulate PCB congeners in Saugatuck water	100

1.6.18  Cumulative frequency distribution - PCB congeners in surficial sediment	100
                                              xx

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1.6.19  Cumulative frequency distribution - PCB congeners in age 5-6 Saugatuck lake trout	  100

1.6.20  PCB congeners in segment 2, Saugatuck  	   101

1.6.21  Air sampling locations	102

1.6.22  Lake Michigan high-resolution 5 km x 5 km grid with 19 sigma layers  .       	102

1.6.23  Cumulative frequency distribution (mean) - PCB congeners in atmospheric vapor phase ....  103

1.6.24  Cumulative frequency distribution (mean) - PCB congeners in atmospheric wet
       deposition	  103

t.6.25  Cumulative frequency distribution (mean) - PCB congeners in atmospheric dry
       deposition	  103

1.6.26  Cumulative frequency distribution (mean) - PCB congeners in segment 2 atmospheric
       data  	  103

1.6.27  PCB congeners in Lake Michigan tributaries  	  104

1.6.28  Comparison of dissolved PCB congeners in Lake Michigan western tributaries to
       segment 2 vapor phase 	105

1.6.29  Comparison of dissolved PCB congeners in Lake Michigan eastern tributaries to
       segment 2 water	  106

1.6.30  Comparison of particulate PCB congeners in Lake Michigan segment 2 to tributaries	  106

1.6.31  Cumulative frequency distribution (mean) - PCB congeners in ages 5 and 6 lake trout	107

1.6.32  Cumulative frequency distribution (mean) - PCB congeners in Saugatuck lake trout	107

1.6.33  Comparison of dissolved PCB congeners in west side-to-east side of Lake Michigan
       tributaries	108

1.6.34  Comparison of particulate PCB congeners in west side-to-east side of Lake Michigan
       tributaries	-	108

1.7.1   Locations of dated cores analyzed for PCBs by Hermanson et al. (1981), Swackhamer
       and Armstrong (1988), Schneider era/. (2001), Golden et al. (1993), and P. Van Hoof
       (personal communication) for the LMMBP	  113

1.7.2   Fit of concentration functions to observed data for core 15	  114

1.7.3   Lake Michigan non-depositional  (0-40 m), transitional (40-100 m), and depositional
       (> 100 m) zones based on water depth and the depth of wind-wave interaction with
       sediments 	  116
                                             XXI

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1.7.4   Comparison of load function to 210Pb focusing factor corrected core 15 loads	  117

2.4.1   The LMMBP sampling locations	  143

2.4.2   Lake-wide phytoplankton carbon calculated from biovolume data and carbon-to-
       chlorophyll a ratios for the eight LMMBP cruises	  148

2.4.3   Level 2 model segmentation for LM3-Eutra	  151

2.4.4   Lake-wide Secchi depths for the eight LMMBP cruises	  153

2.5.1   Level 2 and Level 3 model segmentation	159

2.5.2   LM3-Eutro model versus laboratory primary production	161

 2.5.3   Level 3 LM3-Eutro model predictions versus field data, lake-wide	164

 2.5.4   Level 2 LM3-Eutro model output versus field data for selected segments	165

 2.5.5   Level 3 LM3-Eutro model output versus field data for selected nearshore and
        offshore cells	166

 2.6.1    MICH1 versus LM3-Eutro model predictions and available field data	170

 2.7.1    Scenario 1: Constant Conditions  	172

 2.7.2   Scenario 2: Virtual elimination	172

 2.7.3   Historical total phosphorus loading - Lake Michigan	173

 2.7.4   Scenario 3: Best estimate of current trends resulting from previous actions  	174

 2.7.5   Scenario 4: Scenario 1 with tributary load elimination	175

 2.7.6   Scenario 5: Scenario 1 with atmospheric load elimination	175

 2.7.7   Scenario 6: Scenario 1 with tributary and atmospheric loads increased 20% 	176

 2.7.8   Scenario 7: Application of the GLWQA loads to model	178

 2.7.9   Scenario 8: Estimate of the TMDL to reach the IJC's target total phosphorus
        concentration 	178

 2.7.10 Annual average (1994-1995)  Lake Michigan total phosphorus  loading (kg/year)  	180

 2.7.11 Annual average (1994-1995)  Lake Michigan and Green Bay total phosphorus
       loading (kg/year)	181

3.3.1   MICHTOX PCB  mass balance schematic   	187
                                               XXII

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3.3.2   MICHTOX model segmentation 	  188

3.3.3   Long-term estimates of Lake Michigan total PCB vapor concentrations	   192

3.3.4   Long-term estimates of Lake Michigan total PCB atmospheric deposition loadings	  192

3.3.5   Long-term estimates of Lake Michigan total PCB tributary loadings	  192

3.3.6   Long-term Scenario A predictions of main lake total PCB concentrations   	  192

3.3.7   Comparison of long-term Scenario A predictions to main lake sediment total PCB
       concentrations (sediment cores collected in 1991-1992).	  193

3.3.8   Comparison of long-term Scenario A predictions to GLNPO lake trout data	  193

3.3.9   Comparison of MICHTOX Scenario A total PCB concentrations to Sheboygan Reef
       data	  193

3.3.10 Long-term Scenario B predictions of main lake total PCB concentrations	  193

3.3.11  Comparison of long-term Scenario B predictions to the LMMBP deep water dissolved
       total PCB concentrations	  194

3.3.12 Comparison of Scenario B predictions to main lake sediment total PCB concentrations
       (sediment cores collected in 1991-1992).	  194

3.3.13 Comparison of long-term Scenario B predictions to average total PCB sediment
       concentrations (LMMBP and GBMBP box core samples)	  194

3.3.14 Comparison of long-term Scenario B predictions to GLNPO lake trout data	  194

3.3.15 Comparison of long-term Scenario B total PCB concentrations to Sheboygan Reef
       fish data	  195

3.3.16 Long-term Scenario C predictions of main lake total PCB concentrations	  195

3.3.17 Comparison of Scenario C predictions to main lake sediment total PCB concentrations
       (sediment cores collected in 1991 -1992).	  196

3.3.18 Comparison of long-term Scenario C predictions to GLNPO lake trout data	  196

3.3.19 Comparison of MICHTOX Scenario C total PCB concentrations to Sheboygan Reef
       fish data	  196

3.3.20 Comparison of MICHTOX epilimnetic total PCB concentrations to the LMMBP
       cruise data	  197

3.3.21  Comparison of MICHTOX hypolimnetic total PCB concentrations to the LMMBP
       cruise data	  198
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3.3.22  MICHTOX predicted mass balance fluxes and inventories (kg/year) for 1994-1995,
        whole lake results	  199

3.3.23  MICHTOX predicted mass balance fluxes and inventories (kg/year) for 1994-1995,
        Green Bay and main lake results	  199

3.4.1    The LMMBP biota sampling zones	202

3.4.2   Total PCB concentrations of organisms in Lake Michigan biota zones  	206

3.4.3   Total PCB concentrations in 5.5 year-old lake trout at Saugatuck biota zone  	207

3.4.4a  Sensitivity scenario predicted total PCB concentrations in 5.5 year-old lake trout
        from Saugatuck biota zone	:	208

3.4.4b  Sensitivity scenario total  PCB predictions and the fish consumption target level  	208

3.4.5a  Model application scenario total PCB predictions in 5.5 year-old lake trout from the
        Saugatuck biota zone	208

 3.4.5b  Application  scenario total PCB predictions and the  fish consumption target level	208

 3.4.6  Sensitivity scenario total  PCB concentration predictions for 5.5 year-old lake trout
        at Saugatuck  	209

 A3.4.1  Whole fish to edible portion of fish PCBs and lipid ratios for lake trout	214

 A3.4.2 Comparison of whole fish to fillet PCB ratios and lipid content for various fish species 	214

 4.1.1  Mass budget average for 1994-1995 total PCBs Lake Michigan system (including
        Green Bay) 	  218

 4.1.2  Annual long-term responses of total PCB concentrations in the water column of Lake
        Michigan for the forecast and sensitivity scenarios	219

 4.3.1  Water column segmentation for the LM2-Toxic model  	225

 4.3.2  Surface sediment segmentation for the LM2-Toxic  model 	226

 4.3.3  Cross-sectional sediment segmentation and overlying water column  segments for 10
        Lake Michigan and four Green Bay water columns  	227

 4.3.4  Conceptual framework of organic carbon sorbent dynamics used in the LM2-Toxic model  ... 232

 4.3.5  Schematic  of conceptualization for the steady-state mass balance analysis for  PDC
        vertical transport	234

 4.3.6  Locations of the 30 deployments between 1994  and 2000	236
                                               XXIV

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4.3.7   Regression analysis on the data set (resuspension observed only)	  237

4.3.8   Conceptual framework used by the LM2-Toxic model for PCB congeners in
       Lake Michigan 	238

4.4.1   Locations of 10 tributaries whose flows were considered a part of the water transport
       used in the LM2-Toxic 	249

4.4.2   Locations of monitored and unmonitored tributaries during the LMMBP	253

4.4.3   Lake Michigan water sampling sites during the LMMBP	254

4.4.4   Primary production generated from the LM3-Eutro for Lake Michigan, including
       Green Bay 	257

4.4.5   Lake Michigan sediment sampling sites of organic carbon during the LMMBP	265

4.4.6   Distribution of POC in Lake Michigan surficial sediments (mg/gdw)	265

4.4.7   Distribution of POC in Lake Michigan surficial sediments (mg/L) 	266

4.4.8   IPCBs tributary (11 monitored and 18 unmonitored tributaries) loads to Lake Michigan
       during the LMMBP period	272

4.4.9   Estimated IPCBs atmospheric loads including dry  and wet deposition into Lake Michigan
       during the LMMBP period	273

4.4.10 Lake Michigan atmospheric sampling sites during the LMMBP	275

4.4.11  Seasonal variation of IPCB vapor phase concentrations observed during the LMMBP	276

4.4.12 Lake Michigan sediment sampling sites for PCBs during the LMMBP	277

4.4.13 Distribution of IPCBs in Lake Michigan surficial sediments  (ng/gdw)	278

4.4.14 Distribution of ZPCBs in Lake Michigan surficial sediments  (ng/L)	278

4.4.15 Comparison between the estimated log K'POc,a f°r the LMMBP selected PCB congeners
       based on the  two-phase partitioning model and Kow calculated by Hawker and Connell
       (1988) for all 209 PCB congeners  	281

4.5.1 a Comparison between the temporal profiles for temperature from the LM2-Toxic  	292

4.5.1 b Comparison between the temporal profiles for temperature results from the
       Princeton Ocean Hydrodynamic model	292

4.5.2   Temporal profiles of DOC in the Lake Michigan water column segments for the organic
       carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data	295
                                             xxv

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4.5.3   Temporal profiles of BIG in the Lake Michigan water column segments for the organic
       carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data	298

4.5.4.  Temporal profiles of PDC in the Lake Michigan water column segments for the organic
       carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data	301

4.5.5   Observed data versus the LM2-Toxic predictions for DOC, BIG, and PDC for the
       LMMBP period	304

4.5.6   Temporal profiles of PCB28+31 (dissolved phase + particulate phase) in Lake
       Michigan water column segments for PCB dynamics calibration of the LM2-Toxic and
       the LMMBP cruise  mean data	306

4.5.7   Temporal profiles of IPCBs (dissolved phase + particulate phase) in Lake Michigan
       water segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP
       cruise mean data  	309

4.5.8   Observed data versus the LM2-Toxic predictions for PCB28+31 and IPCBs for the
       LMMBP period	312

4.5.9   Lake-wide average concentrations of (a) 137Cs and (b) 239'240pu computed by the LM2
       radionuclide model over 46 years (1950-1995)	318

4.5.10 Sediment 137Cs inventory comparison between the observed data and the LM2
       radionuclide model outputs	318

4.5.11 Reconstructed historical total PCB loading time functions and sediment core
       LM94-15A total PCB concentration profiles for Lake Michigan	323

4.5.12 Reconstructed total PCB vapor phase concentrations and total PCB loading time
       functions for Lake Michigan 	323

4.5.13 Reconstructed total organic carbon load (primary production + LMMBP  tributary
       loads) for Lake Michigan  	325

4.5.14 The sampling sites of the sediment box core samples (LM94-15A, LM95-61A,
       LM95-87A) taken during the LMMBP for which vertical PCB concentration profiles
       were analyzed and available   	327

4.5.15 Annual lake-wide average total PCB water column concentrations from  the LM2-Toxic
       PCB hindcast simulation   	329

4.5.16 Monthly lake-wide average total PCB water column concentrations from the  LM2-Toxic
       PCB hindcast simulation   	329

4.5.17 Annual average total PCB concentration profiles in the sediment depositional zone
       from the LM2-Toxic PCB hindcast simulation	330

4.5.18 IPCB mass budget of Lake Michigan during the period of the LM2-Toxic PCB
       hindcast (1949-1995)  	332
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4.5.5.1 Model data versus measured total phosphorus loads	427

4.5.5.2 Model output versus measured total phosphorus concentration data	428

4.5.5.3 Relationship between  lake-wide total phosphorus concentration and primary
       productivity based on  model output from the LM3-Eutro model	428

4.6.1   Mass budget average for 1994-1995 total RGBs in the  Lake Michigan system
       (including Green Bay)	432

4.6.2   1994-1995 total PCB Lake Michigan and Green Bay mass budget (averaged)  	433

4.6.3a Annual long-term responses to total PCB concentrations in the water column of
       Lake Michigan for the  forecast scenarios and USEPA water quality criteria for the
       protection of wildlife (U.S. Environmental Protection Agency, 2005) and human
       health (U.S. Environmental Protection Agency, 1997) in the Great Lakes system 	438

4.6.3b Annual long-term responses to total PCB concentrations in the water column of
       Lake Michigan for the  sensitivity scenarios   	438

4.6.4a Monthly long-term responses to total PCB concentrations in the water column of
       Lake Michigan for the  forecast scenarios and USEPA water quality criteria for the
       protection of wildlife (U.S. Environmental Protection Agency, 2005) and human
       health (U.S. Environmental Protection Agency, 1997) in the Great Lakes system  	439

4.6.4b Monthly long-term responses to total PCB concentrations in the water column of
       Lake Michigan for the  sensitivity scenarios   	439

4.6.5a Annual long-term responses to total PCB concentrations in the surficial sediment of
       Lake Michigan for the  forecast scenarios	441

4.6.5b Annual long-term responses to total PCB concentrations in the surficial sediment of
       Lake Michigan for the  sensitivity scenarios	441

4.7.1   Short-term (1994-1995) variations of lake-wide (Green Bay included) organic carbon
       concentrations for ± 50% primary production changes without adjusting settling
       and resuspension rates  	445

4.7.2   Long-term (1994-2055) variations of lake-wide (Green  Bay included) organic carbon
       concentrations for ± 50% primary production changes without adjusting settling
       and resuspension rates  	446

4.7.3   Short-term (1994-1995) variations of lake-wide (Green Bay included) PCB28+31
       (dissolved + particulate) concentrations for ± 50% primary production changes
       without adjusting settling and resuspension  rates  	,	   448

4.7.4   Long-term (1994-2055) variations of lake-wide (Green  Bay included) PCB28+31
       (dissolved + particulate) concentrations for ± 50% primary production changes
       without adjusting settling and resuspension  rates	449
                                              XXVII

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4.7.5   Sensitivity analysis of the LM2-Toxic predictions to varying PCB loads .................. 451

5.3.1    Primary chemical exchange processes between a fish and its environment ............... 458

5.3.2   Comparison of modeling approaches for exposure temperatures in food web models ....... 463

5.4. 1   Biota zones in Lake Michigan  .................................................. 46?

5.4.2a  Typical annual cycles of exposure temperature for Lake Michigan food webs at
        Saugatuck  and Sturgeon Bay .................................................. 485

5.4.2b  Typical annual cycles of exposure temperature for Lake Michigan food web at
        Sheboygan Reef [[[ 485

5.4.2c  Typical annual cycles of exposure temperature for coho salmon in Lake Michigan ......... 486

5.5.1   Agreement between modeled and observed fish PCB concentrations in coho
        salmon using Saugatuck food web (1 994 and 1 995) ................................. 497

5.5.2   Individual comparison between modeled and observed data for PCB congeners in
        lake trout at Saugatuck (1 994 and 1 995) ......................................... 499

5.5.3   Comparison between modeled and observed total PCBs for lake trout at Saugatuck
        (1994 and  1995) [[[ 500

5.7. 1   Sensitivity of PCBs in lake trout (age four) to chemical assimilation efficiency
        presented as ratios of model outputs with modified chemical assimilation efficiency
        to model outputs with the calibrated chemical assimilation efficiency .................... 530

5.7.2   Effect of  changes in food assimilation efficiency on the computed PCB data for lake
        trout in Lake Michigan presented as ratios of model outputs with  modified food
        assimilation efficiency to model outputs with the calibrated food assimilation efficiency ...... 530
 5.7.3   Effect of changes in chemical relative gill transfer coefficient (E^Eo) on the computed
        PCB data for lake trout in Lake Michigan presented as ratios of model outputs with
        modified chemical relative gill transfer coefficient to model outputs with the
        calibrated chemical relative gill transfer coefficient ................................  531

 5.7.4   Effect of changes in SDA on the computed PCB data for lake trout in Lake Michigan
        presented as ratios of model outputs with modified SDA parameter to model outputs
        with the calibrated SDA value ..................................................  532

 5.7.5   Sensitivity of PCBs in lake trout (age four) to fish growth rate presented as the ratios
        of model outputs with zero lake trout growth rate to model outputs with field estimated
        growth rate  [[[  533

 5.7.6   Sensitivity of PCBs in lake trout (age four) to octanol-water partition coefficient (logKow)

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5.7.7   Sensitivity of PCBs in lake trout (age four) to fish diet presented as ratios of model
       outputs with modified fish diet to model outputs with field estimated fish diet	534

5.8.1 a  PCB congener-specific exposure concentrations at Sturgeon Bay predicted by
       LM2-Toxic for Scenario A - PCBs in suspended particles of the water column	538

5.8.1 b  PCB congener-specific exposure concentrations at Sturgeon Bay predicted by
       LM2-Toxic for Scenario A - PCBs in the surface sediment	539

5.8.2   Total PCB concentrations of the lake trout in response to the exposure concentration
       inputs associated with various loading scenarios	540

5.8.3   Total PCB concentrations of the lake trout in response to the exposure concentration
       inputs associated with various loading scenarios	541

6.1     Supporting models and links for MICHTOX and the LM models	545

6.2    Comparison of the  Lake Michigan total PCB mass balance analyses results,
       1994-1995 	547

6.3    Comparison of model output annual average total PCB water concentrations for
       the Constant Conditions Scenario  	548

6.4    Comparison of model output annual average total PCB sediment concentrations for
       the Constant Conditions Scenario	549

6.5    Comparison of model output annual average total PCB water concentrations for
       the Continued Recovery - Fast Scenario	549

6.6    Comparison of the  bioaccumulation model annual average total PCB concentration
       results for the Constant Conditions Scenario	550

6.7    Comparison of the  bioaccumulation model annual average total PCB concentration
       results for the Continued Recovery - Fast Scenario	551
                                             XXIX

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                                      List of Tables
 1.1.1    Characteristics of the LMMBP Modeled Pollutants	   4

 1.1.2    The LMMBP Parameters	   7

 1.3.1    Revised Regression Equations for the LMMBP Total PCBs in All Media	  29

 1.3.2    Ratio of Measured Field Data/Model Results for Congeners 84+92 and 99 in Water	  30

 1.4.1    Summary of Lake Michigan Ice Cover Based Upon Asset (2003)	  57

 1.4.2    Description Wave Statistics for POM Calibration Years (1982-1983) and Study
        Years (1994-1995) Compared to the Period of Record for NOAA's Buoys in
        Northern and Southern Lake Michigan  	  64

 1.5.1    Significant Dates in the History of PCBs in the Lake Michigan Basin  	  67

 1.5.2    Monthly Composite Concentrations of Vapor Phase Total PCBs Measured in Samples
        Collected Around Lake Michigan From April 1994 to October 1995  	  69

 1.5.3    Monthly Composite Concentrations of Total PCBs Measured in Precipitation Samples
        Collected Around Lake Michigan From April 1994 to October 1995  	  73

 1.5.4    Monthly Composite Concentrations of Particulate Phase Total PCBs Measured in
        Samples Collected Around Lake Michigan From April 1994 to October 1995	  75

 1.5.5    Monthly Composite Concentrations of PCBs Measured in Dry Deposition 	  76

 1.5.6    Concentrations of PCBs in 1994-1995 Lake Michigan Water (ng/L)  	  77

 1.5.7    Concentrations of PCBs Measured in Tributaries	  82

 1.5.8    Comparison of PCB Concentrations in Samples Collected From Tributaries in 1994-1995
        With Those in Samples Collected From Tributaries in 1980-1983  	  84

1.5.9    Concentrations of Total PCBs in Lake Michigan Surficial Sediment (ng/g)  	  84

1.5.10   Physical Parameters Associated With LMMBP Cores  	  86
                                            xxx

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1.5.11   Mean Concentrations of PCBs Measured in the 1994-1995 Lake Michigan
        Food Web  	   89

1.6.1    Comparison of Congeners Available for Analysis in All LMMBP Media	   95

1.7.1    Significant Dates in the History of PCBs in the Lake Michigan Basin  	  111

1.7.2    Sedimentary Zones of Lake Michigan	  115

2.3.1    Nutrient State Variables	126

2.4.1    1994-1995 Monthly Atmospheric Total Phosphorus Loads	140

2.4.2    Tributary Total Phosphorus Loads (kg/year)	  141

2.4.3    Sediment Masses, Fluxes, and Loads	  142

2.4.4    The LMMBP Sampling Cruises	142

2.4.5    The LMMBP Open Lake Nutrient Data Summary Statistics	144

2.4.6    Relationship of Field Measurements and Model State Variables	  152

2.4.7    Important LM3 Model Coefficients	153

2.5.1    Coefficients Used in the LM3 Model (Units Correspond to Required LM3 Model Output) ....  162

2.5.2    Summary of Statistical Results of the Calibration 	  165

2.7.1    Final Eutrophication Scenario Results	  179

3.3.1    MICHTOX Segment Geometry  	188

3.3.2    Model Parameters and Coefficients	  189

3.3.3    Cruise and Segment-Specific Average Dissolved Total PCB Concentrations (ng/L)	190

3.3.4    Cruise and Segment-Specific Average Particulate Total PCB Concentrations (ng/L) 	190

3.3.5    Segment-Specific Average Surficial Sediment Total PCB Concentrations (ng/g)	190

3.4.1    Average Total PCB Concentrations in Fish in the Saugatuck Biota Zone	203

3.4.2    Average Total PCB Concentrations in Fish in the Sheboygan Reef Biota Zone	204

3.4.3    Average Total PCB Concentrations in Fish in the Sturgeon Bay Biota Zone	204

3.4.4    MICHTOX Food Chain Age- and Species-Specific Weight, Growth Rate, and Lipid
        Concentrations  	205
                                             XXXI

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3.4.5    MICHTOX Food Chain Model Parameters and Coefficients	206

A3.4.1   Comparison of the LMMBP Lake Trout to MDEQ Lake Superior Lake Trout	213

4.3.1    Geometry Data for Water Column Segments and Lake Michigan (Total)  	229

4.3.2    Initial Geometry Data for Surficial Sediment Segments and Surficial Sediment
        Layer (Total)	230

4.3.3    Processes Considered in Organic Carbon Sorbent Dynamics Constructed for
        the LM2-Toxic	233

4.4.1    Average Annual Flows of the 10 Monitored Tributaries	249

4.4.2    Monthly Average Flows Across the Straits of Mackinac	251

4.4.3   Initial Temperatures in Water Column Segments for the Thermal Balance Model  	251

4.4.4   Cruise-Segment Mean Temperatures for the LMMBP Period	252

4.4.5   The LMMBP Sampling Cruises	255

4.4.6   Initial Chloride Concentrations in Water Column Segments for the Chloride Model	255

4.4.7   Cruise-Segment Mean Chloride Concentrations for the LMMBP Period	256

4.4.8   Annual Average Organic Carbon Loads From 11 Monitored Tributaries to Lake
        Michigan During the LMMBP	259

4.4.9   Annual Average Organic Carbon Loads From 18 Unmonitored Tributaries to Lake
        Michigan During the LMMBP	259

 4.4.10  Annual Average Organic Carbon Internal Loads Generated From the LM3-Eutro
        for Lake Michigan During the LMMBP	26°

 4.4.11   Initial Concentrations of Organic Carbon Sorbents in Water Column Segments for
         the LM2-Toxic	261

 4.4.12   Cruise-Segment Mean Concentrations of DOC (mg/L) for the LMMBP Period	262

 4.4.13   Cruise-Segment Mean Concentrations of BIG (mg/L) for the LMMBP Period	263

 4.4.14   Cruise-Segment Mean Concentrations of PDC (mg/L) for the LMMBP Period 	264

 4.4.15   Concentration of Organic Carbon Sorbents in Surficial Sediments for the LM2-Toxic	267

 4.4.16   Organic Carbon Sorbent Biotransformation Parameters Specified for the LM2-Toxic	268

 4.4.17   Segment-Specific Settling Rates (m/d) for Organic Carbon Sorbents (BIG and PDC)
         Specified for the LM2-Toxic 	269
                                             XXXII

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4.4.18   Segment-Specific Effective PDC Concentrations (Cw) Used in the Steady-State
        Resuspension Calculation Based on the LMMBP Data	270

4.4.19   Segment-Specific Sediment Accumulation Rates (vb) and Thickness of Mixing Layer
        (z) Used in the Steady-State Resuspension Calculation	270

4.4.20   Segment-Specific Critical Wave Heights (Wcr) and Empirical Wave Coefficients (a)
        Used in the Wave-Induced Resuspension Calculation Based on the LMMBP Data  	271

4.4.21   List of PCB State Variables Modeled in the LM2-Toxic	272

4.4.22   Annual Average IPCB Loads From 11  Monitored and 18 Unmonitored Tributaries to
        Lake Michigan During the LMMBP	274

4.4.23   Annual Average IPCB Atmospheric Dry and Wet Loads in the 10 Surface Water
        Column Segments of Lake Michigan During the LMMBP	274

4.4.24   Annual Average Boundary Conditions of IPCB Vapor Phase Concentrations for
        Lake Michigan During the LMMBP	276

4.4.25   Initial Concentrations of IPCBs in Water Column Segments for Lake Michigan	278

4.4.26   Cruise-Segment Mean Concentration of IPCBs (ng/L) for the LMMBP Period	279

4.4.27   Initial Concentrations of IPCBs in Sediment Segments for Lake Michigan	280

4.4.28   Final Partition Coefficients for the LMMBP Selected PCBs Used in the LM2-Toxic  	282

4.4.29   Values of Parameters Used for Air-Water Exchange in the LM2-Toxic for the LMMBP
        Selected PCB Congeners  	283

4.5.1    Results of the Regression Between the LM2-Toxic Calibration Results and the Cruise
        Mean Data for the LMMBP Selected PCB Congeners  	314

4.5.2    Results of the LM2-Toxic Mass Balance Checking for a 62-Year Simulation of an
        Assumed Conservative Tracer 	315

4.5.3    Comparison Between the  LMMBP Field-Generated and the LM2-Toxic-Generated
        Sediment Accumulation Rates (cm/year)  	320

4.5.4    Available  Historical Water Column Total PCB Concentrations for Lake Michigan	325

4.5.5    Sediment PCB Concentration Vertical Profiles Analyzed for Three Sediment Box
        Cores Taken During the LMMBP	326

4.5.6    Available  Inventories of PCBs in Lake Michigan Sediments 	327

4.5.7    Calculations in PCB  Mass Budget Checking for the LM2-Toxic PCB Hindcast	332
                                           XXXIII

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4.6.1    Regression Equations Used for Converting IPCBs to Total RGBs for the LM2-Toxic
        Results	431

4.6.2    Results of Total PCB Mass Budget Analysis for Lake Michigan and Green Bay  	434

4.6.3    Mean and Medium Particulate RGBs/Organic Carbon and Field Data and Scaling
        Factor for Hypolimnetic Level 2, Segments 21, 30, and 37, and for Saugatuck
        Biota Box Hypolimnion	442

4.7.1    Annual Average Concentrations of Water Column Carbon Solids and Annual
        Average Change in Percentage for Water Column Carbon Solids Concentrations
        Resulting From the LM2-Toxic Model Runs for Both the Short-Term (1994-1995)
        and the Long-Term (1994-2055) Simulations with 50% Increase and 50%
        Decrease of the LM3-Eutro Produced Primary Production  	447

4.7.2    PCB28+31 Mass Fluxes and Inventories for Lake Michigan System Results From
        the LM2-Toxic Sensitivity Analysis on Primary Production for the Short-Term (Two-
        Year Period:  1994-1995) Simulations  	448

4.7.3    PCB28+31 Mass Fluxes and Inventories for Lake Michigan System Results From
        the LM2-Toxic Sensitivity Analysis on Primary Production for the Last Two Years
        of the Long-Term (62-Year Period:  1994-2055) Simulations  	449

4.7.4    PCB28+31 Average Inventories of Water Column and Surficial Sediment Results
        From the LM2-Toxic Simulations for the Primary Production Sensitivity Analysis,
        and Changes in Percentage for These Inventories Compared to the Inventories
        From the Original Base Runs 	451

5.4.1    Targeted PCB Congeners and Their Kow  	466

5.4.2a   Annual Dietary Composition of  Lake Trout at Saugatuck (1994-1995) 	469

5.4.2b   Annual Dietary Composition of  Lake Trout at Sheboygan Reef (1994-1995)	470

5.4.2c   Annual Dietary Composition of  Lake Trout at Sturgeon Bay (1994-1995)	472

5.4.3    Dietary Composition of Alewife  in Lake Michigan (1994-1995)	473

5.4.4.   Dietary Composition of Bloater  in Lake Michigan (1994-1995)	474

5.4.5    Dietary Composition of Rainbow Smelt in Lake Michigan (1994-1995)	474

5.4.6    Dietary Composition of Slimy Sculpin in Lake Michigan (1994-1995) 	474

5.4.7    Dietary Composition of Deepwater Sculpin in Lake Michigan (1994-1995)	475

5.4.8    Dietary Composition of Coho Salmon in Lake Michigan (1994-1995) 	475

5.4.9a   Average Weight-Age Relationships for Lake Trout in Lake Michigan (1994-1995)  	477
                                            xxxiv

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5.4.9b   Average Weight-Age Relationships for Coho Salmon in Lake Michigan (1994-1995)	477

5.4.9c   Average Weight-Age Relationships of Forage Fish in Lake Michigan (1994-1995)	478

5.4.9d   Estimated Weight-Age Relationships of Mysis in Lake Michigan	478

5.4.1 Oa  Average Lipid and Protein Fractions (%) of Lake Trout in Lake Michigan (1994-1995)	478

5.4.1 Ob  Average Lipid and Protein Fractions (%) of Coho Salmon in Lake Michigan (1994-1995) ....  479

5.4.1 Oc  Average Lipid and Protein Fractions (%) of Alewife in Lake Michigan (1994-1995)	479

5.4.1 Od  Average Lipid and Protein Fractions (%) of Bloater in Lake Michigan (1994-1995)	480

5.4.1 Oe  Average Lipid and Protein Fractions (%) of Rainbow Smelt in Lake Michigan
        (1994-1995)	480

5.4.1 Of  Average Lipid and Protein Fractions (%) of Slimy Sculpin in Lake Michigan
        (1994-1995)	480

5.4.10g  Average Lipid and Protein Fractions (%) of Deepwater Sculpin in  Lake Michigan
        (1994-1995)	481

5.4.1 Oh  Average Lipid and Protein Fractions (%) of Zooplankton, Mysis, and Diporeia in Lake
        Michigan (1994-1995)	481

5.4.11   PCB Concentrations in Lake Michigan Water Column (1994-1995)  	482

5.4.12   PCB Concentrations in Lake Michigan Surface Sediment (1994-1995)  	484

5.4.13   Bioenergetic Parameters of Lake Michigan Fishes	487

5.5.1    Calibrated Parameter Values for Diporeia Submodel 	494

5.5.2    Calibrated Model Parameters for PCBs in the Sturgeon Bay and Saugatuck Lake
        Trout Food Webs	  494

5.5.3    Calibrated Model Parameters for PCBs in the Sheboygan Reef Lake Trout Food Web   	494

5.5.4    Calibrated Model Parameters for PCBs in Lake Michigan Coho Salmon  	495

7.1.1    Agenda - Lake Michigan Mass Balance PCB Modeling Peer Review	554

7.1.2    Significant Dates in the History of PCBs in the Lake Michigan Basin 	562
                                             xxxv

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                                     Abbreviations
AOCs         Areas of Concern
AREAL        Atmospheric Research and Exposure Assessment Laboratory
BIG           Biotic carbon
BMC          Bayesian Monte Carlo
BNL          Benthic nepheloid layer
CCC          Criterion continuous concentration
CMC          Criterion maximum concentration
CPE          Catch per unit of effort
CTF          Contaminant transport and fate model
DDE          Dichlorodiphenyldichloroethylene
DEA          Deethylatrazine
DIA           Deisopropylatrazine
DIN           Dissolved inorganic nitrogen
DOC          Dissolved organic carbon
DON          Dissolved organic nitrogen
DOP          Dissolved organic phosphorus
DQO          Data quality objectives
DSi           Dissolved silica
EEGLE        Episodic Events-Great Lakes Experiments
EMP          Enhanced Monitoring Program
GLERL        Great Lakes Environmental Research Laboratory
GLNPO       Great Lakes National Program Office
GLWQA      Great Lakes Water Quality Agreement
GBMBP       Green Bay Mass Balance Project
HOC          Hydrophobic organic chemicals
IADN         Integrated Atmospheric Deposition Network
IDL           Instrument detection limit
IDW          Inverse distance weighted
LaMP         Lake-wide Management Plan
LLRFRB      Lake Lakes and Rivers Forecasting Research Branch
LLRS         Large Lakes Research Station
LMMBP       Lake Michigan Mass Balance Project
LOG          Labile organic carbon
LON          Labile organic nitrogen
LOP          Labile organic phosphorus
MCL          Maximum contaminant level
 MDEQ        Michigan Department of Environmental Quality
                                           xxxvi

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MDL          Method detection limit
MED          Mid-Continent Ecology Division
MQO          Measurement quality objectives
NHEERL      National Health and Environmental Effects Research Laboratory
NOAA         National Oceanic and Atmospheric Administration
ORD          Office of Research and Development
PCBs         Polychlorinated biphenyls
PDC          Particulate detrital carbon
PI            Principal Investigator
POC          Particulate organic carbon
POM          Princeton Ocean Model
POP          Persistent organic pollutants
QA           Quality assurance
QAPP         Quality Assurance Project Plan
QC           Quality control
RAPs         Remedial Action Plans
RDMQ        Research Data Management and Quality Control System
RFS          Routine field sample
ROC          Refractory organic carbon
RON          Refractory organic nitrogen
ROP          Refractory organic phosphorus
SA           Available silica
SDA          Specific dynamic action
SDL          System detection limit
SRP          Soluble reactive phosphorus
SU           Biogenic silica
TKN          Total Kjeldahl nitrogen
TMDL         Total maximum daily load
USDOI        United States Department of Interior
USEPA       United States Environmental Protection Agency
USFWS       Unites States Fish and Wildlife Service
USGS         United States Geological Survey
VWA          Volume-weighted average
                                            XXXVII

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                                    Acknowledgments
Special thanks to the United States Environmental Protection Agency, Great Lakes National Program Office
for leadership, support, and collaboration on the Lake Michigan Mass Balance Project. The multiple efforts
by the Principal Investigators for providing data, necessary for the modeling, are greatly appreciated. Thank
you to David H. Miller, Kenneth R. Rygwelski, Timothy J. Feist, Xiaomi Zhang, and James P. Pauer for
providing internal technical reviews of various parts of this document.  Thanks to Kay Morrison for the graphic
renditions and figures and to Debra L. Caudill for formatting and word processing.  Finally, thanks to Robert
B. Ambrose, Jr., Joel E. Baker, Ken G. Drouillard, Barry Lesht, and James L. Martin for serving on  the peer
review panel.
                                             XXXVIII

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                                   Executive Summary
The Lake Michigan Mass  Balance Project (LMMBP)  provided an opportunity to improve the quality of
polychlorinated biphenyl (PCB) mass balance models used to represent large, freshwater ecosystems. A
rigorously quality-assured large supporting data set derived from samples collected during eight cruises in
1994-1995 was used to establish atmospheric and tributary loads, estimate initial conditions, perform model
calibration and confirmation and, to a lesser extent, to assist in estimating a number of kinetic coefficients.
A significant aspect of this modeling effort was modeling PCBs at a congener-level basis.

Lake Michigan is acted upon by a number of physical parameters that impact the hydrology, chemistry, and
biology of the lake.  For a lake the size of Lake Michigan, changes in these parameters can lead to significant
changes, especially when  models are used in long-term predictions to predict the  outcome  of various
scenarios. The primary driving forces are wind, air temperature, and precipitation. These impact tributary
flows, lake levels, waves, water circulation, water temperature, and ice cover. For the period of record, these
driving forces vary from year-to-year.  The period of 1982 to 1983 was used to calibrate the hydrodynamic
models.  For this period of time, hydrodynamic conditions were not at any extreme. This is also true for the
period of 1994 and  1995 when the models were applied.

Major physical forcing functions were not  extreme during the sampling period  of 1994-1995  or  the
hydrodynamic model calibration period of 1982-1983. Precipitation was within the normal range for all years
of modeling interest, resulting in lake levels and tributary flows that were within normal  bounds.

Temperature will impact the eutrophication and contaminant modeling. Air temperature impacts how quickly
the lake warms in any one year. Water temperature is critical to the timing of algae blooms, especially the
spring diatom bloom. It also impacts the volatilization of contaminants.  There appears to be a four-year cycle
of quicker warming which exists within a trend of general warming of the lake. The trend of warming may be
part of a longer term, undocumented cycle, or may be related to climate change.

Models developed at the  United States Environmental Protection Agency's Large Lakes Research Station,
including LM3-Eutro, MICHTOX, LM2-Toxic, and LM Food Chain, utilized results from a hydrodynamic model
to describe the lake's physics and results from air and tributary models to provide loadings to the lake.

LM3-Eutro uses state-of-the-science eutrophication kinetics to simulate the interactions between plankton and
nutrients. LM3-Eutro is a high-resolution (44,042 cells and 19 sigma layers) carbon based model that provides
a highly resolved description of areas such as near and off shore zones,  bays, river confluences, and the
thermocline. Its nutrient variables include dissolved, labile particulate, and refractory particulate forms which
provide a more realistic description  of phytoplankton-nutrient  interactions.  Improvements were made to the
light calculation by using a three-hour rather than 24-hour (one day) average estimate of solar radiation. The
model is driven by the Princeton Ocean hydrodynamics Model which simulates water movements. LM3-Eutro
                                             xxxix

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has 17 state variables, including a single zooplankton class, two phytoplankton classes, and several participate
and dissolved nutrient (including carbon) states.

The model was calibrated on the high-resolution (44,042 cells) Level 3 framework as well as the 41 segment
Level 2 framework. The Level 2 calibration enabled us to visually observe known spatial and temporal trends
such  as the spring diatom bloom and phytoplankton concentration gradients between the epilimnion and
hypolimnion. The Level 3 calibration was performed on a whole-lake basis. The 1994-1995 LMMBP field data
were  used to calibrate the model. The final calibration was chosen based on the best Level 3 calibration, but
Level 2 output was visually inspected to ensure that expected phytoplankton and nutrients trends were
reflected.  Model confirmation was performed by comparing the model to limited total phosphorus data for
1998 and 2000 and to  a historical model, MICH1, which was developed and calibrated in the 1970s and
modified more recently.  All comparisons were done on a whole-lake basis, and LM3-Eutro fits the 1998 and
2000 data well.  LM3-Eutro and MICH1  compared surprisingly well, especially given the fact that they are
based on very different frameworks, kinetics, and segmentation. Compared to field data and LM3-Eutro
predicted, MICH1 underpredicted both total phosphorus concentrations. This was probably due to the fact that
MICH1 does not have any phosphorus sediment recycling. Lower phosphorus values also cause MICH1 to
under-predict chlorophyll a concentrations in the lake.

MICHTOX is a toxic chemical mass balance and food chain bioaccumulation model developed in the early
1990s.   A  Bayesian Monte Carlo uncertainty analysis  demonstrated that  MICHTOX predicted PCB
concentrations should be within a factor of two measured data. During the early part of the LMMBP, MICHTOX
was updated and used as a preliminary assessment tool of the LMMBP PCB data and to provide a screening-
level  analysis of the potential future trends in total PCB concentrations in Lake Michigan water, sediment, and
fish under a variety of contaminant load scenarios. Unmonitored tributary inputs were added to the model and
the applicability of MICHTOX for predicting Lake Michigan total PCB concentrations in water, sediment, and
fish was reconfirmed. MICHTOX was applied using the previously developed parameterization and LMMBP
data  and forcing functions.  The model  fit to data was  acceptable with  no adjustments to the model
parameters. The model also provided a comparison of an older, "off-the-shelf model with the more complex
models developed as part of the Lake Michigan Mass Balance Project (LMMBP). MICHTOX was run for seven
scenarios to help evaluate the impacts on PCB trends caused by various loading sources and to evaluate
loading scenarios.  Results of the MICHTOX modeling indicate that atmospheric exchange is a dominant loss
process of total PCBs in Lake Michigan, and that the reservoir of total PCBs in the sediment has a significant
impact on the future trends in concentrations of total PCBs in lake trout.

LM2-Toxic is a sophisticated and state-of-the-art toxic chemical fate and transport model for Lake Michigan.
 It is a coupled mass balance of organic carbon solids and toxic chemical (PCBs) dynamics. Using the LMMBP
 generated field data, the organic carbon solids dynamics were first calibrated.  This was followed by the
 independent calibration of PCB dynamics.  The temporal variations of both biotic carbon (BIC) and particulate
 detrital  carbon (PDC) resulted from an algal bloom in late spring and early summer.  Primary production was
the dominant organic carbon load to Lake Michigan. The eutrophication model (LM3-Eutro) generated primary
 production accounted for over 90 percent of the total particulate organic carbon load to the lake.

 The  main focus of this model is to address the relationship between sources of toxic chemicals and their
 concentrations in water and sediments of Lake Michigan and to provide the PCB exposure concentrations to
 the bioaccumulation  model (LM2 Food Chain) to predict PCBs concentrations in lake trout tissue. LM2-Toxic
 is a revision of the  USEPA supported WASP4 water quality modeling framework.  It incorporates the organic
 carbon dynamics featured in G BTOX and the sediment transport scheme, a quasi-Lagrangian framework, used
 in the IPX. Both GBTOX and IPX were WASP4-type models and major components in the Green Bay Mass
 Balance Study modeling framework. Another important modification was the addition of updated air-water
 exchange formulations to the model.
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The results at 5 x 5 km2 grid generated by Princeton Ocean Model for the Great Lakes (POMGL) were linked
to the transport fields for LM2-Toxic.  Due to an affinity of PCBs for organic carbon, three organic carbon
sorbents were simulated as state variables in LM2-Toxic. They were BIG, PDC and dissolved organic carbon
(DOC).  The model simulated 54 PCB congeners which accounted for 63% to 85% of the total PCB mass in
various media for Lake Michigan.  This was an enormous effort because individual congeners or co-eluting
congeners  were  modeled  as  separate state  variables in the  mass  balance,  each  with  their  own
physical/chemical properties. Four phases were simulated in LM2-Toxic for the congeners. The four phases
were dissolved, sorbed to PDC, sorbed to BIG, and bound to DOC.

To reduce uncertainties associated with water transport, settling and resuspension, and sedimentation, a
thermal balance model, a chloride model, a long-term simulation using a 137Cs and 239'240pu model, and a long-
term organic carbon simulation using LM2-Toxic were developed and run for LM2-Toxic confirmation.

Air-water exchange of PCBs was the most important process for Lake Michigan. Net sediment resuspension
was the second largest net source. Both the water column and the surficial sediment layer of the lake were
not at steady-state during the LMMBP period.  The model was also applied for forecasting the long-term
responses (60-year simulation,  starting on January 1, 1996) of the  PCBs in Lake Michigan under various
forcing functions and load reduction scenarios. The results indicate that the PCB mass  in the surficial sediment
is large and thus could support PCB concentrations in the water column for a very long time.

LM Food Chain is the food web bioaccumulation model developed for the  LMMBP.  The model established
dynamic relationships between PCBs concentrations in the exposure environments and resulting PCBs levels
in the lake trout food webs of Lake  Michigan. The model was based  upon available theory and data
characterizing the bioaccumulation of toxic chemicals in fish and other aquatic organisms.  Samples collected
for the LMMBP were used to generate data on lake trout and coho salmon food webs in Lake Michigan and
to facilitate refinement of model  parameters to site-specific conditions for forty PCB congeners or co-eluters
that represented toxic chemicals covering a wide range of hydrophobicity.

The food web model was calibrated with PCB data collected in 1994 and 1995 for lake trout food webs at
Sturgeon Bay, Sheboygan Reef, and Saugatuck. The lake trout sub-populations in these three biota zones
were believed to be appropriate representations of lake trout in  Lake Michigan. Model calibration was also
performed for a lake-wide coho salmon food web.  During the model calibration, model parameters were
refined to achieve an  adequate  agreement between model calculations and observed PCB data for a food
web. The focus of model calibration was not limited to top predators or to toxics with a certain hydrophobicity.
The model parameters were systematically optimized for all species at various trophic levels and for PCB
congeners of a wide range of hydrophobicity. Extra care was taken to ensure the refined parameter values
were consistent with the hydrophobicity of individual PCB congeners and with the trophic position of individual
species.  Satisfactory calibration results were achieved for the lake trout food webs at Sturgeon Bay and
Saugatuck. The model parameters calibrated with data from the Sturgeon Bay food webs were independently
tested and validated with data from the Saugatuck food web, and vice versa.

The availability of a complete account of observed data for each food web made this model calibration
probably the most thorough process among similar efforts. Although PCB concentrations in lake trout or coho
salmon were the endpoint of the model computation and the focus of most  model applications, the food web
model with parameters "fine-tuned" for species at all trophic levels can be  used to target any desirable species
in the food web with a high degree of confidence.  Also, the food web model can be used to model toxics with
various hydrophobicities. No food web model intended to simulate as  many toxic chemicals with diverse
hydrophobicity has been previously developed.
                                              xli

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The validated food web model was applied to the lake trout food webs at Sturgeon Bay and Saugatuck and
inferred for the southeastern and northwestern regions of the lake to predict future PCB concentrations.
Several model simulations were performed to predict the expected changes in  future lake  trout  PCB
concentrations in response to different exposure scenarios. Hypothetical long-term PCB exposure scenarios
in the post-1994/1995 period for the food webs at the Sturgeon  Bay and Saugatuck biota zones were
generated by the water quality model LM2-Toxic.  For each lake trout food web, the resulting concentrations
of individual PCB congeners in fish were predicted.  Similar model predictions were  observed for these two
biota zones under each reduction scenario. For the continued fast recovery scenario, current simulations
indicate that the total PCB concentrations in adult lake trout (5.5 years old) were expected to reach the target
level of 0.075 ppm in 2030 for the Saugatuck  biota zone, 2033 for southeastern Lake Michigan, 2036 for
northwestern Lake Michigan,  and 2036 for the Sturgeon Bay biota zone.
                                              xlii

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                                          PART1
                                    INTRODUCTION
Chapter 1. Project Overview

Harry B. McCarty, Ken Miller, Robert N. Brent, and
Judy Schofield
DynCorp (a CSC Company)
601 Stevenson Avene
Alexandrea, Virginia 22304
and
Ronald Rossmann and Kenneth R. Rygwelski
U.S. Environmental Protection Agency
Office of Research and Development
National Health and  Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

The  United  States  Environmental   Protection
Agency's (USEPA) Great Lakes National Program
Office (GLNPO) instituted the Lake Michigan Mass
Balance Project (LMMBP) to measure and model the
concentrations of representative pollutants within
important compartments of  the  Lake  Michigan
ecosystem.   For the LMMBP,  concentrations of
polychlorinated biphenyls (PCBs),  frans-nonachlor,
atrazine,  and mercury in  tributaries,  lake water,
sediments,  food  webs,  and  the  atmosphere
surrounding Lake Michigan  were measured.  This
document  contains  the  PCB  modeling  results
reported  by   staff   and   contractors  of   the
USEPA/Office  of  Research and  Development
(ORD)/National Health and  Environmental  Effects
Research Laboratory (NHEERL)/Large Lakes  and
Rivers Forecasting Research Branch (LLRFRB) staff
and contractors located at the Large Lakes Research
Station (LLRS).

1.1.1  Background

The Great Lakes, which contain 20% of the world's
freshwater, are a globally important natural resource
currently threatened by multiple stressors.  While
significant progress has been made to improve the
quality of the lakes, pollutant loads from point, non-
point, atmospheric, and legacy sources continue to
impair ecosystem functions and limit the attainability
of designated uses  of these resources.   Fish
consumption advisories and beach closings continue
to be issued,  emphasizing  the human  health
concerns from lake contamination. Physical and
biological stressors, such as invasion of non-native
species and habitat loss, also continue to threaten
the biological integrity of the Great Lakes.

The United States and Canada have recognized the
significance and importance of the Great Lakes as a
natural resource and have taken steps to restore and
protect the lakes.  In 1978, both countries signed the
Great Lakes Water Quality Agreement (GLWQA).
This   Agreement  calls  for   the  restoration and
maintenance of the chemical, physical, and biological
integrity of the Great Lakes by developing plans to
monitor and limit pollutant flows into the lakes.

The GLWQA, as well as Section 118(c) of the Clean
Water Act, require the development of a Lake-wide
Management Plan (LaMP) for each Great Lake. The
purpose of these LaMPs is to document an approach
to reduce inputs  of critical pollutants to the Great

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Lakes  and  restore and  maintain Great  Lakes
integrity.  To assist in developing these LaMPs and
to monitor progress in pollutant reduction, Federal,
State,  Tribal, and local  entities  have instituted
Enhanced Monitoring Plans (EMPs).  Monitoring is
essential to the development of baseline conditions
for the Great Lakes and provides a sound scientific
base  of  information  to guide  future  toxic  load
reduction efforts.

The LMMBP is a part of the EMPs for Lake Michigan.
The  LMMBP was  a  coordinated effort  among
Federal, State, and academic scientists to monitor
tributary and atmospheric pollutant loads, develop
source inventories of toxic substances, and evaluate
the fates and effects of these pollutants in Lake
Michigan.   A mass balance modeling approach
provides  the predictive  ability  to determine the
environmental benefits  of specific load reduction
               scenarios for toxic substances and the time required
               to realize those benefits.  This predictive ability will
               allow Federal, State, Tribal, and local agencies to
               make more informed load reduction decisions.

               1.1.2 Description

               The LMMBP  used a mass balance approach to
               evaluate  the  sources,  transport,  and  fate of
               contaminants  in the Lake Michigan ecosystem. A
               mass  balance approach is based on the law of
               conservation of mass, which states that the amount
               of a pollutant entering a system is equal to the
               amount of that  pollutant leaving, trapped in, and
               chemically changed in the system (Figure 1.1.1). In
               the Lake Michigan system, pollutant inputs may
               come from atmospheric deposition or tributary loads.
            Simple Mass Budget for Conservative Substances
                              1
source
              mass ;
                    in
                             water system
             mass out = mass jn + Zsources
                               t
source
            Mass Balance Modeling Approach
              mass i
                    in
                               air system
                                 -N-
                             water system
                           sediment system
                        air sources
             mass out = mass jn + ^sources
             ± air-water exchange
             ± sediment-water exchange
             ± ^internal processes
Figure 1.1.1.  Simplified mass balance approach.

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Pollutants may leave the system through burial in
bottom sediments, volatilization to the atmosphere, or
discharge into Lake Huron through the  Straits of
Mackinaw.  Pollutants within the system may be
transformed through  degradation   or  stored in
ecosystem compartments such as the water column,
sediments, or biota.

For the  LMMBP, contaminant concentrations in
various inputs and ecosystem  compartments over
spatial and  temporal   scales were  measured.
Mathematical models that track the transport and fate
of  contaminants within  Lake  Michigan   were
developed and calibrated using these field data. The
LMMBP was the first lake-wide application of a mass
balance determination for the Great Lakes and will
serve as  a basis for  future  mass budget/mass
balance efforts.

1.1.3 Scope

1.1.3.1 Modeled Pollutants

When the  USEPA  published  the  Water Quality
Guidance for the  Great  Lakes  System (58 FR
20802), the Agency established water quality criteria
for 29 pollutants.  Those criteria were designed to
protect aquatic  life, terrestrial wildlife, and human
health.  PCBs,  frans-nonachlor, and mercury are
included in the list of 29 pollutants. The water quality
criteria and values proposed in the guidance apply to
all of the ambient waters of the Great Lakes System,
regardless  of the sources of  pollutants  in  those
waters.   The proposed  criteria provide a uniform
basis for integrating Federal, State, and Tribal efforts
to protect and restore the Great Lakes ecosystem.

The  number of pollutants  that can  be intensively
monitored and modeled in the Great Lakes System
is limited by the  resources available to collect and
analyze thousands of samples, assure the quality of
the results,  manage the data, and develop  and
calibrate  the  necessary models.   Therefore, the
LMMBP focused on constructing  mass balance
models for a limited group of pollutants. PCBs, trans-
nonachlor, atrazine, and  mercury were selected for
inclusion  in the LMMBP because these pollutants
currently  or potentially pose a  risk  to aquatic and
terrestrial organisms (including humans) in the Lake
Michigan ecosystem (Table 1.1.1). These pollutants
also were selected to cover a wide range of chemical
and physical properties and represent other classes
of compounds  which pose current  or potential
problems.   Once a  mass  budget for selected
pollutants is established and a mass balance model
calibrated, additional contaminants can be modeled
with  limited  data and future  resources  can  be
devoted to activities such as emission inventories
and dispersion modeling.

1.1.3.1.1 PCBs

Polychlorinated biphenyls (PCBs) are a class of man-
made,  chlorinated, organic chemicals that  include
209 congeners,  or specific PCB compounds.  The
highly   stable,   nonflammable,  non-conductive
properties of these compounds made them useful in
a variety of products including electrical transformers
and capacitors,  plastics, rubber, paints, adhesives,
and  sealants.   PCBs were  produced for such
industrial uses in the form of complex mixtures under
the trade name "Aroclor" and  were commercially
available from 1930 through 1977, when the USEPA
banned their production due to environmental and
public health concerns. PCBs also may be produced
by combustion processes, including incineration, and
can be found in stack emissions and ash from
incinerators.

Because they were found by  the USEPA in  the
effluents  from one or  more wastewater treatment
facilities, seven Aroclor formulations were included in
the Priority Pollutant List developed by the USEPA
Office of Water  under the auspices of the Clean
Water Act. Aroclors may have entered the Great
Lakes  through  other  means,  including spills  or
improperdisposal of transformerfluids, contaminated
soils washing into the watershed, or discharges from
ships.    The   PCBs  produced  by   combustion
processes may be released to the atmosphere where
they are  transported in both vapor and particulate
phases and  enter  the lakes  through  either  dry
deposition or precipitation events (e.g., rain).

The stability  and persistence of PCBs, which made
them useful in industrial applications, have also made
these compounds ubiquitous  in  the environment.
PCBs do not readily degrade and thus accumulate in
water bodies and aquatic sediments.  PCBs also
bioaccumulate, or build up, in living tissues. Levels
of PCBs  in some fish  from  Lake Michigan  exceed
U.S. Food and Drug Administration  tolerances,

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Table 1.1.1.  Characteristics of the LMMBP Modeled Pollutants
Pollutant
RGBs
Sources
• Waste incinerators
(unintentional
byproducts of
combustion)
• Industrial
dischargers
• Electrical power
Uses
• Electrical
transformers and
capacitors
• Carbonless copy
paper
• Plasticizers
• Hydraulic fluids
Toxic Effects
• Probable human
carcinogen
• Hearing and vision
impairment
• Liver function alterations
• Reproductive impairment
and deformities in fish and
wildlife
Biocon-
centration
Factor1
1 ,800 to
180,000
USEPA
Regulatory
Standards2
MCL = 0.5 ug/L
CCC = 14 ng/L
HH=0.17ng/L
 frans-Non-
 achlor3
 Atrazine
Application to crops
and gardens
Application to crops
Pesticide on com
and citrus crops
Pesticide on
lawns and
gardens
Herbicide for corn
and sorghum
production
 Probable human           4,000 to      MCL = 2 ug/L
 carcinogen               40,000       CMC = 2.4 (jg/L
• Nervous system effects                  CCC = 4.3 ng/L
 Blood system effects                    HH = 2.1 ng/L
 Liver, kidney, heart, lung,
 spleen, and adrenal gland
 damage

 Weight loss               2 to 100      MCL = 3 ug/L
 Cardiovascular damage                  CMC4 = 350
 Muscle and adrenal                     ug/L
 degeneration                          CCC4 = 12 ug/L
 Congestion of heart,
 lungs, and kidneys
 Toxic to aquatic plants
Mercury
• Waste disposal
• Manufacturing
processes
• Energy production
• Ore processing
• Municipal & medical
waste incinerators
• Chloralkali factories
• Fuel combustion
Battery cells
Barometers
Dental fillings
Thermometers
Switches
Fluorescent lamps
• Possible human 63,000 to
carcinogen 100,000
• Damage to brain and
kidneys
• Adverse affects on the
developing fetus, sperm,
and male reproductive
organs
MCL = 2 ug/L
CMC =1.4 ug/L
CCC = 0.77 ug/L
HH = 50 ng/L
FWA5 = 2.4 pg/L
FWC5 = 12ng/L
Wildlife6 =1.3
ng/L
1From: U.S. Environmental Protection Agency, 1995a, National Primary Drinking Water Regulations, Contaminant Specific
 Fact Sheets, Inorganic Chemicals, Technical Version, EPA 811/F-95/002-T, USEPA, Office of Water, Washington, D.C.;
 and U.S. Environmental Protection Agency, 1995b, National Primary Drinking Water Regulations, Contaminant Specific
  Fact Sheets, Synthetic Organic Chemicals, Technical Version, EPA 811/F-95/003-T, USEPA, Office of Water,
  Washington, D.C.
2MCL = Maximum Contaminant Level for drinking water. CMC = Criterion Maximum Concentration for protection of aquatic
 life from acute toxicity. CCC = Criterion Continuous Concentration for protection of aquatic life from chronic toxicity. HH
 = water quality criteria for protection of human health from water and fish consumption.  Data from: U.S. Environmental
 Protection Agency, 1999, National Recommended Water Quality Criteria-Correction, EPA 822/Z-99/001, USEPA, Office
  of Water, Washington,  D.C.
Characteristics presented are for chlordane.  frans-Nonachlor is a principle component of the pesticide chlordane.
"Draft water quality criteria for protection of aquatic life.  From: U.S. Environmental Protection Agency, 2001 b, Ambient
  Aquatic Life Water Quality Criteria for Atrazine, USEPA, Office of Water, Washington,  D.C.
5FWA = Freshwater acute water quality criterion. FWC = Freshwater chronic water quality criterion. From National Toxics
 Rule (58  FR 60848).
6Wildlife criterion. From the Stay of Federal Water Quality Criteria for Metals (60 FR 22208), 40 CFR 131.36 and the
 Water Quality Guidance for  the Great Lakes System (40 CFR 132).

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prompting closure of some commercial fisheries and
issuance of fish consumption advisories.  PCBs are
a probable human carcinogen, and human health
effects of PCBs exposure include stomach, kidney,
and liver damage; liver and biliary tract cancer; and
reproductive effects, including effects on the fetus
after exposure of the mother.

PCB congeners exhibit a wide range of physical and
chemical   properties   (e.g.   vapor  pressures,
solubilities, boiling points), are relatively resistant to
degradation, and are ubiquitous.  These properties
make them ideal surrogates for a wide  range of
organic compounds from anthropogenic sources.

1.1.3.1.2 Isomer trans-Nonachlor

The  isomer frans-nonachlor is a component of the
pesticide chlordane.   Chlordane  is a  mixture of
chlorinated hydrocarbons that was manufactured and
used as  a  pesticide from 1948 to 1988.   Prior to
1983, approximately 3.6 million pounds of chlordane
were used  annually in the United States.  In 1988,
the  USEPA  banned  all production and use  of
chlordane in the United States.

Like  PCBs, chlordane is relatively persistent and
bioaccumulative.  The taans-nonachlor is the most
bioaccumulative of the chlordanes, and is a probable
human carcinogen.  Other  human health effects
include   neurological  effects,  blood  dyscrasia,
hepatoxicity, immunotoxicity, and endocrine system
disruption.

Historically, frans-nonachlor may have entered the
Great Lakes through a variety of means related to
the application of chlordane, including  improper or
indiscriminate  application,  improper cleaning and
disposal  of pesticide application  equipment,  or
contaminated soils washing into the watershed.  In
the LMMBP, frans-nonachlor served as a model for
the cyclodiene pesticides.

1.1.3.1.3 Atrazine

Atrazine  is a  triazine  herbicide based on  a ring
structure with three carbon  atoms  alternating with
three nitrogen atoms.  Atrazine is  the most widely
used herbicide in the  United States for corn and
sorghum production. Atrazine has been used as an
agricultural herbicide since 1959, and 64 to 75 million
pounds of atrazine are used annually in the United
States.  Atrazine is extensively used in the  upper
Midwest, including the Lake Michigan  watershed,
where it is primarily associated with corn crops.

Unlike PCBs and fra/is-nonachlor, atrazine  is not
bioaccumulative.  It is persistent in water; however,
it is moderately susceptible to biodegradation in soils
with a half-life of about 60-150 days. Atrazine rarely
exceeds the 3 ppb maximum contaminant  level
(MCL) set  by  the  USEPA as a drinking  water
standard, but localized peak values can exceed the
MCL   following   rainfall   events  after  atrazine
application.

In January 31,2003, the U.S. EPA issued an Interim
Reregistration Eligibility Decision (IRED) for atrazine.
In an October 2003 addendum  to the IRED, the
agency concluded that there is sufficient evidence to
formulate a hypothesis that atrazine exposure may
impact gonadal  development  in  amphibians, but
there are currently insufficient data to either confirm
or refute the hypothesis.   Based on available test
data, atrazine is not likely to be a human carcinogen.
The Agency does have concern in regards to the
potential hormonal effects  observed  in  laboratory
animals  exposed  to  atrazine.    Above certain
concentration thresholds, atrazine is toxic to aquatic
plants. In the LMMBP, atrazine served as a model to
describe the transport and fate of a water-soluble
pesticide in current use.

1.1.3.1.4 Mercury

Mercury is a naturally-occurring toxic metal. Mercury
is used in battery cells, barometers, thermometers,
switches, fluorescent lamps, and as a catalyst in the
oxidation of organic compounds. Global releases of
mercury in the environment are both natural and
anthropogenic (caused by human activity).   It is
estimated that about 11,000 metric tons of mercury
are released annually to the air, soil, and water from
anthropogenic sources.   These  sources include
combustion of various fuels such as coal; mining,
smelting and manufacturing activities; wastewater;
and agricultural, animal, and food wastes.

As  an  elemental metal,  mercury  is  extremely
persistent in all media. Mercury also bioaccumulates
with reported bioconcentration factors in fish tissues
in the range of 63,000 to 100,000.  Mercury is a

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possible human carcinogen and causes the following
human health effects: stomach, large intestine, brain,
lung, and kidney damage; blood pressure and heart
rate increase;  and fetal damage.  In the LMMBP,
mercury  served  as  a model for  bioaccumulative
metals.

1.1.3.2 Other Measured Parameters

In addition to  the four chemicals modeled in the
LMMBP, many other chemicals and parameters were
measured in the LMMBP as part of the  EMPs.  A
survey of these chemicals and parameters aids in the
understanding of the overall ecological integrity  of
Lake Michigan. These additional parameters include
various  biological  indicators;   meteorological
parameters; and organic, metal,  and conventional
chemicals in Lake Michigan.  A complete listing  of
parameters included in this study is provided in Table
1.1.2.  A comprehensive listing of parameters and
compartments may be found in Chapter 3 (Appendix
1.3.1).

1.1.3.3 Measured Compartments

In the LMMBP, contaminants were measured in the
following compartments:

•  Open Lake Water Column: The water column in
   the open lake was sampled and analyzed for the
   modeled pollutants.

•  Tributaries: Major tributaries were sampled and
   analyzed for the modeled pollutants.

•  Fish:  Top predators  and forage base  species
   were sampled and analyzed for diet analysis and
   contaminant burden.

•  Lower Pelagic Food Chain: Phytoplankton and
   zooplankton were sampled and analyzed for
   species  diversity,  taxonomy,  and  contaminant
   burden.

•  Sediments:  Cores  were  collected  and  trap
   devices  were  used  to  collect  sediment for
   determination of contaminants and sedimentation
   rates.
• Atmosphere: Vapor, particulate, and precipitation
  phase samples were collected and analyzed for
  the modeled pollutants.

For the  modeled  pollutants,  more than 20,000
samples were collected at more than 300 sampling
locations and analyzed, including more than 9,000
quality control (QC) samples (Figure 1.1.2).  Field
data collection activities were initially envisioned as
a one-year effort.  However, it became evident early
into the project that a longer collection period would
be necessary to provide a full year of concurrent
information  on  contaminant  loads  and ambient
concentrations for modeling purposes. Therefore,
field sampling occurred from April 1994 to October
1995.

 1.1.4  Objectives

The goal of the LMMBP was to develop a sound,
scientific base of  information to guide future toxic
load reduction efforts at the Federal, State, Tribal,
and local levels. To meet this goal, the four following
LMMBP objectives were developed:

>  Estimate pollutant loading rates: Environmental
   sampling of major media will allow estimation of
   relative loading rates of critical pollutants to the
   Lake Michigan Basin.

»•  Establish baseline: Environmental sampling and
   estimated loading rates will establish a baseline
   against which future progress and contaminant
   reductions can be gauged.

-  Predict  benefits  associated   with   load
   reductions: The completed mass balance model
   will  provide a predictive tool that environmental
   decision-makers  and managers  may use  to
   evaluate the benefits of specific load  reduction
   scenarios.

»  Understand ecosystem dynamics: Information
  from the extensive  LMMBP monitoring and
   modeling efforts  will   improve  our  scientific
  understanding of the environmental  processes
  governing contaminant  cycling and availability
  within relatively closed ecosystems.

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Table 1.1.2.  The LMMBP Parameters
                                                              Conventionals (Continued)
 acenaphthene
 acenaphthylene
 aldrin
 anthracene
 atrazine
 cc-BHC
 P-BHC
 5-BHC
 Y-BHC
 benzo[a]anthracene
 benzo[g,/v]perylene
 benzo[b]fluoranthene
 benzo[/f]fluoranthene
 benzo[e]pyrene
 benzo[a]pyrene
 a-chlordane
 Y-chlordane
 chrysene
 coronene
 p,p'-DDE
 p,p'-DDD
Organics

     p.p'-DDT
     endosulfan sulfate
     endosulfan I
     endosulfan II
     endrin
     endrin aldehyde
     endrin ketone
     fluoranthene
     fluorene
     heptachlor
     heptachlor epoxide
     hexachlorobenzene (HCB)
     indeno[1,2,3-cc(|pyrene
     mirex
     frans-nonachlor
     oxychlordane
     PCBs congeners
     phenanthrene
     pyrene
     retene
     toxaphene
                     Metals
aluminum
arsenic
calcium
cadmium
chromium
cesium
copper
iron
mercury
potassium
magnesium
manganese
sodium
nickel
lead
selenium
thorium
titanium
vanadium
zinc
                 Conventionals
 alkalinity
 ammonia
 bromine
 chloride
 chlorine
 sulfate
 conductivity
 dissolved organic
   carbon
 dissolved oxygen
 dissolved phosphorus
 dissolved reactive silica
     particulate organic carbon
     percent moisture
     PH
     phosphorus
     silica
     silicon
     temperature
     total Kjeldahl nitrogen
     total organic carbon
     total phosphorus
     total suspended
       particulates
                                                     dry weight fraction
                                                     element carbon
                                                     nitrate
                        ortho-phosphorus
                        total hardness
                        turbidity
                  Biologicals
 fish species
 fish age
 fish maturity
 chlorophyll a
 fish lipid amount
 zooplankton
fish weight
fish length
fish taxonomy
fish diet analysis
primary productivity
                 Meteorological
 air temperature
 relative humidity
 barometric pressure
 weather conditions
wind direction
wind speed
visibility
wave height and direction
1.1.5 Design

1.1.5.1  Organization

The GLNPO proposed a mass balance approach to
provide coherent, ecosystem-based  evaluation of
toxics  in Lake Michigan.   GLNPO served as the
program sponsor for the LMMBP. GLNPO formed
two committees to coordinate  study  planning, the
Program Steering  Committee  and the Technical
Coordinating  Committee.  These committees  were
comprised  of  Federal,   State,  and  academic
laboratories as well as commercial laboratories (see
Section 1.1.5.2, Study Participants). The committees
administered   a wide  variety  of  tasks including:
planning the project, locating the funding, designing
the sample collection, coordinating sample collection
activities, locating qualified laboratories, coordinating
analytical activities, assembling the data, assuring
the quality of the data, assembling skilled modelers,
developing the models, and communicating interim
and final project results. The Mid-Continent Ecology
Division  (MED) at  Duluth, in cooperation  with the
National Oceanic and Atmospheric Administration
(NOAA)  Great   Lakes  Environmental  Research
Laboratory (GLERL) and the Atmospheric Sciences
Modeling  Division,   supported  the  modeling
component of the mass balance study  by developing
a  suite  of integrated  mass balance models to

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      Menominee
            £Bfewi
                                        Manistique
                                           .e&ktete^
 n  atmospheric monitoring
 »  stations

^ sediment samples

 Q water survey stations

   tributary monitoring
   stations

   monitored tributary
   basins

   unmonitored tributary
   basins

   biota survey boxes
                          Grand Calumet
                                                           St. Joseph
Figure 1.1.2. The LMMBP sampling locations.
                                               8

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simulate the transport, fate, and bioaccumulation of
the study target analytes.

1.1.5.2 Study Participants

The  LMMBP was  a coordinated effort among
Federal,  State,  and academic  scientists;  and
commercial laboratories. The following agencies and
organizations have all played roles in ensuring  the
success of the LMMBP.   Except for  the  three
organizations indicated with an asterisk (*), all of the
participants were members of the LMMBP Steering
Committee.

Federal and International

- USEPA GLNPO (Program Sponsor)
» USEPA Region V Water Division (WD)
> USEPA Region V Air Division
•> USEPA/ORD/NHEERL/MED/LLRFRB
> ORD National Exposure Research Laboratory
•• U.S.  Department  of  Interior  (USDOI)  U.S.
  Geological Survey (USGS) Water  Resources
  Division (WRD)
»• USDOI/USGS Biological Resources Division Great
  Lakes Science Center (GLSC)
- U.S. Fish and Wildlife Service (USFWS)
* U.S. Department of Energy
" U.S. Department of Commerce NOAA/GLERL
- USEPA Office of Air and Radiation*
> USEPA Office of Water*
>• Environment Canada*
>• U.S. Department of Energy Battelle NW

State

" Illinois Department of Natural Resources
>• Illinois Water Survey
*• Indiana   Department   of   Environmental
  Management
*• Michigan Department of Natural Resources
*• Michigan Department of Environmental Quality
  (MDEQ)
" Wisconsin Department of Natural Resources
>• Wisconsin State Lab of  Hygiene

Academic and Commercial

»• Indiana  University
*• Rutgers University
>• University  of Maryland
* University of Michigan
*• University of Minnesota
*• University of Wisconsin
»• Grace Analytical

1.1.5.3 Workgroups

Eleven workgroups were formed to provide oversight
and management of specific project elements. The
workgroups facilitated planning and implementation
of the study in a coordinated and systematic fashion.
The  workgroups communicated  regularly through
participation in monthly conference calls and annual
"all-hands"  meetings.    Workgroup  chairs were
selected and were responsible for managing tasks
under  the  purview   of   the  workgroup  and
communicating the  status  of activities to other
workgroups. The workgroups and workgroup chairs
are listed below.

• Program Steering Committee -  Paul Horvatin
  (USEPA/GLNPO)
• Technical Coordinating Committee-Paul Horvatin
  (USEPA/GLNPO)
• Modeling  Workgroup  -  William  Richardson
  (USEPA/ORD/NHEERL/MED/LLRFRB)
• Air  Monitoring   Workgroup  -  Jackie  Bode
  (USEPA/GLNPO)
• Biota Workgroup - Paul Bertram  (USEPA/
  GLNPO)andJohnGannon(USDOI/USGS/GLSC)
• Chemistry  Workgroup   -   David  Anderson
  (USEPA/GLNPO)
• Data Management Workgroup - Kenneth Klewin
  and Philip Strobel (USEPA/GLNPO)
• Lake  Monitoring  Workgroup  - Glenn Warren
  (USEPA/GLNPO)
• Tributary Monitoring Workgroup - Gary Kohlhepp
  (USEPA/Region V/WD) and Robert Day (MDEQ)
• Quality Assurance Workgroup - Louis Blume and
  Michael Papp  (USEPA/GLNPO)
• Sediment Monitoring Workgroup - Brian Eadie
  (NOAA/GLERL)

1.1.5.4 Information Management

As program sponsor, GLNPO managed information
collected during the LMMBP. Principal Investigators
(Pis) participating in the study reported field and
analytical data to GLNPO. GLNPO developed a data
standard for reporting field and analytical data and a
database for  storing  and  retrieving  study data.

-------
GLNPO was also responsible for conducting data
verification activities and releasing verified data to the
study  modelers and the  public.    The  flow  of
information is illustrated in Figure 1.1.3.

1.1.5.4.1 Data Reporting

Over  20  organizations produced  LMMBP data
through the collection and  analysis of more than
20,000 samples. In the interest of standardization,
specific formats (i.e., file formats  and codes  to
represent certain data values) were established for
reporting the LMMBP data.   Each format specified
the "rules"  by which  data were submitted, and, in
many cases, the allowable values by which they were
to be reported.  The data reporting formats were
designed to minimize the number of data elements
reported from the field crews and laboratory analysis.
Data reporting formats and the resulting Great Lakes
Environmental Monitoring Database (GLENDA, see
Sectionl .1.5.4.2) were designed to be applicable to
projects outside the LMMBP as well.

Principal Investigators (including sampling crews and
the analytical laboratories) supplied sample collection
and  analysis   data  following  the   standardized
reporting formats, if possible. The LMMBP data were
then processed through an automated SAS-based
data   verification   system,   Research  Data
Management and Quality Control System (RDMQ),
for quality assurance  (QA)/QC  checking.  After
verification and validation by the PI, the data sets
were output in a form specific for upload to GLENDA.
Finally, these data sets were uploaded to GLENDA.
Principal
Investigator (PI)
Collect and Analyze
Samples
I
Report Field and
Analytical Data
(According to LMMB Data
Standard)



A
No
II


^^~- 	 -___



















*\.




	 	











GLNPO Data
Management
Workgroup


Receive, Store, and
Transmit Data


^ 	 ^^






•^
Store, Transmit, and
GLENDA

s~
Fulfill Request
data)
1









-^




— *











GLNPO QA
Workgroup


Conduct Data
Verification (Merge Field
and Analytical Data using
RDMQ)
1
<5a1a^r7^e>>
<\!^!^>
No
Produce final verified
data file and provide to
PI for review and
approval

Produce Final Verified
Data File and Transmit
(in GtENDA-compatible
Format)
1


































LMMB Study
Modelers














^ Input Data to Study
* Models







External Parties



















Roquo-t
A


Figure 1.1.3. Flow of information in the LMMBP.
                                               10

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1.1.5.4.2 Great Lakes Environmental Monitoring
Database

Central  to  the  data  management  effort  is  a
computerized  database  system to house LMMBP
and other project results.  That system, the Great
Lakes   Environmental   Monitoring  Database
(GLENDA),  was developed to provide data entry,
storage, access, and analysis capabilities to meet the
needs of mass balance modelers and other potential
users of Great Lakes data.

Development  of GLENDA began in 1993 with a
logical  model  based on the modernized STORET
concept and requirements analysis. GLENDA was
developed with the following guiding principles:

• True multi-media scope - Water, air, sediment,
  taxonomy, fish tissue, fish diet, and  meteorology
  data can  all be housed in the database.

• Data of documented quality - Data quality is
  documented by including results of quality control
  parameters.

• Extensive contextual indicators - Ensure data
  longevity by including enough information to allow
  future or secondary users to make use of the data.

• Flexible  and expandable - Database is able to
  accept data from any  Great Lakes monitoring
  project.

• National compatibility - GLENDA is compatible
  with STORET and allows ease of transfer between
  these large databases.

In an effort to reduce the data administration burden
and ensure consistency of data in this database,
GLNPO developed  several  key  tools.  Features
including standard data definitions,  reference tables,
standard automated data entry applications,  and
analytical tools are (or will soon be) available.

1.1.5.4.3 Public Access to LMMBP Data

All LMMBP data that have been verified (through the
QC process) and validated (accepted by the PI) are
available to  the public.  Currently, GLNPO requires
that written requests be made to obtain the LMMBP
data. The data sets are available in several formats
including WK1, DBF, and SD2.

The primary reason for requiring an official request
form for the  LMMBP data  is  to  keep track  of
requests.  This allows GLNPO to know how many
requests have been made, who has requested data,
and  what use  they  intend  for the data.  This
information assists   GLNPO  in  managing  and
providing public access to Great Lakes data and
conducting public outreach activities. In the future,
after all data are verified and  validated, GLNPO
intends to make condensed versions of the data sets
available on the LMMBP web site for downloading.
This will allow easy public access to the LMMBP
data.

Further information on the information management
for the LMMBP can be found in The Lake Michigan
Mass Balance Study Quality Assurance Report (U.S.
Environmental Protection Agency, 2001 a).

1.1.5.5 Quality Assurance Program

At the outset of the LMMBP, managers recognized
that the data  gathered and the models developed
from the  study  would be used  extensively  by
decision-makers   responsible   for   making
environmental, economic,  and  policy  decisions.
Environmental measurements are never true values
and  always contain  some level  of uncertainty.
Decision-makers, therefore, must recognize and be
sufficiently comfortable  with  the  uncertainty
associated with data  on which their decisions are
based.    In recognition  of this  requirement, the
LMMBP managers established a QA program goal
ofensuring that data  produced under the LMMBP
would meet defined  standards  of  quality  with a
specified level of confidence.

The QA program prescribed minimum standards to
which all organizations collecting data were required
to adhere. Data quality was defined, controlled, and
assessed  through activities  implemented within
various parameter groups (e.g., organic,  inorganic,
and biological parameters).  QA activities included
the following:
                                              11

-------
•• QA Program - Prior to initiating data collection
  activities, plans were developed, discussed, and
  refined to  ensure that  study  objectives were
  adequately defined  and to ensure that  all QA
  activities necessary to meet study objectives were
  considered and implemented.

•> QA  Workgroup  -  USEPA  established a QA
  Workgroup whose primary function was to ensure
  that the overall QA goals of the study were met.

•• QA Project Plans (QAPPs) - USEPA worked
  with Pis to define program objectives, data quality
  objectives  (DQOs),   and measurement  quality
  objectives (MQOs) for use in preparing  Quality
  Assurance Project  Plans  (QAPPs).   Principal
  investigators submitted QAPPs to the USEPA for
  review and approval.  USEPA reviewed each
  QAPPfor required QA elements and soundness of
  planned QA activities.

> Training  -  Before  beginning  data collection
  activities,  Pis conducted  training  sessions  to
  ensure that individuals working on the project were
  capable of properly performing data collection
  activities for the LMMBP.

«• Monthly   Conference   Calls   and   Annual
  Meetings - USEPA, Pis, and support contractors
  participated in monthly conference  calls and
  annual meetings to discuss project status and
  objectives, QA issues, data reporting issues, and
  project schedules.

*• Standardized Data Reporting Format-Pis were
  required to submit all data in a standardized data
  reporting  format that was designed  to  ensure
  consistency  in  reporting  and facilitate data
  verification,   data  validation,   and  database
  development.

>• Intercomparison Studies - USEPA conducted
  studies to  compare performance among different
  Pis analyzing similar samples. The studies were
  used to evaluate the comparability and accuracy
  of program data.

* Technical Systems Audits - During the study,
  USEPA formally audited each Pi's laboratory for
  compliance with  their QAPPs, the overall study
  objectives, and pre-determined standards of good
  laboratory practice.

+ Data Verification - Pis and the USEPA evaluated
  project data against pre-determined MQOs and
  DQOs to ensure that only data of acceptable
  quality  would  be  included  in the  program
  database.

»• Statistical  Assessments  -  USEPA   made
  statistical assessments  of the LMMBP data to
  estimate  elements  of  precision,  bias,  and
  uncertainty.

*• Data  Validation  -   USEPA  and  modelers
  evaluated the data against the model objectives.

Comparability of data among Pis participating in the
LMMBP was deemed to be important for successful
completion  of  the study.   Therefore, MQOs for
several data attributes were developed by the Pis
and defined in the QAPPs.  MQOs were designed to
control various  phases of the measurement process
and to ensure that the total measurement uncertainty
was within  the ranges prescribed by the DQOs.
MQOs were defined in terms of six attributes:

* Sensitivity/Detectability - The determination of
  the low-range critical value that a method-specific
  procedure  can  reliably discern  for  a  given
  pollutant.  Sensitivity measures included, among
  others, method detection limits (MDLs) as defined
  in 40  CFR  Part 136, system  detection  limits
  (SDLs), or instrument detection limits (IDLs).

*• Precision - A measure of the degree to which
  data  generated  from  replicate  or repetitive
  measurements differ from one another. Analysis
  of  duplicate samples  was  used to  assess
  precision.

»• Bias - The degree of agreement between  a
  measured and actual value. Bias was expressed
  in terms  of the recovery  of  an appropriate
  standard reference material or spiked sample.

* Completeness - The measure of the number of
  samples  successfully  analyzed and reported
  compared to the number that  were  scheduled to
  be collected.
                                              12

-------
•• Comparability - The confidence with which one
  data set can be compared to other data sets.

- Representativeness - The degree to which data
  accurately and precisely represent characteristics
  of  a  population,  parameter  variations  at  a
  sampling  point,  a  process  condition,  or  an
  environmental condition.

The Pi-defined MQOs also were used as the basis
for the data verification process.  GLNPO conducted
data verification through the LMMBP QA Workgroup.
The workgroup was chaired by GLNPO's Quality
Assurance   Manager   and  consisted  of  QC
Coordinators that were responsible  for  conducting
review of specific data sets. Data verification was
performed by comparing all field and QC sample
results produced by each PI with their MQOs and
with overall LMMBP objectives.  If a result failed to
meet  predefined criteria, the  QC  Coordinator
contacted the PI to discuss the result, verify that it
was correctly reported, and determine if corrective
actions were feasible.   If the result was  correctly
reported and corrective actions were not feasible, the
results were flagged to  inform data users of the
failure. These flags were not intended to suggest
that data were not useable; rather they were intended
to caution the user about an aspect of the  data that
did not meet the predefined criteria. Data that met all
predefined requirements were flagged to indicate that
the results had been verified and were determined to
meet applicable MQOs. In this way, every data point
was assigned one or more validity flags based on the
results of the QC checks. GLNPO also derived data
quality assessments for each LMMBP data set for a
subset of the  attributes  listed  above,  specifically
sensitivity, precision,  and  bias.    The  LMMBP
modelers and  the LLRS Database  Manager also
performed data quality assessments prior to inputting
data into study models.   Such activities  included
verifying the readability of electronic files, identifying
missing data, checking units, and identifying outliers.
A detailed description of the QA program is included
in The Lake Michigan Mass Balance Project Quality
Assurance Report (U.S.  Environmental Protection
Agency,  2001 a).   A  brief summary  of quality
implementation and assessment is provided in each
of the following parts.
 1.1.6  Project Documents and Products

During  project  planning,  LMMBP  participants
developed  study  tools including  work plans,  a
methods compendium, QAPPs, and data reporting
standards. Through these tools, LMMBP participants
documented many aspects of the study including
information management and QA procedures. Many
of these documents are  available on  GLNPO's
website at http://www.epa.gov/glnpo/lmmb.

 The LMMBP Work Plan

Designers of the LMMBP have documented their
approach in a  report entitled  Lake Michigan Mass
Budget/Mass  Balance  Work  Plan   (U.S.
Environmental  Protection  Agency,  1997a).   The
essential elements of a mass balance study and the
approach  used to  measure and model these
elements in the Lake  Michigan system are described
in the work plan.  This document was developed
based upon the efforts of many Federal and State
scientists and  staff  who participated in  the initial
planning workshop, as well as Pis.

QA Program/Project Plans

The Lake Michigan Mass Balance Project: Quality
Assurance Plan for Mathematical Modeling, Version
3.0 (Richardson et al., 2004) documents the quality
assurance  process  for  the development   and
application   of  LMMBP   models,   including
hydrodynamic,  sediment transport, eutrophication,
transport   chemical   fate,   and  food   chain
bioaccumulation models.

The Enhanced Monitoring Program QA Program
Plan

The   Enhanced  Monitoring   Program   Quality
Assurance  Program  Plan  (U.S.  Environmental
Protection Agency, 1997b) was developed in 1993 to
ensure  that data generated from the  LMMBP
supported its intended use.

The LMMBP Methods Compendium

The Lake Michigan Mass Balance Project Methods
Compendium  (U.S.  Environmental   Protection
Agency, 1997c, 1997d) describes the sampling and
analytical methods used in the LMMBP- The entire
                                              13

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three volumes are available on GLNPO's website
mentioned above.

The LMMBP Data Reporting Formats and Data
Administration Plan

Data management for the LMMBP was a focus from
the  planning  stage  through  data  collection,
verification, validation, reporting, and archiving. The
goal of consistent and compatible data was a key to
the success of  the project. The goal  was met
primarily  through  the development  of  standard
formats for reporting environmental data. The data
management philosophy is outlined on the LMMBP
website mentioned above.

Lake Michigan LaMP

"Annex 2" of the 1972  Canadian-American Great
Lakes Water Quality Agreement (amended in 1978,
1983, and 1987) prompted development of a Lake-
wide Area Management Plan (LaMP) for each Great
Lake. The purpose of these LaMPs is to document
an approach to reducing input of critical pollutants to
the Great Lakes and restoring and maintaining Great
Lakes integrity. The Lake Michigan LaMP calls  for
basin-wide management of toxic chemicals.

GLENDA Database

Central  to  the  data management effort  is  a
computerized  data  system  to  house  LMMBP and
other project results. That system, the Great Lakes
Environmental Monitoring Database (GLENDA), was
developed to provide, data entry, storage, access,
and analysis capabilities to meet the needs of mass
balance modelers and other potential users of Great
Lakes data.

References

Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and
   K.R. Rygwelski (Eds.). 2004. The Lake Michigan
   Mass Balance Project Quality Assurance Plan for
   Mathematical  Modeling.   Prepared by the
   Modeling  Workgroup.     U.S.  Environmental
   Protection  Agency,  Office of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie,  Michigan.
   EPA/600/R-04/018, 233 pp.
U.S. Environmental Protection Agency.   1995a.
   National  Primary  Drinking Water Regulations,
   Contaminant  Specific Fact Sheets, Inorganic
   Chemicals,  Technical  Version.     U.S.
   Environmental  Protection Agency, Office  of
   Water, Washington, D.C.  EPA/811/F-95/002-T.

U.S. Environmental Protection Agency.   1995b.
   National  Primary  Drinking Water Regulations,
   Contaminant  Specific Fact Sheets, Synthetic
   Organic Chemicals, Technical Version.  U.S.
   Environmental  Protection Agency, Office  of
   Water, Washington, D.C.  EPA/811/F-95/003-T.

U.S. Environmental  Protection Agency. 1997a. Lake
   Michigan Mass Budget/Mass Balance Work Plan.
   U.S. Environmental Protection Agency, Great
   Lakes National Program Office, Chicago, Illinois.
   EPA/905/R-97/018, 155 pp.

U.S. Environmental  Protection Agency.  1997b. The
   Enhanced Monitoring Program Quality Assurance
   Program  Plan.  U.S. Environmental Protection
   Agency, Great Lakes National Program Office,
   Chicago,  Illinois. EPA/905/R-97/017, 61 pp.

U.S. Environmental  Protection Agency. 1997c. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume 1: Sample Collection
   Techniques.   U.S. Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago,  Illinois. EPA/905/R-97/012a, 1,440pp.

U.S. Environmental  Protection Agency. 1997d. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium, Volume 2:  Organic and  Mercury
   Sample   Analysis   Techniques.    U.S.
   Environmental Protection  Agency, Great Lakes
   National  Program  Office,  Chicago,   Illinois.
   EPA/905/R-97/012b, 532 pp.

U.S. Environmental  Protection  Agency.    1999.
   National Recommended Water Quality Criteria-
   Correction.    U.S.  Environmental  Protection
   Agency,  Office of Water,  Washington,  D.C.
   EPA/822/Z-99/001, 25 pp.
                                              14

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U.S. Environmental Protection Agency. 2001 a. The   U.S.  Environmental Protection  Agency.   2001 b.
   Lake  Michigan  Mass Balance  Study Quality      Ambient Aquatic Life Water Quality for Atrazine.
   Assurance   Report.     U.S.   Environmental      U.S. Environmental Protection Agency, Office of
   Protection  Agency,  Great   Lakes  National      Water, Washington, D.C.   EPA/822/D-01/002,
   Program, Chicago, Illinois. EPA/905/R-01/013.       230pp.
                                              15

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                                          PART1
                                    INTRODUCTION
Chapter 2. PCBs Modeling Overview

Douglas D. Endicott
Great Lakes Environmental Center
Traverse City, Michigan
and
William L. Richardson
Retired
and
Ronald Rossmann
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

1.2.1 Background

The mass balance project was based  upon the
Enhanced   Monitoring  Program   (EMP),   a
comprehensive,  two-year  synoptic   survey  for
selected toxic  chemicals in  the Lake  Michigan
ecosystem. The EMP included tributary load and
atmospheric deposition monitoring; ambient water
column, biota, and sediment sampling; and additional
measurements to define and confirm transport and
fate  processes.  The toxics studied for the Lake
Michigan Mass Balance Project (LMMBP) included
polychlorinated biphenyls (PCBs), atrazine, trans-
nonachlor, and  mercury. The project was led by the
United States  Environmental  Protection Agency
(USEPA)/Great  Lakes  National  Program Office
(GLNPO).  Modeling support to the  project was
provided  by  the  USEPA/Mid-Continent  Ecology
Division (MED)/Office of Research and Development
(ORD)/Large  Lakes Research  Station (LLRS)  in
cooperation with the Atmospheric Research and
Exposure  Assessment  Laboratory  (AREAL); the
National Oceanic and Atmospheric  Administration
(NOAA)/Great  Lakes  Environmental Research
Laboratory (GLERL); and other cooperators.  The
research  developed  a  suite of  integrated mass
balance models to simulate the transport, fate, and
bioaccumulation of toxic chemicals in Lake Michigan.

1.2.2 Modeling Objectives

Development  of  effective  strategies for toxics
management  requires a quantitative understanding
of the relationships  between sources, inventories,
concentrations, and  effects of contaminants in the
ecosystem. A mass balance modeling approach was
used to address the relationship between sources of
toxic chemicals and concentrations in air, water,
sediment, and biota. This approach integrated load
estimation, ambient monitoring, and research efforts
within a modeling framework that was compatible
with   both   scientific  as  well  as  ecosystem
management  objectives.    The  mass  balance
approach estimated the magnitude of  mass fluxes
that constitute the pathways for toxics transport into
and out of the lake,  that distribute toxics within the
lake water column and sediment, and that lead to
bioaccumulation of the aquatic food webs. Based
upon these estimates, the mass balance was used to
determine the rate of change in concentrations and
inventories of toxics  as inputs such as  atmospheric
                                             16

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and tributary loadings change.   Thus, the mass
balance can serve as a useful tool to estimate or
predict  the   outcome   of   alternatives   under
consideration for toxics management.

Modeling efforts associated with the LMMBP had the
following objectives:

1.  Provide a consistent framework for integrating
   load estimates, ambient monitoring data, process
   research efforts,  and prior  modeling  efforts,
   leading to  a   better  understanding of  toxic
   chemical  sources,  transport,   fate,   and
   bioaccumulation in Lake Michigan.

2.  Estimate the loading of priority toxics, solids, and
   nutrients from  all major  tributaries to  Lake
   Michigan for the duration of the study.

3.  Estimate the atmospheric deposition and air-
   water exchange of priority toxics, including spatial
   and temporal variability over Lake Michigan.

4.  Calibrate and confirm mass balance models for
   priority toxics using project  data, based  upon
   modelsforhydrodynamicandsedimenttransport,
   eutrophication/organic carbon dynamics, toxics
   transport   and   fate,   and   food   web
   bioaccumulation.

5.  Based upon the mass balance models, evaluate
   the magnitude and variability of toxic chemical
   fluxes within and  between lake compartments,
   especially  between the sediment and  water
   column and between the water column and the
   atmosphere.

6.  Apply the calibrated  mass balance models to
   forecast contaminant concentrations in water and
   sediment throughout Lake Michigan, based upon
   meteorological  forcing functions  and  future
   loadings based upon load reduction alternatives.

7.  Predict the bioaccumulation  of persistent toxic
   chemicals through the food web leading to top
   predator  fish (lake trout  and coho  salmon) for
   location-specific fish populations in the lake, in
   order to relate mass balance predictions of water
   and  sediment  exposure  to  this  significant
   impaired use.
8.  Estimate (quantify) the uncertainty  associated
    with estimates of tributary and atmospheric loads
    of  priority  toxics, and model predictions  of
    contaminant concentrations.

9.  Identify  and   prioritize  further  monitoring,
    modeling, and research efforts to (1) address
    additional toxic substances,  (2) further reduce
    uncertainty and improve accuracy of predictions,
    (3) establish additional cause-effect linkages,
    such as ecological risk endpoints and feedbacks,
    and (4) evaluate  additional  source categories,
    such as non-point sources in the watershed.

The purpose of PCBs modeling was to simulate their
transport,   fate,  and  bioaccumulation  in  Lake
Michigan.    PCBs  are  a group of  persistent,
bioaccumulative  hydrophobic  organic  chemicals
(HOCs) that  are  ubiquitous in  the Great Lakes.
Although anthropogenic inputs from production and
disposal largely ceased following  their ban in the
1970s,atmosphericandwatershedtributarytransport
pathways to the lake continue the import of PCBs.  In
addition,   a  large   in-lake  sediment   inventory
represents an  internal source of PCBs, which are
recycled annually.  PCBs have been consistently
identified as the contaminant of greatest concern  to
human and ecosystem health in the Great Lakes
(Ludwig et a/., 1993; Swain, 1991;  Gilbertson et al.,
1991).

1.2.3 Historical Modeling

The modeling design  and approach for the LMMBP
reflects a  progression of prior modeling efforts  in
Lake  Michigan  and throughout the Great Lakes.
These include eutrophication  and  toxic substance
mass balance models, food web  bioaccumulation
models, and predictive hydrodynamic and sediment
transport models.  Although not a comprehensive
review, several of these prior modeling  efforts are
discussed  below.

1.2.3.1 Lake-1

A  eutrophication  model  for  Lake Michigan  was
developed by Rodgers and Salisbury (1981), based
upon the Lake-1 model which was also applied  to
Lakes  Erie, Huron, and Ontario.  The model was
calibrated and tested using data from 1976 and 1977.
                                              17

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The importance of climatic factors on  limnological
(including   eutrophication)  processes  in   Lake
Michigan was demonstrated, as the severe winter
and extensive ice cover of 1976-1977 dramatically
reduced total  phosphorus  concentrations  in the
second  year.  This  work also identified  several
refinements  necessary for  accurate modeling of
eutrophication:   phosphorus  availability  to
phytoplankton  and  particle  transport  including
shoreline erosion and sediment resuspension were
apparently significant influences upon nutrient and
phytoplankton dynamics observed in Lake Michigan.

1.2.3.2 Completely-Mixed Model

A lakes-in-series model for conservative substances
was developed by Sonzogni et al. (1983) and applied
to  forecast chloride concentrations in each of the
Great  Lakes as  a  function  of expected  future
loadings.     This  model  demonstrated   that
concentrations of non-reactive  substances would
substantially "lag" the history of their input. This was
especially the case   for Lake Michigan,  where
maximum chloride concentrations were not predicted
to  occur until the 22nd Century despite declining
loads after the 1970s.  Similarly strong, non-steady-
state behavior may be expected for other chemicals
which are non-reactive and weakly associated to
particles.

1.2.3.3  General  Mass  Balance  Framework for
Toxic Chemicals in the Great Lakes

At about  the same  time,  models were being
developed which  would serve as the foundation for
describing and simulating the transport and fate of
hydrophobic chemicals in the Great Lakes. Thomann
and   Di   Toro   (1983)  and  Robbins   (1985)
demonstrated   that   the   lake-wide,   annual
concentration  trend   of  contaminants  including
cesium-137, plutonium-239/240, and PCBs, were
dependent upon particle transport between the water
column and a resuspendable sediment compartment.
The principal loss mechanisms from the lakes were
found to be burial by sedimentation and (for PCBs)
volatilization. The somewhat paradoxical behavior of
these models was that the water column contaminant
dynamics  were  largely  controlled  by  sediment
parameters.
1.2.3.4  Food Web Bioaccumulation Model

A food web bioaccumulation model was developed
by Thomann and Connolly (1984) and applied to
simulate bioaccumulation of PCBs in Lake Michigan
lake trout.  The model was confirmed  with an
extensive data set collected in 1971, including nine
age classes of trout,  diet characterization by gut
contents analysis,  and  alewife.  The model was
successful in predicting bioaccumulation for mature
age classes of lake trout, although not for juveniles.
Dietary  transfer was  demonstrated  to  be  the
predominant  route  of  PCBs  accumulation,  in
comparison to direct chemical uptake from water.
Substantial residual variance in  lake trout  PCBs
concentrations within  age class  CV = 1 was not
explained  by  this  lake-wide, average-individual
model.

1.2.3.5  MICHTOX

An integrated  mass balance  and bioaccumulation
model for PCBs (modeled as two homologs) and 10
other toxic chemicals was developed as a planning
tool for the LMMBP  (Endicott et at., 2005).  The
MICHTOX   mass  balance  was  calibrated  to
suspended solids  and  plutonium  data  for  the
southern lake basin, while the bioaccumulation model
combined Thomann and Connolly's (1984) effort with
chemical-specific  parameterization  from  Lake
Ontario.  MICHTOX demonstrated that reasonable
predictions of  PCB concentration trends in water,
sediment,  and biota could be developed although
significant uncertainties  regarding sediment-water
and  air-water  contaminant  transport  remained.
These are the most significant transport fluxes for
PCBs   and   presumably  other   hydrophobic
contaminants.   Major data gaps  for other  priority
toxics allowed only order-of-magnitude estimates of
load-concentration relationships.  When this model
was developed and run, available monitoring data for
toxic chemical concentration in tributaries, air, lake
water,  sediment, and  biota were not adequate to
define loading trends or  to relate the distribution of
loadings to  contaminant  gradients observed for
sediment and biota.  Credible model predictions of
toxic chemical transport, fate, and bioaccumulation
would depend upon developing a comprehensive
data set quantifying loadings, sediment inventories,
                                               18

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concentrations, and transport fluxes on a spatially-
resolved basis, and  localized  descriptions of food
web structures.

1.2.3.6 Green Bay Mass Balance Project

The Green Bay  Mass  Balance Project (GBMBP)
demonstrated  the feasibility   of  applying  mass
balance principles to manage toxic chemicals in the
Great Lakes ecosystem.  A two-year (1989-1990)
synoptic sampling program was designed to collect
appropriate and complete data  for the mass balance
study.  A suite  of integrated  mass balance and
bioaccumulation  models were  developed  which,
together, provided an ecosystem-level simulation of
sources,  transport, fate, and  bioaccumulation of
PCBs throughout the Fox River and Green Bay. This
study advanced the state-of-the-art of mass balance
modeling, particularly the ability to construct a fairly
complete and accurate description of contaminant
mass transport.

Several aspects  of the Green Bay modeling effort
were noteworthy. Particle transport and sorption
processes  were  found to   be of  fundamental
importance as bases for  contaminant modeling.
Resuspension of contaminated sediments in the Fox
River constituted the major source of PCBs to the
river as well as the bay. In the bay, particle sorbent
dynamics were strongly affected by phytoplankton
production and decay.  The relative significance of
hydraulic  transport,  sediment  transport,   burial,
volatilization,  and open lake  boundary exchange
processes  upon  the PCBs mass balance  varied
considerably   with   location   in   Green   Bay.
Radionuclide  tracers were  again  essential  for
calibration of particle fluxes and confirmation of long-
term  contaminant  transport   predictions.    The
significance of contaminant accumulation at the base
of the food web, and fish movement in relation to
exposure  gradients,  were  demonstrated  in  the
bioaccumulation model. The LMMBP demonstrated
the linked submodel  approach to ecosystem model
development and application,  and the feasibility of
using such a model for assessing the effectiveness
of toxics management control alternatives.
1.2.3.7 SEDZL

The GBMBP also provided data to test a predictive
two-dimensional,  hydrodynamic  and  sediment
transport model of the Fox River, SEDZL (Gailani et
al., 1991). SEDZL incorporates realistic descriptions
of cohesive sediment resuspension, flocculation, and
deposition processes, and contaminant sorption,
which  are  critical  for  accurate  prediction  of
hydrophobic contaminant transport. These process
descriptions  were based on  laboratory and field
experiments with river, bay, and lake sediments. A
three-dimensional bed  submodel  was used  to
describe sediment bed properties which varied with
depth as well as location. The fine spatial resolution
of the model allowed detailed simulation of in-p!ace
pollutant  transport in both the water column and
sediment bed.  Although computationally intensive
and   requiring   specialized   data,  SEDZL  has
substantially  advanced   the   state-of-the-art for
sediment and contaminant transport modeling in the
Great Lakes.  SEDZL had also been applied to the
Buffalo  and  Saginaw  Rivers as  part  of  the
ARCS/RAM project (Gailani et al., 1994; Cardenas
and Lick, 1996). These applications included long-
term  forecasts  (10-25  years)  of sediment and
contaminant  transport.   SEDZL  had also been
applied to large water bodies such as Lake Erie, and
marine coastal  waters  including Santa  Barbara
Channel, and Atchafalaya Bay (Lick et al., 1994;
Pickens,  1992)  where wave  action  as  well as
currents force sediment resuspension.

1.2.4 Model Resolution

Model resolution is the spatial and temporal scale of
predictions, as well as the definitions of model state
variables. While factors such as  data availability,
model sophistication,  and  computer resources
constrain resolution to a degree, different levels of
model resolution are possible and, are  in fact,
necessary.   Three  "levels"  of spatial  resolution,
indicated  by  the segmentation  grid of the lake
surface, are illustrated in Figure 1.2.1.  Level 1 was
resolved  at the scale of  lake basins (characteristic
length, L = 150 km), with  an associated seasonal
temporal resolution.   This was a screening-level
model resolution used in MICHTOX. Level 2 was
                                               19

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  LEVEL 1 - MICHTOX
  (Screening)
  6 surface segments
  9 water segments
LEVEL 2 - LM-2

10 surface segments
41 water segments
LEVEL 3 - LM-3
(High resolution 5km X 5km grid)
2318 surface segments
44,042 water segments
19 sigma layers
Figure 1.2.1. Surface water segmentation for alternative Lake Michigan mass balance model levels.
resolved at a regional scale defined by food webs (L
= 40 km) including gross resolution of the nearshore
and  offshore  regions;  temporal resolution  was
weekly-to-monthly.   This  resolution  was  roughly
comparable to that achieved by models developed in
the GBMBP.   Level  3 was a hydrodynamic scale
resolution (L = 5 km), with associated daily temporal
resolution. Level 3 was scaled to resolve and predict
particle transport processes as well as hydrodynamic
transport.

Although the Lake-wide Management Plan  (LaMP)
and the Great Waters Program objectives are "lake-
wide," both of these emphasize biotic impairments
occurring primarily  in localized, nearshore regions.
LaMP objectives also require that the transport of
contaminants from tributaries and other nearshore
sources to the open lake be resolved.  Therefore, the
                Level  1  model  was  not  adequate for the  study
                objectives. Level 2 resolution was adequate for most
                modeling objectives,  but not for  resolution of
                significant  hydrodynamic  and  sediment transport
                events. Level 3 resolution  was required for accurate
                hydrodynamic and sediment transport modeling and
                was desirable for predicting nearshore gradients,
                especially  those formed  by  transients such as
                thermal   bars,   upwelling,  and   storm-induced
                resuspension; as well as  more persistent features
                such as tributary plumes, thermal stratification, and
                the  benthic  nepheloid layer.   Level  3 transport
                resolution was also valuable in relating toxics loading
                from the  10 Areas of Concern (AOCs) adjoining Lake
                Michigan, which must be addressed by the Remedial
                Action Plan  (RAP) process, to the LaMP via the
                LMMBP.
                                               20

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The modeling design for the LMMBP was based on
the development of a number of models at three
levels of resolution. For the contaminant transport
and fate (CTF) models, MICHTOX was resolved at
Level 1, and LM2-Toxic was resolved at Level 2. For
the eutrophication models, MICH1 was resolved at
Level 1 and LM3-Eutro was resolved at Level 3.  The
Princeton  Ocean Model  (POM)  and atmospheric
loading models were resolved at Level 3. Results of
the  hydrodynamic  model  were   spatially  and
temporally averaged prior to coupling to the Level 2
model.   The  rationale  for  specifying  different
resolutions was the hydrodynamic models require a
Level 3 resolution to offer the best  capability for
transport  simulation and forecasting.   A lower
resolution was specified for LM2-Toxic because this
model had been demonstrated at  this resolution.

 1.2.5  Models Developed and Applied

The  model design for the LMMBP was based upon
the linked submodel approach used in the GBMBP.
Models developed, refined, and applied by the Large
Lakes and Rivers  Forecasting Research  Branch
(LLRFRB) included eutrophication/sorbent dynamics
(MICH1 and LM3-Eutro), contaminant transport and
fate  (MICHTOX  and  LM2-Toxic),  and food  web
bioaccumulation (LM Food Chain)  models (Figure
 1.2.2).   Models developed  and  run  elsewhere
included a hydrodynamics model (POM) (Schwab
and  Beletsky, 1998), an atmospheric loading model
(Green  et al.,  2000;  Miller et al.,  2001),  and a
tributary loading model (Hall and Robertson, 1998).
Only the models developed, refined, and applied at
LLRFRB  will  be discussed in  detail within  this
document.

 1.2.5.1 Lake Process Models

The  mass balance for toxics in Lake Michigan was
comprised  of  linked   hydrodynamic  (POM),
eutrophication/sorbent  dynamics   (LM3-Eutro),
contaminant transport and transformation  (LM2-
Toxic), and bioaccumulation simulations (LM Food
Chain).  In addition, Level 1 eutrophication/sorbent
dynamics (MICH1) and contaminant transport and
transformation/bioaccumulation   (MICHTOX)
simulations were run for comparison to Level 3 and
2  results,  respectively.    Each  of these  models
represented significant processes  affecting the mass
balance for toxic chemicals.   The hydrodynamic
model  predicted water movements necessary to
describe the three-dimensional transport of dissolved
and particulate constituents in the water column. The
eutrophication  models described the production,
respiration, grazing, and decomposition of planktonic
biomass within the lake. The contaminant transport
and fate models described contaminant partitioning
between dissolved and sorbed phases, mass transfer
between   media  (air,  water,  sediment),   and
biogeochemical   transformations.     The
bioaccumulation  models  simulated  contaminant
accumulation from water and sediments to predator
fish via direct exposure and trophic transfer through
benthic and  pelagic food webs.  Together, these
submodels formed an integrated description of toxic
chemical cycling in the aquatic ecosystem with which
to predict the  relationship between loadings  and
concentrations of PCBs.

1.2.5.2 Hydrodynamics (POM)

The Princeton Ocean Model  (POM) (Blumberg and
Mellor, 1980, 1987) was  used to compute three-
dimensional  current fields in  the lake.  The POM
simulated large- and medium (km)-scale circulation
patterns,  vertical  stratification  and   velocity
distribution, seiche, and surface waves. This model
was also used to simulate a thermal balance for the
lake.  The POM is a primitive equation, numerical
hydrodynamic circulation  model that predicts three-
dimensional water column transport in response to
wind stress, temperature, barometric pressure, and
Coriolis force. The POM has been demonstrated to
accurately simulate the predominant physics of large
water bodies (Blumberg and  Mellor, 1983, 1985;
Blumberg  and Goodrich, 1990). This model  was
used to develop year-long simulations on a 5 km
horizontal  grid,  with nine  sigma vertical  layers, at
one-hour intervals for Lake Michigan (Schwab and
Beletsky,  1998).     Observed  and   simulated
meteorological data were used to  define  model
forcing functions.   Extensive measurements of
temperature  and current distributions  collected in
Lake  Michigan  during  1982-1983 were  used to
provide the necessary data for  model calibration;
measurements  of daily surface temperature  and
current  distributions  were  used  to   confirm
hydrodynamic simulations for 1994-1995.
                                             21

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   model type
                         Level 1 models
Level 2 models
Level 3 models
 Hydrodynamic and
      load models
                                                                                    POM
                                                                                  advective/
                                                                                  dispersive
                                                                                  transport and
                                                                                  bottom
                                                                                  ,shear stress
    Eutrophication/
  sorbent dynamics
                                                               internal carbon
                                                               aggregated to
                                                               Level 2
     Contaminant
  transport and fate
                transport
              "aggregated
               to Level 2
        Food web
   bioaccumulation
Figure 1.2.2. Model construct used for the LMMBP to model PCBs.
1.2.5.3  Eutrophication/Sorbent Dynamics (LM3-
Eutro)

The eutrophication/sorbent dynamics (LM3-Eutro)
model predicted the production, transformation, and
decay of plankton biomass in response to seasonal
dynamics  of   temperature,   light,   and  nutrient
concentrations.  In the open lake, living and dead
plankton  comprise  the majority  of  suspended
particles and  generate significant autochthonous
loads of particulate and dissolved organic carbon
(POC  and  DOC)  to  which  PCBs  and  other
contaminants preferentially partition (Richardson et
al., 1983; DePinto era/., 1993).  LM3-Eutrosimulated
the non-conservative, seasonally-variable dynamics
of the biotic organic pool, which has a significant
influence upon partitioning of  HOCs (Dean et al.,
1993). A similar, less resolute model was applied to
simulate the dynamics of organic carbon states in
Green Bay as part of the GBMBP (DePinto et al.,
1993). Model outputs included autochthonous solids
  loads and transformation and decay rates that were
  used as inputs for LM2-Toxic.

  1.2.5.4  Contaminant Transport and Fate (LM2-
  Toxic)

  The mass balance for toxic chemicals in the lake was
  computed in a contaminant transport and fate model
  (LM2-Toxic) which described contaminant transport,
  intermedia  exchange,  phase  distribution,  and
  biogeochemical transformations  in both the water
  column and sediments. LM2-Toxic was calibrated
  and confirmed for selected individual congeners and
  the sum of PCBs congeners. Mass balance analyses
  were performed for  total  PCBs to  evaluate the
  significant source, transport, and loss pathways. The
  effectiveness of alternative load reduction scenarios
  upon reducing  total  PCB concentrations  were
  forecast.
                                               22

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1.2.5.5  Food  Web Bioaccumulation (LM Food
Chain)

A  bioaccumulation   model  simulated  chemical
accumulation  in  the food  web  in  response to
chemical exposure,  based  upon chemical mass
balances for aquatic biota. The general form of the
bioaccumulation  equation was well-defined,  and
equated the rate of change in chemical concentration
within a fish (or other aquatic organism) to the sum of
chemical fluxes into  and  out of the animal. These
fluxes included direct uptake of chemical from water,
the flux of chemical into the animal through feeding,
and  the loss  of  chemical  due to elimination
(desorption and excretion) and dilution due to growth.
To predict bioaccumulation for top predator fish, the
bioaccumulation  mass   balance  was  repeatedly
applied to animals at each trophic level to simulate
chemical  biomagnification   from  primary   and
secondary producers, through forage species to top
predators.  Food web bioaccumulation models have
been successfully applied for PCBs and other HOCs
in several large-scale aquatic ecosystems (Thomann
and Connolly, 1984; Connolly and Tonelli, 1985) and,
most recently, for the GBMBP (Connolly etal., 1992).
The model developed for that project, FDCHN, was
adapted for use in Lake Michigan (LM Food Chain).
FDCHN is  a time-variable,  population-based age
class  model, incorporating realistic descriptions of
bioenergetic,  trophodynamic,  and  toxicokinetic
processes.  The  general features of FDCHN were
well-suited to a modeling application such as the
LMMBP.  For  Lake  Michigan, bioaccumulation of
PCB congeners was modeled for lake trout and coho
salmon food webs. Food web bioaccumulation was
simulated for sub-populations of lake trout in three
distinct biotic zones.

1.2.6 Model Quality Assurance

A  Quality  Assurance Project Plan (QAPP)  was
prepared and implemented for the PCBs modeling
(Richardson et al.,  2004).   The  QAPP specified
procedures  for   code   development,   testing,
modification, and documentation; as well as methods
and  measures  applied  in  model  calibration,
confirmation, and  uncertainty analysis.
1.2.7      Model   Application   and
Computational Aspects

1.2.7.1 Annual Simulations

Annual simulations were run with the models for the
period of 1994-1995. Results were analyzed in terms
of regional and lake-wide contaminant loads, fluxes
and inventories, and spatial and temporal gradients
of contaminant concentrations.   Bioaccumulation
simulations were  analyzed in terms  of  relative
accumulation   pathways,   spatial  and  temporal
variability  of  contaminant concentration  ratios
(bioconcentration factor,  bioaccumulation factor,
biota/sediment accumulation factor, predator/prey),
and influence of diet, age, and migration factors.

1.2.7.2 Long-Term Simulations

Long-term simulations were used to forecast the
impact of various management scenarios. Forecasts
were performed to determine time to steady-state for
both continuing and discontinued loads. Forecasts
were also  run  to evaluate  reductions  in exposure
concentrations resulting from elimination of tributary
and/or atmospheric loading. These forecasts  were
propagated through the food web  bioaccumulation
model  for PCBs to estimate  time  for sport fish
contaminant concentrations to decline below criteria
limits.

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Sonzogni,  W.C., W. Richardson, P. Rodgers,  and
   T.J.  Monteith.  1983. Chloride  Pollution of the
   Great  Lakes.   Water Pollut. Contr.  Fed.  J.,
   55(5):513-521.

Swain, W.R.  1991.  Effects of  Organochlorine
   Chemicals  on the  Reproductive  Outcome of
   Humans  Who  Consumed Contaminated Great
   Lakes Fish: An Epidemiological Consideration. J.
   Toxic. Environ. Health, 33:587-639.

Thomann, R.V. and D.M. Di Toro. 1983.  Physico-
   Chemical Model of Toxic Substances in the Great
   Lakes. J. Great Lakes Res., 9(4):474-496.

Thomann, R.V. and J.P. Connolly.  1984. An Age
   Dependent Model of  PCB in  a Lake Michigan
   Food Chain.   U.S. Environmental  Protection
   Agency,  Office of Research and Development,
   ERL-Duluth, Large  Lakes Research Station,
   Grosse lie, Michigan. EPA/600/S3-84/026, 3 pp.
                                             25

-------
                                         PART1
                                    INTRODUCTION
Chapter 3. Information Management

David A. Griesmer
Computer Sciences Corporation
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

To support the modeling efforts of the Lake Michigan
Mass Balance Project (LMMBP), large amounts of
data were collected and analyzed by a number of
State and government agencies and universities
(Appendix 1.3.1).  Data were collected, analyzed,
and  sent to the United  States  Environmental
Protection Agency (USEPA) Great Lakes National
Program Office  (GLNPO)  in  Chicago, Illinois.
GLNPO staff, under the direction of Lou Blume, were
responsible for quality assurance (QA) assessment,
organization, and consolidation of all data.   To
facilitate  the  QA assessment  process,  a SAS
application Research Data Management and Quality
Assurance System (RDMQ), developed by Syd Allen,
a private contractor, was used to automate the  QA
process (Sukloff, 1995).  RDMQ is a  menu-driven
SAS program.  It has capabilities for loading data,
applying quality control checks, adding validity flags,
viewing  and editing  data, producing  user-defined
tables and graphs, and exporting data in ASCII files.
These tasks are performed  through a set of menu-
driven SAS programs and macros.  Data which had
been  put  through the assessment  process  and
approved for release by both  GLNPO  and  the
Principal Investigator (PI) were then sent to USEPA,
Office of Research and Development (ORD)/National
Health   and  Environmental   Effects  Research
(NHEERL)/Large Lakes  and  Rivers  Forecasting
Research Branch (LLRFRB)/Large Lakes Research
Station (LLRS) for use by the modeling staff.

1.3.1   Overview   of   Information
Management at the LLRS

Data received from GLNPO were usually in the form
of electronic media.  Data were typically E-mailed,
but sometimes they were downloaded from GLNPO
databases or received on CD-ROM.  Data were
reformatted by GLNPO into a form facilitating entry
into database programs at the LLRS. Upon arrival,
raw data were copied to the "Immb" folder on Dave
Griesmer's personal network space ("n:\" drive). In
addition, data were  imported into one of several
Microsoft   Access   databases  in   the
"\Access_2000\lmmb" folder on Mr. Griesmer's "n:\"
drive.   The "n:\"  drive  was used to facilitate data
security because this file space  is  backed up
regularly and is only available to Mr. Griesmer. Data
were placed in the Microsoft Access databases to
facilitate data review/assessment  and later retrieval
for the modeling  team.

Prior to use, several reviews were done of the data
received to look for errors in the  data sets.  At the
LLRS, this  review was broken up into two  parts.
First,  an initial  review was  made to  check for
completeness of information, to look for transcription
errors,  programming errors, and  formatting errors,
and to review comments  added  by collection and
analysis personnel.  Second, a review was done by
the data users   to determine if the data  made
environmental sense.   This  type  of review was
conducted for the open lake,  surficial sediment and
sediment  trap,   lower  food  chain,  and  fish
                                             26

-------
polychlorinated  biphenyl   (PCB)  data   sets.
Atmospheric PCB fluxes/loadings and tributary PCB
loadings did not go through this review process at the
LLRS, but they were assessed by study members
assigned  with providing loading values.  Tributary
PCB loading assessment was done by David Hall,
U.S. Geological Survey (USGS).  All atmospheric
PCB loading/concentration data were assessed by
Keri  Hornbuckle,  University  of   Iowa.     The
assessment process used  by these individuals is
unknown.

For data reviewed at LLRS, samples which GLNPO
determined had failed the RDMQ QA process were
flagged with the value of -9999. GLNPO preserved
the values in the data sets that were received and
flagged the analytical remark field for that parameter.
Flagging these values as -9999 facilitated processing
by  analytical  software such  as  IDL.   Parameter
values with analytical remark  flags of "INV" (invalid
data, as determined by GLNPO QA evaluation), and
"NAI" (no  result  reported  - interference)  were
changed  to -9999.  Samples with the  analytical
remark flag of "LAC" (no results reported, laboratory
accident) were removed.

Documentation associated with the data was studied.
RDMQ data warning fields (RS_NMAND,RS_WARN,
RS_UPDAT) were checked to verify that there were
no   problems  flagged  by   RDMQ  which  were
inadvertently included in the database. Every routine
field sample (RFS) and field  duplicate (FD#) was
checked to verify that a valid station name, sampling
date, and depth collection information were included.
The value ranges (minimum, maximum, average) for
all congeners  was checked to look for any obvious
errors. Data ranges of all data  were also checked for
obvious errors.  Data were checked to verify units
and to confirm whether blank, dilution, or surrogate
corrections were  done.    Quality  Control  (QC)
Coordinator(RECSTAT),stationnotes(STNNOTES),
and record  (RECSTAFF)  comment fields  were
checked for comments associated with a sample. All
of  this  information was  recorded on  a  Data
Verification Checklist (Appendix 1.3.2).  If questions
or errors  were found, they were referred back to
GLNPO for resolution.

Upon completion  of this initial data check, readme
files were created to describe  the data, and the raw
data set(s) and readme files were copied to a data
archive on the LLRS Unix systems.  This archive is
located at \usr\lmmbdata on the Alpha workstation
named Ilrssrv2 and is available to modeling staff at
the LLRS.   Each study  has  its  own  directory
(LMI0001   LMI0041) within the Immbdata archive.
PCB data for the LMMBP can be found in directories:
LMI0029 (daily gas phase congener, total PCBs, and
frans-nonachlor for each surface cell in the LMMBP
5 km  grid); LMI0032  (particulate and precipitation
congener, total PCBs, and frans-nonachlor data for
eight  onshore  sampling  stations  around  Lake
Michigan  and  for shipboard sampling from the Lake
Guardian);  LMI0035  (open lake congener, total
PCBs, and frans-nonachlor data collected during the
eight  LMMBP cruises  conducted in  1994-1995);
LMI0036  (phytoplankton, zooplankton, Mysis and
Diporeia congener, total PCBs, and frans-nonachlor
data  collected  during  eight  LMMBP  cruises);
LMI0037  (forage  and predator  (lake trout,  coho
salmon)  fish  congener, total  PCBs, and frans-
nonachlor data collected for the LMMBP); LMI0040
(surficial sediment and sediment trap congener, total
PCBs, and frans-nonachlor data); and LMI0041 (daily
tributary congener, total PCBs, and frans-nonachlor
loading data from 11 monitored and 18 unmonitored
Lake Michigan tributaries).

At the same time, information  about data received
(metadata) was stored in  a searchable  Microsoft
Access database.  The database is found on the
LLRS common drive "\\giord2\grlcommon", which is
also known as  the "r:\" drive.  This database  is
named "Imtrack2000.mdb" and  is found in the
r:\access2000A folder. This database is available to
all staff. This database can be searched  by library
number (consecutive  number assigned when data
are logged in, corresponds to LMI folder name in
Immbdata archive), PI, parameter, PI and parameter,
or library number and parameter  (Appendix 1.3.3.).

After initial review of a data set was completed, data
were retrieved from the Microsoft Access databases
and exported  into files (usually Microsoft Excel) for
assessment by the modeler who would be using the
data set.  Water and sediment PCB data were given
to Xiaomi Zhang.  Lower food  chain data  were
assessed by Xin Zhang and Katie Taunt. Forage and
predator fish  data were assessed by Xin Zhang.
Initially, only routine field samples and field duplicates
were  given to the data assessors.   If issues  or
problems were found, the person  assessing the data
                                              27

-------
would  then  request  additional  QA  data.    If
questions/problems could not be resolved by looking
at QA data, they were referred back to GLNPO for
resolution.

In several instances, data which passed GLNPO QA
checks from the analytical point of view were rejected
during   assessment  because values  were  not
environmentally   reasonable.     For   example,
particulate and dissolved water PCB values from
station MB63 from the October 1995  cruise were
orders  of  magnitude  higher  than  values  for
surrounding stations. In addition, their values were
orders of magnitude higher than  values from the
same   station  collected   on   different  cruises.
Environmentally these results were  unreasonable,
and they were not used  by  the modelers. GLNPO
was informed whenever we  rejected data.

After the assessment process was completed, files
were created which could be used in  IDL, which is a
software package used for visualization and analysis
of LMMBP data. Standard formats were developed
for water, sediment, and fish  data (Appendices 1.3.4,
 1.3.5,  1.3.6). All files  were  fixed format ASCII text
files. One of the principal uses of IDL was to develop
volume-weighted  averages (VWA)  estimates of
parameter  concentrations  for  each  cell  in the
modeling grid. These VWA  estimates could then be
compared to model results.

 1.3.2 Calculation of Total PCBs

 In general, total PCBs were  calculated by  the PI
 reporting the data. In the case of tributary loads for
total PCBs, total PCBs were calculated by the PI, and
 loads were calculated by David Hall, USGS.  In a
similar fashion, total PCBs were calculated by the PI,
and atmospheric loads were calculated by  Keri
 Hornbuckle, University of  Iowa.   Open lake total
 PCBs  were calculated by GLNPO contractor staff
(Marcia  Kuehl).   A GLNPO contractor, DynCorp,
verified total PCB values. However, the method used
to calculate total PCBs was not consistent from Pl-to-
Pl. Some Pis blanked corrected data; some included
invalid  data (samples with  INV analytical  remark
field); and some did not surrogate correct data. In
those instances when invalid samples were included
in the total PCB calculation  or surrogate correction
was not done,  total  PCBs were recalculated by
DynCorp to correct these problems. Attached are
documents  from    Marcia  Kuehl,  PCBs  QA
Coordinator,  describing  how  total  PCBs were
calculated by each PI (Appendix 1.3.7).

1.3.3  Regression Analysis of Measured
Congener, Total PCB Data

As the modeling  study was originally devised, all
modeling was to  be done  at the congener level;
however,  at a  later date  it  was decided that
simulation of total PCBs  would also be desirable.
The  Level  2 (LM2)  and Level 3  (LM3)  LMMBP
models did not model  total PCBs;  therefore, a
method  was  devised  to   calculate  total PCB
concentrations for model results based on the set of
congeners  modeled.  Regressions and ratios were
calculated  comparing the Pis' measured total PCB
field  values (the independent variable) to  the Pis'
measured sum of the congeners that were modeled
at the LLRS (the  dependent variable) in all media
modeled (atmospheric vapor phase, wet  and dry
deposition, dissolved and particulate tributary water,
total,  dissolved  and particulate  water,  surficial
sediment,  phytoplankton, zooplankton, Diporeia,
Mysis,  and all forage and  predator fish species).
Note that total PCBs in water were not measured, but
were derived by adding up dissolved and particulate
PCBs for each sample. With R**2 values of .90 or
greater, these regression analyses produced very
good results (Table 1.3.1).

Additional analysis was then performed to  produce
an uncertainty estimate for the regression equations.
A mean was calculated for the slope of the line in the
linear regression  (z), and 95  percent confidence
intervals were calculated for z using the formula:

z = x/y

where

x  =   total of PCB congener subset that  was
       modeled

y  =   true total PCBs as calculated by the PI.

David Miller, statistician at LLRS, verified that the z
values  were generally  normally distributed.  This
allowed us to calculate a mean, standard deviation,
                                               28

-------
Table 1.3.1.  Revised Regression Equations for the LMMBP Total PCBs in All Media
Media
Atmospheric Vapor Phase
Atmospheric Dry Deposition
Atmospheric Wet Deposition
Tributary Loading Data
Dissolved Water
Particulate Water
Dissolved + Particulate Water
Surficial Sediment
Phytoplankton
Zooplankton
Diporeia
Mysis
Alewife < 1 20 mm
Alewife > 1 20 mm
Bloater < 1 60 mm
Bloater > 1 60 mm
Deepwater Sculpin
Slimy Sculpin
Adult Smelt
Hatchery Coho
Coho Yearling
Coho Adult
Adult Lake Trout
Ratio of PI Calculated
Total PCBs to Summed
Modeling Congeners
1 .2944
1 .3597
1 .5775
1 .2476
1 .4822
1 .2948
1.4147
1.1805
1 .3842
1 .3923
1.3652
1.3162
1 .4458
1 .4281
1 .4761
1 .4827
1.5157
1 .4976
1 .4447
1 .2836
1.497
1.444
1 .4897
Pis' Calculated Total PCBs
Versus Summed Modeling
Congeners: Regression
Equation
y = 1 .2707x + 0.0891
y= 1.3204X + 0.2159
y= 1. 691 7x- 0.0322
y= 1. 21 34x + 0.7752
y = 1 .2738x + 0.0268
y= 1 . 2251 x + 0.0051
y = 1 .2427x + 0.0347
y= 1.1668X + 0.6125
y= 1. 2871 x + 3.621 6
y = 1 .2058x + 22.833
y= 1.3763X-3.4124
y= 1.3829X- 12.842
y= 1.4534X- 1.296
y= 1.3338X + 38.145
y = 1. 431 7x + 19.505
y = 1.4146X + 38.14
y = 1 .3752x + 38.735
y = 1 .5272x - 8.4009
y = 1 .46693x - 5.2828
y = 1 .4009x - 1 1 .024
y = 1.6263X- 16.984
y = 1.4392x + 2.7179
y = 1 .4875x + 3.7424
R2
0.9997
0.9623
0.9672
0.991
0.9413
0.9992
0.9829
0.997
0.9584
0.9595
0.9795
0.9833
0.9784
0.947
0.9851
0.9426
0.9897
0.9257
0.9044
0.994
0.9835
0.9927
0.9977
and 95 percent confidence intervals for the z value in
all media modeled (Appendix 1.3.8).

A comparison of field measured water congeners
values to model results from Xiaomi Zhang, WelSo
modeler at LLRS, indicated that there were some
problems with measured field results for congeners
84+92 and 99.  In both  dissolved and particulate
water fractions, concentrations for both  of these
congeners were much higher than model results.

Investigation into this issue revealed a contamination
issue with congener 99.  The analytical technique
used  to measure the  water congener  could  not
adequately  separate  congener  99 from trans-
nonachlor, which caused a co-elution problem.  The
reason for the high field values for congener 84+92
was unclear, but it also is believed to be caused by a
co-elution problem. Xiaomi Zhang developed ratios
comparing field data to model results (Table 1.3.2).

These ratios were used to correct 84+92 and 99
congener values by the following formula:

Corrected congener nn value = Measured congener
nn value/ratio

where

nn =   congener 84+92 or 99.
                                              29

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Table 1.3.2. Ratio of Measured Field Data/Model
Results for Congeners 84+92 and 99 in Water
Congener
84+92
99
Ratio in
Dissolved
Water
2.08
1.15
Ratio in
Particulate
Water
3.41
1.94
Regression analysis was then redone for dissolved,
paniculate, and dissolved + particulate water.

These revised regression equations  were then
applied to summed modeled congeners to calculate
modeled total PCBs.   Regression equations  for
dissolved and particulate fractions of total PCBs have
a positive y-intercept.  This means that when these
regressions are used to calculate  total PCBs,  the
value  will  never  drop to zero  even  if modeled
congeners drop to zero. Meetings were held with the
modeling staff to discuss this issue. It was believed
that this  bias was caused  by  1) lack of  blank
correction of congener data, 2) detection limits, and
3) inherent  uncertainty in  the regression process.
Since it was not possible to correct these problems,
the decision was made  to use  the regression
equations,  and carefully explain these  difficulties
when documenting modeling scenarios.

1.3.4 Summary

The  LMMBP  data received at the  LLRS  were
carefully evaluated prior to use to insure that the
field  data being used by the modelers were as
accurate as possible.  In addition, data were archived
and cataloged to protect these valuable data sets
and make it easier for users to find the information.
Incorporation of this information into LLRS Microsoft
Access databases has given us flexibility in retrieving
the information needed by the modeling staff at the
LLRS. These data were used to develop regression
equations which were used to approximate total PCB
concentrations for modeled data.

References

Sukloff, W.B., S. Allan, and K. Ward. 1995.  RDMQ
   User Manual. EnvironmentCanada, Atmospheric
   Environment Service,  North   York,  Ontario,
   Canada. 91 pp.
                                               30

-------
                                        PART1
                                  INTRODUCTION
  Appendix 1.3.1.  List of Parameters Analyzed and Principal Investigators for the
                                        LMMBP
      Parameter
  Focus
  Group
      Media
    Notes
 Principal Investigator
Atrazine, Deethylatrazine   IUAA
(DEA),
Deisopropylatrazine
(DIA), Terbuthylazine
Atrazine, DEA, DIA,
d5-Atrazine
Atrazine


Atrazine
WSAA
Atmospheric
Vapor and
Particulate Phase,
Precipitation


Atmospheric
Vapor and
Particulate Phase,
Precipitation
RULA      Open Lake
RUTA      Tributary
                             Sleeping Bear
                             Dunes site
                             only.
All stations
except
Sleeping Bear
Dunes site.
Ron Hites, Indiana
University. Keri
Hornbuckle, U. of Iowa
used this data to
calculate loadings.

Clyde Sweet,  Illinois
State Water Survey.
Keri Hornbuckle, U. of
Iowa used this data to
calculate loadings.

Steven Eisenreich,
Rutgers University

Steven Eisenreich,
Rutgers University.
David Hall, USGS used
this data to calculate
loads.
Alkalinity, Conductivity,    GPLN      Open Lake
Hardness, pH, Turbidity
Alkalinity, Chloride,
Conductivity, NO2+NO3,
Organic Carbon, pH,
Total Phosphorus, TKN
GRAN     Atmospheric
                                  Marvin Palmer,
                                  GLNPO.  Analysis by
                                  Grace Analytical Labs.

                                  Glenn Warren, GLNPO.
                                  Analysis by Grace
                                  Analytical Labs.
                                           31

-------
Parameter
Conductivity, DO, pH,
Temperature
Alkalinity, Chloride,
Focus
Group
USTN
LHTN
Media
Tributary
Tributary
Notes Principal Investigator
Peter Hughes, USGS
George Bowman,
Conductivity, NH4,
NO2+NO3J
Orthophosphorus,
SiCySiOg, Sulfate, Total
Phosphorus, TSS, TKN

Chloride,
Ammonium-Nitrogen
(NH4N), N02+N03,
Dissolved Phosphorus,
Orthophosphorus, Total
Phosphorus, Dissolved
Silica ,  TKN

Phosphorus (Base
Extractable as PO4),
Total Phosphorus,
Biogenic Silica (SiO2-bio)

Total Organic Carbon,
Total Organic Nitrogen

Organic Carbon (DOC,
POC), TSS

Organic Carbon (DOC,
POC)
Mercury
Mercury
Mercury
GRLN
GLSN
MNPH
Open Lake
Sediment,
Sediment Traps
NASN
BALN
WWTN
Sediment,
Sediment Traps
Open Lake
Tributary
MIAH      Atmospheric
           Vapor, Particulate,
           and Precipitation
                   Monthly
                   deposition/
                   concentration
                   calculated by
                   Matt Landis,
                   University of
                   Michigan
MDLH      Open Lake
Plankton
                                              Wisconsin State Lab of
                                              Hygiene.  David Hall,
                                              USGS used this data to
                                              calculate loads.
Glenn Warren, GLNPO,
Analysis by Grace
Analytical Labs.
Thomas Johengen,
NOAA/GLERL
Brian Eadie,
NOAA/GLERL

Eric Crecelius, Battelle
Marine Sciences

Martin Shafer,
University of Wisconsin
Water Quality
Laboratory

Jerry Keeler, University
of Michigan
Robert Mason, U. of
Maryland.  David Hall,
USGS used this data to
calculate loads.

Edward Nater, U. of
Minnesota
                                             32

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      Parameter
 Focus
 Group
Media
Notes
Principal Investigator
Mercury
LLSH      Sediment,
           Sediment Trap
                             Ronald Rossmann,
                             LLRS
Mercury, Methylmercury   WWTH     Tributary
Mercury
Congener PCBs,
frans-nonachlor
Congener PCBs,
frans-nonachlor
Congener PCBs,
frans-nonachlor
Congener PCBs,
frans-nonachlor
Congener PCBs,
frans-nonachlor
Congener PCBs,
frans-nonachlor

Congener PCBs,
frans-nonachlor

Congener PCBs,
frans-nonachlor
MIFH      Fish
I DAP      Atmospheric
           Vapor and
           Particulate Phase,
           Precipitation


WSAP     Atmospheric
           Vapor and
           Particulate Phase,
           Precipitation


RUAP     Atmospheric Dry
           Deposition
BALP      Open Lake
LHTP      Tributary
NASP      Sediment,
           Sediment Trap

MNPP      Plankton, Mysis,
           Diporeia

MNFP      Forage Fish, Lake
           Trout, Coho
           Salmon
             Sleeping Bear
             Dunes site
             only.
             All stations
             except
             Sleeping Bear
             Dunes site.
             Includes both
             land and over-
             lake sampling
             sites.
            Jim Hurley, U. of
            Wisconsin, Water
            Quality Laboratory.
            David Hall, USGS used
            this data to calculate
            loads.

            Jerome Nriagu, U. of
            Michigan

            Ron Hites, Indiana
            University. Keri
            Hornbuckle, U. of Iowa
            used this data to
            calculate loadings.

            Clyde Sweet,  Illinois
            State Water Survey.
            Keri Hornbuckle, U. of
            Iowa used this data to
            calculate loadings.

            Steven Eisenreich,
            Rutgers  University
                             Eric Crecelius, Battelle
                             Marine Sciences
                             Laboratory

                             William Sonzogni,
                             Wisconsin State Lab of
                             Hygiene

                             Pat Van Hoof,
                             NOAA/GLERL

                             Deborah Swackhamer,
                             U. of Minnesota

                             Deborah Swackhamer,
                             U. of Minnesota
                                            33

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      Parameter
 Focus
 Group
      Media
    Notes
 Principal Investigator
Seabird Temperature,
Chlorophyll a,
Transmissivity


Chlorophyll a
N/A
Primary Productivity
Abundance/Biomass
Diet Information
Diet Information
Diet Information
Cs-137andPb-210
Sediment Bulk Density,
Fraction Dry weight,
Porosity, Sediment
Mixing Depth, Vertical
Sediment Transport, Net
Mass Accumulation Rate
BSDB
FSDB
NASR
N/A
Open Lake
WSLH      Tributary
GRLY      Open Lake
           Phytoplankton


GRLP      Phytoplankton
Abundance/Biomass      GRLZ     Zooplankton
BSDB      Forage Fish
Lake Trout
Coho Salmon
Sediment
Sediment
Chlorophyll a
calculated from
fluorescence
data
Glenn Warren, GLNPO
                George Bowman,
                Wisconsin State Lab of
                Hygiene.  David Hall,
                USGS used this data to
                calculate loads.

                Glenn Warren, GLNPO.
                Analyzed by Grace
                Analytical Laboratory

                Glenn Warren, GLNPO.
                Analyzed by Grace
                Analytical Laboratory.

                Glenn Warren, GLNPO.
                Analyzed by Grace
                Analytical Laboratory.

                John Gannon/
                Jacqueline Savino,
                USGS, National
                Biological Survey

                John Gannon/Edward
                Brown, USGS, National
                Biological Survey

                Mark Holey, U.S. Fish
                and Wildlife Service

                John Robbins,
                NOAA/GLERL

                John Robbins,
                NOAA/GLERL
                                            34

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                                     PART1

                                INTRODUCTION
              Appendix 1.3.2.  Example of Data Verification Checklist
                               Used for the LMMBP
Data Verification Checklist

FOCUS	   Version Number	   Date Received

Description:	
1.  Read any documentation which came with data files:

2.  Make sure I understand field names in RDMQ files:
3. Check fields which according to RDMQ should not be flagged/or indicate some question, with data (e.g.

RS_NMAND, RS_WARN, RSJJPDAT).

RS_NMAND	

RS_WARN	

RS UPDAT
4. Make sure every RFS and field duplicate has station, date, depth collected information.
5. Check to make sure every sample has station name that is valid.
                                        35

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6. Check number of RFS and field duplicates for every analyte. Total Samples




Analyte	    RFS	   FDn	




Analyte	    RFS	   FDn	




Analyte	    RFS	   FDn	




Analyte	    RFS	   FDn	




Analyte	    RFS	   FDn	



Analyte	    RFS	   FDn	
7.     Analysis Results for RFS and field duplicates for every analyte.



       Analyte	Avg        Min	    Max	Count



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



       Analyte



      Analyte



      Analyte



      Analyte
Avq
Ava
Ava
Avg
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Ava
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Min
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Max
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
Count
                                              36

-------
Analvte
Analyte
Analvte
Analvte
Avg
Avg
Ava
Ava
Min
Min
Min
Min
Max
Max
Max
Max
Count
Count
Count
Count
8.     Check date ranges of data to see if they are reasonable.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max



Analyte	Min	Max



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



Analyte	Min	Max.



9.  Check to verify units information looks alright.
10. Number of significant digits for each analyte.
11.  Number of negative values for each analyte.
                                               37

-------
12.    Check flags on RFS and field duplicates.
13. Core slice range (sediment)/species, age, length, weight (fish).
14. Check blank correction, dilution, and surrogate correction fields.
15. Questions about QC Coordinator remarks (RECSTAT). Check flags for whole record (RECSTATF).




Questions about Station Notes (STNNOTES), Field Remarks (FREMARK), and Sample Description




(SAMPDESC).
18. Additional Questions.
                                             38

-------
                                           PART1
                                     INTRODUCTION
       Appendix 1.3.3. Printout of Information Stored in the LMMBP Tracking
                     Database (R.-\\Access2000\\lmmb\lmtrack.mdb)
                           LMMBP DATA ARCHIVE - QUICK REPORT

Note: All Data Archived on Ilrssrv2 in /usr/lmmbdata.
 Library No.


 Description


 Library No.

 Description



 Library No.

 Description



 Library No.

 Description



 Library No.

 Description
LM10001
PI:
David Scwab
Hourly Lake Michigan wind, wave, and atmospheric data (5 km grid) for 1982, 1983, 1994, 1995.
Original data files were converted to SEDZL and POM formerly by M. Settles. Also, bathymetric
data for Lake Michigan.
LM 10002
PI:
William Richardson
STORET conventional and general chemistry data for Lake Michigan, April 1962 - August 1993.
Note: Date range varies by parameter, includes original file, reformatted spreadsheet, and MS
Access file.
LM 10003
PI:
David Scwab
2D and 3D GLERL hydrodynamics data for the Lake Michigan 5 km grid. 2D data: January 1982-
September 1983; 3D: covers January-July 1982. Program//llrssrv2/~model/dev/PATRIC2D/RCS is
for 2D processing, no 3D programming yet.
LM 10004
PI:
Steven Eisenreich
Open Lake (RULA) and tributary (RUTA), atrazine, DEA, DIA data for LMMBP  Open lake 325
samples (1/17/94 - 4/17/95). Tributary: 126 samples (4/4/95 - 5/15/96).  Revised version of data
sent 2/19/98.
LM 10005
PI:
Angela Bandemehr
Hourly meteorological data (air temperature, solar radiation, relative humidity, wind speed and
direction, and precipitation) from 13 air sampling sites both in and outside of the Lake Michigan
basin. 11/30/90 -12/31/96 (Dates vary by site).
                                              39

-------
Library No.

Description



Library No.

Description



Library No.

Description
LM10006
                                                    PI:
            Glenn Warren
Seabird water temperature data for seven LMMBP surveys, April 1994 - October 1995. Data
collected at 0.5 m intervals. Does not include January 1994 survey. Note: Data received was
extensively revised from original version.
LM10007
PI:
David Hall
Tributary flow data for 11 tributaries to Lake Michigan (Fox, Grand, Indiana Harbor, Kalamazoo,
Manistique, Menominee, Milwaukee, Muskegon, Pere Marquette, Sheboygan, St. Joseph), 1/1/94-
12/31/95. Some data estimated.
LM10008
PI:
Glenn Warren
Open lake organic carbon (dissolved and particulate), and solids data for eight LMMBP cruises.
Sampling date was 4/14/94 -10.13/96. Data also received in D-base (dbf) format.  Focus: BALN
Library No.

Description



Library No.

Description



Library No.

Description



Library No.

Description
LM10009
PI:
Debra Piper
Open Lake Michigan nutrient data (chlorophyll, ammonia, NO2+NO3, total phosphorus, dissolved
phosphorus, orthophosphate, silica, and TKN), 4/24/94 -10/13/95 (orthophosphate, NH4-N started
10/14/94).  Focus GRLN, collected and analyzed by Grace Labs. Focus: GRLN.
LM10010
PI:
David Hall
Total and dissolved mercury loading estimates for monitored and unmonitored tributaries to Lake
Michigan -1/1/94 -12/31/95.  Note: Associated flow data is included in an earlier release of this
data.
LM10011
PI:
David Schwab
Lake Michigan final report, hourly circulation, meteorology, and wave data (5 km grid) for 1982,
1983,1994, 1995. Includes intake, cruise, mooring, water level data. Also, HTML files and images,
model results (XDR format), Fortran and IDL programs.
LM10012
PI:
David Rockwell
Open lake conventional data (alkalinity, conductivity, total hardness, pH, turbidity) from eight
LMMBP cruises, 4/24/94 -10/13/95. Data received in D-base, and Lotus formats. Focus: GPLN.
Library No.

Description



Library No.

Description
LM10013
PI:
Peter Hughes
Conventional data (conductivity, dissolved oxygen, pH, temperature) collected from 11 Lake
Michigan tributaries, 3/29/94 -12/5/95. Note: Five dissolved oxygen samples were flagged as
invalid (INV) and should not be used. Files in D-base and Lotus formats. Focus: USTN.
LM10014
PI:
Robert Mason
Open lake mercury data (particulate and total), collected 6/17/94 -10/10/95. Note: There are 11
invalid samples (flagged as INV) in this data set which should not be used. Data received in D-
base, Lotus, SAS, and tabular delimited formats. Focus: MDLH.
Library No.    LM10015                              PI:          David Rockwell

Description    Secchi depth data collected during eight LMMBP cruise, 1/16/94 -10/11/95. Focus: GPLS.
Library No.

Description
LM10016
PI:
Matt Landis
Mercury deposition/concentration data estimated into the NOAA/GLERL 5 km over water grid. All
data are monthly averages. Covers time period: July 1994 - October 1995.
                                                 40

-------
Library No.

Description
LM10017
PI:
Glenn Warren
By species and by functional group, abundance, and biomass value, for open lake zooplankton
data collected during eight LMMBP cruses (4/24/94 -10/10/95).  Focus Group: GRLZ.
Library No.    LM10018                              PI:          George Bowman

Description    Tributary chlorophyll a data, collected 3/24/94 -10/31/95.  Focus Group: LHTL.
Library No.

Description



Library No.

Description
Library No.

Description



Library No.

Description


Library No.

Description



Library No.

Description



Library No.

Description



Library No.

Description
LM10019
PI:
Tom Johengen
Sediment nutrient data (NaOH extractable PO4, total phosphorus, biogenic silica), 6/24/94 -
5/22/96. Note: One sample was flagged as invalid (INV) for all parameters and should not be used.
Focus Group: GLSN.
LM10020
PI:
Keri Hornbuckle
Atmospheric atrazine and nutrient (NO3, total phosphorus, TKN) wet deposition loading data for
Lake Michigan 5 km grid cells used in hydrodynamic model. Atrazine wet deposition and
particulate monthly concentration data.  Data for 10/94 -10/95 (nutrient) and 5/94 -10/95
(atrazine).
LM10021
PI:
David Hall
Tributary inorganic/nutrient loading data for 10 parameters (alkalinity, ammonia, chloride, NO2+NO3,
orthophosphate, dissolved silica, TKN, total nitrogen, total phosphorus, total suspended solids)
collected from 11 monitored Lake Michigan tributaries. Data collected 1/1/94 -12/31/95.
LM 10022
PI:
David Hall
Atrazine, DEA, DIA tributary loading data for 11 monitored tributaries and atrazine data for
unmonitored tributaries to Lake Michigan. Data covers the time period: 1/1/94 -12/31/95.
LM 10023
PI:
Glenn Warren
Primary productivity data collected during eight surveys for the LMMBP study. Data covers the
time period: 4/24/94 -10/13/95. Data did not go through RDMQ, but was QA'd by Deb Piper, Grace
Analytical. Focus Group: GRLY.
LM 10024
PI:
Glenn Warren
Abundance and biomass for plankton samples collected during the LMMBP.  Data covers the time
period: 4/24/94 -10/10/95. Data is reported both by individual species and by functional group.
Focus: GRLP.
LM 10025
PI:
John Robbins
Sediment radiochemistry (Pb-210, Cs-137), physical properties (mass, fraction dry weight, soluble
fraction, bulk density, porosity, age) of Lake Michigan sediment collected 1994-1996. Station
location and modeled data (mixing rates, settling) also included. Focus: NASR.
LM 10026
PI:
Nathan Hawley
Current velocity, water transparency, temperature from three stations, 10/31 /94 -10/11 /95.  In-situ
sediment resuspension from sediment flume experiments (8/12/95 - 9/23/98). Also profile data -
temperature, dissolved oxygen, conductivity, BAT, pH, fluorescence, TSM data from  six stations in
Lake Michigan (1/4/95 -11/29/95).
                                                  41

-------
Library No.

Description



Library No.

Description



Library No.

Description
LM10027
                                                    PI:
            Barry Lesht
Current velocity and direction, bottom wave orbital velocity, temperature, beam attenuation, and
TSM data collected from Tripod Station 98(latitude 42 52.18, longitude 87 42.41), during the
EEGLE project, 4/2/98 -12/1/98. Data collected every 30 minutes.
LM 10028
PI:
Michael Settles
NEMA and NOAA wind speed and direction, wave height and period data for six stations in Lake
Michigan, retrieved from ACOE Web Site (http://bigfoot.wes.army.mil/c300.html).  1980-1998 (not
all stations cover entire date range). NEMO-Daily data, NOAA-Hourly data.
LM 10029
PI:
Keri Hornbuckle
Daily gas phase congener, total PCBs, and frans-nonachlor concentration data for LMMBP 5 km
grid. Covers time period: 1/1/94 - 9/30/95.
Library No.

Description


Library No.

Description



Library No.

Description



Library No.

Description



Library No.

Description


Library No.

Description
LM 10030
PI:
Catherine Taylor
Sedflume data gathered from Catherine Taylor's masters thesis. Excel files contain erosion data at
different energy levels and graphs of shear stress at different erosional stresses.
LM 10031
PI:
Ronald Rossmann
Surficial sediment/sediment trap mercury data collected during eight LMMBP cruises, 7/18/94 -
8/28/95.  Surficial sediment samples collected by R. Rossmann, trap samples by Brian Eadie,
NOAA. All analysis by R. Rossmann.  D-base files from RDMQ output. GLNPO Focus LLSH.
LM 10032
PI:
Keri Hornbuckle
Congener, total PCBs, frans-nonachlor monthly and annual loading and flux data (particulate and
precipitation data) for eight land stations around Lake Michigan and from Lake Guardian. Monthly
and annual precipitation loading for LMMBP 5 km grid (text files). 4/94 - 9/95.
 LM 10033
PI:
Ken Klewin
Nutrient data (chlorophyll, NO2+NO3, silica, total dissolved phosphorus, and total phosphorus for
1998 spring and summer GLNPO surveys of all five Great Lakes. Lake Michigan data was
collected 4/25/98 - 5/10/98 and August 1998 - first week in September 1998.
LM 10034
PI:
Rich Quintal
1994-1995 Lake Michigan diet, length, weight, age, and migration data (coho only) for forage fish,
coho salmon and lake trout. Data compiled by Lauri Davis based on data received from R. Quintal.
LM 10035
PI:
Marcia Kuehl
Open water congener PCBs and frans-nonachlor data collected during eight LMMBP cruises,
4/24/94 -10/13/95. Data analyzed by Battelle Marine Science Lab (Focus: BALP).
Library No.

Description



Library No.

Description
LM 10036
PI:
Deborah Swackhamer
Lower food chain (phytoplankton, zooplankton, Mysis, Diporeia) congener PCBs, total PCBs, frans-
nonachlor, lipids and moisture data collected during LMMBP cruises (6/17/94 -10/10/95).  Focus
group: MNPP.
LM 10037
PI:
James Mickey
Lipids, moisture, congener PCBs data for composite samples of two predator fish species (coho
salmon, lake trout) and five forage fish (alewife, bloater, deepwater sculpin, slimy sculpin, and
smelt). Length/weight data also. Data collected during LMMBP
                                                  42

-------
Library No.

Description



Library No.

Description


Library No.

Description



Library No.

Description
LM10038
PI:
Ken Klewin
Nutrient data (chlorophyll, dissolved and total phosphorus, dissolved silica, NO2+N03) collected and
analyzed by GLNPO for spring (4/9 - 4/30/00) and summer (8/2 - 8/28/00) 2000 cruises for selected
stations for all five Great Lakes.  Note: File is a Lotus 123 file, the rest are Excel files.
LM 10039
PI:
Glenn Warren
Seabird chlorophyll a transmissivity profiles for eight LMMBP cruises (4/25/94 -10/13/95).
Chlorophyll a calculated by John Goldsmith, GLNPO.  Most 1/2 m depth intervals.
LM 10040
PI:
Pat Van Hoof
Surficial sediment and sediment trap PCB congeners, total PCBs, and frans-nonachlor data
collected 10/5/94 - 5/22/96 (surficial), 7/4/94 - 8/28/95 (traps), analyzed by Pat Van Hoof. Samples
are from top 1 cm of box and gravity cores, and ponar samples.
LM10041
PI:
David Hall
Daily tributary loading data (1/1/94 -12/31/95) for PCB congeners, total PCBs, and frans-nonachlor
for 11 monitored and 18 unmonitored tributaries to Lake Michigan. Data analyzed by Wisconsin
Laboratory of Hygiene.  Loading estimates by David Hall and Faye Blondin.
                                                  43

-------
                        PART1
                    INTRODUCTION
Appendix 1.3.4. Generalized Format for the LMMBP Water Data to be
                Analyzed With IDL Programs
Beginning -
Ending
Columns
1 7
8-8
9- 14
15-15
16-22
23-23
24-35
36-36
37-44
45-45
46-53
54-54
55-58
59-59
Variable Description
Cruise Name
Blank Space
Latitude (ddd.ddd)
Blank Space
Longitude (-ddd.ddd)
Blank Space
Station Name
Blank Space
Depth Sampled
Blank Space
Sampling Start Date
(mm/dd/yy)
Blank Space
Sampling Start Time
(24-hour clock)
Blank Space
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
A7
1X
F6.3
1X
F7.3
1X
A12
1X
F8.0
1X
A8
1X
A4
1X
Sort Order
(A = Ascending,
D = Descending,
Blank = None)
A
N/A

N/A

N/A
A
N/A
A
N/A
A
N/A

N/A
Missing Data
Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
                          44

-------
Beginning -
Ending
Columns
60-67
68-68
69-72
73-73
74-75
76-76
77-79
80-80
81 -88
89-103
104-111
112-126




Variable Description
Sampling End Date
(mm/dd/yy)
Blank Space
Sampling End Time
(24-hour clock)
Blank Space
Filter Fraction
Blank Space
Sample Type
Blank Space
Value Parameter 1
Parameter 1 Flags
Value Parameter 2
Parameter 1 Flags
1
t
Value Parameter n
Parameter n Flags
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
A8
1X
A4
1X
A2
1X
A3
1X
F8.0
A15
F8.0
A15


F8.0
A15
Sort Order
(A = Ascending,
D = Descending,
Blank = None)
A
N/A

N/A
A
N/A
D
N/A








Missing Data
Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
-9999
Blank
-9999
Blank


-9999
Blank
45

-------
                         PART1
                     INTRODUCTION
Appendix 1.3.5. Generalized Format for the LMMBP Sediment Data to be
                 Analyzed With IDL Programs
Beginning -
Ending
Columns
1 -6
7-7
8-14
15-15
16-27
28-28
29-36
37-37
38-47
48-48
49-58
59-59
60-65
66-66
Variable Description
Latitude (ddd.ddd)
Blank Space
Longitude (-ddd.ddd)
Blank Space
Station Name
Blank Space
Station Depth
Blank Space
Sampling Start Date
(mm/dd/yyyy)
Blank Space
Sampling End Date
(mm/dd/yy)
Blank Space
Top of Core Slice
Blank Space
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
F6.3
1X
F7.3
1X
A12
1X
F8.0
1X
A10
1X
A8
1X
F6.0
1X
Sort Order
(A = Ascending,
D = Descending,
Blank = None)

N/A

N/A
A
N/A

N/A

N/A

N/A
A
N/A
Missing
Data Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
                            46

-------
Beginning -
Ending
Columns
67-72
73-73
74-75
76-76
77-79
80-80
81 -87
88-88
89-96
97 - 1 1 1
112-119
120-134




Variable Description
Bottom of Core Slice
Blank Space
Filter Fraction
Blank Space
Sample Type
Blank Space
Collection Method
Blank Space
Value Parameter 1
Parameter 1 Flags
Value Parameter 2
Parameter 1 Flags
1
t
Value Parameter n
Parameter n Flags
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
F6.0
1X
A2
1X
A3
1X
A7
1X
F8.0
A15
F8.0
A15


F8.0
A15
Sort Order
(A = Ascending,
D = Descending,
Blank = None)

N/A
A
N/A
D
N/A

N/A








Missing
Data Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
-9999
Blank
-9999
Blank


-9999
Blank
47

-------
                        PART1
                    INTRODUCTION
Appendix 1.3.6. Generalized Format for the LMMBP Fish Data to be
               Analyzed With IDL Programs
Beginning -
Ending
Columns
1 13
14 14
15-27
28 28
29-41
42-42
43-45
46-46
47-49
50-50
51 -52
53-53
54-58
59-59
60 64
Variable Description
Species
Blank Space
Biota Zone
Blank Space
Station Name
Blank Space
No. of Samples in
Composite
Blank Space
Minimum Age
Blank Space
Maximum Age
Blank Space
Minimum Length
Blank Space
Maximum Length
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
A13
1X
A13
1X
A13
1X
13
1X
13
1X
12
1X
F5.0
1X
F5.0
Sort Order
(A = Ascending,
D = Descending,
Blank = None)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Missing
Data Code
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
.
Blank
N/A
Blank
                          48

-------
Beginning -
Ending
Columns
65-65
66-87
88-88
89-99
100-100
101 110
111 111
112-119
120-120
121 -128
129-129
130-137
138-138
139-146
147 147




Variable Description
Blank Space
Gender
Blank Space
Sampling Start Date
(mm/dd/yyyy)
Blank Space
Sampling End Date
(mm/dd/yy)
Blank Space
Parameter Units
Blank Space
Biota Part Sampled
Blank Space
Value Parameter 1
Blank Space
Value Parameter 2
Blank Space
1
T
Value Parameter n
Parameter n Flags
Format
(A = Alpha, F =
Floating Point No.,
1 = Integer, X = Skip)
1X
A22
1X
A11
1X
A10
1X
A8
1X
A8
1X
F8.0
1X
F8.0
1X


F8.0
A15
Sort Order
(A = Ascending,
D = Descending,
Blank = None)
N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A




Missing
Data Code
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
Blank
N/A
-9999
N/A
-9999
N/A


-9999
Blank
49

-------
                                          PART1

                                     INTRODUCTION
     Appendix 1.3.7. Documents From Marcia Kuehl Discussing How Total PCBs
                             Were Calculated by Focus Group
 PCBs total routines
 2/28/01

 IUAP: reported by PI as pcbtot, is in rdmq output. PI added all congeners except 30, 204,14, 65,166 , and
 in two cases 44 due to interference. As PI added prior to rdmq verification, INV data included in totals and
 totals not surrogate corrected. Dyncorp redid, using surrogate corrected values for summation and excluding
 INV results and #44 as indicated by PI. Values are called pcbtot2 in the rdmq output.

 WSAP: PI added prior to rdmq verification and gave to modeler. Not reported to GLNPO and therefore not
 in rdmq output. INV data included in totals. Added all congeners except 30, 204, 14, 65, 166. Dyncorp did
 totals,  using surrogate corrected values for summation and excluding INV results. Some samples have no
 pcbtot due to whole sample invalidated from biased Lake Guardian sampling location.

 RUAP: PI added and included mention of protocol  in ES&T journal article. Not reported to GLNPO and
 therefore not in rdmq output. Added all congeners except 30, 204, 14, 65, 166, and 4+10, 6, 8+5, 7+9 due
 to interference/contamination. Each sample had its associated field blank (FMB) subtracted from it. No mention
 of how to handle negative results included, but I would assume negative values revert become zero. Dyncorp
 did totals including INV results and blank subtraction with  negative results reverting to zero.  Totals were
 qualified as field blank corrected (FBC).

 LHTP:  Modeler added. Not reported to GLNPO and therefore not in rdmq output. INV data  not included in
 totals. Added all congeners except 30,204,14,65,166. Dyncorp did using these rules and totals were verified
 against Faye Blondin's totals.

 BALP: Not reported to GLNPO and therefore not in rdmq output.  Excluded 14, 65,166,103,30,204 and any
 INV flagged  results for first run at totals. Subsequent runs may also exclude  INT, UNC  values and/or
 replacement  of zeros with  a value (my thesis work). Some samples have no pcbtot due to whole sample
 invalidated due to extremely low surrogate recoveries.  Dyncorp has done using these rules.

NASP:  Not reported to GLNPO and therefore not  in  rdmq output. PI developed routine which excludes
congeners that had small peak heights: 12,13,77,119,129,130,189 and those with interference: 33,49,201
and low recoveries in QC spikes:  16, 19.  All trap samples lab blank corrected on a congener by congener


                                             50

-------
basis, negative results become zero. Low level (< 4 ng/g) ponar samples lab blank corrected if total pcbs in
lab blank is > 10% of total pcbs in sample, blank correction done on a congener by congener basis, negative
results become zero. If > 33% of blank corrected results are 0, and/or NAI, pcbtot flagged with LOB. Any lab
blank corrected pcbtot values flagged with (LBC). INV results excluded from any pcbtot calculation. Dyncorp
has done using these rules.

MNPP: As per PI:" We generally summed all congeners, and those below the MDL were assigned a value
of zero for summation purposes. HOWEVER we then applied lots of expert judgement in looking at the sums,
and applied some other criteria (for instance, if a single congener made up more than 10% of the mass of the
sum  we  rejected it entirely) -  and if you simply sum you will definitely get different results than we are
publishing (on a significant number of samples)." PI supplied totals using below MDL values as reported and
considering them zero. I chose  to have Dyncorp enter the PI totals that used the below MDL values in the total
as the biggest difference between the two methods was < 7 %.

BSFP: Not reported to GLNPO and therefore not in rdmq output. Added all congeners reported  (no 14, 65,
166,  30, 204 reported for BSFP) for each RFS. No INV DATA TO BE INCLUDED.
                                             51

-------
Spreadsheet Highlighting Differences and Similarities in How Total PCBs Were Calculated In Different
LMMBP Focus Groups



FOCUS
IUAP

WSAP
RUAP




BALP
LHTP
NASP














MNPP




.BSFP
INV
Flagged
Congeners
Excluded
Y

Y
Y




Y
Y
Y














Y




Y


Surrogate
Corrected
Y

Y
N




Y
Y
Y














Y




Y
LOB
Flagged
Congeners
Excluded*
N

N
N




N
N
N














N




N
HIB
Flagged
Congeners
Excluded*
N

N
N




N
N
N














N




N
MDL
Flagged
Congeners
Excluded*
Y

Y
Y




Y
Y
Y














Y




Y


Blank
Corrected
N

N
Y,
flagged
FBC*


N
N
Y,
flagged
LCB*,
LOB* if >
33%
results =
0 or NAI*








N




N



Other Conditions
BZ #44 excluded
in two cases

If field blank
subtraction
yielded negative
value, reverted to
0


If lab blank correc-
tion yielded neg.
value, reverted to
0. Small peak
height congeners
excluded: BZ#12,
13,77,119,129,
130, 189. Ex-
cluded congeners
with interferences:
BZ# 33, 49, 201 .
Excluded low
matrix spike re-
covery congeners:
BZ#16, 19.
"Expert judgment"
and any congener
> 1 0% total was
excluded and
retotal done

*Key to Flags: LOB = Low Bias Flag, HIB = High Bias Flag, MDL = Method Detection Limit Flag, FCB = Field
Corrected Blank, LCB = Lab Corrected Blank, NAI = Not Analyzed Due to Interference Flag
                                             52

-------
                              PART1
                          INTRODUCTION
Appendix 1.3.8. Uncertainty of Z Values (Slope of Linear Regression Line) for PCBs
               in Media Modeled at the LLRS for the LMMBP
Media
Dissolved Water
Particulate Water
Surficial Sediment
Phytoplankton
Zooplankton
Diporeia
Mysis
Alewife <120mm
Alewife > 120mm
Bloater <1 60mm
Bloater >1 60mm
Deepwater Sculpin
Slimy Sculpin
Adult Smelt
Adult Lake Trout
Z - Mean
1.53924569
1.353079101
1.17528269
1.432619553
1 .462624095
1 .3641 858
1.312835
1.439713618
1 .43439061 9
1 .479266265
1.487545716
1 .533308409
1.495781353
1.44344149
1.489208126
Z-sd
0.290911927
0.120677122
0.05661207
0.355361646
0.399167732
0.0432741
0.04129
0.140086181
0.08071193
0.049654336
0.109039931
0.089680915
0.110647788
0.166498179
0.06449037
Number
of
Samples
369
372
132
86
77
42
62
60
70
70
67
74
69
73
246
95%
Confidence
Interval Lower
1 .509305468
1 .340709374
1.16550663
1 .356363486
1.371918262
1 .3506909
1 .302347
1 .403380756
1 .41 5096777
1 .467396609
1 .46090302
1.512520571
1.469140496
1.41625314
1.481079185
95%
Confidence
Interval Upper
1.569185912
1 .365448829
1.18505875
1 .50887562
1 .553329928
1 .3776807
1 .323322
1 .47604648
1 .45368446
1.491135922
1.514188411
1 .554096246
1.52242221
1 .470629841
1 .497337067
                                 53

-------
Coho Salmon
Hatchery
Coho Salmon -
Yearling
Coho Salmon -
Adult
1 .280547696
1.485910089
1 .44826731 7
0.028621549
0.112380374
0.074873035
5
8
54
1 .24501 5047
1.391942816
1 .427797757
1.316080345
1 .579877362
1 .468736876
Z values for the LMMBP Total PCBs Data in Various Media.  Z = Y/X, where X = Sum of all modeled
congeners, as measured by Principal Investigator (PI), and Y = PI Calculated Total PCBs.
Notes from David Miller, Statistician at the LLRS:

"I used Histograms with a normal distribution overlayed upon them as well as quantile-quantile plots (Q-Q
Plots) to analyze these data sets for normality.

The assumption of normality appears to be supported for these data sets, although the following observations
should be noted.

Dissolved and particulate water, surficial sediment, phytoplankton, zooplankton, deepwater sculpin, and adult
coho, appear to exhibit some degree of kurtosis. Thus, the confidence intervals for the mean of these data
sets will be conservative.

Bloater >160mm and adult lake trout appear to have a few values at the upper end of the distribution that fall
higher than the rest of the distribution (appear under-represented according to a quantile-quantile plot).

Surficial sediments have some outlying values on the  low  end of the distribution as examined by the
quantile-quantile plot.

Coho salmon - Hatchery,and coho salmon   Yearling have too few data to make a determination."
                                               54

-------
                                         PART1
                                    INTRODUCTION
Chapter  4.   Representativeness of the
LMMBP Years Relative to Lake Michigan's
Historic Record

Ronald Rossmann, Kenneth  R. Rygwelski, and
Russell G. Kreis, Jr.
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
and
Gregory J. Gerstner, Xiaomi  Zhang, and  Brent
Burman
Welso Federal Services, LLC
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

1.4.11ntroduction

A major concern related to modeling contaminants in
the lake was the representativeness of the years of
sampling (1994-1995) relative to the historical record.
This  was  particularly important  when  using  the
models to predict future conditions in the lake. The
models were calibrated  using  1994-1995  data
collected during the project.  If these data did not
represent something close to average conditions, the
resulting predictions could be biased. Parameters
considered most important to the performance of the
models included  ice cover, air temperature, water
temperature, lake water levels, precipitation, tributary
flows, and  wave heights.   Each of  these  were
investigated for the representativeness of the 1994-
1995 project data relative to the available historical
data record.

7.4.2 Ice Cover

Ice cover impacts the volatilization, absorption, and
physical mixing of the lake during the winter months.
In locations where there is ice cover, gas exchange
between the water and atmosphere is prevented by
the physical barrier.  Physical mixing  includes not
only the mixing of the water column  but also the
interaction  of  waves with the  lake bottom to
resuspend sediments. Winters having extensive ice
cover yield a more poorly mixed water column, and
a large region of the lake becomes depositional due
to lack of wave resuspension of sediments. Once ice
retreats in the spring, sediments accumulated during
ice cover will be resuspended as a pulse. Ice cover
can cause significant changes in  winter circulation
patterns in a large lake (Campbell  etal., 1987). The
years of interest that were important were 1982,
1983,1994, and 1995. The hydrodynamic modeling
included three-dimensional lake circulation, surface
flux for atmospheric input, and  wind-wave models
(Schwab and Beletsky, 1998). These were calibrated
for the  period of 1982-1983 using temperature,
current, water level, and wind-wave measurements.
The calibrated model was applied to 1994-1995 and
validated. There was no ice modeling component for
the version of hydrodynamic model applied. Thus,
ice cover was  important  for  understanding any
potential   weakness   associated   with  the
                                             55

-------
hydrodynamic results as well as the dynamics of
exchanges between the water and the atmosphere.

Ice cover data  were available  from the National
Oceanic and Atmospheric Administration (NOAA)/
Great Lakes Environmental Research  Laboratory
(GLERL) (Assel, 2003).  This data  set is partially
described in Assel etal. (2002). Tabular information
presented in Assel (2003)  were summarized in a
manner that seemed appropriate for this discussion
(Table 1.4.1). For the period when ice was recorded
on Lake Michigan, the mean and median daily ice
cover were 16.7%  and 14.7%, respectively.   Ice
years began with the first ice.  For example, 1982
may include December of 1981. Both  1982 and 1994
were greater than the mean and median; whereas,
1983 and 1995 were less than the mean and median.
None of the four years represented  an  extreme of
mean daily ice  cover.  The lowest  ice cover was
observed in 2002, and the highest was observed in
1977. Results for each winter's maximum daily ice
cover were similar to mean daily ice cover. None of
the four years represented an extreme of maximum
daily ice  cover.   As before, 1982 and  1994 were
above the mean and median, and 1983 and 1995
were below the  mean and median.   The maximum
mean occurred  in 1977,  and the minimum mean
occurred in 2002. For all four years, 1982 and 1994
were  above average for number of days ice was
observed, and 1983 and 1984 were slightly below the
average.   None of the four years  represented a
minimum  or maximum extreme.   Ice cover is
extremely variable from year-to-year.  The impact
upon hydrodynamics as modeled was believed to be
minimal with respect to 1983 and 1995 when ice
cover was quite low. Though high ice cover occurred
during the winters of 1982 and 1994, these periods
were not  a part of the hydrodynamic model period.
Using the  hydrodynamic   model information  for
models that are  used to predict future conditions
could  lead to potential errors.  Modeled circulation
patterns could be in error and impact a high bias to
modeled current velocities during the  winters of high
ice cover years due to the lack of an ice model within
the hydrodynamics model.

7.4.3  Water and Air Temperatures

Water and air temperature data were retrieved from
the National Data Buoy Center (U.S.  Department of
Commerce, 2002). The buoy numbers are 45002
(north buoy) and 45007 (south buoy) (Figure 1.4.1).
Water temperature sensors were located 1 m below
the water surface, and air temperature sensors were
located  4 m  above the surface.  Water and air
temperature data were available 1979 through 2002
for the north buoy and 1981  through 2002 for the
south buoy.

Water temperature is highly variable from year-to-
year.  The data had been stratified in two ways for
presentation.   First, monthly  mean  temperatures
were calculated and plotted for the south  (Figure
1.4.2) and  north (Figure  1.4.3) buoys.  Years of
importance to  the hydrodynamic  model were
highlighted. It was interesting to note that 1983 and
Figure 1.4.1. Location of the NOAA's buoys in
Lake Michigan.
                                              56

-------
Table 1.4.1.  Summary of Lake Michigan Ice Cover Based Upon Assel (2003)


Year
1973
1974
1975
1976
1977
1978
1979
1980
1981
| 1982
| 1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
| 1994
| 1995
1996
1997
1998
1999
2000
2001
2002
Mean
Median
Minimum
Maximum
Mean Daily
Ice Cover
During Ice Period
13.3
16.9
13.9
15.5
46.5
26.6
35.2
18.2
24.6
24.0
8.2
15.6
20.1
25.3
9.1
16.6
13.1
17.5
10.0
8.3
11.0
27.3
7.2
19.4
13.4
6.1
8.7
9.2
13.4
6.0
16.7
14.7
6.0
46.5
Days of
Observed
Ice
104
122
113
119
132
132
132
106
112
135
118
127
119
126
100
104
140
132
120
149
126
134
120
161
156
109
111
103
134
116
124
121
100
161
Maximum
Daily Ice
Cover
33.0
39.4
28.1
29.5
93.1
66.6
92.3
38.6
53.8
60.2 [
23.6 j
43.3
41.3
66.8
19.3
32.7
30.9
32.4
21.5
32.8
32.2
82.7 |
21.6 j
75.0
37.8
15.1
23.0
27.2
29.5
12.4
41.2
32.8
12.4
93.1
                                          57

-------
   25
   20-

 O
 a> 15

 I
 (D
 9-10-
    5-

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c
•
t
south buoy
_»_ data years for Lake W
hydrodynamic modeli

»
i
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•



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chigan
ng

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t


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rf

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-------
This, in part, explained why sampling  missed the
spring 1995 diatom bloom.

The exchange of PCBs between the air and water
were dependent on both water and air temperatures.
Air temperature varied from year-to-year at the south
and north buoys (Figure 1.4.6 and 1.4.7).  Because
air temperature drives observed water temperature,
it was not surprising that patterns  observed and
conclusions made for  water temperature  are the
same for air temperature. The cyclic pattern of June
mean water temperatures was also found for the air
temperatures (Figures 1.4.8 and 1.4.9). As additional
data become available, future modeling efforts will
need to address these cyclic patterns and long-term
temperature trends for water and air temperatures.

 1.4.4 Lake Water Levels

Lake levels can affect model geometry. If segment
volume deviates significantly from the volumes used
at the time of calibration, model results  can be
impacted.  On a percentage basis, the impact will be
most noticeable for  shallow water  segments and
predictions from the  hydrodynamic  model and
surface water model could be  affected.   Monthly
mean lake water levels varied  between 175.5 and
177.5 m for the period of record (1918-1997). Lake
levels during 1994 and  1995 were near the  average
for the period of record (Figure  1.4.10).

 1.4.5 Precipitation

Precipitation  influences  the   flux  of   airborne
contaminants to the  lake, impacts tributary loading
rates, and controls water levels. The 1982 and 1983
hydrodynamic years, and the 1994 and 1995 project
years were compared  to the previous 50 years of
data (Croley and Hunter, 1994).

1.4.5.1 Annual Comparisons

Precipitation to  Lake Michigan for 1982,1983,1994,
1995 were close to the 50-year mean for  the lake
(Figure 1.4.11).  1982 and 1983 were slightly above
the mean, and 1994 and 1995 were slightly below the
mean. 1995 total annual precipitation was very close
to the 50-year mean  for over-lake precipitation. No
visual trend was apparent in the total annual amounts
of precipitation over the 50-year period.
1.4.5.2 Monthly Comparisons

The  monthly mean precipitation  for 1982, 1983,
1994, and 1995 were compared to the 50-year mean
for the period of 1949 through 1998 (Figure 1.4.12).
For the years of interest, January, July, November
and December of 1982; May of 1983; and October of
1995 had relative high  amounts of precipitation,
exceeding one standard deviation of the  50-year
mean. For the four years of interest, February  of
1982; June of 1983; March, May and December  of
1994; and June of 1995 had relatively low amounts
of precipitation.  This illustrates that, in any one year,
precipitation varies from month-to-month while the
precipitation for  the year can be  at or near the
average expected.

1.4.6 Tributary Flows

Tributary flows impact the delivery of materials to the
lake, including nutrients and contaminants.  During
high flow events triggered by spring snow melt or rain
events, tributary flows increase and materials can be
carried from the watersheds to the tributaries. Within
thetributary, sediments containing contaminants may
resuspend.  Thus, the fluxes of solids, nutrients, and
contaminants to the  lake  have  the potential  to
increase  during high flow events.  Tributary flows
were  obtained from the United States Geological
Survey (USGS) website (www.usgs.gov). A historical
average and  median daily flow were calculated for
each tributary for the period of record, as well as for
the 1994-1995 and 1982-1983 time periods. During
1982  and 1983, tributary flows were approximately
20% greater than the average flows (Figure 1.4.13).
The  1994-1995 time period had relatively ordinary
tributary flows (Figure  1.4.14).

7.4.7 Wave Heights

In Lake Michigan, waves are the driving force for the
resuspension of sediments and  their associated
contaminants.  As waves move from offshore  to
inshore, they  begin to  interact with the lake bottom.
The  energy  associated  with the waves  serves  to
resuspend the sediments.  Lake Michigan  is deep
enough such that it can be divided into three zones
based upon the potential for waves to  resuspend
sediments.    The  zones  are  non-depositional,
                                              59

-------
   25
  20:


§15-
0)

f 10:
m
o.

£
'S
   0:
   -5
            south buoy
          _«_ data years for Lake Michigan
             hydrodynamic modeling
       T—i—i—i—|—i—r
    1980     1985
                    1990
                             1995
                                     2000
                                              2005
Figure 1.4.6. Monthly mean air temperatures in
southern Lake Michigan.
 O
 25

 20:

 15:

 '10:
 ID
 t
 £
 •5 -5:

  -10:
            north buoy
           ,. data years for Lake Michigan
            hydrodynamic modeling
 -15-
  1980
            1985
                     1990
                             1995
                                      2000
                                              2005
                                                         25
                                                          20-
                                                         O
                                                          10-
                                                                                               north buoy
                                                                                                 June
                                                           1980
                                                                    1985
                                                                            1990
                                                                                      1995
i  I i
2000
                                                                                                      2005
                                                        Figure  1.4.9.   Mean June air temperatures in
                                                        northern Lake Michigan.
                                                               Lake Michigan and Lake Huron water levels (1918-1997)
                                                               data' US Army Corps ol Engineers. Detroil District
                                                               —— monthly mean water lovcb (meters - IGLD 1985}
                                                               	, alt-time, record monthly hjgh and tow water levels
                                                        Figure  1.4.10. Record of mean  monthly water
                                                        levels for Lake Michigan.
Figure 1.4.7. Monthly mean air temperatures in
northern Lake Michigan.
   20
   16-
O

%
?
55
o.
4>
   12-
    4-
                                      south buoy
                                        June
    1980
           1985
                     1990
                              1995
                                      2000
                                               2005
Figure 1.4.8.  Mean June air temperatures  in
southern Lake Michigan.
                                                                    f\
                                                                                                50 y»at annual mMnj
                                                                                              -total annual precipilation
                                                        Figure  1.4.11.  Annual  precipitation  to  Lake
                                                        Michigan between 1949 and 1998.
                                                     60

-------
               1200 -
               1000 -
               600 •
               200
   Monthly mean precipitation
—*— 1949-1998mean
- -+- - 1949-1998 mean + 1 std.dev.
	•- -1949-1998 mean - 1 std.dev
  o 1982
—a—1983
	o—1994
— o— 1995
                    JAN    FEB   MAR    APR   MAY   JUN   JUL    AUG   SEP    OCT   NOV   DEC

Figure 1.4.12. Comparison of 1982,1983,1994, and 1995 monthly mean precipitation to the means for
the period of 1949 through 1998.
bUUU'
5000-



4000'

CD
£ 3000
o
2000
1000'
ni





















1












p.








f

Tributary flow
c historical mean
• 1982-83 mean

n 1982 mean
01983 mean



N r-«-n c-«










rn










.lfH.rfl.Ffnj I j









n
(n jn[ ]
Ifli;
.MII.J / rrn

                 '•s   i
                                                                         2"
                                                                         ra
                            '^   9
                                                          -^   TO
Figure 1.4.13. Comparison of tributary flow for hydrodynamic model calibration (1982-1983) to the
historic means.
                                                 61

-------
6000"
5000-
4000'

-------
following equation (U.S. Army Coastal Engineering
Research Center, 1973).
 'max
where,
        2TTL
                                        (1.4.2)
H =  wave height, cm

For both 1994 and 1995, mean wave heights at the
northern and southern buoys were similar to historic
mean and median  observations  (Table  1.4.2).
Maximum wave heights for these years were high at
the northern buoy relative to the historical mean and
median heights for both years; however, they were
less than the historical maximum wave height at both
locations.  At the southern buoy, maximum wave
heights were high in 1982,1983, and 1995 relative to
historic mean and median maximum heights. At this
location, the 1994 maximum height was lower than
historic means and medians. For all calibration and
study years, the maximum wave heights were neither
an extreme high or low for the period of  observation
at the two locations.

At both buoys, the annual maximum water depths of
wave interaction with sediments were not unusual.
Though  at the maximum in the southern basin (78
m), the maximum was achieved for more than one-
half the years of record (Table 1.4.2).

During the calibration and study years, the annual
maximum horizontal component of wave orbital
velocity ranged between 11.2 and 15.0 cm/s (Table
1.4.2).   These  observations were  close  to their
historical means.   Chambers  and Eadie  (1981)
hypothesized that thermal bar migration generated
currents of four to 13.4 cm/s which were enough to
resuspend surficial shelf sediment. For fine/medium
sand in southwestern Lake Michigan, a near-bottom
wave orbital velocity of 17.8 cm/s was enough to
initiate resuspension (Lesht, 1989).  Similar results
(18 cm/s) were found for silty sand in southeastern
Lake Michigan (Lesht and Hawley 1987). Sediment
resuspension  was found in  Hamilton  Harbor  at
bottom current speeds of 4.8 cm/s  (Brassard and
Morris, 1997).  Thus the annual maximum horizontal
component of wave orbital velocity was  sufficient to
at least once,  on an  annual basis, resuspend fine-
grained (silt and clay) sediment.
Therefore, it appeared that the repetitious use of the
1994 through 1995 Princeton Ocean Model (POM)
results will not introduce bias to the results.  Though
1994 and 1995 were not  the perfect mean  and
median situation, they were not singular extremes of
what has historically occurred within the lake.

1.4.8  Summary

Lake Michigan is acted upon by a number of physical
parameters that impact the physics, chemistry, and
biology of the lake.  For a  lake  the  size of Lake
Michigan, changes in these  parameters can lead to
significant changes,  especially when  models are
used in long-term predictions to predict the outcome
of various scenarios. The primary driving forces are
wind, air temperature, and precipitation.   These
impact tributary flows, lake levels, waves, water
circulation, water temperature, and ice cover. For the
period  of record, these driving forces varied from
year-to-year. The period of  1982 to 1983 was used
to calibrate the hydrodynamic models. Fortunately
for the period of time the models were calibrated,
conditions were not at any extreme. This was  also
true  for the period  of 1994 and 1995  when the
models were applied. However,  the impact of ice
cover remains a concern and will have to be dealt
with  in  the future.

Temperature can impact atrazine modeling.   Air
temperature impacts  how quickly the lake warms in
any  one year.   Water  temperature  impacts the
volatilization of atrazine. There appears to be a four-
year cycle of quicker warming which exists within a
trend of general warming of the lake.  The trend of
warming may be part of a longer term undocumented
cycle or may be related to climate change.  For future
modeling, these cycles and trends will have to be
considered to improve long-term predictions.

Precipitation will impact both lake levels and tributary
flows. Tributary flows have an impact on the delivery
of contaminants to the lake.  Precipitation was within
the normal range for all years of modeling interest,
resulting in lake levels and tributary flows that were
within normal bounds. Changes in lake levels as well
as the response of tributaries to precipitation events
will need to be considered for future modeling used
to predict changes of contaminants within the lake.
                                              63

-------
Table 1.4.2. Descriptive Wave Statistics for POM Calibration Years (1982-1983) and Study Years (1994-
1995) Compared to the Period of Record for NOAA's Buoys in Northern and Southern Lake Michigan
                                            Historical    Historical
      Description        Historical   Historical    Minimum    Maximum
                        Mean    Median      (Year)      (Year)
                       1982
         1983
        1994
       1995
 Northern Buoy

 Annual Maximum Wave       4.3        4.1
 Height, m

 Wave Height, m             0.7        0.6
 Annual Maximum Water
 Depth of Wave Interaction     14.1        65
 with Bottom Sediments, m

 Annual Horizontal
 Component of Maximum       4.9       13.4
 Orbital Wave Velocity,
 cm/s
3.1(1996)   5.9(1991)     4.0
          4.5
        4.6
   0.6         0.9
(numerous)    (several)
        5.3
  0.8      0.9       0.6      0.7
(Mean)    (Mean)   (Mean)   (Mean)
            96(1991)     65       78      78      78
           18.0(1984)    12.1      11.3      12.7     15.0
 Southern Buoy

 Annual Maximum Wave       4.5        4.2
 Height, m

 Wave Height, m             0.6        0.5
 Annual Maximum Water
 Depth of Wave Interaction    12.7        65
 with Bottom Sediments, m

 Annual Horizontal
 Component of Maximum       4.6        13.6
 Orbital Wave Velocity,
 cm/s
2.8(1991)   6.2(1998)     4.9
          5.3
        3.7
              78
           (numerous)
  65
           19.2(1998)    13.1
78
          15.0
65
        11.2
        5.2
   0.5         0.8        0.8       0.8      0.6      0.5
 (several)   (numerous)  (Mean)   (Mean)   (Mean)   (Mean)
78
       14.7
References

Assel, R.A., D.C. Norton, and K.C. Cronk. 2002.  A
    Great Lakes Digital Ice Cover  Data Base for
    Winters 1973-2000.    National  Oceanic  and
    Atmospheric  Administration,   Great   Lakes
    Environmental Research Laboratory, Ann Arbor,
    Michigan.  NOAA Technical Memorandum ERL
    GLERL-121,46pp.

Assel, R.A.  2003.   An Electronic  Atlas of Great
    Lakes Ice Cover. NOAA Great Lakes Ice Atlas.
    National   Oceanic   and    Atmospheric
    Administration,  Great  Lakes   Environmental
    Research Laboratory, Ann Arbor, Michigan.
       Brassard, P. and W. Morris.  1997.  Resuspension
           and  Redistribution  of  Sediments  in  Hamilton
           Harbor.  J. Great Lakes Res., 23(1):74-85.

       Campbell, J.E., A.M. Clites, and G.M. Green. 1987.
           Measurements of Ice Motion in Lake Erie Using
           Satellite-Tracked Drifter Buoys. National Oceanic
           and  Atmospheric Administration, Great Lakes
           Environmental Research Laboratory, Ann Arbor,
           Michigan. NOAA Technical  Memorandum  ERL
           GLERL-30, 22 pp.
                                                64

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Chambers, R.L and B.L Eadie. 1981.  Nepheloid
   and   Suspended  Particulate  Matter   in
   Southeastern Lake  Michigan.  Sedimentology,
   28(3):439-447.

Croley, I.E., II and T.S.  Hunter.  1994.  Great Lakes
   Monthly Hydrologic Data.  National Oceanic and
   Atmospheric  Administration,   Great  Lakes
   Environmental Research Laboratory, Ann Arbor,
   Michigan. NOAA Technical Memorandum ERL
   GLERL-83, 13pp.

Lesht, B.M. and N. Hawley.  1987.  Near-Bottom
   Currents  and   Suspended   Sediment
   Concentration in Southeastern Lake Michigan. J.
   Great Lakes Res., 13(3):375-386.

Lesht,  B.M.   1989.   Climatology  of Sediment
   Transport on Indiana Shoals, Lake  Michigan.  J.
   Great Lakes Res., 15(3):486-497.

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
   Mass Balance  Study:  Hydrodynamic Modeling
   Project.  National  Oceanic and  Atmospheric
   Administration,   Great  Lakes  Environmental
   Research  Laboratory,  Ann Arbor,  Michigan.
   NOAA Technical Memorandum ERLGLERL-108,
   53pp.
Sverdrup, H.U., M.W. Johnson, and R.H. Fleming.
   1942. The Oceans:  Their Physics, Chemistry,
   and   General   Biology.     Prentice-Hall,
   Incorporated, New Jersey, 1,087 pp.

U.S.  Army Coastal Engineering  Research Center.
   1973. Shore Protection Manual, Volumes 1, 2,
   and 3. U.S. Army Corps of Engineers, U.S. Army
   Engineering  Waterways  Experiment  Station,
   Vicksburg, Mississippi.

U.S.  Department of Commerce.  2002.  National
   Data Buoys. National Weather Service, National
   Oceanic and Atmospheric Administration, Ann
   Arbor, Michigan. National Data Buoy Center files
   downloaded from www.ndbc.noaa.gov.
                                             65

-------
                                          PART1
                                    INTRODUCTION
Chapter 5. PCBs in the Lake  Michigan
Ecosystem

Ronald Rossmann
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

1.5.1 Introduction

Within this chapter, polychlorinated biphenyl (PCB)
data for Lake Michigan are summarized. These data
are then compared to historical data.  Summaries of
the 1994-1995 data  are taken directly from the
"Results of the Lake Michigan Mass Balance Study:
Polychlorinated Biphenyls and frans-Nonachlor Data
Report"  (McCarty et al., 2004) and are referenced
appropriately. Presentation of the data are arranged
by media.

Details of the use of PCBs within the Lake Michigan
basin are difficult to document.  However, a list of
significant dates for the basin was compiled  from a
variety of online sources. PCBs appear to have been
first purchased for use in the basin in 1948  (Table
1.5.1).  Use appears to  have been phased  out by
1972.   Remediation of PCBs at various locations
began in the late 1980s with Sheboygan Harbor
being the earliest completed in 1991. Remediation
continues to this day.

1.5.2 Atmospheric

Atmospheric concentrations measured include vapor
phase, precipitation, particulate, and dry deposition
PCBs. Each of these is plotted for stations within the
basin that are representative of the annual seasons
for 1994 and 1995.

1.5.2.1 Vapor Phase

Median vapor phase concentrations of PCBs are
elevated  in the  northern  end  of the lake and
especially offshore of Chicago (Figure 1.5.1). The
elevation in the northern end of the lake is related to
the air station at  Beaver  Island.  The site was
discovered  to be  impacted by a local source of
contamination and was not representative of that
region of the lake.

Complete  data are summarized  in Table 1.5.2.
Monthly mean composite concentrations ranged from
110  pg/m3  outside the basin to 2600 pg/m3 at IIT
Chicago (McCarty et al., 2004).

Historic information are sparse.  Data sets vary in
temporal and spatial scales. Data sets found include
the period of  1976, 1979, and 1992-2002 (Murphy
and  Rzeszutko,   1977;  Rice et  al.,  1982; The
Integrated Atmospheric Deposition Network, 2004).
The  Integrated Atmospheric  Deposition  Network
(IADN) data for 2001 and 2002 were presented in a
graphic by  Boughton (2004) and attributed to the
IADN Steering Committee, unpublished 2004. Vapor
                                             66

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Table 1.5.1. Significant Dates in the History of PCBs in the Lake Michigan Basin


         Date                                         Event

         1865          First PCB-like chemical discovered
         1881          First PCBs synthesized
         1914          Measurable amounts of PCBs found in bird feathers
         1927          PCBs first manufactured at Anniston, Alabama
         1935          PCBs manufactured at Anniston, Alabama and Sauget, Illinois
       1948-1971        Outboard Marine Corporation at Waukegan, Illinois purchased eight million
                           gallons of hydraulic fluid with PCBs
 Mid-1950s to Mid-1960s  PCBs loaded to Kalamazoo River from deinking
    1950s to 1980s      PCBs discharged to Manistique River and Harbor
         1954          Appleton Paper Company began using PCBs as PCB-coated carbonless
                           copy paper
       1959-1971        PCBs used by Tecumseh Products Company as a hydraulic fluid was loaded
                           to Sheboygan River
       1959-1972        Outboard Marine Corporation at Waukegan, Illinois used hydraulic fluid with
                           PCBs for die-casting
       1969-1970        Paper company discharges of PCBs to Fox River peaked
         1970          PCB production peaked at 85 million pounds and huge contamination noted
                           at Sauget, Illinois plant
       1971 -1972        Appleton Paper Company and NCR Corporation phased out PCB use.
                           Recycling of carbonless paper had occurred for several decades
         1973          U.S. Food and Drug Administration (USFDA) establish 5 ppm PCB tolerance
                           level in fish
         1975          124,000 cans of salmon from Lake Michigan seized because of PCBs
         1977          PCB production ends
         1984          USFDA lowered PCB tolerance level in fish to 2 ppm
         1985          Commercial fishing for carp and other valuable species outlawed on Green
                           Bay
         1991          End Sheboygan River PCB remediation of upper river
         1991          U.S. Department of Health and Human Services label PCBs as possible
                           carcinogen
         1992          End Waukegan Harbor PCB remediation
         1998          The eight Great Lakes states agreed on a "Great Lakes Protocol for Fish
                           Consumption Advisories" that lowered the regional standard from the
                           USFDA commercial standard of 2 ppm down to 0.05 ppm
       1997-1998        Milwaukee River PCB remediation
         2001          Manistique Harbor PCB remediation completed
         2002          Possibly begin Grand Calumet River PCB remediation
                                            67

-------
                                                Naubinway
                                      Manistic
                       Escanab

             Scale

       0 km  40 km 80 km

               Menominee
              Charlevoix
               Green Bay
               Manitowoc,
              Sheboyga
            Milwaukee


                Racine


                      I
             Waukegan



                  Chicago^
                   Mackinaw City
                                           Muskegon
                                            Grand Haven
    Saugatuck


  South Haven

Benton Harbor
                           Gary
                                  Michigan City
Figure 1.5.1. Median concentration of vapor phase PCBs in the atmosphere during 1994 and 1995 for
all seasons of both years.
                                       68

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Table 1.5.2.  Monthly Composite Concentrations of Vapor Phase Total PCBs Measured in Samples
Collected Around Lake Michigan From April 1994 to October 1995
                     Sampling Station
             N     Mean (pg/m3)   SD (pg/m3)
Shoreline Atmospheric Beaver Island
Stations Chiwaukee Prairie
I IT Chicago
Indiana Dunes
Manitowoc
Muskegon
Sleeping Bear Dunes
South Haven
Out-of-Basin Atmospheric Bondville
Stations Brule River
Eagle Harbor
Over-Water Atmospheric Empire Michigan
Stations GB24M
1
5
6
110
18M
23M
27M
280
310
380
40 M
41
47M
MB19M
11M
19
19
19
19
19
18
15
19
19
19
4
4
4
5
6
4
3
5
4
5
4
3
1
4
2
5
1
2
970
320
2600
680
350
490
380
400
250
110
260
170
940
990
670
1200
810
560
490
360
480
650
290
340
21
410
280
2200
880
230
1900
580
260
410
550
360
150
110
370
77
1600
1500
590
1800
1200
930
600
410
570
430
NA
460
6.4
630
NA
3100
 phase PCBs have decreased dramatically since the
 very first measurements in 1976  (Figure  1.5.2);
 however,  the  IADN   data   set  vapor   phase
 concentrations at Sleeping Bear Dunes does not
 show a definitive trend between  1992 and 2002
 (Figure 1.5.3). For the period of 1992 through 2001,
 an examination of the temperature corrected PCB
 partial  pressure IADN data revealed that  partial
 pressures were declining with a half-life rate of 8.3 ±
 1.5 years (Buehler  et al., 2004).  The  1994-1995
 concentrations measured during the Lake Michigan
 Mass Balance Project (LMMBP) are considerably
 higher  (21  to  2,600  pg/m3)  than  concentrations
 observed at Sleeping Bear Dunes (50 to 110 pg/m3).
Thus it appears that Sleeping Bear Dunes does not
represent the Lake Michigan basin. Concentrations
observed at the Chicago IADN station were greater
than 1,000 pg/m3 for all the years (1993-2000)  of
observation (Buehler and Hites, 2002). Recently, an
average concentration of  1,900  pg/m3  PCBs was
reported for June 2001 at Milwaukee, Wisconsin
(Wethington and Hornbuckle, 2005).

1.5.2.2 Precipitation

Median precipitation  concentrations of PCBs are
elevated  in the  southern end  of  the  lake and
                                              69

-------
              8000
                 1970 1975 1980 1985 1990 199520002005
                                   'Year

Figure 1.5.2. Time variation of vapor phase PCBs in Lake Michigan.
               250
                 1990
1995         2000
       Year
2005
Figure 1.5.3. Time variation of vapor phase PCBs in Lake Michigan at Sleeping Bear Dunes based on
IADN data.
                                      70

-------
especially  offshore  of  Chicago  (Figure  1.5.4).
Concentrations decline from south-to-north.

Complete  data  are  summarized  in Table  1.5.3.
•Monthly mean composite concentrations ranged from
290 pg/L outside the basin to 16,000 pg/L at I IT
Chicago (McCarty et al., 2004).

Historic concentrations of RGBs in precipitation were
only available from IADN (2004). The time trend of
the data are plotted in  Figure 1.5.5.  There is no
apparent trend in the data.  Again, the 1994-1995
basin results (360 to 16,000 ng/L) are higher than
those reported by IADN for Sleeping Bear Dunes
(955 to 2,849  ng/L).  Sleeping Bear Dunes may
under-represent  the   PCB   concentration  in
precipitation.    Chicago concentrations  ranged
between 3,500 and 8,600 ng/L for the 1996-2000
period of observation (IADN, 2004).

1.5.2.3 Participate

Median atmospheric particulate concentrations of
PCBs are  elevated in the southern end of the lake
and especially offshore of Chicago (Figure 1.5.6).
Concentrations decline from south-to-north and then
increase slightly in the northern end of the lake. The
elevation in the northern end of the lake is related to
the air station at Beaver Island.  The  site was
discovered to  be impacted by  a  local  source of
contamination and was not representative  of that
region of the lake.

Complete  data are  summarized  in Table  1.5.4.
Monthly mean composite concentrations ranged from
0.37 pg/m3 at an over-water station to 91 pg/m3 at NT
Chicago (McCarty et al., 2004).

Historic particulate concentrations of PCBs were only
available from IADN (2004). The time trend of the
data are plotted in Figure 1.5.7. Again, the 1994-
1995 basin results (0.37 to 91 pg/m3) are higher than
those reported by IADN at Sleeping Bear Dunes (7.8
to  9.2  pg/m3).   Sleeping Bear  Dunes  under-
represents the PCB concentration  in particulates.
Samples collected in June  2001 at Milwaukee
averaged  50 pg/m3  (Wethington and Hornbuckle,
2005).
1.5.2.4 Dry Deposition

Some   measurements   were   made   of   PCB
concentrations  in  dry deposition  (Table 1.5.5).
Highest  mean  concentration   (315,000  ng/m2)
occurred at South  Haven  and the lowest  mean
concentration (1,830 mg/m2) occurred at the Chicago
SWFP crib intake (McCarty et al., 2004).  Thus the
mean range was very large and fluxes could be high.
However, the limited number of data points and high
standard   deviations   precluded  any further
interpretation of the data.

1.5.3  Lake Water

Water concentrations measured included dissolved,
particulate, and total PCBs. Results are plotted for
the 1994  through  1995  stations.  Only stations
representative of all annual seasons were included in
the figures.

1.5.3.1 Total PCBs

Total PCB concentrations in lake water were elevated
in southern and central Lake Michigan (Figure 1.5.8).
Of  note  were  high  concentrations  offshore  of
Milwaukee  and along the eastern  shoreline  from
South Haven to Manistee.  Highest concentrations
were  found  along  the  shoreline of  the   lake.
Concentrations are summarized in Table  1.5.6 and
averaged 0.25 ppb in  1994 and 0.27  ppb in  1995
(McCarty et at., 2004).

Historic data were available for the period of  1976
through 1994 (Chambers and Eadie,1980; Rice etal.,
1982; Anderson et al., 1999, Offenberg and Baker,
2000; Bicksler, 1996; Murphy and Rzeszutko, 1977;
Swackhamer and Armstrong, 1987; Filkins et al.,
1983; Lefkovitz, 1987; Pearson et al., 1996).  Since
1976, concentrations have dramatically decreased
(Figure 1.5.9). Expanding the scale and looking only
at the 1986 and later data, concentrations decrease
from 1986 to  1993; however, they appear constant
since 1993 (Figure 1.5.10).

1.5.3.2 Dissolved PCBs

Dissolved PCBs had a pattern similar to that of total
PCBs.  Concentrations were highest in the southern
basin of the lake with special note of the Milwaukee,
Michigan  City,  Saugatuck,  and  Grand  Haven
                                              71

-------
                                               Naubinway
                                     Manistique
                     Escanaba
            Scale
      0 km  40 km 80 km

             Menominee
               Charlevoix
             Green Bay
             Manitowoc
            Sheboygan,
          Milwaukee

              Racine



           Waukegan
                   'Mackinaw City
                                          Muskegon
                                           Grand Haven
    Saugatuck


  South Haven

Benton Harbor
                Chicago
                         Gary
                                 Michigan City
Figure 1.5.4.  Median concentration of wet (precipitation) PCBs in the atmosphere during 1994 and
1995 for all seasons of both years.
                                      72

-------
Table 1.5.3. Monthly Composite Concentrations of Total PCBs Measured in Precipitation Samples
Collected Around Lake Michigan From April 1994 to October 1995
Sampling Station
Shoreline Atmospheric Beaver Island
Stations Chiwaukee Prairie
NT Chicago
Indiana Dunes
Manitowoc
Muskegon
Sleeping Bear Dunes
South Haven
Out-of-Basin Atmospheric Bondville
Stations Brule River
Eagle Harbor
Over-Water Atmospheric Empire Michigan
Stations GB17
GB24M
1
5
23M
380
3000 n
2400 -
^1800 -
Q.
CD
01200 -
Q_
600 -
0 -
19'

: li
: A
/ \
/ I A
^ ^^

30 1995 2000
N Mean (pg/L)
20 1900
20 1800
17 16000
21 1500
20 2600
20 2600
16 1300
21 3800
21 1700
19 1700
4 290
4 2000
1 2300
1 680
1 750
1 1500
1 360
1 510







2005
SD (pg/L)
2800
1200
28000
1500
4200
4000
880
10000
1100
2900
300
2000







                                           Year

 Figure 1.5.5. Time variation of precipitation PCBs in Lake Michigan at Sleeping Bear Dunes based on
 IADN data.
                                           73

-------
                                              Naubinway
                                    Manistique
                    Escanaba.
           Scale
           ••=
     0 km  40 km  80 km

            Menominee
               Charlevoix
             Green Bay
             Manitowoc
            Sheboyga
          Milwaukee
              Racine
          Waukegan
                   'Mackinaw City
                                         Muskegon

                                          Grand Haven
    Saugatuck



  South Haven


Benton Harbor
                Chicago
                        Gary
                                Michigan City
Figure 1.5.6. Median concentration of particulate PCBs in the atmosphere during 1994 and 1995 for
all seasons of both years.
                                      74

-------
Table 1.5.4. Monthly Composite Concentrations of Particulate Phase Total PCBs Measured in Samples
Collected Around Lake Michigan From April 1994 to October 1995
Sampling Station
Shoreline Atmospheric Beaver Island
Stations Chiwaukee Prairie
I IT Chicago
Indiana Dunes
Manitowoc
Muskegon
Sleeping Bear Dunes
South Haven
Out-of-Basin Atmospheric Bondville
Stations Brule River
Eagle Harbor
Over-Water Atmospheric Spatial Composites
Stations Empire Michigan
GB24M
1
5
6
10 -
8 -
CO
£ 6-
D)
Q.
CQ 4 .
0 ^
Q_
2 -
0 -
19!

: \
: / 	
: /
: "j

90 1995 2000
N Mean (pg/m3)
18 52
19 22
19 91
19 33
19 26
16 24
15 18
18 23
19 25
18 21
4 14
18 19
4 14
2 3.9
1 2.6
3 17
1 0.37






2005
SD (pg/m3)
29
6.1
48
12
22
12
21
12
14
14
4.7
21
4.0
5.2

27


                                        Year

Figure 1.5.7. Time variation of atmospheric particulate PCBs in Lake Michigan at Sleeping Bear Dunes
based on IADN data.
                                          75

-------
 Table 1.5.5. Monthly Composite Concentrations of PCBs Measured in Dry Deposition
Sampling Station
Chicago SWFP Crib Intake
Harrison Crib
NT Chicago
Sleeping Bear Dunes
South Haven
N
9
1
13
8
11
Mean (ng/m2)
1830
5400
7060
6120
315,000
SD (ng/m2)
1710

6480
7940
1 ,020,000
                     NI
                    Scale
              0 km  40 km 80 km
                     Menominee
                     Green Bay


                     Manitowoc
                    Sheboygan)—^'
                  Milwaukee

                      Racine


                  Waukegan


                       Chicago1
                                           Manistique
                                                    Naubjnway
               >^  fCharlevoix
                                                               Mackinaw City
                                Gary
       ?\Muskegon
          Grand Haven

          Saugatuck

         South Haven

 /& / Benton Harbor

'Michigan City
Figure 1.5.8. Distribution of total PCBs (ng/L) in 1994-1995 Lake Michigan water.
                                           76

-------
Table 1.5.6.  Concentrations of PCBs in 1994-1995 Lake Michigan Water (ng/L)
  Year
Descriptive Statistics
Dissolved PCBs    Particulate PCBs    Total PCBs
  1994
  1994
  1994

  1995
  1995
  1995
      Mean
 Standard Deviation
 Number of Samples

      Mean
 Standard Deviation
 Number of Samples
     0.17
     0.11
     181

     0.21
     0.16
     142
0.079
0.088
 181

0.066
0.060
 142
0.25
0.16
181

0.27
0.19
142
                     1975    1980
                       1985    1990
                           Year
             1995    2000
Figure 1.5.9. Time variation of total PCBs in Lake Michigan water. Historic data from Chambers and
Eadie (1980), Rice et al. (1982), Anderson et al. (1999), Offenberg and Baker (2000), Bicksler (1996),
Murphy and Rzeszutko (1977), Swackhamer and Armstrong (1987), Filkins et al. (1983), Lefkovitz
(1987), and Pearson et al. (1996).
                                           77

-------
                         1985
1990
1995
2000
                                            Year
Figure 1.5.10. Time variation of total PCBs in Lake Michigan water since 1986. Historic data from
Anderson etal. (1999), Offenberg and Baker (2000), Bicksler (1996), Lefkovitz (1987), and Pearson et
al. (1996).
locations (Figure 1.5.11). Green Bay appears as a
source of dissolved PCBs to the lake.  For the lake
as a whole, concentrations averaged 0.17 ppb with a
standard deviation of 0.11 ppb in 1994 and 0.21 ppb
with a standard deviation of 0.16 ppb in 1995 (Table
1.5.6) (McCarty et al., 2004).

During the summer thermal stratification, dissolved
PCB concentrations in the hypolimnion are notably
high offshore of the region bounded by Racine and
Michigan City and centered on Waukegan (Figure
1.5.12).  This region  appears to  continue to be
impacted  by   the  historical  contamination at
Waukegan (Swackhamer and Armstrong, 1988).

Dissolved  PCB  data were  found for the  period of
1991 through 1994 (Anderson et al.,  1999; Offenberg
and Baker 2000; Pearson et al., 1996).  What few
data are available illustrate a  downward trend  in
concentration (Figure 1.5.13).  This is similar to the
trend observed for total PCBs.

1.5.3.3 Particulate PCBs

Particulate PCB concentrations were highest along
the shoreline, notably at Saugatuck and especially at
Milwaukee (Figure 1.5.14). Concentrations averaged
0.079 ppb with a standard deviation of 0.088 ppb in
1994 and 0.066 ppb with a standard deviation of
0.060 ppb in 1995 (Table  1.5.6) (McCarty et al.,
2004).
            Particulate  PCB  data were  derived  from  the
            difference   between  total   and   dissolved
            concentrations for the period of 1991 through 1994
            from the data of Anderson et al. (1999), Offenberg
            and Baker (2000), and Pearson et al. (1996). The
            variation of these and the project data is one of a
            decreasing trend (Figure 1.5.15).

            1.5.4 Tributaries

            Tributary water concentrations measured included
            dissolved and particulate forms.  Mean particulate
            concentrations  were  higher than mean dissolved
            concentrations  in the  Fox, Grand Calumet, Grand,
            Kalamazoo, Pere  Marquette, Sheboygan, and  St.
            Joseph Rivers (Table  1.5.7) (McCarty et al.,  2004).
            Concentrations of dissolved PCBs were highest in
            the Grand Calumet River and  lowest in  the Pere
            Marquette River. Concentrations of particulate PCBs
            were  highest in the Sheboygan  River and lowest in
            the Muskegon River.  Loads of total PCBs to Lake
            Michigan were highest for the Fox River and  lowest
            for the Manistique River (Figure 1.5.16).

            One historic data set for PCBs in Lake  Michigan
            tributaries was  found  (Marti and Armstrong,  1990).
            These data permitted comparison of the Fox, Grand
            Calumet,   Grand,    Kalamazoo,  Manistique,
            Menominee, Milwaukee, Muskegon, Pere Marquette,
            Sheboygan, and St. Joseph Rivers for dissolved and
            particulate PCB concentrations. Between 1980 and
            1995, dissolved PCB concentrations decreased at all
                                              78

-------
                                    Manistique
                                              Naubinway
                    Escanaba
           Scale
    0 km  40 km 80 km
            Me nominee
               Charlevoix
            Green Bay

            Manitowoc


           Sheboyga
         Milwaukee

             Racine


          Waukegan
                                                         Mackinaw City
                                         Muskegon
                                          Grand Haven
    Saugatuck

  South Haven

Benton Harbor
               Chicago
                        Gary
                                Michigan City
Figure 1.5.11. Distribution of dissolved PCBs (ng/L) in 1994-1995 Lake Michigan water.
                                   79

-------
                                              Naubinway
                    Escanaba.
           Scale
     0 km 40 km 80 km

            Menominee
             Green Bay


             Manitowoc,
           Sheboyga
          Milwaukee
              Racine
          Waukegan
                                    Manistique
               Chicago
               Charlevoix
                   Mackinaw City
                                         Muskegon
                                          Grand Haven
    Saugatuck


  South Haven

Benton Harbor
                        Gary
                                Michigan City
Figure 1.5.12. Distribution of dissolved PCBs (ng/L) in 1994-1995 summer hypolimnetic Lake Michigan
water.
                                     80

-------
                     0.75
                      0.6 -
                  •B>0.45 --
                  DO
                  O   0.3
                  a.
                     0.15 -
                         0

                         1990  1991   1992  1993  1994  1995  1996
                                               Year

Figure 1.5.13. Time variation of dissolved PCBs in  Lake Michigan water.


                                                       Naubinway
                                               Manistiq
                                  Escanaba.


                           Scale
                          ^•=n
                     0 km 40 km 80 km

                           Menominee
                            Green Bay



                            Manitowoc.



                           Sheboyga






                         Milwaukee


                            Racine




                         Waukegan



                              Chicago^
            Charlevoix
               'Mackinaw City
Pentwater
  Muskegon

   Grand Haven
   Saugatuck



  South Haven


Benton Harbor
                                           Michigan City
                                     Gary
Figure 1.5.14. Distribution of particulate PCBs (ng/L) in 1994-1995 Lake Michigan water.
                                              81

-------
                     -0.1
                       1990 1991  1992  1993  1994  1995  1996
                                         Year
Figure 1.5.15. Time variation of particulate PCBs in Lake Michigan water.
Table 1.5.7. Concentrations of PCBs Measured in Tributaries
        Fraction
Tributary
N
Mean (ng/L)     SD (ng/L)
Dissolved Fox River
Grand Calumet
Grand River
Kalamazoo
Manistique
Menominee
Milwaukee
Muskegon
Pere Marquette
Sheboygan
St. Joseph
Particulate Fox River
Grand Calumet
Grand River
Kalamazoo
Manistique
Menominee
Milwaukee
Muskegon
Pere Marquette
Sheboygan
St. Joseph
39
15
47
38
28
24
38
28
28
36
33
39
15
47
38
28
24
38
28
28
36
33
14
35
0.76
6.9
0.76
1.4
13
0.58
0.43
26
1.0
39
41
1.6
16
0.41
0.52
11
0.25
0.47
55
1.9
7.6
6.5
0.35
2.1
0.39
2.1
4.0
0.40
0.19
8.3
0.53
25
22
0.63
9.6
0.37
0.27
6.2
0.14
0.32
31
0.98
                                            82

-------
0.7-
-50.6-
TO
TJ
jj>0.5-
.f 0.4-
ra
00.3-
-Q
U
Q-0.2-
0.1 -
n -











II.. 	
x o ^^ £ ^ c fl) o c
o o c2 a. c ra 0 « o fcS =
u. Neacv ra a) ^ c a>£ocr
E O-= o O o g E J crtS
.20? •glg^iS'c
5 S5 ss^ssi
Figure 1.5.16.  Relative loads of PCBs to Lake Michigan from tributaries.
but one of the tributaries that could be compared
(Table  1.5.8).   Particulate  PCB  concentrations
declined at all locations  between 1980 and 1995
(Table 1.5.8).

1.5.5 Sediment

PCB concentrations in Lake Michigan surficial (0.5 to
1.5 cm  in thickness) sediments  ranged between
0.066 and 220 ng/g for samples collected between
1994 and 1996 (Table 1.5.9). This is similar to the
range reported by  Swackhamer and Armstrong
(1988)  for samples  collected between  1978 and
1980. The mean concentration in 1994-1996 was 47
ng/g. This is within the range of means reported by
Swackhamer and Armstrong (1988) for depositional
(81 ng/g), transitional (26 ng/g), and non-depositional
(7.2 ng/g) regions of the lake as defined by Cahill
(1981). All of  these results are considerably higher
than those reported by Frank et al. (1981) for 1975
(Table  1.5.9).   Results of the  1975  samples
represented samples that had  been freeze-dried.
Freeze-drying  can result in the loss of  volatile
contaminants  from the  sediment samples.  The
spatial variation of PCBs in the main lake surficial
sediments in  1994-1996 is  consistent with  that
reported by Frank et al. (1981) and Swackhamer and
Armstrong (1988). Concentrations are elevated in
southeastern Lake Michigan, offshore of Sheboygan,
and offshore of Frankfort (Figure 1.5.17). Of these
three areas,  PCB concentrations  are  highest  in
southeastern Lake Michigan.

Currently, LMMBP PCB results are available for six
sediment cores (Figure 1.5.18) (Eadie and Van Hoof,
personal communication). All of these cores illustrate
the decline in  PCB concentrations in recent times.
The  cores are of varying resoluteness  due to the
thickness of the surface mixed layer which results in
varying  particle residence times in the mixed  layer
(Table 1.5.10). Historic records indicate the first use
of PCBs in the basin began no earlier  than 1948.
PCBs occurring in cores prior  to 1948 represent
vapor phase  PCBs transported  to the  basin, the
failure of sealed sources within the basin, or physical
processes within the lake associated with  the surface
mixed layer of the cores. The surface mixed layer is
a surficial zone of the sediment that is consistently
mixed by physical or biotic processes. This mixing
homogenizes the sediment to a given depth referred
to as the mixed  layer.  The residence time of a
particle and its associated PCBs varies from core to
core (Bobbins  et al., 1999).  For the six cores this
residence time varies from 0.0 to 31.5 years.  Thus
some cores are  highly  resolved (LM-94-15)  and
some are poorly resolved (LM-95-58). This results in
differences for the apparent time of appearance of
PCBs in the cores.
                                              83

-------
Table 1.5.8. Comparison of PCB Concentrations in Samples Collected From Tributaries in 1994-1995
With Those in Samples Collected From Tributaries in 1980-1983 (Marti and Armstrong, 1990)

                      1980-1983          1994-1995         1980-1983         1994-1995
                    Dissolved Mean    Dissolved Mean    Particulate Mean    Particulate Mean
                     Concentration      Concentration      Concentration       Concentration
     Tributary           (ng/L)              (ng/L)	(ng/L)	(ng/L)
Fox River
Grand Calumet
Grand River
Kalamazoo
Manistique
Menominee
Milwaukee
Muskegon
Pere Marquette
Sheboygan
St. Joseph
17
24
16
9
6
6
28
4
4
34
7
14
35
0.76
6.9
0.76
1.4
13
0.58
0.43
26
1.0
81
220
41
31
18
9
69
5
10
69
7
39
41
1.6
16
0.41
0.52
11
0.25
0.47
55
1.9
Table 1.5.9. Concentrations of Total PCBs in Lake Michigan Surficial Sediment (ng/g)

   Year    Number of            Standard
 Collected   Samples     Mean    Deviation   Minimum   Maximum   Median        Source
1994-
1996
113
47
48
0.066
220
29
LMMBP
              179        9.7       15.7                                    Frank et a/., 1981

 1978-         60      7.2-81                  1.0        201               Swackhamer and
 1980                                                                     Armstrong, 1988
                                            84

-------
                           t
                           N      Escanaba,
                          Scale
                          ^•czi
                    0 km  40 km 80 km
                           Menominee
                            Green Bay


                           Manitowoc.


                          Sheboyga,




                         Milwaukee

                            Racine


                         Waukegan


                              Chicago*
                                                Manistique
                                                         Naubinway
                   Charlevoix
                       Mackinaw City
                                      Gary
         Muskegon
         Grand Haven

         Saugatuck

        South Haven

     ' Benton Harbor

^Michigan City
Figure 1.5.17.  Total PCBs in 1994-1995 Lake Michigan surficial sediments (ng/g).
                    D)
                      150 :
                       50
                                       AB
                                       --- A--
                        0 T i i—i—i—i—i—i—M-
                        2000  1980  1960  1940  1920 1900  1880
                                            Year

                                    • LM-94-15 V LM-95-86 A  LM-95-61

Figure 1.5.18. Vertical variation of PCBs in dated sediment cores collected for the LMMBP.
                                              85

-------
Table 1.5.10. Physical Parameters Associated With LMMBP Cores (Bobbins etal., 1999; Eadieand Van
Hoof, Personal Communication)
 Station Number
 Sedimentation
Rate g/cm2/year
  Mixed Layer
Residence Time,
     Year
Year of Peak PCB
  Concentration
Year of PCB Onset
Above Background
LM-94-15
LM-95-58
LM-95-61
LM-95-86
LM-95-103
LM-95-108
0.2235
0.0357
0.1064
0.0312
0.0266
0.051 1
0.0
31.5
7.8
14.9
23.1
12.5
1965, 1972

1969
1968
-1953
-1961
1943

1916
1917
Prior to 191 6
1932
Historic cores include those reported by Swackhamer
and  Armstrong (1988), Hermanson  et al. (1991),
Golden et al. (1993), and Schneider et al. (2001)
(Figures 1.5.19 to  1.5.22).  The core reported  by
Swackhamer and Armstrong (1988) was collected in
1980 (Figure 1.5.19).  It failed to capture any decline
in   PCBs  attributable  to  the ceasing  of  the
manufacture of PCBs in 1977. For cores collected in
1984, a decline in PCBs was documented in four of
the five cores (Figure 1.5.20).  For these cores, the
apparent peak of PCBs occurred after 1965. Cores
collected in 1991  and  1992  had  a  peak  PCB
concentration between the  early 1960s and  early
1980s (Figure 1.5.21), and the core collected in 1998
had a  peak concentration in the mid-1970s (Figure
1.5.22).  Peak concentrations in all these cores are
consistent with the  LMMBP cores which had a peak
concentration between the  early 1960s and  early
1970s (Figure 1.5.18).

7.5.6 Biota

Various biota  were analyzed  for  PCBs.   These
include phytoplankton, zooplankton, Diporeia, Mysis,
alewife, bloater, deepwater sculpin, smelt, slimy
sculpin, coho, and lake trout (Table 1.5.11).  Mean
concentrations ranged from 49 ng/g in phytoplankton
to  3,000 ng/g in lake trout.  For  alewife and bloater,
the fish were divided into two size classes based on
length.  For both of these, PCB concentrations were
higher in the larger  fish. Concentrations of PCBs in
members of the lake trout food web increase with
trophic  level, with  concentrations  lowest  in the
plankton, higher in benthos, and highest in the forage
                              fish,  illustrating biomagnification of  PCBs  (Figure
                              1.5.23).   For  lake  trout,  PCB  concentrations
                              increased in a predicable way with fish age until age
                              10 (Figure 1.5.24).  All PCB concentrations in lake
                              trout exceed  those of their prey.   For unknown
                              reasons,  age  11  fish  and   older  have  PCB
                              concentrations that vary in no predictable way with
                              increasing age.

                              PCB concentrations in lake trout have been declining
                              since 1975 (Figure 1.5.25). Similarly, concentrations
                              of PCBs have declined in bloater since 1974 (Figure
                              1.5.26).  These appear  to be  responding to the
                              phase-out of PCB use in the basin which occurred at
                              most locations by the early 1970s (Table 1.5.1).

                              1.5.7 Summary

                              Concentrations  of PCBs have declined  in  Lake
                              Michigan since  the phase-out of use by industries
                              within the basin.  Concentrations are highest in
                              southern  Lake Michigan for most media. For air and
                              water  media,  concentrations  are  highest  near
                              shoreline sources.

                              References

                              Anderson, D.J., T. B. Bloem, R.K. Blankenbaker, and
                                 T.A.  Stanko.     1999.     Concentration  of
                                 Polychlorinated Biphenyls in the Water Column of
                                 the Laurentian Great  Lakes: Spring 1993. J.
                                 Great Lakes Res. 25(1 ):160-170.
                                              86

-------
            .O)

            ^5>
            c
100



 80 ::



 60 :;



 40 ::



 20 -'-



   0
                         H	1	1-
H	1	1	1	1	1	1-
                  2000    1980    1960    1940   1920    1900

                                         Year
                                     -•- LM-80-18



Figure 1.5.19.  Vertical variation of PCBs in dated sediment cores reported by Swackhamer and

Armstrong (1988).
£3\J -I
onn
ZUU
-i t;n
1 ou
i nn
IUU
KH
ou
n
V

W DT
0
•
D
; 	 * v v
t * ° ° A P-
I ! I 1 i r 1 _ _l . _l 	 J.I I _ 1__J 	 !_„! 	 1 .. ]. 	 1 	 1 	 1 	 1 	
                  2000  1980   1960  1940  1920  1900  1880

                                      Year

                      -A- LM-84-SLM-F -m- LM-84-SLM-D -^- LM-84-CLM-M


                      -V- LM-84-NLM-B -Q- LM-84-NLM-E


Figure 1.5.20. Vertical variation of PCBs in dated sediment cores reported by Hermanson etal. (1991).
                                       87

-------
                ,o>
                ^)
200


160


120 '- -•-£•-*


 80 --


 40
                                  D
                                             P |  P ir
                     0

                     2000   1980   1960   1940   1920   1900

                                         Year


                            -m- LM-91-68R-A- LM-91-18 -B- LM-92-47s


Figure 1.5.21. Vertical variation of PCBs in dated sediment cores reported by Golden et al. (1993).
                      75
                      60 --
                     «45
                    O)
                    c
                      30
                      15 -
                       0

                       2000   1980    1960   1940   1920   1900

                                           Year
                                        LM-98-HMS-1
Figure 1.5.22. Vertical variation of PCBs in dated sediment cores reported by Schneider et al. (2001).
                                        88

-------
Table 1.5.11. Mean Concentrations of PCBs Measured in the 1994-1995 Lake Michigan Food Web

(McCarty et al. 2004)
            Sample Type
N
Mean (ng/g)
Standard Deviation (ng/g)
Phytoplankton
Zooplankton
Mysis
Diporeia
Smelt
Slimy Sculpin
Deepwater Sculpin
Alewife < 1 20 mm
Alewife > 1 20 mm
Bloater < 1 60 mm
Bloater > 1 60 mm
Coho - Hatchery
Coho - Yearling
Coho - Adult
Lake Trout (All Age Classes)
71
70
53
39
73
69
74
60
70
70
67
5
8
54
246
49
170
250
420
310
430
420
250
580
650
830
120
200
810
3000
38
74
61
100
83
130
200
150
140
180
210
27
90
520
2300
                 1000
                  800
               3  600
               m
               o
               a.


               $

               2.  400
                  200


































































C
'a.
0
CO
Iff
1 1 3
s ^ ^
5 D 1
o
a
a
E
en
I -S i









0
c
i
C
0
c :
: s
3 •*
Q.
O
S
"5.




c
^
J
2
£
Q

L



















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>





































































ff^
E
CO







































L.
9)
13
o
m







































Figure 1.5.23. PCB concentrations in various members of the lake trout food web during the LMMBP.
                                           89

-------
10000 -



8000-



I? 6000 -
c
CO
O
Q.
•§ 4000 -

2000-
r\
















































































CD K









0
0
O)
•^


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0
O)

oo
D)
<
f|,
CD

0
CD
CM
(P
m

^
CO
0
O)
T
1
0
n>
1
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< <






























T


IO

5. *
0
_ TO
2 < i
0
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3)
f


















Figure 1.5.24. PCB concentrations in various age classes of lake trout during the LMMBP.
                   E 20
                   Q.
                   a


                   m
                   o
                   Q. 10 +
                                      i
                       1970   1975   1980   1985   1990   1995   2000   2005


                                              Year

Figure 1.5.25.  Time variation of PCB concentration in five to six year-old lake trout from Lake

Michigan.
                                            90

-------
                    6.0
                    4.8 -
                CO
                o
                Q_
2.4  -•
                    1.2 -
                    0.0
                      1970        1980        1990        2000
                                           Year
Figure 1.5.26. Time variation of PCB concentrations in bloater from Lake Michigan.
Bicksler, J. 1996. PCBs in the Spring-Time Water
   Column of  the  Great  Lakes.   M.S. Thesis,
   University of Minnesota, Minneapolis, Minnesota.
   160pp.

Boughton, L.  2004.  Human Impact. Presented at
   the State of the Lakes Ecosystem Conference.
   Toronto, Ontario, Canada, October 6-8, 2004.

Buehler, S.S. and R.A. Hites.  2002.  The Great
   Lakes'  Integrated  Atmospheric  Deposition
   Network.  Environ. Sci. Technol., 36(17):354A-
   359A.

Buehler, S.S., I. Basu, and  R. A. Hites.  2004.
   Causes of  Variability in  Pesticide  and PCB
   Concentrations  in Air Near the  Great Lakes.
   Environ. Sci. Technol., 38(2):414-422.

Chambers, R.L. and B.J. Eadie.  1980. Nearshore
   Chemistry in the Vicinity  of the Grand River,
   Michigan.   National Oceanic and Atmospheric
   Administration,   Great  Lakes  Environmental
   Research  Laboratory,  Ann  Arbor,  Michigan.
   NOAA Technical  Memorandum ERL GLERL-28,
   28pp.
                            DeVault, D.S., R. Hesselberg, P.W. Rodgers, and
                               T.J. Feist. 1996.  Contaminant Trends in Lake
                               Trout and Walleye From the Laurentian Great
                               Lakes.  J. Great Lakes Res., 22(4):884-895.

                            Filkins, J.C., J.M. Townsend, and S.G. Rood. 1983.
                               Organochlorines in Offshore Waters of the Great
                               Lakes,  1981.   U.S. Environmental Protection
                               Agency, Office of  Research and Development,
                               ERL-Duluth,  Large  Lakes  Research Station,
                               Grosse lie, Michigan. 12pp.

                            Frank, R., R.L. Thomas, H.E. Braun, D.L. Gross, and
                               T.T. Davies.  1981.  Organochlorine Insecticides
                               and PCB in Surficial Sediments of Lake Michigan
                               (1975). J. Great Lakes Res., 7(1):42-50.

                            Golden,  K.A., C.S. Wong,  J.D. Jeremiason, S.J.
                               Eisenreich,   G.  Sanders,   J.  Hallgren,  D.L.
                               Swackhamer, D.R. Engstrom, and D.T. Long.
                               1993. Accumulation and Preliminary Inventory of
                               Organochlorines in  Great  Lakes Sediments.
                               Water Sci. Technol., 29(8-9):19-31.

                            Hermanson, M.H., E.R. Christensen, D.J. Buser, and
                               L.  Chen. 1991.  Polychlorinated Biphenyls in
                               Dated Sediment Cores From Green Bay and
                               Lake  Michigan.    J.   Great  Lakes  Res.,
                               17(1):94-108.
                                             91

-------
Integrated Atmospheric Deposition Network.  2004.
   Download   from    the   web   site
   www.msc.ec.gc.ca/iadri/Data/form/form_e.html.
   "The Integrated Atmospheric Deposition Network,
   established  in  1990,  is  implemented by  the
   Canadian Federal  (Environment Canada) and
   Provincial (Ontario Ministry of the Environment)
   Governments  and the  U.S.  Environmental
   Protection Agency as Mandated in Annex 15 of
   the Great  Lakes  Water Quality  Agreement
   (GLWQA)."

Lefkovitz. L.F.    1987.   The  Particle Mediated
   Fractionation of PCBs in  Lake Michigan. M.S.
   Thesis,  University of  Wisconsin, Madison,
   Wisconsin. 238 pp.

Marti,  E.A.   and   D.E.   Armstrong.     1990.
   Polychlorinated Biphenyls  in  Lake  Michigan
   Tributaries. J. Great Lakes Res., 16(3):396-405.

McCarty, H. B., J. Schofield, K. Miller, R. N. Brent, P.
   Van Hoof, and B. Eadie.  2004. Results  of the
   Lake   Michigan   Mass   Balance  Study:
   Polychlorinated Biphenyls and frans-Nonachlor
   Data Report.   U.S. Environmental Protection
   Agency,  Great Lakes National Program Office,
   Chicago, Illinois. EPA/905/R-01/011, 289 pp.

Murphy, T.J. and C.P. Rzesutko.  1977.  Precipitation
   Inputs of PCBs to Lake Michigan. J. Great Lakes
   Res., 3(3-4):305-312.

Offenberg, J.H. and J.E. Baker. 2000.  PCBs and
   PAHs in Southern Lake Michigan  in 1994 and
   1995:   Urban  Atmospheric  Influences and
   Long-Term Declines. J. Great Lakes Res. 26(2):
   196-208.

Pearson, R.F., K.C. Hornbuckle, S.J. Eisenreich, and
   D.L. Swackhamer.   1996.   PCBs in Lake
   Michigan Water  Revisited.   Environ. Sci.
   Technol., 30(5):1429-1436.
Rice, C.P., B. J. Eadie, and K.M. Erstfield. 1982.
   Enrichment of PCBs in Lake Michigan Surface
   Films.  J. Great Lake Res., 8(2):265-270.

Robbins, J.A., N.R. Morehead, R.W.  Rood, D.N.
   Edgington, and S. Meyer.  1999.  Accumulation
   and  Near-Shore Mixing of Sediments in Lake
   Michigan as Determined for the Lake Michigan
   Mass Balance Program. Part 1. Cores Collected
   between 1994  and 1996  (2 Volumes).   Final
   Report. U.S. Environmental Protection Agency,
   Office  of  Research and  Development, ERL-
   Duluth, Large Lakes Research Station, Grosse
   lie, Michigan. 274 pp.

Rodgers, P.W. and W.R. Swain. 1983.  Analysis of
   Polychlorinated Biphenyl (PCB) Loading Trends
   in Lake Michigan.   J. Great Lakes Res. 9(4):
   548-558.

Schneider, A.R., H.M. Stapleton, J. Cornwell, and
   J.E.  Baker.  2001.   Recent Declines in PAH,
   PCB, and  Toxaphene  Levels  in the Northern
   Great Lakes as Determined from High Resolution
   Sediment   Cores.    Environ.  Sci.  Technol.,
   35(19):3809-3815.

Swackhamer,  D.L. and D.E.  Armstrong.   1987.
   Distribution and Characterization of PCBs in Lake
   Michigan  Water.    J.  Great  Lakes  Res.,
   13(1):24-36.

Swackhamer,  D.L. and D.E.  Armstrong.   1988.
   Horizontal and Vertical Distribution  of PCBs in
   Southern  Lake Michigan  Sediments and the
   Effect of Waukegan Harbor as a Point Source. J.
   Great Lakes Res., 14(3):277-290.

Wethington, D.M.  And  K.C.  Hornbuckle.   2005.
   Milwaukee, Wl, as a Source of PCBs to Lake
   Michigan.   Environ. Sci. Technol., 39(1):57-63.
                                             92

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                                          PART1
                                    INTRODUCTION
Chapter 6. Congener Pattern Matching of
Data Collected  for  the  Lake  Michigan
Mass Balance Project (LMMBP)

David A. Griesmer
Computer Sciences Computer
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

1.6.1 Introduction

As part of the quality assurance (QA) process for the
Lake Michigan  Mass Balance Project (LMMBP), a
LMMBP PCB Modeling Peer Review Conference was
held on July 27-28, 2004 at the Crowne Plaza Hotel
in Romulus,  Michigan  to  review  the  LMMBP
polychlorinated biphenyl (PCB) models developed for
this study. One of the recommendations to come out
of this peer review  was  to "Investigate  congener
patterns in air, water, fish, and sediment.  How do
these compare?" (Part 7, Appendix 1).  In response
to this question, the peer review response document
states that: "The PCB patterns of multiple media will
be  compared  to   determine   similarities   and
differences within and among media. This technique
is commonly referred to as PCB fingerprinting or PCB
signature recognition and has had mixed success in
the  past.  This  recommendation   has  minor
implications to the modeling; however, it is a data
analysis tool and has merit for data presentation and
interpretation purposes. The relative percent of total
PCBs represented  by  each  congener  will be
computed and  then  expressed  as  a cumulative
frequency plot for comparative purposes. These will
represent data for an entire study period, will be
tested with both mean and median values, and will
be a composite expression of seasonal and spatial
data.  In addition, selected  evaluation of  pattern
recognition using the LMMBP data set can be found
in  Kuehl  (2002)  and   McCarty et  al.  (2004).
Fingerprints will be calculated for sediment, water
column (dissolved and particulate), vapor phase, wet
and dry atmospheric deposition, and age 5-6 year-old
lake trout signatures from the Saugatuck  biota site.
Atmospheric signatures will be based on a subset of
all congeners  because  vapor phase data were
computed by Keri Hornbuckle for the study, and over-
lake  concentrations  were only calculated  for the
congeners that are being modeled at Grosse lie. In
addition,  PCB  patterns  associated  with  water
discharging from the Kalamazoo River near the
Saugatuck biota site and other selected  tributaries
will be compared/contrasted to the lake water".

One  of the main objectives of  this analysis was to
see if there was a correlation between  congener
patterns in the  LMMBP biota  boxes  and  the
atmospheric, tributary,  and  sediment sources of
congener  contamination.   To  accomplish  this,
congener pattern matching analysis was  expanded
from that originally suggested in the  peer review
modelers' response.  This included congener pattern
matching for all 11  major tributaries, as  well as a
comparison of atmospheric inputs for all 10 surface
segments defined in the LM2-Toxic model.   In
addition, ages 5-6 year-old lake trout were evaluated
in Sturgeon Bay, Sheboygan Reef, and Saugatuck
biota boxes (Figure 1.6.1),  instead  of just in the
Saugatuck biota box. Multiple year classes (age 2,
age 3, and age 9), of lake trout were analyzed for the
Saugatuck biota box, to see if there were differences
in the congener patterns of different aged lake trout.
                                             93

-------
   segment-. /'' A/-'1 .i, '
     9 "  '
segment <*•
  10 ;;'
            __
   x< ../  Sturgeon Bay
    /   LMMB-biota1
                               LM2 surface water
                               segmentation and
                               LMMB biota boxes
 Sheboygan Reef si?
  LMM3-biota2 ife^f

     } segment 1
HI
biota survey boxes
segment 2 I """•-..,
                 Saugatuck .
                LMMB-blota3
Figure 1.6.1.  LM2 surface water segmentation
and LMMBP biota boxes.
 1.6.2  Analytical Approach

This exercise  was  not  meant to be a rigorous
statistical analysis of the data, rather it is strictly an
empirical look at congener patterns in different media
collected for the  LMMBP.   All available congener
data were included in the analysis.  Analysis was
done for most of the media collected for PCBs during
the  LMMBP (atmospheric,  tributary, lake  water,
sediment, and biota).  Kuehl (2002) previously had
done a comparison of congener patterns of the biota
media (phytoplankton, zooplankton, Mysis, Diporeia,
bloater chub, slimy and deepwater sculpin, alewife,
rainbow smelt, coho salmon, and lake trout) collected
for the LMMBP.  Therefore, with the exception of lake
trout, these biota media were not examined in the
present analysis. For all of the analyses done for the
present study, comparisons were made by plotting
the congener patterns and doing a visual comparison
for obvious similarities or differences.

1.6.3  Methodology

PCB  congener  analysis  for  the  LMMBP were
performed  by  a  number of  different principal
investigators, using  different  instrumentation and
techniques, which are detailed in the LMMB Methods
Compendium   (U.S.   Environmental   Protection
Agency, 1997).   These variation in methods may
have had some impact of the comparison of samples
from different media due to co-elution and congener
detection differences from media-to-media.

All analyzed congeners were used to maximize the
amount of data  available.  All analyses were done
using  Microsoft  Excel  spreadsheets.  Congeners
were ordered in each media by congener number,
with co-eluting congeners ordered by the lowest co-
eluting congener number (see Table 1.6.1)  Data
were  analyzed  by  media,  where the  mean and
median values were calculated for each congener.
Cumulative frequency analysis was then performed
on the means and medians for each congener. The
percent frequency of each congener was calculated
by dividing the means and median of each congener
by the total sum of all the congeners in that media to
give  a percent  value to each congener.   These
percentages  were  added  together  to  give  a
cumulative frequency distribution which totals to one.

These  data were graphed, and visual comparisons
were done.

1.6.4  Results

1.6.4.1    Comparison of  Modeled  Congener
Patterns to All  Analyzed Congener Patterns

In an effort to make use of as  many congeners as
possible in this  analysis, all congeners analyzed in
each media were used rather than just the modeled
                                              94

-------
Table 1.6.1 Comparison of Congeners Available for Analysis in All LMMBP Media
Available Congeners in All Media
Modeled
Congeners
8+5
15+17
16+32
018
026
28+31
033
37+42
044
049
052
56+60
066
70+76
074
77+110
081
92+84
085
087
089
099
101
132+153+105
118
123+149
163+138
146
151
170+190
172+197
180
187+182
195+208
196+203
201

















Vapor
Phase
8+5
15+17
16+32
018
026
28+31
033
37+42
044
049
052
56+60
066
70+76
074
77+110
081
92+84
085
087
089
099
101
132+153+105
118
123+149
163+138
146
151
170+190
172+197
180
187+182
195+208
196+203
201

















Wet
Deposition
8+5
012
013
15+17
016
018
026
31+28
032
033
037
042
044
049
052
56+60
066
70+76
074
077
081
92+84
085
087
089
099
101
132+153+105
110
118
123+149
163+138
170+190
172
180
187+182
208+195
196
197
201
203












Dry
Deposition
8+5
012
013
15+17
016
108
1026
31+28
032
033
037
042
044
049
052
56+60
066
70+76
074
077
081
92+84
085
087
089
099
101
132+153+105
110
118
123+149
163+138
146
151
170+190
172
180
187+182
108+195
196
197
201
203










Open Lake
Water
4+10
8+5
006
7+9
012
013
014
15+17
016
018
019
021
022
024
025
026
027
31+28
029
032
033
37+42
040
41+71
043
044
045
046
47+48
049
051
052
053
56+60
063
064
066
70+76
074
77+110
081
082
083
92+84
085
087
089
091
095
097
099
100
101
Surficial
Sediment
8+5
006
7+9
012
013
15+17
016
018
019
021
022
24+27
025
026
31+28
029
032
033
37+42
040
41+71
042
043
044
045
046
47+48
049
051
052
053
56+60
063
064
066
70+76
074
77
081
082
083
92+84
085
087
089
091
095
097
099
100
101
105+132+153
107
Tributary
003
4+10
8+5
006
7+9
15+17
16+32
017
018
019
022
24+27
025
026
31+28
033
37+42
040
41+71+64
044
045
046
47+48
049
051
052
053
56+60
063
066
66+95
70+76
074
77+110
082
083
92+84
085
087
089
091
095
097
099
101
105+132+153
118
123+149
128
132+153
135+144
136
137+176
Lake
Trout
022
24+27
31+28
029
033
040
41+71
042
044
47+48
049
052
56+60
063
064
066
70+76
074
77
81+87
082
083
92+84+89
085
091
095
097
099
101
105
107
110
114
118
119
123
126
128
129
131
132+153
134
135+144
137+176
163+138
141
146
149
151
156
157
158
167
                                          95

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Table 1.6.1.  Comparison of Congeners Available for Analysis in All LMMBP Media (Continued)
Available Congeners in All Media
Modeled Vapor Wet Dry Open Lake
Congeners Phase Deposition Deposition Water
103
105+132+153
107
114+131
118
123+149
147+124
128
129
130
134
135+144
1.36
137+176
163+138
141
146
151
156
157+200
158
167
170+190
202+171
172+197
173
174
175
177
178
180
187+182
183
185
189
191
193
194
208+195
196
198
199
201
203
205
206
207
Total # 209
Congeners:
Surficial
Sediment
110
114+131
118
119
123+149
128
129
130
134
135+144
136
137+176
163+138
141
146
151
156
157+200
158
167
170+190
202+171
172
173
174
175
177
178
180
187+182
183
185
189
191
193
194
208+195
203+196
197
198
199
201
205
206
207
209



Tributary
163+138
141
146
149
151
158
167
170+190
202+171
172
172+197
174
177
178
180
187+182
183
185
193
194
208+195
203+196
198
199
201
206
207






















Lake
Trout
170+190
171
172
173
174
175
177
178
180
187+182
183
185
189
191
193
194
195
203+196
197
198
199
200
201
202
205
206
207
208
209




















     54
54
                           54
                      56
                                                 127
                                            123
107
98
                                           96

-------
congeners. Because most of the comparisons were
in the same media, this would give the  maximum
number of data points.  However, there were some
concerns when comparing data from different media.
To see if this  approach was feasible,  an  initial
comparison was done to see if the congener pattern
trends seen using only modeled  congeners were
different from the congener patterns seen when "all
available congeners" were analyzed (Table 1.6.1).
This  analysis was done for a number of different
media  (vapor  phase,  wet  and  dry deposition,
dissolved and particulate PCBs in lake water, surficial
sediments, and 5-6 year-old lake trout).

The definition of "all  congeners" varied somewhat,
depending on the medium  being  examined.  For
dissolved and particulate water, sediment, tributary,
and lake trout data,  all congeners reported were
used in this analysis.  This number varied from a low
of 98 congeners in the lake trout analysis to a high of
127 congeners in the open lake water samples. For
the atmospheric samples, Keri Hornbuckle calculated
over-water  concentrations   of  only  modeled
congeners in vapor phase, as well as wet  and dry
deposition data (Table 1.6.1);  therefore, the  only
congeners available in this media are the 54 modeled
congeners.   While  over  100  congeners  were
available  from Hornbuckle in the atmospheric wet
and dry deposition data sets, only a subset were
used because  data  needed to  be aggregated by
LM2-Toxic modeling  segment.  These aggregated
data sets contained only 54  congeners of wet
deposition data and 56  congeners  of dry deposition
data (Table 1.6.1).

This comparison was limited to the samples collected
in or near the Saugatuck Biota Box, LMMBP-BiotaS,
for lake trout and surficial sediment, or to LMMBP
modeling  segment 2  (Figure 1.6.1), which contains
the Saugatuck biota  box, for the atmospheric and
lake  water samples.   In addition, dissolved  and
particulate water data for the Kalamazoo River were
included,  because  this  is  the  tributary which
discharges water closest to the Saugatuck biota box.

Cumulative frequency plots comparing all available
congener data  and modeled congener data were
then  created.   This  comparison  showed that the
trends in the different media were similar, whether
modeled congeners or all available congeners were
used  (Figures  1.6.2-1.6.10).   This  is  not too
surprising because the 54 modeled congeners made
up the bulk of the congener mass in all media
sampled, accounting for an average of 74% of the
mass across all media (77.3% of vapor phase PCB
mass, 63.4% of wet deposition PCB mass, 73% of
dry deposition  PCB mass, 80.2% of tributary water
PCB mass,  67.5% of  dissolved lake  water  PCB
mass, 77.2% of particulate lake  water  PCB mass,
84.7% of surficial sediment PCB mass, and 67.1 % of
lake trout PCB mass). Because  these trends were
similar, it was decided that all available congeners
would be used  in the data analysis, so that we could
take  advantage of  the  maximum amount of  data
points.

1.6.4.2 Comparison  of Median to Mean Data

A comparison between mean and median values in
different media was done to see  how much results
varied between averaging methods. In vapor phase
samples, dry deposition samples, dissolved and
particulate tributary water samples, there was almost
no difference  between median  and  mean  plots
(Figures 1.6.11  1.6.14). In wet deposition samples,
dissolved and  particulate  water  samples,  surficial
sediment samples, and 5-6 year-old lake trout
samples, there were some small differences in the
plots (Figures 1.6.15   1.6.19), with median values
being somewhat lower in all instances due to the fact
that  some  of  the  congeners have  zero  median
values.  These zero median values were the result
of a large number of congeners with reported values
of zero  or near zero.  We  decided to use mean
values for the  rest of the analysis because  there
appeared to be little difference between median and
mean plots and to avoid the zero  median values.

1.6.4.3  Comparison of Congener  Patterns in
Different Media in Segment 2/Saugatuck Biota
Box

When the congener patterns for different  media in
segment 2  and the  Saugatuck biota box  were
compared (Figure  1.6.20),  the  following  general
trends were identified. Vapor phase data appeared
to have a higher percentage of lower chlorinated
congeners than any of the other  media. Dissolved
PCB congeners in water from the Kalamazoo River
most nearly matched  the congener pattern seen in
the vapor phase, with  the  congener pattern for
dichloro,  tetrachloro  -  decachloro's  most closely
                                              97

-------
   1.0
   0.9
   0.8-
   0.7
   0.6-
   0.5
   0.4-
   0.3-
   0.2
   0.1 •
                              PCB congeners in
                            segment 2 vapor phase
                             B» all congeners
                             — modeled congeners
      1  17 "33" 49" 65 81 97" 113 129 145 161 177 193 209
                      congener

Figure 1.6.2.  Cumulative frequency distribution
- PCB congeners in segment 2 vapor phase.
                             dissolved PCB congeners
                               in segment 2 water
                              — all congeners
                              — modeled congeners
                                                           1  17 33 49  65  81  97 113129145161177193209
                                                                             congener

                                                     Figure 1.6.5. Cumulative frequency distribution
                                                     - dissolved PCB congeners in segment 2 water.
                               PCB congeners in
                            segment 2 dry deposition
                              'an, all congeners
                              — modeled congeners
                                                        i.o
            33  49  65  81  97 113129145161177193209
                       congener

 Figure 1.6.3.  Cumulative frequency distribution
 - PCB congeners in segment 2 dry deposition.
                                                      g  0.9-
                                                      o>
                                                      g 0.8-
                                                        0.6-
o
C 0.
j! 0.3-1
o
c 0.2
                                                         0-M
                                                                                  participate PCB congeners
                                                                                    in segment 2 water
                                                                                   ~=- all congeners
                                                                                   — modeled congeners
                                                          1  17  33  49"65 81 gy'flS 129 145 161 177 193209
                                                                            congener
                                                     Figure 1.6.6.  Cumulative frequency distribution
                                                     - particulate PCB congeners in segment 2 water.
                               PCB congeners in
                            segment 2 wet deposition
                                all congeners
                             — modeled congeners
      1  17  33 49  65  8l""g'f'm 'l29 i"45"l61 "H~7 193"'209
                       congener
Figure 1.6.4. Cumulative frequency distribution
- PCB congeners in segment 2 wet deposition.
                                                                                     PCB congeners in
                                                                                 segment 2 surficial sediment
                                                                                    «- all congeners
                                                                                    — modeled congeners
                                                                 33  49  65  81 97 113 129 145 161 177 193 209
                                                                             congener
                                                     Figure 1.6.7.  Cumulative frequency distribution
                                                     -   PCB  congeners   in   segment  2  surficial
                                                     sediment.
                                                  98

-------
 1.0
 0.9-
 0.8-
 0.7-
 0.6-
 0.5
 0.4
 0.3-
 0.2
                            dissolved PCB congeners
                            in Kalamazoo River water
                             -»- all congeners
                             — modeled congeners
                           ~	              r
      1  17 33 49 65  81  97  113129145161177193209
                       congener
Figure 1.6.8.  Cumulative frequency distribution
- dissolved PCB congeners in Kalamazoo River
water.
                                                                                     PCB congeners in
                                                                                   segment 2 vapor phase
                                                                                        -^i median
                                                                                                   r
                                                              17  33  49  65  81  97 113129145161 177193209
                                                                            congener
                                                     Figure 1.6.11.  Cumulative frequency distribution
                                                     - PCB congeners in segment 2 vapor phase.
   1.0
   0.9
   0.7 ^
« 0.5-
g 0.4-
3 0.3-
o
£ 0.2-
                           particulate PCB congeners
                           in Kalamazoo River water
                             -=- all congeners
                             — modeled congeners
      1  17 33 49 65 81  97 113129145161177193209
                      congener
Figure 1.6.9.  Cumulative frequency distribution
- particulate PCB congeners in Kalamazoo River
water.
                                                                                    PCB congeners in
                                                                                  segment 2 dry deposition
                                                                                        *"« median
                                                             17  33  49  65  81  97 113 129 145 161 177 193 209
                                                                            congener
                                                     Figure 1.6.12.  Cumulative frequency distribution
                                                     - PCB congeners in segment 2 dry deposition.
   1.0
   0,9
   0.8
   0.7
   0.6-
   0.5
   0.4'
   0.3-
   0.2-
   0.1 •
    0-
                          Age 5-6 Saugatuck Lake Trout
                          |  — all PCB congeners     :
                          |  — modeled PCB congeners :
                          !	    r
      1  17 33 49  65  81  97 113129145161 177193209
                       congener
Figure 1.6.10. Cumulative frequency distribution
- age 5-6  Saugatuck lake trout.
                                                                                dissolved PCB congeners in
                                                                                  Kalamazoo River water
                                                                                      =^ median
                                                              17 33 49 65  81  97  113129145161177193209
                                                                            congener
                                                     Figure 1.6.13. Cumulative frequency distribution
                                                     - dissolved PCB congeners in Kalamazoo River
                                                     water.
                                                  99

-------
                           particulate PCB congeners
                            in Kalamazoo River water
                                 ecu median
                                 — mean
           33 49 65 81 97 113129145161177193209
                      congener
Figure 1.6.14. Cumulative frequency distribution
- particulate PCB congeners in Kalamazoo River
water.
   1.0
   0.9-
   0.8
   0.7-
   0.6-
   0.5-
   0.4-
   0.3-
   0.2
   0.1 •
     0-
    PCB congeners     :
in segment 2 wet deposition
      -' - median       !
      — mean
      1   17  33  49  65  81  97 113129145161177193209
                      congener

Figure 1.6.15.  Cumulative frequency distribution
- PCB congeners in segment 2 wet deposition.
                             dissolved PCB congeners
                               in Saugatuck water
                                  a-n median
                                  — mean
      1  17
            33 49  65  81  97  113129145161177193209
                       congener
Figure 1.6.16. Cumulative frequency distribution
- dissolved PCB congeners in Saugatuck water.
                                                      particulate PCB congeners '
                                                      :   in Saugatuck water
                                                      i      B.«, median
                                                            — mean
                                   17  33  49  65 81  97 113129145161177193209
                                                 congener
                         Figure 1.6.17. Cumulative frequency distribution
                         - particulate PCB congeners in Saugatuck water.
                                                           PCB congeners in
                                                           surficial sediment
                               1   17  33 49  65 81  97 113129145161177193209
                                                congener
                         Figure 1.6.18. Cumulative frequency distribution
                         - PCB congeners in surficial sediment.
                                                                                 PCB congeners in age 5-6
                                                                                   Saugatuck lake trout
                                                                                       !«= median
                                                          1  17  33  49  65  81  97 113129145161177193209
                                                congener
                         Figure 1.6.19. Cumulative frequency distribution
                         - PCB congeners in age 5-6 Saugatuck lake trout.
                                                 100

-------
           All PCB congeners
           segment 2 / Saugatuck
                dissolved water
           •^~ Kalamazoo River dissolved water
           •	»" Kalamazoo River participate water
           	 participate water
           '"""'"" wet deposition
                dry deposition
                vapor phase
                surficial sediment
                age 5-6 year lake trout
              17    33    49    65    81    97   113   129   145   161   177   193   209
                                        congener

Figure 1.6.20. PCB congeners in segment 2, Saugatuck.
matching the vapor phase data. The pattern seen for
open lake dissolved water samples did not closely
match any of the other media. Open lake dissolved
PCB congeners in water had a lower percentage of
lower chlorinated congeners than  either the vapor
phase or dissolved water congener samples from the
Kalamazoo River, but it had a higher percentage of
these congeners than any of the other media.

The Kalamazoo River particulate PCB  congeners in
water had a higher percentage of lower congeners
than open lake particulate PCB congeners in water
and closely matched the congener patterns seen in
both wet and dry deposition data for segment 2.
Open lake  particulate PCB congeners data most
closely matched the surficial sediment for segment 2.
Age  5-6 year-old  lake trout  had a  much  lower
percentage of lower chlorinated congeners than any
of the other media.
1.6.4.4
Data
        Comparison of Atmospheric Congener
Atmospheric PCB samples for vapor phase and wet
and dry deposition media were collected from  a
number  of  land-based  stations  around  Lake
Michigan, as well as from shipboard stations (Figure
1.6.21)   on  several  LMMBP   surveys  (U.S.
                                             101

-------

    H+
              +
                            air sampling locations
                   •H   K,
           HH
Figure 1.6.21. Air sampling locations.
                        r LEVELS
                       7 High resolution 5km X 5km grid
                         19 sigma layers
                       1.  2318 surface segments
                       ~> 44,042 water segments
Figure 1.6.22.  Lake Michigan high-resolution 5
km x 5 km grid with 19 sigma layers.
Environmental Protection Agency,  1997).  These
data were then used by Hornbuckle to generate over-
water concentrations of PCB congeners for all 5 km
surface grid cells used for the high-resolution model
(Green, 2000; Miller etal., 2001) (see Figure 1.6.22).
Hornbuckle only analyzed modeled congeners, thus
the congener set for this media is somewhat smaller
than  in  other  media.   These  data  were  then
aggregated for modeling into surface segment cells
for the LM2-Toxic model  (Figure 1.6.1).  As stated
previously, the Saugatuck biota box (LMMBP-BiotaS)
is located within LM2 segment 2. A congener pattern
comparison was done to see if the congener patterns
                                              102

-------
seen in segment 2 were similar to patterns seen in
other segments.  This was  indeed the  case with
vapor phase and wet deposition data (Figures 1.6.23
and  1.6.24).   For dry  deposition samples,  LM2
segment 1, which is the southwest corner of the lake,
is somewhat  lower in tetrachloro and pentachloro
congeners than all of the other segments, which have
a very similar congener pattern (Figure 1.6.25).
                                 PCB congeners
                                 in atmospheric
                                   vapor phase
                                    Segment 1
                                 	Segment 2
                                 -  - • Segment 3
                                 	Segment 4
                                 	Segment 5
                                 	Segment 6
                                 	Segment 7
                                  - - Segment 8
                                    Segment 9
                                    Segment 10
        17  33 49 65 81  97 113129145161 177193209
                     congener

Figure 1.6.23. Cumulative frequency distribution
(mean) - PCB congeners in atmospheric vapor
phase.
1.0 j
0.9-

O.B-


0.6

0.5

0.4

0.3


0.1-

 o-lu
Segment 1
	 Segment 2
- - Segment 3
	 Segment 4
— Segment 5
	 Segments
	 Segment 7
Segment 8
- - - Segment 9
— Segment 10

S~
                                PCB congeners
                                in atmospheric
                                wet deposition
     1  17  33 49 65 81 97 113129145161177193209
                     congener
Figure 1.6.24. Cumulative frequency distribution
(mean) - PCB congeners  in  atmospheric wet
deposition.
A comparison of vapor phase PCB samples from
segment 2 to wet and dry deposition samples from
this segment (Figure 1.6.26) showed a somewhat
similar congener  distribution for  wet  and   dry
 •e1'0
 g 0.9-
 o>
 i> 0.8-

 I 0.7]
 £ 0.6
                                                  0.5-

                                                  0.4-

                                                  0.3
                                                   E 0.2-
                           PCB congeners in
                           atmospheric dry deposition
                           •=» segment 1
                           — all other segments
      1  17 33 49 65 81  97 113129145161177193209
                      congener
Figure 1.6.25. Cumulative frequency distribution
(mean) - PCB  congeners in atmospheric  dry
deposition.
   1.0
   0.9
   0.8-
   0.7-
   0.6-
   0.5-
   0.4-
   0.3-
   0.2-
   0.1 •
                                                                        PCB congeners in
                                                                        segment 2 atmospheric data
                                                                        «"- vapor phase
                                                                        — wet deposition
                                                                        	dry deposition
                                                       1  17  33  49 65 81 97 113129145161177193209
                                                                       congener
                                                  Figure 1.6.26.  Cumulative frequency distribution
                                                  (mean)  -  PCB  congeners  in  segment  2
                                                  atmospheric data.
deposition samples.   The vapor phase congener
pattern  was  quite different with a  much higher
percentage of lower chlorinated congeners.

1.6.4.5    Comparison  of  Tributary Congener
Patterns

An examination of 11 monitored LMMBP tributaries
show that dissolved  water samples  clearly had a
higher percentage of lower  chlorinated congeners
than do particulate water samples (Figure 1.6.27).
The Fox River dissolved PCBs in water had a higher
percentage of lower congeners than any of the other
dissolved tributary samples.  This same trend held
true for Fox River particulate water samples.
                                               103

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                                Manistique
                                  River
          Menominee
               River,
         Sheboygan
              River
       Milwaukee
           River
                                    Kalamazoo River
                                   St. Joseph River
          Fox River


    Kalamazoo River


Grand Calumet River


        Grand River


    Sheboygan River


    Milwaukee River


     St Joseph River


    Muskegon River


   Menominee River


    Manistique River


     Pere Marquette
	dissolved
^—— participate

    -  dissolved
—— particulale
	  dissolved
	participate

	dissolved
	particulate

       dissolved
       particulate

— —  dissolved
	particulate

— —  dissolved
	particulate

—     dissolved
______ particulate

       dissolved
       particulate

— —  dissolved
	particulate
       dissolved
       particulate
                                                                          PCB congeners in
                                                                      Lake Michigan tributaries
                                              81     97    113    129   145    161    177   193    209
                                                    congener

Figure 1.6.27. PCB congeners in Lake Michigan tributaries.
                                                   104

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When segment 2 data for other media were overlaid
on the tributary data, it appears that vapor phase
data most closely matched dissolved water tributaries
data from  the western  side  of  the lake  (Figure
1.6.28), dissolved water data from segment 2 most
closely matched dissolved water tributary data from
rivers in the lower peninsula  of  Michigan  (Figure
1.6.29), and particulate water data from segment 2
generally fell in the middle of the particulate tributary
data (Figure 1.6.30).

1.6.4.6 Comparison of Ages 5 and 6 Lake Trout
Congener Patterns in All Biota Boxes

Five and six year-old lake trout were collected and
analyzed  from  all  three  LMMBP  biota  boxes
(Sturgeon  Bay:  LMMBP-Biotal, Sheboygan Reef:
LMMBP-Biota2, and  Saugatuck:   LMMBP-BiotaS).
Congener  patterns were similar for  all three biota
boxes, with Saugatuck having a somewhat higher
percentage of lower congeners,   Sheboygan  reef
having the lowest percentage of lower congeners,
and Sturgeon Bay being in between (Figure 1.6.31).

1.6.4.7  Comparison of Different Lake Trout Age
Class Congener Patterns in Saugatuck Biota Box

A  comparison  was done between different age
classes of lake trout in the Saugatuck biota box to
see  if  the congener  patterns  were  different for
different  age classes.   Lake trout data  for  year
classes 2, 3, and 9 were compared to age classes 5
and 6 lake trout (Figure 1.6.32). This comparison
showed that there was very little  difference between
the congener patterns of these different age classes.

1.6.5  Conclusions

When all media were looked at, there were very clear
differences in the congener patterns that were
observed.
                                                        dissolved PCB congeners in
                                                            Lake Michigan water
                                          2 E E 1i segment 2
                                              vapor phase
                 iiiiiiiiiiiiiiiiiirTlininTiTiiiiiTirmnTiiniMMiiiiiiiiHMi in inn iiiiniiiiiiiini in in MIMMIIHIIIIIIIIIHIII MI HIM m in niiiiiiiiiiiiii n n MIIIIII mi inn HIM mi mini HI
           1     17    33    49    65    81     97    113   129   145   161   177   193   209
                                             congener

Figure 1.6.28. Comparison  of dissolved PCB congeners in Lake  Michigan western tributaries to
segment 2 vapor phase.
                                              105

-------
                                                                      dissolved PCB congeners in
                                                                           Lake Michigan water
                                                           i segment 2
                                                            dissolved water
                                                       - - - Pere Marquette
                                                       	i Muskegon River
                                                       aa ass Grand River
                                                       	 Kaiarnazoo River
                                                          - St Joseph River
   'ere Marquette
   :iver

   luskegon River
 J~~Grand River
 Kalamazoo River

St. Joseph River
                rillUIIIIIMIlllMllllHinniMIHniMIIIIIIMIllMIIIHnllnTnTnTIIIMIIIMIIIIIIIIIHIHIIirnillirnilHIIIIIIIHIMIIIMIIIIIIIiniHIIIIIIMIIMnuUMIIMIIIIUUMIIIllllllllltlllMiniMI
               1     M     33    49     65     81     97    113    129    145    161    177   193   209
                                                     congener
Figure 1.6.29. Comparison of dissolved PCB congeners in  Lake Michigan  eastern tributaries to
segment 2 water.
                                                                                     segment 2
                                                                                     particulate water
                                                                                     Manistique River
                                                                                     Menominee River
                                                                                     Fox River
                                                                                     Sheboygan River
                                                                                     Milwaukee River
                                                                                     Grand Calumet
                                                                                - — Pere Marquette
                                                                                ...... Muskegon River
                                                                                     Grand River
                                                                                	Kalamazoo River
                                                                                	St Joseph River
                                                                     particulate PCB congeners in
                                                                       Lake Michigan tributaries
                                                      97     113   129    145    161    177   193    209
                                                      congener
Figure 1.6.30. Comparison of particulate PCB congeners in Lake Michigan  segment 2 to tributaries.
                                                     106

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                               PCB congeners in
                               age 5 & 6 lake trout
                                  Saugatuck
                               — Sheboygan Reef
                               	Sturgeon Bay
       17 33 49 65 81  97 113129145161177193209
                     congener

Figure 1.6.31. Cumulative frequency distribution
(mean) - PCB congeners in ages 5 and 6 lake
trout.
   1.0

   0.9-
   0.8

   0.7-

   0.6-
   0.5
   0.4

   0.3

   0.2
   0.1
    04
PCB congeners in
Saugatuck lake trout
—« age 2 years
— age 3 years
	age 5-6 years
— age 9 years
     1   17 33 49 65 81  97  113129145161 177193209
                     congener

Figure 1.6.32. Cumulative frequency distribution
(mean) - PCB congeners in Saugatuck lake trout.
Vapor phase data had  the highest percentage of
lower chlorinated congeners  of  all  the  media
examined.  In all cases, the dissolved fraction of
water samples had a higher percentage of lower
chlorinated  congeners  than  the  corresponding
paniculate  fraction  of  water  samples.    The
Kalamazoo  River dissolved  and particulate water
samples  had  a  higher  percentage  of  lower
chlorinated congeners than the corresponding open
lake dissolved and particulate water samples.  The
Kalamazoo  River dissolved water  samples most
closely resembled the vapor phase  data, while the
Kalamazoo River particulate water data most closely
resembled segment 2 wet and dry deposition data.
This could be due to the possibility that PCBs in the
river are from a newer (non-weathered) source of
contamination than open lake samples.

Open lake particulate samples most closely matched
the surficial sediment data for segment 2. This result
probably is not too surprising considering the close
relationship between sediments and particulates and
the continuous  deposition  and  resuspension  of
bottom sediments in the water column.

Atmospheric congener patterns were very uniform
over the  entire lake, with  the  exception  of  dry
deposition data  which had  a  somewhat different
congener patten in segment 1 than in all of the other
model segments. Wet and dry deposition samples
showed similar congener patterns for segment 2 with
dry deposition  samples  having  a  slightly  lower
congener distribution  pattern than wet deposition
samples.  This is the same trend that was seen in
lake water and  tributary  samples.   Vapor phase
samples for segment 2 had a very different congener
pattern  with a  much  higher percentage  of  lower
chlorinated congeners.

Dissolved tributary  water samples  had a higher
percentage of  lower  chlorinated  congeners than
particulate water samples.  It is interesting that
dissolved water tributary data, with the exception of
the Kalamazoo River, from the western side of Lake
Michigan  (Fox,  Grand   Calumet,  Sheboygan,
Milwaukee, Manistique,  and Menominee  Rivers)
generally had a higher concentration of the lower
chlorinated congeners than tributaries from the lower
peninsula of  Michigan (Grand,  Muskegon, Pere
Marquette, and St. Joseph Rivers) (Figure 1.6.33).
This same trend generally holds true for the tributary
particulate data (Figure 1.6.34).   Overall,  the Fox
River had the highest percentage of lower chlorinated
congeners  for  both  dissolved  and  particulate
samples.

Fish from all three biota  boxes had very similar
congener patterns.  In addition, fish from  different
age classes in the same biota box (Saugatuck) also
had very similar congener patterns. This would seem
to indicate that the same processes are  responsible
for determining the congener pattern distribution in
fish.  This pattern was also similar to the trend that
Kuehl observed in  her analysis of LMMBP biota
samples (Kuehl,  2002), where she  reported that
                                              107

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CD
c
CD
0)
O
O
DO
O
Q.
"CD
**—
o
E
"5
c
o
"o
CD


1.0-
0.9-
0.8-


0.7-


0.6-

0.5-

0.4-
0.3-


0.2-
0.1 -
o-
                                                                      dissolved PCB congeners in
                                                                        Lake Michigan tributaries
                                              west side of Lake Michigan
                                                   Manistique River
                                                   Menominee River
                                                   Fox River
                                                   Sheboygan River
                                                   Milwaukee River
                                                   Grand Calumet
                                              Menominee
                                                  River
                                                                  ere Marquette
                                                                 River
                                                        Sheboygan
                                                            River
                                                                                       •''Muskegon River
                                                                                          Grand River
                                                                                       Kalamazoo River
                       east side of Lake Michigan
                       — - Pere Marquette
                            Muskegon River
                            Grand River
                       	Kalamazoo River
                       - - - St Joseph River
                                                                                      St. Joseph River
               Ti II11 Ml I IN 111 NIIHIIHII11III HI I MM I IHt III III11II11III III 11 til 111 III11III III ml I III! HI II ill III11II11III 111 I III 11II11 III HI
17     33
                                 49
65     81
                               97    113
                              congener
                                                      129   145   161    177    193    209
Figure 1.6.33. Comparison of dissolved PCB congeners in west side-to-east side of Lake Michigan
tributaries.
             1.0'
          CD
          CD
          O)
          O
          O
         DO
         O
         CL
          o
          E
             0.9-
             0.8-
0.7-
0.6-
             0.5-
             0.4-
          c  0.3-
          o
          fo  0.2-
                                                        west side of Lake Michigan
                                                        	Manistique River
                                                        	Menominee River
                                                        	Fox River
                                                        	Sheboygan River
                                                        ^^_ Milwaukee River
                                                        	Grand Calumet

                                                        east side of Lake Michigan
                                                        - - - Pere Marquette
                                                        • ••••' Muskegon River
                                                        m EH Grand River
                                                        	Kalamazoo River
                                                          — St Joseph River

                                             particulate PCB congeners in
                                               Lake Michigan tributaries
17    33     49    65
                                                81    97    113    129    145   161    177   193   209
                                                      congener
Figure 1.6.34. Comparison of particulate PCB congeners in west side-to-east side of Lake Michigan
tributaries.
                                                      108

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"With the exception of deepwater sculpin samples
collected in Biota Box #3 near Saugatuck, all LMMBP
biota sample congener patterns were homogeneous",
and "A relatively consistent pattern of the selected
PCB congeners was measured  in all of the  biota
species or classifications." Also, while the age class
5-6 year-old lake trout congeners pattern for  biota
box number 3 most nearly matched the congener
patterns of surficial sediment and particulate water
samples from modeling segment 2, this was not a
very close match.

From this data investigation, it was clear that it was
impossible to relate PCB contamination of lake trout
to a tributary source based  on congener  pattern
matching.  This  is due  to the  fact that tributary
congener patterns for Lake Michigan, regardless of
source,  had  congener   patterns  which   were
significantly  different from  all lake trout congener
patterns.  In addition, congener patterns seen in lake
trout from different  biota boxes had very similar
congener patterns, which would  indicate that there
are no spatial differences in lake trout congener
patterns.

References

Green, M.L  2000.  Geographic Information System
    Based  Modeling  of   Semi-Volatile  Organic
    Compounds Temporal  and  Spatial  Variability.
    Ph.D. Thesis,  University of New York, Buffalo,
    New York. 250 pp.
Kuehl,  M.  2002.  Polychlorinated Biphenyl (PCB)
   Congener  Patterns  in  Lake  Michigan  Mass
   Balance Study Biota. M.S. Thesis, University of
   Wisconsin, Green Bay, Wisconsin. 120 pp.

McCarty, H.D., J. Schofield, K. Miller, R.N. Brent, P.
   Van Hoff, and B.  Eadie.  2004. Results of the
   Lake  Michigan   Mass  Balance  Study:
   Polychlorinated Biphenyls and frans-Nonachlor
   Data Report.   U.S. Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois. EPA/905-R-01/011, 289 pp.

Miller,  S.M., M.L. Green, J.V. DePinto, and  K.C.
   Hornbuckle.   2001.  Results from the Lake
   Michigan Mass Balance Study: Concentrations
   and Fluxes  of  Atmospheric Polychlorinated
   Biphenyls and  frans-Nonachlor.  Environ.  Sci.
   Techno!., 35(2):278-285.

U.S. Environmental Protection Agency. 1997. Lake
   Michigan Mass Balance Study (LMMBP) Methods
   Compendium,  Volume 2: Organic and Mercury
   Sample   Analysis  Techniques.     U.S.
   Environmental Protection Agency, Great Lakes
   National   Program  Office,  Chicago,  Illinois.
   EPA/905/R-97/012b, 532 pp.
                                              109

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                                         PART1
                                    INTRODUCTION
Chapter 7. Hindcasting and Forecasting
Functions for PCBs in the Lake Michigan
Ecosystem

Ronald Rossmann
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

1.7.1 Introduction

The challenge for both hindcasting and forecasting is
the development of  functions  that  represent the
change of polychlorinated biphenyl (PCB)  loads to
the lake  with time.   To accomplish this, quality
historical databases are required that bracket both
sides of the peak load to the lake.  The use of PCB
production information are  of  limited usefulness.
They do not represent the historical use of PCBs in
the Lake Michigan basin. The early use of PCBs was
primarily  in sealed  containers  as capacitors and
transformers.  PCBs were not  released except at
points of manufacture and locations where capacitors
and transformers were manufactured. The number
of these sites was limited.  None were within the
Lake Michigan basin.  The open use of PCBs within
the basin were primarily for the manufacture of
carbonless paper and as a hydraulic fluid for die-
casting.   The first  recorded  use  of  PCBs  for
manufacture of carbonless paper was in 1954 in the
Fox River Valley at  Green Bay, Wisconsin  (Table
1.7.1).   In the Fox  River  Valley,  its use for this
purpose peaked in 1969-1970. Its use for production
of the paper as well  as recycling of the paper was
phased out in 1971 -1972. Other locations associated
with  production  of  this carbonless paper  or  its
recycling included the Manistique  and Kalamazoo
Rivers. The use of hydraulic fluids with PCBs began
in 1948 and ended  in 1971 at  Waukegan, Illinois.
Another location where these hydraulic fluids were
used was Sheboygan, Wisconsin.   It appears that
these two uses are most responsible for the loads of
PCBs to Lake Michigan.  These uses are  poorly
quantified.  Therefore,  historical  observations  of
PCBs in various media were used to construct load
functions for both forecasts and hindcasts.

1.7.2  Forecast Functions

Functions developed for the purpose of forecasting
PCBs in various media were developed  from the
1994-1995 Lake Michigan Mass Balance Project
(LMMBP) data and data from various published and
unpublished sources.   The data  were  used to
develop functions that describe the apparent loading
trends  for PCB  loads  from  the atmosphere and
tributaries.  Data were extremely limited.  Historical
data are of unknown quality due to  changes in
methodology.   Specifically, those  data generated
using packed columns rather than capillary columns
for the gas chromatography are suspect.  Packed
columns did not provide a good separation of PCB
peaks.  Older results were expressed as Aroclors
rather than congeners. These weaknesses can lead
to a high bias.  However, these data were  all that
                                             110

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Table 1.7.1. Significant Dates in the History of PCBs in the Lake Michigan Basin
         Date
                                  Event
         1865
         1881
         1914
         1927
         1935
       1948-1971

         1954
  Mid-1950s to Mid-1960s
     1950s to 1980s
       1959-1972

       1959-1971

       1969-1970
         1970

       1971-1972

         1973
         1975
         1977
         1984
         1985
         1991
         1991
         1992
         1998
       1997-1998
         2001
         2002
First PCB-like chemical discovered
First PCBs synthesized
Measurable amounts of PCBs found in bird feathers
PCBs first manufactured at Anniston, Alabama
PCBs manufactured at Anniston, Alabama and Sauget, Illinois
Outboard Marine Corporation at Waukegan, Illinois purchased eight million gallons of
       hydraulic fluid with PCBs
Appleton Paper Company began using PCBs as PCB-coated carbonless copy paper
PCBs loaded to Kalamazoo River from deinking
PCBs discharged to Manistique River and Harbor
Outboard Marine Corporation at Waukegan, Illinois used hydraulic fluid with PCBs for
     die-casting
PCBs used by Tecumseh  Products Company as a hydraulic fluid was loaded to
     Sheboygan River
Paper company discharges of PCBs to Fox River peaked
PCB production peaked at 85 million pounds and huge contamination noted at Sauget,
     Illinois plant
Appleton Paper Company and NCR Corporation phased out PCB use. Recycling of
  carbonless paper had occurred for several decades
U.S. Food and Drug Administration (USFDA) establish 5 ppm PCB tolerance level in fish
124,000 cans of salmon from Lake Michigan seized because of PCBs
PCB production ends
USFDA lowered PCB tolerance level  in fish to 2 ppm
Commercial fishing for carp and other valuable species outlawed on Green Bay
End Sheboygan River PCB remediation of upper river
U.S. Department of Health and Human Services label PCBs as possible carcinogen
End Waukegan Harbor PCB remediation
The eight Great Lakes states agreed  on a "Great Lakes Protocol for Fish Consumption
    Advisories" that lowered the regional standard from the USFDA commercial
    standard of 2 ppm down to 0.05  ppm
Milwaukee River PCB remediation
Manistique Harbor PCB remediation completed
Possibly begin Grand Calumet  River PCB remediation
were available at the time of model development and
execution.

1.7.2.1 Tributary Loads

Several data sets were used to develop a first-order
exponential decay function for tributary loads.  The
derivation of the PCB loading attenuation rate half-life
for tributaries is straightforward. It utilized 1994 and
1995 LMMBP tributary data (McCarty ef a/., 2004)
along with  data for the Fox River for  1989-1990
(Velleux and Endicott, 1994) and for Lake Michigan
tributaries for 1982 (Marti and Armstrong,  1990).
The loading data for the Fox River, Lake Michigan
                           tributaries, and 1994-1995 were fit using Equation
                           1.7.1.
                               = C2e
                                     -kt
(1.7.1)
                           where:
                           C,  =  load at time t1

                           C7  =  load at time t,
                           k  =  attenuation rate
                           t   =  interval of time between t, and t2
                                                111

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The equation was solved for k.  For the Fox River
and Lake Michigan tributaries, k was calculated to be
0.053/year  and 0.054/year,  respectively.   These
yielded the half-lives of 12-13  years.  The k of
0.054/year was used.

Recent reports of half-lives for individual tributaries
range from 6.1  years  (k  = 0.114/year) in  the
Kalamazoo River (Blasland,  Bouck and Lee, Inc.,
2000) to 6.8 years (k = 0.102/year) at the Fox River
DePere  Dam and 9.0 years (k = 0.077/year) at the
river mouth (Limno  Tech,  Inc.,  2002).  Thus, the
choice of a k equal to 0.054 is probably conservative
for  the  purposes  of a forecast; that  is,  tributary
loadings could be biased high, leading to later dates
when lake  trout will cease to have  consumption
advisories.

1.7.2.2 Atmospheric Loads

For the period of 1992 to 1997, Simcik et al. (2000)
reported a half-life of 6.9 ± 3.5 for precipitation. The
half-life  for atmospheric vapor  phase PCBs was
taken as reported  by Schneider et al. (2001) based
upon the work of Hillery et al. (1997).  Hillery et al.
(1997)  based  their  work  on  the  1992-1995
International Atmospheric Deposition Network (I ADN)
data. At  that time, the reported half-life was  six
years. This is one of the bounding half-lives used for
all  forecasts.  More recently published half-lives
include  those of Simcik et al. (1999) who  reported
half-lives of 2.7 years and 3.0 years over water and
land, respectively.  Their results were based  on
1992-1997 IADN  data.   For the period  of 1992
through 2000 at Sleeping Bear Dunes, Buehler et al.
(2002) reported half-lives of 3.1 ± 0.7 years for 1992
through 1995,4.9 ± 0.9 years for 1992 through 1997,
and 20.0 ±  8.6 years for 1992 through 2000. For the
period of 1992 through 2001, an examination of the
temperature-corrected PCB partial pressure IADN
data revealed that partial pressures were declining at
Sleeping Bear Dunes with a half-life rate of  8.3 ± 1.5
years (Buehler et  al.,  2004).   Because of the
uncertainty concerning the  rate of decline and the
apparent increase in half-life with the addition of
more recent  data, bounding half-lives  of 6 and 20
years were used for the purposes of the forecasts.
The uncertainty in the half-life of atmospheric vapor
phase PCBs suggests that a half-life of 13 years for
tributaries  is within reason.
1.7.3  Hindcast Functions

Development of a hindcast load function was more
problematic. The only data that exist for years prior
to the year of peak load are for preserved museum
forage fish  specimens (Neidermyer and Mickey,
1976) and lake trout (DeVault et al., 1996). Though
the forage fish data can not be used for a hindcast
because they are  preserved  specimens, they do
provide valuable information about when PCBs first
appeared in Lake Michigan   fish.  The fourhorn
sculpin data are of most interest.  PCBs were not
detected in the fish collected in 1949 but were found
in fish collected in  1951.  For rainbow smelt, PCBs
were not detected  in 1942 but were detected and
measured in 1960. This is consistent with the first
known reported  purchase  of  hydraulic fluids with
PCBs in 1948 for use at Waukegan, Illinois. Thus, it
appears contamination of the lake with PCBs did not
begin until  after 1948.  The lake trout annual data
only  go back to  1972 (DeVault et al., 1996).  The
peak in the lake trout occurred in 1974-1975; hence,
not enough data for establishing a function for the
onset of contamination.

The only way currently available to reconstruct the
load  function of PCBs for the lake was  to utilize
information available from dated sediment cores. The
number  of  sediment  cores  for  which data  are
available are limited  (Figure 1.7.1).  Cores SLMD,
SLMF, CLMM, NLMB, and NLME are from the work
of Hermanson et al. (1991). The  18S core is from
Swackhamer and Armstrong (1988), the HMS1  core
is from Schneider et al. (2001), and cores 18G, 47s,
and 68k are from Golden et al.  (1993). The LMMBP
cores are from  Stations 15, 61, and 86 (Van Hoof
and Eadie, personal communication). All the cores
are of varying quality.   Core  quality is dependent
upon sedimentation rate, depth of surficial sediment
mixing by physical and biological processes, location
with  respect to  sources,  and thickness of the core
interval samples.  Of 13 cores,  core 15 is of the
highest  quality.    Sedimentation  rate  (0.2235
g/cm2/year) is one  of the highest for Lake Michigan
(core is highly  resolved with the  surficial  1  cm
representing 1.2 years of deposition), its mixed layer
is less than the 1 cm interval sampled, and it is
located in an area of the lake that is very responsive
to loadings. The location of core 15 is in the region
impacted by an annual spring  plume of suspended
                                              112

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        A

        N       Escanaba

       Scale
  __ _  ••=	
0 km   40 km  80 km

        Menominee
                                    Manistique
                                              Naubinway
                                                    Charlevoix
            Green Bay
            Manitowoc
         Milwaukee

             Racine


         Waukegan
                                                         Mackinaw City
           Sheboygan.
               Chicago
                        Gary
                                         Muskegon
                                         Grand Haven
                                     Saugatuck


                          SLMFV   /south Haven

                                  Benton Harbor


                           Michigan City
Figure 1.7.1. Locations of dated cores analyzed for RGBs by Hermanson etal. (1981), Swackhamer and
Armstrong (1988), Schneider etal. (2001), Golden etal. (1993), and Van Hoof (personal communication)
for the LMMBP.
                                      113

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 participate matter that has concentrations four to 10
 times that of the lake (Eadie et al., 1996).  Because
 core 15 is highly resolved, relatively undisturbed by
 post-depositional mixing processes (mixed layer less
 than 1 cm), and in a region  responsive to an annual
 transport event in the lake, it was chosen for the
 development of a total PCB loading function for the
 lake.

 The distribution of RGBs within the core with time can
 be broken into two linear functions; one prior to peak
 concentration  and one  after  peak  concentration
 (Figure 1.7.2).  The pre-peak function was based on
 the period  of  1947 to 1965, and the  post-peak
 function was based on the period 1972 to 1994. The
 two functions crossed one another in 1967, indicating
 this should be the peak load  year. The  exact location
 of the peak was problematic because  there are two
 peaks in the observed data.  The year of peak
 loading was constrained by the  date of the peak
 concentration of  PCBs in  lake trout.  This peak
 occurred between  1974 and 1975 for five to six year-
 old fish (DeVault era/., 1996). Thus, peak exposure
 could have occurred as early as 1968 and as late as
 1970. This was used as guidance when selecting the
 year ranges from which  the linear functions were
                   300
chosen. The peak concentration for the intersection
of the two functions needed to be near the range of
1968 to  1970.    In  addition,  the  simple linear
regression model for the onset of contamination had
to intersect the x-axis around 1 949 when PCBs were
first noted in forage fish. The function derived for the
onset of PCB loading to the lake is:


CPCB = [(-26316.85)  + (13.509334 * yr)]
       (R2 = 0.865)                     (1.7.2)

where:

CpCB =• concentration of PCBs in ng/g

yr   = calendar year.

The function derived for the decline of PCB load to
the lake is:
      = [(15647.899) + (-7.823438 * yr)]
      (R2 = 0.897)                     (1.7.3)
                     0
                      1940  1950  1960   1970  1980   1990  2000
                                            Year
                     - Measured               _B_ 1947-! 965 Linear Fuction
                     - 1972-1994 Linear Function
Figure 1.7.2.  Fit of concentration functions to observed data for core 15.
                                             114

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The two function lines crossed in roughly 1967. This
was considered acceptable and assumed to be the
peak load year.

Finally, results from these concentration functions
were converted to annual loads. This was done by
using  mass  sedimentation   rate   information
(Rossmann and Edgington, 2000; Rossmann, 2002).
The flux of PCBs to the lake can be described as:
                                        (1.7.4)
where
 PCB
       -  flux of PCBs in ng/cm2/year
MSP  -  mass sedimentation rate in g/cm2/year.

This calculated flux was then corrected for physical
processes that redistribute sediment within the lake.
Sediment focusing is the process by which sediments
are  moved from  one location  to another.   The
process  includes  sediment  resuspension  and
transport by currents until  deposition  at another
location.  The  materials  that  are  preferentially
resuspended  are  the  fine-grained  fraction  of
sediments. This fraction of the sediments has 210Pb,
137Cs, PCBs, and other contaminants associated with
it.   Thus  sediments at the  new  deposition  site
become enriched in these while those at the original
resuspension  site may become  depleted in these.
The focusing factor  applied was that for 210Pb
(Rossmann and  Edgington,  2000).   The 210Pb
focusing factor is defined as:
                                        (1.7.5)
where:
 FF,
           210
   2WPb  ~
'Pb focusing factor
           Activity of 210Pb stored in the core for
           time period year

           Activity of 210Pb supported by radium in
           the sediment

           Decay  corrected   activity  of  210Pb
           deposited for time period year.
                                    The 210Pb flux from the atmosphere is constant, and
                                    the  amount  stored  is  calculated  from  core
                                    measurements. The focusing factor corrected PCB
                                    flux (FFFPCB) in ng/cm2/year is calculated using:
                                    FFF
PCB
           *FF,
                                                       210Pt>
                                                                             (1.7.6)
                                    This flux is then converted to a load to the lake using
                                    the depositional and transitional areas of the lake.
                                    The depositional area is defined as the area of the
                                    lake with  water depths greater  than  100 m,  the
                                    transitional area is defined as the areas of the lake
                                    with water depths between  40 and  100 m, and the
                                    non-depositional area is defined as the area of the
                                    lake with water depths less than 40 m (Figure 1.7.3).
                                    Together (excluding Green Bay), the transitional and
                                    depositional areas of the lake represent 68.6% of the
                                    lake's total area of 58,016 km2 (Table 1.7.2).
                                    Table 1.7.2. Sedimentary Zones of Lake Michigan
Region of the
Lake
Non-Depositional
Transitional
Depositional
Green Bay
Percent of the
Lake's Area
24.7
32.8
35.8
6.7
Area, km2
14,334
19,032
20,756
3,894
                                     Total
                  100.0
58,016
                                    Flux is converted to load using the equation:

                                    LL
                                     -PCB
                                           *(1010cm2//f/772)*>\
                                                                                          (1.7.7)
                                                                             TD
                                                  where:
                                    LPCB  =  Total  load  to  lake  depositional  and
                                             transitional areas in kg/year

                                    ATD   =  Total depositional and transitional area of
                                             the lake in km2.
                                              115

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                                                              Naubinway
           Okm
                                                  Manistique
                     A


                     N
            Escanaba
 Scale
•	
40km     80km

                  .^S
  Menominee X    J f'
                    Green Bay
                      Manitowoc
                     Sheboygan
                  Milwaukee
                       Racine
                   Waukegan
                        Chicago
                                           Michigan City
                                  Gary
                                                       Mackinaw City
                                                                         Legend
                                                                           Depositional
                                                                             Zone
                                                                            Transitional
                                                                              Zone
                                                       Non-Depositional
                                                            Zone
Figure 1.7.3. Lake Michigan non-depositional (0-40 m), transitional (40-100 m), and depositional (> 100
m) zones based on water depth and the depth of wind-wave interaction with sediments.
                                                116

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The remaining terms  are unit conversion terms.
Loads in the post-peak function were normalized to
the 1989 load (2,731 kg/year) because the best linear
fit line went through 1989.  The result of this function
in 1967 of 8,129 kg/year was used to normalize the
onset function  so that its result in 1967 was also
8,129 kg/year.  The resulting load function had  a
PCB load onset in 1949, a peak load of 8,129 kg/year
in  1967, and a 1994 load of  1,504 kg/year (Figure
1.7.4). The 1994 total load was 1.9 times higher than
the measured load of 786 kg/year.   This was
considered within  reason because  at  least one
additional loading source was  identified  since the
project. The source is Milwaukee, Wisconsin with an
estimated load of 130 kg/year  (Wethington and
Hornbuckle, 2005).  In addition, atmospheric loads
did not include the coarse particulate fraction which
would contribute an additional load.  Franz et al.
(1998)  estimated  a particle dry deposition flux of
1,100 kg/year which was  considerably higher than
previously reported dry deposition fluxes of 16 to 170
kg/year.  For the LMMBP, the average  particulate
PCB flux reported was 120 ng/m2/month (Miller et al.,
2001).  This converts to roughly 85 kg/year for the
whole lake. Thus the coarse particulate  load could
be as high as 1,000 kg/year. Therefore, using a load
function that results in a 1994 load of 1,504 kg/year
seems quite plausible given the uncertainties in the
dry deposition fluxes.  Finally,  it is suspected that
other  loads,   similar  to  the  Milwaukee  load,
were undetected due to the lack of sampling stations
in all metropolitan areas.

1.7.4  Estimated PCB Storage

To provide an estimate of the PCBs stored in Lake
Michigan sediment, the three LMMBP core PCB
results were manipulated  in  the  same way  as
described above.   This included the application of
210Pb focusing factors to each core so that storage in
the entire lake could be estimated. Cores 15, 61,
and 86 yielded a  lake-wide  storage of 209,239,
64,533, and  41,192 kg, respectively. The mean of
these is 104,988 kg. This is higher than the 75,000
kg reported by Golden et al. (1993). As seen from
the three LMMBP cores, storage results are  highly
variable and  location dependent. Application of the
above procedure used for estimating storage in the
lake's sediments to cores reported  in the literature
(Golden  et  al., 1993;  Hermanson et al.,  1991;
Schneider et al., 2001), the mean storage in the main
lake is  estimated  to  be  46,466 kg.   This is
comparable to more recent work by Brian J.  Eadie
and  Patricia Van   Hoof (National Oceanic and
Atmospheric Administration (NOAA), Great  Lakes
Environmental Research Laboratory (GLERL), Ann
Arbor, Michigan, personal communication) based on
the LMMBP data which has yielded an estimate of
40,700 kg for the main lake and 60,000  kg for the
main lake plus Green Bay.
                        10000
                     ^
                     D)
                            0 ji 1*1*1 f I i i i  i I i i i i I i i i i I i i  i i I i i i i
                            1940 1950 1960 1970 1980  1990 2000
                                             Year
                                   Core 15 Load —e— Load Function
Figure 1.7.4.  Comparison of load function to 210Pb focusing factor corrected core 15 loads.
                                              117

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References

Blasland, Bouck, and  Lee, Incorporated.  2000.
   Allied   Paper,   Incorporated/Portage
   Creek/Kalamazoo River Superf und Site Remedial
   Investigation/Feasibility  Study.     Remedial
   Investigation Report  Phase I.  A 10/30/2000
   draft for state and federal review.

Buehler, S.S., I. Basu, and R.A. Hites.  2002. Gas-
   Phase   Polychlorinated  Biphenyl   and
   HexachlorocyclohexaneConcentrationsNearthe
   Great Lakes: A Historical Perspective. Environ.
   Sci. Technol., 36(23):5051-5056.

Buehler, S.S. and R.A. Hites.  2002.  The Great
   Lakes'  Integrated  Atmospheric   Deposition
   Network.  Environ.  Sci. Technol., 36(17):354A-
   359A.

Buehler,  S.S.,  I.  Basu, and  R.A.  Hites.  2004.
   Causes of  Variability  in  Pesticide  and  PCB
   Concentrations in  Air Near the Great  Lakes.
   Environ. Sci. Technol, 38(2):414-422.

DeVault, D.S., R.  Hesselberg,  P.W. Rodgers, and
   T.J.  Feist.  1996. Contaminant Trends in Lake
   Trout and Walleye From the Laurentian Great
   Lakes.  J. Great Lakes Res., 22(4):884-895.

Eadie, B.J., D.J. Schwab,  R.A. Assel, N. Hawley,
   M.B. Lansing,  G.S. Miller,  N.R. Morehead, and
   J.A. Robbins. 1996. Development of Recurrent
   Coastal Plume in Lake Michigan Observed for the
   First  Time.    EOS,  Transaction,  American
   Geophysical Union, 77:337-338.

Franz, T.P., S.J. Eisenreich, and T.M. Holsen. 1998.
   Dry  Deposition of  Particulate  Polychlorinated
   Biphenylsand Polycyclic Aromatic Hydrocarbons
   to Lake Michigan.   Environ.   Sci.  Technol.,
   32(23):3681-3688.

Golden,  K.A.,  C.S. Wong, J.D. Jeremiason,  S.J.
   Eisenreich,  G.  Sanders,   J.  Hallgren,  D.L.
   Swackhamer, D.R.  Engstrom, and D.T. Long.
   1993. Accumulation and Preliminary Inventory of
   Organochlorines  in Great  Lakes Sediments.
   Water Sci. Technol., 29(8-9):19-31.
Hermanson, M.H., E.R. Christensen, D.J. Buser, and
   L.Chen.  1991.  Polychlorinated Biphenyls in
   Dated Sediment Cores From Green Bay and
   Lake Michigan. J. Great Lakes Res., 17(1):94-
   108.

Hillery, B.L., I. Basu, C.W. Sweet, and  R.A. Hites.
   1997.  Temporal and Spatial Trends in a Long-
   Term Study of Gas-Phase PCB Concentrations
   Near the Great Lakes.  Environ. Sci. Technol.,
Hillery, B.L., M.F. Simcik, I. Basu, R.M. Hoff, W.M.J.
   Strachan, D. Burniston, C.H. Chan, K.A. Brice,
   C.W. Sweet, and R.A. Hites. 1998. Atmospheric
   Deposition of Toxic Pollutants to the Great Lakes
   as  Measured  by the  Integrated Atmospheric
   Deposition Network.   Environ. Sci.  Technol.,
   32(1 5):221 6-2221.

Limno  Tech,  Incorporated.  2002.  Recent Data
   Collection  Efforts and Trends  in  Data.   A
   presentation at the Fox River Group Comment
   Meeting.

Marti,   E.A.   and   D.E.   Armstrong.     1990.
   Polychlorinated  Biphenyls  in  Lake  Michigan
   Tributaries. J. Great Lakes Res., 1 6(3):396-405.

McCarty, H.B.; J. Schofield, K. Miller, R.N. Brent, P.
   Van Hoof, and B. Eadie.  2004.  Results of the
   Lake   Michigan   Mass   Balance   Study:
   Polychlorinated Biphenyls and frans-Nonachlor
   Data Report.   U.S.  Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois. EPA-905/R/01 -01 1 , 289 pp.

Miller,  S.M., M.L. Green,  J.V.  DePinto,  and K.C.
   Hornbuckle.  2001.   Results  From  the Lake
   Michigan Mass Balance Study: Concentrations
   and Fluxes  of  Atmospheric  Polychlorinated
   Biphenyls and  frans-Nonachlor.  Environ.  Sci.
   Technol., 35(2):278-285.

Neidermyer,  W.J.   and   J.J.  Hickey.     1976.
   Chronology of  Organochlorine Compounds in
   Lake Michigan Fish, 1929-1966. Pest. Monit. J.,
   10(3):92-95.
                                              118

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Rossmann, R. and D.N. Edgington. 2000.  Mercury
   in 1987-1990 Green Bay, Lake Michigan Surficial
   Sediments. J. Great Lakes Res., 26(3):323-339.

Rossmann, R.  2002.  Lake  Michigan 1994-1996
   Surficial Sediment Mercury. J. Great Lakes Res.,
   28(1):65-76.

Schneider, A.R., H.M. Stapleton, J. Cornwell, and
   J.E. Baker.   2001.  Recent Declines  in PAH,
   PCB,  and Toxaphene  Levels in  the Northern
   Great  Lakes  as   Determined   From  High
   Resolution  Sediment  Cores.   Environ.  Sci.
   Technol., 35(19):3809-3815.

Simcik, M.F., I.  Basu, C.W. Sweet, and R.A. Hites.
   1999. Temperature Dependence and Temporal
   Trends of Polychlorinated Biphenyl Congeners in
   the Great Lakes Atmosphere.   Environ. Sci.
   Technol., 33(12):1991 -1995.
Swackhamer, D.L. and  D.E.  Armstrong.   1988.
   Horizontal and Vertical Distribution of PCBs in
   Southern  Lake Michigan Sediments  and the
   Effect of Waukegan Harbor as a Point Source.  J.
   Great Lakes Res., 14(3):277-290.

Velleux, M.L. and D. Endicott. 1994. Development
   of a Mass Balance Model for Estimating PCB
   Export From the Lower Fox River to Green Bay.
   J. Great Lakes Res., 20(2):416-434.

Wethington,  D.M. and  K.C. Hornbuckle.   2005.
   Milwaukee, Wl as a Source of Atmospheric PCBs
   to Lake  Michigan.    Environ.  Sci.  Technol.,
   39(1):57-63.
                                             119

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                                         PART 2

                                      LM3-EUTRO
                              James J. Pauer and Katie W. Taunt
                                 Welso Federal Services, LLC
                                            and
                                       Wilson Melendez
                                Computer Sciences Corporation
                                 Large Lakes Research Station
                                       9311 Groh Road
                                  Grosse lie, Michigan 48138
Chapter   1.     Conclusions  (Executive
Summary)

LM3-Eutro was developed in conjunction with several
other  mathematical models  as part  of the Lake
Michigan Mass Balance Project (LMMBP).  These
models work together to  determine  contaminant
concentrations in Lake Michigan fish predators under
present and future conditions.

LM3-Eutro was based on the CE-QUAL-ICM model
transport framework (Cerco  and Cole, 1995) and
used state-of-the-science eutrophication kinetics to
simulate the interactions  between plankton and
nutrients.  LM3-Eutro is a high-resolution framework
containing  44,042  water column segments.  The
model  is  driven   by  the  Princeton   Ocean
hydrodynamics Model (POM) (Schwab and Beletsky,
1998).  A sediment model is under development.
Until developed, LM3-Eutro  includes  user-defined
fluxes to simulate sediment-water interactions. The
model has  17  state variables, including a  single
zooplankton class, two phytoplankton  classes, and
several particulate and dissolved nutrient (including
carbon) states.
LM3-Eutro has several advantages over historical
Great Lakes models:

• A high-resol ution segmentation framework (44,042
  cells  and 19 sigma layers), enabling a better
  description of  areas such as nearshore  and
  offshore zones, bays, river confluences, and the
  thermocline.

• Use of POM to simulate water movement is a
  significant improvement over  historical models
  which traditionally used  tracers,  chloride,  and
  temperature to estimate diffusive and advective
  flows.

• The  model  is carbon-based,  which  is  an
  improvement over  chlorophyll a due to  high
  variability of this pigment in phytoplankton.

• The expansion of nutrient  variables to include
  dissolved, labile  particulate,  and  refractory
  particulate forms  allows  for  more  realistic
  description of phytoplankton-nutrient interactions.
                                             120

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• Important improvements were made to the light
  calculation by using a three-hour rather than 24-
  hour (one day) average estimate of solar radiation.
  The 24-hour average approach has been criticized
  by some scientists.

Another  advantage of this modeling effort was the
large supporting data set. These data were used to
establish atmospheric and tributary loads,  estimate
initial  conditions,  perform  model  calibration and
confirmation and,  to  a lesser extent,  assist in
estimating a number of kinetic coefficients. Most of
the data  were collected during eight sampling cruises
in  1994-1995  (U.S.   Environmental  Protection
Agency, 1997). Limited data were  also collected in
1998  and  2000.  The data  went through rigorous
quality assurance  (QA) and quality control (QC)
procedures (Richardson et a/.,  2004).  In general,
most of  the emphasis was placed on the main lake
as relatively little field data were collected from Green
Bay.  Supplemental data were gathered for loads
such  as shoreline  erosion  and internal  sediment
fluxes (Monteith  and  Sonzogni,  1976;  Hall and
Robertson, 1998).   Most  of  the kinetic model
coefficients were derived from  the literature and
historical Great Lakes models (e.g., Thomann and
DiToro,  1975; Ambrose et at., 1993).

The model was calibrated  on  the high-resolution
(44,042  cells) Level 3 framework as well as the 41
segment Level 2 framework.  The Level 2 calibration
enabled us to visually observe known spatial and
temporal trends such as the spring diatom bloom and
phytoplankton concentration gradients between the
epilimnion and hypolimnion.  The Level 3 calibration
was performed on a whole-lake basis.  Model output
was compared to field data  for different calibration
 runs using simple  statistical parameters  such as
slope and squares of the correlation coefficient. The
 1994-1995 LMMBP field data were  used to calibrate
the model. The final calibration was chosen based
on the best Level  3 calibration, but Level 2 output
was visually  inspected to  ensure that  expected
phytoplankton and  nutrients trends were reflected.
Overall,  the calibrated model fits the data well.  We
were especially satisfied with  how well the model was
able to mimic the particulate organic carbon (POC)
field data because  providing carbon production for
use in LM2-Toxic was the most important objective of
this modeling effort. The phytoplankton fit was not as
good, but could be partly explained by the uncertainty
in using an in s/fufluorometer (Seabird Instrument) to
estimate phytoplankton concentrations and the large
natural  variation  in  phytoplankton  communities
(Clesceri et at., 1998).

Model confirmation was performed by comparing the
model to limited total phosphorus data for 1998 and
2000 and to a historical  model, MICH1, which was
developed and calibrated in the 1970s and modified
more  recently.   All comparisons were  done on a
whole-lake basis, and LM3-Eutro fits the 1998 and
2000 data well.  LM3-Eutro and MICH1 compared
surprisingly well, especially given the fact that they
are based on very different frameworks, kinetics, and
segmentation.  Compared  to field  data and LM3-
Eutro  predicted,  MICH1  underpredicted  both total
phosphorus concentrations. This was probably due
to the fact that MICH1 does not have any phosphorus
internal sediment recycle. Lower phosphorus values
also caused MICH1 to  underpredict chlorophyll a
concentrations in the lake.

Several model forecast scenarios were performed,
and   long-term  total   phosphorus,   POC,  and
phytoplankton  predictions  were observed.  One
scenario utilized alternating  1994 and 1995 tributary
and atmospheric phosphorus loads for 30 years. The
autochthonous solid (primary production  carbon)
output from this model was used in the contaminant
fate and transport polychlorinated  biphenyl (PCB)
model.  The  model predicted a steady-state total
phosphorus concentration of 4.3 ug/L, a steady-state
POC concentration of  0.2 mg/L, and an epilimnetic
spring chlorophyll a peak of 2.36 ug/L. Steady-state
was reached within 28 years. Several load reduction
scenarios were  performed, and total phosphorus,
POC,   and  chlorophyll  a  concentrations  were
predicted.    The  Great   Lakes  Water  Quality
Agreement (GLWQA) target total phosphorus load of
5,600 MT was revisited and the impact of increasing
the load to  this  level  was  predicted  for total
phosphorus and  chlorophyll a concentrations in the
lake (International Joint Commission, 1978). Under
the GLWQA  loading  levels,  total  phosphorus
concentrations were predicted to reach 7.5 ug/L, and
spring epilimnetic chlorophyll a peaked at 4.0 ug/L.
The  model  was   used  to   estimate  the total
phosphorus  loading   required  to  reach   the
International   Joint   Commission's   (IJC)  total
phosphorus concentration guideline of 7 ug/L (Great
Lakes Research  Advisory Board, 1978). The model
                                              121

-------
predicted that an annual load of 5,020 maximum total
(or a  Total Maximum  Daily Load  (TMDL)  of  14
maximum total/day) would result in the 7 ug/L steady-
state total  phosphorus  concentration and a spring
maximum epilimnetic chlorophyll a concentration of
3.7 ug/L.

References

Ambrose,  R.B., Jr.,  T.A. Wool,  and J.L. Martin.
   1993.  The Water Quality Analysis Simulation
   Program, WASPS; Part A: Model Documentation.
   U.S.   Environmental  Protection   Agency,
   Environmental  Research  Laboratory,  Athens,
   Georgia. 202 pp.

Cerco, C. and T. Cole.  1995.  User's Guide  to the
   CE-QUAL-ICMThree-DimensionalEutrophication
   Model.   U.S. Army Corps of Engineers, U.S.
   Army Engineer Waterways  Experiment Station,
   Vicksburg, Mississippi. Technical Report EL-95-
   15,2,420pp.

Clesceri, L.S., A.E.  Greenberg,  and A.D.  Eaton
   (Eds.).   1998.    Standard Methods for  the
   Examination of Water and Waste Water, 20th
   Edition. American  Public  Health Association,
   American Water Works Association, and Water
   Environment Federation,  Hanover,  Maryland.
   1,205pp.

Great Lakes  Research Advisory Board.   1978.
   Annual  Report  to the   International  Joint
   Commission.  International Joint Commission,
   Windsor, Ontario, Canada.  44 pp.

Hall,  D.  and  D. Robertson.  1998.  Estimation of
   Contaminant  Loading  from   Monitored  and
   Unmonitored Tributaries to Lake Michigan for the
   USEPA Lake Michigan  Mass  Balance Study.
   Quality  Systems  and  Implementation  Plan.
   Submitted October 23,1998. U.S. Environmental
   Protection   Agency,  Great  Lakes  National
   Program Office, Chicago, Illinois.  19 pp.
International Joint Commission.  1978.  Great Lakes
   Water Quality Agreement of 1978, with Annexes
   and  Terms of Reference, Between the United
   States  and  Canada.    Signed  at Ottawa,
   November  22,  1978.    International  Joint
   Commission, Windsor, Ontario, Canada. 60 pp.

Monteith, T.J. andW.C. Sonzogni.  1976. U.S. Great
   Lakes Shoreline Erosion Loadings.  Great Lakes
   Basin Commission, Ann Arbor, Michigan.  223
   PP-

Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and
   K.R. Rygwelski (Eds.). 2004. The Lake Michigan
   Mass Balance Project Quality Assurance Plan for
   Mathematical  Modeling.    Prepared by  the
   Modeling  Workgroup.    U.S.  Environmental
   Protection  Agency, Office  of Research  and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-04/018, 233 pp.

Schwab, D.J. and D. Beletsky. 1998.  Lake Michigan
   Mass Balance Study:  Hydrodynamic Modeling
   Project.  National Oceanic and Atmospheric
   Administration,  Great  Lakes  Environmental
   Research  Laboratory, Ann  Arbor,  Michigan.
   NOAATechnical Memorandum ERLGLERL-108,
   53pp.

Thomann, R.V., D.M. DiToro, R.P. Winfield, and D.J.
   O'Connor.  1975.   Mathematical  Modeling of
   Phytoplankton in Lake Ontario, Part 1  Model
   Development  and  Verification.     U.S.
   Environmental Protection  Agency,  Office of
   Research   and Development,  ERL-Corvallis,
   Large  Lakes  Research  Station,  Grosse lie,
   Michigan.  EPA/660/3-75/005,  177 pp.

U.S. Environmental Protection Agency. 1997.  Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume 1: Sample  Collection
   Techniques.   U.S. Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois. EPA/905/R-97/012a, 1,440pp.
                                             122

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                                          PART 2

                                       LM3-EUTRO
Chapter 2. Recommendations

LM3-Eutro captured  nutrient  and  phytoplankton
trends in Lake Michigan and fit the project field data
relatively well. This built confidence in how well the
model will be able to describe the system and predict
total  phosphorus,  phytoplankton, and particulate
organic carbon (POC) concentrations under different
loading  scenarios.   However,  LM3-Eutro  has a
number of  limitations  and  there  are  several
improvements that can be made to improve  the
accuracy and predictive capability of the model.

• Presently  the framework does not  include a
  sediment  sub-model and  instead  uses  user-
  defined  fluxes.     A  sediment   model   is
  recommended to describe nutrient interactions
  between  the  sediment and water  column.   A
  sediment submodel, coupled  with  the  present
  water column model, would provide an integrated
  framework that conserves mass in both the water
  and sediments.

• The initial conditions were estimated on the Level
  2 segmentation scheme. Because the model is
  sensitive to the initial conditions, especially over
  the first few years of predictions,  it would be
  preferable to calculate initial condition on the high-
  resolution Level 3 segmentation scheme.

• Few laboratory  and field measurements  were
  performed to estimate kinetic coefficients. Limited
  laboratory   production  experiments   were
  conducted for use in the model. Measurements
  estimating Lake Michigan specific coefficients,
   especially the phytoplankton growth coefficients,
   would improve the reliability of the model.

•  Additional field and laboratory data would have
   benefitted  the  construction,  calibration,  and
   confirmation of the LM3-Eutro model.

•  Although the lake was sampled eight times during
   the 1994-1995 period as part of the Lake Michigan
   Mass Balance Project (LMMBP), additional field
   measurements  would have allowed  this  high-
   resolution model to be better constrained.

•  Additional    sampling   during   the   spring
   phytoplankton bloom period would  improve our
   understanding of Lake Michigan phytoplankton
   dynamics and  thus  assist  in construction and
   calibration of the model.

•  Laboratory  chlorophyll   a  measurements,  in
   addition to Seabird fluorescence estimates, should
   be used for future phytoplankton estimates.

•  No zooplankton concentration estimates below the
   thermocline   were  available  for  this  study.
   Hypolimnetic or whole water column zooplankton
   tows would improve the accuracy of the model.

•  Limited soluble reactive phosphorus (SRP) data
   were collected for this study and  most of the
   results were below the detection limit of 1 ug/L. A
   more complete  SRP data  set, using  an analytical
   method with  a lower detection limit,  would be
   helpful.
                                             123

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Access to an independent data set(s) to confirm
the model would improve the  credibility of the
model.

Relatively few samples were taken in Green Bay
during this project. Examination of the Green Bay
Mass Balance Project (GBMBP) data set suggests
that these data are already  "out-of-date" due to
improvements in  conditions  in the bay.  A more
complete recent  data set for Green Bay would
improve the model's ability to describe the bay
and, to a  lesser  extent,  benefit  the  overall
mathematical framework.

The  hydrodynamics for  the  project year were
incomplete. A constant overall lake temperature
was assumed for the first three months of 1994 in
the Princeton Ocean Model (POM) hydrodynamics
calculation.   No hydrodynamics exist  beyond
December  21,  1995.   A  complete  two-year
hydrodynamics data set  would be useful during
model  calibration   and   forecast  scenario
simulations.

Limited sensitivity analyses  (not included in this
document)  have been performed to date. No
uncertainty analyses  have been performed.
Sensitivity and uncertainty analyses will identify
the effect of the many processes and coefficients
on the model and indicate how accurate the model
predictions  are.    However,  both  of  these
procedures are a major undertaking in a high-
resolution  model like LM3-Eutro due to the large
number of  segments  and time required  to
complete a single simulation.  Commonly used
methods such as Monte Carlo analysis are almost
impossible to perform due to the time and disk
space requirements for a single simulation (and
hundreds,  if not thousands, of simulations  will
probably be  necessary).    In addition,  these
methods provide only an estimate of parameter
uncertainty and do not address issues such as
structure or scenario uncertainty.

A longer record of hydrodynamics data for Lake
Michigan (e.g. 1983-1995) would be useful in
running  forecast scenarios.   This would allow
determination of "typical" hydrodynamics data for
use in longer model simulations.

Updated phosphorus loading data  (and to a lesser
extent nitrogen and silica) would allow for a more
accurate estimation of future loading trends used
in longer model scenarios.
                                           124

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                                         PART 2

                                      LM3-EUTRO
Chapter 3. Model Description

The LM3-Eutro model offers the current "state-of-the-
science" in modeling and is capable of providing the
type  of spatial information  lacking  in the MICH1
model (Rodgers and Salisbury, 1981a,b) and many
other historical Great Lakes frameworks (Di Toro and
Connolly, 1980; Thomann and Di Toro, 1975). The
model is  based on  the standard  eutrophication
kinetics used in the  WASP  family  of  models
(Thomann and Di Toro, 1975; Ambrose et a/., 1993)
as well as the U.S. Army Corps of Engineers' QUAL
models (Cerco  and  Cole,  1995).    Important
improvements over earlier  Great  Lakes models
include the high-resolution segmentation and the use
of a sophisticated hydrodynamics model  (Princeton
Ocean Model [POM], Schwab and Beletsky, 1998) to
drive the lake's hydrodynamics. Earlier models used
tracers such as chloride or temperature to calibrate
water movement and account for the transport within
a system. These approaches frequently introduced
large  uncertainties which are, for the most part,
avoided with the implementation of the POM.

2.3.1 Transport Scheme for Lake Michigan

Considerable attention had  been paid to correctly
simulate water column transport in the Lake Michigan
Mass Balance Project  (LMMBP).   The correct
implementation of hydrodynamics flow and dispersion
and the simulation  of concentration gradients had
been identified as  key elements  of water quality
analysis for the Lake Michigan System,  and they
were given particular emphasis throughout the model
development.
The computational transport scheme for the LMMBP
consisted of three linked submodels in which the
output of one submodel  was  used as input for
another submodel.   The  models  consisted of  a
hydrodynamics   model  that  simulated   three-
dimensional velocity and temperature fields in the
lake, a wave model, and a particle transport model.

The hydrodynamics model was  based on the POM
which  was  adapted to Lake Michigan  by David
Schwab (Schwab and Beletsky,  1998). This model
simulated currents, dispersion coefficients, and water
temperature over a 5 km grid. The grid was three-
dimensional and consisted of 2,318 horizontal cells
and  19 vertical layers that resulted in a  total of
44,042 water column segments.  This 5 km grid was
also  used in, LM3-Eutro.    Tributary inflows, the
Chicago River olitflow, and the  Straits of Mackinac
were incorporated into POM by David Schwab as
part of the hydrodynamics simulation.  The POM
output consisted of water temperature, horizontal and
vertical  dispersion, and  horizontal  and  vertical
currents for each segment in the  water column. This
output was used as input for LM3-Eutro. The 1994-
1995 POM simulation assumed  a constant  uniform
water temperature of 2°C for the  period January 1 to
March 31, 1994, while no hydrodynamics data were
available after December 21,1995. In order to obtain
hydrodynamics data for the complete  1994-1995
period (used  in  model calibration and  long-term
simulations), the first three  months of  1994 were
replaced with January to March 1995 data (including
temperatures), while the corresponding 1994 data
was used for the last 10 days in  1995.
                                            125

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The transport model was fairly complex and was
incorporated within LM3-Eutro itself.  This transport
model was based on the ULTIMATE QUICKEST
transport scheme, originally developed by Leonard
(1991) and subsequently augmented for use with
variable  grid  sizes  by Chapman et al.  (1997).
ULTIMATE QUICKEST  was  also  modified  by
Chapman et al. (1997) to incorporate particle settling
velocities  into the  vertical  transport calculation,
resulting in a more realistic simulation of settling in
Lake Michigan. The resulting transport algorithm has
been  coded   in  Fortran  and   applied   to the
Chesapeake Bay (CE-QUAL-ICM) model (Cerco and
Cole, 1994, 1995).   A linkage between POM and
LM3-Eutro was developed by Chapman et al. (1997).
The linkage was essentially a mapping of POM cell
numbers with ULTIMATE QUICKEST flow  face
numbers and the relationship between horizontal and
vertical   components.  The  LM3-Eutro   model
calculation  performed  numerical integration  of
spatially varying  particle  concentrations   using
quadratic interpolation of the concentration to infer its
value at flow faces.  It  also performed analytic
integration  over  space and time to account for
changes in the concentration at the cell wall during
each time step. Further details of the dimensional
derivation  of  ULTIMATE  QUICKEST  transport
method  can be found in Settles et al. (2002).

2.3.2 Sediments

The  sediments are leaky sinks  of  nutrients and
carbon  in  Lake Michigan.   Phytoplankton and
particulate detrital matter containing carbon, nitrogen,
phosphorus, and silica settle to the lake bed and are
recycled back to the water column  via resuspension,
diagenesis, and diffusion.  The ultimate goal of the
eutrophication modeling effort was  to develop a
coupled  water column  and  sediment  transport
framework.  As a short-term approach, the model
code was  modified  to incorporate  user-specified
sediment fluxes.  Although the framework has the
flexibility to specify fluxes  for any  of the  state
variables,  we  only used  fluxes  of the  dissolved
nutrients  (soluble  reactive  phosphorus  [SRP],
ammonia [NHJ, dissolved silica [DSi])  and dissolved
organic  carbon (DOC). These fluxes, in effect, are
loads that are evenly distributed  over the bottom
sediments. These loads were input into the cells of
the lowest water column layer, with each cell
receiving exactly the same load value.  The loads
were, thus, independent of time and space. It is well
documented that the majority of nutrient mass is
recycled within the lake on an annual basis (Meyers
and  Eadie 1993).  Using this knowledge, nutrient
sediment  fluxes were calculated.   These values
compared favorably  to  limited  published nutrient
fluxes (Quigley and Bobbins, 1986;  Conley, et al.,
1988).

2.3.3   Formulation  of   Eutrophication
Equations

Two important  features of  eutrophication models
were the  multiple interactions  among  nutrients,
plankton, and sediments and the complexity of the
transformation reactions describing the conversions
between  dissolved and  particulate  phases.  The
model simulated two phytoplankton classes, diatoms
and "non-diatoms," a single herbivorous zooplankton
class, and several nutrient  state variables (Table
2.3.1).  In a modeling framework, each  interaction
was described as a mathematical equation and the
challenge  was  to  define  a   relatively  simple
expression to approximate complex biochemical
processes. Most of the equations formulated and
used here were based on the WASP family of models
(Thomann and Di Toro, 1975; Ambrose era/., 1993)
and  the CE-QUAL-ICM model  (Cerco  and Cole,
1995).
Table 2.3.1. Nutrient State Variables
Nutrient
Phosphorus
Nitrogen
Silica
Carbon
Dissolved
Species
Soluble reactive,
dissolved organic
Ammonia, nitrate,
dissolved organic
Biogenic silica
Dissolved carbon
Particulate Organic
Species
Labile, refractory
Labile, refractory
Unavailable silica
Labile, refractory
The complete set of mathematical equations used in
this model can be found in Appendix 2.3.1.  Here we
provide a brief explanation of the changes made in
formulating the  equations describing  algal light
                                             126

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dependence, as this was a significant improvement
over previous eutrophication modeling approaches.

The  general  equation   for  expressing   net
phytoplankton production is given below.

Net production = gross production - mortality

 dp  i\f   if \ o  if  7                  (231)


where

P   =  phytoplankton concentration (mass/volume)

f    =  time

kg   =  phytoplankton  growth rate (time"1)

kd   =  phytoplankton  mortality rate (time"1)

kg;  =  predation rate  (time"1)

Z   =  zooplankton concentration (mass/volume)

The growth rate can be  written as:

k9-kgma}(f(N)f(T)f(l)                  (2.3.2)

where

*gmax =  optimum growth rate (time"1)

f(N) =  nutrient growth dependency

1(1)  =  light growth dependency

f(T) =  temperature growth dependency

A number of equations  had been proposed to
describe the effect of light intensity on phytoplankton
production. Steele's equation (Steele, 1962) is one
of the most commonly used expressions, while a light
saturation equation (similar to the Monod equation)
is also frequently used (Di Toro et al.,  1971). We
described light dependency in this model according
to Steele's equation:
    1(1) = j-  exp
           s
   —+ 1
    /„
              (2.3.3)
   where

   1(1)  =   light limitation (fraction between 0 and 1)

   /    =   solar light intensity (energy/time/area)

   ls   =   saturating light intensity (energy/time/area)

   The Beer-Lambert equation was used to estimate the
   light penetration in the water:
   /z = /0exp(-frez)

   where
                             (2.3.4)
   lz   =  the    light    intensity   at   depth   z
           (energy/time/area)

   /0   =  the surface light intensity (energy/time/area)

   ke   =  light extinction coefficient (1/length)

   z   =  depth (length)

   Substituting this equation into the previous equation
   yields:

        „   /Oexp(-/cz)
          exp
/0 exp (- ke z)
      I,
+ 1
                                            (2.3.5)
   This equation calculates the light limitation at  an
   instantaneous  time  and  at  a  specific  depth.
   However, for models like ours, light limitation must be
   estimated in a certain cell (with a given depth range)
   and over a time period (the time step). Thus, we
   needed to integrate this equation over time and
   depth. Di Toro et al. (1971) formulated  an equation
   assuming  a  constant  light  intensity over  the
   photoperiod. They integrated Steele's equation over
   a 24-hour period and the total depth of a segment.
127

-------
                                        (2.3.6)
where

where

fd   =   the photoperiod

la    =  average   light   intensity   over   the
        photopheriod (energy/time/area)

This approach is still commonly used, although it has
been  criticized for losing the power to represent
midday  surface inhibition  (Di Toro  et a/.,  1971;
Kremer and Nixon, 1 978). LM3-Eutro had the luxury
of performing variable time averaging from hourly to
1 2-hour averages  and it allowed observation of the
differences in time steps.  However, if one wanted to
estimate the light limitation for less than a day and
the average light intensity of that period is known,
one can solve Steele's equation as follows (note:  it
is only integrated over depth, but not over time):
       z2
       z1
       exp
            - /. exp (- k. z)
                   L
dz
                                        (2.3.7)
The solution is almost the same as before, without
the fraction of daylight in the equation.
                   - a,) -exp(-c,0),
The average light intensity (la) can be calculated as
follows:
                                                 '.=
                                                     J 'o (0
                                                                                         (2.3.9)
                                                 where
              t
                                                      =  measured   incident   solar   radiation
                                                         (energy/time/area)

                                                      =  time
and can, thus, be approximated by

     1  "
 a ~ ~n  ,-=1  °                          (2.3.10)

where

n    =  number of discrete time intervals at which I0
        is measured

The ability to estimate light limitation on a three-hour
basis (the time interval used  in LM3-Eutro) rather
than an average daily basis allowed a more accurate
portrayal of the environment in which phytoplankton
grow. The frequency of light measurements in the
LMMBP allowed an important model improvement.

References

Ambrose,  R.B.,  Jr., T.A.  Wool,  and J.L.  Martin.
   1993.  The Water Quality Analysis  Simulation
   Program, WASPS: Part A: Model Documentation.
   U.S.   Environmental  Protection   Agency,
   Environmental  Research  Laboratory, Athens,
   Georgia.  202 pp.

Cerco, C.  and T. Cole.  1994. Three-Dimensional
   Eutrophication Model of Chesapeake Bay. U.S.
   Army Corps of Engineers, U.S. Army Engineer
   Waterways   Experiment  Station,  Vicksburg,
   Mississippi.  Technical Report EL-94-4, 658 pp.

Cerco, C.  and T. Cole. 1995.  User's Guide to the
   CE-QUAL-ICM Three-Dimensional Eutrophication
   Model.   U.S. Army Corps of Engineers, U.S.
   Army Engineer Waterways Experiment Station,
   Vicksburg, Mississippi. Technical Report EL-95-
    15,2,420pp.
                                              128

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Chapman, R.S., T.M. Cole, and T.K. Gerald.  1997.
   Development  of  Hydrodynamic/Water Quality
   (POM-IPXMT) Linkage for  the Lake  Michigan
   Mass Balance Project.   Final  Report.   U.S.
   Environmental  Protection  Agency,  Office  of
   Research and Development, ERL-Duluth,  Large
   Lakes Research Station,  Grosse lie, Michigan.
   63pp.

Conley,  D.J., M.A. Quigley, and C.L.  Schelske.
   1988.  Silica  and   Phosphorus  Flux  From
   Sediments: Importance of Internal Recycling in
   Lake Michigan.  Canadian J. Fish. Aquat. Sci.,
   45(6): 1030-1035.

Di Toro, D.M., D.J. O'Connor, and R.V. Thomann.
   1971. A Dynamic Model of the Phytoplankton
   Population in the Sacramento-San Joaquin Delta.
   Adv. Chem., 106:131-180.

Di  Toro,   D.M.  and   J.P.   Connolly.     1980.
   Mathematical Models of Water Quality in  Large
   Lakes.  Part 2: Lake Erie.  U.S. Environmental
   Protection Agency,  Office  of Research and
   Development,  ERL-Duluth,  Large  Lakes
   Research  Station,  Grosse   lie,   Michigan.
   EPA/600/3-80/065,  97pp.

Kremer, J. and S. Nixon. 1978. A Coastal Marine
   Ecosystem Simulation and  Analysis.  Springer
   Verlag, New York, New York. 210 pp.

Leonard, B.  1991.  The ULTIMATE Conservative
   Difference Scheme Applied to Unsteady One-
   Dimensional Advection.  Comp.  Methods Appl.
   Mechan. Engin., 88(1 ):17-74.

Meyers, P.A.  and B.J.  Eadie.  1993.   Sources,
   Degradation and  Recycling of Organic Matter
   Associated with  Sinking  Particles  in  Lake
   Michigan.  Org. Geochem., 20:47-56.
Quigley, M.A. and J.A. Robbins. 1986. Phosphorus
   Release Processes in Nearshore Southern Lake
   Michigan.   Canadian  J.  Fish.  Aquat. Sci.,
   43(6):1201-1207.

Rodgers, P.W. and D. Salisbury.  1981 a. Modeling
   of Water Quality in Lake Michigan and the Effect
   of the Anomalous Ice Cover of 1976-1977. Great
   Lakes  Environmental  Planning Study,  Great
   Lakes Basin Commission, Ann Arbor, Michigan.
   Contribution Number 44, 53 pp.

Rodgers, P.W. and D. Salisbury.  1981b.  Water
   Quality   Modeling   of  Lake  Michigan and
   Consideration of the Anomalous Ice Cover of
   1976-1977. J. Great Lakes Res., 7(4):467-480.

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
   Mass Balance Study:  Hydrodynamic Modeling
   Project.   National  Oceanic  and Atmospheric
   Administration,  Great  Lakes   Environmental
   Research Laboratory,  Ann  Arbor,  Michigan.
   NOAATechnical Memorandum ERLGLERL-108,
   53pp.

Settles, M., W. Melendez, and J. Pauer. 2002. LM3:
   The  Lake  Michigan  Mass  Balance  Model.
   Internal Report. U.S. Environmental Protection
   Agency, Office of Research and Development,
   National  Health and  Environmental  Effects
   Research Laboratory, MED-Duluth, Large Lakes
   Research Station, Grosse lie,  Michigan. 203 pp.

Steele, J.H.    1962.   Environmental  Control  of
   Photosynthesis in the Sea.  Limnol. Oceanogr.,
   7:137-150.

Thomann, R.V., D.M. Di Toro, R.P. Winfield, and D.J.
   O'Connor.  1975.   Mathematical Modeling of
   Phytoplankton in Lake Ontario,  Part 1   Model
   Development  and  Verification.     U.S.
   Environmental  Protection  Agency,  Office  of
   Research and  Development,   ERL-Corvallis,
   Large  Lakes Research Station, Grosse lie,
   Michigan.  EPA/660/3-75/005, 177 pp.
                                            129

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                                         PART 2

                                      LM3-EUTRO
Appendix 2.3.1.   Development of  LM3-
Eutro Equations

Important characteristics of eutrophication modeling
are the many interactions among nutrients, plankton,
and sediments and  the  transformation reactions
describing the conversions between dissolved and
paniculate phases. In a modeling framework, each
interaction is described as a mathematical equation,
and the challenge is to define a relatively  simple
expression  to  approximate   more  complex
biochemical processes. The model equations used
in LMS-Eutro are described here.

A2.3.1.1 Phytoplankton Growth

Although several phytoplankton groups are present
in Lake Michigan, the lake is dominated by diatoms
and flagellates.  The  major differences between
these classes are silica dependence by the diatoms,
settling rates, carbon content, and growth rates at
different times of the year. It has been speculated
that diatoms grow faster than "non-diatoms" and that
they grow better at cold temperatures because their
blooms are usually observed during the spring in the
Great  Lakes.   The kinetic equations used  in this
model are based on the WASP  family of models
(Thomann and Di Toro, 1975; Di Toro and Connolly,
1980; Rodgers and Salisbury, 1981 a, b) and CE-
QUAL-ICM, developed by the U.S. Army Corps of
Engineers (Cerco and Cole, 1993). These equations
do not include settling rates.  Settling was included
as part of the  model transport in the LM3 model
framework.    The  basic phytoplankton   growth
equation can be written as:
Net Production = Gross Production -  Mortality

^ = (ka-kd)P-kgzZ             (A2.3.1.1)
       g
where

P    -  phytoplanktonconcentration(mass/volume)

t    =  time

kg   =  phytoplankton growth rate (1/time)

kd   =  phytoplankton mortality rate (1/time)

     =  predation rate (1/time)

     =  zooplankton concentration (mass/volume)

The growth rate can be written as:

        __             )             (A2.3.1.2)

where

kgmax   =  optimum growth rate (1/time)

f(N)    =  nutrient growth dependency

f(l)    -  light growth dependency

f(T)    -  temperature growth dependency

For the nutrient growth dependency, we used the
standard  Monod  equation,  but  treated diatoms
slightly differently than non-diatoms. Assuming that
Kg



Z
 kg = kgmax f(N) f(T)
                                             130

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a fraction of the dissolved organic phosphorus (OOP)
was  readily  available  for algal uptake,  available
phosphorus,  Pav, was defined as follows:
Pav =
              f
               DOP
                                     (A2.3.1.3)
where
f,
 OOP
      = fraction of available OOP
DOP=  dissolved    organic   phosphorus
        concentration (mass/volume)

For the non-diatoms, the Liebig's law of minimum
applied with no silica dependency.
 f(N) = min
                NH4 + NO3
                 av
                                     (A2.3.1.4)


where

NH3  =  ammonia concentration (mass/volume)

NO3  =  nitrate concentration (mass/volume)

ksat-N -  half-saturation   coefficient  for  nitrogen
        uptake (mass/volume)

ksat-p =  half-saturation  coefficient for phosphorus
        uptake (mass/volume)

The diatoms were described using the product of the
silica limitation and the minimum  of nitrogen and
phosphorus:
 f(N) =
           Si
                   mm
                                  NO
                                     (A2.3.1.5)
where

*..
             half-saturation  coefficient for silica
             uptake
                                                 The temperature dependency was expressed using
                                                 an equation analogous to the Arrhenius temperature
                                                 correction. Thus:
                                                 f(T) =
                                                 where
                                                         exp
                                                                                    ' M
                                                                                       (A2.3.1.6)
                                                         temperature   effect   below   optimum
                                                         temperature (°C)
                                                         temperature   effect
                                                         temperature (°C)
                                                                               above   optimum
                                                 TM   -  optimum temperature  for  phytoplankton
                                                         growth (°C)

                                                 7   =  temperature (°C)

                                                 A number  of equations had  been  proposed to
                                                 describe the effect of light intensity on phytoplankton
                                                 production.  Steele's equation (Steele, 1962) is one
                                                 of the most commonly used expressions, while a light
                                                 saturation equation (similar to the Monod equation)
                                                 is also frequently used (Di Toro et ai, 1971). In this
                                                 model, light dependency is described according to
                                                 Steele's equation.
                                                     = — exp
                                                        s
                                                 where
                                                                                       (A2.3.1.7)
1(1)  =  light limitation (fraction between 0 and 1)

/    =  incident  solar   light  intensity   (energy/
        time/area)

/s    =  saturating light intensity (energy/time/area)

The Beer-Lambert equation was used to estimate the
light penetration in the water:
                                                 /z =  /0 exp (- ke z)

                                                 where
                                                                                      (A2.3.1.8)
                                                 lz    =  the light  intensity  at  depth z  (energy/
                                                         time/area)
                                              131

-------
I0    =  the surface light  intensity  (energy/time/
        area)

ke    =  light extinction coefficient (1/length)

z    =  depth (length)

Substituting  Equation  (A2.3.1.8) into  Equation
(A2.3.1.7):
 1(1
         /Oexp(-/rez)
      exp
           /o exp (- ke z) + 1
                 L
                                      (A2.3.1.9)
This equation calculated the light limitation at an
instantaneous  time  and  at  a   specific  depth.
However, the need to estimate the light limitation at
a certain cell (with a given depth range) and over a
time period (the time step) was desired.  Thus, it was
necessary to integrate this equation over time and
depth. Di Toro et al. (1971) formulated an equation
assuming a constant light intensity  over photoperiod.
They  integrated  Steele's  equation  (Equation
A2.3.1.9) over a 24-hour period and the total depth of
a segment.
           Az
                    - at) - exp (- a0)]
where
 "0 =    exp (-*,
 a, = y- exp(- kgz2)
                                     (A2.3.1.10)



                                     (A2.3.1.11)


                                     (A2.3.1.12)
and
fd   =  the photoperiod (time)

la    =  average light intensity over the photoperiod
        (energy/time/area)

This approach is still very commonly used, although
it has been criticized for losing the power to represent
midday  surface inhibition (Di Toro et al.,  1971;
                                                   Kremer and Nixon, 1978). In our model, we had the
                                                   luxury of performing  variable time averaging from
                                                   hourly  to  12-hour averages  and  observing  the
                                                   difference.

                                                   However, if one wanted to estimate the light limitation
                                                   for less than a day and the average light intensity of
                                                   that period was  known, one can  solve Steele's
                                                   equation as follows (note: it is only integrated over
                                                   depth, but not over time):
                                                         z1
                                                         exp
                                                              - la exp (- kg z)
                                                                              + I
                                                                                    dz
                                                                                       (A2.3.1.13)
                                                  The solution was almost the same as  Equation
                                                  (A2.3.1.10), without the fraction of daylight in the
                                                  equation.
                                                                                        (A2.3.1.14)

                                                  The average light intensity (la) here can be calculated
                                                  as follows:
>a =
     f /o (0 dt
        J*
                                                                                        (A2.3.1.15)
where

/„  =   measured    incident   solar   radiation
       (energy/time/area)

t  =   time

and can, thus, be approximated by:


la = -  I  I0(tn)                      (A2.3.1.16)


where

n  =   number of discrete time intervals at which I0
       is measured.
                                               132

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A2.3.1.2 Zooplankton Kinetics

Zooplankton predation is important in regulating the
phytoplankton densities in Lake Michigan, especially
during the stratified summer months (Scavia et at.,
1988). Lake Michigan zooplankton are dominated by
herbivorous species, with copepods making up the
majority of the total biomass for most of the year.
However, cladocerans exhibit significant peaks in the
late summer and fall. Due to the limited zooplankton
data reported for the lake and the lack  of kinetic
laboratory and field studies for Lake  Michigan
zooplankton  populations,  we  avoided  complex
zooplankton equations (e.g., Bowie et a/., 1985), and
chose a relatively simple formulation.  The equations
describing  herbivorous  zooplankton growth were
based on formulations from the literature  (Bowie et
a/., 1985; Di Toro and Connolly, 1980; Di Toro and
Matystik,  1980;  Thomann  and   Mueller,  1987).
Carnivorous zooplankton were not directly simulated
here, but  were  represented  in  a herbivorous
zooplankton mortality term.

The following equation was used:
      ii    i  \  -*                 I A1? ^ 1 17\
      /p  If  	 If  \  f                 ^/^^.O. \ , \ I I


where

Z  =  zooplankton  concentration (mass/volume)

t  =  time

kgz =  growth rate (1/time)

kdz =  mortality (1/time)

e  =  growth efficiency
t-
where
               P     r.T-T.
                                    (A2.3.1.18)
      =  maximum growth rate (1/time)
P     =  diatom   and   greens   concentration
         (mass/volume)
                                                 ks    =  half-saturation coefficient (mass/volume)

                                                 6     =  temperature correction factor

                                                 Tu    =  reference temperature (°C)

                                                 The maximum growth rate is a term that lumps the
                                                 filtration and assimilation rates into a  single term.
                                                 The mortality term lumps respiration, excretion, and
                                                 higher predation in a single term. We can, thus, write
                                                 the overall equation:
                                                  dZ
                                                  dt
       ek.
         gzmax
                             *dz
                                    (A2.3.1.19)
                                                 A2.3.1.3 Carbon Interactions
Several carbon interactions were described in the
model,  including  phytoplankton  and  zooplankton
carbon, carbon  loads from tributaries, shoreline
erosion, and detrital carbon from plankton.  The
carbon state variables in this model were diatom,
non-diatom, and zooplankton carbon; labile detrital
carbon; refractory detrital carbon; and dissolved
organic carbon (DOC). Carbon dioxide (CO2) was
not simulated, although a mineralization reaction was
included.   Diatom  and non-diatom carbon were
simulated,  as described  in the  previous  section.
Labile detrital carbon referred to the organic detrital
carbon from the phytoplankton species which breaks
down,  as the  name implies, relatively rapidly.  In
contrast, the  refractory   detrital carbon  is  the
combination of the fraction of the plankton breaking
down, as well as other forms of organic carbon in the
system, e.g., carbon from tributaries, the sediments,
etc.  These forms of carbon break down slowly, but
are not totally refractory. In the equation, we specify
the fractions of labile and refractory carbon.   We
assumed that phytoplankton  utilizes  CO2 as the
carbon source during photosynthesis and releases
carbon as dissolved (CO2 and DOC) and paniculate
(refractory organic carbon  [ROC] and labile organic
carbon [LOG]) forms.

Phytoplankton Mortality and Decay

Phytoplankton   respiration   and   non-predatory
mortality were grouped together  in the model as a
"mortality" term. The release of carbon as C02 from
these processes was split  into different fractions of
DOC and POC.
                                              133

-------
dDOC
  dt
             *-p
                                    (A2.3.1.20)
                                                  simulate any higher predation such as carnivorous
                                                  zooplankton).    We  assumed  that  the  detrital
                                                  zooplankton carbon consisted of dissolved, labile,
                                                  particulate, and refractory particulate fractions.
where

fcdm  =  fraction of DOC from mortality

 dROC _ f   .
— -j: -- 'cm Ka

where

fcrm  =  fraction of ROC from mortality

 dLOC
                                                  dDOC
                                                    dt
                                                  where
                                                                                       (A2.3.1.26)
                                    (A2.3.1.21)    fcdz   = fraction of DOC from zooplankton mortality
         f
       =
          clm
                                     (A2.3.1.22)
where

fclm  =  fraction of LOG from mortality

However, phytoplankton carbon was also converted
to detrital  and DOC  through  predation  (messy
feeding) and zooplankton-imposed mortality.
 dDOC = f  n _  \ k  z
   JA      cdp *     '  gz
   or

where

fcdp  =  fraction of DOC from predation
                                    (A2.3.1.23)
where
          crp

                                     (A2.3.1.24)
 crp
     =  fraction of ROC from predation
 dLOC
  dt

where
                                     (A2.3.1.25)
 clp
     -  fraction of LOG from predation
Zooplankton Mortality and Decay

The zooplankton mortality term included respiration,
non-predatory mortality, and predation (we did not
                                                  dLOC
                                                    dt
                                                         = f
                                                           clz
                                                                                      (A2.3.1.27)
                                                  where

                                                  fc,z    = fraction of LOG from zooplankton mortality

                                                  dROC   ,  .   -,
                                                    dt

                                                  where
                                                                                       (A2.3.1.28)
                                                  fcr2   = fraction of ROC from zooplankton mortality

                                                  Particulate fractions (both labile and refractory) were
                                                  hydrolyzed to DOC, while DOC mineralized to C02.

                                                  Since we did  not explicitly  model bacteria  in  this
                                                  model, their breakdown of carbon was modeled by
                                                  including a dependency on the phytoplankton, which
                                                  acted as a surrogate of the heterotrophic bacterial
                                                  activity in the lake. We also calculated a temperature
                                                  limitation to the hydrolysis and mineralization.  The
                                                  equations can be written as follows:
                                                                                       (A2.3.1.29)

                                                                                       (A2.3.1.30)
                                                  Tfhdr = exp(TkMr(T-Trhdr)]

                                                  where
                                                  Tfmnl  = temperature correction for  mineralization
                                                          (°C)

                                                  Tfhdr  = temperature correction for hydrolysis (°C)

                                                  Tkmn, = mineralization temperature coefficient (°C"1)

                                                  Tkhdr = hydrolysis temperature coefficient (°C"1)
                                               134

-------
7rmn/ =
Trhrir =
optimum  temperature   correction   for
mineralization (°C)

optimum  temperature   correction   for
hydrolysis (°C)
dDOC
  dt

dROC
  dt

dLOC
  dt
where
 = Tfmn, (kdc
                                   (A2.3. 1 .31 )


                                   (A2.3.1.32)
kdc   =  DOC minimum mineralization rate (1/time)

kdcp  =  DOC   mineralization    relating   to
        phytoplankton (volume/mass/time)

krc   =  ROC minimum hydrolysis rate (1/time)

krcp  =  ROC hydrolysis relating to  phytoplankton
        (volume/mass/time)

klc   =  LOG minimum hydrolysis rate (time"1)

kicp  =  LOG hydrolysis relating to  phytoplankton
        (volume/mass/time)

Examining the last two equations, we calculated that
the gain in DOC equaled the sum of the loss of ROC
and LOC.

A2.3.1.4 Phosphorus

In  our model, phosphorus existed as one of four
species  (in  addition  to   being  tied  up  in  the
phytoplankton). Note that all four forms were in the
same oxidation state,  thus, no oxidation reactions
occurred.    The  forms   were  soluble  reactive
phosphorus  (SRP),   DOP,  and  two  forms  of
particulate organic phosphorus (POP)  - a labile
(LOP) and a refractory (ROP) form. SRP and a small
fraction  of  the DOP were  taken  up  by  the
phytoplankton during production (photosynthesis). It'
was  released  due to mortality  and  predation.
                                                Particulate phosphorus was hydrolyzed to DOP and
                                                DOP to SRP.

                                                Phosphorus Uptake by Phytoplankton

                                                Soluble Reactive Phosphorus Uptake:
                                                 dSRP
                                                  dt

                                                where
= - /•„
                                                                       SRP
       9   SRP +
                             (A2.3.1.34)
                                                                     DOP
                                                rpc    = the P:C ratio
                                   (A2.3.1.33)   Dissolve Organic Phosphorus Uptake:
                                                 dDOP
                                                   dt
                                                                             (A2.3.1.35)
                                                where
                                                 pc
                                                      = the P:C ratio
                                                An  interesting   concept,   common  in  many
                                                phytoplankton  models, is the way in  which the
                                                nutrients, including phosphorus, are accounted for
                                                within the phytoplankton. The model kept track of the
                                                carbon  and   used  a  constant  carbon:nutrient
                                                relationship to make these determinations.

                                                Phytoplankton  in the water column were  hydrolyzed
                                                and mineralized to all four phosphorus forms. During
                                                algal metabolic/mortality processes, phytoplankton-
                                                phosphorus  was  converted to  particulate  and
                                                dissolved organic forms as well as directly to SRP.
                                         dSRP
                                           dt
                                                 dLOP
                                                         r  f   k
                                                         rpc 'dop Kd
                                                       -r   f   k P
                                                         rpc 'lop Kd r
                                                         r   f
                                                         rpc 
-------
where
fsrp    = fraction SRP from metabolic processes

fdop   = fraction DOP from metabolic processes

flop    = fraction of LOP from metabolic processes

frop    = fraction of ROP from metabolic processes

During the phytoplankton predation,  zooplankton
assimilated only a fraction of the phytoplankton and
the remainder of the  detrital  phytoplankton  was
released directly  to the  water.  This process  is
commonly  referred to  as "messy  feeding."  The
phosphorus was released in both the dissolved and
particulate forms.
dSRP _
  dt

dDOP
  dt

dROP
  dt


dLOP
  dt
            d -
rpc kgz
                   •pc "0z
                                     (A2.3.1.40)
                                     (A2.3.1.41)
                                     (A2.3.1.42)
where

fpip   =  fraction of SRP from predation

fpdp   =  fraction of DOP from predation

fplp   -  fraction of LOP from predation
•prp
      - fraction of ROP from predation
The model  also  included  equations  to describe
zooplankton mortality. Phosphorus was released to
the  water column  in  both the  dissolved  and
particulate forms.
       _ f     k
         'piz rpc Kdz
         f  r  k   Z
         'pdz 'pc Kdz *-
                  (A2.3.1.45)
                                                  dLOP _
                                                   dt

                                                  dROP
                                                    dt

                                                 where
                                                              pc
                                                                    (A2.3.1.46)


                                                                    (A2.3.1.47)
                                                  fPiz    = fraction of SRP from zooplankton mortality

                                                  fpdz    = fraction of DOP from zooplankton mortality

                                                  fplz    = fraction of LOP from zooplankton mortality
                                                  •prz
                                                        - fraction of ROP from zooplankton mortality
                               Particulate phosphorus was hydrolyzed to DOP and
                               DOP was mineralized back to SRP as follows:
                                 dDOP
                                   dt
                                                    dLOP
                                                     dt
                                                                         "'sat-pt
k*»PSRP+k.
                                                                             'sat-ptj
                                                                         sat-pt
                                                                           ^sat-pt)
                  (A2.3.1.43)      dRQp
                                                     dt
                                                          -k
                                                                                      (A2.3.1.48)
                                                                   (A2.3.1.49)
                                                                                      (A2.3.1.50)
                                                                            nsat-pt)
                               where

                               kdp   = DOP mineralization coefficient (1/time)

                               klp   = LOP hydrolysis coefficient (1/time)
                                                  Vp
                                                        = ROP hydrolysis coefficient (1/time)
                               kdpa   = DOP   mineralization  coefficient   algal
                                       dependence (volume/mass/time)
                               klpa   = LOP   hydrolysis    coefficient   algal
                                       dependence (volume/mass/time)
                                    (A2.3.1.44)    k,
                                rpa   = ROP   hydrolysis   coefficient   algal
                                       dependence (volume/mass/time)
                                                  ksat-Pt = mean saturation coefficient of algal classes
                                                          for SRP
                                               136

-------
A2.3.1.5 Nitrogen

The  transformation  of  nitrogen  was  similar to
phosphorus, although nitrogen existed in more than
one oxidation state. Algal nitrogen was released as
two forms of particulate organic nitrogen  (labile
[LON], refractory [RON]), dissolved organic nitrogen
(DON), and ammonia (NH4). Particulate forms were
hydrolyzed to DON. DON was further mineralized to
NH4 and NH4 is oxidized to nitrate (NO3).

In our model, we assumed that phytoplankton had no
preference between NH4 and NO3 as a  nitrogen
source.
 dDIN
  dt
      ~   rnc kg P
(A2.3.1.51)
where

DIN = NH4 + NO3
rnc    - N:C ration
Because we assumed no preference, then

          NH4
  dt
 dN03
  dt
          DIN
          NO,
                                    (A2.3.1.52)
          DIN
(A2.3.1.53)
Similar  to  phosphorus,   nitrogen   bound  to
phytoplankton can be released as particulate organic,
dissolved organic and NH4 forms.
 dNH4
      _
      = rnc 'din
       _    f
  ft   ~ rnc 'don Kd ^
                                    (A2.3.1.54)
                                    (A2.3.1.55)
                                                 where

                                                 fdin   = fraction NH4 from metabolic processes
                                                 'don
                                                 'Ion
                   =  fraction DON from metabolic processes

                   =  fraction LON from metabolic processes
                                                 fron   = fraction RON from metabolic processes

                                                 As  described for carbon  and  phosphorus,  the
                                                 nitrogen balance was affected by the zooplankton
                                                 through "messy feeding" and zooplankton mortality.
  dt
              dDON
                dt
              dLON
                dt
              dRON
                dt
                                                                   nc
                                                                    nc
                                                                                     (A2.3.1.58)
                                                                                     (A2.3.1.59)
                                                                                     (A2.3.1.60)
                                                           nrp'
                                                                                     (A2.3.1.61)
where

fnip   = fraction of NH4 from predation

fndp   = fraction of DON from predation

fnlp   = fraction of LON from predation
                                                 'nrp
                                                       = fraction of RON from predation
             The release of nitrogen during zooplankton mortality
             can be expressed similarly to the phosphorus.
              Qf/VH4

               dt
                                                            rnc kdz
                                                                                     (A2.3.1.62)
dLON
      _
      ~ rnc 'ion Kd
(A2.3.1.56)
                                                  dDON
             rnc kd.
                                                                                     (A2.3.1.63)
         r  f
         'nc 'ran
                                    (A2.3.1.57)
                                                   dt
                                                             nc
                                                  (A2.3.1.64)
                                              137

-------
dRON
  dt
                                    (A2.3.1.65)
where

fniz    = fraction of NH4 from zooplankton mortality
'ndz
 nlz
- fraction of DON from zooplankton mortality

= fraction of LON from zooplankton mortality

= fraction of RON from zooplankton mortality
A2.3.1.6 Silica

The behavior of silica was  similar  to that of
phosphorus  and  nitrogen.   Two  silica  species,
biogenic silica (SU) and available silica (SA), were
simulated in the lake. Dissolved silica was utilized by
phytoplankton, while  both dissolved and  biogenic
silica were released via phytoplankton  mortality,
predation upon phytoplankton by zooplankton, and
zooplankton mortality. The major difference from the
other nutrients was that only diatoms had a silica
dependency.

The diatom  silica consumption  can be written as
follows:
  dt

where
            k P
              P
                               (A2.3.1.66)
rsc    = Si:C ratio

Note that in  all the silica equations, the variable
phosphorus refers only to the diatom concentration.
Like the other  nutrients, silica  was released  via
diatom mortality.
 dSU
  dt
                              (A2.3.1.67)
Both  classes  of silica  could  be  released via
zooplankton predation.
dSA
         ,
       — r   r  If
         sap 'sc ngz
              dSU
               dt

             where
                                                 = - f   r   k
                                                      sup 'sc ngz
                                                                                      (A2.3.1.69)
             fsap   = fraction of SA from predation

             fsup   = fraction of SU from predation

             We assumed that  no silica accumulated within the
             zooplankton so there were no terms for silica release
             from zooplankton mortality.

             References

             Bowie,  G.L.,  W.B.  Mills,   D.B.  Porcella,  C.L
                Campbell,  J.R. Pagenkopf,  G.L.  Rupp, K.M.
                Johnson, P.W.H. Chan, S.A. Gherini, and C.E.
                Chamberlin. 1985. Rates, Constants and Kinetic
                Formulations in Surface Water Quality Modeling,
                2nd Edition.   U.S.   Environmental  Protection
                Agency, Environmental  Research Laboratory,
                Athens, Georgia. EPA/600/3-85/040, 455 pp.

             Cerco, C.F. and T.  Cole.  1993.  Three-Dimensional
                Eutrophication  Model of  Chesapeake Bay. J.
                Environ. Engin., 119(6):1 006-1 025.

             Di Toro, D.M., D.J. O'Connor, and R.V.  Thomann.
                1971.  A Dynamic Model of the Phytoplankton
                Population in the Sacramento-San Joaquin Delta.
                Adv. Chem., 106:131-180.
                                            Di
                                                 Di
                 Toro,  D.M.   and  J.P.  Connolly.    1980.
                 Mathematical Models of Water Quality in Large
                 Lakes.  Part 2: Lake Erie.  U.S. Environmental
                 Protection Agency,  Office  of Research  and
                 Development,   ERL-Duluth,  Large   Lakes
                 Research   Station,   Grosse  lie,   Michigan.
                 EPA/600/3-80/065, 97 pp.

                 Toro,  D.M.  and  W.F.  Matystik, Jr.   1980.
                 Mathematical Models of Water Quality in Large
                 Lakes.  Part 1 : Lake Huron and Saginaw  Bay.
                 U.S. Environmental Protection Agency, Office of
                 Research and Development, ERL-Duluth, Large
                 Lakes Research Station, Grosse lie, Michigan.
                 EPA/600/3-80/056, 180 pp.
(A2.3.1.68)
                                              138

-------
Kremer, J. and S. Nixon.  1978. A Coastal Marine
   Ecosystem Simulation and Analysis.  Springer-
   Verlag, New York, New York. 210 pp.

Rodgers, P.W. and D. Salisbury. 1981 a.  Modeling
   of Water Quality in Lake Michigan and the Effect
   of the Anomalous Ice Cover of 1976-1977. Great
   Lakes  Environmental Planning  Study,  Great
   Lakes Basin Commission, Ann Arbor, Michigan.
   Contribution Number 44, 53 pp.

Rodgers,  P.W. and D.  Salisbury.  1981b.  Water
   Quality  Modeling   of  Lake  Michigan  and
   Consideration of the Anomalous Ice Cover of
   1976-1977. J. Great Lakes Res., 7(4):467-480.

Scavia, D., G.A. Lang, and J.F. Kitchell.   1988.
   Dynamics of  Lake Michigan Plankton: A Model
   Evaluation of Nutrient Loading, Competition, and
   Predation.   Canadian  J.  Fish. Aquat.  Sci.,
   45(1 ):165-177.
Steele,  J.H.   1962.   Environmental  Control  of
   Photosynthesis in the Sea.  Limnol. Oceanogr.,
   7:137-150.

Thomann, R.V., D.M. Di Toro, R.P. Winfield, and D.J.
   O'Connor.   1975.   Mathematical  Modeling  of
   Phytoplankton in Lake Ontario, Part 1   Model
   Development   and   Verification.     U.S.
   Environmental Protection  Agency,  Office  of
   Research and  Development,  ERL-Corvallis,
   Large  Lakes  Research Station,  Grosse Me,
   Michigan. EPA/660/3-75/005, 177 pp.

Thomann, R.V. and J.A. Mueller. 1987. Principles of
   Water Quality Modeling and Control.  Harper and
   Row Publishers, New York, New York. 644 pp.
                                             139

-------
                                         PART 2

                                      LM3-EUTRO
Chapter 4. Model Input and Field Data

2.4.1   Loading  and  Sediment-Water
Interactions

2.4.1.1 Atmospheric Loads

Measurements were made at eight locations around
Lake  Michigan   (Miller  et  a/.,  2000;  U.S.
Environmental Protection Agency, 1997) and loads
were calculated for total phosphorus, total Kjeldahl
nitrogen (TKN),  and nitrate (NO3).   Monthly total
loads were available for March 1994 through October
1995  (Table  2.4.1).    Table  2.4.1  shows the
phosphorus loads for this period. In order to obtain
a complete two-year record (necessary for model
calibration and forecast simulations), January and
February of 1994 were assumed to be the same as
January and February of 1995, while November and
December of 1995 were assumed to be the same as
November and  December of 1994.  The total
phosphorus loads were split between labile organic
phosphorus (LOP) (67% of total phosphorus) and
soluble reactive phosphorus (SRP) (33% of total
phosphorus).  All other forms were assumed to be
insignificant. We assumed that the TKN atmospheric
loading is split evenly between labile organic nitrogen
(LON) and refractory organic nitrogen (RON) forms.

2.4.1.2 Tributary Loads

Loads from 11 monitored tributaries were calculated
using the stratified  Beale ratio estimator model (Hall
and Robertson, 1998). Loads from 18 unmonitored
tributaries  (two of which represented  portions  of
monitored tributaries) were also  estimated based on
results from monitored watersheds  and individual
watershed and flow attributes (Hall and  Robertson,
Part 7, Appendix  2).  Monitored tributaries were
sampled at sites as far downstream as  possible to
provide   the   most  accurate  load  estimates.
Composite samples were prepared from  two depths
at three points along a cross-sectional transect of the
river.  Most samples were taken during high flow
Table 2.4.1.   1994-1995 Monthly Atmospheric
Total Phosphorus Loads	

    1994 Atmospheric Total Phosphorus Loads
                  (kg/month)
         March
          April
          May
          June
          July
         August
       September
        October
       November
       December
17552
34665
27465
14429
38184
38303
29908
17334
32418
7937
    1995 Atmospheric Total Phosphorus Loads
                  (kg/month)
January
February
March
April
May
June
July
August
September
October
22748
8430
17552
34665
27465
14429
26457
39458
18893
43254
                                            140

-------
periods (Hall and Robertson, 1998). Loads provided
in the original Great Lakes National Program Office
(GLNPO) data set included chlorophyll a, dissolved
organic carbon (DOC),  participate organic  carbon
(POC), total phosphorus, SRP, TKN, ammonia-N
(NH4), NO3, and dissolved silica (DSi).  Daily loads
were provided  in  units  of  kg/d for the period of
January 1,1994 to December 1, 1995. Table 2.4.2
provides a summary of the total phosphorus loads for
the 11 monitored tributaries.

Loads  provided for  each tributary  were used to
calculate  additional  parameters   of  interest.
Chlorophyll a loads were converted to phytoplankton
carbon by assuming a  40:1 carbon-to-chlorophyll
ratio. This ratio was chosen to maintain consistency
within the model (see Section 2.4.2.2). This carbon
value was then converted into diatom carbon and
non-diatom carbon  by  assuming  that  tributary
phytoplankton populations  were 75% diatom and
25% non-diatom (Allan,  1995).  Labile paniculate
organic carbon (LOG)  and refractory particulate
organic carbon (ROC) were estimated by subtracting
total algal carbon from POC and multiplying by 0.55
for LOG and 0.45 for ROC. Algal phosphorus was
estimated by assuming a phosphorus:carbon ratio of
0.01  (algal carbon  multiplied by  .01).  Organic
phosphorus was taken to be total phosphorus minus
         the sum of algal phosphorus and SRP-  From the
         estimate of organic phosphorus,  dissolved organic
         phosphorus (OOP) was assumed to  be 10% and
         LOP and refractory organic phosphorus (ROP) were
         both assumed  to  be 45%.  Algal nitrogen was
         estimated  using a nitrogen:carbon ratio of 0.2 (algal
         carbon multiplied by 0.2).  Organic nitrogen was
         calculated as TKN  minus the sum of algal nitrogen
         and NH4.  As in the case of phosphorus, dissolved
         organic nitrogen (DON) was represented by 10% of
         organic nitrogen, while labile organic nitrogen (LON)
         and  refractory organic nitrogen (RON) were  each
         represented as 45% of organic nitrogen.

         2.4.1.3 Shoreline Erosion

         Shoreline erosion, mainly along the western shore,
         contributes significantly to the solids concentration in
         Lake Michigan.  The  shoreline erosion estimates
         were based on the long-term, county-level estimates
         of Monteith and Sonzogni (1976).  David Schwab,
         National Oceanic and Atmospheric Administration
         (NOAA),  Great  Lakes  Environmental Research
         Laboratory (GLERL),  Ann Arbor, Michigan,  used
         these estimates to calculate erosion loads of coarse-
Table 2.4.2.  Tributary Total Phosphorus Loads (kg/year)
 River
 Total
  1994
  1995
2676481
2315700
Two-Year Average
Menominee
Fox
Sheboygan
Milwaukee
Calumet
St. Joseph
Kalamazoo
Grand
Muskegon
Pere Marquette
Manistique
Total Monitored Tributaries
Total Unmonitored Tributaries
83753
562865
28424
33731
44710
275772
176318
663972
62490
34937
25966
1992937
683544
127281
595991
21703
31320
39782
264341
137918
351250
43497
26828
25367
1665276
650424
105517
579428
25063
32525
42246
270057
157118
50761 1
52993
30882
25667
1829107
666984
     2496091
                                             141

-------
and fine-grained particles (personal communication).
Organic carbon makes up a very small fraction of the
bluff material (Monteith and Sonzogni, 1976) so we
used a carbon fraction of 0.5% for  the fine-grain
material in estimating the POC erosion loads to the
lake. We assume this POC is in the refractory form.

2.4.1.4 Sediment

A brief description of the sediment component of the
model  was previously  provided  in the  model
description section (Part 2, Chapter 3). A summary
of the phosphorus fluxes,  settled  masses, and
reported  literature values can  be found in  Table
2.4.3.
Table 2.4.3. Sediment Masses, Fluxes, and Loads


               Mass      Mass      Literature
    State      Settled   Recycled   Comparison
   Variable   (kg/year)  (kg/year)    (kg/year)

  Phosphorus  7x106    4x106     3.6-12 x106*
	1.1 x106"

* Quigley and Bobbins, 1986.
**Conley et al., 1988.
 2.4.2 Field Data

 Large amounts of data were collected between April
 1994 and  October 1995 during eight  sampling
 cruises  (Table  2.4.4).   Sampling stations were
 scattered throughout the  lake (Figure 2.4.1). The
 data  sets  included lake nutrient concentrations;
 physical measurements such as solar radiation and
 temperature;  and  biological   data  related  to
 phytoplankton, zooplankton, and fish communities in
 the lake. These data have been used to describe the
 current  state-of-the-lake  and  to gain  a  better
 understanding  of the  lake as a whole and  the
 processes affecting it.  They were also  useful in
 model calibration.  Many of the samples collected
 were  analyzed in situ or on  the  ship immediately
 following collection, while others were  carefully
 preserved and  sent out for analysis  by several
 laboratories  around  the   country.    Detailed
 descriptions of  sampling techniques and sample
 analyses used can be found  in the Lake  Michigan
Table 2.4.4. The LMMBP Sampling Cruises
Cruise
Number
Cruise 1
Cruise 2
Cruise 3
Cruise 4
Cruise 5
Cruise 6
Cruise 7
Cruise 8
Start Date
April 24, 1994
June 17, 1994
Augusta, 1994
October 14, 1994
January 16, 1995
March 23, 1995
Augusts, 1995
September 16, 1995
End Date
May 11, 1994
June 26, 1994
August 26, 1994
November 7, 1994
January 25, 1995
April 18, 1995
August 16, 1995
October 13, 1995
Mass   Balance   Project   (LMMBP)   Methods
Compendium   (U.S.   Environmental   Protection
Agency, 1997).

All LMMBP data were subjected to rigorous water
quality   assurance  (QA)  procedures   (U.S.
Environmental Protection Agency, 2004).   Once
available to the modelers, data were examined for
completeness and content. Any data which seemed
suspect (unusually high or low values as compared
to historical data, missing values or codes, etc.) were
resubmitted to GLNPO for  additional examination.
Data which appeared reasonable and complete were
subjected to  a  standardized  data  assessment
protocol  by  individual  modelers.    This  data
assessment  provided basic statistical information
about the data (mean, minimum, maximum, median,
standard deviation), identified outliers, and evaluated
sample  normality.  In some cases, data averaging
and  grouping were necessary  before adequate
assessment  could be performed.  Once the data
assessment was completed by the modeling team,
data were imported into the  modeling database and
kept unchanged for the rest of the data evaluation,
model development, and model validation. Summary
statistics for the nutrient data are shown  in Table
2.4.5.   Due  to the limited  number of Green Bay
samples,  the  table includes only the  open lake
statistics.

2.4.2.1 Open Lake Nutrient and Carbon Data

2.4.2.1.1 Total Phosphorus

Total phosphorus  represented  the sum  of  all
phosphorus  species in the  sample, including the
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                                                 Muskegon River

                                               Cterand River
                                                                  Sample Type

                                                                  water survey stations
                                                               Q\ biota survey stations
                                                               D tributary monitoring
                                                                  stations
                Sheboygan
                  River
               Milwauke
                 River
                                             •.vat'id Kalamazoo River
                                               . Joseph River
               Chicago
              Grand Calumet
                /    Harbo?
Figure 2.4.1. The LMMBP sampling locations.
                                           143

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Table 2.4.5. The LMMBP Open Lake Nutrient Data Summary Statistics

Nitrate
SRP
Total Phosphorus
DSi
TKN
NH4
POC
DOC
Number
of
Samples
847
504
846
847
845
505
363
364
Minimum
0.01
0.0
1.8
0.038
0.01
0.0
36.15
1.05
Maximum
0.45
6.70
28.7
2.11
0.43
0.30
989.26
2.9
Median
0.28
0.60
4.6
0.52
0.15
0.01
201.7
1.54
Mean
0.27
0.71
5.0
0.54
0.16
0.02
203.0
1.55
Standard
Deviation
0.064
0.69
2.1
0.29
0.07
0.03
92.0
0.19
Outliers*
None
19
31
10
10
13
6
9
 Note: POC, SRP, and total phosphorus are expressed in ug/L, all others are in mg/L.
 *Larger than twice the standard deviation.
 phosphorus dissolved  in  the  water;  phosphorus
 sorbed  to particles  such as  iron, calcium,  and
 magnesium; and the phosphorus contained within the
 phytoplankton,  zooplankton,  and detrital particles.
 Large spatial  and  temporal  changes  were not
 expected in the lake over the project years (1994-
 1995) because of the conservative nature of total
 phosphorus.   Changes were  typically  limited  to
 particulate settling and incoming and outgoing loads.
 It appeared  that the LMMBP data  verified this,
 suggesting small, if any, changes in concentration
 and no  apparent nearshore/offshore or north/south
 trends in the lake. Seasonal trends were observed,
 with complete mixing early in the year and  slightly
 higher total phosphorus in surface waters during the
 spring/early summer bloom. Total phosphorus was
 lower in the  surface waters later in  the summer,
 possibly due to settling out of the algal phosphorus.
 Higher concentrations were observed at the bottom.
 On  a  few  occasions,  unexpectedly  high total
 phosphorus values  were  observed.   These may
 result from local inputs  (tributary) or natural  or
 sediment disturbances during sampling.

 2.4.2.1.2  Dissolved Phosphorus

 Dissolved  phosphorus   was   defined   as  the
 concentration of phosphorus found in a sample after
 filtration  through  a membrane filter.   SRP, the
 preferred form of phosphorus  used by algae,  was
 some portion of dissolved  phosphorus.  A  general
seasonal   concentration  trend   in   dissolved
phosphorus was observed on a lake-wide basis.
Early in spring, the concentration was relatively low,
with values just above 2 ug/L. There was a slight
increase in early summer, followed by a decrease in
summer to a level frequently below detection limits.
Dissolved  phosphorus increased in the fall and the
pattern repeated for the following year.

2.4.2.1.3 Soluble Reactive Phosphorus

SRP is one of the most important nutrients because
it is widely considered to be the driving force for algal
primary productivity in  Lake Michigan (Tarapchak
and Nalewajko, 1987). There has been considerable
discussion in  the  literature  about  the meaning,
measurement, and  role of SRP, but most agree that
SRP levels can be used to  predict algal growth
(Tarapchak  and Nalewajko, 1987).  SRP  was  not
analyzed for samples collected during the first three
cruises and the majority of the data  from the other
five cruises fell  below the detection  limit of 1 ug/L
(U.S. Environmental Protection Agency, 1997). The
remarkably  good  correlation between  SRP  and
dissolved   phosphorus,  which   was   previously
discussed, was useful in making estimates of SRP
for the first two cruises.  However, because of  the
lack of actual SRP data, we did not speculate about
trends  in the lake.  This weakness in this important
data set made the analysis and subsequent modeling
exercise difficult.
                                               144

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2.4.2.1.4 Nitrate

Nitrate analysis methods actually measure the sum
of N03  and  nitrite  (NO2).    Nitrite values were
assumed to  be  low  enough to be  considered
negligible. No obvious nitrate spatial trends were
observed (nearshore versus offshore or  northern
basin versus southern basin), but it appeared that the
concentration, on average, was slightly lower in the
summer than in the winter. This was probably due to
the uptake of dissolved nitrogen during phytoplankton
production in the summer.

2.4.2.1.5 Ammonia

Ammonia is the most reduced nitrogen form and is,
therefore, the most  available for algal uptake.  It
occurred in the lake at very low concentrations, often
below the detection limit of 20 ug N/L. Though the
data set was incomplete because no samples for
ammonia analysis were taken during the first three
sampling cruises, no obvious spatial or temporal
trends were observed.

2.4.2.1.6 Total Kjeldahl Nitrogen

TKN is  a measure of all of  the  reduced  nitrogen
present  in  the water,  including  organic  nitrogen
(particulate and dissolved) and ammonia. No spatial
or temporal trends were observed for the TKN values
but  noticeably  higher  values  were  observed
throughout the lake during the August 1994 cruise.

2.4.2.1.7 Dissolved Silica

Only the dissolved form of silica (DSi) was measured
in this study.  A reasonably good representation of
the open lake concentration could be constructed.
Silica concentrations followed a distinct pattern, with
highest observed values occurring uniformly in the
lake early in the year. The silica was depleted during
the spring and summer by diatom consumption in the
epilimnion,  while  silica  increased  in the  lake's
hypolimnion during this part of the year, mainly due
to diatom settling and detrital silica (Laird et at.,
1988). Toward the end of the summer and early fall,
silica, in the strongly stratified  epilimnion, decreased
to approximately 0.2  mg/L, while it was greater than
1 mg/L in the hypolimnion. This seasonal trend was
observed for both  project  years.   No  obvious
differences could be observed between the Michigan
or Wisconsin shores or between the southern and
northern parts of the lake. Epilimnion values tended
to be higher during the summer in shallow nearshore
sites than in  deeper open lake sites.

2.4.2.1.8 Dissolved Organic Carbon

DOC   remained  remarkably  constant  in  Lake
Michigan over the two-year period. We observed few
spatial or temporal  trends  in the  lake, although
significantly  higher and lower concentrations were
observed at individual stations in the lake.

2.4.2.1.9 Particulate Organic Carbon

As expected, there was  a large variation in POC
concentrations in the lake. Typical concentrations for
the open lake ranged from 100 to 300 ug/L. In Lake
Michigan, POC consisted mainly of phytoplankton
carbon, detrital carbon,  and to a lesser extent,
zooplankton  carbon.  High POC was strongly related
to the timing  and locations of  phytoplankton blooms.
In general,  POC was higher in  the euphotic zone
during  the warmer summer  months.  Early in the
spring, POC was higher in the nearshore, probably
due to higher temperatures which resulted in early
spring phytoplankton production.

2.4.2.1.10 Green Bay Nutrient Data

While Lake Michigan is classified as an oligotrophic
system, Green Bay is eutrophic  and has drastically
different properties than  the  Lake Michigan proper.
Green  Bay exhibited  much higher concentrations of
nutrients and large phytoplankton and zooplankton
populations   (and,   thus,   higher   carbon
concentrations).    Most  state  variables had  a
concentration gradient, with highest levels (several
times higher than the open lake) close to the Fox
River mouth  and lowest concentrations close to the
confluence  with the lake.   This gradient  was
especially prominent for phosphorus, phytoplankton,
and carbon.  During the LMMBP (1994-1995), Green
Bay was only sampled in two locations (Figure 2.4.1).
This lack of data complicated the estimation of many
state variables. Available historical data and scaling
of  open  lake  data  were used   to  estimate
concentrations in many  instances  (Bierman et a/.,
1992;  DeStasio and Richman,  1998; Sager and
Richman, 1991).
                                              145

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2.4.2.2 Plankton

2.4.2.2.1 Phytoplankton

Data were collected during eight cruises between
April 1994 and October 1995. Samples represented
composites of 1, 5,10, and 20 m sub-samples. Data
were   communicated   by  group  densities  and
biovolumes (diatoms, "all else" (primarily flagellates),
greens, non-nitrogen fixers, and nitrogen-fixers) and
species densities and biovolumes. Sampling stations
were distributed through the lake (Figure 2.4.1).

Sampled phytoplankton populations differed in overall
density and biovolume in  1994 and 1995. Diatoms
and "all else" occurred in higher numbers  in 1994
than 1995, while greens and blue-greens occurred in
similar  numbers during both  years.  This density
difference was reflected in the  1994  and 1995
biovolume data.  Overall, phytoplankton biovolume
was much higher in 1994 as a result of higher diatom
and "all else" biovolume.  This finding could be the
result  of sampling  which was not evenly divided
across the calendar year, with 1994 being spring-
weighted and 1995 being  fall-weighted.

Blue-green algae (non-nitrogen fixers and nitrogen-
fixers categories) dominated the samples in the total
number of cells present.  Blue-greens were  the
dominant cell type present  in all  months.  Peak
densities of blue-greens occurred in August-October
1994 and August-September 1995.  Peak densities
of  diatoms were observed in  May-June 1994 and
April-August 1995.  "All else" category phytoplankton
peaked in number in May-June  1994, again in
October-November 1994, and then  remained stable
throughout the 1995 sampling months.

Diatoms dominated phytoplankton biovolume in April-
June 1994 and again in January-August 1995.  "All
else" phytoplankton dominated total biovolume in
October 1994,  November  1994,  and  September
1995.  Diatoms and "all else" contributed similarly to
total phytoplankton biovolume  in August 1994 and
October 1995.  Green algae and blue-green algae
contributed  slightly more to  total biovolume  in
August-October 1994 and August-October 1995 but
never contributed more than approximately 20% and
15%, respectively. In general, diatoms and "all else"
composed   >75%   of the   total   phytoplankton
biovolume  every month, while the  blue-greens
contributed approximately 6% of the phytoplankton
biomass.

Average sizes for  each  phytoplankton  category
further supported the  biovolume data.   Diatoms
averaged 898.6  um3/cell, "all else" 574.3 urrvYcell,
greens  374.4 um3/cell,  non-nitrogen fixers 12.2
um3/cell and nitrogen-fixers 167.7 um3/cell. Because
total carbon content was expressed as a function of
cell biovolume and diatoms and "all else" dominated
the total biovolume of the epilimnetic waters, it was
safe to assume that the major phytoplankton carbon
source  would  be   the  diatom  and "all  else"
phytoplankton categories. The blue-greens, although
high  in  numbers,  made  up  an  insignificant
percentage of phytoplankton carbon mass.

2.4.2.2.2 Chlorophyll a

Chlorophyll a data were provided by GLNPO for the
1994-1995  LMMBP field  season.   Data  were
collected using a Seabird fluorometer and calibrated
to extracted chlorophyll a data. Due to laboratory
error, extracted chlorophyll a data from all cruises
except Cruise 8 (September-October 1995)  were
declared invalid. Thus, Seabird data for the 1994-
1995 sampling season were calibrated with fall 1995
and 1997 extracted chlorophyll  a  data (Goldsmith,
1999). This was accepted  as the best alternative,
and the chlorophyll a profiles  generated from the
calibrated  data generally agreed  with trends and
overall concentration levels expected for the lake.

Raw,   station-specific  chlorophyll  a  data  files
contained  information  such as station code and
location,  date,  time,   depth  of measurement,
chlorophyll  a  (mg/L), and  percent  transmissivity.
Most chlorophyll a depth profile measurements were
taken in 0.1-0.5 m increments, although occasionally
only 1 m increments were provided.  Some level of
"cleaning" was required for all files.  All data with
depth measurements less  than or  equal to zero
meters were discarded in the analysis, as were data
reporting a measurement of -0.18.  Both of these
values were utilized as data flags by GLNPO.  In
addition, chlorophyll a data near the surface or
bottom were frequently reported as a long-series of
identical measurements.  The chlorophyll a profile
used  in the  analysis  included the last  of  these
"repeats" and  its coordinating depth if the repeats
occurred at surface depths,  or  the first of the
                                               146

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"repeats" and coordinating depth if occurring at lake
bottom. These repeating values were likely the result
of equipment limitations and sampling error (hitting
bottom, etc.) and could not be deemed reliable.

2.4.2.2.3  Phytoplankton Carbon

The eutrophication model required phytoplankton to
be expressed as carbon and divided into diatom and
non-diatom classes.  Multiple data transformations
were necessary to satisfy these requirements. The
determination of which approach should be used to
estimate phytoplankton carbon was a complicated
first  step.    The  LMMBP  data  set  included
phytoplankton biovolume data from 0-20 m integrated
samples as well as chlorophyll a depth  profiles.
Biovolume data could be converted to phytoplankton
carbon using equations published by Strathmann
(1967) and Rocha and Duncan (1985).  While this
approach  is generally accepted in  the  scientific
community, some researchers question whether it is
possible to avoid propagating error using this method
(Sicko-Goad etal., 1984).  In calculating biovolume,
organism  dimensions are  measured  and  then
multiplied to yield cubic volume. Any  measurement
error is, thus, cubed and then further compounded by
inclusion of the erroneous value in the volume-to-
carbon equation.   The microscopic  nature  of
phytoplankton makes some degree of measurement
error inevitable.  Another issue was the presence of
vacuoles  and thick  walls in some phytoplankton
species. These would  be included in  a microscopic
measurement of an organism as biovolume but
contribute relatively little to the carbon content of the
organisms (Sicko-Goad et al., 1984).  In addition to
methodological  difficulties,  the   limitation  of
phytoplankton biovolume data to integrated samples
from the  top 20  m of the water column made it
difficult to estimate phytoplankton carbon for discrete
depths and deeper waters using these data.

Another method of estimating phytoplankton carbon
is  converting chlorophyll  a  using  a carbon-to-
chlorophyll  a  ratio.   This  approach  also has
shortcomings. Carbon-to-chlorophyll ratios may vary
with species and light and nutrient conditions.  Some
researchers have found  that the variation was
greatest  under  nutrient   limitation,  a  common
occurrence in Lake Michigan (Riemann etal., 1989).
The chlorophyll a calibration difficulties encountered
during the LMMBP, and  discussed earlier,  further
complicated the issue, as the chlorophyll a data set
was not as reliable  as desired.  The LMMBP
chlorophyll a data set, however, was quite thorough
and any error contained within it as a result of actual
measurement or calibration was probably consistent
across the entire data set.  The chlorophyll a data
set also lent itself  to comparison  with the large
volume of historical data from Lake Michigan, as well
as measurements taken in Green Bay as part of the
Green Bay Mass Balance Project (GBMBP) modeling
effort (Bierman etal., 1992).

A  cruise-by-cruse comparison  of  biovolume and
chlorophyll a derived carbon data for the entire lake
was made (Figure 2.4.2).  Chlorophyll a values from
the top 20 m of the water column were averaged and
converted to carbon using several commonly cited
carbon-to-chlorophyll a ratios (35:1, 40:1, and 50:1)
(Riemann et al.,  1989; Montagnes et al., 1994;
Cloern et al., 1995).  Visual analysis of the results
presented in Figure 2.4.2 suggested  that a  40:1
carbon-to-chlorophyll ratio provided the best fit with
biovolume  carbon data  over all  eight  sampling
cruises.   It was  our belief that this chlorophyll a
carbon estimation approach  provided the greatest
consistency among  integrated 0-20 m  samples,
deeper water samples, and Green Bay estimates,
and it  provided the best fit to biovolume carbon
estimation methods.

The40:1 carbon-to-chlorophyll relationship was used
to generate carbon values for model fitting exercises.
Chlorophyll a values for each station and cruise were
converted to carbon at each depth along the depth
profile, and  then separate average carbon values
were calculated for the 0-10 m and 11 -20 m intervals.
Diatom/non-diatom proportions were taken from the
corresponding 0-20 m phytoplankton biovolume data
and used to divide the total average carbon value
into diatom and non-diatom categories. Estimates of
phytoplankton carbon deeper in the water column
were also calculated from chlorophyll a data.   Set
depths of 25 m and 40 m were chosen, and the total
depth between 50 m and the bottom for each station
was split into thirds, and the  midpoint of each  third
was used for carbon estimation.  When total depth
was less than 65 m, a few set depths were used
instead  (50 m,  60  m,  etc.).   Occasionally, an
additional  depth  was  added  to allow  better
representation  of  the  deep  chlorophyll  layer.
Phytoplankton carbon at these depths was estimated
                                              147

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      160
  D)
  C
  o
  .a
  CO
  O
                                            Phytoplankton carbon estimates
                                         r- C predicted from chlorophyll-a data (C:Chl 50 :1)
                                            C predicted from chlorophyll-a data (C:Chl 40 :1)
                                       - * - C predicted from chlorophyll-a data (C:Chl 35 :1)
                                       -•— C calculated from biovolume data

     0.35
     0.30
                                                                 6
                                   o  c predicted from chlorophyll-a data (C:Chl 40 :1)
                                   •  C calculated from biovolume data
      0.25
      0.20
  c
  o
  .0
  l_
  CD
  O
0.15
                                                       9>
                                             4         5
                                               Cruise #
                                                            6
Figure 2.4.2.   Lake-wide phytoplankton carbon  calculated from  biovolume  data and carbon-to-
chlorophyll a ratios for the eight LMMBP cruises.
                                                148

-------
by again assuming a carbon:chlorophyll ratio of 40:1.
Total carbon was then split into diatom and non-
diatom carbon using station- and cruise-specific 0-20
m diatom proportions.  Biovolume data were  not
available for many stations and some station/cruise
combinations and cruise average diatom proportions
were used in these instances.

2.4.2.2.4 Zooplankton

Zooplankton were collected with plankton net tows
from 20 m to the surface. At stations that were less
than 20 m in depth, the zooplankton tow was done
from 1 m above the bottom to the surface.  Data
were  communicated  by  group  densities  and
biovolumes  (Bythotrephes, carnivores,  detrivores,
Dreissena veligers,  herbivores,  and Mysis)  and
species densities and biovolumes. Sampling stations
were distributed throughout the lake.

Zooplankton  species  level  analyses  revealed that
several species had high average  abundances.
Conochilus unicornis, Polyarthra vulgaris, Polyarthra
major, Dreissena veliger, copepod nauplii, Keratella
cochlearis, Synchaeta,  Kellicottia longispina,  and
Diaptomus copepodites all occurred at average
densities greater than 5,000/m3. Of these organisms,
only Dreissena, copepod nauplii, and Diaptomus are
not rotifers.  Other species were found in virtually all
of the samples and were well distributed throughout
the lake,  regardless  of season.  These included
copepod  nauplii, Cyclops copepodites,  Diaptomus
copepodites,   Diaptomus  minutus,   Keratella
cochlearis,   Synchaeta,   Cyclops   bicuspidatus,
Diaptomus  ashlandi,  Kellicottia longispina,  and
Polyarthra vulgaris.

Zooplankton were further divided into groups by class
(rotifer, copepod, and cladocera). As  suggested by
the species level data, rotifers dominated the overall
zooplankton abundance, although copepods were
more important in early spring and winter (April-June
1994, January-May   1995).   Rotifer abundance
peaked in July-August 1994 and July-August 1995
while copepods seemed to peak in August of both
years.    Cladocerans experienced  a   brief   but
significant peak  in  number in  mid-August  and
September  of  each year.   Total  zooplankton
abundance peaked at 400,000 organisms/m3 in 1994
and 700,000 organisms/m3 in  1995.  This annual
difference  may  be  the  result  of  differential
reproductive success between years or the timing of
sampling, as discussed for the phytoplankton data.

Copepods overwhelmingly dominated zooplankton
biomass  throughout most of the year, with  peaks
from August to mid-October  1994 and August to
September  1995.    Cladocerans did experience
seasonal peaks, however, in which they accounted
for most of the zooplankton  biomass present in the
lake over a  very  short  period  of  time.  These
cladoceran biomass peaks coincided with peaks in
the Daphnia galeata  population at  the sampling
stations.  The peaks occurred in mid-August 1994
and  early-August 1995,  with a smaller peak in
October 1995. Rotifer biomass was always quite low
despite  peaks  in abundance and generally high
numbers. Overall, total zooplankton biomass peaked
in the 225,000 to 275,000 mg/m3 range  in August
1994 and August 1995.

In order  to better   understand  the  impact  of
zooplankton on phytoplankton populations, trends of
carnivorous species versus herbivorous species were
examined. Detrivores and Dreissena did account for
10-30% of the total zooplankton abundance during
several sampling months, but no biomass data were
available for these organisms. Carnivores accounted
for less than 25% of the total zooplankton biomass
and  abundance  during  all  months   sampled.
Carnivore abundance and biomass, in  fact, were
relatively static, with  only  small peaks in each
observed in August of 1994 (around  46,000 mg/m3
and 12,000 organisms/m3) and 1995 (around 48,000
mg/m3   and  4,000  organisms/m3).   Herbivore
abundance increased from April 1994 to August 1994
and then began to decline.  The same abundance
peak was observed  in  1995,  but  with a  slight
resurgence in October of 1995. Herbivore biomass
increased from April 1994 through August 1994 and
slowly declined through December 1994. Herbivore
biomass began  to increase again in April 1995 and
followed a similar pattern to that of 1994 over the rest
of  1995.    Herbivore   abundance peaked  at
approximately 210,000 organisms/m3 in 1994 and
275,000  organisms/m3 in 1995,  while biomass
peaked at around 225,000 mg/m3 both years.

Differences in carnivore and herbivore abundance
and biomass among sampling stations were analyzed
using a one-way ANOVA.  No significant differences
were found among stations for  herbivore abundance
                                             149

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or biomass, but significant differences  did  exist
among  stations for  carnivore  abundance  and
biomass. Statistical differences in carnivore biomass
arose primarily from a difference between Stations
47M and MB19M.   Carnivore abundance differed
between many stations and Station GB24M.  This
was not unexpected since GB24M is located in
Green Bay rather than the open lake.

2.4.2.2.5 Zooplankton Carbon

LMMBP  zooplankton  data were provided  as dry
weight biomasses (mg/m3).  Data corresponding to
herbivorous species were extracted from the data set
for further  analysis.  Herbivorous species  were
selected because their  grazing  activities  directly
impacted phytoplankton and were, thus, important to
the eutrophication  model.  Herbivore data  were
converted to units of g/L and then converted to
carbon by assuming that carbon accounted for 50%
of the dry  weight  (Baudouin and  Ravera, 1972;
Hessen, 1990;  Andersen  and  Hessen,  1991).
Carbon  data were incorporated into the model with
accompanying  station and  date  information.  No
zooplankton carbon values were estimated for
segments below 20  m due to the lack of applicable
LMMBP or historical data.

2.4.3 Initial Conditions

The model simulation started in January 1994, but no
field  data  were available  until  late April 2004.
Seasonal changes of the state variables were much
larger than changes  (increases or decreases) over a
one-year period.  We, therefore, based  our  initial
conditions for the nutrients, carbon, and plankton on
January 1995  (LMMBP Cruise 5) field data.  The
carbon  estimates were derived from the LMMBP
chlorophyll  a data for the 41  segments in the LM2
model.  Level 2 segmentation is detailed in Figure
2.4.3.    A  40:1  carbon:chlorophyll  a ratio was
assumed and used throughout. Diatom/non-diatom
proportions  were  taken  from   the   0-20  m
phytoplankton  biovolume data wherever possible,
and the same diatom/non-diatom proportions  were
maintained  throughout the water column.  When
insufficient phytoplankton biovolume data existed (as
was the case for many  segments in Cruise  5), a
cruise average value (52% diatoms) was used.
When no chlorophyll a profiles were available for a
given segment for Cruise 5, values from neighboring
segments were used.  In general, if no values were
available for segments 4, 5, and 6, the average of
total phytoplankton carbon for segments 1, 2, and 3
were used  (diatom/non-diatom carbon proportions
were assigned later based on segment specifics).  If
values were available for segment 6 but 4 and 5 were
missing,  segment  6  values  were  assigned  to
segment 5 and segment 3 values were assigned to
segment 4.   These  estimated surface segment
values were  then  mirrored throughout the water
column.

Inadequate Green  Bay data existed  to follow the
previously described approach for assigning initial
conditions.  After review of the LMMBP chlorophyll
a profiles available for Green Bay stations, January
chlorophyll  a  was  estimated  to  be  1  ug/L for
segments 7 and 8, 2 ug/L  for segment 9, and 3 pg/L
for   segment  10.    Using  Green  Bay  specific
diatom/non-diatom  proportions estimated from the
literature and a 40:1  carbon-to-chlorophyll ratio,
these values were converted to diatom  and non-
diatom carbon initial conditions (Sager and Richman,
1991; DeStasio and Richman, 1998). Values for
deeper  segments  mirrored  the surface  values.

Carbon data for zooplankton collected from waters 0-
20 m depth in January  1995 (Cruise 5), March 1995,
and April 1994 and 1995 (Cruises 1  and 6) were
examined in order to estimate initial conditions for the
lake. No zooplankton samples existed for several
surface segments within the lake and most estimates
for these segments follow from estimated values of
neighboring segments. Carbon values varied with
segment, but,  generally, the  same value was
assigned to the  0-10  m, 10-20 m, and 20-30 m
segments within each surface segment sector, and a
value of 150% of  this 0-10  m carbon value was
assigned to the 30-50 m depth segment. The bottom
segment (50 m maximum depth) was assigned a
carbon value equal to the  0-10 m carbon value.

Many of the non-biological field measured variables
did not directly relate to the state variables used in
the model. As a result, assumptions were made and
calculations were   performed   to  determine  the
appropriate initial conditions for the modeled state
variables. Table 2.4.6 lists the field measurements
and modeled state variables for the nutrients and
carbon.  Specific assumptions and calculations used
in estimating these model state variables from field
                                              150

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3
13
22
31
38
10
10
10
20
212
1
11
20
29
36

10
10
10
20
103
2
12
21
30
37
10
10
10
20
113
5
15
24
33
40
10
10
10
20
44
                                                 water column
                                                     segment
                                                     numbers
11
                                                             20
                                                             29
                                                             36
10 average
10 depth of
10 segment

20
   103
Figure 2.4.3.  Level 2 model segmentation for LM3-Eutro.
                                        151

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Table 2.4.6. Relationship of Field Measurements
and Model State Variables
  Variable
     Field
Measurements
  Model State
    Variable
 Phosphorus
 Nitrogen
  Silica
  Carbon
Total Phosphorus
Dissolved
  Phosphorus
Soluble Reactive
  (SRP)
Total Kjeldahl
  (TKN)
Ammonium (NH4)
Nitrate (NO3)
Dissolved (DSi)
 Particulate Organic
  (POC)
 Dissolved Organic
  (DOC)
Labile Organic (LOP)
Refractory Organic
  (ROP)
Soluble Reactive
  (SRP)
Dissolved Organic
  (OOP)
Labile Organic (LON)
Refractory (RON)
Dissolved Organic
  (DON)
Ammonium (NH4)
Nitrate (NO3)
Dissolved (DSi)
Biogenic (BSi)
Labile Organic (LOG)
Refractory Organic
  (ROC)
Dissolved Organic
  (DOC)
 measurements can be found in Appendix 2.4.1.  It
 was assumed that the particulate forms for carbon,
 phosphorus, and nitrogen were split evenly between
 the labile and refractory forms. It was also assumed
 that the DON was insignificant.

 2.4.4 Parameter Estimation

 One  of the most  challenging tasks in the model
 development process was  the  estimation  of  the
 different model coefficients.  A limitation of this
 project was  the  lack  of  field  and  laboratory
 experiments to determine  values for the  many
 coefficients. Some physical data were available for
 model coefficient estimation, and these instances are
 detailed  below.  In  addition,  the use of  primary
 productivity experiments to assist with the estimation
 of production-related coefficients will be discussed.
 Values for all  other  parameters were  obtained
initially from the literature, with further refinement via
calibration.

2.4.4.1  Physical Measurements

2.4.4.1.1 Secchi Disk

Secchi disk measurements were performed during
the eight sampling cruises in 1994-1995 to obtain an
estimate of water clarity.  Cruise averages for all
available stations were calculated and are shown in
Figure 2.4.4.  Secchi disk values  were used in an
empirical equation (Thomann and Mueller, 1987) to
estimate the light extinction coefficients  used in the
eutrophication model.

2.4.4.1.2  Solar Radiation and Temperature

Primary productivity was strongly  affected  by  both
available light (solar radiation) and temperature. As
part  of the output  of  the  hydrodynamics model
(Princeton Ocean Model [POM]) used to generate
Lake Michigan hydrodynamic parameters, lake-wide
short wave solar radiation and temperature data were
generated (Schwab and  Beletsky,  1998).  Solar
radiation was one of the forcing functions driving the
phytoplankton growth.  In the model, it was  referred
to as incident solar light intensity (I0).

2.4.4.2 Primary Production Estimates

The rates at which phytoplankton grow and utilize
available nutrients are among the most important and
complex processes in  any  eutrophication  model.
Primary productivity laboratory  experiments were
conducted as part of the LMMBP. However, due to
the difficulty in converting laboratory production rates
into   reasonable  in  situ   primary   production
information,  the  model  production  rates  were
generated using coefficients gleaned from published
literature and the model calibration process (Table
2.4.7).     The  laboratory   primary  production
experiments  were  used to  verify  the   overall
production  rates  in the model  (Figure  2.5.2).
Laboratory  productivity data  were  provided by
GLNPO for the 1994-1995 project field season. The
14C incubation productivity determination method was
utilized.   This  method  calls  for the inoculation of
water sub-samples with 13C radiotracer followed by
incubation at varying light intensities for two to four
hours.  Sub-samples were filtered and radioactivity
                                                152

-------
         0

         1-

         2-

         3-
Average Lake Michigan Secchi Disk Depth  1994-1995
    Error bars represent 95% confidence intervals
Figure 2.4.4. Lake-wide Secchi depths for the eight LMMBP cruises.
Table 2.4.7. Important LM3 Model Coefficients
Parameter
CCHLD
CCHLG
KHPD

KHPG
KHSD
PMD
PMG
TMD
TMG
Unit
No Unit
No Unit
M9/L

ug/L
mg/L
1/day
1/day


Value
40
40
0.5

0.5
0.03
2.5
2.1
20
20
Literature
Values
10-1002'3'4
10-1001-4
0.5-1.02'3'4

0.5-1.01'4
0.03 - 0.062'3'4
0.58 - 8.02'3'4
0.58 - 8.01 4
2Q2.3.4
2Q1-4
Description
Carbon:chlorophy!l ratio (diatoms)
Carbon :chlorophyll ratio (non-diatoms)
Phosphorus half-saturation coefficients for diatoms
Phosphorus half-saturation coefficients for non-
diatoms
Si half-saturation coefficient for diatoms
Diatom growth coefficient
Non-diatom growth coefficient
Optimum diatom growth temperature
Optimum non-diatom growth temperature
1Rodgers and Salisbury, 1981
2Di Toro and Connolly, 1980
3Bowieefa/., 1985
"Thomann and Di Toro, 1975
                                            153

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of algal cells  was measured (U.S. Environmental
Protection Agency, 1997).  Measured radioactivity
should be proportional to the amount of carbon fixed
by the algae. Other variables used in calculating the
final productivity estimate include light  intensity,
length  of  incubation,  temperature,  and  basic
information about carbon and chlorophyll levels in the
water samples.  Variables reported included station
code, date,  sample depth, temperature, sample
identification number, productivity results (mg C/L/h),
total incubation time  and incubation light level
(mE/m2/s). Each station was sampled several times
from April 1994 to October 1995,  and 12 sub-
samples were incubated (at different light intensities)
for each  station/date/depth combination.  Discrete
and integrated samples were collected, and efforts
were made to include hypolimnetic samples during
stratification.

Most of the analysis effort was devoted to determine
how productivity changes with light, temperature,
phytoplankton carbon, chlorophyll a, etc.,  and to
compare these changes with the output of the model
equation. Data appeared to follow typical irradiance
versus production curves, with production increasing
with increasing light levels and then  reaching  a
plateau.  Limited light ranges, however, prevented
determination of the presence/absence or degree of
light inhibition.  For purposes of further analysis of
laboratory versus  model productivity  predictions,
optimum light levels were designated. For each set of
experiments, optimum light was taken to be that light
at  which maximum  production  (mgC/L/h) was
reported.

There was some degree of uncertainty associated
with all  estimates  of phytoplankton production
derived from  incubation experiments.   It is well-
known that results from  short  experiments (<  6
hours) are frequently higher than  those estimated
from longer experiments (24 hours).  It is generally
believed  that  short-term 14C incubations  measure
something  between  gross and  net  production
(Fahnenstiel and Scavia,  1987).   This is a  factor
which  must  be   considered   when  comparing
laboratory data to predictions from model equations.
References

Allan, J.D.  1995.  Stream  Ecology: Structure and
   Function of Running Waters. Chapman and Hall,
   London, England.  104pp.

Andersen, T. and  D.O. Hessen.  1991.  Carbon,
   Nitrogen, and Phosphorus Content of Freshwater
   Zooplankton. Limnol. Oceanogr., 36(4):807-814.

Badouin, M.F. and O.  Ravera.  1972. Weight, Size
   and Chemical Composition of Some Freshwater
   Zooplankters: Daphnia hyalina (Leydig). Limnol.
   Oceanogr., 17(4):645-649.

Bierman, V.J., Jr.,  J.V. DePinto, T.C. Young, P.W.
   Rodgers,  S.C.  Martin,  and R.  Raghunathan.
   1992.    Development  and Validation  of  an
   Integrated Exposure Model for Toxic Chemicals
   in Green Bay, Lake Michigan. Final Report. U.S.
   Environmental  Protection  Agency,  Office  of
   Research and Development, ERL-Duluth, Large
   Lakes Research Station, Grosse He,  Michigan.
   381 pp.

Bowie,  G.L, W.B.   Mills,  D.B.  Porcella,  C.L
   Campbell, J.R.  Pagenkopf, G.L.  Rupp,  K.M.
   Johnson,  P.W.H. Chan, S.A. Gherini, and C.E.
   Chamberlin. 1985. Rates, Constants and Kinetic
   Formulations in Surface Water Quality Modeling,
   2nd Edition.    U.S. Environmental Protection
   Agency,  Environmental Research Laboratory,
   Athens, Georgia. EPA/600/3-85/040, 455 pp.

Cloern, J.E., C. Grenz,  and L. Vidergar-Lucas. 1995.
   An  Empirical  Model   of  the  Phytoplankton
   ChlorophylkCarbon Ratio  - The  Conversion
   Factor Between Productivity and Growth Rate.
   Limnol. Oceanogr., 40(7):1313-1321.

Conley, D.J.,  M.A. Quigley,  and C.L.  Schelske.
   1988.    Silica  and  Phosphorus  Flux From
   Sediments: Importance  of Internal  Recycling in
   Lake Michigan.  Canadian J. Fish. Aquat. Sci.,
   45(6): 1030-1035.
                                              154

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DeStasio,  B.T.,  Jr.  and  S.  Richman.   1998.
   Phytoplankton Spatial and Temporal Distributions
   in Green Bay, Michigan, Prior to Colonization by
   the Zebra Mussel (Dreissena polymorpha). J.
   Great Lakes Res., 24(3):620-628.

Fahnenstiel, G.L andD. Scavia.  1987.  Dynamics of
   Lake   Michigan   Phytoplankton:   Primary
   Production and Growth. Canadian J. Fish. Aquat.
   Sci., 44(3):499-508.

Goldsmith, J.C.   1999.    Calibration of  In  Vivo
   Fluorometer  Response  Measurements  With
   Known Amounts  of  Extracted Chlorophyll a.
   Internal   report  and  presentation.     U.S.
   Environmental Protection Agency, Great Lakes
   National Program Office, Chicago, Illinois. April
   29,1999.

Hall, D. and D. Robertson.  1998.  Estimation of
   Contaminant  Loading  from  Monitored  and
   Unmonitored Tributaries to Lake Michigan for the
   USEPA  Lake Michigan Mass  Balance Study.
   Quality  Systems  and  Implementation  Plan.
   Submitted October 23,1998. U.S. Environmental
   Protection  Agency,  Great   Lakes   National
   Program Office,  Chicago, Illinois. 19 pp.

Hessen,  D.O.   1990.    Carbon,  Nitrogen  and
   Phosphorus Status in Daphnia at Varying Food
   Conditions. J. Plankton Res., 12(6): 1239-1249.

Laird, G.A., D. Scavia, G.L. Fahnenstiel, LA. Strong,
   and G.A.  Lang.  1988.   Dynamics of Lake
   Michigan Phytoplankton: Relationship to Nitrogen
   and Silica Fluxes. Canadian J. Fish. Aquat. Sci.,
   45(8): 1459-1466.

Miller,  S.M.,  C.W. Sweet, J.V. DePinto, and  K.C.
   Hornbuckle.   2000.  Atrazine and  Nutrients in
   Precipitation:  Results from the Lake Michigan
   Mass Balance Study.   Environ. Sci. Technol.,
   34(1):55-61.

Montagnes, D.J.S., J.A. Berges, P.J. Harrison, and
   F.J.R.  Taylor.   1994.   Estimating  Carbon,
   Nitrogen,  Protein,  and  Chlorophyll  a  From
   Volume  in Marine  Phytoplankton.    Limnol.
   Oceanogr., 39(5): 1044-1060.
Monteith.T.J.andW.C.Sonzogni. 1976. U.S. Great
   Lakes Shoreline Erosion Loadings. Great Lakes
   Basin Commission, Ann  Arbor, Michigan. 223
   pp.

Quigley, M.A. and J.A. Robbins. 1986. Phosphorus
   Release Processes in Nearshore Southern Lake
   Michigan.   Canadian  J.  Fish.  Aquat. Sci.,
   43(6): 1201-1207.

Richardson, W.L., D.D. Endicott, R.G.  Kreis, Jr., and
   K.R. Rygwelski (Eds.). 2004. The Lake Michigan
   Mass Balance Project Quality Assurance Plan for
   Mathematical  Modeling.    Prepared  by the
   Modeling  Workgroup.    U.S.  Environmental
   Protection  Agency, Office  of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes  Research Station, Grosse  lie, Michigan.
   EPA/600/R-04/018, 233 pp.

Riemann,  B.,  P.  Simonsen, and L. Stensgaard.
   1989.  The Carbon and Chlorophyll Content of
   Phytoplankton From Various  Nutrient Regimes.
   J. Plankton Res., 11(5):1037-1045.

Rocha, O.  and A. Duncan. 1985.  The Relationship
   Between Cell  Carbon  and  Cell Volume  in
   Freshwater Algal Species Used in Zooplanktonic
   Studies.  J. Plankton Res., 7(2):279-294.

Rodgers, P.W.  and D. Salisbury.  1981. Modeling of
  " Water Quality in Lake Michigan and the Effect of
   the Anomalous Ice Cover of 1976-1977.  Great
   Lakes  Environmental  Planning  Study,  Great
   Lakes Basin Commission, Ann Arbor, Michigan.
   Contribution Number 44, 53 pp.

Sager, P.E.  and S. Richman.   1991.  Functional
   Interactions of Phytoplankton and Zooplankton
   Along the Trophic Gradient in Green Bay, Lake
   Michigan.   Canadian  J.  Fish.  Aquat. Sci.,
   48(1):116-122.

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
   Mass Balance Study:  Hydrodynamic Modeling
   Project.  National  Oceanic  and  Atmospheric
   Administration,  Great  Lakes  Environmental
   Research  Laboratory, Ann  Arbor,  Michigan.
   NOAA  Technical Memorandum  ERL GLERL-
   108, 53pp.
                                             155

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Sicko-Goad, L.M., C.L. Schelske, and E.F. Stoermer.
   1984.  Estimation of  Intracellular Carbon and
   Silica  Content   of  Diatoms  From  Natural
   Assemblages Using Morphometric Techniques.
   Limnol. Oceanogr., 29(6):1170-1178.

Strathmann, R.R.  1967.  Estimating the Organic
   Carbon  Content  of  Phytoplankton From Cell
   Volume or Plasma Volume.  Limnol. Oceanogr.,
   12:411-418.

Tarapchak, S.J. and C. Nalewajko. 1987. A Review:
   Phosphorus-Plankton Dynamics and Phosphorus
   Cycling in Aquatic Systems. National Oceanic
   and  Atmospheric Administration, Great Lakes
   Environmental Research Laboratory, Ann Arbor,
   Michigan.  NOAA Technical Memorandum ERL
   GLERL-60, 57 pp.
Thomann, R.V., D.M. Di Toro, R.P. Winfield, and D.J.
   O'Connor.   1975.   Mathematical Modeling of
   Phytoplankton in Lake Ontario, Part 1 - Model
   Development   and  Verification.     U.S.
   Environmental  Protection  Agency,  Office  of
   Research and  Development, ERL-Corvallis,
   Large  Lakes  Research Station, Grosse lie,
   Michigan. EPA/660/3-75/005, 177 pp.

Thomann, R.V. and J.A.Mueller. 1987. Principles of
   Water Quality Modeling and Control. Harper and
   Row Publishers, New York, New York. 644 pp.

U.S. Environmental Protection Agency. 1997. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume  1:  Sample  Collection
   Techniques.    U.S.  Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois.  EPA/905/R-97/012a, 1,440pp.
                                            156

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                                        PART 2

                                     LM3-EUTRO
Appendix  2.4.1.     Modeled   Versus
Measured Variables

Not all  model  output variables could  be  directly
compared to the field measurements.  In order to
compare these model variables to the field data, the
following calculations were performed:
                                              APCP =  phosphorus to carbon ratio (a constant
                                                        value of 0.01)
                                  (A2.4.1.1)
 Total Phosphorus

TP = LOP + POP + OOP + SRP

   + APCP (DIA + GFtE + ZOO)

 where

 Field measurements (expressed as mass/volume)

 TP   =  total phosphorus

 SRP  =  soluble  reactive  phosphorus  (also a
         model output variable)

 Model output variables (expressed as mass/volume)

 LOP  =  labile particulate organic phosphorus

 ROP  =  refractory particulate organic phosphorus

 OOP  =  dissolved organic phosphorus

 DIA   =  diatoms (expressed as carbon)

 GRE  =  non-diatoms (expressed as carbon)

 ZOO  =  zooplankton (expressed as  carbon)
                                               Dissolved Phosphorus

                                               DP = OOP + SRP

                                               where
                                    (A2.4.1.2)
DP   =  dissolved  phosphorus  (expressed  as
         mass/volume)(field measurement)

Total Kjeldahl Nitrogen

TKN = LON + RON +  DON + NH3

     + ANCP(DIA + GRE + ZOO)     (A2.4.1.3)

where

Field measurements (expressed as mass/volume)

TKN  =  total Kjeldahl nitrogen

NH3   =  ammonia (also a model output variable)

Model output variables (expressed as mass/volume)

LON  =  labile particulate organic nitrogen

RON  =  refractory particulate organic nitrogen

DON  =  dissolved organic nitrogen

ANCP =  nitrogen-to-carbon ratio (a constant value
         of 0.25)
                                           157

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Particulate Organic Carbon                         Total Algal Carbon
POC  = LOG + ROC + DIA + GRE + ZOO         Total a/0a/  carbon = DIA  + GRE     (A2.4.1.5)
                                    (A2.4.1.4)
                                                Total algal carbon was neither  a field nor a model
where                                           variable.  It was a summed total of the diatoms and
                                                greens (performed for both the  field and model
Field measurements (expressed in mass/volume)     output).

POC  =  particulate organic carbon

Model output variables (expressed as mass/volume)

LOC  =  labile particulate organic carbon

ROC  =  refractory particulate organic carbon
                                            158

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                                          PART 2

                                       LM3-EUTRO
Chapters. Calibration

2.5.? Description of Process

After model equations were formulated and coded as
a computer program, model calibration was the next
step. The goal of calibrating water quality models
was to adjust the model coefficients in order to obtain
the best possible fit between the model output and
the  field   data.     Challenges  of  calibrating
eutrophication models included the many degrees of
freedom (independent model coefficients) and the
uncertainty of many of these model coefficients. A
traditional model calibration approach was used for
LM3-Eutro.   The model  coefficients were initially
estimated using values and ranges reported in the
literature (see Table 2.4.7) and these parameters
were then adjusted to provide the best model fit to
the field data.  In this  study,  values  for many
coefficients  were  derived from  available  Lake
Michigan and Great Lakes historical data collected by
reputable agencies  such as  the United States
Environmental Protection  Agency's (USEPA) Great
Lakes National Program Office (GLNPO), National
Oceanic and Atmospheric Administration's (NOAA)
Great Lakes Environmental  Research  Laboratory
(GLERL) and the University of Michigan. Very few
field and laboratory experiments were performed to
estimate kinetic coefficients for LM3-Eutro.  Limited
14C primary production experiments were performed
and  used  in determining phytoplankton  growth
coefficients.   Phytoplankton  (diatoms and  non-
diatoms), particulate organic carbon (POC),  total
phosphorus,  and  dissolved silica (DSi) were  the
most   important  state  variables   in  model
calibration. However, all variables were evaluated
during the calibration process.

The   model  was   calibrated  on  the  Level  3
segmentation framework (Figure 2.5.1). The main
calibration emphasis was placed  on the main lake
due  to  inadequate  Green  Bay data.   The  high-
resolution (Level 3) segments were also collapsed to
the Level 2 segmentation scheme to provide a visual
representation of how well the model reflected the
field  data in different areas of the lake and captured
expected trends, such as spring epilimnetic diatom
peaks   and  nutrient  depletion.    It was  not
LEVEL2-LM-2

10 Surface Segments
41 Water Segments


Figure  2.5.1.
segmentation.
     LEVEL 3 - LM-3
     (High Resolution 5km X 5km Grid)
     2318 Surface Segments
     44,042 Water Segments
     19 "Sigma" Levels

Level  2  and  Level 3  model
                                             159

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feasible to visually compare model output versus field
data on the Level 3 segmentation framework due to
the large number (44,042) of 5 km2 cells.  Instead,
we regressed model output versus field data for each
of the 5 km2 cells where a field data  point was
available.    This  enabled calculation  of  simple
statistical  parameters  such  as  square  of  the
correlation  coefficient  (r2)  and  slope and  direct
comparison of the different model calibration runs.

During the calibration process, small changes to the
model initial conditions were made to observe the
effect on the model fit. This was done because of the
uncertainty of the initial condition estimates and the
fact  that the initial conditions  have  a  significant
influence on the model output.

Data  from   laboratory  primary   productivity
experiments were used  to constrain and confirm
values for the growth coefficients that were used in
the model.  Productivity experiment results;  light and
temperature parameters;  and the Lake Michigan
Mass  Balance  Project (LMMBP) field  data  were
applied to  LM3-Eutro  productivity  equations  to
generate model production estimates comparable to
those  generated  in  the  laboratory experiments.
Model constants were then adjusted in order to best
reflect the primary production trends observed in the
laboratory experiments (Figure 2.5.2).

2.5.2  Selection of Best Calibration

After performing several hundred model simulations,
we  selected our  best  run  based  on statistical
parameters including square of correlation coefficient
(r2) and slope. The coefficients of the final run were
constrained to ensure that all model coefficients fell
within reasonable and  reported ranges. The best
model fit was also evaluated visually on the Level 2
segmentation scheme.  Important criteria  included
the  model  fit with the  overall  field data, ability to
capture  observed  and  expected   phytoplankton
peaks, and how well hypolimnetic and  epilimnetic
nutrient,  carbon,  and  plankton  trends  and
concentrations were predicted.  The calibrated final
model coefficients are listed in Table 2.5.1. Figure
2.5.3 shows the overall Level 3 fit for phytoplankton,
total phosphorus,  POC, and DSi. Statistical results
are summarized in Table 2.5.2.  Figure 2.5.4 shows
model versus field data plots for selected Level  2
segments. Selected Level 3 5 km2 cells representing
nearshore  regions  and  offshore  regions  are
presented in Figure 2.5.5. Model output for the 5 km2
cells was much more dynamic than for the larger
Level 2 segments and  there were far fewer data
points  for  model  fit  determination.  The  model
appeared to fit the available data very well in some of
the cells and not as well in others.  Level 3 model
versus data comparisons in individual 5 km2 cells
were not used in our calibration exercise.

Although we attempted  to calibrate all of the state
variables, less emphasis was placed on the nitrogen
states  because Lake  Michigan is phosphorus and
silica-limited.    We  also  did  not  perform any
comparison of model output with the field data for
particulate silica (SU) or soluble reactive phosphorus
(SRP), since there was  no SU field data and more
than 80%  of the  SRP  field data  fell below  the
detection limit.  As stated, we did not spend much
time calibrating Green Bay because of the limited
sampling done  in the bay. As a consequence, the
final Green Bay calibration was not as good as the
rest of the lake.  This was especially true for the
portion of the bay closest to the Fox River.

With the exception of zooplankton, the final model
calibration was reasonably good, and the model was
able to fit the field data well and capture important
spatial and seasonal trends.  A brief discussion of
individual calibration results for phytoplankton, POC,
total phosphorus, and DSi follows.

2.5.2.1 Phytoplankton

The model somewhat underestimated the field data
for phytoplankton.  Seabird chlorophyll a data were
used for all phytoplankton field values.  Part of the
explanation for the  underestimation  was poor
chlorophyll a field data.   The  Seabird fluorescence
instrument, like  many in vivo fluorescence methods,
is   notorious  for  its  inaccuracy  in  measuring
chlorophyll a (U.S. Environmental Protection Agency,
1997; Clesceri  et  a/., 1998).  The square of  the
correlation  coefficient  of 0.37  was acceptable,
especially  given  the   inherent   variation   in
phytoplankton communities over space  and time.
Our fit was in-range of other published eutrophication
models (Thomann, 1982; Cerco and Cole, 1994).
The model was  able to capture spatial and temporal
trends such as the spring diatom blooms (Figure
2.5.4)  and earlier  phytoplankton  blooms  in  the
                                               160

-------
   0.016'
   0.014
   0.012
 O  0.01
 D)

 £ 0.008

 t>
 £ 0.006
 Q
 Q.
   0.004

Phytoplankton carbon versus productivity (discrete samples)
D
D ^- ^
» ^ ^
* * D ^_ -•- "
^ — n
* _^^ LJ
* * -- * ""
V\i-«^^oD*
— ^F 	 . — n 	 ^ 	 . 	 _
^
a Chlorophyll-a based model
productivity results (mgC/L/hr)
4 Lab results (mgC/L/hr)

*

   0.002
                      0.05
0.1             0.15            0.2
    phytoplankton carbon (mg/L)
0.25
0.3
   0.025
    0.02
           Seasonal productivity in Lake Michigan
                                                n Model results
                                                4 Lab results
                1      2      3
            5         6
          LMMB cruises
Figure 2.5.2. LM3-Eutro model versus laboratory primary production.
                                                 161

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Table 2.5.1.  Coefficients Used in the LM3 Model (Units Correspond to Required LM3 Model Output)
Coefficient
ANCP
APCP
ASCD
BMRD
BMRG
CCHLD
CCHLG
CGZ
FCDD
FCDG
FCLD
FCLG
FCRD
FCRG
FCDP
FCDZ
FCLP
FCLZ
FCRP
FCRZ
FNDD
FNDG
FNDP
FNDZ
FNID
FNIG
FNIP
FNIZ
FNLD
FNLG
FNLP
FNLZ
FNRD
FNRG
RNRP
RNRZ
FPDD
FPDG
FPDP
FPDZ
FPID
FPIG
FPIP
FPLD
FPLG
FPLP
FPLZ
FPRD
FPRG
FPRP
FPRZ
Value
0.25
0.01
2.3
8.6E-07
8.6E-07
40
40.
3.1E-06
0.05
0.05
0.3
0.3
0.3
0.3
0.35
0
0.5
0.4
0.15
0.1
0.5
0.5
0
0
0.5
0.5
0.5
0.5
0
0
0.4
0.4
0
0
0.1
0.1
0.1
0.1
0.2
0.2
0.3
0.3
0.5
0.5
0.3
0.15
0.15
0.3
0.3
0.15
0.15
Unit Description
Nitrogen:carbon ratio (mass basis)
Phosphorus:carbon ratio (mass basis)
Silica:carbon ratio (mass basis)
1/s Diatom mortality
1/s Greens mortality
Carboirchlorophyll ratio (diatoms)
Carboirchlorophyll ratio (greens)
m3/kg/s Zooplankton grazing rate coefficient
Dissolved organic carbon fraction from diatom mortality
Dissolved organic carbon fraction from greens mortality
Labile organic carbon fraction from diatom mortality
Labile organic carbon fraction from greens mortality
Refractory organic carbon fraction from diatom mortality
Refractory organic carbon fraction from greens mortality
Dissolved organic carbon fraction from algal predation
Dissolved organic carbon fraction from zooplankton mortality
Labile particulate dissolved carbon fraction from algal predation
Labile particulate dissolved carbon fraction from zooplankton mortality
Refractory particulate dissolved carbon fraction from algal predation
Refractory particulate dissolved carbon from zooplankton mortality
Dissolved organic nitrogen from diatom mortality
Dissolved organic nitrogen fraction from greens mortality
Dissolved organic nitrogen fraction from algal predation
Dissolved organic nitrogen fraction from zooplankton mortality
Dissolved inorganic nitrogen fraction from diatom mortality
Dissolved inorganic nitrogen fraction from greens mortality
Dissolved inorganic nitrogen fraction from algal predation
Dissolved inorganic nitrogen fraction from zooplankton mortality
Labile organic nitrogen fraction from diatom mortality
Labile organic nitrogen fraction from greens mortality
Labile organic nitrogen fraction from algal predation
Labile organic nitrogen fraction from zooplankton mortality
Refractory organic nitrogen fraction from diatom mortality
Refractory organic nitrogen fraction from greens mortality
Refractory organic nitrogen fraction from algal predation
Refractory organic nitrogen fraction from zooplankton mortality
Dissolved organic phosphorus fraction from diatom mortality
Dissolved organic phosphorus fraction from greens mortality
Dissolved organic phosphorus fraction from algal predation
Dissolved organic phosphorus fraction from zooplankton mortality
Dissolved inorganic phosphorus fraction from diatom mortality
Dissolved inorganic phosphorus fraction from greens mortality
Dissolved inorganic phosphorus fraction from algal predation
Dissolved inorganic phosphorus fraction from zooplankton mortality
Labile organic phosphorus fraction from greens mortality
Labile organic phosphorus fraction from algal predation
Labile organic phosphorus fraction from zooplankton mortality
Refractory organic phosphorus fraction from diatom mortality
Refractory organic phosphorus fraction from greens mortality
Refractory organic phosphorus fraction from algal predation
Refractory organic phosphorus fraction from zooplankton mortality
                                          162

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Table 2.5.1.  Coefficients Used in the LM3 Model (Continued)
Coefficient
FSAP
GREFF
ILUMO
ISMIN
KDC
KDCALG
KDN
KDNALG
KDP
KDPALG
KE
KECHL
KHND
KHNG
KHNNT
KHPD
KHPG
KHSD
KLC
KLCALG
KLN
KLNALG
KLP
KLPALG
KRC
KRCALG
KRN
KRNALG
KRP
KRPALG
KSUA
KSZ
KTBD
KTBG
KTGD1
KTGD2
KTGG1
KTGG2
KTHDR
KTMNL
KTNT1
NTNT2
KTSUA
NTM
NTM
PMD
PMG
TMD
TMG
TMNT
TRD
Value
0
0.6
25
400
1.16E-08
O.OOE+00
1 .74E-07
O.OOE+00
1.16E-09
6.0E-03
0.15
1.7E+04
2.50E-05
2.50E-05
0.0000
5.0E-07
5.0E-07
6.0E-05
1.0E-07
O.OOE+00
3.47E-07
O.OOE+00
1 .OOE-09
6.00E-03
1.00E--07
O.OOE+00
3.47E-08
OOE+00
1 .OE-09
6.0E+03
2.5E-07
1.0E-04
0.074
0.074
0.0025
0.006
0.0025
0.006
9.9E-02
7.4E-02
0.004
0.004
0.069
0.074
2.50E-11
2.90E-05
2.60E-05
18
18
30
20
Units


W/m2
W/m2
1/s
m3/kg/s
1/s
m3/kg/s
1/s
m3/kg/s
1/m
m2/kg
kg/m3
kg/m3
kg/m3
kg/m3
kg/m3
kg/m3
1/s
m3/kg/s
1/s
m3/kg/s
1/s
m3/kg/s
1/s
m3/kg/s
1/s
m3/kg/s
1/s
m3/kg/s
1/s
kg/m3
1/°C
1/°C
1/°C2
1/°C2
1/°C2
1/°C2
1/°C
1/°C
1/°C2
1/°C2
1/°C
1/°C
kg/m3/s
1/s
1/s
°C
°C
°C
°C
Description
Dissolved silica fraction from diatom predation
Zooplankton grazing coefficient
Constant illumination (first 90 days)
Optimum light illumination
Dissolved organic carbon mineralization coefficient
Dissolved organic carbon algal dependency coefficient
Dissolved organic nitrogen mineralization coefficient
Dissolved organic nitrogen algal dependency coefficient
Dissolved organic phosphorus mineralization coefficient
Dissolved organic phosphorus algal dependency coefficient
Background light attenuation
Light attenuation for chlorophyll a
Nitrogen half -saturation coefficients for diatoms
Nitrogen half-saturation coefficients for greens
Nitrate half-saturation coefficient for nitrification
Phosphorus half-saturation coefficients for diatoms
Phosphorus half-saturation coefficients for greens
Silica half-saturation coefficients for diatoms
Labile organic carbon hydrolysis coefficient
Labile organic carbon algal dependency coefficient
Labile organic carbon hydrolysis coefficient
Labile organic nitrogen algal dependency coefficient
Labile organic phosphorus hydrolysis coefficient
Labile organic phosphorus algal dependency coefficient
Refractory organic carbon hydrolysis coefficient
Refractory organic carbon algal dependency coefficient
Refractory organic nitrogen hydrolysis coefficient
Refractory organic nitrogen algal dependency coefficient
Refractory organic phosphorus hydrolysis coefficient
Refractory organic phosphorus algal dependency coefficient
Biogenic silica dissolution rate
Zooplankton half-saturation (for algae)
Diatom mortality temperature coefficient
Greens mortality temperature coefficient
Diatom growth temperature coefficient (< optimum)
Diatom growth temperature coefficient (> optimum)
Greens growth temperature coefficient (< optimum)
Greens growth temperature coefficient (> optimum)
Hydrolysis temperature dependency coefficient
Mineralization temperature dependency coefficient
Nitrification temperature coefficient (< optimum)
Nitrification temperature coefficient (> optimum)
Silica dissolution temperature coefficient
Diatom mortality temperature coefficient
Nitrification rate coefficient
Diatom growth coefficient
Greens growth coefficient
Optimum diatom growth temperature
Optimum greens growth temperature
Optimum nitrification temperature
Optimum diatom mortality temperature
                                           163

-------
Table 2.5.1.  Coefficients Used in the LM3 Model (Continued)
Coefficient
TRG
TRHDR
TRMNL
TRSUA
TZREF
ZDTH
ZTHET
VDIA
VGRE
VLOC
VROC
VLON
VRON
VLOP
VROP
VSU
Value
20
20
20
20
20
5.0E-07
1.0
1.15E-06
0.85E-06
2.0E-06
2.0E-06
2.0E-06
2.0E-06
2.0E-06
2.0E-06
2.0E-06
Units
°C
°C
°c
°c
°c
1/s

m/s
m/s
m/s
m/s
m/s
m/s
m/s
m/s
m/s
Description
Optimum greens mortality temperature
Optimum hydrolysis temperature
Optimum mineralization temperature
Optimum silica dissolution temperature
Optimum predation temperature
Zooplankton mortality rate coefficient
Arrhenius temperature coefficient for predation
Diatoms settling coefficient
Greens settling coefficient
Labile organic carbon settling coefficient
Refractory organic carbon settling coefficient
Labile organic nitrogen settling coefficient
Refractory organic nitrogen settling coefficient
Labile organic phosphorus settling coefficient
Refractory organic phosphorus settling coefficient
Biogenic silica settling coefficient
          S
          CO
P3 •«

0.25-



0.2-


0.15.



0.1.


0.05.
x
Phytoplankton (mg/L) x
X'
X
X
s'
X
X
X
^ x
' • • x
• • " "A
'?.:•;-',:? ^il

**• > -eT/" * ^* * *
•i&KpsV? " -''J •
'*»!?« ' " «

JJF?f-'*~

30.


25.


S20.
-D
T3
•5515-
i^=

10-


5-
0-
Total Phosphorus (M9/L) '
s'
S
/
/•
s
• , /
• /
; \; x
• .. X

x.
r: x
i • x
T " " •/
mZ' '
jgp
/
                   0.05  0.1   0.15   0.2   0.25   0.3
                          model results
0   5   10   15   20   25   30   35
           model results
1.2

1.0


0.8-

0.6-


0.4-
0.2-


o.

Particulate Organic Carbon (mg/L) x '
x
x
x
x
. x
X
X
' x
X
• X
l X
'x
' /. X
•$$&?- •
**3lSi$ * ' "
/'aSS'**" * •* •
x "*"• '


2


oj1-5-
"ra
•a

m
•S 1-
0.5-


0-
Dissolved Silica (mg/L) x
x
x
x
x
x
; " x'
xx

.• x"
f:33*/



,
                   0.2   0.4   0.6   0.8   1.0   1.2
                         model results
0    0.5     1     1.5
           model results
                              2.5
Figure 2.5.3.  Level 3 LM3-Eutro model predictions versus field data, lake-wide.
                                                 164

-------
Table 2.5.2.  Summary of Statistical Results of the Calibration
Variable Regression Coefficient (r2) Slope
Phytoplankton 0.37 0.67
Particulate Organic Carbon 0.39 0.95
Total Phosphorus 0.37 1 .4
Dissolved Silica 0.37 1 .2
Zooplankton 0.13 0.43
0.2
O.15
0.1
O.O5
O
J
1S
O.6
O.5
O.4
0.3
0.2
0.1
°j;
13
2O
15
10
5
°J:
19
O.2
O.15
O.1
O.O5
O

Phytoplankton (mg/L)
Segment 6

/v/v
an July Jan July Dec
94 1994 1995 19951995
Particulate
Organic Carbon (mg/L)
Segment 1
,/tv - yv
i
in July Jan July Dec
®4 1994 1995 19951995
Total Phosphorus (pg/L)
Segment 3

- • . >
} ' " i *
an July Jan July Dec
94 1994 1995 19951995
Dissolved Silica (mg/L)
Segment 2

^V! /'J~~V/" '
1^ ••*
O.2
O.15
O.1
O.O5
O
J<
19
O.6
O.5
O.4
0.3
O.2
0.1
°l:
19
2O
15
1O
5
°J<
19
O.2
0.15
O.1
O.O5
O

Phytoplankton (mg/L)
Segment 13
.
J\7V
an July Jan July Dec
94 1994 1995 19951995
Particulate
• Organic Carbon (mg/L) -
Segment 1 1
• :/sv /V n
1
in July Jan July Dec
94 1994 1995 19951995
Total Phosphorus (pg/L)
Segment 11

: . '
•TT - -T«-
sn July Jan July Dec
94 1994 1995 19951995
Dissolved Silica (mg/L)
Segment 11

^V -X^VJ^
-;
O.2
0.15
O.1
O.O5
O
j£
19
0.6
O.5
O.4
0.3
O.2
O.1
19
20
15
1O
5
9,
19
O.2
O.15
O.1
O.O5
Q

Phytoplankton (mg/L)
Segment 24
A A
AjV
in July Jan July Dec
94 1994 1995 19951995
Particulate
• Organic Carbon (mg/L)
Segment 21
/~^~\
,/rvy-.
n July Jan July Dec
94 1994 1995 19951995
Total Phosphorus (pg/L)
Segment 31

- 1 . i ' --.•

in July Jan July Dec
94 1994 1995 19951995
Dissolved Silica (mg/L)
Segment 29
'
~^^A~^-^—T^
•
O.2
O.15
O.1
O.O5
0
J;
19
O.6
O.5
O.4
O.3
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O.1
9,
19
2O
15
1O
5
9=
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O.15
O.1
O.O5
0

Phytoplankton (mg/L)
Segment 38
. -
t .
an July Jan July Dec
Q4 1994 1995 19951995
Particulate
- Organic Carbon (mg/L)
Segment 3O

1
an July Jan July Dec
94 1994 1995 19951995
Total Phosphorus (pg/L)
Segment 41

^— ~^~
an July Jan July Dec
94 1994 1995 19951995
Dissolved Silica (mg/L)
Segment 36
; , * •
^---•— j~^~











                   July Dec
       1994 1994 1995 19951995
    July
1994 1994 1995 19951995

water column
segment
numbers
Lake Michigan Level II
Water Column Seqmentation
10 surface water segments
41 total segments


1
11
20
29
36
1 0 average depth
1O of segment
10
20
103
Figure 2.5.4. Level 2 LM3-Eutro model output versus field data for selected segments.
                                             165

-------
  0.0928
          non-diatoms
          near-shore segment 10738
                                          segment
                                           10738
                                           5 meter
                                           depth
  0.0025
          i  i i  i  i i  i i  i  i i  i i  i  i i  i  i i  i i  i  i i
       Jan.94   Jun.94    Dec.94    Jun.95    Dec.95
  0.3408



  0.2957



  0.2506
 j
 n

  0.2054
   0.1603-
   0.1152
particulate organic carbon
near-shore segment 10738
           i i  i i  i—i i  i i—i—i—i—r~i—i—n—i i  i  i '—TT
       Jan.94  Jun.94    Dec.94    Jun.95    Dec 95


    5.515-
    5.120-



    4.726-



    4.332-



    3.937-
    3.543
           total phosphorus
           near-shore segment 10738
   I  I I  I  I I  I I  I  I I  I I
Jan.94   Jun.94    Dec.94
                                                             0.0799
                                                             0.0643-
                                                  0.0486-
                                                             0.0330-
                                                           |> 0.0173-1
                                                             0.0017
                                                                                         diatoms
                                                                            off-shore segment 4096
                                                                                         \
                                                            I ii  } i  i i  i  r r i  i r i  i i  i i  i i  ii i
                                                       Jan.94   Jun.94    Dec.94     Jun.95    Dec.95
                                                              0.331
                                                   0.282-
                                                   0.234-
                                                              0.185-
                                                              0.136-
                                                              0.087
                                                                                       *  \
                                                                                    particulate organic carbon
                                                                                      off-shore segment 4096
                                                            i i  i i  i  i i  i  i i  i  i i  i i  i  i i  i i  i p t
                                                      Jan.94   Jun.94    Dec.94    Jun.95    Dec.95
                                                              5.298
                                                   4.887
                                                   4.475-
                                                   4.064-
                                                              3.652
                                                              3.241
                                                                                 total phosphorus
                                                                            off-shore segment 4096
                                  Jun.95    Dec.95
                                                          i  i i  i i  i  i i  i  i i  i  i r i  i i  i i  i i  i  i i
                                                      Jan.94   Jun.94     Dec.94     Jun.95    Dec.95
Figure 2.5.5.  Level 3 LM3-Eutro model output versus field data for selected nearshore and offshore
cells.
                                                     166

-------
nearshore cells than  in the offshore cells (Figure
2.5.5).

2.5.2.2 Particulate Organic Carbon

The model fits the field data well (Figure 2.5.3), with
a slope of almost  one (0.95) and a square of the
correlation coefficient of almost 0.4 (r2 = 0.39). The
POC data exhibited  less  scatter than the other
variables and were reflective of the phytoplankton in
the lake.  Additionally, we had a  great  deal  of
confidence in the POC data measurement technique.
The ability of  the  model  to  capture the POC data
trend increased our confidence in the model's overall
eutrophication predictions.  Examination of Level 2
segments showed that the model captured important
trends, such as higher POC concentrations during
the spring diatom bloom (Figure 2.5.4).

2.5.2.3 Total Phosphorus

The model fits the total phosphorus data reasonably
well  with  a  slope of 1.4  and a  square of the
correlation coefficient of 0.37.  In general, the total
phosphorus concentrations  in the lake were fairly
constant, with most measurements falling between 4
and 5 ug/L and little  seasonal  variation observed.
The overall model  fit was acceptable (Figure 2.5.4).
However, there were several higher total phosphorus
values measured in Green Bay close to the Fox River
and at other nearshore stations close to rivers and/or
areas  where  there  were  significant  sediment
resuspension.  In  these cases, the model was not
able to mimic  the data,  probably due  to initial
conditions that  were too low  and the lack of a
sediment resuspension term.

2.5.2.4 Dissolved  Silica

The dissolved silica model fits the field data well, with
a slope of 1.2 (influenced by several very high silica
field data points) and a regression coefficient of 0.37.
The  model  predicted the  expected trends,  with
highest silica in the winter, a steep decline in the
epilimnion in the spring coinciding with the diatom
bloom, and a recovery toward the end  of the  year
(Figure 2.5.4).

In general, the model fit the field data  reasonably
well.  Seasonal  and  spatial  trends for important
variables,  such  as phytoplankton and silica, were
captured.   There  were  difficulties in  accurately
predicting  Green  Bay phytoplankton and nutrient
concentrations, but this was not unexpected.

References

Cerco, C.  and T. Cole.  1994. Three-Dimensional
   Eutrophication Model of Chesapeake Bay.  U.S.
   Army Corps of Engineers, U.S.  Army Engineer
   Waterways  Experiment  Station,   Vicksburg,
   Mississippi.  Technical Report Number EL-94-4,
   658 pp.

Clesceri,  L.S., A.E.  Greenberg, and  A.D.  Eaton
   (Eds.).   1998.    Standard  Methods for  the
   Examination of Water and Wastewater,  20th
   Edition.  American Public Health Association,
   American Water Works Association, and Water
   Environment  Federation,  Hanover, Maryland.
   1,205 pp.

Thomann, R.V.  1982. Verification of Water Quality
   Models.  J. Environ. Engin., 108(EE5):933-940.

U.S. Environmental Protection Agency.  1997. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,   Volume  1: Sample Collection
   Techniques.   U.S.  Environmental  Protection
   Agency,  Great  Lakes National Program Office,
   Chicago, Illinois. EPA/905/R-97/012a, 1,440pp.
                                              167

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                                         PART 2

                                      LM3-EUTRO
Chapter 6. Model Confirmation

The most common approach to model confirmation
is  the  comparison of  the model output  to  an
independent field data set. Ideally, this independent
field data set describes a different year or years than
the calibration field data. We did not have the luxury
of access to a second field data set as thorough as
the 1994-1995 Lake Michigan Mass Balance Project
(LMMBP) data used in model calibration. However,
limited total phosphorus field data were available.  In
addition, we were able to compare LM3-Eutro to the
historical MICH1  model, which was calibrated and
applied  in  the 1970s  and 1980s (Rodgers and
Salisbury, 1981 a,b) and recently extended to predict
more   recent   chlorophyll   and  phosphorus
concentrations in Lake  Michigan  (Pauer  et al.,
Submitted).

2.6.1  Additional Field Data

Limited  Great  Lakes  National  Program  Office
(GLNPO)   monitoring  data   were  available  for
comparison to model predictions. GLNPO data were
collected on an  annual basis for the  purpose  of
monitoring  long-term trends  in the Great Lakes
(Barbiero era/., 2002). Samples were collected from
a  set of stations that formed a north-south transect
through Lake Michigan (Barbiero et al., 2002). Green
Bay was not sampled as part of this lake monitoring
effort.   Samples  were  taken from discrete  depths
throughout the water column (Barbiero et al., 2002).
Data from the entire water column were averaged to
produce 1998 spring total phosphorus and 1998
summer total phosphorus lake-wide values.  The
1998 spring and summer chlorophyll a data were
averaged to provide seasonal epilimnion (0-20 m in
depth) chlorophyll a values.

2.6.2 MICH1 Model

The Lake Michigan eutrophication model (MICH1)
was developed as part of the International Joint
Commission's  (IJC)  Great  Lakes  International
Surveillance Plan.  The framework was constructed
by Rodgers and Salisbury (1981 a, b) based on the
Great Lakes  model LAKE1  which was originally
developed and tested for Lake Ontario (Thomann ef
al., 1975). It is a four-segment model, simulating two
zooplankton classes, a single phytoplankton class (as
chlorophyll), and several nutrient species. However,
it does not have a sediment component and the
segmentation excludes Green Bay.  MICH1 was
calibrated using field data from the Lake Michigan
intensive survey of 1976-1977  (Rockwell et al.,
1980).   This model was recently resurrected  and
extended to  run  from 1976 through  1995  and
compared to the LMMBP field data. Changes were
also made to  the MICH1  model by  reducing the
detrital settling rate by 20%, which results in a better
model fit with the  LMMBP field data (Pauer et al.,
Submitted).

2.6.3   Comparison  of LM3-Eutro to  the
MICH1 Model and Field Data

In order to  compare  LM3-Eutro to the historical
MICH1 model, some modifications and qualifications
were necessary.  The 1994  and 1995  loads were
repeated for the period 1996-2000 in both models.
However, the total phosphorus loads were averaged
for MICH1, while the loads were alternated in LM3-
                                             168

-------
Eutro. Although the two approaches did not result in
any significant long-term differences, we observed
short-term differences.  Because the two models
used  very  different  segmentation  schemes,  all
comparisons were  made on  a lake-wide  basis,
excluding Green Bay. LM3-Eutro algal carbon was
converted to chlorophyll a using a 40:1 carbon-to-
chlorophyll a ratio. All MICH1 simulations started in
1976 and ran through 2000, while LM3-Eutro was
only simulated from 1994 to 2000.

The results are shown in Figure 2.6.1.  In general,
the two models compared reasonably well, which
was remarkable  because the models are very
different in structure. MICH1 total phosphorus output
was lower than that of LM3-Eutro and the 1994-1995,
1998, and 2000 field data. The revised MICH1 (20%
reduced settling rate) compared more favorably with
the field data and LM3-Eutro.

The epilimnetic chlorophyll a  concentration also
compared reasonably well between the models,
although LM3-Eutro  predictions were higher than
both MICH1  predictions.  The  lower MICH1  output
values (as compared to LM3-Eutro)  were probably
due to  the  absence of a sediment phosphorus
recycle mechanism.  It was difficult to compare the
model versus field data for the chlorophyll due to the
steep peaks and large seasonal variation  in the
chlorophyll a data.

The overall strength of the comparison between the
models and the model fit with limited 1998 and 2000
field  data  built  confidence  in  the  LM3-Eutro
framework and confirmed that the model was able to
represent the eutrophication state variables in Lake
Michigan.
References

Barbiero, R.P.,  M.L. Tuchman, G.J. Warren, and
   D.C. Rockwell.  2002.  Evidence of Recovery
   from Phosphorus Enrichment in Lake Michigan.
   Canadian J. Fish. Aquat. Sci., 59(10):1639-1647.

Pauer, J.J., K.W. Taunt, W.  Melendez, and R.G.
   Kreis, Jr. Submitted. Resurrection of the Lake
   Michigan Eutrophication Model, MICH1. J. Great
   Lakes Res, Submitted for publication.

Rockwell, D.C., D.S. DeVault, III; M.F. Palmer, C.V.
   Marion, and R.J. Bowden.  1980.  Lake Michigan
   Intensive   Survey,   1976-1977.     U.S.
   Environmental Protection Agency, Great Lakes
   National  Program  Office,  Chicago,  Illinois.
   EPA/905/4-80/003A, 155 pp.

Rodgers, P.W. and D. Salisbury.  1981 a. Modeling
   of Water Quality in Lake Michigan and the Effect
   of the Anomalous Ice Cover of 1976-1977. Great
   Lakes  Environmental  Planning  Study,  Great
   Lakes Basin  Commission, Ann Arbor, Michigan.
   Contribution  Number 44, 53 pp.

Rodgers, P.W. and D. Salisbury.  1981b.  Water
   Quality  Modeling   of  Lake   Michigan  and
   Consideration of the Anomalous Ice Cover of
   1976-1977. J. Great Lakes Res., 7(4):467-480.

Thomann, R.V., D.M. Di Toro, R.P. Winfield, and D.J.
   O'Connor.   1975.   Mathematical Modeling of
   Phytoplankton in Lake  Ontario,  Part 1:  Model
   Development  and   Verification.     U.S.
   Environmental  Protection  Agency,  Office of
   Research  and Development,  ERL-Corvallis,
   Large  Lakes  Research  Station, Grosse lie,
   Michigan.  EPA/660/3-75/005, 177 pp.
                                             169

-------
    6.00
    5.00-
    4.00-
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           MICH1 and LM3-Eutro epilimnion chlorophyll-a
                                                           — MICH1
                                                           — MICH1 settling reduced 20%
                                                           	LM3-Eutro
                                                           • 1994-1995 LMMB
                                                           D 1998
       1976
            1979
1982
1985
1988
1991
1994
1997
2000
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       1976
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            1	1	1	\	1	1	1	T
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                               —I	1	1	1	\	T
                                1994      1997      2000
Figure 2.6.1.  MICH1 versus LM3-Eutro model predictions and available field data.
                                              170

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                                          PART 2

                                       LM3-EUTRO
Chapter 7. Results - Application of Model    2.7.1.2 Results and Discussion
A  total of seven  model scenarios were  run  to
evaluate future lake conditions under different total
phosphorus loads.  Because total phosphorus is not
a model state variable, the individual phosphorus
state variables (soluble reactive phosphorus [SRP],
dissolved organic phosphorus [OOP], labile organic
phosphorus   [LOP],   and   refractory   organic
phosphorus [ROP]) were scaled  accordingly  to
accomplish the total phosphorus load increases or
reductions. The assumptions and conditions for each
model scenario are briefly described followed by a
Results and Discussion section. We evaluated future
lake-wide total phosphorus and particulate organic
carbon (POC) concentrations as well as epilimnetic
and   hypolimnetic  chlorophyll   concentrations
(assuming a 20 m  thermocline). Model simulation
time for the scenarios ranged between 20 and 30
years, with  scenarios starting in  1994 and  load
increases and decreases beginning in 2005.

2.7.7 Scenario  1 - Constant Conditions

2.7.1.1 Description of Assumptions

The scenario was started on January 1, 1994.  A
constant   user-specified   net   sediment  total
phosphorus flux was applied in both space and time.
Total phosphorus tributary and atmospheric loads for
1994 and 1995 were repeated in a two-year cycle.
Hydrodynamics for 1994  and 1995 were similarly
repeated.  The model was run until steady-state was
achieved.
The model reached steady-state within 28 years
(2021). The steady-state lake-wide concentrations for
total phosphorus and maximum POC were 4.3 ug/L
and 0.2 mg/L, respectively (Figure 2.7.1), while the
values for the  spring  maximum  chlorophyll  a
concentrations were 2.36 ug/L for the epilimnion and
1.07 ug/L for the hypolimnion (Figure 2.7.1).
2.7.2   Scenario 2 -
(Lower Bound)
Virtual Elimination
2.7.2.1 Description of Assumptions

The scenario was started on January  1, 1994. A
constant  user-specified  net   sediment  total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from January 1, 1994 to  December 31, 2004.
Beginning  on  January  1,   2005,  tributary  and
atmospheric total phosphorus loads were reduced by
100% and the sediment total phosphorus fluxes were
assumed to be zero. The model was run for a total
of 30 years (through December 2023).

2.7.2.2 Results and Discussion

As expected, the  total  phosphorus concentration
significantly  declined once the  atmospheric  and
tributary total phosphorus loads were turned off. The
model  predicted  that after  30 years  (December
2023), the  lake-wide total phosphorus concentration
would be 0.54 ug/L (Figure 2.7.2). The epilimnetic
maximum chlorophyll a concentration after 30 years
would be less than 0.1 ug/L, while the hypolimnetic
                                            171

-------
         epilimnion chlorophyll-a
                                                             hypolimnion chlorophyll-a
1994   1998   2002   2006   2010  2014   2018 2021
                                                                1994   1998   2002   2006   2010  2014   2018 2021
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                                                                 1994   1998   2002   2006   2010   2014   2018 2021
Figure 2.7.1. Scenario 1:  Constant Conditions.
   epilimnion chlorophyll-a
      1994   1998  2002  2006   2010  2014  2018   2022
                                                              1.2
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Figure 2.7.2. Scenario 2:  Virtual elimination.
                                                       172

-------
concentration  would be approaching zero (Figure
2.7.2).  In 2023, the lake-wide POC concentration
maximum was around 0.02 mg/L (Figure 2.7.2).

2.7.3   Scenario 3 -  Best  Estimate  of
Current Trends Resulting From Previous
Actions

2.7.3.1  Description of Assumptions

The scenario was started on January 1, 1994.  A
constant  user-specified   net  sediment  total
phosphorus flux was applied in both space and time.
Tributary and  atmospheric total phosphorus loads
declined at rates observed over the last two decades
(1981-1995).  The model was run for a total of 20
years (through December 2013).

The rate of total phosphorus decay was based on the
downward trend of total phosphorus in Lake Michigan
since the 1980s.  In order to calculate the  decay, it
was assumed that the total phosphorus equation was
an  exponential.   Figure 2.7.3 shows the  Lake
Michigan  historical  total   phosphorus  loading.
Although historical  total phosphorus loading data
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prior to 1981 were available, only the loading values
between 1981  and 1995 were used because it was
believed this provided the most realistic picture of the
                                             present  loading trend.    No loading data were
                                             available after 1995.  Total phosphorus loads were
                                             assumed to follow the equation:
                                                 = L(t0)exp[k(t-t0)]
                                        (2-7.1)
where

t    =  time in units of years

t0   =  initial time (1981)

L(t)  -  total phosphorus load at time t

L(t0) =  total phosphorus load at time t0

k   =  total phosphorus  decay rate in units of
        1/year.

The decay rate k was  calculated by applying the
Least Squares Fitting method and has a value of
-2.21 x 10'2/year.

2.7.3.2 Results and Discussion

The total phosphorus concentration steadily declined
over 20 years to approximately 3.5 ug/L (Figure
2.7.4).  The epilimnetic chlorophyll a reached a value
of approximately 2.0 ug/L, while the hypolimnion fell
below 0.9 ug/L (Figure 2.7.4). In 2013, the lake-wide
POC maximum concentration was around 0.18 mg/L
(Figure 2.7.4).

2.7.4    Scenario  4 -  Scenario  1  With
Instantaneous  Reduction  of  Tributary
Loads to Zero

2.7.4.1  Description of Assumptions

The scenario was started on January  1, 1994. A
constant   user-specified  net  sediment  total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from  January  1, 1994 to December 31, 2004.
Beginning on January  1,  2005,  tributary total
phosphorus loads were reduced by 100%. The 1994
and 1995 atmospheric  load cycle was continued.
The model was run for a total of 30 years (through
December 2023).
                                             173

-------
       epilimnion chlorophyll-a
     1994   1997   2000   2003   2006   2009   2012
        hypolimnion chlorophyll-a
     1994  1997  2000  2003  2006  2009   2012
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      1994  1997  2000  2003  2006  2009   2012
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Figure 2.7.4. Scenario 3: Best estimate of current trends resulting from previous actions.
2.7.4.2 Results and Discussion

There  was  a  significant decline  in  the total
phosphorus concentration when the tributary loads
were turned off  in January 2005.(Figure  2.7.5).

However, this decline was not as steep as  that
observed in Scenario 2 (Virtual Elimination).  The
total phosphorus concentration at the end of 2023
was 0.91  ug/L, which was higher than the 0.54 ug/L
observed in the Virtual Elimination scenario (Scenario
2).    Similarly,   after  30  years,  the  maximum
chlorophyll a concentration fell to approximately 0.4
ug/L and less than 0.1 ug/L for the epilimnion and
hypolimnion, respectively (Figure  2.7.5), and the
lake-wide POC  maximum was 0.03  mg/L (Figure
2.7.5). These values were all somewhat higher than
their equivalents in Scenario 2.

2.7.5  Scenario  5  - Scenario  1  With
Instantaneous Reduction of Atmospheric
Loads to Zero

2.7.5.1 Description of Assumptions

The scenario was started in January 1, 1994.  A
constant   user-specified  net   sediment  total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from January 1, 1994 to December 31, 2004.
Beginning on January  1, 2005, atmospheric  total
phosphorus loads were reduced by 100%. The 1994
and 1995 tributary load cycle was continued.  The
model  was run for a total of  20 years (through
December 2013).

2.7.5.2  Results and Discussion

Turning off the atmospheric total phosphorus loads
had little effect on the total phosphorus, chlorophyll a,
and POC concentration (Figure  2.7.6) as compared
to the Constant Conditions scenario (Figure 2.7.1).
2.7.6    Scenario
Tributary  and
increased 20%
6  -  Scenario 1  With
Atmospheric  Loads
2.7.6.1 Description of Assumptions

The scenario was started on January 1, 1994.  A
constant  user-specified   net  sediment  total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from January 1, 1994 to December 31, 2004.
                                             174

-------
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Figure 2.7.5. Scenario 4:  Scenario 1 with tributary load elimination.
         epilimnion chlorophyll-s
       1994   1997   2000   2003   2006   2009   2012
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                                                      1994   1997   2000   2003   2006   2009   2012
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Figure 2.7.6. Scenario 5:  Scenario 1 with atmospheric load elimination.
                                                      175

-------
Beginning  on  January  1,  2005,  tributary  and
atmospheric total phosphorus loads were increased
by 20%. The model was run for a total of 20 years
(through December 2013).

2.7.6.2 Results and Discussion

The 20% increase in total phosphorus loads had a
relatively small influence on the total phosphorus
concentration,   the  epilimnetic   chlorophyll  a
concentration, and the POC concentration (compare
Figure 2.7.7  [20% load increase] with Figure 2.7.1
[Constant  Conditions]).     The  lake-wide  total
phosphorus concentration after 20 years was 4.6
ug/L  (Figure  2.7.7).  Maximum  chlorophyll  a
concentrations  after 20 years were 2.2-2.5  ug/L
(epilimnion)  and 1.1 ug/L  (hypolimnion)  (Figure
2.7.7). The lake-wide POC maximum concentration
was 0.22 mg/L (Figure 2.7.7).
2.7.7 Scenario 7 - Application of Great
Lakes Water Quality Agreement Loads to
Model

2.7.7A Description of Assumptions

The scenario was  started on January  1, 1994. A
constant  user-specified   net  sediment   total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from January 1, 1994 to  December 31,  2004.
Beginning on January 1,2005, the 1978 Great Lakes
Water Quality Agreement (GLWQA) specified total
phosphorus target loading of 5,600 MT/year was
applied (International Joint Commission, 1978). The
1994  and  1995  atmospheric load  cycle  was
continued.  A new user-defined net  sediment total
phosphorus  flux  was  estimated,  assuming that
approximately  95% of the  phosphorus load was
retained in the sediment and 5% was recycled back
to the water column. The model was run to steady-
state.
       epilimnion chlorophyll-a
     1994   1997   2000   2003   2006   2009   2012
       hypolimnion chlorophyll-s
                                                     1994  1997  2000  2003  2006  2009   2012
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      1994  1997   2000   2003   2006   2009   2012
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       partculate organic carbon
                                                      1994   1997  2000   2003   2006  2009  2012
Figure 2.7.7.  Scenario 6: Scenario 1 with tributary and atmospheric loads increased 20%.
                                             176

-------
2.7.7.2 Results and Discussion

Applying the GLWQA total phosphorus load of 5,600
MT/year resulted in a lake-wide steady-state  total
phosphorus  concentration  of  7.5  ug/L and an
epilimnetic chlorophyll a maximum of 4.0 ug/L (Figure
2.7.8).  The hypolimnetic chlorophyll a maximum at
steady-state was 1.6 ug/L (Figure 2.7.8).  Steady-
state lake-wide maximum POC was approximately
0.28 mg/L (Figure 2.7.8).  Steady-state was reached
within 30 years.

2.7.5   Scenario  8  -  Estimate  of Total
Maximum   Daily   Loads   to   Reach
International Joint Commission's Target
Total Phosphorus Concentration

2.7.8.1 Description of Assumptions

The scenario was started on January 1,  1994.  A
constant  user-specified  net   sediment   total
phosphorus flux was applied in both space and time.
The Constant Conditions scenario (Scenario 1) was
run from January 1,  1994 to  December 31, 2004.
Through trial-and-error,   a  total phosphorus  load
(tributary and atmospheric) that resulted in steady-
state total phosphorus concentration of  7 ug/L (the
lake-wide International  Joint  Commission's [IJC]
target) was  determined  (Great Lakes  Research
Advisory Board, 1978).   A new user-defined net
sediment total  phosphorus flux  was  estimated,
assuming that approximately 95% of the phosphorus
load was retained in the sediment and 5%  was
recycled back to the water column.  The IJC  total
phosphorus concentration target was chosen with the
goal of  returning  Lake  Michigan  to its  "natural
oligotrophic state" under the GLWQA (International
Joint Commission, 1978).  The model was run to
steady-state.

2.7.8.2 Results and Discussion

An average annual total phosphorus load of 5,020
MT resulted  in  a  steady-state  lake-wide total
phosphorus concentration of 7 ug/L (Figure 2.7.9).
This equated to a total phosphorus total maximum
daily load (TMDL) of 14 MT/d. This also resulted in
a  spring  epilimnetic   maximum  chlorophyll  a
concentration of 3.7 ug/L and a spring hypolimnetic
chlorophyll a concentration of 1.6 ug/L (Figure 2.7.9).
Steady-state lake-wide maximum POC was around
0.33 mg/L (Figure 2.7.9). Steady-state was reached
within approximately 30 years.

2.7.9     Scenario   Comparison  and
Discussion

A summary of the final total phosphorus, chlorophyll
a, and POC concentrations is shown in Table 2.7.1.
Examining  the  scenarios  revealed a  number of
interesting conclusions regarding Lake Michigan. It
was apparent from comparing Scenarios 1,  4, and 5
that tributary   loading  was  considerably  more
important than atmospheric loading in driving Lake
Michigan total phosphorus and chlorophyll a  (Figures
2.7.1, 2.7.5, and 2.7.6). However, as Scenario 6
revealed, a small increase  in loading (tributary and
atmospheric) does not have a large impact on the
lake (Figure 2.7.7). Scenario 2 confirmed that Lake
Michigan's reaction to significant loading changes is
immediate but buffered by the large water volume
and slow retention time of the lake (Figure 2.7.2).
Scenario 3 suggested that  if current loading trends
continue, lake-wide total phosphorus and chlorophyll
a will continue to slowly  decline (Figure 2.7.4).
Scenarios  7 and 8 demonstrated  the   drastic
increases  in chlorophyll a, total  phosphorus, and
POC that would occur if loading to Lake Michigan
were  allowed  to  increase  to  GLWQA/IJC limits
(Figures 2.7.8 and 2.7.9).

2.7.70 Mass Budget

The sources, sinks,  and  lake  inventory  of total
phosphorus  was estimated.   Figure  2.7.10 is  a
graphical representation of the average annual loads,
sinks, and total phosphorus  inventory for Lake
Michigan based on 1994 and 1995 modeled and
measured data.  It was clear that the internal recycle
(settling and sediment feedback) accounted for the
majority of total phosphorus dynamics occurring in
the  lake. A significant mass of phosphorus settled to
the  lake bed, but a large  percentage (~  60%) is
recycled back to the water column. The monitored
tributaries  made  up  the  largest external total
phosphorus   source   to   the   lake,   while  the
unmonitored tributaries and atmospheric loads were
relatively small components. The total phosphorus
export at the Straits of Mackinac and the  Chicago
diversion were estimated to be a small fraction of the
                                             177

-------
         epilimnion chlorophyll-a
                                                           hypolimnion chlorophyll-a
      1994  1998  2002  2006  2010  2014  2018  2022
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                                                        1994  1998  2002  2006   2010   2014  2018  2022
                                                           particulate organic carbon
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2.0
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       1994  1998   2002  2006  2010  2014  2018  2022
                                                        1994  1998  2002  2006  2010  2014  2018  2022
Figure 2.7.8.  Scenario 7: Application of the GLWQA loads to the model.
          epilimnion chlorophyll-a
                                                          hypolimnion chlorophyll-a
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1994
   particulate organic carbon
                                                                                       2024
2034    2044
       1994
           2004
2014
                                 2024
                                     2034
                          2044
                                                            1994
                                                                2004
                                                      2014
                           2024     2034
Figure 2.7.9.   Scenario  8:   Estimate of the  TMDL to  reach the IJC's target total  phosphorus
concentration.
                                                     178

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Table 2.7.1. Final Eutrophication Scenario Results
Scenario
and
Length
1 (28
years)
2(30
years)
3(20
years)

4(30
years)

5(30
years)

6(20
years)

7(30
years)
8(30
years)
Description
Constant Conditions Remain
From 1994-1 995
Virtual Elimination (Lower
Bound)
Best Estimate of Current
Trends Resulting From
Previous Actions
Scenario 1 With
Instantaneous Reductions of
Tributary Loads to Zero
Scenario 1 With
Instantaneous Reductions of
Atmospheric Loads to Zero
Scenario 1 With Tributary
and Atmospheric Loads
Increased 20%
Application of GLWQA
Loads to Model
Estimate of TMDL to Reach
IJC Target Total
Spring
Epilimnion
Chlorophyll
a(ug/L)
2.4

0.1

2.0


0.4


2.3


2.5


4.0

3.7

Maximum
Hypolimnion
Chlorophyll
afog/L)
1.1

~0

0.9


0.1


1.1


1.1


1.6

1.6

Maximum
Total Particulate
Phosphorus Organic
(ug/L) Carbon
(mg/L)
4.3 0.20

0.54 0.02

3.5 0.18


0.91 0.03


4.1 0.20


4.6 0.22


7.5 0.34

7.0 0.33

            Phosphorus Concentration
                                             179

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     atmospheric
      deposition
       290,000
                        sediment recycle
                           4,000,000
                                                                export to
                                                               Lake Huron
                                                                 140,000
        i  export via
           Chicago
          Diversion
            13,000
                                                Total Phosphorus Inventory
                                               water column = 24,000,000 kg
 sediment1
    burial
3,000,000
                                       tributary loading
                                  monitored - 1,830,000
                                 unmonitored - 670,000
                              (Lake Michigan watershed)
Figure 2.7.10. Annual average (1994-1995) Lake Michigan total phosphorus loading (kg/year).
total export.  Overall, the best estimate using the
average of the 1994-1995 loads was that there was
a 5% annual loss of total phosphorus in the lake,
which suggested that there would be a small, but
steady,  decrease  in the  total  phosphorus  lake
concentration given constant total phosphorus loads.
Figure 2.7.11 is a breakdown of the total phosphorus
loads and inventory of the main lake and Green Bay.
It depicts  the loads  entering  the  bay and  lake
separately and the phosphorus exchange between
these two system.  Phosphorus from Green Bay
accounted  for approximately  13%  of the  total
phosphorus input into Lake Michigan. The bulk of
the  Green  Bay total phosphorus load was from the
Fox River.
                                    References

                                    Great Lakes Advisory Board.  1978. Annual Report
                                       to  the   International   Joint   Commission.
                                       International Joint Commission, Windsor, Ontario,
                                       Canada.  44 pp.

                                    International Joint Commission. 1978. Great Lakes
                                       Water Quality Agreement of 1978, with Annexes
                                       and Terms of Reference,  Between the United
                                       States and Canada, Signed at Ottawa, November
                                       22,  1978.    International  Joint Commission,
                                       Windsor,  Ontario,  Canada.  60 pp.
                                             180

-------
    Green Bay
   monitored and
    un monitored
  tributary loading
      930,000
                     atmospheric
                        loads
                       18,000
                                                   Green Bay Mass Budget
        atmospheric
         deposition
          270,000
                                   Green Bay
                                     export
                                    230,000
                           sediment recycle  /
                              3,700,000 A
                                                                       export to
                                                                      Lake Huron
                                                                       140,000
                            settling
                          6,500,000
  export \i\a
^ Chicago
  Diversion
   13,000
                                                                Total Phosphorus Inventory
                                                                                 Main Lake:
                                                               water column = 23,300,000 kg
                                                                                 Green Bay:
                                                                  water column = 700,000 kg
     sediment'
        burial
    2,800,000
                                      main lake monitored and
                                  unmonitored tributary loading
                                    (Lake Michigan watershed
                                        excluding Green Bay)
                                                  1,570,000
Figure 2.7.11. Annual average (1994-1995) Lake Michigan and Green Bay total phosphorus loading
(kg/year).
                                             181

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                                         PART 2

                                      LM3-EUTRO
Chapter  8.
Toxic
Results Provided for  LM2-   2.8.2 Manipulation of Results
2.8.1  Description

LM2-Toxic was developed to simulate congener-
specific   polychlorinated  biphenyl   (PCB)  state
variables. Due to the importance of organic carbon
in the fate and transport of PCBs, the model also
simulated three carbon states: biotic carbon (BIG),
particulate detrital carbon (PDC),  and dissolved
organic carbon (DOC). LM2-Toxic relied on external
calculations (measured or modeled) to estimate the
autochthonous and allochthonous carbon loads. The
internally produced carbon made up the majority of
carbon entering the lake. Thus, a reliable estimate of
this  internal load was of  utmost  importance  in
accurately simulating the organic  carbon in the
system. The  main purpose  of the eutrophication
model  (LM3-Eutro) in the Lake Michigan  Mass
Balance  Project  (LMMBP)  was  to   provide
autochthonous (internally produced) phytoplankton
carbon to the PCB fate and transport model (LM2-
Toxic).
                                 LM3-Eutro  and  LM2-Toxic  utilize  very  different
                                 modeling frameworks, with different segmentation
                                 schemes,   hydrodynamics,  and   transport
                                 mechanisms. Several modifications were made to
                                 LM3-Eutro  to  ensure  data  compatibility when
                                 exporting the autochthonous carbon to LM2-Toxic.
                                 Because all necessary changes were  made within
                                 LM3-Eutro code, no post-processing was necessary.
                                 LM3-Eutro generated carbon from primary production
                                 at each model time step.  The model used a variable
                                 time step of approximately three hours. The carbon
                                 was totaled on a daily basis, and the high-resolution
                                 LM3-Eutro  5 km2  segments were collapsed to the
                                 Level 2 segmentation scheme  to generate daily
                                 allochthonous carbon loads for each of the 41 Level
                                 2  segments.  These loads were generated for the
                                 1994-1995 calibration years, as well as a long-term
                                 simulation where  the 1994 and 1995  loading and
                                 hydrodynamics data were repeated for approximately
                                 28 years (see Part 2, Chapter 7, Section 2.7.1 -
                                 Constant Conditions Remain From 1994-1995).
                                            182

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                                         PARTS


                                  LEVEL 1  MODELS

                                     Douglas D. Endicott
                               Great Lakes Environmental Center
                                   Traverse City, Michigan
                                            and
                                       Timothy J. Feist
                                      Gregory  Gerstner
                                 Welso Federal Services, LLC
                                 Large Lakes Research Station
                                      9311 Groh Road
                                 Grosse lie, Michigan 48138
                                            and
                                    Kenneth R. Rygwelski
                             U.S. Environmental Protection Agency
                              Office of Research and Development
                  National Health and Environmental Effects Research Laboratory
                                Mid-Continent Ecology Division
                      Large Lakes and Rivers Forecasting Research Branch
                                 Large Lakes Research Station
                                      9311 Groh Road
                                 Grosse lie, Michigan 48138
Chapter  1.      MICHTOX  PCB  Model
Executive Summary

MICHTOX is a toxic chemical mass balance and food
chain bioaccumulation model.  In this project, the
model was used to provide a screening-level analysis
of the potential future trends in total polychlorinated
biphenyl (PCB) concentrations in Lake Michigan
water,  sediment, and  fish under  a  variety  of
contaminant  load scenarios.   The model  also
provided a comparison  of an older, "off-the-shelf"
model with the more complex models developed as
part of  the Lake Michigan Mass  Balance  Project
(LMMBP).   Results  of  the MICHTOX  modeling
indicate that atmospheric exchange  is a dominant
loss process of total PCBs in Lake Michigan, and that
the reservoir of total PCBs in the sediment has a
significant  impact   on  the  future  trends   in
concentrations of total PCBs in lake trout.

MICHTOX was developed  in the  early 1990s  to
provide guidance for the Lake Michigan Lake-wide
Management Plan (LaMP) and to assist with the
planning  of the LMMBP  (Endicott et al., 2005).
During the early part of the LMMBP, MICHTOX was
updated and used as a preliminary assessment tool
of the LMMBP PCB data (Endicott, 2005).  For the
present  study, the  updated fate  and transport
submodel  was  used  to  provide  exposure
concentrations to the food chain submodel under
                                            183

-------
seven scenarios  of future  PCB loadings to Lake
Michigan.

Chapter 3 summarizes the work with the MICHTOX
fate and  transport  submodel.  This includes  the
evaluation of historical loading trends in the earlier
project (Endicott,  2005).  The preliminary modeling
suggested that the scenario of historical PCB loads
to the lake that best fit the available data was one in
which loads increased from zero at a start date of
1940, peaked in  1961-1963,  and then  declined to
present levels.  This hindcast was later updated
using the LM2-Toxic model, as described in Part 4.

A Bayesian Monte Carlo (BMC) uncertainty analysis
was  also  conducted in the earlier  project that
demonstrated  that  MICHTOX  predicted  PCB
concentrations should be within a factor of two of the
measured data.

For the present  study,  atmospheric and tributary
loads, including unmonitored  tributary inputs, were
calculated for the  1994-1995 LMMBP  sampling
period. The model was run using these inputs and
the previously developed parameterization, and the
applicability of MICHTOX as a screening model for
predicting Lake Michigan total  PCB concentrations in
water, sediment, and fish was reconfirmed.

Chapter 4 summarizes the work with the MICHTOX
food chain submodel and the application of the model
for predicting potential  total  PCB  concentrations
under  different  loading  scenarios.     Data  to
parameterize the food  chain model was obtained
from the LMMBP sampling effort. The applicability of
the food chain model was confirmed by applying it to
the previous hindcast scenarios and to 1994-2000
Lake Michigan lake trout data.

MICHTOX was run for seven scenarios to help
evaluate future loading  trends and the impacts on
PCB  concentrations  of various loading  sources.
These scenarios included:

>  Continued loading at 1994-1995 levels
*  Continued recovery - fast  rate
*•  Continued recovery - slow rate
»•  Zero atmospheric deposition
>•  Zero tributary loads
*  Zero atmospheric deposition and zero tributary
   loads
*  Lake-wide sediment cleanup

The scenario model runs indicated that if declining
trends in loading sources occurred at the faster of
rates found in the scientific literature, the total PCB
concentrations in an average 5-6 year-old lake trout
in southern Lake Michigan would be reduced below
the fish  consumption advisory target  level by
approximately  the year 2025.  If loading  sources
declined at the slower rates found in the literature,
total PCB concentrations in an average 5-6 year-old
lake trout would be reduced below the target level by
approximately  2053.   The  sensitivity  scenarios
indicated that  the system was more affected by
atmospheric vapor concentration and deposition than
tributary loadings, and that the sediment reservoir of
total PCBs played a large role in the concentrations
observed in lake trout.

References

Endicott, D.D.  2005.   2002 Lake Michigan Mass
   Balance Project: Modeling Total PCBs Using the
   MICHTOX  Model.   In:   R. Rossmann  (Ed.),
   MICHTOX:     A   Mass  Balance   and
   Bioaccumulation Model for  Toxic Chemicals in
   Lake  Michigan,  Part  2.   U.S.  Environmental
   Protection  Agency,  Office  of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-05/158, 140 pp.

Endicott,  D.D., W.L. Richardson, and D.J. Kandt.
   2005.  1992 MICHTOX:  A Mass  Balance and
   Bioaccumulation Model for  Toxic Chemicals in
   Lake  Michigan.   In:   R.  Rossmann  (Ed.),
   MICHTOX:     A   Mass  Balance   and
   Bioaccumulation Model for  Toxic Chemicals in
   Lake  Michigan,  Part  1.   U.S.  Environmental
   Protection  Agency,  Office  of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse Me, Michigan.
   EPA/600/R-05/158, 140 pp.
                                              184

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                                         PARTS
                                  LEVEL 1  MODELS
Chapter 2.  MICHTOX Recommendations

MICHTOX  was adapted from the general model,
WASP4, and  has served well as a screening-level
model for  Lake Michigan  over the past  several
decades. Much of the model development took place
prior to the availability of  an extensive data set
collected for  the Lake  Michigan Mass  Balance
Project (LMMBP) during 1994-1995 and, therefore,
depended heavily on  existing historical data.   In
contrast, the LM2-Toxic model and LM Food Chain
model were constructed using the most recent data
from the LMMBP. Some of the advantages of using
LM2-Toxic instead of MICHTOX as a screening-level
model for future contaminants of interest include the
following:

• LM2-Toxic   has   a  significant  amount   of
  documentation.

• LM2-Toxic  algorithms are all contained within the
  WASP code, whereas MICHTOX utilizes Excel
  spreadsheets for some of the calculations. This
  makes code modifying in LM2-Toxic easier.

• LM2-Toxic  automatically corrects the Henry's Law
  Constant for temperature.

• LM2-Toxic  has a better treatment  of  carbon
  (including having  biotic and abiotic carbon  and
  carbon decay).
•  LM2-Toxic is as easy and fast as MICHTOX in
   preparing model runs for similar numbers of state
   variables.

•  LM2-Toxic utilizes output from the hydrodynamic
   model to compute advective flows and vertical
   exchanges.

•  LM2-Toxic carbon state variables are from the
   LM3-Eutro  model  for defining  autochthonous
   carbon generation.

•  LM2-Toxic handles sediment as a limited source
   for resuspension; whereas, MICHTOX does not.

•  LM Food Chain has more organisms in its food
   web.

•  LM2-Toxic has a higher spatial resolution in both
   the water and sediment.  This higher resolution
   allows one to utilize this resolution if data sets
   related to a new contaminant of interest are well-
   populated.

Therefore, future enhancements of MICHTOX are not
warranted.
                                            185

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                                         PARTS
                                  LEVEL 1  MODELS
Chapter  3.   MICHTOX  PCB  Fate  and
Transport Modeling

MICHTOX is a toxic chemical mass balance and
bioaccumulation model for  Lake Michigan.   The
model was developed to simulate the transport and
fate of polychlorinated biphenyls (PCBs) and other
toxic chemicals in Lake Michigan, and it has served
as the screening-level model in  the United  States
Environmental Protection Agency (USEPA), Large
Lakes Research  Station's  (LLRS)  suite of PCB
models.

MICHTOX contains both   a  fate  and transport
submodel  and  a  food  chain  bioaccumulation
submodel. The fate and transport submodel predicts
water and sediment concentrations that are used as
exposure concentrations by the food chain submodel.

MICHTOX was developed  in the early 1990s to
provide guidance for the Lake Michigan Lake-wide
Management Plan (LaMP)  and  to assist with the
planning of the Lake Michigan Mass Balance Project
(LMMBP) (Endicott  et al., 2005). The model was
later  updated and  used  as a  tool for a rapid,
preliminary assessment  of LMMBP  PCB data
(Endicott, 2005).

The focus of the MICHTOX modeling in the present
study was to provide a screening-level evaluation of
the effects of different loading scenarios on lake trout
PCB concentrations, using the MICHTOX food chain
submodel and the LMMBP data for organisms in the
lake  trout  food  chain.  The  organism data and
descriptions of the scenarios are included in Chapter
4. For the present study, the same  MICHTOX fate
and transport submodel parameterization was used
as that used for the early LMMBP preliminary mass
balance modeling assessment conducted by Endicott
(2005). Loads were adjusted to reflect later findings
of the LMMBP. This  chapter summarizes this work
to provide background for the food chain modeling
assessments  presented   in  Chapter 4.    The
MICHTOX modeling also provided an opportunity to
compare an established, "off-the-shelf model to the
more complex Level 2 models developed during the
LMMBP.

The following chapter contains a brief description of
MICHTOX,  a description  of the data used in the
modeling,  a discussion  of  the hindcast  model
confirmation, a comparison of model results to the
1994-1995 data, and a discussion of the evaluation
of uncertainty in the  model results using Bayesian
Monte Carlo (BMC)  methods.  A more complete
description of the model, the hindcast confirmation,
and the BMC evaluation can be found in Endicott et
al. (2005) and Endicott (2005).

3.3.1 Description

MICHTOX  was implemented using  the  USEPA
WASP4 modeling framework (Ambrose et al., 1988).
The model was  modified  by the USEPA to run on
Hewett-Packard (HP) Alphas running Tru64 Unix. As
part of the early LMMBP data assessment (Endicott,
2005), MICHTOX was revised  to reflect updated
information on the chemical properties of PCBs and
fate processes.  In addition to using the new PCB
data,  the following modifications or additions were
made at that time: the water balance was corrected
to maintain continuity for long model runs,  the
                                            186

-------
boundary condition at the Straits of Mackinac was
enhanced, the segment-specific atmospheric vapor
concentrations were  specified, and  the  chemical
volatilization  rate formulations and  Henry's  Law
Constant parameterizations were updated.  This is
the version of the model used for the hindcast study
described in Sections 3.3.3.1 and 3.3.3.2.  For the
most recent study discussed in Section 3.3.3.3 and
Chapter 4, MICHTOX predictions were improved by
updating atmospheric deposition PCB loadings and
adding  unmonitored tributary PCB loadings.

MICHTOX used  differential  equations  and  the
concept of mass balance to solve for concentrations
of contaminants,  in this case PCBs, both temporally
and spatially in the water  column and sediments.
Water  movement,  contaminant  transport,  and
               contaminant fate processes were used in the model
               to track the mass of PCBs from sources (tributary
               and  atmospheric) to sinks.   The mass balance
               equation for PCBs included terms for loads to the
               system, advective transport, vertical and horizontal
               dispersive   exchange,   settling,  resuspension,
               atmospheric deposition, vapor exchange  with the
               atmosphere (volatilization and absorption), sediment
               water diffusion, and burial from the surficial sediment
               segments  to   deep  sediment  (Figure  3.3.1).
               Contaminant concentrations were accounted for both
               in their  dissolved and particulate  states.   The
               complete coupled mass balance equations for the
               contaminant concentrations can be found  in "1992
               MICHTOX: A Mass Balance and Bioaccumulation
               Model  for  Toxic  Chemicals  in Lake Michigan"
               (Endicott et al., 2005).
   Watershed
                Atmosphere
            Tributary
             Loading
   Epilimnion
Hypolimnion
      Surficial
    Sediment
                                                                            Transport and
                                                                            Exchange
Absorption
Volatilization
                         sorbed chemical

                              POC
                                      -- Partitioning
                                                 Resuspension    Settling
                                              output to food
                                               chain model
                                                         sorbed chemical

                                                              POC
                                                 Resuspension
                                              output to food
                                              chain model
                                            Exchange
Figure 3.3.1. MICHTOX PCB mass balance schematic.
                              Deep
                            sediment
                                            187

-------
MICHTOX simulates total PCBs as the sum of two
homologs  (tetrachlorobiphenyl   [PCB4]   and
pentachlorobiphenyl [PCB5]).   Although  this  is
technically incorrect (there are ten  homologs), this
representation of total PCBs as two PCB homologs
was considered a reasonable compromise between
pre-LMMBP loading and concentration data, mostly
quantified as total PCBs or Aroclors, and congener-
specific  estimates  available for physicochemical
model parameters.  Loads of total PCBs to the model
were evenly divided between the two homologs.

MICHTOX model segmentation included both water
column and surficial sediment segments. The water
column  segmentation  included  ten  segments  of
varying geometry (Table 3.3.1), consisting of three
epilimnetic  segments   in the  main  lake, three
corresponding hypolimnetic  segments in the main
lake, three water column segments in Green Bay,
and one segment for the lower Fox River (segment 4)
that was not used for the LMMBP modeling (Figure
3.3.2). There were  seven sediment  segments; each
corresponded to an  epilimnetic/hypolimnetic segment
in the water column.  The  Fox  River sediment
segment (segment  14), underlying the unused water
column  segment,  was  not  used for the LMMBP
modeling.
Table 3.3.1.  MICHTOX Segment Geometry
  Segment     Volume (m3)
Depth (m)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
2.61 E+11
2.11E+11
7.49E+10
1.46E+07
2.23E+09
1.89E+10
4.61 E+10
1.70E+12
2.27E+12
1.91 E+11
5.81 E+08
4.09E+08
4.95E+07
1.97E+05
7.60E+06
4.20E+07
3.51 E+07
10
10
10
2.22
5.87
13.3
16.3
65.3
108
25.5
0.033
0.033
0.033
0.10
0.04
0.04
0.04
n
m
seasonally-stratified
water column
m completely-mixed
water column
frrf
4 °
surficial
sediment
sediment sampling
location

                                               Figure 3.3.2. MICHTOX model segmentation.
                                               The parameters used for all MICHTOX model runs
                                               are shown in Table 3.3.2.  Volatilization rates were
                                               input as a monthly time series and were based on the
                                               Wanninkhoff (1992)  formulation  for  water mass
                                               transfer  resistance and  the Schwarzenbach  et al.
                                               (1993) formulation for gas mass transfer resistance.
                                               Henry's Law Constants were also input as a monthly
                                               time series as a function of average monthly surface
                                               water temperatures (Bamford et al., 2000).  Endicott
                                               (2005) contains a complete description of the revised
                                               gas exchange equations in MICHTOX.
                                            188

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Table 3.3.2. Model Parameters and Coefficients
 Parameter
Value
Unit
 Particle settling velocity

 Pore water diffusion coefficient

 Monthly fraction organic carbon:
   for surface segments 1 -7
   for deep segments 8-10
   for sediment segments 11-17

 Log(organic carbon partition
   coefficientXKJ:
   -PCB4
   -PCB5

 Monthly water temperatures

 Monthly temperature-dependent
   Henry's Law Constants
   -PCB4

   -PCB5
 Monthly volatilization rates:
   -PCB4
   -PCB5
1.5

1.8e-5
0.127-0.290
0.039-0.090
0.023-0.052
6.18
6.46

1.7-19.2
1.2e-4-
 2.6e-4
1.5e-4-
 3.2e-4
0.55-1.72
0.56-2.02
m/day

m2/day
             atm-
             m3/mol
             m/day
3.3.2    Description   of  Data   Used  in
MICHTOX

The following is a description of the data used in the
MICHTOX PCB modeling for the LMMBP.

3.3.2.1 Water Column PCB Concentrations

Water-column total PCB concentrations (McCarty et
al., 2004) were averaged  for each water column
segment and cruise of the LMMBP using a volume-
weighted  averaging (VWA)  procedure (Appendix
4.4.1). Dissolved (filtered) and paniculate total PCB
concentrations were averaged separately.  Average
dissolved total PCB concentrations are presented in
Table 3.3.3, and particulate total PCB concentrations
are presented in Table 3.3.4.

3.3.2.2 Surficial Sediment PCB Concentrations

Sediment total PCB concentrations were measured
in 133 surficial sediment samples; 50 were collected
from the top 1 cm increment of box cores, and 65
were Ponar samples (McCarty et al., 2004). All total
PCB data were interpolated onto a uniform grid using
a natural-neighbor algorithm and averaged for each
MICHTOX  surficial sediment segment (Appendix
4.4.1). Average total PCB concentrations were also
calculated for the box core samples in each main
lake sediment segment (Segments 11 -13). Relatively
few sediment samples were collected in Green Bay;
therefore, surficial sediment total PCB concentrations
from cores collected during  the 1989-1990 Green
Bay Mass Balance Project (GBMBP) (Manchester-
Neesvig era/., 1996) were used to calculate average
concentrations in Green Bay sediment (Segments
15-17).   Segment-specific   average  total  PCB
sediment concentrations are presented in Table
3.3.5.  Model  runs that started in 1994 used the
segment-average box core data for initial conditions.

3.3.2.3 Atmospheric and Tributary Loads

Forcing functions used in  MICHTOX consisted of
atmospheric vapor concentrations, atmospheric (wet
and dry) deposition, and tributary loads. The forcing
functions developed for PCBs by the LMMBP were
believed to  be accurate estimates for the 1994-1995
period, based upon the data  quality objectives and
well-developed  estimation procedures.  Previous
studies  (Endicott  et al., 2005;  Endicott, 2005),
including the hindcast scenarios described below,
used tributary loads from the major  river systems.
The present study, including  the 1994-1995 model
runs and the forecast scenarios, also included load
estimates from unmonitored tributaries.

A number of sources were available to characterize
trends of the usage and release of PCBs in the Great
Lakes during the 20th Century, and these were used
to  extrapolate  the LMMBP forcing functions both
backward in time for estimates of historical loading
trends (Endicott, 2005) and forward in time for model
scenarios (the present study, Chapter 4). The total
PCB forcing functions were based upon the LMMBP
estimated  long-term trends  in PCB  usage  and
loadings to and concentrations in Lake Michigan and
the Great Lakes (McCarty et al., 2004).  Estimation
of hindcast  forcing functions is described in the next
section, Section  3.3.3.  Estimation of the forecast
forcing functions is discussed in Chapter 4.
                                              189

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Table 3.3.3. Cruise and Segment-Specific Average Dissolved Total PCB Concentrations (ng/L)


         Segment  Segment  Segment  Segment   Segment   Segment   Segment   Segment   Segment
 Date        12356        7        8         9         10
May 94
Jun 94
Aug94
Oct94
Jan 95
Apr 95
Aug95
Sep95
0.173
0.124
0.167
0.204
0.162
0.196
0.216
0.244
0.145
0.114
0.172
0.184
0.160
0.169
0.183
0.172
0.072
0.110
0.129
0.146
0.160
0.141
0.189
0.127
0.912

0.567
0.404

0.478
0.234
0.253
0.916

0.565
0.400

0.473
0.234
0.253
0.152

0.348
0.190

0.261
0.234
0.142
0.133
0.131
0.181
0.214

0.173
0.270
0.254
0.121
0.106
0.167
0.194

0.150
0.221
0.176
0.064
0.100
0.138
0.154

0.137
0.210
0.138
Table 3.3.4. Cruise and Segment-Specific Average Particulate Total PCB Concentrations (ng/L)
Date
May 94
Jun 94
Aug94
Oct94
Jan 95
Apr 95
Aug95
Sep95
Segment
1
0.147
0.088
0.031
0.102
0.138
0.099
0.046
0.036
Segment
2
0.137
0.065
0.030
0.042
0.136
0.084
0.026
0.023
Segment
3
0.132
0.071
0.024
0.030
0.138
0.075
0.025
0.020
Segment
5
1.653

0.574
0.608

0.805
0.048
0.572
Segment
6
1.659

0.577
0.598

0.789
0.050
0.571
Segment
7
0.272

0.103
0.121

0.245
0.064
0.126
Segment
8
0.114
0.090
0.080
0.099

0.089
0.111
0.052
Segment
9
0.120
0.070
0.055
0.045

0.073
0.058
0.026
Segment
10
0.123
0.072
0.045
0.030

0.071
0.050
0.025
Table 3.3.5. Segment-Specific Average Surficial Sediment Total PCB Concentrations (ng/g)


                 All LMMBP Sediment      Surficial Samples From    Surficial Samples From
   Segment	    Samples             LMMBP Box Cores        GBMBP Box Cores
11
12
13
15
16
17
56.2
35.2
4.99
17.1
127
52.9
103
63.4
27.9






695
643
07 3
                                          190

-------
3.3.3 Model Confirmation

A thorough  recalibration  of MICHTOX  was  not
conducted after the enhancements were made to the
model  as part of  the  preliminary  assessment
modeling described in this chapter (Endicott, 2005).
However, the  model predictions were  confirmed
against  data  using two   methods.    Hindcast
simulations were produced to confirm the suitability
of and to establish confidence in MICHTOX model
parameters and model predictions over a long-term
model run.  In addition, MICHTOX was run for the
1994-1995 LMMBP study period and model results
were compared to the LMMBP data.  This section
contains discussion of the hindcast and comparison
to the  LMMBP data for  both of the MICHTOX
submodels.

3.3.3.1  Description of Hindcast Process

For the hindcast simulations developed by Endicott
(2005), estimated  long-term  trends were used to
develop the continuous total PCB forcing functions
from an uncontaminated initial condition in  1940.
Although somewhat speculative, a similar procedure
was demonstrated  for  PCBs  in  Lake  Ontario
(Mackay, 1989; Gobas et al., 1995).  The hindcast
forcing  functions and model runs described in this
section were developed early in the LMMBP, before
the availability  of the LMMBP sediment core data
used to develop the more rigorous  hindcast forcing
functions (Part 1, Chapter 7) used for the LM2-Toxic
hindcast (Part 4, Chapter 5).

The development of  hindcast  forcing  functions
required estimates of the date when contamination
began,  the rate of increase in the magnitude of the
forcing  function,  the date  and  duration of  the
loading/forcing function peak, and the rate of decline
in the  magnitude  of the forcing  function.   The
assumptions were that PCB contamination of Lake
Michigan commenced in 1940, the rate of increase in
vapor concentrations and tributary loadings was the
same as the rate of decline, atmospheric deposition
loadings followed  the same long-term  trends  as
vapor concentrations, and monthly  variability in the
magnitude of forcing functions followed the 24-month
pattern  established by the LMMBP estimates.

Rates of change in vapor phase PCB concentrations
for Lake Michigan and the Great Lakes region have
been published by a number of researchers (Hillery
et al.,  1997, 1998; Baker  and Eisenreich, 1990;
Green et al., 2000; Schneider et al., 2001). Although
there   is  some  disagreement  as  to  whether
atmospheric measurements support the belief that
vapor phase PCB concentrations are declining over
Lake Michigan, Schneider et al. (2001) indicate that
PCB  concentration  profiles   in  highly-resolved
sediment cores from Grand Traverse Bay located in
northeastern Lake  Michigan support the view that
vapor  phase  PCB  concentrations  have  been
declining at a  rate  of  about 0.115/year,  which
corresponds to a six-year half-life, over the past 25
years.

Similarly, rates of change in PCB tributary loadings
were determined from loading estimates based upon
measurements  for the  Fox River in 1989-1990
(Velleux  and  Endicott,  1994) and from  similar
estimates for other Lake Michigan tributaries for 1982
(Marti  and  Armstrong,  1990)  and  1994-1995
(McCarty et al.,  2004).   This  information yielded
estimates for the rate of decline in tributary loadings
of 0.053 to 0.054/year, corresponding to a 12- to 13-
year half-life.

The date and duration of the peak in the PCB forcing
function was not easily defined.  Schneider et al.
(2001)  suggested that forcing  functions  peaked in
1972 and declined with  the decline in chemical
production after  1972 and a halt of production in
1977.   On the other hand, Gobas et  al. (1995)
estimated that PCB loadings to Lake Ontario peaked
in 1962. The reason for such a difference between
the lakes is unclear and  perhaps  reflects  the
subjectivity of  these  estimates.  Ultimately, three
different estimates for long-term hindcast total PCB
forcing functions were developed:

•  Scenario A  Total PCB forcing functions peaked
   in 1970 and declined after 1972.

•  Scenario B - Total PCB forcing functions peaked
   in 1961 and declined after 1963.

•  Scenario C - Total PCB forcing functions peaked
   in 1961 and  declined after 1972.

Plots of the three forcing function scenarios were
provided  on  a   whole-lake  basis  for   vapor
concentrations   (Figure   3.3.3),   atmospheric
                                              191

-------
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    16
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     total PCB
    vapor-phase
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  - - - scenario A
  	 scenario B
      scenario C
     0
     1940   1950   1960
                                                     2000
                    1970   1980
                    year
         1990   2000
Figure  3.3.3.    Long-term estimates  of
Michigan total PCB vapor concentrations.
                                      Lake
                                                  ^
                                                  "en
                                                  en
                                                  "
                                                  m
1800-
1600-
1400-
1200-
1000-
 800-
 600-
 400-
 200-
  total PCB
tributary loading
  estimates

—  scenario A
	 scenario B
    scenario C
                                                    0
                                                    1940   1950   1960
                      1970   1980   1990   2000

                      year
                                              Figure  3.3.5.    Long-term estimates  of  Lake
                                              Michigan total PCB tributary loadings.
deposition  (Figure 3.3.4), and tributary loadings
(Figure 3.3.5).

3.3.3.2 Hindcast Results

Hindcast simulations, commencing in 1940 from zero
concentration (i.e., clean) initial conditions for total
PCBs, were conducted for each of the three long-
term forcing function scenarios (Endicott, 2005). The
results of these simulations were compared to the
LMMBP  data  (1994-1995)  and  long-term  PCB
concentration  data with  the  goals of  confirming
parameters and determining which long-term loading
scenario  simulated  the  data most  accurately.
  noooo
                                  total PCB
                                 wet and dry
                             atmospheric estimates
                                 - • scenario A
                                	 scenario B
                                  - scenario C
                                     1990  2000
Figure  3.3.4.   Long-term  estimates  of  Lake
Michigan total  PCB atmospheric deposition
loadings.
                                              Predictions  for  Scenario  A  were  plotted  and
                                              compared to available data in Figures 3.3.6-3.3.9.
                                              Predictions  in the main Lake  Michigan  segments
                                              were compared to whole  lake volume-weighted
                                              average water column concentrations in Figure 3.3.6.
                                              Clearly,  the   model  predictions of  total  PCB
                                              concentrations in the water column were low for this
                                              scenario.   Predicted  surficial sediment  total  PCB
                                              concentrations were also low in comparison to the
                                              data (Figure 3.3.7) (see Figure 3.3.2 for sediment
                                              core locations). The same tendency of Scenario A to
                                              underpredict total PCBs concentrations occurred with
                                               en
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                                                          scenario A
                                                          predictions
                                                            segment 1
                                  — segment 2
                                    segment 3
                                	segment 8
                                    segment 9
                                    segment 10
                                  a  water data
                                                        1940
                                                                                          2000
                                              Figure 3.3.6.  Long-term Scenario A predictions
                                              of main lake total PCB concentrations.
                                               192

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Figure 3.3.7. Comparison of long-term Scenario
A predictions  to main lake sediment total PCB
concentrations (sediment  cores  collected  in
1991-1992).
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A total PCB concentrations to Sheboygan Reef
data.
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                            	age 10
                            	age 9
                            	age 8
                            •-.-•r. ..- age 7
                            O  GLNPO data
0
1940  1950   1960
                       1970
                       year
1980   1990   2000
                                                  Figure 3.3.14. Comparison of long-term Scenario
                                                  B predictions to GLNPO lake trout data.
                                               194

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        	segment 11
        - - - segment 13
           D site 18 field data
           A site 47s field data
           o site 68k field data
                      on
               0   0
     0-
     1900
            1920    1940    1960    1980   2000
                       year
Figure  3.3.17.   Comparison  of  Scenario C
predictions to main lake sediment total PCB
concentrations  (sediment  cores  collected  in
1991-1992).
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                                                 C total PCB  concentrations to Sheboygan Reef
                                                 fish data.
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D cn o
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scenario C
lake trout prediction
	 age 10
- - - age 8
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980 1990 200C
s




                       year
 Figure 3.3.18. Comparison of long-term Scenario
 C predictions to GLNPO lake trout data.
                                                 In general, the Scenario B long-term simulations tend
                                                 to agree most favorably with the available PCB data.
                                                 The  model  predictions  for  this  scenario  were
                                                 probably at least as accurate as the forcing functions
                                                 themselves; this was judged to be an adequate level
                                                 of model confirmation for this assessment. However,
                                                 further refinement of forcing  functions and model
                                                 parameters could improve the agreement between
                                                 data and predictions.

                                                 3.3.3.3 Comparison to the LMMBP Data

                                                 For the most recent study, MICHTOX simulations
                                                 were conducted for the 1994-1995 LMMBP period as
                                                 a check on model performance and in preparation for
                                                 the forecast scenarios discussed in Part 3, Chapter
                                                 4.

                                                 The total PCB forcing functions used for this model
                                                 run  included  both  monitored  and  unmonitored
                                                 tributary   loads  and  the  revised  atmospheric
                                                 deposition estimates. They were calculated following
                                                 the same procedure used for the LM2-Toxic forcing
                                                 functions described in Section 4.4.3.1  and Section
                                                 4.6.3.  For comparison to model-predicted  water
                                                 column PCB concentrations, the LMMBP cruise
                                                 sample data  were extrapolated using the natural
                                              196

-------
neighbor method and aggregated to the MICHTOX
water column segments (Appendix 4.4.1).

Differences between model predictions and sampling
data concentrations  were evident,  although  the
differences were  probably  reasonable  for  a
screening-level model application.  Modeled total
PCB concentrations for the main lake  epilimnetic
segments  were consistently lower than observed
data (Figure 3.3.20).  Model-predicted  main lake
epilimnetic total PCB concentrations averaged over
the 1994-1995 period  were 30-40%  lower than
sample data average concentrations.

Predicted  hypolimnetic  PCB concentrations were
important as these were passed to  the  MICHTOX
food chain submodel as exposure concentrations.
Modeled   main  lake  hypolimnetic  total   PCB
concentrations  exhibited   substantial   seasonal
variation,   but  the data  showed little  seasonal
variation  (Figure  3.3.21).    While  the baseline
concentrations predicted  by the model were in
general agreement  with the observed data,  the
seasonal  peaks resulted in study-period averaged
total PCB concentrations from the model  being 50%
higher in southern Lake Michigan, 7% higher in
central Lake Michigan, and 25% higher in northern
Lake Michigan.

The results of  the model simulations for the 1994-
1995 period were also interrogated in terms of total
PCB mass transport fluxes and inventories (Figures
3.3.22-3.3.23).    Mass  balance  diagnostics
demonstrated that air-water fluxes clearly dominated
the transport pathways for PCBs in Lake Michigan.

The net volatilization (gross volatilization  minus gas
absorption) of 3,078 kg/year was the most significant
net gain or net loss to the system.  Particulate settling
and resuspension resulted in large amounts of PCBs
cycling between the water column and sediments.

The general agreement between model and data,
coupled with the hindcast simulation conducted in the
Endicott (2005) study, confirmed the applicability of
MICHTOX for screening-level modeling assessments
of PCBs in Lake Michigan. A robust calibration of the
model should be conducted if it is desired to  use
MICHTOX in a future management context.  The
results  from this study suggested  that important
areas  for  future   review   included   MICHTOX
parameterization   related   to  partitioning,
settling/resuspension, and volatilization/absorption.

Further confirmation of the model is presented in the
following discussion  of the MICHTOX  food  chain
submodel,  which  uses  the  fate and transport
submodel water-column and sediment PCB output as
exposure concentrations.
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        Central Lake Michigan
        (segment 9)
                       hypolimnetic total PCB
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        Southern Lake Michigan (segment 8)
       Jan
      1994
                     Jan
                    1995
Jan
1996
during model development  reduced  the errors
associated with model conceptualization and coding.
Errors associated with model parameterization and
forcing functions can  be  examined  through  an
uncertainty analysis.

Uncertainty in MICHTOX model predictions had been
examined through both conventional Monte Carlo
analyses (Endicott et ai, 2005) and Bayesian Monte
Carlo (BMC)  analyses  (Endicott,  2005).  The
uncertainty analyses were conducted on a steady-
state  version  of the model because  of run time
limitations. Both analyses were conducted before the
availability of the LMMBP data; however, the results
should be  representative.   The  BMC analyses
indicated that  total PCB concentrations should be
well  within a  factor of two of model  predictions
(Endicott, 2005).  It  is  possible that repeating the
BMC  analysis with  the LMMBP data and forcing
functions would result in smaller confidence intervals
for prediction, and less uncertainty.

References

Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W.
   Schanz. 1988.  WASP4,  a  Hydrodynamic and
   Water Quality Model - Model Theory, User's
   Manual  and Programmer's   Guide.    U.S.
   Environmental  Protection  Agency,  Office  of
   Research  and   Development,  Environmental
   Research   Laboratory,  Athens,   Georgia.
   EPA/600/3-87/039, 297 pp.

Baker,   J.E.    and   S.J.   Eisenreich.   1990.
   Concentrations and Fluxes of Polycyclic Aromatic
   Hydrocarbons and Polychlorinated  Biphenyls
   Across the Air-Water Interface of Lake Superior.
   Environ. Sci.  Technol., 24(3):342-352.
Figure  3.3.21.     Comparison   of  MICHTOX
hypolimnetic total PCB concentrations to the
LMMBP cruise data.
3.3.4 Model Uncertainty

Model predictions contain uncertainty for numerous
reasons:  conceptual  errors and/or simplifications,
errors in parameterization, uncharacterized system
variability, and systematic errors in forcing functions
and calibration data.  Peer review and quality control
                                             Bamford, H.A., D.L. Poster, and J.E. Baker. 2000.
                                                Henry's  Law  Constants  of   Polychlorinated
                                                Biphenyl Congeners and Their Variation with
                                                Temperature. J. Chem. Engin., 45:1069-1074.

                                             DeVault, D.S., R. Hesselberg, P.W. Rodgers, and
                                                T.J.  Feist.  1996.  Contaminant Trends in Lake
                                                Trout and Walleye From the Laurentian Great
                                                Lakes. J. Great Lakes Res., 22(4):884-895.
                                              198

-------
                        gross volatilization
                                  5387
                        gas absorption
                               2309
                                                                              traits of Mackinac
                                                                                  export
                                                                                    -2
    atmospheric
     deposition
wet deposition 226
dry deposition 781
                                                                      water column = 1511 kg
                                                                        sediment = 27,045 kg
                              sediment
                                 burial
                                  984
                                            379
                                  monitored and unmonitored
                                       tributary loading
                                  (Lake Michigan watershed)
Figure 3.3.22. MICHTOX predicted mass balance fluxes and inventories (kg/year) for 1994-1995, whole
lake results.
                         Green Bay
                        monitored and
                        unmonitored
                       tributary loading
               atmospheric    226-
               deposition"
             wet deposition
            dry deposition
                         Green Bay
                        volatilization
                              804
                     Green Bay
                     gas absorption
                     70
                                                   Green Bay
                                                   sediment burial
                                                   53
                                                        Green Bay Mass Budget
                                                    atmospheric
                                                     deposition
                                                  wet deposition 212
                                                  dry deposition 531
                         main
                       gas absorption
                           2240
                   gross
             volatilization
                   4583
                                    Green Bay
                                      export
                       resuspension
                          2966
                                                  Straits of Mackinac
                                                       export
                                                         -2
                                                                                PCB Inventory
                                                                                   Main Lake:
                                                                        water column = 1411 kg
                                                                          sediment = 18,749 kg
                                                main lake monitored and
                                              unmonitored tributary loading
                                                (Lake Michigan watershed
                                                 excluding Green Bay)
                                                         153
                                                                      Green Bay:
                                                            water column = 100 kg
                                                              sediment = 8,296 kg
931
Figure 3.3.23. MICHTOX predicted mass balance fluxes and inventories (kg/year) for 1994-1995, Green
Bay and main lake results.
                                                   199

-------
Endicott, D.D.  2005.  2002 Lake Michigan Mass
   Balance Project: Modeling Total Polychlorinated
   Biphenyls Using the MICHTOX Model.  In:  R.
   Rossmann (Ed.), MICHTOX:  A Mass Balance
   and Bioaccumulation Model for Toxic Chemicals
   in Lake Michigan, Part 2.  U.S. Environmental
   Protection Agency, Office of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600-R-05/158, 140 pp.

Endicott, D.D., W.L. Richardson, and  DJ.  Kandt.
   2005.  1992 MICHTOX:  A Mass Balance and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan.   In:   R.  Rossmann  (Ed.),
   MICHTOX:    A  Mass   Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan, Part  1.  U.S. Environmental
   Protection Agency, Office of  Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600-R-05/158, 140pp.

Gobas, F.A.P.C., M.N.Z. Graggen, and X. Zhang.
   1995.  Time Response of the  Lake  Ontario
   Ecosystem to  Virtual  Elimination  of  PCBs.
   Environ. Sci. Techn., 29(8):2038-2046.

Green, M.L., J.V. DePinto, C.W. Sweet, and K.C.
   Hornbuckle.  2000.   Regional  Spatial  and
   Temporal Interpolation  of Atmospheric  PCBs:
   Interpretation of Lake  Michigan Mass Balance
   Data. Environ. Sci. Technol., 34(9): 1833-1 841.

Hillery, B.L.,  I. Basu, C.W. Sweet, and R.A.  Hites.
   1997. Temporal and Spatial Trends in a  Long-
   Term Study of Gas-phase PCB Concentrations
   Near the Great Lakes.  Environ. Sci. Technol.
Hillery, B.L, M.F. Simcik, I. Basu, R.M. Hoff, W.M.J.
   Strachan, D. Burniston, C.H. Chan, K.A. Brice,
   C.W. Sweet, and R.A. Hites. 1998. Atmospheric
   Deposition of Toxic Pollutants to the Great Lakes
   as Measured by the Integrated Atmospheric
   Deposition  Network.  Environ. Sci. Technol
   32( 1 5) :22 16-2221.
Mackay, D. 1989. Modeling the Long-Term Behavior
   of an Organic Contaminant in a  Large Lake:
   Application to PCBs in Lake Ontario. J. Great
   Lakes Res., 15(2):283-297.

Manchester-Neesvig, J.B, A.W. Andren, and D.N.
   Edgington.     1996.     Patterns  of   Mass
   Sedimentation and  of Deposition  of Sediment
   Contaminated by PCBs in Green Bay. J. Great
   Lakes Res., 22(2):444-462.

Marti,   E.A.   and   D.E.   Armstrong.    1990.
   Polychlorinated  Biphenyls  in  Lake Michigan
   Tributaries. J. Great Lakes Res., 16(3):396-405.

McCarty, H.B., J. Schofield, K. Miller, R.N. Brent, P.
   Van Hoff, and B. Eadie.  2004.  Results of the
   Lake   Michigan   Mass  Balance  Study:
   Polychlorinated  Biphenyls and frans-Nonachlor
   Data Report.   U.S. Environmental  Protection
   Agency,  Great Lakes National Program  Office,
   Chicago, Illinois.  EPA/905/R-01/011, 289 pp.

Schneider, A.R., H.M. Stapleton,  J. Cornwell, and
   J.E. Baker. 2001. Recent Declines in PAH, PCB,
   and Toxaphene Levels in the Northern Great
   Lakes  as  Determined from  High Resolution
   Sediment  Cores.  Environ.   Sci.  Technol.,
   35(19):3809-3815.

Schwarzenbach, R.P., P.M. Gschwend, and D.M.
   Imboden.  1993.      Environmental  Organic
   Chemistry. John Wiley and Sons, Incorporated,
   New York, New York. 681 pp.

Velleux, M.L. and D. Endicott. 1994. Development of
   a Mass Balance Model for Estimating PCB Export
   from the Lower Fox River to Green Bay. J. Great
   Lakes Res., 20(2):416-434.

Wanninkhoff, RJ. 1992. Relationship Between Gas
   Exchange and Wind Speed Over the Ocean.  J
   Geophys. Res., 97:7373-7381.
                                            200

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                                         PARTS
                                  LEVEL 1  MODELS
Chapter  4.
Modeling
MICHTOX   Food   Chain
MICHTOX  is a toxic chemical mass balance and
bioaccumulation model. The model was used at the
beginning  of the Lake  Michigan Mass  Balance
Project (LMMBP) as a planning tool (Endicott et al.,
2005).  After the LMMBP data were collected, the
model provided preliminary mass balance modeling
assessments for polychlorinated biphenyls (PCBs) in
Lake Michigan  (Endicott,  2005).  For the present
phase of the LMMBP, MICHTOX was used to provide
a screening-level analysis of the effects of various
pollutant loading sources on bioaccumulation in Lake
Michigan lake trout, and to predict the length of time
until PCB concentrations in lake trout declined below
health advisory target levels under different forecast
scenarios.  Chapter 4 discusses this aspect of the
MICHTOX  application. MICHTOX also provided a
comparison of an established "off-the-shelf" model to
the more complex Level 2 models developed during
the LMMBP.

MICHTOX  contains  both  a fate and  transport
submodel  and  a  food  chain  bioaccumulation
submodel.  This chapter provides information on the
application  of the  MICHTOX   food   chain
bioaccumulation submodel to Lake Michigan. The
food  chain  submodel is  briefly described, with a
complete description included  in Endicott  et al.
(2005).  The model  coefficients and data are also
briefly described.   The remainder of  the chapter
includes a discussion of seven model scenarios that
were conducted as a screening-level assessment of
the fate and sources of PCBs in the system, as a
preliminary  evaluation of the potential range of future
PCB concentrations in Lake Michigan under different
possible loading scenarios, and for a comparison to
predictions of the Level 2 models.

3.4.1 Model Development

MICHTOX was originally  developed  in the early
1990s as a screening model for the Lake Michigan
Lake-wide  Management  Plan  (LaMP)  and  for
development of the LMMBP (Endicott  et al., 2005).
The model  was later updated with newer process
formulations, parameters, and  the LMMBP data
(Endicott, 2005), as described in Part 3, Chapter 3.

The  screening-level food chain  bioaccumulation
modeling for Part 3 was completed using the food
chain submodel of MICHTOX.  This submodel was
essentially  unchanged from the  original version
(Endicott et al., 2005), except for adaptations to the
program code to accommodate 62-year model runs
for the forecast modeling.

The MICHTOX food chain bioaccumulation submodel
was adapted from version 3.20 of the Manhattan
College Food Chain Model, which was based upon
the WASTOXv4 food chain  model (Connolly and
Thomann, 1985; Connolly, 1991).  It used the time-
variable water column dissolved and particulate PCB
concentrations output from the  MICHTOX fate and
transport  submodel  as  the  PCB  exposure
concentrations for the trophic levels of the food chain.
The MICHTOX food chain submodel  was applied
separately to paired water column and sediment
segment output from the fate and transport submodel
for each  area  of interest. As  with the  fate and
transport submodel, the food chain  submodel
                                            201

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simulated total PCBs as the sum of two homologs:
tetrachlorobiphenyl (PCB4) and pentachlorobiphenyl
(PCBS).

MICHTOX treats bioaccumulation as a chemical
mass balance within  individual organisms, and a
bioaccumulation differential equation was solved for
each  individual  age class  of organism  (Equation
3.4.1 ) (Endicott ef a/., 2005):
= kuicfd
E
                             - K',v,
where

/  =  the organism of interest

/  =  the prey organism

v, =  chemical   concentration  in   organism  i
       (Mcherr/Mwet)

kui =  uptake rate (L3/T/Mwet)

c =  chemical concentration in water (M/L3)

fd =  dissolved  chemical  fraction  in the water
       column
                                n
Pij -  feeding  preference  factor £ (p^  = 1 )  of
       organism i for organism j   y=i

a/y =  chemical assimilation efficiency across gut

Cv =  food consumption rate (Mprey,W9t/MprediW9t)

K'; =  chemical elimination rate (1/T)

In general,  PCB concentrations in an organism was
equal to the  sum of the  PCB  uptake from water
(across the gill) and from consumption minus the
PCB   concentrations   lost  through  elimination
(excretion and dilution through growth). Equations
for the consumption rate, PCB uptake rate, and  PCB
excretion rate are fully described in "1 992 MICHTOX:
A  Mass   Balance  and   Bioaccumulation
Model  for  Toxic Chemicals  in  Lake  Michigan"
(Endicott, 2005).

For phytoplankton, PCB accumulation was assumed
to be a partitioning process, assuming 2% organic
carbon composition on a wet weight basis. The PCB
concentrations in detritus, the food source for benthic
organisms, was assumed to be equal to that of the
surficial sediment.

3.4.2  Description of the Data  Used  in
MICHTOX Food Chain

3.4.2.1 Description of Data

Fish and lower food chain organism data used for
MICHTOX modeling were collected  in three biota
zones  (Figure 3.4.1)  (McCarty et al., 2004).  The
biota zones  were geographical areas on the lake
chosen to compare and contrast the fish population
characteristics in different regions of Lake Michigan.
                                                         0,-
                                 Figure 3.4.1. The LMMBP biota sampling zones.
                                             202

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The Saugatuck biota zone is located on the eastern
side  of Lake  Michigan (MICHTOX water column
segments 1 and 8).  The Sturgeon Bay biota zone is
located east of the Door Peninsula (MICHTOX water
column segments 2 and 9).  The Sheboygan Reef
biota zone in southern Lake Michigan is located just
south of the segment partition between MICHTOX
water column segments 1 and 2. The Sheboygan
Reef data are only shown for information purposes.
Because forage fish were collected in a substantially
different location than the lake trout at this site and
were possibly not representative of prey items on the
reef, the Sheboygan Reef data were not used for
model confirmation.  Tables  3.4.1, 3.4.2, and 3.4.3
show the biota data for their respective zones. In the
higher trophic levels, it can be seen that Saugatuck
organisms had a consistently higher amount of total
PCB  concentrations  than  organisms   at  other
locations (Figure 3.4.2).

The representation of the Lake Michigan food chain
in the MICHTOX food chain  modeling included five
organisms: phytoplankton, Mysis, Diporeia, alewife,
and lake trout.
                       3.4.2.2 Sources and Choice of Constants

                       For the most recent study, LMMBP data were used
                       to update  alewife growth rates  and biota  zone-
                       specific lipid concentrations.  In addition, biota zone-
                       specific growth rates and biota zone-specific lipid
                       concentrations were used for lake trout. The age-
                       and  species-specific weight,  growth rate, and lipid
                       concentrations for all organisms are shown in Table
                       3.4.4.  Table 3.4.5 includes  the food assimilation
                       efficiencies and the chemical assimilation coefficients
                       for the organisms and PCB homologs used  in  the
                       MICHTOX food chain model.

                       3.4.3 Model Confirmation

                       In previous work with the MICHTOX model and the
                       LMMBP   data  (Endicott,   2005),   a  hindcast
                       confirmation of the MICHTOX fate and transport
                       submodel  and the  food chain submodel was
                       conducted to establish confidence in the model and
                       model parameters. The hindcast simulations of both
                       submodels were discussed in Part 3, Chapter 3.3.
                       The  food chain organism weights, specific growth
                       rates, and  lipid concentrations were subsequently
Table 3.4.1. Average Total PCB Concentrations in Fish in the Saugatuck Biota Zone
  Species
Age (Years)
    Average PCB
Concentrations (ng/g)
PCB Standard Deviation
         (ng/g)
Alewife < 120 mm
Alewife > 1 20 mm
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
1-2
3-7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
304
592
175
904
883
1287
2068
3185
3609
4511
5728
8209
7477
8116
6666
6799
4014
167
140

171
288
241
532
1126
809
921
1645
4101
2515
2997
872
794
3268
                                             203

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Table 3.4.2. Average Total PCB Concentrations in Fish in the Sheboygan Reef Biota Zone
Species
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Age (Years)
3
4
5
6
7
8
9
11
12
13
14
Average PCB
Concentrations (ng/g)
547
706
1202
1395
1974
2668
3102
5322
4692
4466
3483
PCB Standard Deviation
(ng/g)
184
217
204
192
320
1001
1022
1215
1234
217

Table 3.4.3. Average Total PCB Concentrations in Fish in the Sturgeon Bay Biota Zone
Species
Alewif e < 1 20 mm
Alewife > 120 mm
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Age (Years)
1-2
3-7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Average PCB
Concentrations (ng/g)
170
589
350
395
889
1268
1707
2487
2656
3360
4211
5283
5939
4420
4324
5254
7192
PCB Standard Deviation
(ng/g)
71
171
163
107
159
270
309
577
509
559
757
1168
1543
1185
438
1345
683
                                           204

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Table 3.4.4.  MICHTOX Food Chain Age- and Species-Specific Weight, Growth  Rate, and Lipid
Concentrations
MYSIS
Age
1
2
3
4
Weight (g)
0.00021
0.0022
0.0081 1
0.01977
Growth
Rate (1/day)
0.0193
0.0107
0.0073
0.0056
Lipid %
4
4
4
4






DIPOREIA
Age
1
2
3
Weight (g)
0.00007
0.00300
0.00630
Growth
Rate (1/day)
0.00398
0.00203
0.00313
Lipid %
3
3
3






ALEWIFE
Age
1
2
3
4
5
6
7
Weight (g)
3
15
27
37
45
50
53
Growth
Rate (1/day)
0.00441
0.00161
0.00086
0.00054
0.00029
0.00016
0.00010
Average
Lipid %
Sheboygan
Reef
7.2
8.5
9.0
10.5
11.5
12.0
12.2
Average
Lipid %
Saugatuck
5.5
5.5
6.0
7.5
9.0
10.0
11.0
Average
Lipid %
Sturgeon
Bay
4
6
6
6
6
6
6




STOCKED TROUT

Age
1
2
3
4
5
6
7
8
9
10
11
12

Weight (g)
Sheboygan
Reef
20
128
244
490
900
1378
1900
2600
3400
4000
4400
4700

Growth
Rate (1/day)
Sheboygan
Reef
0.005082
0.001766
0.001909
0.001665
0.001166
0.000879
0.000859
0.000734
0.000445
0.000261
0.000181
0.000114

Lipid %
Sheboygan
Reef
2.30
3.66
7.90
9.36
12.48
15.56
18.60
19.36
19.34
19.10
20.73
22.40

Weight (g)
Saugatuck
90
180
550
1100
2050
2850
3400
4000
4500
5400
6500
6900

Growth
Rate
(1/day)
Saugatuck
0.001898
0.003058
0.001898
0.001704
0.000902
0.000483
0.000445
0.000322
0.000499
0.000508
0.000164
0.000078

Lipid %
Saugatuck
2.30
3.66
7.13
9.52
14.77
18.96
21.05
18.56
19.12
20.68
22.00
23.00

Weight
(9)
Sturgeon
Bay
98
120
350
800
1500
2700
3200
3700
4400
5000
5500
5600
Growth
Rate
(1/day)
Sturgeon
Bay
0.000554
0.002931
0.002263
0.001721
0.001609
0.000465
0.000397
0.000474
0.000350
0.000261
0.000049
0.000096

Lipid %
Sturgeon
Bay
4.80
4.68
9.21
11.81
17.04
18.30
19.13
20.52
20.15
22.63
22.50
20.53
                                          205

-------
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8000-
7000-
6000-
5000-

4000-
3000-

2000-

1000-

0-













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• Sheboygan Reef
D Sturgeon Bay







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                          ,£ E
                          '5 E s E
                          _0)0 .0)0
                          03 (N TOtN
                                          lake trout age class
Figure 3.4.2. Total PCB concentrations of organisms in Lake Michigan biota zones.


Table 3.4.5.  MICHTOX Food Chain Model Parameters and Coefficients
 Parameter
                   Value (Unitless)
 Mysis Assimilation of Ingested Food
 Diporeia Assimilation of Ingested Food
 Alewife Assimilation of Ingested Food
 Stocked Lake Trout Assimilation of Ingested Food
 Mysis Chemical Assimilation (PCB4)
 Diporeia Chemical Assimilation (PCB4)
 Alewife Chemical Assimilation (PCB4)
 Stocked Lake Trout Chemical Assimilation (PCB4)
 Mysis Chemical Assimilation (PCB5)
 Diporeia Chemical Assimilation (PCB5)
 Alewife Chemical Assimilation (PCB5)
 Stocked Lake Trout Chemical Assimilation (PCB5)
                        0.400
                       0.0288
                        0.800
                        0.800
                        0.800
                        0.405
                        0.800
                        0.600
                        0.575
                        0.165
                        0.575
                        0.600
updated as described in Section 3.4.2.  Estimates of
unmonitored tributary PCB loads were also added.
The hindcast model runs were repeated with the new
food chain parameterization, and the  results were
again in general agreement with the available data.

While  previous applications of  MICHTOX  used
hindcast model runs to confirm model performance
and evaluate past loading (Endicott et al., 2005;
Endicott, 2005), the  most recent application of the
model focused on forecast model runs. For the latest
study,  the model  performance representing Lake
Michigan food chain PCB dynamics was confirmed
by comparing forecast model predictions to lake trout
data collected from the Saugatuck biota zone.  The
forecast  confirmation  model  run was compared
against the same Saugatuck  biota zone historical
lake trout data (1970 to 2002) as the hindcast runs
previously  described   (DeVault et  al.,  1996;
Swackhamer, 2003).  For these studies, lake trout
were  collected in  a specific  size range with  an
average  weight   of   2,600   grams,  which  is
approximately the average weight of the five year-old
and six year-old lake trout at Saugatuck (Table 3.4.4)
collected during the LMMBP.  The average of the
five year-old  and  six year-old lake  trout will  be
referred  to  as "5.5  year-old" lake  trout.  The
MICHTOX  model  was  set   up  with  Constant
Conditions for the 1994-2050 time frame:  vapor
concentrations, tributary loadings, and  atmospheric
                                             206

-------
loads were repeated from those measured during
1994-1995.    The  original  food  chain  model
coefficients were found  to  give an  acceptable fit
between model and the 5.5 year-old lake trout total
PCB data (Figure 3.4.3).   Chemical assimilation
efficiencies were adjusted to see if a better fit could
be obtained, but no substantial improvement was
obtained.
   25
   20
 = 15
   10
age 5.5 Saugatuck lake trout
 • total PCB data
— MICHTOX
    1970 1980 1990 2000  2010 2020 2030  2040 2050 2060
                       year

Figure 3.4.3.  Total PCB concentrations  in 5.5
year-old lake trout at Saugatuck biota zone.
3.4.4 Results - Forecast Scenarios

The ability to forecast future pollutant concentrations
based upon changes in pollutant loadings is one of
the most useful capabilities of models.  MICHTOX
was  used to  forecast  the reduction in total  PCB
concentrations in  the  Lake Michigan  food chain,
especially those trophic levels that  would impact
human health risk by consumption such as lake trout.

The forecast simulations in Sections 3.4.4 and 3.4.5
were run for 62 years, from January 1, 1994 through
December 31, 2055.    Measured  LMMBP  PCB
concentrations were used to define initial conditions
in water, sediment, and fish (McCarty et al., 2004).
All simulations used the 1994-1995 forcing functions
for the first two years of the model run.  The forcing
functions were determined from measured values of
the  LMMBP   and included  atmospheric vapor
concentration, wet and dry atmospheric deposition
loads, and monitored  and unmonitored  tributary
loads. These forcing functions were the same as
those used in  the MICHTOX model run for Section
3.3.3.3 and were calculated in  the same manner as
the congener-specific  functions of the  LM2-Toxic
model. The calculation procedures are described in
Sections 4.4.3.1  and 4.6.3. After January 1, 1996,
forcing  functions were varied  according  to  the
specified conditions of the forecast scenario.

The scenario results were evaluated against a fish
advisory consumption guideline target level  for the
total PCB body burden in lake trout. The Protocol for
a Uniform  Great Lakes Sport  Fish Consumption
Advisory (Great  Lakes  Sport Fish Advisory Task
Force, 1993) derived a target concentration in  the
edible  portion of lake  trout  of 0.05 ppm.   For
comparison to the LMMBP model output, this value
was converted to a whole fish concentration of 0.075
ug total PCBs/g fish (Appendix 3.4.1).  Model output
for the average of five and six  year-old  lake trout
("5.5 year-old fish") was  selected for evaluation
against the target level to be consistent with the size
range of fish in the long-term data set (DeVault et al.,
1996).

In this section,  MICHTOX was applied  to  three
loading scenarios.   The  first scenario  assumed
constant loads at the 1994-1995 level.  The second
and third scenarios had decreasing loads based upon
decline rates observed in the literature. The results
of the  scenario  simulations for Saugatuck are
displayed in  Figures 3.4.4a and 3.4.4b,  and the
Sturgeon Bay results are displayed in Figures 3.4.5a
and  3.4.5b.   The  figures include  an expanded
concentration  scale to allow  a comparison of
predicted total PCB concentrations to the 0.075 ug
total PCBs/g  fish  consumption advisory  target
concentration.    While  the  model   results  are
referenced to data from the Saugatuck and Sturgeon
Bay biota zones, MICHTOX has relatively coarse
segmentation and the model results only represent
the southern and middle sections of Lake Michigan.

3.4.4.1  Conditions Remain the Same as 1994-
1995 (Constant Conditions)

For this scenario,  1994-1995  forcing  functions
(tributary loads, atmospheric deposition loads, and
atmospheric vapor concentrations) were assumed to
remain  constant  from  January  1,  1994 through
December 31, 2055. The two-year cycle of forcing
functions was repeated for the entire 62-year period
of the scenario. This scenario provides insight into
the equilibrium status of the system to the 1994-1995
conditions,   but   it  likely overestimates  future
                                              207

-------
                           — constant conditions
                              slow recovery
                           — fast recovery
          -i	1	r
   1990  2000   2010   2020  2030  2040  2050  2060
Figure 3.4.4a. Sensitivity scenario predicted total
PCB concentrations in 5.5 year-old  lake trout
from Saugatuck biota zone.
   1.0-
 •° 06-
 a
 c:
 
-------
Total PCB concentrations in lake trout were predicted
to decrease at an exponential  rate (Figures 3.4.4a
and 3.4.5a).  Total PCB concentrations in 5.5 year-
old  lake trout were  predicted to be less than the
0.075 ug total PCBs/g fish consumption advisory
target concentration  in the year 2025 for Saugatuck
and 2018 for  Sturgeon  Bay  (Figures 3.4.4b and
3.4.5b).

Recent studies (Buehler et at., 2002) have suggested
that historical rates of decline have recently slowed,
and   thus  this  scenario,  while  realistic,  may
overestimate the future rate of decline.

3.4.4.3 Continued Recovery - Slow

This scenario also simulated the system response to
declines  in  PCB  loads   and   atmospheric
concentrations, but assumed that a slower observed
decline  rate of PCB loadings (Section  1.7.2) will
continue for the 62-year simulation period.  Forcing
functions were assumed to decrease from 1994-1995
levels  at a  20-year half-life  for   atmospheric
components (vapor phase PCB concentrations and
wet and dry atmospheric deposition loadings) and a
13-year half-life for PCB tributary loadings).

Total PCB concentrations in lake trout again declined
exponentially but at a slower rate than in the previous
scenario (Figures 3.4.4a  and 3.4.5a).  Total  PCB
concentrations  in 5.5 year-old lake trout were
predicted to take 28 years  longer to  reach the
consumption   advisory  target concentration  at
Saugatuck.  The 0.075 ug total PCBs/g fish target
concentration was achieved in the year 2053 for
Saugatuck and 2037 for  Sturgeon Bay (Figures
3.4.4b and 3.4.5b).

The atmospheric vapor and deposition decline rate
selected for this scenario was  on the conservative
side of possible rates, and the predicted dates of
compliance with the consumption advisory target are
likely the upper bound of possible dates.

3.4.5 Model Sensitivity

Four  model  sensitivity runs  were  conducted  to
analyze the importance of different total PCB loading
sources to the  Lake  Michigan  system.    Each
sensitivity  run eliminated one or more  loading
sources to determine the impact of the sources on
PCB concentrations in the water and lake trout.  The
remaining loading sources repeated the 1994-1995
values, similar to the constant conditions scenario.
The loading sources removed included all tributary
loads, atmospheric deposition loads, the combination
of tributary and atmospheric deposition loads,  and
internal  loads from the  sediment.   Results were
displayed for the Saugatuck area,  but the trends
were similar for all areas of the main lake.

3.4.5.1 No Atmospheric Wet and Dry Deposition
Loadings

This simulation analyzed the sensitivity of MICHTOX
to the elimination  of  atmospheric  wet  and  dry
deposition  loadings.  The first two years of  the
simulation  used the 1994-1995 forcing functions.
After January 1, 2005, the atmospheric deposition
loadings were set to zero for the remaining 60 years
of the  model run.  The remainder of the forcing
functions repeated the two-year 1994-1995 values.

The 5.5 year-old lake trout total PCB concentrations
decreased  at a rate considerably faster than  the
Constant  Conditions  Scenario  and  achieved  a
significantly lower steady-state concentration (Figure
3.4.6).  The scenario with no atmospheric deposition
was predicted to reach an approximate steady-state
total PCB concentrations in lake trout of 0.30 ug/g,
compared to the constant conditions steady-state
concentration of 0.47 ug/g.
  3.0
 ^2.0
 o
 -£ 1.5-
 <§ 1.0
 CO
 o
 2=0.5-
	constant conditions
	no atmospheric deposition
	no tributary loading
— .no atmospheric deposition
    or tributary loads
—— clean sediment
    0
    1990   2000   2010  2020  2030   2040  2050  2060

Figure  3.4.6.   Sensitivity scenario  total  PCB
concentration  predictions for 5.5 year-old lake
trout at Saugatuck.
                                               209

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3.4.5.2 No Tributary Loadings

This simulation analyzed the sensitivity of MICHTOX
to the elimination of total PCB tributary loads to the
Lake Michigan system.  The first two years of the
simulation used the 1994-1995 forcing functions, but
after January 1,2005, the tributary loadings were set
to zero for the remainder of the model run. All other
forcing functions  repeated the two-year 1994-1995
values.

Compared to the  Constant Conditions Scenario, the
5.5 year-old  lake trout total PCB  concentrations
declined faster and  reached a lower steady-state
concentration, but the reduction  was small  (Figure
3.4.6).  The  scenario  with no  tributary loadings
reached  an approximate steady-state total PCB
concentrations of  0.42 ug/g,  compared  to  the
constant  conditions  steady-state concentration of
0.47 ug/g.

Based upon these results, the system is predicted to
have a greater sensitivity to wet and dry atmospheric
deposition loadings than to tributary loadings. This
result was not surprising, because MICHTOX treats
tributary   loadings  and  atmospheric  deposition
loadings  in the same manner and the atmospheric
deposition loadings were more than 2.6 times greater
than tributary loadings in 1994-1995.

However, the relative magnitudes of tributary and
atmospheric deposition loads were confounded by
the available load estimate methodologies.  The
atmospheric deposition  loads included estimates of
the coarse particle loads, which were not  directly
measured. While comprising a large portion of the
total load, the estimate of the coarse particle load is
only approximate and may be subject to significant
error.

The results of this  scenario do  not suggest that
tributary loadings are not important. While they have
a relatively small  impact  when looking at Lake
Michigan on a large scale, such as MICHTOX does,
tributary loadings have  a large impact on the local
receiving waters which they enter.  Tributary loadings
and their watershed sources  may also have a
significant   effect   on   atmospheric   vapor
concentrations and deposition to the lake.   Thus,
clean-up  of watershed PCB sources may have an
effect on loadings which are not directly quantified by
the water quality model.

3.4.5.3   No Atmospheric Deposition and  No
Tributary Loadings

This simulation combined the removal  of tributary
loadings and atmospheric deposition loadings. The
first two years of the simulation used the 1994-1995
forcing  functions.    After January 1,  2005, the
atmospheric deposition  loadings and the tributary
loadings were  set to zero.  Other forcing functions
repeated the  1994-1995 values  throughout the
simulation period.

The 5.5 year-old lake trout total PCB concentrations
decreased at a  rate and achieved a steady-state
concentration  only slightly  below those  of the
scenario with  only atmospheric deposition  loads
removed (0.25 ug/g versus 0.30 ug/g) (Figure 3.4.6).
As with the previous two scenarios, this suggested
that atmospheric components had a greater effect on
MICHTOX-predicted  total PCB  concentrations in
Lake Michigan lake trout than tributary loadings.

3.4.5.4  Sediment Total PCB Concentration Initial
Conditions  Set to Zero

This scenario  was  conducted  to evaluate the
sensitivity of the total PCB concentrations in lake
trout to the reservoir of total PCBs in the sediment of
Lake   Michigan.     Measured   LMMBP  PCB
concentrations were used to define initial conditions
in water, sediment, and fish (McCarty et al., 2004).
The 1994-1995 forcing functions were repeated for
the entire period of the model run. On January 1,
1996, the sediment total PCB concentrations were
re-set to zero, after which the model simulation was
allowed to run normally for a 60-year period.

The  shape  of  the lake trout PCB concentration
curves overtime was influenced by PCB dynamics in
the water column and sediments and a time lag in the
MICHTOX food chain. Water column concentrations
dropped for two years as PCBs in the water column
settled  out  and volatilized faster than loadings
entering the system. After two years, however, water
column concentrations  began to  recover due to
tributary and   atmospheric  deposition loadings,
resuspension of newly contaminated sediments, and
absorption of PCBs from the atmosphere.
                                              210

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While the lower levels of the food chain immediately
responded to the  reduced PCBs  in water  and
sediment, the higher food chain  organisms had a
residual body burden, and the response was slower.
MICHTOX predicted a drop in the 5.5 year-old lake
trout total PCB concentrations for a period of six
years, then the concentrations steadily  increased
until reaching the same steady-state concentration as
the Constant  Conditions Scenario (Figure 3.4.6).
The lake trout total PCB concentrations were within
5% of the concentrations of the Constant Conditions
Scenario  within a period of 30 years, and reached
steady-state concentration about 45 years after the
sediment clean-up.  This was about the same time
period required for the Constant Conditions Scenario
to reach steady-state concentrations.

This scenario demonstrated the importance of the
reservoir of total PCBs in the sediments on the total
PCB concentrations in the higher levels of the food
chain. Response time would be greatly influenced by
the sediment settling and resuspension dynamics in
the model.

References

Buehler, S.S., I. Basu, and R.A. Hites.  2002.  Gas-
    Phase   Polychlorinated   Biphenyl   and
    Hexachlorocyclohexane Concentrations Near the
    Great Lakes: A  Historical Perspective. Environ.
    Sci. Technol., 36(23):5051-5056.

Connolly, J.P. and R.V. Thomann. 1985. WASTOX,
    A Framework for Modeling the  Fate of Toxic
    Chemicals in Aquatic  Environments.  Project
    Report.  U.S. Environmental Protection Agency,
    Office of  Research and Development,  ERL
    Duluth, Large Lakes Research Station, Grosse
    lie, Michigan. 52 pp.

Connolly, J.P. 1991. Documentation for Food Chain
    Model,  Version 4.0.     Manhattan  College,
    Riverdale, New York.
DeVault, D.S., R.  Hesselberg, P.W. Rodgers, and
   T.J. Feist.  1996.  Contaminant Trends in Lake
   Trout and Walleye From the Laurentian Great
   Lakes.  J. Great Lakes Res., 22(4):884-895.

Endicott, D.D.  2005.  2002 Lake Michigan Mass
   Balance Project: Modeling Total Polychlorinated
   Biphenyls Using the MICHTOX Model. In: R.
   Rossmann  (Ed.), MICHTOX: A Mass Balance
   and Bioaccumulation Model for Toxic Chemicals
   in Lake Michigan, Part 2.  U.S. Environmental
   Protection  Agency, Office  of  Research  and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-05/158, 140 pp.

Endicott, D.D., W.L. Richardson, and D.J. Kandt.
   2005.  1992 MICHTOX: A Mass Balance and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan.    In:   R.  Rossmann (Ed.),
   MICHTOX:     A   Mass   Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake Michigan, Part  1.   U.S.  Environmental
   Protection  Agency, Office  of  Research  and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-05/158, 140 pp.

Great Lakes Sport Fish Advisory Task Force. 1993.
   Protocol for a  Uniform Great Lakes Sport  Fish
   Consumption Advisory.  86 pp.

McCarty, H.B., J. Schofield, K. Miller, R.N. Brent, P
   Van Hoff, and  B. Eadie. 2004.  Results of the
   Lake   Michigan   Mass   Balance  Study:
   Polychlorinated Biphenyls and frans-Nonachlor
   Data Report.  U.S.  Environmental  Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois.  EPA/905/R-01/011, 289 pp.

Swackhamer, D.  2003.  Personal communication.
   University of Minnesota, Madison, Wisconsin.
                                             211

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                                          PART 3
                                   LEVEL 1  MODELS
Appendix   3.4.1   Derivation    of   a
Hypothetical Lake Michigan  Lake Trout
Fish Consumption Criteria for PCBs

Brent Burman
Welso Federal Services, LLC
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138
and
Kenneth R. Rygwelski
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

The hypothetical fish consumption criteria that we
derived for Lake Michigan lake trout has not been
officially adopted by any Federal,  State,  or local
authorities. We  proceeded to calculate  this target
concentration  because we could not find any other
polychlorinated  biphenyl   (PCB)   consumption
concentrations for Lake Michigan lake trout that we
could  readily   relate  to   our  model-predicted
concentrations in whole fish. This fish consumption
criteria was used in  our hypothetical PCB forecast
scenarios for Lake Michigan found elsewhere in this
report.
A  reduction factor  to convert whole  fish PCB
concentrations to fillet PCB  concentrations was
needed for the comparison of model output data to
fish consumption advisories. The data gathered for
the Lake Michigan Mass Balance Project (LMMBP)
for lake trout PCB concentrations was based on the
whole body of the fish, less the stomachs. Available
consumption advisories are based on the edible
portion of the fish (Great Lakes Sport Fish Advisory
Task Force, 1993).  The edible  portion refers to a
fillet which includes all flesh from  the back of the
head to the tail, including the skin and fatty belly flap.
The Health Protection Value for this fillet portion has
been established at 0.05  ug PCBs/(kg/day) and is
sufficient to keep cancer incidence at less than 1 per
10,000. Using a standard of 225 meals per year, the
amount of PCBs  allowable in the edible portion for a
70 kg individual is 0.05 ppm.  The derivation of this
figure is described in the Protocol for a Uniform Great
Lakes  Sport Fish  Consumption Advisory  (Great
Lakes Sport Fish Advisory Task Force, 1993).

Research  was necessary  to determine if PCBs are
concentrated equally in the fillet and whole body of
lake trout.  If it was determined that  PCBs are
concentrated either higher or lower in the fillet than in
the whole fish, then the target  level  of 0.05 ppm
would have to be adjusted accordingly.

Limited research was performed on Lake Michigan
lake trout in this regard.  Relevant research was done
on  Lake Superior  lake trout  (Miller and Schram,
2000)  and Lake Michigan rainbow trout and coho
salmon (Amrhein etai, 1999), but nothing was found
that specifically looked  at  Lake Michigan lake trout.
Amrhein era/. (1999) found whole fish:fillet derived
                                             212

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ratios  of 2.47 for rainbow trout, and 2.7 for coho
salmon, but it was uncertain as to whether the ratio
would be similar  for lake trout. Miller and Schram
(2000) found a whole fish:fillet ratio for siscowet lake
trout of 2.5, but the lipid content of siscowet and Lake
Michigan lake trout are vastly different. A data set of
lean lake trout from Lake Superior was provided by
the Michigan Department of Environmental Quality
(MDEQ) which had PCB concentrations for both
whole fish and fillets (Day, 1997).  The fish in the
data set provided were  nearly identical  in length,
weight, and lipid  content, so we believed that they
would be sufficient to use in our comparison to the
Lake Michigan data set (Table A3.4.1).

Statistical analysis of the  MDEQ data set resulted in
a whole fishrfillet ratio of 1.525; that is, PCB levels
were found to be  1.525 times higher in the whole fish
than in the fillets. Based upon this calculation, we
concluded that a factor of 1.5 would be justified  in
converting the  target level of 0.05 ppm PCBs for
fillets to 0.075 ppm PCBs as the new target level for
comparison  of our  model output for whole fish.
Further examination of the MDEQ data set revealed
that the whole fish:fillet PCB ratio was closely related
to the whole fish:fillet lipid ratio. A ratio of 1.50 was
found for the lipid concentrations and is shown  in
Figure A.3.4.1.  The result was not surprising as it is
known that PCBs are concentrated in the lipids.

Further research was initiated to validate the factor of
1.5 for lake trout in light of the work of Amrhein et al.
(1999). Coho salmon and rainbow trout were both
found to have much higher ratios, which if used for
lake trout, would raise the target level  for a fish
consumption advisory to 1.25 ppm of PCBs, or even
higher.   To add validity to  the factor we had
calculated, a review of several common Great Lakes
sport fish was conducted.  Because the PCB ratio in
question was shown in Figure 3.4.1 to be closely
related to lipid  concentrations, a comparison was
made between eight Great Lakes sport fish using
additional data. The lake trout from the MDEQ data
set and the rainbow trout and coho salmon from
Amrhein  et al. (1999)  were compared with  five
additional species of fish from the Fox River  and
Green Bay.  The additional species examined were
carp, walleye, northern pike,  smallmouth bass,  and
yellow  perch  (Fox   River  Model  Evaluation
Workgroup, 1999). It was found that as whole fish
lipid concentrations decreased, that the relative ratio
of whole fish:fillet PCBs increased (Figure 3.4.2).  It
can be reasoned that fish with higher lipid contents
store   more  lipids   in  the  fillet  portion  than
comparatively less fatty fish, which store most of their
lipids in the viscera and head which are not included
in the edible portion.  Because Lake  Michigan lake
trout have higher lipid contents than the rainbow trout
and coho salmon studied by  Amrhein et al.  (1999),
they will also have a relatively lower whole fish:fillet
PCBs ratio.

References

Amrhein, J.F., C.A. Stow, and C. Wible.   1999.
   Whole-Fish Versus Fillet Polychlorinated Biphenyl,
   Concentrations: An Analysis Using Classification
   and Regression Tree Models.  Environ. Toxicol.
   Chem., 18(8): 1817-1823.

Day,  R.    1997.   Michigan Fish   Contaminant
   Monitoring Program Annual Report. Surveillance
   Water Quality Division, Michigan Department of
   Environmental Quality, Surface Water  Quality
   Division, Lansing,  Michigan.   Report  Number
   MI/DEQ/SWQ-97-125.

Fox River  Model Evaluation Workgroup.   1999.
   Analysis of Bioaccumulation in the Fox River.
   Technical   Memorandum   Document,  Number
   8600B6A.001 1001 0299 DN08, 24 pp.
Table A3.4.1. Comparison of the LMMBP Lake Trout to MDEQ Lake Superior Lake Trout
                             Length (cm)   Weight (g)
           Whole Fish PCBs
Lipid
Lake Superior (MDEQ)
Lake Michigan (LMMBP)
58.1
57.83
1519
1943.34
0.24075
2.03646
15.63
16.07
                                              213

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     2.5
     2.0-
 co
 o
     1.5
                                  Lake Trout

                                  PCB
                                3 lipid
                         	1.52 - average PCB ratio

                         	 1.50 - average lipid ratio
 o
 .c
 5
     1.0-
     0.5-
         50.8  51.1  51.1  51.8  52.1  52.3  55.9  56.9  57.4  57.9  58.7  59.9  61.0  61.2  62.5 62.7  63.2  63.5 66.0 66.0

                                              individual fish (by length - cm)


Figure A3.4.1. Whole fish to edible portion of fish PCBs and lipid ratios for lake trout.
                                                                                                      18.00%
_g
"ro

CD
O
CL
-C
 CO
JD
 O
                                                                                      PCB ratio

                                                                                —A— lipid content
           lake
           trout
carp
rainbow
 trout
 coho
salmon
                                  walleye
northern
  pike
T	r
  smallmouth
    bass
                                                                                                     -0.00%
yellow
perch
                                                 fish species
Figure A3.4.2. Comparison of whole fish to fillet PCB ratios and lipid content for various fish species.
                                                     214

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Great Lakes Sport Fish Advisory Task Force. 1993.    Miller, M.A. and S.T. Schram.  2000. Growth and
   Protocol for a Uniform Great Lakes Sport Fish       Contaminant Dynamics of Lake Superior Lake
   Consumption Advisory. 86 pp.                      Trout. J. Great Lakes Res., 26(1 ):102-111.
                                             215

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                                         PART 4

                                      LM2-TOXIC
                                       Xiaomi Zhang
                                 Welso Federal Services, LLC
                                Large Lakes Research Station
                                      9311 Groh Road
                                 Grosse He, Michigan 48138
Chapter 1. Executive Summary

As one  of  the components in  the  overall  Lake
Michigan Mass Balance Project (LMMBP) modeling
framework,   a   comprehensive   polychlorinated
biphenyl (PCB) congener-based water quality model,
LM2-Toxic,  was developed to simulate fate and
transport of PCBs in both water and sediment of
Lake Michigan.  The main focus of this model was to
address the relationship between sources  of toxic
chemicals and  their concentrations in  water and
sediments of Lake Michigan, and provide the PCB
exposure concentrations  to the bioaccumulation
model   (LM2  Food   Chain)  to  predict  PCB
concentrations  in lake trout tissue.   This report
provides   detailed  model   description   and
development, model input and  field data, model
calibration procedures and confirmation, PCB mass
budget analysis, the results of model predictions, and
sensitivity analyses.

LM2-Toxic  is a  revision  of the United  States
Environmental   Protection  Agency  (USEPA)-
supported WASP4 water quality modeling framework.
It incorporates the organic carbon dynamics featured
in GBTOX and the  sediment transport  scheme, a
quasi-Lagrangian framework, used in the IPX. Both
GBTOX and IPX were WASP4-type models and
major components in the Green Bay Mass Balance
Project (GBMBP)  modeling framework.   Another
important modification was the addition of updated
air-water exchange formulations to the model.

There were 94 segments in the spatial segmentation
for the LM2-Toxic.  Forty-one of them were water
column segments, and 53 of them were surficial
sediment segments.  Temporal  resolution for the
model input was on a daily time scale.  Most of the
kinetic  functions   were  segment-specific  time
functions.  Good representation of water circulation
was essential for the accuracy of outputs from the
water quality model. The results at 5 x 5 km2 grid
generated by Princeton Ocean Model (POM) for the
Great Lakes were linked to the transport fields for
LM2-Toxic. Due to an affinity of PCBs for organic
carbon,  three  organic  carbon  sorbents  were
simulated  as  state variables in LM2-Toxic.  They
were biotic carbon (BIC), particulate detrital carbon
(PDC), and dissolved organic carbon (DOC).  The
model simulated 54 PCB congeners which accounted
for roughly 70% of the  total PCB mass in  Lake
Michigan.  Four phases were simulated in LM2-Toxic
for the congeners.  The four phases were dissolved,
sorbed to PDC, sorbed to BIC, and bound to DOC.

LM2-Toxic is a coupled mass balance of organic
carbon solids and  toxic chemical (PCBs)  dynamics.
Prior to the  organic carbon dynamics  and  PCB
                                            216

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dynamic calibrations, vertical dispersion coefficients
were determined using a thermal balance model.
Using the LMMBP-generated field data, the organic
carbon solids dynamics were first calibrated. This
was followed by the independent calibration of PCB
dynamics. The temporal variations of both BIG and
PDC resulted from an algal bloom in late spring and
early summer. Primary production was the dominant
organic  carbon  load to  Lake  Michigan.    The
eutrophication model (LMS-Eutro)-generated primary
production accounted for over 90 percent of the total
paniculate organic carbon  (POC) load to the lake.
The PCB concentrations in the dissolved phase was
about double the concentration in the particulate
phase in the main lake. There was some degree of
temporal  variation  in  the  water  column  PCB
concentration  controlled  by   a  combination  of
seasonal variation of external  loads, atmospheric
concentrations, and sediment resuspension events.
There was also a slight longitudinal concentration
gradient throughout the main  lake.  The highest
concentrations were found in the southern segments
due  to  higher  PCB  atmospheric deposition  and
concentrations  observed  in the  area  close to
Chicago. There was little vertical gradient of PCB
concentrations found based on main lake cruise
mean data.  As an important part of the modeling
effort to reduce  uncertainties associated with water
transport, settling, resuspension, and sedimentation,
a chloride model, a  long-term  simulation using a
137Cs and 239'240pu model, a long-term organic carbon
simulation, and a 47-year PCB  hindcast simulation
using LM2-Toxic were developed and run  for LM2-
Toxic confirmation.  These confirmation steps were
crucial and laid down a credible  foundation for long-
term projections using LM2-Toxic.

After calibration of organic carbon and PCB congener
dynamics and model  confirmation, a mass budget
analysis was done for the  LMMBP period (1994-
1995) to identify the  critical contaminant  sources,
sinks, and  key  environmental  processes in Lake
Michigan.  Figure 4.1.1 provides a summary of the
results  of the total PCB  mass budget diagnosis in
Lake Michigan.  The average masses of total PCBs
presented in  the water  column and the surficial
sediments (0-1  cm) of the lake during 1994-1995
were 1,216 kg and 13,085 kg, respectively. The
inventories divided by the volumes of the water and
the surficial sediment layer of the lake  lead to an
average concentration of total PCBs equal to 0.259
ng/L in the water column and 12,037 ng/L in the
surficial sediment layer.  The information on the
fluxes of total PCBs in Figure 4.1.1 shows the single
largest flux leaving the Lake Michigan system was
gross volatilization. This flux was countered by the
flux from gas absorption as the largest source to the
lake.   The air-water  exchange  was  the most
important process for Lake Michigan. It produced the
largest  PCB   net  loss  out  from   the lake.
Resuspension  was a major influx of PCBs to the
water column   offset  by the  flux from  settling.
Resuspension  and  settling were very important
processes in the lake system.  The results of these
processes made the net flux between resuspension
and settling the second largest net source. The total
external load (tributary loads + atmospheric loads) to
the water column of the lake was the largest net PCB
source to the lake water column. The flux by burial
was the largest net loss from the surficial sediment
layer. There was a net loss of 1,863 kg/year of total
PCBs for the entire Lake Michigan system (the water
column + the surficial sediment layer of both Green
Bay and Lake  Michigan).  This indicated both the
water column and the surficial sediment layer of the
lake were not  at steady-state during the LMMBP
period.

The model was also applied for forecasting the long-
term  responses (62-year  simulation, starting on
January 1, 1994) of the  PCBs  in  Lake Michigan
under various forcing functions and load reduction
scenarios.   Seven  PCB  forecast  and  sensitivity
scenarios were conducted. These seven long-term
forecast and sensitivity scenarios were:

A. Constant Conditions (Upper Bound) -  Repeat
    1994-1995  conditions.

B. Continued   Recovery  (Fast)  - Atmospheric
   components (vapor phase concentration, dry and
   wet depositions) decline with a six-year half-life
   (Hillery  et  al., 1997; Schneider  et  at., 2001).
   Tributary loads decline with a  13-year  half-life
   (Endicott, 2005; Marti and Armstrong, 1990).

C. Continued   Recovery  (Slow)  -  Atmospheric
   components (vapor phase concentration, dry and
   wet depositions) decline with a 20-year half-life
   (Buehler et al., 2002).  Tributary loads decline
   with a 13-year half-life.
                                              217

-------
                  volatilization
                        3439
             gas absorption
                    1507
         atmospheric
         deposition
            980
                                                                            .input from
                                                                           Lake h(uron
                                                                                 V. 4
                            resuspension
                                                                          export to
                                                                         Lake Huron
                                                                             12
                                                                            PCB Inventory kg
                                                                      water column = 1216
                                                                     active sediment = 13,085
                                                                    (0-4 cm interval)
                                         monitored and unmonitored
                                              tributary loading
                                         (Lake Michigan watershed)
Chicago,
 River
 export)
   8
           sediment
              burial
               1284
Figure 4.1.1.  Mass budget average for 1994-1995 total PCBs in the Lake Michigan system (including
Green Bay).  Unit of the masses transported (arrows) is in kg/year.
D. No Atmospheric  Deposition -  Stop all  PCB
   atmospheric deposition (dry and wet).

E. No Tributary Loadings - Stop all tributary PCB
   loads.

F. Lake  Sediment Clean-up  - Remove all  PCB
   mass from the surficial sediments.

G. No Atmospheric  Deposition and No Tributary
   Loadings - Stop all PCB atmospheric deposition
   (dry and wet) and tributary  loads.

All of the control actions started on January 1,1996.
Figure 4.1.2 presents the long-term responses of
total  PCBs in the water column of  Lake Michigan
under the seven forecast and  sensitivity scenarios.
The results from the Constant Conditions Scenario
simulation clearly demonstrated that, during the
                                          LMMBP period, the Lake Michigan system was not at
                                          steady-state with  respect to the  1994-1995 loads,
                                          vapor  phase  concentrations,  and the  level  of
                                          sediment total PCB inventory. The results from this
                                          scenario also indicate that,  if there is no decline in
                                          the current (1994-1995) forcing conditions, the water
                                          column PCB concentration  in the future will never
                                          meet  the  USEPA water  quality criteria  for the
                                          protection of wildlife (0.12 ng/L) (U.S. Environmental
                                          Protection Agency, 2005) and human health (0.026
                                          ng/L) (U.S. Environmental Protection Agency, 1997)
                                          in  the Lake  Michigan system.   The  long-term
                                          response from  Scenario B  -  Continue Recovery
                                          (Fast) shows that it takes about five years for the
                                          water column  concentration to  reach the  USEPA
                                          water quality criterion for the  protection of wildlife and
                                          more than two decades to reach the USEPA water
                                          quality criterion for the protection of human health.
                                          The water column PCB concentrations predicted in
                                              218

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                          0.30
                        ? 0.25-
                        c
                        •B
                        cc

                        § 0.15H

                        "
                       CD
                       o
                          0.10-
                          0.05-
             	Constant conditions - scenario a
             	 Continue recovery (slow) - scenario c
             «™= Continue recovery (fast) - scenario b
              »  LMMBP data -1994-95; EEGLE data 2000
             — • EPA water quality criteria for protection of
                 wildlife (2005)
             	 EPA water quality criteria for protection of
                 human health (1997)
                            0
                            1994   2004   2014    2024
                                                year
                               2034   2044
                                            2054
                          0.30
                        c
                        .g
                        ~m
•B 0.20-
                        | 0.15H

                        o
                        0 0.10-
                        00
                        O
                        °- 0.05-
             	No atmospheric deposition - scenario d
             	 No tributary loadings - scenario e
             	Lakewide sediment cleanup - scenario f
             —— No atmospheric deposition & tributary
                 loadings - scenario g
              *  LMMBP data - 1994-95; EEGLE data 2000
                            1994
                                  2004
                                         2014
                                                2024
                                                year
                                                      2034
                                                             2044
                                                                   2054
Figure 4.1.2. Annual long-term responses of total PCB concentrations in the water column of Lake
Michigan for the forecast and sensitivity scenarios.
Scenario C - Continue Recovery (Slow) declined at
a much slower pace.  The model  results indicated
that it takes about 12 years for the water column PCB
concentrations in the lake to reach the USEPA water
quality criterion for the protection of wildlife and that
the water column PCB concentration will reach the
USEPA water  quality criterion for the protection of
human health around 2046 (five decades after 1996).
The rates used in Scenarios B and  C may not be
realistic rates for the Great Lakes in the future.  With
the addition of  more recent data, it  appears that the
rate of decline in the atmospheric components could
be slower than the decline rate used  in Scenario C.

The results from the sensitivity scenarios (Scenarios
D, E, F, and G) suggested that the long-term PCB
                            concentrations  in  the  water  column  are  more
                            sensitive to atmospheric deposition  (dry and wet)
                            than to tributary loads. By  eliminating PCB total
                            inventory in the lake sediments on January 1,1996,
                            the water column concentration had a steep drop
                            initially, and then gradually increased and reached a
                            value  close  to the   steady-state  concentration
                            predicted by Scenario A - Constant Condition.

                            LM2-Toxic is  a sophisticated and state-of-the-art
                            toxic chemical fate  and transport model for  Lake
                            Michigan.  There are still many improvements that
                            can be made to the modeling framework and more
                            systematic tests could be conducted to address the
                            impacts of each process conceptualized in the LM2-
                            Toxic on the  model outcomes.  The  results and
                                                219

-------
predictions from the LM2-Toxic clearly demonstrate
the ability of the model to quantitatively analyze the
behavior of PCBs in the Lake Michigan system and
to forecast  the long-term  PCB  dynamics  under
various external forcing conditions.

References

Buehler, S.S., I. Basu, and R.A. Hites. 2002.  Gas-
   Phase   Polychlorinated   Biphenyl   and
   Hexachlorocyclohexane Concentrations Near the
   Great Lakes: A Historical Perspective. Environ.
   Sci. Techno!., 36(23):5051-5056.

Endicott,  D.D.  2005. 2002 Lake Michigan Mass
   Balance Project: Modeling Total PCBs Using the
   MICHTOX  Model.   In:  R. Rossmann  (Ed.),
   MICHTOX:     A   Mass  Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan,  Part 2.   U.S.  Environmental
   Protection  Agency,  Office of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-05/158, 140 pp.

Hillery, B.L., I.  Basu, C.W. Sweet, and R.A.  Hites.
   1997. Temporal and Spatial Trends in a  Long-
   Term Study of Gas-Phase PCB Concentrations
   Near the Great  Lakes. Environ. Sci. Technol.,
Marti,   E.A.   and   D.E.   Armstrong.    1990.
   Polychlorinated  Biphenyls  in  Lake Michigan
   Tributaries. J. Great Lakes Res., 16(3):396-405.

Schneider, A.R., H.M. Stapleton, J. Cornwell, and
   J.E. Baker.  2001.   Recent Declines in PAH,
   PCB, and Toxaphene  Levels  in the Northern
   Great  Lakes  as   Determined  From  High
   Resolution  Sediment  Cores.   Environ.  Sci.
   Technol., 35(19):3809-3815.

U.S.  Environmental  Protection Agency.   1997.
   Revocation  of the  Polychlorinated Biphenyl
   Human  Health Criteria in  the Water  Quality
   Guidance for the Great Lakes System. Federal
   Register, October 9, 1997, Volume 62, Number
   196.  [DOCID:fr09oc97-9].   From the Federal
   Register   Online   via  GPO  Access
   [wais.access.gpo.gov].

U.S. Environmental Protection Agency. 2005. Water
   Quality Guidance for the Great Lakes System.
   Code of Federal Regulations, Title 40, Volume
   21,  Chapter 1, Part 132.   http://www.access.
   gpo.gov/nara/.
                                            220

-------
                                           PART 4

                                        LM2-TOXIC
Chapter 2. Recommendations

LM2-Toxic was developed for its efficient conduct of
model calibrations and its capability to forecast long-
term impacts resulting  from a variety of  control
actions applied to an aquatic system.  There are still
quite a few improvements  that can be done to
enhance the credibility of the model predictions. The
recommendations for the further improvements and
tests are listed below.

1.  The results from the long-term cesium hindcast
   simulation should not be limited to comparison to
   the water  column  concentration and total
   sediment cesium inventory. They should include
   comparisons with the available 137Cs sediment
   core profiles.   This  will  provide  an additional
   confirmation  on the  solid vertical transport
   dynamics  (settling,  resuspension,  and  burial
   rates) in Lake Michigan.

2.  Instead  of the  empirical approach to estimate
   sediment  resuspension  of  each  sediment
   segment, a  more sophisticated  mechanistic
   sediment transport  model (e.g.,  SEDZL  or
   SEDZLJ), should  be used to provide  a more
   accurate and realistic sediment resuspension.

3.  Model verification (a post-audit) should continue
   using the latest field data collected around Lake
   Michigan to verify 1) parameters and rates used
   in the  model and  2) some of the conclusions
   made from the long-term forecast scenarios.

4.  As  an  extended  confirmation  process,   1)
   comparison between net settling fluxes of organic
   carbon generated from the model with available
   sediment  trap  data,  and  2)  more research
   conducted on carbonrchlorophyll a ratio including
   its spatial variation in the lake are needed.

5.  To investigate  the  potential impacts on the
   outcomes of the model by critical environmental
   processes and under different physical, chemical,
   and meteorological conditions, more systematic
   analyses are needed, including:

   A.  Sensitivity analysis of the fluxes across the
       air-water interface by using different gas and
       liquid transfer formulations.

   B.  Sensitivity analysis of model  responses  by
       changing surficial sediment initial conditions.

   C.  Investigation of  model  responses  in both
       water column and sediment to various mass
       fluxes across the sediment-water interface by
       changing diffusion  coefficient  and/or mixing
       length between the  water column and surficial
       sediment.

   D.  Investigation of potential impacts of ice-cover
       and  water surface elevation on the  model
       outcome.

   E.  Investigation of the impacts of carbon internal
       loads on the results  of LM2-Toxic PCB
       hindcast.

   F.  Investigation of system responses to different
       hydrodynamic transport fields.
                                             221

-------
     G.  Sensitivity analysis of organic carbon decay
        rates in the surficial sediment.

All of the above analyses should be conducted on
both short-term and long-term scales to see the
short-term and long-term effects  on the model
simulation results.

6.   Explore the effect  of a  huge resuspension
     event on outcomes of the model hindcast and
     forecast.

7.   Addition of a benthic nepheloid layer (BNL) to
     the  model  configuration  to  address  the
     importance,  effects, and benefits of  this
     compartment on the overall organic carbon and
     hydrophobic organic chemicals cycling in the
     lake system.

8.   Expand the  model to more applications and
     additional contaminants including a) mercury
     and  frans-nonachlor   modeling  for   Lake
     Michigan and b) finer model resolutions  (e.g.,
     Level 3) in both spatial and kinetic processes.

9.   Couple an air model to  the LM2-Toxic to
     compute  vapor  phase   concentration
     dynamically for more accurate calculation of the
     fluxes  across the interface between air and
     water.

10.  Collect a higher density of sediment samples
     from  shallow  high-energy areas   of  Lake
     Michigan  that   will greatly   enhance  the
     representativeness of carbon sorbent or toxic
     chemical dynamics in these zones. These may
     significantly influence sediment-water exchange
     and toxic chemical dynamics in Lake Michigan.

11.  Apply a different  segmentation (still similar to
     the  spatial  resolution   as   the  current
     segmentation used in LM2-Toxic) to the model
     to 1) more  efficiently utilize  nearshore and
     offshore  data, 2)  document  nearshore and
     offshore  gradients observed  in the  data
     collected for the Lake Michigan Mass Balance
     Project (LMMBP) for most congeners, and 3)
     investigate  the  impacts of  using  different
     segmentations (even on  the same spatial
     resolution) on the model  outcomes.

12.  Conduct  sensitivity  analysis  on  potential
     polychlorinated bipheny!  (PCB) decay rates in
     both water column and sediments  of Lake
     Michigan. The model currently assumes no
     PCB decay in both  compartments.

13.  I nvestigate the uncertainty associated with the
     selected  parameters for specific  processes
     conceptualized in the LM2-Toxic using Monte
     Carlo or other uncertainty analyses.

Considering   the   feasibility  of   these
recommendations, they  can  be  categorized as
follows:

A.   The  recommendations that can be done in a
     relatively short time period with limited effort
     include Numbers 5A, B, C, G; and 12.

B.   The recommendations that can be done within
     a relatively moderate time frame and effort
     once the necessary data are available include
     Numbers 3; 4; 5D, E, F; and 6.

C.   The  recommendations that can be done in a
     long time frame and significant effort include
     Numbers 1, 2, 8,  and 13.

D.   The  recommendations that can be done in a
     very long time period and demand full effort
     include Numbers 7, 9, 10, and 11.
                                              222

-------
                                         PART 4

                                       LM2-TOXIC
Chapter 3. Model Description

As one of the submodels in the overall Lake Michigan
Mass   Balance  Project   (LMMBP)  modeling
framework, LM2-Toxic was specifically developed to
simulate transport and fate of toxic chemicals, such
as polychlorinated biphenyl (PCB) congeners, in both
the water and  sediment of Lake Michigan.  The
principal focus of this model was to quantitatively
define the relationship among external carbon and
toxic chemical  loads,  internal  cycling of  organic
carbon  and toxic chemicals, and toxic chemical
concentrations in the water and sediments of the
lake. The PCB  exposure concentrations predicted
from the LM2-Toxic model  were  then used as a
forcing function  to compute  PCB concentrations in
fish tissue.

LM2-Toxic evolved  by  combining  the IPX  and
GBTOX models  in an attempt to better represent and
integrate processes  considered to be important in
Lake Michigan (Velleux  et at., 2000; Settles, 1997;
Bierman etal., 1992). Both the IPX and GBTOX are
descendants from the WASP4 water quality modeling
framework (Ambrose et a/., 1988).   LM2-Toxic
incorporates the organic carbon dynamics highlighted
in GBTOX and  the sediment transport scheme, a
quasi-Lagrangian framework, used in the IPX model.
It allows decay and transformation between organic
carbon states in  both the water column and sediment
bed and variation of the surficial sediment layer
thickness  in response  to  net  settling  or  net
resuspension of sediments.  In addition to the above
features, an updated air-water exchange formulation
(Bamford  et ai, 1999;  Wanninkhof  et  a!., 1991;
Schwarzenbach etal., 1993) was implemented in the
model.

4.3.1 Model Framework

LM2-Toxic  was   based   on   the   principle  of
conservation  of mass.   It used the same  finite
segment  modeling  approach used  in the United
States Environmental Protection Agency (USEPA)-
supported WASP4 modeling framework.  The mass
of a chemical or solid in each segment of a water
system is controlled by water movement between
adjacent  segments,  solid  dynamics  and chemical
dynamics within the system, internal and  external
loads, and boundary concentrations. A group of
mass  balance equations  representing the above
processes was used in the model to compute change
of mass  for  a state  variable  (constituent)  in a
segment  at a certain time. The model traced and
described where and how a mass of constituent was
transported and transformed.   The  general time-
dependent finite  differential equation in  a  given
segment  can  be written to describe the  change of
mass for a state variable at a certain time.

In a water column segment:
   dC
                                      (4.3.1.)
In a sediment segment (assume  mixing only in
vertical direction):
                                            223

-------
   dt
where

Vj   =  volume of segment j (L3)

Cj   =  concentration of water quality constituent in
        segment j (M/L3)

Cjj   =  concentration of water quality constituent at
        the interface  between segment i  and  j
        (M/L3)

QJJ   -  net flow  across the  interface  between
        segment i and j  (defined as positive when
        entering segment j  and  negative when
        leaving segment j) (L3/T)

n   =  number of adjacent segments
Ay  =
AX,
 W,  =
 'aw,/ -
         ,    bulk   dispersion/diffusion
coefficient (L3/T)

mixing   (dispersion/diffusion)  coefficient
(L2/T)

interfacial area between  segment i and j
(L2)

characteristic  mixing   length   between
segments i and j (L)

internal  and external  loading  rate  of
segment j (M/T)

mass change  rate due  to   air-water
exchange process between segment j and
air directly above segment j (M/T)

mass change rate due to sediment-water
exchange processes between segment j
and  adjacent sediment segments  (M/T).
The  processes   include   settling   and
resuspension

mass change rate due to sum of kinetic
transformation processes within segment j
(M/T), positive is source, negative is sink
Sb   =  mass change rate due to burial process
        from  surficial  mixing  layer  to deeper
        sediment layer (M/T)

Note:  L = length; M = mass; T = time.

The following sections of this chapter will provide a
detailed description of model segmentation,  water
circulation, solid dynamics, and chemical dynamics.
The  direct  loads,  including  external and internal
loads,  and  boundary conditions are discussed in
detail in Chapter 4.

4.3.2  Model Configuration

4.3.2.1  Spatial  Resolution - Segmentation

Compared  to MICHTOX (Level  1 contaminant
transport and  fate  model  developed  for  Lake
Michigan)  segmentation (Figure 3.3.2), the  LM2-
Toxic Level 2 model had finer resolution. Most water
column segments  in   the  LM2-Toxic  model
segmentation share  the  same or  portion of the
segment boundaries used in the MICHTOX-Toxic
model. Green Bay segments had similar features as
those used in GBTOX (Bierman et a/., 1992) and
MICHTOX (Endicott et at, 2005).   The significant
differences between  Level 1 and Level 2 models
were with respect to model structure, state variables,
and related physical and chemical processes.  They
are discussed in detail in the following sections.

The  spatial segmentation for the LM2-Toxic model
was developed from digitized  bathymetric (5x5 km2
grid) and shoreline data for Lake Michigan provided
by  Dr. David  Schwab,  National  Oceanic and
Atmospheric Administration (NOAA) (Schwab and
Beletsky, 1998). The lake, including Green Bay, was
divided into 10 horizontal columns, five water column
layers, and one  surficial sediment layer. A detailed
spatial and cross sectional segmentation of the LM2-
Toxic model is illustrated in Figures 4.3.1 to  4.3.3.
There are 94 segments in total. Segments 1 -41 were
water  column segments.  Most  of them had an
interface with surficial sediments.  Segments 1-10
were surface water segments with an interface with
the atmosphere. Segments 42-94 were the surficial
sediment segments with  sizes not  identical  to the
upper  water column segments.   The sediment
segmentation of the  LM2-Toxic  model  was very
                                             224

-------
5
15
24
33
40

10
8
9
15
19

3
13
22
31
38
10
10
10
19
80
1
11
20
29
36
10
10
10
18
42
4
14
23
32
39
10
10
10
20
84
2
12
21
30
37
10
10
10
18
48
6
16
25
34
41
10
10
9
16
21
                                      water
                                    column
                                   segment
                                   numbers
1
11
20
29
36
10
10
10
20
100
average
segment
thickness
in meters
Figure 4.3.1. Water column segmentation for the LM2-Toxic model.
                                      225

-------
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'•4

•i?
52
b2
S?
Si
bJ
U

5262
•»?S4
52c-i
M M
*^ 55
3

                                                        Lake Michigan 5 Kilometer Grid
                                                               Level 2 Model
                                                           [#] depositional zones
                                                           (¥] transitional zones
                                                           0 non-depositional zones
                                                            # segment numbers
Figure 4.3.2.  Surface sediment segmentation for the LM2-Toxic model.
                                             226

-------
                                                  |  | water column
                                                  [g] non-depositional area
                                                  |H transitional area
                                                  •I depositional area
Figure 4.3.3. Cross-sectional sediment segmentation and overlying water column segments for 10
Lake Michigan and four Green Bay water columns.
                                             227

-------
different from the one used in MICHTOX.  The
surficial sediment  was differentiated  into  non-
depositional  areas,   transitional   areas,  and
depositional areas (Figure 4.3.2).  The areas in a
surficial sediment segment were not  necessarily
adjacent to each other. The surficial mixing sediment
layer was not uniformly distributed and its thickness
varied from 1 to 4 cm in transitional and depositional
areas.

The principal criteria considered for the LM2-Toxic
model  water  column   segmentation   were  the
following:

1. Circulation  patterns (Schwab  and  Beletsky,
   1998).

2. Bathymetry (Schwab and Beletsky, 1998).

3. Horizontal and vertical gradients of temperature
   and concerned constituent concentrations.

4. Comparability to MICHTOX segmentation and its
   results.

The principal criteria considered for the LM2-Toxic
model  surficial  sediment segmentation were the
following:

1. Bathymetry data - 5 x 5 km grid (Schwab and
   Beletsky, 1998).

2. Distribution of sediment characteristics  (Cahill,
   1981; Bobbins etal., 1999).

3. Results generated from the LMMBP sediment
   measurements - box cores, ponar, and gravity
   samples (Robbins  et  a/.,   1999;  Eadie and
   Lozano, 1999).

Geometry-related data used in the LM2-Toxic model
such  as volumes, surface  areas,  and average
thickness of all segments are listed  in Tables 4.3.1
and 4.3.2.

4.3.2.2 Temporal Resolution

Two  levels of temporal  resolution for model inputs
and outputs were applied to LM2-Toxic. One was a
daily time scale that was used for  the LM2-Toxic
calibrations. Another was a monthly time scale that
was used for the purpose of long-term forecasts to
be used as a lake management tool. Both temporal
resolutions were detailed enough for physical and
chemical processes in the lake that occur at monthly
or  seasonal  time   scales,  such  as  thermal
stratification,  general  water circulation  patterns,
carbon  internal loads (primary  production), PCB
atmospheric concentrations, and resuspension. Final
interpretation of carbon and PCB mass budgets and
the results from long-term load reduction scenarios
on a lake-wide basis were at an annual time scale
using annually averaged results.

4.3.3  Water Balance

Water balance is one of the major components in a
traditional water quality modeling framework.  Water
movement directly controls the transport of solids and
chemicals in dissolved and  particulate phases  in a
water system.  In terms of LM2-Toxic model inputs,
the data in transport fields such as advective flows
and dispersive exchanges or mixing were used to
describe the water balance in the model.   The
components and their sources used in  LM2-Toxic
model transport fields are listed below:

1.   Bi-direction horizontal advective flows (provided
   by David Schwab,  NOAA; originally based on
   Schwab and Beletsky, 1998).

2.  Net vertical advective flows (provided by David
   Schwab, NOAA; originally based on Schwab and
   Beletsky, 1998).

3.  Tributary flows and flows across the Straits of
   Mackinaw (Endicott etal., 2005; Quinn, 1977).

4.  Water balancing flows.

5.  Vertical dispersion coefficients.

Components such as precipitation, evaporation, and
groundwater infiltration were not  considered in the
water transport fields used in the LM2-Toxic model.

Correct water circulation is essential for the accuracy
of outputs from the LM2-Toxic model. The Princeton
Ocean Model (POM) has been demonstrated for its
ability to accurately simulate water movement for a
given large water body (Schwab and Beletsky, 1997;
Blumberg and Mellor,  1987).  Using an extensively
                                              228

-------
Table 4.3.1.  Geometry Data for Water Column Segments and Lake Michigan (Total)
Segment
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Volume (m3)
1 .2729E+1 1
1 .3000E+1 1
1.1475E+11
8.6000E+10
4.0850E+10
3.8500E+10
1.1925E+10
1.1475E+10
1.3213E+10
1 .7250E+09
1.2123E+11
1 .2924E+1 1
1.1475E+11
8.6000E+10
3.1775E+10
3.7263E+10
8.4125E+09
7.7625E+09
6.4375E+09
1.1214E+11
1 .2303E+1 1
1.1444E+11
8.6000E+10
1.9625E+10
3.1988E+10
3.7625E+09
2.1500E+09
5.3750E+08
1.9198E+11
2.1649E+11
2.1339E+11
1.6908E+11
2.481 3E+10
4.2725E+10
2.8750E+08
3.6038E+1 1
4.6783E+1 1
7.8266E+1 1
6.8133E+11
1.6913E+10
3.4625E+10
Average
Thickness (m)
9.97
10
10
10
9.95
10
9.25
9.7
9.49
4.36
9.66
9.93
10
10
8.05
9.63
8.44
7.48
6.34
9.6
9.69
9.97
10
8.99
9.1
6.36
4.53
2.13
18.01
18.22
18.85
19.63
14.55
15.63
4.5
42.01
48.1
79.58
84.05
19.38
21.11
Surface Area (m2)
1.2750E+10
1.3000E+10
1.1475E+10
8.6000E+09
4.1000E+09
3.8500E+09
1 .2750E+09
1.1750E+09
1 .3750E+09
3.5000E+08
1.2500E+10
1.3000E+10
1.1475E+10
8.6000E-1-09
3.8500E+09
3.8500E+09
9.7500E+08
1.0000E+09
9.5000E+08
1.1625E+10
1.2650E+10
1.1475E+10
8.6000E+09
2.1500E+09
3.4750E+09
5.5000E+08
4.2500E+08
2.0000E+08
1.0600E+10
1.1825E+10
1.1275E+10
8.6000E+09
1 .6750E+09
2.7000E+09
5.0000E+07
8.4750E+09
9.6250E+09
9.8000E+09
8.0750E+09
8.5000E+08
1 .6000E+09
Water Segment
Above










1
2
3
4
5
6
7
8
9
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
29
30
31
32
33
34
   Total
4.8148E+12
5.7950E+10
                                          229

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Table 4.3.2. Initial Geometry Data for Surficial Sediment Segments and Surficial Sediment Layer (Total)
Segment
Number
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
Total
Volume (m3)
2.5000E+05
8.7500E+05
1.0250E+06
2.1250E+06
2.6975E+07
6.8850E+07
6.4375E+07
1 .8058E-I-08
7.6850E+07
2.1850E+07
1 .6500E+07
8.2500E+05
8.2500E+05
3.5000E+05
2.0000E+05
1 .4750E+06
4.9725E+07
2.1850E+07
1.3510E+08
1.0985E+08
5.0375E+07
3.5600E+07
5.2500E+06
2.5000E+05
1 .7000E+06
4.7500E+05
8.2500E+05
1.7500E+06
1 .2600E+07
4.5000E+06
2.0000E+06
6.8750E+06
7.7500E+06
1.1000E+06
7.7500E+05
3.7500E+05
7.0000E+06
2.3000E+07
1 .2000E+07
3.0000E+06
2.0000E+06
1.0000E+06
1.0000E+06
2.0000E+07
1.7000E+07
1.2000E+07
1 .7000E+07
2.0000E+07
5.0000E+06
1.3000E+07
1.2000E+07
1.0000E+06
1 .OOOOE+06
1.0871E+09
Average
Thickness (m)
0.001
0.001
0.001
0.001
0.013
0.018
0.025
0.031
0.029
0.019
0.012
0.001
0.001
0.001
0.001
0.001
0.013
0.019
0.028
0.026
0.031
0.016
0.010
0.001
0.001
0.001
0.001
0.010
0.024
0.030
0.010
0.011
0.010
0.001
0.001
0.001
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04

Surface Area (m2)
2.5000E+08
8.7500E+08
1.0250E+09
2.1250E+09
2.0750E+09
3.8250E+09
2.5750E+09
5.8250E+09
2.6500E+09
1.1500E+09
1.3750E+09
8.2500E+08
8.2500E+08
3.5000E+08
2.0000E+08
1.4750E+09
3.8250E+09
1.1500E+09
4.8250E+09
4.2250E+09
1.6250E+09
2.2250E+09
5.2500E+08
2.5000E+08
1 .7000E+09
4.7500E+08
8.2500E+08
1.7500E+08
5.2500E+08
1.5000E+08
2.0000E+08
6.2500E+08
7.7500E+08
1.1000E+09
7.7500E+08
3.7500E+08
1 .7500E+08
5.7500E+08
3.0000E+08
7.5000E+07
5.0000E+07
2.5000E+07
2.5000E+07
5.0000E+08
4.2500E+08
3.0000E+08
4.2500E+08
5.0000E+08
1 .2500E+08
3.2500E+08
3.0000E+08
2.5000E+07
2.5000E+07
5.7950E+10
Water Segment
Above
1
11
20
29
36
36
36
37
37
37
30
30
21
12
22
31
38
38
38
39
39
39
32
5
15
24
33
40
40
40
41
41
41
34
25
16
8
18
27
27
27
35
35
26
17
7
9
19
19,28
19,28
10
10
10

                                           230

-------
tested version of POM for the Great Lakes (POMGL),
transport fields were generated for Lake Michigan at
different spatial and temporal resolutions for use in a
series of mass balance models adapted for LMMBP
(Schwab and Beletsky,  1998).  The hydrodynamic
model for Lake Michigan had 20 vertical levels and a
uniform  horizontal grid size of 5 km  (Schwab  and
Beletsky, 1998).  Because the  LM2-Toxic model
segmentation was constructed based on the 5 x 5
km2 grid used in the POMGL for Lake Michigan, the
hydrodynamic model results were easily aggregated
to the resolution used in LM2-Toxic  (Schwab  and
Beletsky, 1998).   The aggregated  horizontal bi-
direction flows at each interface  provided a good
approximation of horizontal advective and dispersive
transport components  at the  interface.    The
advantage of using bi-direction flows at an interface
was  that it  bypassed the  tedious and necessary
horizontal dispersion coefficient calibration procedure
required when  only net flow is  available  at  the
interface.

The vertical transport field was calculated in the form
of net  vertical flow (provided by  David Schwab,
NOAA; originally based on Schwab and Beletsky,
1998).   Therefore, vertical exchange coefficients
were calculated and calibrated to define the vertical
mixing   process   between  vertically  adjacent
segments. A summer period  of strong stratification
and a non-stratified period of intense vertical mixing
were the most important limnological features of the
Great Lakes (Chapra and Reckhow, 1983; Thomann
and  Mueller, 1987).  Therefore, determining  the
dynamics of  vertical mixing was considered  as an
important task  in modeling  development for  the
LMMBP. A thermal balance model was constructed
to calibrate the vertical exchange coefficients at the
interfaces  (Zhang et  al.,   1998,  2000).    The
coefficients  were  calibrated  using 250 observed
vertical temperature profiles collected at 40 stations
in Lake Michigan during the 1994-1995 LMMBP
period.    The  model  calibration  results versus
temperature  measurements in each water column
segment and temporal plots of calibrated exchange
coefficients are listed in Appendix 4.5.1. A detailed
discussion on how the thermal model was run can be
found in Part 4, Chapter 4.

Water  balancing   flow  was another advective
component added into the water transport field for
LM2-Toxic.    The  aggregated  advective  flows
provided by NOAA were not balanced in individual
segments  over  the  two-year  LMMBP   period.
However,  the total  water mass  was perfectly
balanced on a whole lake basis.  Over the two-year
LMMBP period, some segments lost or gained a
certain amount of water. This problem could be very
significant  for long-term simulations for the LM2-
Toxic. It stops the model simulation once the volume
of a segment reached zero.  To counter the amount
lost or gained in each segment, a balancing flow was
introduced to keep the volume of water unchanged in
each segment at any time during the simulation. The
balancing  flows  were  generated based  on  the
aggregated advective flows (provided  by David
Schwab, NOAA;  originally  based on Schwab and
Beletsky, 1998), original  volume of each segment,
and the general water circulation patterns during the
LMMBP period.

Tributary flows and flows  through the  Straits of
Mackinac were based on MICHTOX model inputs
(Endicott et al., 2005), literature (Quinn, 1977), and
water circulation patterns during the LMMBP period
(provided by David Schwab, NOAA; originally based
on Schwab and Beletsky, 1998).

After vertical exchange coefficients were  calibrated,
a conservative constituent,  chloride, was simulated
using the LM2 model configuration to verify that the
water transport components described above were a
good representation of the overall water transport
field for the LM2-Toxic. The chloride model was run
just  once  without adjusting  any  parameter or
coefficient.   The model results had  very good
agreement with the observations during the LMMBP
period (Appendix 4.5.4). A detailed discussion of the
chloride results can be found in Part 4, Chapters 4
and 5.

4.3.4  Solid Balance

PCBs have an affinity for organic carbon.  Each type
of organic  carbon sorbs PCBs differently. Settling
rates and  resuspension rates, decay rates,  etc.,
impact the organic carbon  fractions. Figure 4.3.4
shows the conceptual framework and the processes
related to organic carbon sorbent dynamics in Lake
Michigan.   Three organic carbon  sorbents were
simulated as  state variables in LM2-Toxic.  They
were biotic carbon (BIG), particulate detrital  carbon
(PDC), and dissolved organic  carbon (DOC).   BIG
                                             231

-------
Primary
WBIC Production
1 1
Advection In -
Water
Surface
Mixed
Sediment
Layer
~B
r-*' BIC


Net
Settling

HTC°2
BIC to ^
PDC
Wpoc
1
4-
PDC

Gross
Settling


>,

>
;

r
k
Primary
WDOC Production
1 1

PDC to ^
DOC
i-t
DOC
i
Resuspension

PDC


^TC°2
PDC to
DOC
\
k
^ DOC
i> Decay

Diffusion
r
DOC

DOC
> Decay

->• Advection Out

                                   Burial >


                 BIG = Biotic Carbon; PDC = Particulate Detrital Carbon;
                 DOC = Dissolved Organic Carbon; W = Load

Figure 4.3.4. Conceptual framework of organic carbon sorbent dynamics used in the LM2-Toxic model.
represented participate organic carbon (POC) in live
phytoplankton biomass. PDC represented participate
detrital   carbon  derived  from  phytoplankton
decomposition,   zooplankton   excretion,  and
allochthonous sources. DOC represented colloidal-
sized particles  that pass through ashed 47 mm
diameter glass fibers (U.S. Environmental Protection
Agency, 1997).

Other than the components related to the water
transport field, numerous processes were considered
important  in controlling the three organic carbon
solids concentrations in either the water column or
sediment or both (Table 4.3.3).

The  segment-specific internal  primary production
load generated from the eutrophication model (LM3-
Eutro) was a crucial input to the LM2-Toxic. Primary
production is the dominant organic carbon load to
Lake  Michigan.    LM3-Eutro   generated  primary
production accounted for over 90 percent of the total
POC  load to the  lake.   Further discussions  on
parameterization of  processes such  as organic
carbon  sorbent  decay   and  resuspension  are
presented in the following sections.  In Chapter 4,
detailed discussion of the input  data used  in the
processes related to organic carbon dynamics will be
provided.
4.3.4.1 Solid Kinetics

Decay of organic carbon sorbent was the only kinetic
process considered for solid dynamics in the LM2-
Toxic.  This process transforms significant amounts
of carbon species in  both the water  column  and
sediment segments.  To  capture  general carbon
sorbent loss mechanisms in the water column without
introducing too much complication, pseudo-first-order
carbon decay rates for BIG, PDC,  and DOC were
formulated   as  Michaelis-Menten  functions  of
respective   carbon   concentrations  and  then
temperature-corrected  according to an  Arrhenius
relationship with a temperature coefficient, 6 = 1.05
(Bierman et al.,  1992). The decay equations for the
three organic carbon sorbents in water column are:
                         * 0(7- 20)
                          »0(7--20)
(4.3.3)
(4.3.4)
                                              232

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Table 4.3.3.  Processes Considered in Organic Carbon Sorbent Dynamics Constructed for the LM2-
Toxic
 Biotic Carbon (BIC)
               Particulate Detrital Carbon (PDC)   Dissolved Organic Carbon (DOC)
 External Tributary Loads

 Internal Primary
 Production Loads

 Net Settling

 Decay and Yield to PDC
               External Tributary Loads

               Yield From BIC Decay


               Gross Settling

               Resuspension

               Decay and Yield to DOC in Both
               Water Column and Sediments

               Burial to Deeper Subsurface
               Sediment Layer
                     External Tributary Loads

                     Internal Loads Derived From Primary
                     Production Loads

                     Yield From PDC Decay

                     Diffusion at Sediment-Water
                     Interface

                     Decay in Both Water Column and
                     Sediments
d(DOC)
          ^1/2(000)
        'DOC
where
                         *0(T-20)
(4.3.5)
 kd  = decay  rate of a carbon  sorbent in water
      column (d~1)

 k  = substrate saturated decay rate of a carbon
  d    sorbent in water column at 20°C (d~1)

 k1/2 = Michaelis-Menten half-saturation constant for
      a carbon sorbent in water column (mg C/L);
      where C = carbon, L = liter

 C  = segment-specific concentration of a carbon
      sorbent in water column (mg C/L); where C =
      carbon, L = liter

 Q  = Arrhenius   temperature    coefficient
      (dimensionless)

 T  = measured segment-specific temperature (°C)

Therefore,  the  final  decay  rates for the  carbon
sorbents were temperature- and spatially-dependent.
The rates were calculated at each time step during
the model simulation period. Simpler equations were
used for decay of  carbon species in the  surficial
segments.  BIC in  sediments was assumed to be
zero. The equations used for PDC and DOC decay
in sediments were:
                                            -
                                      ds(DOC) ~ Kds(DOC)
                                                      Q(7"-20)
                                                      °
                                     If      -If       *fl(r-2°)
                                     Kds(PDC) ~ *ds(PDC)  °
                                                (4.3.6)
                                     where

                                     kds  = segment-specific and temperature-dependent
                                           decay rate of a carbon sorbent in sediments
                                     ~k   = decay rate of a carbon sorbent in sediment at
                                           20°C
                                     T   = measured segment-specific temperature (°C)

                                     4.3.4.2 Sediment Transport

                                     The transport and fate of PCBs in natural waters and
                                     sediments are governed by the transport and fate of
                                     POC.   Based on the  data  collected  during the
                                             233

-------
LMMBP period, sediment PCB concentrations were
approximately four or five orders of magnitude higher
than water  column  PCB  concentrations.   The
interaction between the water column and surficial
sediments for POC was critical to PCB dynamics,
mass budget, and especially long-term concentration
in the lake. Therefore, it was necessary to describe
the important processes involved in vertical particle
transport, including settling velocity,  resuspension
rate, and burial rate within the LM2-Toxic. There is
no unique set of carbon solids transport rates that
could be determined without the aid of a solids tracer
compound (Thomann and Di Toro, 1983; DePinto,
1994).  Even though the burial  rate could be fixed
based on the Pb-210 core dating technique, there still
was an infinite set of settling and resuspension rates
that could close the solids mass balance for vertical
particle transport.

4.3.4.2.1 Steady-State Resuspension Calibration

The overall modeling design for the LM2-Toxic was
intended to minimize the parameters needing to be
calibrated. It was originally planned not to calibrate
any particle transport parameters,  including the
sediment resuspension rate. The original plan was
that LM3-Eutro and SEDZL  would provide carbon
sorbent settling velocities   and  estimate  POC
resuspension rates, respectively.  However, it was
not clear in late 1999 that the particle transport rates
would  be available  in  time  for  the LM2-Toxic
execution  due to the departures of key project
personnel. After a series of discussion among staff,
it was decided that a steady-state PDC mass balance
approach would be used. A similar approach was
used for the Green  Bay Mass  Balance Project
(GBMBP) (Bierman et al., 1992). This approach was
used to estimate the segment-specific sediment POC
resuspension.  In the water column,  POC = BIG +
PDC, while in the sediment, POC = PDC.

There was only one sediment layer specified in LM2-
Toxic model segmentation  (Figure  4.3.3).   The
thickness of each surficial mixing sediment segment
was estimated based on the surficial sediment mixing
layer thickness derived from the LMMBP box cores
(Robbins et al., 1999).   Figure 4.3.5 shows a
schematic of the concepts used  for the steady-state
PDC mass balance analysis.   Due to the simple
sediment segmentation within the  LM2-Toxic, the
procedure for  determining  the segment-specific
   water
                              V,
   surficial sediment
        Cs = f (O, FD, p)
Figure 4.3.5. Schematic of conceptualization for
the steady-state mass balance analysis for PDC
vertical   transport   where   4>  =   porosity
(dimensionless), FD = fraction of organic carbon,
9dv/9» P = bulk density of surficial sediments, wet
weight, g/cm3.
sediment PDC resuspension velocities was much
more simplified.   The set of PDC mass balance
equations for  each  sediment  segment  for PDC
vertical transport was reduced to only one.  The
simplified equation can be written as:
    dt
       -vb*A*Cs-kds*Vs*Cs
                 dC,
                                        (4.3.7)
At  steady-state, —p  =  0,  and  Vs =  A*z,  the
resuspension velocity can be solved as:
                                        (4.3.8)
where
Vs  =  volume of the surficial sediment segment (m3)

Cs  =  bulk concentration  of  PDC in the surficial
       sediment segment  (mg C/m3); where C =
       carbon

vs  =  PDC gross settling velocity (m/d)
                                             234

-------
A   = PDC settling area for a  surficial sediment
      segment (m2)

Cw  = effective PDC concentration in water column
      segment right above the sediment segment
      (mg C/m3); where C = carbon. Cw = CWPDC +
      (VSBIC/VSPDC)*CWBIC,  where vsBIC = BIC  gross
      settling velocity, m/d; VSPDC = vs = PDC gross
      settling  velocity,  m/d;   CWPDC  =  PDC
      concentration in the water column segment,
      mg C/m3;  CwBIC = BIC concentration in  the
      water column segment, mg C/m3.

vr  = PDC resuspension velocity (m/d)

vb  = sediment (PDC) burial  rate (m/d)

kds = PDC decay rate in sediments (d"1)

z   = thickness  of the  surficial  sediment  mixing
      layer (m)

Equation  4.3.8 was manipulated  differently  for
different sediment zones. In the areas where  the
water depth was greater than 100 m, resuspension
was considered as non-wave-induced resuspension.
The resuspension rates in these areas were directly
calculated using  Equation 4.3.8.   Although it is
arguable that there is any resuspension in the area
above which the water is deeper than 100 m (Part 1,
Chapter 4), the resuspension rate used in LM2-Toxic
was estimated. This was  because of a lack of
available sediment transport models that were well
tested for  not only  accurately computing sediment
resuspension  rate,  but also satisfying the particle
mass balance in both water column and sediments
under this kind of coarse spatial resolution used in
LM2-Toxic. After combining wave height information
for Lake Michigan estimated for the LMMBP period,
resuspension  velocities in non-depositional areas
were  computed   using  empirical  wave-induced
resuspension derived from Equation 4.3.8.  Further
detailed  data  reductions and  discussion of  the
parameters used in Equation 4.3.8 will be provided in
Chapters 4 and 5.

4.3.4.2.2 Empirical Wave-Induced Resuspension
Calculation

An equation similar to the one used in the GBMBP
(Bierman etal., 1992) was developed for LM2-Toxic
to estimate the resuspension rates in
non-depositional zones. The equation is:

vr=  a(W-Wcr)

where
                                       (4.3.9)
vr   =  estimated   daily,   segment-specific
        resuspension velocity (m/d)

W   =  segment-specific  surface  daily average
        wave height (m)

Wcr  =  segment-specific critical wave height for the
        segment below which there is no wave-
        induced resuspension (m)

cr   =  segment-specific empirical wave coefficient
        (d-1)

Due to lack of accurate ice cover information and
winter concentrations of BIC and PDC during the
LMMBP period, it was not  feasible to estimate the
segment-specific  base (W <  Wcr) resuspension
velocity (v^, i.e., non-wave-induced resuspension in
non-depositional  area)  formulated in  the original
wind-induced resuspension calculation presented in
the GBMBP report (Bierman etal., 1992). Therefore,
vr0 was removed from the original formula used in the
GBMBP report (Bierman etal., 1992). This made the
resuspension in the non-depositional areas a function
of wave heights, as shown in Equation 4.3.9.

As part of the  LMMBP, Schwab and Beletsky, using
the NOAA/Great  Lakes Environmental  Research
Laboratory (GLERL) Donelan wave model, generated
surface wave heights (Schwab and Beletsky, 1998).
These  values were  made available on  a high-
resolution grid of 5 x 5 km2 and on time scales of
one-hour.   These  data were then averaged and
aggregated on a  daily basis for  the 53 sediment
segments used in LM2-Toxic. The segment-specific
empirical wave coefficient (a) was estimated using
the following equation:
v,=
     a

(4.3.10)
                                             235

-------
where

vr   =  segment-specific average resuspension
        velocity calculated for Equation 4.3.8 (m/d)

n    =  number of days during LMMBP period

Wj   =  segment-specific daily wave height (m)

Note: The term (Wj - Wcr) in  the Equation 4.3.10
becomes zero when Wj < Wcr.

The Equation  4.3.10  was  derived  under  the
assumption that the cumulative resuspension flux
computed from Equation 4.3.9 on a daily basis for the
LMMBP period was equal to the total resuspension
flux computed for the same period from the steady-
state sediment  carbon  mass  balance in Equation
4.3.8.  The empirical wave coefficient (a) was then
formulated as:
 a =
4.3.11
      n
The segment-specific critical wave height (Wcr) was
a very crucial parameter for calculating wave-induced
resuspension flux.  The availability of field data
relating local resuspension to surface critical wave
heights was  extremely important for defining the
segment-specific  or depth-specific  critical wave
heights in this approach.  Fortunately, a relatively
good set of the wave-induced resuspension field data
(Appendix 4.3.1) was available to use with a great
deal of help from Nathan Hawley at NOAA/GLERL
(Hawley, 1999).  The data set included the critical
wave  heights required for resuspension and the
water depths at which local sediment resuspension
was observed.  These data were  based  on  30
deployments during 1994-2000 for which sediment
concentration near the bed were plotted against the
wave heights from the GLERL/Donelan wave model
(Schwab and Beletsky, 1998).  Figure 4.3.6 shows
deployment locations in Lake Michigan during this
period (Lesht and  Hawley,  1987;  Hawley, 1999;
Hawley, 2001).  The details on this method are
discussed in Lesht and Hawley (1987) and Hawley
(1999,2001). The deployment locations were limited
to the southwest region of the lake and the number of
the deployments were relatively small.  There were
         Figure 4.3.6. Locations of the 30 deployments
         between 1994 and 2000.
         indications that for similar water depths, different
         surface  critical wave heights were required to
         resuspend sediment in different regions of the lake
         (Hawley, 2001). However, the data set was the best
         available data (Appendix 4.3.1) and was applied to
         the sediment segments within the depositional area.

         Based on this  data set, a simple linear regression
         was  performed on  the  data (deployment  depth,
         critical  wave  height) for stations  at which  local
         resuspension  occurred  (Figure  4.3.7).   Visual
         examination of Figure 4.3.7 revealed that the linear
         regression line was a reasonable approximation of
                                              236

-------
                 Relation b/w Critical Wave Height and Station Depth
                         (Data provided by Nathan Hawley, NOAA, GLERL)
                                                   Vllcr = 0.0417Depth + 0.5688
                                                          R2 = 0.6ls41
                 20
40
             100
120
                       60         80

                       Station Depth (m)

resuspension observed  e  no resuspension 	Linear (resuspension observed)
140
Figure 4.3.7. Regression analysis on the data set (resuspension observed only). For the convenience
of the viewer, the data at the stations with no resuspension observed are also put on this plot.
the relationship  between water depth  and critical
wave heights.

Assuming this linear relationship between the critical
wave height and water depth, the parameterization of
the segment-specific critical wave height (Wcr) is:
Wcr =  0.0417  *Depth + 0.5688

where
           (4.3.12)
Depth =  deployment depth at  the  resuspension
         station (m)

Therefore, the daily wave-induced resuspension in
the  non-depositional  area was  calculated  using
Equation   4.3.9   given  the   segment-specific
parameters a and Wcr.

For  the  30 deployments used  to  calculate  the
relationship,  there  were  11   cases   with  no
resuspension observed.  These deployments were
either at very large depths or occurred during the
stratified period.  Previous observations (Lesht and
Hawley, 1987; Hawley,  1999;  Hawley and Lesht,
1995; Hawley and Murthy, 1995),  combined with
those listed in Appendix 4.3.1, lead to the conclusion
that resuspension events during the stratified period
are confined to shallow water regions (< 13 m) where
the epilimnion was the entire water column.  It is
arguable whether Equation 4.3.12 should be applied
to both unstratified and stratified periods.  Based on
Nathan Hawley's suggestion  (Hawley, 2001) and
data  in  Appendix  4.3.1,  separate  criteria for
unstratified and stratified periods were unnecessary.
Therefore, it was decided that the Equation 4.3.12
would be used in the wave-induced resuspension
calculation without considering seasonal variability.

4.3.4.2.3 The Sediment Bed - Semi-Lagrangian
Option

Another  important  aspect  related  to  sediment
transport was constructing the sediment bed. The
Semi-Lagrangian sediment bed option in IPX Version
2.74 (Velleux et a\., 2000) was incorporated into the
                                              237

-------
LM2-Toxic  model by  taking  advantage  of the
flexibility in  IPX, which allows a moving sediment-
water interface. Unlike the Eulerian option, the Semi-
Lagrangian  option does not allow material to move
into  or out  of the  modeled system across the
sediment bottom boundary. Therefore, the system
being modeled would not be artificially introducing or
losing mass through the sediment bed, especially for
cases when the total inventory of sediments is not
well-defined. In this option, the solids concentration
in all sediment segments was held constant. The
volume (thickness) of all surficial sediment segments
was  allowed  to  vary  from  their  initial values in
response to net settling and net resuspension fluxes.
In response to net settling (deposition), the thickness
of the surficial sediment segment increased, and the
segment  was split  into  two  vertically  adjacent
segments when its maximum thickness was reached.
In response to net resuspension  (scouring), the
thickness  of  the   surficial  sediment   segment
decreased, and the  subsurface sediment  segment
replaced  the surficial  sediment segment  when its
minimum thickness was reached.  When there was
not  enough  sediment to be  resuspended, the
resuspension  was  stopped  regardless  of the
resuspension velocity specified. Once sediment was
deposited, the resuspension was resumed in the
surficial sediment segment.

4.3.5  Chemical Balance

The  transport and  fate  of  hydrophobic  organic
chemicals such as RGBs  are closely linked to the
movement of carbon sorbents.  The approaches
used in the past to model PCBs for the Great Lakes
were based on either homolog or total PCBs.  LM2-
Toxic is a PCB congener-based model. It simulates
54 PCB congeners that accounted for roughly 70% of
the total PCB mass in Lake Michigan.  Figure 4.3.8
shows  the conceptual framework  and processes
related to PCB dynamics  in  Lake Michigan.  Four
phases were simulated in the LM2-Toxic for the
congeners. The four phases were dissolved, sorbed
to PDC, sorbed to BIG, and bound to DOC.  The
processes considered  important for PCB dynamics
in Lake Michigan water column and sediments are:
                          Conceptual Framework of Toxic Chemical Dynamics
                                                 Atmosphere
c
Advection in
Surface
Water
Layer
Tributary +
ther inflow
loading
_>. *

voiaiinzation
Deposition + Absorption
X A

bound chemical
BIG | PDC
1
DOC

T
TCDOC
A A
i
unbound
chemical
(dissolved)



Advection out
	 >
Exchange
                              Settling
                                         Net advection
      Exchange
Advection in
Sub-Water
Layers

>,.
*
I
i i
bound chemical
BIG | PDC
tl
DOC
t
Resuspension 1 [settling
II >
TTpoc unbc
^ 	 ^- chen
T^QOC (dissc
y
3iffusion
>
und
nical
>lved)
k


Diffusion
f
Advection out
	 >-
Exchange
                    Surface
                   Sediment
                      Layer
                                 bound chemical
                                  PDC     DOC
                                  f Sedimentation
 Figure 4.3.8.  Conceptual framework used by the LM2-Toxic model for PCB congeners in  Lake
 Michigan.
                                              238

-------
'   Equilibrium partitioning between dissolved phase
   and sorbed phases of PCBs.

•   Air-water exchange of dissolved PCBs including
   both volatilization and absorption.

•   External loadings of  PCBs including tributary
   loads and atmospheric wet and dry deposition.

•   Gross settling of particulate PCBs.

•   Resuspension of particulate PCBs.

•   Pore-water diffusion  of dissolved PCBs at the
   sediment-water interface.

•   Advection  across interfaces  between water
   segments.

•   Dispersion of PCBs across interfaces between
   water segments.

•   Sediment burial of PCBs.

The   following  subsections   present  detailed
descriptions of PCB equilibrium partitioning, PCB air-
water exchange, and parameters associated  with
these processes.

4.3.5.1 PCB Partitioning

Due to PCBs' hydrophobic  nature, the  partitioning
process  and the  movement  of  organic carbon
particles were  very important for describing the
transport  and fate of PCBs  in  Lake  Michigan.
Therefore, estimation of PCB partitioning coefficients
was one of the major steps to secure a successful
calibration of LM2-Toxic.

Partitioning  of a hydrophobic organic contaminant
such as PCBs in a dilute water system is, in general,
governed by the following relationship (Eadie et al.,
1990,1992; Bierman et al., 1992):
 V
where
Kp  = partitioning coefficient (L/kg)
                                      (4.3.13)
Cp  =  particle  associated  contaminant  (PCBs)
       concentration (kg/L)

m  =  sorbent (either total suspended solid, total
       suspended matter (TSM), or POC in LM2-
       Toxic concentration (kg/L)

Cd  -  dissolved PCBs concentration (kg/L)

Based on this fundamental equation, two of the many
partition theories have been widely used to describe
the distribution of PCBs in a diluted water system
such as a lake or a river.  These were a two-phase
PCB partitioning model and  a three-phase  PCB
partitioning model  (Swackhamer and Armstrong,
1987; Eadie etal., 1990,1992; Bierman etal., 1992).

In the two-phase PCB partitioning model,  PCBs is
either in dissolved phases or in a particulate phase.
The dissolved phases include both dissolved  PCBs
and PCBs sorbed to DOC.  The particulate phase is
the PCBs sorbed to POC (POC = BIC + PDC). The
partition coefficient is described as:
1/-I
A POC,a


where
             'POC
                             (4.3.14)
      'd,a
K'
  POC, or
 'POC   ~
in situ partition  coefficient for PCBs on
POC (L/kg OC)

concentration of PCBs bound to  POC
(kg/L)
[POC] =  concentration of POC (kg OC/L)
 'a,a
concentration of PCBs in an analytically
defined dissolved phase (i.e.,  dissolved
and sorbed to DOC) (kg/L)
The  two-phase model  is a very simple,  straight-
forward method and most commonly used to quickly
compute the in situ POC partition coefficients (K'POc.a)
for  PCB congeners  without  using sophisticated
statistical analysis.  The computed K'POc,a can tnen
be conveniently compared to the measured octanol-
water partition  coefficient Kow (Karickhoff,  1981;
Baker  and  Eisenreich,  1986; Swackhamer and
Armstrong, 1987; Bierman et al., 1992).
                                             239

-------
In the three-phase PCB partitioning model,  PCBs
was  distributed  between  the  dissolved,  POC
participate,  and  DOC  participate phases.   The
partition coefficients are defined as follows:
          •'DOC
 we
and
       [DOC]Cd
                                       (4.3.15)
 K"
            •'POC
   POC
         [POC]Cd
                                       (4.3.16)
where
       -  partition coefficient for  PCBs on DOC
          (L/kg OC)
K'poc  =  in situ partition coefficient for PCBs on
          POC (L/kg OC)

[DOC] =  DOC concentration (kg OC/L)

CDOC   =  concentration of  PCBs bound to DOC
          (kg/L)

Cd     -  dissolved PCB concentrations (kg/L)

The total concentration of PCBs (CT) equals the sum
of the three phases:
CT=Cd+ C
           DOC + CPOC
   = Cd(1 + KDOC[DOC} + K'POC[POC])
                                       (4.3.17)

Because of the  difficulty  of  directly  measuring
dissolved PCB concentrations (Cd), the POC (K'POc)
partition coefficients had to be estimated using either
a simple linear regression technique (Brannon etal.,
1991; Bierman et al., 1992)  or a statistical analysis
such as a combination of the Levenberg-Marquardt
nonlinear  least squares  routine and root  mean
square error algorithm (Bierman et al.', 1992). The
initial  estimation of the  K'pnr and Knnr for each
                                 XDOC
selected PCB congener (total 40 congeners) was
done  by  applying the  simple  linear regression
technique to the following equation for the LMMBP-
generated data (see Karickhoff et al., 1979 and
                                                 Bierman et al., 1992 for detailed derivation of this
                                                 equation).
                                                     •'POC
               K'
                                                                 POC
K'
                                                                                POC
The terms on the left-hand side and [DOC] on the
right-hand side are the measured concentrations for
each  water  column  sample.   The simple linear
regression was applied to the above equation to yield
a slope of KDOC/K'poc and an intercept of 1/K'POC-
From  this, both partition coefficients K'Poc anc' KDQC
were calculated.  Pairs of partition coefficients were
estimated by repeating the regression analysis for
each PCB congener selected.

After  a thorough analysis of the  PCB congener
partitioning coefficient results estimated using both
the two- and three-phase  partitioning  models, the
two-phase partitioning model was selected as the
approach to initially compute the POC  partition
coefficients (K'POc,a) that would be used in LM2-Toxic.
It was selected because 1) the two-phase partitioning
model was simple and very efficient in terms of data
analysis  procedure;  2)  though  the  regression
technique used in the three-phase partitioning model
worked well  for a well-behaved  system, it was not
applicable   to  a   natural  water  system  with
heterogeneous organic carbon concentrations, PCB
characteristics, and PCB concentrations; and 3) the
three-phase  model-estimated   PCB  congener
partitioning coefficients on DOC (KDOC) did not have
similar trends as the ones on POC (K'POc)- Previous
publications  suggested that the KDOC and K'POc °f
PCB congeners should have similar variation trends,
and that the value of KDOC should  be within  1 to 2
orders of magnitude less  than  the value of K'POc
(Carter and Suffet, 1982; Landrum et al., 1984,1987;
Hassettand  Milicic, 1985; Chiou et al., 1986,1987;
Eadie et al.,  1990,1992; Bierman et al., 1992).

4.3.5.2  PCB Air-Water Exchange

Previous studies (Endicott et al., 2005; Endicott,
2005; DePinto etal., 2003) have suggested that net
volatilization  to   the  atmosphere  may  be  the
predominant loss  mechanism  for   hydrophobic
organic contaminants such as PCBs in Great Lakes.
Therefore, it was very important to precisely compute
                                              240

-------
the net  PCB mass transfer across the water-air
interface in  order to  satisfy  the overall  PCBs
inventory and mass budget in  the Lake Michigan
system for the LMMBP period, and forecast PCB
concentrations  in  both the  water column  and
sediments with a certain degree of confidence.  The
mass change rate term (Sawj) for PCBs due to air-
water exchange processes in Equation 4.3.19  was
calculated as a product of the overall net mass
exchange flux and surface area of the water segment
'dwj
                   H
                     f)
(4.3.19)
where
 kol =  the overall mass exchange  rate coefficient
       (m/d)

 Crfn, =  dissolved  PCB  concentrations  in  water
 'dm
       (ng/m3)

 Ca =  atmospheric PCB concentrations (ng/m3)

 H' =  temperature   dependent   Henry's    Law
       Constant   for   a   PCB   congener
       (dimensionless)

 Aj  =  surface area of the water segment j (m2)

 The overall mass exchange rate coefficient (k0[) was
 calculated  using  the  Whitman  two-film theory
 formulation (Whitman, 1923) given as:
           1
                                       (4.3.20)
      k,   K,*H'
where

k,  =  the liquid film mass transfer rate coefficient
       (m/d)

kg  =  the gas film mass transfer  rate coefficient
       (m/d)

The LMMBP Atmospheric Workgroup recommended
that the Wanninkhoff (Wanninkhoff, 1992) formulation
for  water  mass  transfer  resistance  and  the
          Schwarzenbach  (Schwarzenbach  et  al.,   1993)
          formulation for gas mass transfer resistance were the
          most  appropriate  for  modeling  the  air-water
          exchange of  PCBs  in  Lake  Michigan.    The
          Wanninkhoff equation for k,, with correction for PCBs
          molecular diffusivity in reference to carbon dioxide
          (CO2) molecular  diffusivity across the  air-water
          interface, is given as:
                                                 k, =  0.45

                                                 where
                       D..
                      ''-co.
                              *u
                                                           1.64
                                                           10
                                                 (4.3.21)
                                                        =  chemical molecular diffusivity in  water
                                                           (cm2/s)
                                                   I co
                                                               molecular diffusivity in water (cm2/s)
          uw    =  wind velocity measured at  10  m above
                    water surface (m/s)

          The Schwarzenbach formulation for kg with correction
          of PCB molecular diffusivity in reference to water
          vapor  molecular diffusivity across the  air-water
          interface is given as:

                                 p.  r
             = (0.2*U10 + 0.3)*
                                Dg_H2o
          where
          Da     -  chemical  molecular  diffusivity  in  air
                    (cm2/m)

          Dg H O  =  water vapor molecular diffusivity in gas
                    phase (cm2/m)

          Another   recommendation   from  the  LMMBP
          Atmospheric Workgroup was to use the equation of
          Henry's Law Constant for PCB congeners updated
          by Bamford using recently developed data (Bamford
          et a/.,  2000).   The equation originated from  the
          Gibbs-Helmholtz equation as:
          InH' = -•
                   AH,
                      H
                                                     AS
                             H
                                                                           (4.3.23)
          Afer rearranging  the equation,  the  temperature
          dependent Henry's Law constant is given as:
                                              241

-------
 H' = e
where
         fl*r
                                       (4.3.24)
H'    =  temperature  dependent  Henry's  Law
          Constant (dimensionless)

AHH  =  the enthalpy of phase change for a PCB
          congener (kJ/mol)

ASH  =  the entropy of phase change for a PCB
          congener (kJ/mol)

R    -  the  ideal  gas constant,  8.315 x 10"3
          kJ/(mol)(°K)

T    =  interfacial temperature (°K)

These  volatilization  formulas  were  coded into
subroutines of LM2-Toxic, and the parameters H', k,,
and kg were calculated at every time step for each
LM2-Toxic segment.

4.3.5.3  PCB-Specific Parameterization

PCB congener-specific parameters input into LM2-
Toxic model included PCB congener partitioning
coefficients, molecular weight, enthalpy, and entropy.
Chapter 4 of this part provides detailed information
on  the  values used  in  the LM2-Toxic for these
parameters, their data sources, and parameterization
procedures.

4.3.6 Modification

LM2-Toxic features some important updates from
both the IPX and GBTOX models.   The project-
specific  modifications  were:

•  Incorporation of organic carbon sorbent dynamics
  used  in GBMBP (Bierman etal., 1992).

•  Organic   carbon   kinetic   processes   and
  biotransformation.

•  Incorporation of the quasi-Lagrangian framework
  for sediment transport scheme used in the IPX
  model.
• Added the Gibbs-Helmholtz formulation (Bamford
  etal., 2000) to compute the Henry's Law Constant
  for PCB congeners.

• Added the Wanninkhoff formulation for liquid film
  mass  transfer   rate   coefficient  and   the
  Schwarzenbach formulation for gas film transfer
  rate coefficient in  the PCB overall volatilization
  equations.

• Parameterization   of   segment-specific
  resuspension velocity of carbon sorbent (PDC) as
  a function of daily average wave height.

• Added  subroutines  for  performing   segment-
  specific and lake-wide mass budget calculations,
  a very useful tool to identify programming errors,
  to  identify  artificial  gain/loss  of solid/chemical
  mass to a water system, and to construct overall
  mass budget for the system simulated.

• Capability  for specifying segment-specific and
  daily water temperature time functions.

• Capability  for specifying segment-specific and
  daily air temperature time functions.

• Capability  for specifying segment-specific and
  daily wind speed time functions.

• Capability for specifying segment-specific,  PCB
  congener-specific,  and daily  atmospheric  PCB
  concentration time functions.

• Reorganization/rewrite of many subroutines and
  minor bug fixes to Euler integration. Use of utility
  libraries and organized error handling (UT library)
  in the codes. Modification to input format - using
  FIREAD.

LM2-Toxic was developed and tested on both the
Unix  and the  Linux  platforms and used  both
FORTRAN 77 and FORTRAN 90 compilers.
                                             242

-------
References

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   Shanz.  1988.  WASP4, A Hydrodynamic and
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Baker, J.E. and S.J. Eisenreich. 1986.  Influence of
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Bamford, H.A., J.H. Offenberg, R.K. Larsen, F.C. Ko,
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Bamford, H.A., D.L. Poster, and J.E. Baker.  2000.
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Carter, C.W. and I. H. Suffett. 1982. Binding of DDT
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Chiou, C.T., R.L. Malcolm, T.I. Brinton, and D.E. Kile.
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Eadie,  B.J., N.R. Morehead,  and P.P.  Landrum.
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Eadie,  B.J., N.R. Morehead, J.V.  Klump, and P.P.
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                                             243

-------
Eadie,  B.J. and S. Lozano.  1999.   Grain  Size
   Distribution of the Surface Sediments Collected
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Endicott,  D.D.  2005.  2002 Lake Michigan Mass
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Endicott,  D.D., W.L. Richardson, and  D.J. Kandt.
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Hassett, J.P. and E. Milicic. 1985. Determination of
   Equilibrium and Rate Constants for  Binding of a
   Polychlorinated Biphenyl Congener by Dissolved
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Hawley,  N. and B.M.  Lesht. 1995.   Does Local
   Resuspension  Maintain  the  Benthic Boundary
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   65:69-76.

Hawley, N. and C.R. Murthy.  1995.  The Response
   of the Benthic Nepheloid Layer to a Downwelling
   Event. J. Great Lakes Res., 21(4):641-651.
Hawley, N.  1999.  Sediment  Resuspension and
   Transport in Lake Michigan.  Final Report. U.S.
   Environmental  Protection  Agency,  Office of
   Research and Development, ERL-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   240 pp.

Hawley, N.  2001. Critical Wave Heights. National
   Oceanic and Atmospheric Administration, Great
   Lakes Environmental Research Laboratory, Ann
   Arbor, Michigan.  E-mail sent to Xiaomi Zhang on
   February 1, 2001 (Appendices 4.3.1 and 4.3.2).

Karickhoff, S.W.,  D.S. Brown, and T.A. Scott. 1979.
   Sorption  of Hydrophobic Pollutants  on Natural
   Sediments. Water Res.,  13(3):241-248.

Karickhoff, S.W. 1981. Semi-Empirical Estimation of
   Hydrophobic  Pollutants on  Natural  Sediments
   and Soils. Chemosphere, 10(8):833-846.

Landrum, P.F., S.R. Nihart, B.J. Eadie,  and  W.S.
   Gardner.   1984.   Reverse-Phase Separation
   Method for  Determining  Pollutant  Binding to
   Aldrich  Humic  Acid and Dissolved Organic
   Carbon   of  Natural  Waters.   Environ. Sci.
   Technol., 18(3):187-192.

Landrum, P.F., S.R. Nihart, B.J. Eadie, and LR.
   Herche.  1987.  Reduction  in Bioavailability of
   Organic  Contaminants  to  the  Amphipod
   Pontoporeia hoyi by Dissolved Organic Matter of
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   Chem., 6(1 ):11-20.

Lesht, B.M.  and  N.  Hawley.  1987.  Near-Bottom
   Currents   and   Suspended  Sediment
   Concentrations in Southeastern Lake Michigan.
   J. Great Lakes Res., 13(3):375-386.

Quinn,  F.H.   1977.  Annual and Seasonal  Flow
   Variations Through the Straits  of   Mackinac.
   Water Resources Res., 13(1):137-144.
                                             244

-------
Bobbins,  J.A.,  N.R.  Morehead, R.W.  Rood, D.N.
   Edgington, and S. Meyer.  1999. Accumulation
   and Near-Surface Mixing of Sediments in Lake
   Michigan as Determined for the Lake Michigan
   Mass Balance Program,  Volumes 1  and 2.
   Project Report.  U.S. Environmental Protection
   Agency, Office of Research and Development,
   ERL-Duluth, Large  Lakes Research  Station,
   Grosse He, Michigan. 503 pp.

Schwab,  D.J. and D. Beletsky.  1997.  Modeling
   Thermal  Structure  and  Circulation  in  Lake
   Michigan.  In: Estuarine and Coastal Modeling,
   pp. 511-522. Proceedings of the 5th International
   Conference  of the  American  Society  of Civil
   Engineers, Alexandria, Virginia.  October 22-24,
   1997.

Schwab, D. and D. Beletsky. 1998. Lake Michigan
   Mass Balance Study: Hydrodynamic Modeling
   Project.  National Oceanic and  Atmospheric
   Administration,  Great  Lakes  Environmental
   Research  Laboratory,  Ann Arbor,  Michigan.
   NOAATechnical Memorandum ERLGLERL-108,
   53pp.

Schwarzenbach, R.P., P.M. Gschwend, and D.M.
   Imboden.    1993.   Environmental  Organic
   Chemistry.  John  Wiley and Sons, Incorporated,
   New York, New York. 681 pp.

Settles, M.  1997.  IPX: A Review of Planning and
   Development.  U.S. Environmental Protection
   Agency, Office of Research and Development,
   ERL-Duluth, Large  Lakes Research  Station,
   Grosse lie, Michigan. Technical Note TEC-16,11
   PP.

Swackhamer, D.L. and D.E.  Armstrong.   1987.
   Distribution and Characterization of PCBs in Lake
   Michigan Water.  J. Great Lakes Res., 13(1):24-
   36.

Thomann, R.V. and D.M. Di Toro.  1983.  Physico-
   Chemical Model of Toxic  Substances in the Great
   Lakes. J. Great Lakes Res., 9(4):474-496.

Thomann, R.V. and J.V.Mueller. 1987.  Principles of
   Water Quality Modeling and Control. Harper and
   Row Publishers, New York, New York.  644 pp.
U.S. Environmental Protection Agency.  1997.  Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume 1: Sample Collection
   Techniques.   U.S.  Environmental Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois. EPA/905/R-97/012a, 1,440pp.

Velleux, M., S. Westenbroek, J. Ruppel, M. Settles,
   and  D.D. Endicott.  2000.  A User's Guide to
   IPX, The In-Place Pollutant Export Water Quality
   Modeling  Framework, Version  2.7.4.    U.S.
   Environmental  Protection  Agency, Office  of
   Research and Development, National Health and
   Environmental  Effects  Research  Laboratory,
   MED-Duluth,  Large  Lakes Research Station,
   Grosse He, Michigan.  179 pp.

Wanninkhoff, R., J.R. Ledwell,  and J. Crusius.  1991.
   Gas Transfer Velocities on Lakes Measured with
   Sulfur Hexafluoride.  In: S.C. Wilhelm and J.S.
   Gulliver (Eds.), Air-Water Mass Transfer, pp.441-
   458. American Society of Civil Engineers, New
   York, New York.

Wanninkhoff, R.J. 1992. Relationship Between Gas
   Exchange and Wind Speed Over the Ocean. J.
   Geophys. Res., 97:7373-7381.

Whitman, W.G.  1923.  A Preliminary Experimental
   Confirmation of the Two-Film Theory of Gas
   Absorption. Chem. Metall. Eng., 29:146-148.

Zhang, X., D. Endicott, and W. Richardson.  1998.
   Transport Calibration Model With Level 2 Model
   Segmentation Scheme.   First  Lake  Michigan
   Mass Balance Project Science Panel Review,
   Southgate, Michigan.  June 23,1998.  12pp.

Zhang, X., W. Richardson, and K. Rygwelski.  2000.
   Preparation and Verification Transport Field for
   LMMBP  Level 2 Contaminant: Transport and
   Fate Models.   Second  Lake Michigan   Mass
   Balance   Project  Science   Panel   Review,
   Southgate, Michigan.  September 27, 2000.  15
   pp.
                                             245

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                             PART 4

                           LM2-TOXIC
Appendix 4.3.1. Lake Michigan Resuspension Field Data Set (One of the Two
       Attachments in Nathan Hawley's E-Mail on February 2, 2001)
Station
Musk Wl
M24
M27
M19
Musk Wl
Musk Wl
M24
M27
M19
Musk Wl
M24
M27
M19
Leshtl
Lesht2
LeshtS
MWI
M24
M27
W1
W2
W3
S Haven
MO4
MO9
M11
MWI
MWI
M25
M55
Deployed
01.11.1994
01.11.1994
01.11.1994
01.11.1994
25.05.1995
12.07.1995
12.07.1995
12.07.1995
12.07.1995
31.08.1995
31.08.1995
31.08.1995
31.08.1995
02.04.1998
23.07.1998
28.10.1998
24.07.1998
24.07.1998
24.07.1998
15.10.1998
15.10.1998
27.10.1998
15.10.1999
03.03.2000
03.03.2000
03.03.2000
07.04.2000
13.09.2000
13.09.2000
13.09.2000
Retrieved
24.05.1995
24.05.1995
25.05.1995
24.05.1995
12.07.1995
21.08.1995
21.08.1995
21.08.1995
21.08.1995
17.11.1995
17.11.1995
12.10.1995
12.10.1995
30.04.1998
24.08.1998
01.12.1998
13.08.1998
13.08.1998
13.08.1998
11.11.1999
20.04.1999
10.05.1999
17.11.1999
22.05.2000
22.05.2000
22.05.2000
29.05.2000
30.10.2000
30.10.2000
27.11.2000
Latitude
4312.30'N
4313.75'N
43 09.50'N
42 02.93'N
4312.30'N
43 12.30'N
4313.75'N
43 09.50'N
43 02.93'N
43 12.30'N
43 13.75'N
43 09.50'N
43 02.93'N
42 39.90'N
42 52.22'N
4252.18'N
43 12.32'N
4311.33'N
43 10.04'N
42 08.09'N
41 44.1 4'N
42 57.50'N
42 24.23'N
41 55.58'N
42 14.87'N
42 17.36'N
43 12.21'N
43 12.23'N
43 12.24'N
43 12/73'N
Longitude
86 20.83'W
86 25.46'W
85 25.87'W
86 38.57'W
86 20.83'W
86 20.83'W
86 25.46'W
86 25.87'W
86 38.57'W
86 20.83'W
86 25.46'W
86 25.87'W
86 38.57'W
87 44.89'W
87 42.41 'W
87 42.41 'W
86 20.44'W
86 22.76'W
86 25.87'W
86 29.50'W
86 54.45'W
87 48.79'W
86 19.68'W
86 39.92'W
86 24.74'W
86 30.60'W
86 21 .OO'W
8621.32'W
86 22.90'W
86 28.65'W
Depth
13
28
58
100
13
13
28
58
100
13
28
58
100
15
25
25
14
30
60
10
10
16
18
20
18
38
15
17
26
55
Wave Height
0.7
1
3.1
>4.5
1
>1.5
">1,5"
>1.5
>1.6
0.8
1.7
>1.7
>3.3
1.5
>1.7
2.6
>1.4
>1.4
>1.4
1
0.8
2
.4
.1

2

.6
2
2.7
Comments



No resuspension

No resuspension
No resuspension
No resuspension
No resuspension


No resuspension
No resuspension

No resuspension

No resuspension
No resuspension
No resuspension











                               246

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                                          PART 4

                                       LM2-TOXIC
 Appendix 4.3.2. Notes (One of the Two Attachments in Nathan Hawley's E-Mail on
      February 2, 2001) From Nathan Hawley on the Data Set in Appendix 4.3.1


Dear Xiaomi:

As you requested, I am attaching the spreadsheet with the critical wave heights required for resuspension.
These were determined for the thirty deployments by plotting the sediment concentration near the bed against
the wave heights from the GLERL wave model. This  method was used by Barry and I in our paper analyzing
a data set from 1981  (Lesht and Hawley, 1987, Journal of Great Lakes Research, v. 13, 375-386, see fig 6
for an example).  In the present case this method assumes that a) high sediment concentrations are caused
only by local resuspension, and b) that local resuspension is caused mainly by wave action.  If both these
assumptions are true then high sediment concentrations will occur only when the wave height exceeds a
certain value. For the thirty deployments listed in the spreadsheet, these assumptions appear to hold in about
1/3 of the cases. In another 1/3 of the cases no resuspension occurred at all, in these cases the maximum
waves during the deployment can be used as a lower bound for the critical height (the  height required for
resuspension must exceed the height listed). These deployments were either at a very large depth (M19,
100m) or occurred during the stratified period. In the  remaining cases there was no clear  critical wave height
but resuspension did occur. In  these cases, I determined the wave height by visually examining the time
series observations of concentration and wave height and  then estimating the critical height as best I could.
In most cases the results aren't totally consistent (there are instances where waves larger than the critical
height do not correlate with increased sediment concentrations), but I did the best I could.

We might do a bit better if we  used  the combined (waves plus currents) bottom stress as the  forcing
parameter, but this depends upon the wave period as well,  and the wave model doesn't do a real good job of
calculating the wave period.

If you look at the data carefully, there are indications  that for similar water depths larger waves are required
to resuspend sediment on the western side of the lake than on the eastern side. There is also some indication
that the sediment properties at a given location vary somewhat throughout the year, but I don't think that there
is enough data to say anything more. I did a rough plot of  the data and fitted a straight line by eye.  My line
suggests that a wave height of about 4.8 m would be required to resuspend sediment at 100m.
                                            247

-------
                                          PART 4

                                       LM2-TOXIC
Chapter 4. Model and Field Data

The inputs necessary for running LM2-Toxic came
from two major sources. One was the Lake Michigan
Mass  Balance Project  (LMMBP)-generated data
collected between 1994 and  1995.  Another was
historical data.   These  were  primarily  physical,
chemical, and biotransformation data.  Most of the
parameters initially selected for the LM2-Toxic input
were from the Green Bay Mass Balance  Project
(GBMBP)  (DePinto  et  al.,  1993).   All  project-
generated data were subjected to quality assurance
(QA)/quality control (QC) coordinated by the United
States Environmental Protection Agency (USEPA),
Great Lakes  National Program Office (GLNPO)
(McCarty et al.,  2004).   Detailed  descriptions of
sampling collection and sample analysis techniques
can be found in the  LMMB Methods Compendium
(U.S.  Environmental Protection Agency,   1997).
Further  analysis  and preprocessing of these data
followed the guidance detailed in the LMMBP Quality
Assurance  Project Plan (QAPP) for Mathematical
Modeling (Richardson et al., 2004).  Descriptions of
model input, data analysis procedures to generate
the model inputs, and sources of data are described
in the following sections.

4.4.1  Water Transport

Advective flows and  bulk dispersion between water
segments  drive  the transport  of all  constituents
among water column segments of  Lake Michigan.
Water transport data was among the major  critical
inputs for the LM2-Toxic. Water transport fields and
associated data sources were provided in Chapter 3.
The   following   subsections  provide  additional
information on the transport field.

4.4.1.1 Circulation

The advective components of water transport in the
LM2-Toxic input consisted of bi-directional horizontal
flows,  net  vertical  flows, balancing flows,  and
tributary  and boundary flows.   Other than the
tributary and boundary flows, all of the flows were
expressed as daily time series for the model.  Bi-
direction horizontal flows and net vertical flows were
aggregated and  provided by Dr. David Schwab
(National Oceanic and Atmospheric Administration
[NOAA],  Great  Lakes  Environmental  Research
Laboratory [GLERL]) based on the 5 x 5 km2 grid
results from the  Princeton Ocean Model for Great
Lakes (POMGL, Schwab and Beletsky,  1998).
Balancing flows  were computed at  each interface
based on  the   residual  flows  for  the  adjacent
segments. The horizontal and vertical flows provided
by Dr. David Schwab were 10  days short of the
complete two-year LMMBP period.  For consistency
with other model  input time series and convenience
to  conduct  long-term  management  scenario
simulations, a complete two-year time function for
advective flows was preferred and necessary. To
avoid unnecessary complication, the flows of the last
10 days of 1994 were duplicated at the end of 1995.

Ten tributary flows (Figure 4.4.1) were input into the
model input as annual averaged flows.  Tributary
flows are listed in Table 4.4.1. The boundary flows
across the Straits of Mackinac were input into the
LM2-Toxic as monthly average flows.  The lower
returning  flows  at the  boundary  entered  from
segments 15 and  16 and were routed out from
                                             248

-------
                                          Manistique
                     Menominee River
                      Milwaukee
                          River
                                                   uskegon River
                                                     rand River
                       Sheboygan
                           Rive
                                                    alamazoo River
                                                  Joseph River
Figure 4.4.1. Locations of 10 tributaries whose flows were considered a part of the water transport
used in the LM2-Toxic.
Table 4.4.1. Average Annual Flows of the 10 Monitored Tributaries
Tributary Name
Milwaukee River
Sheboygan River
Fox River
Menominee River
Manistique River
Pere Marquette River
Muskegon River
Flow (m3/s)
19
11
199
166
84
30
98
                                             249

-------
(Figures 4.3.1 and 4.3.3).  Table 4.4.2 presents the
monthly boundary flows (i.e., the lower returning
flows). Both the tributary and boundary flows were
assembled based on the flows used in MICHTOX
(Endicottefa/., 2005).

4.4.1.2 Vertical Dispersion

Another major component of the water transport field
defined/used in the LM2-Toxic input is the vertical
dispersion.   In order to reasonably estimate the
vertical  exchange  coefficients for the interfaces
between  vertically  adjacent segments, a thermal
balance  model  with the  same   Level 2 spatial
segmentation was used to simulate temperature
using   LMMBP-generated   temperature  field
measurements. Instead of chloride, temperature was
the state variable being simulated. The same data
reduction and interpolation procedures used  for
chloride were used for temperature. Table 4.4.3 lists
the initial temperature for 41 segments in the thermal
balance model inputs. Table 4.4.4 contains cruise-
segment mean  temperatures generated  using
inverse distance weighted interpolated matrix (IDW)
and volume-weighted average  (VWA). The values
listed in Table 4.4.4 were used for comparison with
the thermal balance model  output.   Heat fluxes
provided by  Dr.  David   Schwab (Schwab and
Beletsky, 1998) were input as loads in the model.
The initial estimation of vertical exchange coefficients
was computed using a simple equation (Chapra and
Reckhow, 1983) presented below.
     V*(dT,/dt)

 /f= At*(Tu-T,)
 Et=vt*zt
where
(4.4.1)


(4.4.2)
 v,  =  vertical heat exchange coefficient across the
       interface between upper segment and lower
       segment (m/d)

 V  =  average volume of the upper  and lower
       segments (m3)

 7",  =  temperature in lower segment (°C)

 7"0  =  temperature in upper segment (°C)
At =   surface area of the interface between upper
       segment and lower segment (m2)

t  =   time, d - days

£, =   vertical exchange coefficient (m2/d)

z, =   mixing  length for  the  upper segment  and
       lower segment (m)

Following a substantial calibration effort, the final set
of vertical dispersion coefficients was determined.
The calibration results of the thermal balance model
were very good.  Further discussion on the results
will be provided  in Chapter  5.   The  complete
calibration results from the thermal balance model
and the final set of vertical dispersion coefficient plots
are presented in Appendix 4.5.1.

4.4.1.3 Verification of Water Transport Fields

The inputs for the water transport field used in the
LM2-Toxic were composed  of advective flows  and
vertical dispersion.  The water transport field was
verified by simulating chloride,  a  conservative
constituent, in Lake Michigan for the LMMBP period.
In addition to the data related to water transport field,
chloride tributary loads and  segment-specific initial
chloride concentrations were the  only necessary
input data to run the chloride model. Daily chloride
tributary loads for the complete two-year period from
11  monitored  tributaries  (Figure 4.4.2)  were
calculated using the stratified Beale ratio model (Hall
and Robertson,  1998;  Beale,  1962; Baun,  1982;
Cohn   et  aL,  1989; Richards, 1998).   The  18
unmonitored tributary loads  were not input into the
chloride model because they were not available by
the time (June 1999) model executions were finished.

Eight field sampling cruises  occurred between April
1994 and October 1995 at 41 water survey stations
throughout Lake Michigan,  Green  Bay, and Lake
Huron (Figure 4.4.3). The eight field cruise sampling
periods are listed  in  Table 4.4.5.  Multiple field
samples were collected at different depths at most of
the water survey stations.  Some  of the samples
were duplicates. The segment-specific chloride initial
conditions (Table 4.4.6) and eight  cruise-segment
mean  chloride concentrations (Table  4.4.7) were
generated  using an interpolation  procedure  that
combined the  IDW interpolation formulation and a
                                              250

-------
Table 4.4.2. Monthly Average Flows Across the Straits of Mackinac


                        Lower Return Flow (m3/s) From      Lower Return Flow (m3/s) From
        Month	Segment 16                      Segment 15
January
February
March
April
May
June
July
August
September
October
November
December
40
452
0
0
0
1500
3000
2000
1000
0
0
0
36
400
0
0
0
1034
2096
1726
989
0
0
0
 Table 4.4.3. Initial Temperatures in Water Column Segments for the Thermal Balance Model


       Segment           Temperature (°C)          Segment          Temperature (°C)
1 2.01
2 2.01
3 1.99
4 1.99
5 2.00
6 2.00
7 1.99
8 1.99
9 2.01
10 2.05
11 2.01
12 2.01
13 2.00
14 2.00
15 2.01
16 2.01
17 2.02
18 2.02
19 2.03



20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
2.01
2.01
2.00
2.00
2.01
2.01
2.02
2.03
2.04
2.01
2.01
2.01
2.01
2.01
2.01
2.01
2.50
2.50
2.50
2.50
2.00
2.00
                                          251

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Table 4.4.4.
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cruise-Segment Mean Temperatures
Cruise 1
3.93
4.51
2.54
2.64
1.99
1.93
2.70
2.78
4.74
4.65
3.56
3.77
2.49
2.57
2.01
1.90
2.71
2.83
4.54
3.23
3.41
2.54
2.59
2.10
1.93
2.66
2.79
4.30
2.94
2.93
2.20
2.33
2.06
1.86
2.52
2.68
2.72
2.55
2.62
2.14
1.96
Cruise 2
18.95
19.02
16.29
15.28
18.02
17.86
15.61
16.06
17.07
17.03
16.28
16.26
13.50
12.25
16.47
16.17
12.41
13.19
12.20
8.23
8.89
7.63
7.13
11.08
10.05
8.15
9.00
8.82
5.40
5.64
5.41
5.04
7.60
6.39
8.65
4.13
4.05
4.04
4.03
5.31
4.51
Cruise 3
21.04
23.22
21.16
21.27
21.20
21.17
21.83
22.12
21.53
21.48
10.54
13.70
14.71
13.95
14.47
14.36
14.89
14.79
15.00
6.34
6.36
8.03
7.31
8.00
7.80
9.49
9.56
9.43
5.44
5.20
6.16
5.67
5.69
5.58
6.75
4.11
4.12
4.32
4.23
4.25
4.19
for the LMMBP Project Period
Cruise 4
2.82
3.14
2.96
2.93
2.94
2.94
0.00
0.00
0.00
0.00
2.86
3.15
2.96
2.93
2.94
2.94
0.00
0.00
0.00
2.92
3.17
2.96
2.93
2.95
2.94
0.00
0.00
0.00
3.43
3.80
3.75
3.81
3.68
3.70
0.00
4.13
4.12
4.13
4.13
4.12
4.12
Cruise 6
2.37
2.36
2.30
2.50
2.05
2.01
1.56
1.48
1.26
1.26
2.38
2.38
2.31
2.51
2.06
2.00
1.57
1.48
1.27
2.35
2.34
2.33
2.53
2.06
1.98
1.59
1.49
1.29
2.34
2.24
2.40
2.57
2.08
1.88
1.39
2.42
2.40
2.58
2.59
2.01
1.80
Cruise 7
11.32
12.13
11.01
12.18
12.59
12.92
12.45
12.84
13.39
13.33
10.76
11.91
10.36
11.75
12.40
12.67
11.57
12.16
12.87
9.79
11.27
8.70
10.49
11.78
11.80
9.01
9.64
11.60
6.65
7.13
5.77
5.95
9.17
7.21
7.32
4.18
4.14
4.19
4.15
5.62
4.89
Cruise 8
15.22
15.25
15.57
14.90
17.07
16.80
16.58
16.68
16.72
16.72
13.74
14.77
14.78
14.34
16.99
16.62
16.40
16.57
16.63
8.90
10.37
11.77
11.80
14.49
13.61
13.02
13.46
14.64
5.14
5.08
6.93
6.91
9.18
7.44
8.59
4.07
4.05
4.18
4.10
5.12
4.78
252

-------
                          \A/U> *• u  Sturgeon
                          Whitefishx    a   t
                                                 Millecoquins
                        Escanaba
         Menominee River
               Peshtiga
              Oconto  "~
          Pensaukee;
             Manitowac
           Sheboygan
                River
          Milwaukee
               River
 Manistee
Pere Marquette River
                                            uskegon River
                                            Grand River
                                             alamazoo River
                                             \
                                           Black

                                        St. Joseph River
                                         • Monitored tributaries
                                         O Unmonitored tributaries
Figure 4.4.2.  Locations of monitored and unmonitored tributaries during the LMMBP.
                                        253

-------
                                     Q
    Menominee River
      Milwaukee
          River
                                     Pere Marquette River
      Sheboyga
           River
                                       Kalamazoo River
                                St. Joseph River
Figure 4.4.3.  Lake Michigan water sampling sites during the LMMBP.
                                          254

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Table 4.4.5. The LMMBP Sampling Cruises
        Cruise Number	Start Date	End Date

           Cruise 1                     April 24, 1994                  May 11, 1994
           Cruise 2                     June 17, 1994                  June 26, 1994
           Cruise 3                     August 3, 1994                August 26, 1994
           Cruise 4                   October 14, 1994              November 7, 1994
           Cruise 5                   January 16, 1995               January 26, 1995
           Cruise 6                     March 23, 1995                 April 18, 1995
           Cruise 7                     August 3, 1995                August 16,1995
           Cruise 8                  September 16, 1995              October 13, 1995
 Table 4.4.6. Initial Chloride Concentrations in Water Column Segments for the Chloride Model

       Segment            Chloride (mg/L)           Segment           Chloride (mg/L)
1 10.39
2 10.78
3 10.25
4 10.33
5 9.93
6 9.94
7 10.55
8 10.65
9 12.66
10 12.55
11 10.38
12 10.68
13 10.24
14 10.32
15 9.82
16 9.80
17 10.56
18 10.67
19 12.51



20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
10.17
10.18
10.21
10.21
10.08
9.82
10.45
10.49
10.40
10.15
10.14
10.21
10.19
10.26
10.23
10.51
10.17
10.18
10.17
10.16
10.12
10.09
                                            255

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Table 4.4.7. Cruise-Segment Mean Chloride Concentrations for the LMMBP Period
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cruise 1
10.39
10.78
10.25
10.33
9.93
9.94
10.55
10.65
12.66
12.55
10.38
10.68
10.24
10.32
9.82
9.80
10.56
10.67
12.51
10.17
10.18
10.21
10.21
10.08
9.82
10.45
10.49
10.40
10.15
10.14
10.21
10.19
10.26
10.23
10.51
10.17
10.18
10.17
10.16
10.12
10.09
Cruise 2
10.25
10.46
10.19
10.20
10.22
10.23
10.20
10.20
10.19
10.19
9.98
10.24
10.19
10.20
10.23
10.24
10.20
10.20
10.19
10.13
10.16
10.16
10.14
10.17
10.16
10.17
10.17
10.18
10.12
10.16
10.13
10.12
10.13
10.13
10.13
9.99
10.00
10.12
10.11
10.13
10.12
Cruise 3
10.47
10.57
10.36
10.44
10.12
10.28
10.63
10.74
11.61
11.56
10.36
10.46
10.36
10.36
10.42
10.44
10.53
10.53
11.18
10.27
10.41
10.24
10.25
9.91
9.45
10.40
10.40
10.40
10.24
10.31
10.23
10.23
10.08
10.17
10.14
10.19
10.24
10.25
10.25
10.10
10.15
Cruise 4
10.45
10.55
10.23
10.30
9.79
9.86
10.45
10.51
11.09
11.05
10.43
10.58
10.26
10.32
9.74
9.80
10.43
10.49
11.01
10.39
10.58
10.23
10.31
9.76
9.41
10.39
10.48
10.90
10.29
10.28
10.18
10.18
10.05
10.14
10.22
10.19
10.24
10.19
10.20
10.14
10.17
Cruise 6
10.30
10.61
10.24
10.27
9.99
10.01
10.44
10.49
11.54
11.52
10.51
11.11
10.30
10.37
9.95
10.02
10.47
10.54
11.51
10.56
11.17
10.37
10.42
10.16
10.11
10.34
10.39
10.30
10.14
10.13
10.24
10.25
10.14
10.17
10.41
10.09
10.05
10.16
10.13
10.08
10.04
Cruise 7
10.46
10.38
10.24
10.27
10.16
10.16
10.47
10.56
10.38
10.36
10.15
10.14
10.07
9.98
8.34
8.33
10.17
10.18
10.15
10.17
10.14
10.03
10.00
8.61
8.27
9.99
10.02
10.12
10.20
10.19
10.40
10.35
10.50
10.45
10.49
10.22
10.19
10.37
10.35
10.45
10.42
Cruise 8
10.40
10.47
10.36
10.39
10.22
10.27
10.53
10.58
10.94
10.92
10.34
10.46
10.33
10.36
10.16
10.20
10.53
10.59
10.91
10.23
10.32
10.26
10.32
10.03
9.90
10.36
10.45
10.87
10.21
10.24
10.26
10.28
10.31
10.29
10.36
10.15
10.17
10.26
10.25
10.31
10.31
                                         256

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VWA  algorithm  (Appendix 4.4.1).    The major
advantages of this interpolation approach were its
convenience and effectiveness. The subroutines/-
programs of IDW and VWA are widely available in
many major software packages.  On the other hand,
the  conventional  approach  of  calculating  the
arithmetic mean and standard deviation of actual field
measurements is constrained by a data theory  that
is only valid for a randomly sampled data set with
normal  distribution.    Most  environmental field
samples, including water quality  samples, were
collected  based  on  biased  sampling  designs.
Appendix 4.4.1  and publications (Lesht,  1988a,  b;
Bierman et al., 1992)  provided further details on the
interpolation approach.   Lake  Michigan chloride
distribution  was  simulated  once  for the LMMBP
period  without adjustment  on any parameter  or
coefficient.  The results were very good.  Further
details are presented  in Chapter 5. Appendix 4.5.4
provides the complete simulation  results from the
chloride model.

4.4.2  Organic Carbon

Three carbon sorbents were simulated as principal
state  variables in the  LM2-Toxic.   They were
dissolved organic carbon (DOC), biotic carbon (BIC),
and  particulate  detrital  carbon (PDC).   Measured
data used for directly generating carbon  sorbent
concentrations and estimating their loadings were
DOC and particulate  organic carbon (POC). POC
was then divided into  BIC and PDC by first applying
a fixed carbon/chlorophyll a ratio (C:Chl) to estimate
BIC  (BIC is  equal to  the product  of  the ratio  and
measured  chlorophyll a).  PDC was calculated by
subtracting BIC from POC. Negative values were not
allowed during the derivation. A final value (see Part
2, LM3-Eutro) Pauer  et al., 2005) was selected for
splitting BIC  and  PDC from  POC.  DOC was
characterized as colloidal-sized particles that did not
settle.  It was assumed that BIC did not accumulate
in sediment.  Instead, BIC settling to the sediment
surface would instantly convert to  PDC.  DOC  had
very   little   variation   spatially  and temporally
throughout the lake. The concentration of DOC was
about five times higher than POC (BIC + PDC). The
typical  concentration  of DOC in the  lake was 1.6
mg/L BIC was strongly controlled  by seasonal and
local   phytoplankton   blooms  and  associated
dynamics.  The typical range of BIC concentrations
for the lake was between 0.05 and  0.25 mg/L. PDC
in the lake was highly controlled by phytoplankton
decomposition  and  settling  and  resuspension
processes.  Therefore, the variation of PDC in the
lake was also seasonally and spatially characterized.
The typical range of PDC concentrations varied from
0.1   to  0.25  mg/L  in  Lake  Michigan.    The
concentration of PDC was doubled or tripled in Green
Bay where  carbon  loads from  tributaries and
resuspension  fluxes   were   more  dominant
components than in the main lake.  The average
PDC concentration range  in sediments was from
2,000 to 7,000 mg/L.

4.4.2.1 Loads

Loads of organic carbon sorbents were classified as
external and internal. External loads entered the lake
from tributaries. Internal loads were generated from
gross primary production  of phytoplankton within
Lake Michigan.

Segment-specific  primary   production  was
aggregated from the high-resolution eutrophication
model LM3-Eutro. The gross primary production had
strong seasonal variations.  Figure 4.4.4 shows the
seasonal fluctuation  of gross primary  production
generated from the LM3-Eutro for the lake during the
LMMBP period. By assuming 20% of gross  primary
production released from phytoplankton was DOC,
the internal loads were then further divided into BIC
and DOC (Bierman era/., 1992).
5
^1

o
'•8

1
Q.

to
   1.60E+07
   1.40E+07
   1.20E+07
   1.00E+07
   8.00E+06
   6.00E+06
   4.00E+06
   2.00E+06
        0
  Lake Michigan Primary Production
        Jan94
Jun94
                         Dec94
                                  Jun95
Nov95
Figure 4.4.4. Primary production generated from
the LM3-Eutro for Lake Michigan, including Green
Bay.

External  loads  provided  by  the  United  States
Geological Survey  (USGS) included loads from 11
monitored tributaries and 18 unmonitored tributaries
(Hall and Robertson, 1998).  Tributary loads were
                                              257

-------
received as DOC, POC, and chlorophyll loads.  A
carbon/chlorophyll ratio of 40 was then used to split
POC loads into BIC and PDC loads.

Both external and internal loads were input into the
LM2-Toxic as daily time functions. Summaries of the
annual average organic carbon external and internal
loads are  presented  in Tables 4.4.8, 4.4.9, and
4.4.10. During the two-year LMMBP period, virtually
all of BIC load (98.6%) came from internal primary
production, virtually all of the POC load (POC = BIC
+  PDC,  97.3%) came   from   internal  primary
production,  and 88%  of total organic carbon load
(TOC = DOC +  BIC  + PDC) came from internal
primary production. Therefore, the internal primary
production dictates the level of all three organic
carbon concentrations in the lake, and thus, it also
influences observed polychlorinated biphenyl (PCB)
concentrations in the system, including sediments.

4.4.2.2 Field Data and Initial Conditions

By including  some  field  data  (sediment  data)
collected during the GBMBP, the LMMBP-generated
data were used to generate segment-specific water
column   initial  concentrations,   sediment
concentrations,   and   cruise-segment  mean
concentrations for the three organic carbon sorbents
simulated  in the LM2-Toxic.  Samples for water
column DOC and POC were collected at 41 water
survey stations (Figure 4.4.3) between April 1994
and October 1995. The original  POC cruise data
were split into BIC and PDC by applying a carbon
and  chlorophyll a ratio of 40.   Both the inverse
distance and natural-neighbor algorithms (Appendix
4.4.1) were used for interpolation of all three organic
carbon concentrations in the water column. In order
to choose the best interpolation for the water column
carbon data set, the results from both interpolation
methods  were compared.   The  two  interpolation
methods gave quite different results when there were
not enough samples and poor spatial resolution. The
inverse distance algorithm was  selected for the
interpolation of the organic carbon sorbents because
the interpolation results using the inverse distance
algorithm  showed more  realistic organic  carbon
distributions in the water column than the ones from
the  natural-neighbor  algorithm.    Table  4.4.11
presents the water column initial concentrations of all
three organic sorbent state variables used  in the
LM2-Toxic.     The  cruise-segment  mean
concentrations for DOC, BIC, and PDC are listed in
Tables 4.4.12, 4.4.13, and 4.4.14, respectively.

Sediments  were collected  during the  LMMBP
sampling period  of 1994 and 1995, and sediment
organic carbon data were collected and analyzed
(Eadie and Bobbins, 2004). A total of 116 sediment
samples (Figure  4.4.5) were used to generate
sediment  segment-specific   organic  carbon
concentrations, including 53 box core samples, 60
Ponar grab samples, and three gravity core samples.
Among these sediment samples, only four stations
(15B1,  41B1, 86B1,  and 112B1) had complete
vertical profile analysis done for organic carbon; the
rest of the stations had only the top 1 cm of sediment
analyzed. The reported sediment  organic carbon
(POC)  concentrations were reported as mg carbon
per g dry sediment (mg/gdw). In order to convert the
unit (mg/gdw) into the standard unit (mg/L, i.e., bulk
concentration) used  in LM2-Toxic, the following
equation was used for the calculation:
                    FD *
                            103
(4.4.3)
where
sPOC
sPOC  -
          POC concentration in surficial sediments
          (mg/L), the unit required for the LM2-
          Toxic

          POC concentration in surficial sediments
          (mg/gdw), the unit of field measurements

          bulk density of surficial sediments,  wet
          weight (g/cm3).   To convert cm3 to  L
          (liter), a factor of 103 is needed  in the
          equation.
       = fraction of organic carbon (gdv/g)
The sediment POC data from  the  GBMBP  were
incorporated  into the  data set to  generate the
sediment  POC contour maps for Lake  Michigan,
including Green Bay. Figures 4.4.6 and 4.4.7  show
the distribution of POC  in Lake Michigan sediments
in units of mg/gdw and mg/L, respectively.  The
natural-neighbor algorithm  (Appendix 4.4.1) was
used for the interpolation and the segment-specific
                                              258

-------
Table 4.4.8. Annual Average Organic Carbon Loads From 11 Monitored Tributaries to Lake Michigan
During the LMMBP
Tributary (Monitored)
Milwaukee River
Sheboygan River
Calumet River
St. Joseph River
Kalamazoo River
Grand River
Muskegon River
Pere Marquette River
Manistique River
Menominee River
Fox River
Total
DOC (ton/year)
2508.50
1549.45
1974.49
18202.45
10205.13
31922.79
13191.20
3990.09
11502.03
25727.66
33972.50
154746.30
BIC (ton/year)
284.13
237.09
61.85
2688.78
1585.62
4646.54
394.89
104.17
95.80
621.73
7059.93
17780.53
PDC (ton/year)
225.91
169.43
426.65
2801.15
1 827.60
3496.82
881 .68
738.60
1232.81
2477.21
4743.01
19020.87
Table 4.4.9.  Annual Average Organic Carbon Loads From 18 Unmonitored Tributaries to Lake
Michigan During the LMMBP
Tributary (Unmonitored)
Manitowac
Root
Galien
Black
Kalamazoo-Minor
Kewaunee
Pere Marquette-Minor
Manistee
Betsie
Millecoquins
Jordan
Sturgeon
Whitefish
Escanaba
Cedar
Peshtigo
Oconto
Pensaukee
DOC (ton/year)
4507.49
1188.18
2530.82
31 32.23
268.82
4106.73
6841.12
11095.82
3909.74
4406.39
8012.93
4385.19
3997.10
5815.29
6544.03
7236.21
5568.69
2702.66
BIC (ton/year)
689.71
134.58
373.84
486.67
41.77
853.43
204.79
289.68
117.04
36.70
239.87
36.52
96.59
140.53
158.14
174.87
1157.25
561 .65
PDC (ton/year)
492.89
107.01
389.47
560.94
48.14
573.35
457.25
2053.92
261.32
472.28
535.57
470.01
384.86
559.93
630.10
696.74
777.46
377.33
 Total
86249.43
5793.65
3896.44
                                          259

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Table 4.4.10. Annual Average Organic Carbon Internal Loads Generated From the LM3-Eutro for Lake
Michigan During the LMMBP
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
DOC (ton/year)
700929.10
689189.92
518083.37
403259.87
189956.41
170745.86
68920.37
69189.01
124169.03
78248.13
250457.73
246500.23
188306.18
145633.10
53584.35
56110.59
15072.21
14053.69
13520.81
50927.44
49818.55
39893.25
31062.96
6879.84
10523.70
1388.52
816.87
213.29
9827.70
9681 .05
8149.94
6621.80
1017.37
1648.21
19.20
293.42
314.62
276.19
244.65
18.65
33.69
BIG (ton/year)
2803716.40
2756759.68
2072333.48
1613039.48
759825.64
682983.44
275681.48
276756.05
496676.11
312992.51
1001830.93
986000.92
753224.70
582532.39
214337.41
224442.38
60288.86
56214.78
54083.22
203709.77
199274.20
159573.00
124251.84
27519.37
42094.79
5554.07
3267.48
853.14
39310.79
38724.21
32599.76
26487.18
4069.47
6592.86
76.80
1173.68
1258.48
1104.74
978.61
74.59
134.75
             Total
422560.09
                                                                     1690240.34
                                          260

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Table 4.4.11. Initial Concentrations of Organic Carbon Sorbents in Water Column Segments for the
LM2-Toxic
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
DOC (mg/L)
1 .5694
1 .7768
.5311
1.6138
1 .6464
1.6113
1 .7583
1.968
3.6044
3.6154
1 .5338
1.6015
1.532
1.6247
1 .5429
1 .4676
1.7187
2.0062
3.4877
1.5442
1 .5581
1 .6276
1 .6385
1 .5571
1.763
.8679
.8811
.8553
.5242
.5286
.7129
.6961
.8431
1.87
1.8
1.415
1 .3966
1 .6693
1 .6572
1 .7343
1 .7286
BIC (mg/L)
0.038332
0.038024
0.030031
0.029434
0.033097
0.030774
0.034256
0.036219
0.035715
0.07318
0.036831
0.036452
0.029746
0.029355
0.032192
0.030419
0.033151
0.03439
0.034217
0.035098
0.034508
0.029078
0.02881 1
0.030666
0.029705
0.031902
0.03355
0.034123
0.031241
0.03062
0.026917
0.026645
0.027635
0.027417
0.031769
0.024359
0.024124
0.019674
0.020367
0.023314
0.024052
PDC (mg/L)
0.1484
0.1383
0.1818
0.168
0.1763
0.1768
0.2616
0.2616
0.2616
0.2616
0.1544
0.1441
0.1802
0.1651
0.1761
0.1753
0.2616
0.2616
0.2616
0.1294
0.1218
0.1598
0.151
0.2015
0.1699
0.1865
0.1959
0.1777
0.1246
0.1222
0.1411
0.146
0.1538
0.1575
0.1388
0.0833
0.0811
0.1144
0.1169
0.1183
0.1182
                                          261

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Table 4.4.12. Cruise-Segment Mean Concentrations of DOC (mg/L) for the LMMBP Period




 Segment   Cruise 1   Cruise 2    Cruise 3    Cruise 4    Cruise 5    Cruise 6   Cruise 7   Cruise 8
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
1.569
1.777
1.531
1.614
1.646
1.611
1.758
1.968
3.604
3.615
1.534
1.602
1.532
1.625
1.543
1.468
1.719
2.006
3.488
1.544
1.558
1.628
1.639
1.557
1.763
1.868
1.881
1.855
1.524
1.529
1.713
1.696
1.843
1.870
1.800
1.415
1.397
1.669
1.657
1.734
1.729
1.574
1.593
1.565
1.594
1.569
1.573




1.519
1.575
1.558
1.587
1.559
1.563



1.440
1.417
1.379
1.388
1.388
1.391



1.438
1.404
1.620
1.572
1.582
1.567

1.433
1.422
1.446
1.433
1.305
1.312
1.623
1.577
1.707
1.637
1.608
1.629
2.285
2.378
2.885
2.858
1.720
1.615
1.742
1.716
1.632
1.688
2.408
2.468
2.755
1.461
1.470
1.618
1.587
1.514
1.461
1.698
1.662
1.733
1.459
1.473
1.633
1.606
.566
.591
.661
.540
.475
.451
1.419
1.406
1.387
1.611
1.635
1.596
1.609
1.349
1.401
1.930
1.948
2.432
2.427
1.622
1.635
1.593
1.613
1.363
1.412
1.883
1.924
2.331
1.650
1.619
1.517
1.518
1.338
1.349
1.496
1.457
1.534
1.555
1.547
1.592
1.585
1.557
1.568
1.448
1.584
1.539
1.565
1.554
1.388
1.317
1 .503 .559
1 .482 1 .637
1.496 1.515
1 .499 1 .51 1
1.497 1.484
1.497 1.483
1.672
1.797
2.770
2.776
1.561
1.592
1.569
1.535
1.505
1.504
1.649
1.820
2.700
1.535
1.487
1.605
1.530
1.524
1.543
1.730
1.730
1.730
1.531
.550
.453
.474
.382
.371

1.476
1.480
1.457
1.440
1.383
1.371
1.594
1.611
1.676
1.639
1.646
1.624
2.108
2.248
1.965
1.939
1.604
1.620
1.683
1.647
1.663
1.630
2.111
2.244
1.976
1.385
1.391
1.348
1.326
1.284
1.253
1.430
1.430
1.430
1.354
1.375
1.377
1.345
1.318
1.318
1.397
1.433
1.400
1.433
1.409
1.311
1.320
1.709
1.681
1.691
1.724
1.701
1.821
1.789
1.852
2.149
2.135
1.668
1.690
1.633
1.683
1.606
1.738
1.691
1.780
2.037
1.540
1.578
1.483
1.551
1.436
1.466
1.419
1.444
1.395
1.471
1.481
1.392
1.416
1.488
1.687
1.445
1.443
1.439
1.464
1.489
1.542
1.713
                                           262

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Table 4.4.13. Cruise-Segment Mean Concentrations of BIC (mg/L) for the LMMBP Period
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cruise 1
0.092
0.093
0.090
0.090
0.098
0.095
0.100
0.100
0.100
0.100
0.093
0.093
0.088
0.083
0.097
0.089
0.100
0.100
0.100
0.093
0.095
0.091
0.084
0.127
0.105
0.101
0.101
0.102
0.095
0.093
0.091
0.083
0.098
0.099
0.102
0.064
0.067
0.046
0.042
0.049
0.049
Cruise 2
0.087
0.094
0.106
0.107
0.104
0.104




0.181
0.151
0.129
0.137
0.133
0.135



0.104
0.090
0.096
0.096
0.094
0.094



0.078
0.069
0.079
0.079
0.077
0.077

0.049
0.043
0.063
0.058
0.066
0.065
Cruise 3
0.041
0.044
0.046
0.050
0.045
0.048
0.071
0.077
0.184
0.179
0.037
0.044
0.053
0.059
0.050
0.055
0.076
0.082
0.158
0.077
0.088
0.111
0.130
0.088
0.092
0.082
0.078
0.086
0.079
0.092
0.108
0.128
0.090
0.096
0.078
0.023
0.020
0.027
0.023
0.029
0.034
Cruise 4 Cruise 5
0.065 0.046
0.064 0.048
0.052 0.046
0.053 0.046
0.055 0.047
0.053 0.047
0.097
0.103
0.141
0.141
0.067
0.066
0.048
0.052
0.054
0.052
0.084
0.095
0.128
0.070
0.065
0.041
0.047
0.051
0.049
0.045
0.047
0.043
0.015
0.014
0.017
0.018
0.026
0.023
0.043
0.009
0.009
0.005
0.006
0.004
0.004
Cruise 6
0.058
0.063
0.056
0.047
0.069
0.062
0.088
0.092
0.122
0.122
0.054
0.055
0.065
0.051
0.070
0.064
0.081
0.082
0.115
0.053
0.053
0.059
0.045
0.076
0.064
0.075
0.075
0.075
0.051
0.049
0.063
0.059
0.058
0.057

0.041
0.039
0.030
0.031
0.045
0.051
Cruise 7
0.026
0.024
0.038
0.037
0.040
0.038
0.080
0.090
0.069
0.068
0.026
0.023
0.040
0.038
0.042
0.039
0.080
0.089
0.070
0.052
0.046
0.044
0.046
0.052
0.057
0.037
0.037
0.037
0.047
0.044
0.048
0.047
0.047
0.047
0.040
0.021
0.018
0.025
0.023
0.027
0.024
Cruise 8
0.050
0.047
0.041
0.044
0.043
0.044
0.087
0.094
0.217
0.214
0.049
0.048
0.043
0.044
0.044
0.045
0.073
0.085
0.187
0.021
0.019
0.029
0.031
0.026
0.027
0.028
0.031
0.026
0.010
0.009
0.018
0.017
0.015
0.010
0.031
0.007
0.006
0.012
0.010
0.013
0.009
                                         263

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Table 4.4.14. Cruise-Segment Mean Concentrations of PDC (mg/L) for the LMMBP Period
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cruise 1
0.148
0.138
0.182
0.168
0.176
0.177
0.262
0.262
0.262
0.262
0.154
0.144
0.180
0.165
0.176
0.175
0.262
0.262
0.262
0.129
0.122
0.160
0.151
0.202
0.170
0.187
0.196
0.178
0.125
0.122
0.141
0.146
0.154
0.158
0.139
0.083
0.081
0.114
0.117
0.118
0.118
Cruise 2
0.202
0.203
0.224
0.221
0.220
0.219




0.165
0.187
0.215
0.210
0.210
0.208



0.114
0.126
0.147
0.137
0.144
0.141



0.091
0.108
0.086
0.087
0.089
0.090

0.078
0.077
0.080
0.079
0.079
0.079
Cruise 3
0.221
0.231
0.206
0.204
0.159
0.175
0.283
0.306
0.525
0.514
0.210
0.222
0.204
0.206
0.141
0.162
0.295
0.317
0.439
0.145
0.102
0.144
0.128
0.146
0.145
0.167
0.185
0.149
0.149
0.120
0.141
0.130
0.148
0.148
0.186
0.122
0.105
0.079
0.083
0.059
0.057
Cruise 4 Cruise 5
0.222 0.113
0.207 0.113
0.210 0.118
0.202 0.119
0.239 0.119
0.227 0.120
0.357
0.356
0.397
0.397
0.222
0.209
0.197
0.198
0.242
0.225
0.321
0.333
0.374
0.185
0.185
0.152
0.168
0.223
0.220
0.146
0.142
0.149
0.177
0.196
0.126
0.134
0.142
0.142
0.140
0.103
0.097
0.072
0.076
0.064
0.065
Cruise 6
0.139
0.152
0.178
0.142
0.202
0.188
0.237
0.248
0.332
0.333
0.127
0.135
0.177
0.144
0.203
0.189
0.255
0.266
0.320
0.118
0.120
0.163
0.128
0.196
0.176
0.270
0.270
0.270
0.122
0.124
0.159
0.155
0.193
0.198

0.096
0.095
0.088
0.093
0.149
0.176
Cruise 7
0.135
0.130
0.187
0.178
0.195
0.186
0.372
0.438
0.306
0.293
0.133
0.129
0.174
0.170
0.200
0.186
0.374
0.436
0.311
0.158
0.140
0.177
0.162
0.170
0.168
0.183
0.183
0.183
0.113
0.127
0.135
0.134
0.148
0.145
0.173
0.106
0.108
0.091
0.089
0.094
0.091
Cruise 8
0.144
0.139
0.162
0.151
0.150
0.147
0.272
0.295
0.480
0.474
0.138
0.130
0.166
0.152
0.144
0.143
0.239
0.272
0.427
0.114
0.111
0.144
0.141
0.149
0.144
0.180
0.196
0.165
0.080
0.077
0.094
0.083
0.096
0.069
0.196
0.074
0.074
0.062
0.059
0.079
0.061
                                         264

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                           Muskegon River
                              Grand River
                      St. Joseph River
                                                                         Sediment Participate
                                                                            Organic Carbon
                                                                           (mg/g dry weight)
                                                                                1001
                                                                                 90

                                                                                 80*
                                                                                      -
Figure 4.4.5. Lake Michigan sediment sampling   Figure  4.4.6.   Distribution  of POC  in  Lake
sites for organic carbon during the LMMBP.       Michigan surficial sediments (mg/gdw).
                                            265

-------
                            Sediment Particulate
                               Organic Carbon
                              bulk concentration
                                  (mgC/L)
 Figure 4.4.7.   Distribution  of  POC  in  Lake
 Michigan surficial sediments (mg/L).
averaged sediment organic carbon concentrations
are presented in Table 4.4.15.

4.4.2.3  Parameterization

There were a number of organic carbon sorbent-
specific parameters that had to be specified as input
for  the  LM2-Toxic.  These parameters included
carbon biotransformation parameters (carbon decay
and  yield),  carbon  sorbent   vertical  transport
parameters (settling velocities, resuspension rates,
burial  rates),  and  parameters  derived  during
empirical wave- induced resuspension (critical wave
height and empirical wave coefficient). Most of these
parameters were not project-generated parameters.
Initial values of these  parameters were specified
based on values from the literature or were derived
from empirical relationships. Their values could have
a large variation, and results from the LM2-Toxic are
very  sensitive to  some  of  these  parameters.
Therefore, final values  of these parameters were
defined by calibration.  Tables 4.4.16 through 4.4.19
present   final  lists  of  carbon  sorbent-specific
parameters, definitions, units, their final values, and
data sources used in the LM2-Toxic.

The carbon biotransformation parameters are listed
in Table 4.4.16.  Initial values of the  parameters in
this table were from  the GBMBP  (Bierman et a/.,
1992; DePinto et  at.,  1993).  Table 4.4.17 presents
segment-specific settling velocities for both BIG and
PDC, respectively. All of the settling velocities were
calibration parameters. More detailed discussion of
the LM2-Toxic model calibration can be found in
Chapter 5.   The water column segment-specific
effective  PDC concentrations  (CJ  used  in  the
steady-state  resuspension  calculation (Equation
4.3.8) were computed by averaging the eight cruise
segment-mean concentrations derived from samples
collected   during the  LMMBP.     The  PDC
concentrations (Cw) are  presented in Table 4.4.18.
An  accurate  estimation of the burial rate  was
essential in computing the steady-state resuspension
rate   and  calibrating   organic   carbon  sorbent
dynamics.  A reliable burial  rate could  reduce one
degree of freedom in Equation  4.3.8,  making  the
carbon settling velocity the only calibration parameter
in the equation. Table 4.4.19 provides the segment-
specific  burial  rates  along  with  the thickness of
mixing layer for  the surficial  sediment segments
based  on the analyzed  results of  the LMMBP-
generated sediment core samples (Robbins et a/.,
1999). Table 4.4.20 lists the segment-specific critical
wave heights and empirical wave coefficients for
calculating wave-induced resuspension based on the
LMMBP-generated data using linear-regression (see
Section 3.4.2 for discussion and  details).  The final
segment-specific resuspension rates were computed
as daily time functions and input into the LM2-Toxic.
                                              266

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Table 4.4.15. Concentration of Organic Carbon Sorbents in Surficial Segments for the LM2-Toxic
Segment
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
DOC (mg/L)
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
PDC (mg/L)
2981.3
3342.3
4190.0
3782.3
6384.2
6056.2
7040.6
4561 .8
4688.9
5354.8
4058.7
1942.4
3009.7
1709.3
1778.6
2906.6
4447.1
5557.6
4768.6
4968.2
4770.0
3519.9
3024.3
6175.6
5771 .8
4533.5
3970.2
Segment
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94

DOC (mg/L)
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.7
2
2
2
2
2
1.9
1.9
1.9
1.9
2
3.4
3.4
3.4
3.4
3.6
3.6
3.6

PDC (mg/L)
3634.1
4120.2
4340.0
2712.5
2642.3
2626.8
2172.5
2433.5
4322.5
6147.7
5817.2
5705. 1
6422.5
5699.2
5831.2
5987.4
6665.9
6354.6
5672.5
5040. 1
5877.2
6611.8
5917.6
6399.6
5460.0
5293.0

                                          267

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Table 4.4.16. Organic Carbon Sorbent Biotransformation Parameters Specified for the LM2-Toxic
   Parameter
             Definition and Units
                                                             Value
Source
    d(DOC)
Substrate saturated decay rate of DOC in water
column at temperatures equal to 20°C, d'1
                                                             0.005     DePinto era/., 1993;
                                                                            Calibration
                  Michaelis-Menten half-saturation constantfor DOC     3.4
                  decay in water column, mg/L
                                                          DePinto etal., 1993;
                                                              Calibration
ds(DOC)
                  Decay rate for  DOC in surficial sediments  at    0.015
                  temperature equal to 20°C, d'1
                                                          DePinto et ai, 1993;
                                                              Calibration
    d(BIC)
Substrate saturated decay  rate of BIG in water    0.56
column at temperature equal to 20°C, d1
                                                                       DePinto et al., 1993;
                                                                            Calibration
                  Michaelis-Menten half-saturation constant for BIG
                  decay in water column, mg/L
                                                 0.4       DePinto era/., 1993;
                                                              Calibration
   '(BIC PDC)
Yield coefficient (percentage) of PDC during BIC
decay in water column, dimensionless
                                                              90%     DePinto et al., 1993;
                                                                            Calibration
    d(PDC)
Substrate saturated decay rate of PDC in water
column at temperature equal to 20°C, d"1
                                                              0.04     DePinto etal., 1993;
                                                                            Calibration
                  Michaelis-Menten half-saturation constantfor PDC    0.55
                  decay in water column, mg/L
                                                         DePinto etal., 1993;
                                                              Calibration
   Y(PDC DOC)       Yield coefficient (percentage) of DOC during PDC    60%      DePinto ef al., 1993;
                  decay in water column, dimensionless                            Calibration
   ^ds(PDC)
Decay rate  for PDC  in  surficial  sediments at   0.0001
temperature equal to 20°C, d"1
                                                                       DePinto etal., 1993;
                                                                            Calibration
  YS(PDC  DOC)     Yield coefficient (percentage) of PDC decay in
                 surficial sediments, dimensionless
                                                60%     DePinto et al., 1993;
                                                              Calibration
                                              268

-------
Table 4.4.17.  Segment-Specific Settling Rates (m/d) for Organic Carbon Sorbents (BIC and PDC)
Specified for LM2-Toxic
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
BIC Settling Velocity
0.15
0.15
0.15
0.15
0.15
0.15
0.2
0.2
0.2
0.2
0.15
0.15
0.15
0.15
0.15
0.15
0.2
0.2
0.2
0.06
0.06
0.06
0.06
0.06
0.06
0.2
0.2
0.2
0.06
0.06
0.06
0.06
0.06
0.06
0.08
0.06
0.06
0.06
0.06
0.06
0.06
PDC Settling Velocity
0.15
0.15
0.15
0.15
0.15
0.15
0.2
0.2
0.2
0.2
0.15
0.15
0.15
0.15
0.15
0.15
0.2
0.2
0.2
0.25
0.25
0.25
0.25
0.25
0.25
0.8
0.8
0.8
0.4
0.4
0.4
0.4
0.4
0.4
0.75
0.75
0.75
0.75
0.75
0.75
0.75
Source
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
Calibration
                                          269

-------
Table 4.4.18.   Segment-Specific Effective PDC Concentrations (Cw) Used in  the Steady-State
Resuspension Calculation Based on the LMMBP Data
Segment Cw (mg/L)
1 223.63
2 223.74
3 242.84
4 232.14
5 245.25
6 240.86
7 384.32
8 410.02
9 522.45
10 515.67
1 1 236.60
12 233.70
13 253.99
14 243.83
15 257.86
16 252.19
17 373.12
18 402.88
19 386.85



Segment
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cw(mg/L)
153.59
145.00
171.26
161.49
193.38
182.99
204.06
210.65
197.85
130.39
132.76
135.10
133.21
147.27
144.40
190.47
97.10
93.47
86.09
87.14
94.54
95.34
 Table 4.4.19.  Segment-Specific Sediment Accumulation Rates (vb) and Thickness of Mixing Layer (z)
 Used in the Steady-State Resuspension Calculation (Original Data Source: Bobbins etal., 1999)
Segment
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
vb (m/d)
0
0
6.12E-09
1 .59E-07
4.97E-07
1 .36E-06
3.11E-06
9.94E-06
7.29E-06
3.96E-06
6.37E-06
1 .36E-06
1 .28E-06
1 .87E-07
0
9.87E-08
7.56E-07
2.90E-06
6.06E-06
7.47E-06
7.58E-06
5.06E-06
3.76E-06
3.65E-08
8.78E-08
9.83E-09
4.83E-07
z(m)
0.001
0.001
0.001
0.001
0.013
0.018
0.025
0.031
0.029
0.019
0.012
0.001
0.001
0.001
0.001
0.001
0.013
0.019
0.028
0.026
0.031
0.016
0.001
0.001
0.001
0.001
0.001
Segment
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94

vb (m/d)
1.49E-06
3.19E-06
5.31 E-06
0
4.18E-07
9.05E-08
1 .47E-07
3.36E-07
4.94E-07
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
1 .36E-06
2.72E-06
2.72E-06
2.72E-06
2.72E-06
2.60E-06
2.60E-06
2.60E-06

z(m)
0.001
0.024
0.03
0.001
0.011
0.01
0.001
0.001
0.001
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04

                                           270

-------
Table 4.4.20. Segment-Specific Critical Wave Heights (Wcr) and Empirical Wave Coefficients (a) Used
in the Wave-Induced Resuspension Calculation Based on the LMMBP Data
Segment
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Wcr(m)
0.93
1.20
1.63
2.24
3.75
4.01
5.52
4.83
4.50
4.15
2.28
2.22
1.62
1.29
1.74
2.29
3.84
5.02
7.89
7.66
5.47
3.81
2.40
0.95
1.22
1.63
2.19
a
52
70
131
565
0
0
0
0
0
0
61
257
56
88
0
0
0
0
0
0
0
0
0
96
143
520
0
Segment
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94

Wcr(m)
3.49
3.33
3.90
3.57
3.76
3.35
2.21
1.65
1.24
0.90
1.22
1.57
1.60
1.69
2.07
1.94
1.65
1.25
0.85
0.92
1.22
1.31
1.33
0.74
0.82
0.82

a
0
0
0
0
0
0
0
0
214
99
207
0
0
0
0
0
0
142
175
273
0
0
0
142
157
146

4.4.3  PCBs

Thirty-six PCB state variables representing a total of
54 PCB congeners were modeled in the LM2-Toxic
as either individual PCB congeners or co-eluting PCB
congeners.  The mass for the sum of all 54 PCB
congeners (ZPCB) accounted for approximately 70%
of total PCB mass  in Lake Michigan.  Table 4.4.21
provides a list of these PCB congeners by IUPAC
numbers.  Because  of the huge amount of PCB
congener data generated during the LMMBP, it was
impossible to  provide summaries of field data for
each modeled PCB congener.  Thus, PCB loadings,
field data, and related information in the following
subsections are reported only as the sum of the 54
congeners  (IPCBs).   Organic carbon  sorbent
dynamics, PCB  partitioning,  and  PCB  air-water
exchange  dictated  the  variation  of  PCB
concentrations in Lake  Michigan.  The lake-wide
average concentrations for  vapor  phase IPCBs
varied  from  0.28 to 0.42 ng/m3.   The average
concentrations in the main lake for dissolved and
particulate  IPCBs  were 0.15  and  0.07  ng/L,
respectively.    In  Green   Bay,   the  average
concentrations of dissolved and particulate IPCBs
were almost double or triple,  and their values were
0.3 and 0.36 ng/L, respectively. The average IPCB
concentrations in the surficial sediments ranged from
650 to 25,000 ng/L. Data requirements for PCB input
to the  LM2-Toxic  included  loads  (tributary and
                                            271

-------
Table 4.4.21.  List of PCB State Variables Modeled in the LM2-Toxic
    PCB Congeners
PCB Congeners
PCB Congeners
PCB Congeners
PCB8+5
PCB15+17
PCB16+32
PCB18
PCB26
PCB28+31
PCB33
PCB37+42
PCB44
PCB49
PCB52
PCB56+60
PCB66
PCB70+76
PCB74
PCB77+110
PCB81
PCB87
PCB92+84
PCB89
PCB99
PCB101
PCB118
PCB123+149
PCB1 05+1 32+1 53
PCB151
PCB163+138
PCB170+190
PCB172+197
PCB180
PCB187+182
PCB1 95+208
PCB1 96+203
PCB201
PCB85
PCB146
 atmospheric),   boundary  conditions  (Straits  of
 Mackinac  and   atmospheric   gas   phase
 concentrations), initial conditions in both the water
 column and surficial sediments, process   related
 parameters, and  kinetic time functions such  as
 temperature and wind speed. The segment-mean
 concentration for each cruise, generated by IDWdata
 interpolation with VWA, was an essential part of field
 data analysis used to calibrate the LM2-Toxic. See
 Part 4, Appendix 4.4.1.

 4.4.3.1 Loading

 Tributary and atmospheric sources were the two
 major external loads of PCBs  to Lake Michigan.
 Tributary loads included loads from 11 monitored
 tributaries and 18 unmonitored tributaries (Hall and
 Robertson, 1998).  Atmospheric  loads were derived
 from monthly atmospheric dry and wet deposition
 fluxes (McCarty et al., 2004; Miller et al., 2001).
 Tributary loads were input as daily time functions into
 the LM2-Toxic.  The seasonal variation of the IPCB
 tributary load represents the  sum of the  tributary
 loads  including 11  monitored and 18 unmonitored
 tributaries (Figure 4.4.8). There  were 18 months of
 atmospheric dry deposition measurements during the
 LMMBP  period.  The data set provided  for the
 modeling was organized for the period from April
 1994 to  March 1995.  For consistency with other
 input loading time series in the model, the loads for
 the first three months of 1995 were used as the loads
 for the same period of 1994.  The loads for April-
 December of 1995 were assumed  to be identical to
 the loads for the same  period of  1994.  Eighteen
                         3.0

                         2.5

                         2.0
      Lake Michigan IPCB tributary load
      (11 monitored, 18 unmonitored)
                               i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r  .
                           Jan94   Jun94     Dec94     Jun95   Nov95
                     Figure 4.4.8. IPCB tributary (11 monitored and
                     18 unmonitored tributaries)  loads  to  Lake
                     Michigan during the LMMBP period.
                     months of monthly atmospheric wet deposition were
                     available  starting  in  April  1994 and  ending in
                     September 1995. Among the 18 months, the loads
                     in April and May of 1994 were significantly higher
                     (about 10 times higher) than the monthly loads of the
                     remaining 16 months. Therefore, the approach used
                     in the first three months of 1994 for dry loads was not
                     applicable for wet deposition. In order to construct a
                     complete two-year atmospheric wet load time series,
                     monthly wet loads for January, February, and March
                     of 1994 were assumed to be the same as that for
                     April 1994, and the loads for the last three months in
                     1995 were assumed to be the same as the loads for
                     the last three months of 1994. Figure 4.4.9 provides
                     the information on lake-wide temporal variation of
                     total atmospheric deposition (dry  +  wet) for the
                     IPCBs.  Based on the loads generated for the two-
                     year LMMBP period,  the annual average tributary
                                              272

-------
1 g 1 ato IMi^hinan VDOR
-D ' °
CD 1 K
O
., 14.
"a> 1.2
a. -a 1 0
§ ^ 0.8
ro 0.6
S 0.4-
a. 0.2 j
w o-
Ja
atmosphRrir, (dry

/ \
and wet) load



I
I
I
I — /^, / \ XA
17 \^J ^ ^ \^
n94 Jun94 Dec94
Jun95 Nov95
                                                used   to   generate   segment-specific  sediment
                                                concentrations  of  individual   PCB  congeners.
                                                Equation 4.4.4 was used to convert sediment PCB
                                                measurements from the unit of ng/gdw to the standard
                                                unit (ng/L, i.e., bulk concentration) used in the LM2-
                                                Toxic.
                                                 C*  =  Csb * p *  (1 -<(>) * 103

                                                where
                                                                  (4.4.4)
Figure 4.4.9. Estimated ZPCB atmospheric loads
including dry  and wet deposition  into  Lake
Michigan during the LMMBP period.
and atmospheric loads of the ZPCBs are presented
in Tables 4.4.22 and 4.4.23, respectively.  For the
two-year LMMBP period, the ZPCB external loads to
Lake Michigan from tributary and atmospheric loads
were roughly equal.
                          Cs" =
                                                 C," =
PCB concentrations in surficial sediments
(ng/L), the unit required for LM2-Toxic

PCB concentrations in surficial sediments
(ng/gdw), the unit of field measurements
                              =  bulk density of surficial sediments
                                 constant (2.45 gdw/cm3). To convert cm3 to L
                                 (liter),  a factor
                                 equation.
               of 103  is needed in the
4.4.3.2    Field  Data,
Boundary Conditions
Initial  Conditions, and   (p  =  porosity (dimensionless)
PCB congener level data were collected at eight
shoreline sites (Figure  4.4.10), 41  water column
stations (Figure 4.4.3), and  about  120 sediment
locations (Figure 4.4.5) during the LMMBP.  The
LMMB Methods  Compendium provides detailed
descriptions  of  sampling methods and analysis
procedures (U.S. Environmental Protection Agency,
1997).   The LM2-Toxic required inputs of PCB
congener  concentrations  in  air,  water,  and
sediments.   Daily vapor phase PCB  boundary
concentrations were interpolated and calculated at a
spatial resolution of 5 x 5 km2 grid cells (Green et a/.,
2000; Miller  et al., 2001) based on air samples
collected at eight shoreline sites and 14 over-water
sites (Figure 4.4.10). These results were aggregated
to the LM2-Toxic  segmentation for  input into the
model. Strong seasonal variation was observed for
PCB vapor phase  concentrations during the study
period (Figure 4.4.11).   Table 4.4.24 presents the
segment-specific annual average ZPCB vapor phase
concentrations above the 10 surface segments of the
LM2-Toxic.  The same  interpolation methods and
averaging  procedures  used  for  organic  carbon
species were applied to individual PCB congeners in
both the water column and sediments. One hundred
and sixteen sediment samples (Figure 4.4.12) were
                          PCB concentrations for the sediment segments in
                          Green  Bay were estimated  based  on only four
                          sediment samples (SD89,  95G1,  SD106P, and
                          113G1) collected during the two-year LMMBP period.
                          Figures 4.4.13 and 4.4.14 show the  distribution of
                          ZPCBs in Lake Michigan sediments in units of ng/gdw
                          and ng/L, respectively.  Table 4.4.25 presents the
                          water column initial concentrations expressed as the
                          sum (ZPCBs) of the individual PCB congener initial
                          concentrations used in the LM2-Toxic.  The cruise-
                          segment mean concentrations for ZPCBs are listed
                          in Table 4.4.26.   The segment-specific sediment
                          concentrations for ZPCBs are provided in  Table
                          4.4.27. The boundary conditions for individual PCB
                          congeners used the measurements taken   at a
                          sampling location in Lake Huron, LM54M.

                          4.4.3.3 Parameterization

                          The PCB congener-specific parameters that must be
                          specified for input to the LM2-Toxic include partition
                          coefficients,   molecular  weights,  enthalpy,  and
                          entropy. The  POC partition coefficients, K'POc,a.
                          were  computed  initially  using  the  two-phase
                          partitioning  model  (Equation  4.3.14)  for   each
                          selected PCBs computed initially using the two-phase
                                             273

-------
Table 4.4.22. Annual Average IPCB Loads From 11 Monitored and 18 Unmonitored Tributaries to Lake
Michigan During the LMMBP
 Tributary (Monitored)   IPCB Loads (kg/year)  Tributary (Unmonitored)  ZPCB Loads kg/year)
Milwaukee River
Sheboygan River
Calumet River
St. Joseph River
Kalamazoo River
Grand River
Muskegon River
Pere Marquette River
Manistique River
Menominee River
Fox River

Total





9.00
9.40
29.86
8.79
30.31
8.60
1.71
0.51
1.17
4.01
174.62

277.98





Manitowac
Root
Galien
Black
Kalamazoo-Minor
Kewaunee
Pere Marquette-Minor
Manistee
Betsie
Millecoquins
Jordan
Sturgeon
Whitefish
Escanaba
Cedar
Peshtigo
Oconto
Pensaukee
1.20
4.26
1.22
9.30
0.80
0.75
0.89
1.41
0.51
0.45
1.04
0.45
0.62
0.91
1.02
1.13
1.02
0.50
                                           Total
                                           21.84
 Table 4.4.23. Annual Average IPCB Atmospheric Dry and Wet Loads in the 10 Surface Water Column
 Segments of Lake Michigan During the LMMBP
           Segment
         Total Loads
IPCB Atmospheric Dry Loads   IPCB Atmospheric Wet Loads
         (kg/year)                     (kg/year)
1 18.13
2 13.90
3 12.27
4 9.20
5 4.38
6 4.12
7 1.36
8 1.26
9 1.47
10 0.37
35.67
34.69
22.29
16.89
9.04
8.46
2.54
2.34
2.79
0.65
          66.46
                                                                     135.35
                                         274

-------
                    Manitowoc
                  Kenosha
                 IIT-Chic
                                                  Sleeping Bear
                                                  Dunes
                                                      based sampling
                                                  water based sampling
                                                South Haven
Figure 4.4.10. Lake Michigan atmospheric sampling sites during the LMMBP.
                                         275

-------
SPCB vapor concentratior
(ng/m3)
1 .U
0 9
0.8
0 7
0 6
0.5
0.4
0.3
0.2
0.1
n
Lake Michigan 2.PCB
vapor concentration A
\

/ \ / \
/ \ / \
/ \ / \
/ \ J \


                    Jan94    Jun94      Dec94      Jun95     Nov95
Figure 4.4.11. Seasonal variation of IPCB vapor phase concentrations observed during the LMMBP.
Table 4.4.24. Annual Average Boundary Conditions of IPCB Vapor Phase Concentrations for Lake
Michigan During the LMMBP
                 Segment
ZPCBs (ng/m3)
                      1
                      2
                      3
                      4
                      5
                      6
                      7
                      8
                      9
                     10
    0.418367
    0.374108
    0.297252
    0.284844
    0.299123
    0.282733
    0.30676
    0.307972
    0.312705
    0.300599
                                         276

-------
               Menominee River
                                               Pere Marquette River
                 Milwaukee
                 Sheboygan
                      River
                                                Muskegon River
                                                   Grand River
                                                 Kalamazoo River
w o *-*
D

' 0 °
0 D
N o
*
\ (-»

D D / \
an /
9] /
• °A
x . l^ . _.

Core types
D box
O ponar
• gravity

                                           St. Joseph River
Figure 4.4.12. Lake Michigan sediment sampling sites for PCBs during the LMMBP.
                                       277

-------
                            Total PCS
                         (ng/g dry weight)
Figure 4.4.13.   Distribution of IPCBs in Lake
Michigan surficial sediments (ng/gdw).
                  Figure 4.4.14.  Distribution of IPCBs in  Lake
                  Michigan surficial sediments (ng/L).
Table 4.4.25. Initial Concentrations of IPCBs in Water Column Segments for Lake Michigan
        Segment
IPCBs (ng/L)
Segment
ZPCBs (ng/L)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0.1903
0.2414
0.2264
0.2680
0.1597
0.2350
0.5910
0.6349
2.4938
2.5051
0.1141
0.1888
0.2106
0.1682
0.1859
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
0.1401
0.1872
0.2053
0.1438
0.1621
0.1604
0.1388
0.1639
0.1643
0.1492
0.2039
0.1663
0.1148
0.1560
0.1531
                                           278

-------
Table 4.4.26. Cruise-Segment Mean Concentration of ZPCBs (ng/L) for the LMMBP Period
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Cruise 1
0.192
0.372
0.227
0.269
0.214
0.236
0.591
0.807
2.494
2.505
0.154
0.194
0.212
0.227
0.196
0.199
0.550
0.847
2.373
0.184
0.188
0.212
0.181
0.170
0.179
0.550
0.847
2.373
0.210
0.204
0.167
0.162
0.162
0.161
0.847
0.170
0.178
0.111
0.117
0.117
0.118
Cruise 2
0.124
0.172
0.136
0.138
0.148
0.150




0.133
0.180
0.138
0.140
0.150
0.152



0.146
0.180
0.145
0.152
0.154
0.157



0.166
0.206
0.144
0.150
0.154
0.157

0.137
0.146
0.112
0.113
0.108
0.108
Cruise 3
0.136
0.136
0.112
0.103
0.078
0.083
0.195
0.186
0.486
0.475
0.129
0.145
0.113
0.107
0.084
0.085
0.293
0.321
1.327
0.179
0.173
0.142
0.128
0.122
0.122
0.293
0.321
1.327
0.178
0.174
0.143
0.130
0.126
0.127
0.293
0.200
0.201
0.116
0.125
0.103
0.102
Cruise 4 Cruise 5
0.199 0.241
0.241 0.230
0.158 0.232
0.157 0.232
0.116 0.233
0.123 0.233
0.211
0.221
0.839
0.833
0.190
0.240
0.168
0.160
0.119
0.125
0.196
0.221
0.733
0.136
0.170
0.168
0.155
0.111
0.116
0.196
0.221
0.733
0.359
0.410
0.213
0.224
0.189
0.197
0.196
0.226
0.229
0.201
0.202
0.147
0.132
Cruise 6
0.214
0.249
0.181
0.206
0.130
0.155
0.376
0.452
1.039
1.043
0.217
0.236
0.171
0.199
0.132
0.155
0.362
0.465
0.997
0.219
0.216
0.194
0.218
0.185
0.196
0.362
0.465
0.997
0.171
0.177
0.145
0.151
0.136
0.135
0.362
0.156
0.161
0.126
0.126
0.128
0.130
Cruise 7
0.183
0.211
0.169
0.171
0.166
0.166
0.199
0.199
0.199
0.199
0.184
0.211
0.169
0.171
0.167
0.166
0.199
0.199
0.199
0.296
0.281
0.235
0.221
0.239
0.217
0.199
0.199
0.199
0.251
0.259
0.241
0.228
0.269
0.260
0.199
0.189
0.200
0.146
0.149
0.151
0.149
Cruise 8
0.233
0.154
0.143
0.109
0.117
0.114
0.170
0.185
0.660
0.648
0.240
0.138
0.091
0.088
0.092
0.094
0.154
0.179
0.564
0.350
0.296
0.179
0.286
0.197
0.217
0.154
0.179
0.564
0.341
0.243
0.178
0.194
0.162
0.161
0.154
0.289
0.194
0.140
0.143
0.151
0.156
                                         279

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 Table 4.4.27. Initial Concentration of IPCBs in Sediment Segments for Lake Michigan
Segment
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
ZPCBs (ng/L)
11238.3
10438.8
8680.7
9765.4
8451 .6
8349.3
12396.8
14131.3
9141.6
9960.8
13205.0
2528.2
8925.6
3730.8
626.7
1447.1
3814.2
4599.3
6756.3
8685.5
5617.1
4717.8
3818.2
2183.8
3071.1
2872.5
2148.1
Segment
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94

IPCBs (ng/L)
2178.5
2453.6
1740.4
1711.2
1877.1
1501.6
1556.9
1802.7
2855.3
12509.3
13727.2
7267.3
10515.1
9084.9
7084.6
9136.2
10282.1
11444.5
11787.7
21459.7
15602.5
25096.6
20717.4
7957.1
9250.4
8975.4

 partitioning  model  (Equation  4.3.14)  for  each
 selected PCB congener. A number of studies have
 shown  a strong  relationship between  measured
 organic carbon partition coefficients and the octanol-
 water partition coefficient (Kow) for PCBs in natural
 water systems (Karickhoff,  1981;  Hassett  et  a/.,
 1980; Di Toro, 1985; Thomann and Mueller,  1987).
 For the purpose of comparison in general variation
 trend, the set of estimated POC partition coefficients,
 K'POc,a> were Plotted (Figure 4.4.15) against the Kow
 calculated by Hawker and Connell (1988) for 209
 PCB congeners. Figure 4.4.15 illustrates the strong
 relationship between the POC partition coefficients
 estimated from the two-phase partitioning model and
 Kow computed by Hawker and Connell (1988). This
 result supported the conclusion of Eadie etal. (1990)
that "ambient Great Lakes POC is similar to octanol
as a substrate  for binding" and  indicated  the
reasonable accuracy of the initially estimated K'POc,a
coefficients from the two-phase partitioning model.
There were some exceptions for less-chlorinated and
high-chlorinated congeners.   Assuming the DOC
partition coefficients (KDOC) has a similar variation
trend as  K'POc,a and that  it is about two orders
magnitude less than K'POc a (Carter and Suffet, 1982;
Landrum et a/., 1984,1987; Hassetand Milicic, 1985;
Chiou etal., 1986, 1987; Eadie etal., 1990, 1992;
Bierman  et a/., 1992),  the  KDOC for each PCB
congener were estimated by multiplying the value of
KVoc,a by  a factor of 10'2.  When necessary, the
values of the pair of partition coefficients for a PCB
congener  were adjusted during the course of the
LM2-Toxic PCB calibration.  Table 4.4.28 presents
the  final  values  of  POC  and DOC  partition
coefficients used in the LM2-Toxic.
                                              280

-------
              Hawker's log Kow vs Two Phase log K'poc.a Partition Coefficients
     8.0-


     7.5'
• log Kow
Dlog K'poc.a
                                                                                       5.0
        0   10   20  30  40  50  60  70  80  90  100 110 120 130 140 150 160  170 180 190 200
                                        PCB Congener

Figure 4.4.15. Comparison between the estimated log K'POc,afor tne LMMBP selected PCB congeners
based on the two-phase partitioning model and Kow calculated by Hawker and Connell (1988) for all
209 PCB congeners.
Air-water exchange  was another crucial process
affecting  PCB concentrations in Lake  Michigan.
Parameters related to volatilization were molecular
weight, enthalpy, and entropy of each PCB congener.
Table 4.4.29 provides a listing of molecular weights,
enthalpy, and entropy, including their sources for
each PCB congener or co-eluting PCB congeners
required for the LM2-Toxic.  During the LM2-Toxic
PCB calibration, the values of enthalpy for some PCB
state variables were adjusted within  their allowed
ranges (i.e., one standard error). It is worth noting
that the enthalpy and entropy for co-eluting PCB
congeners were  estimated  by  simple  arithmetic
averaging of the values for the individual congeners.
Because quantitative information for  the mass of
each PCB congener in a co-eluting PCB mixture was
lacking, a  certain  amount  of  uncertainty was
associated with their estimated enthalpy and entropy.
Thus, some adjustments on these parameters were
done during the LM2-Toxic PCB calibration.

4.4.3.4 Kinetic Time Functions

The kinetic time functions for the LM2-Toxic included
reaeration,  segment-specific wind   speed  time
functions, segment-specific water temperature time
functions, and segment-specific air temperature time
functions (Schwab and Beletsky, 1998).
                                  References

                                  Bamford, H.A., D.L  Poster, R.E. Huie,  and J.E.
                                      Baker.    2002.    Using Extrathermodynamic
                                      Relationships  to   Model  the  Temperature
                                      Dependence of Henry's Law Constants of 209
                                      PCB  Congeners.    Environ.  Sci.  Technol.,
                                      36(20):4395-4402.

                                  Baun, K.  1982. Alternative Methods of Estimating
                                      Pollutant Loads in Flowing  Water.  Wisconsin
                                      Department of Natural Resources, Madison,
                                      Wisconsin. Technical Bulletin 133, 11 pp.

                                  Beale, E.M.L.  1962.  Some Uses of  Computers in
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                                  Bierman, V.J., Jr., J.V.  DePinto, T.C. Young, P.W.
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                                      Environmental Protection Agency,  Office  of
                                      Research and Development, ERL-Duluth, Large
                                      Lakes Research  Station,  Grosse  lie, Michigan.
                                      381 pp.
                                             281

-------
Table 4.4.28.  Final Partition Coefficients for the LMMBP Selected PCBs Used in the LM2-Toxic
PCB Congeners
5+8
15+17
16+32
18
26
28+31
33
37+42
44
49
52
56+60
66
70+76
74
77+110
81
92+84
85
87
89
99
101
105+132+153
118
123+149
163+138
146
151
170+190
172+197
180
1 87+1 82
1 95+208
1 96+203
201
log K'POc.a1(M
-------
Table 4.4.29. Values of Parameters Used for Air-Water Exchange in the LM2-Toxic for the LMMBP
Selected PCB Congeners (Bamford et al., 2002)
PCB Congeners Molecular Weight (g/mol)
5+8
15+17
16+32
18
26
28+31
33
37+42
44
49
52
56+60
66
70+76
74
77+110
81
92+84
85
87
89
99
101
105+132+153
118
123+149
163+138
146
151
170+190
172+197
180
187+182
195+208
196+203
201
223.10
240.30
257.54
257.54
257.54
257.54
257.54
274.77
291 .99
291 .99
291 .99
291 .99
291.99
291 .99
291.99
309.21
291.99
326.43
326.43
326.43
326.43
326.43
326.43
349.40
326.43
343.66
360.88
360.88
360.88
395.32
412.55
395.32
395.32
446.99
446.99
429.77
Enthalpy (kJ/mol)
46.0
46.5
44.5
35.0
41.0
34.0
43.0
41.0
26.0
25.0
32.5
28.5
27.5
27.0
25.0
35.0
33.0
25.0
21.0
33.0
27.0
22.0
30.0
61.0
44.0
47.5
80.0
61.0
32.0
145.0
137.0
137.0
97.0
146.0
143.0
138.0
Entropy (kJ/mol °K)
0.115
0.120
0.110
0.080
0.100
0.085
0.110
0.095
0.050
0.050
0.070
0.065
0.060
0.060
0.050
0.090
0.070
0.040
0.050
0.070
0.040
0.020
0.070
0.190
0.130
0.135
0.250
0.170
0.100
0.485
0.460
0.450
0.295
0.250
0.490
0.460
                                          283

-------
Carter, C.W. and I.H. Suffet.  1982.  Binding of DDT
   to Dissolved Humic Materials.   Environ.  Sci.
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Chapra, S.C. and K.H. Reckhow  (Eds.).  1983.
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   Science Publishers, Ann  Arbor,  Michigan.  492
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Chiou, C.T., R.L Malcolm, T.I. Brinton, and D.E. Kile.
   1986.  Water Solubility Enhancement  of Some
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Chiou, C.T., D.E. Kile, E.I. Brinton, R.L. Malcolm, and
   J.A.  Leenheer.  1987.  A Comparison  of Water
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Conn, T.A., L.L. DeLong, E.J. Gilroy, R.M. Hirsch,
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DePinto, J.V.,  R.  Raghunathan, P  Sierzenga, X.
   Zhang, V.J. Bierman, Jr., P.W. Rodgers, and T.C.
   Young.   1993.   Recalibration of GBTOX:   An
   Integrated Exposure Model for Toxic Chemicals
   in Green Bay, Lake Michigan. Final Report. U.S.
   Environmental  Protection  Agency,  Office of
   Research and Development, ERL-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   132pp.

Di Toro, D.M. 1985. A Particle Interaction Model of
   Reversible  Organic   Chemical   Sorption.
   Chemosphere, 14(10):1503-1538.

Eadie,  B.J., N.R. Morehead,  and  P.P. Landrum.
   1990. Three-Phase Partitioning of Hydrophobic
   Organic  Compounds  in Great Lakes  Water.
   Chemosphere, 20(1/2):161-178.

Eadie, B.J.,  N.R. Morehead, J.V. Klump,  and  P.P.
   Landrum.  1992.  Distribution  of Hydrophobic
   Organic  Compounds Between  Dissolved  and
   Particulate Organic Matter in Green Bay Waters.
   J. Great Lakes Res., 18(1):91-97.
Eadie, B.J. and J.A. Robbins.  2004.  Composition
   and Accumulation of Lake Michigan Sediments.
   In: M. Munawar andT. Edsall (Eds.), The State of
   Lake Michigan. In press.

Endicott,  D.D.,  W.L. Richardson, and D.J. Kandt.
   2005.   1992 MICHTOX: A Mass  Balance and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan.   In:   R.  Rossmann  (Ed.),
   MICHTOX:     A  Mass  Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan,  Part  1.   U.S.  Environmental
   Protection  Agency, Office of  Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse lie, Michigan.
   EPA/600/R-05/158, 140 pp.

Green, M.L., J.V. DePinto, C.W. Sweet, and K.C.
   Hornbuckle.   2000.   Regional  Spatial and
   Temporal  Interpolation of  Atmospheric PCBs:
   Interpretation of Lake Michigan  Mass Balance
   Data. Environ. Sci. Technol., 34(9):1833-1841.

Hall, D.  and D. Robertson.  1998.   Estimation of
   Contaminant  Loading  from Monitored  and
   Unmonitored Tributaries to Lake Michigan for the
   USEPA Lake  Michigan Mass Balance Study.
   Quality Systems  and  Implementation  Plan.
   Submitted October23,1998. U.S. Environmental
   Protection   Agency,   Great  Lakes  National
   Program Office, Chicago, Illinois. 19 pp.

Hassett, J.P., J.C. Means, W.L. Barnwart, and S.G.
   Wood.  1980.  Sorption Properties of Sediments
   and    Energy-Related   Pollutants.      U.S.
   Environmental Protection Agency, Environmental
   Research   Laboratory,   Athens,  Georgia.
   EPA/600/3-80/041, 133 pp.

Hasset, J.P. and E. Milicic. 1985. Determination of
   Equilibrium and Rate Constants  for Binding of a
   Polychlorinated Biphenyl Congener by Dissolved
   Humic  Substances.   Environ.  Sci. Technol.,
   19(7):638-643.

Hawker, D.W. and D.W. Connell.  1988.  Octanol-
   Water Partition Coefficients  of  Polychlorinated
   Biphenyl Congeners.   Environ. Sci. Technol.,
   22(4):382-387.
                                             284

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Karickhoff, S.W. 1981. Semi-Empirical Estimation of
   Hydrophobic Pollutants on Natural Sediments
   and Soils.  Chemosphere, 10(8):833-846.

Landrum, P.P., S.R. Nihart, B.J. Eadie, and W.S.
   Gardner.  1984.  Reverse-Phase  Separation
   Method  for  Determining Pollutant  Binding to
   Aldrich  Humic  Acid  and  Dissolved Organic
   Carbon  of  Natural  Waters.    Environ.  Sci.
   Technol., 18(3): 187-192.

Landrum, P.P.,  S.R. Nihart, B.J.  Eadie, and  L.R.
   Herche.  1987. Reduction in  Bioavailability of
   Organic   Contaminants  to  the  Amphipod
   Pontoporeia hoyiby Dissolved Organic Matter of
   Sediment  Interstitial Waters.  Environ. Toxicol.
   Chem., 6(1 ):11-20.

Lesht, B.M. 1988a. Comparison of Two Computer
   Programs for  Volume-Weighted Averaging of
   Limnological  Data.     Final  Report.     U.S.
   Environmental Protection Agency, Great Lakes
   National Program Office, Chicago, Illinois.

Lesht, B.M.  1988b. Nonparametric Evaluation of the
   Size  of  Limnological  Sampling  Networks:
   Application to the Design of a Survey of Green
   Bay. J. Great Lakes Res., 14(3):325-337.

McCarty, H.B., J. Schofield, K. Miller, R.N. Brent, P.
   Van Hoff,  and B. Eadie.  2004.  Results of the
   Lake  Michigan  Mass   Balance   Study:
   Polychlorinated Biphenyls and frans-Nonachlor
   Data Report.   U.S. Environmental Protection
   Agency, Great Lakes National Program Office,
   Chicago, Illinois. EPA/905/R-01/011, 289 pp.

Miller, S.M., M.L. Green,  J.V. DePinto, and  K.C.
   Hornbuckle.  2001.   Results from  the  Lake
   Michigan Mass Balance Study: Concentrations
   and  Fluxes  of  Atmospheric   Polychlorinated
   Biphenyls and frans-Nonachlor.  Environ. Sci.
   Technol., 35(2):278-285.
Richards, *R.P. 1998. Estimation of Pollutant Loads
   in Rivers and Stream: A Guidance Document for
   NPS Programs.  U.S. Environmental Protection
   Agency, Region VIII, Denver, Colorado. 108 pp.

Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and
   K.R. Rygwelski (Eds.). 2004. The Lake Michigan
   Mass Balance Project Quality Assurance Plan for
   Mathematical  Modeling.    Prepared  by  the
   Modeling  Workgroup.    U.S.  Environmental
   Protection Agency, Office  of Research  and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes  Research Station, Grosse  lie, Michigan.
   EPA/600/R-04/018, 233 pp.

Robbins,  J.A., N.R. Morehead,  R.W. Rood, D.N.
   Edgington, and S. Meyer. 1999.  Accumulation
   and Near-Surface Mixing of Sediments in Lake
   Michigan as Determined for the Lake Michigan
   Mass  Balance  Program, Volumes  1  and  2.
   Project Report.   U.S. Environmental Protection
   Agency, Office of Research and Development,
   ERL-Duluth, Large  Lakes  Research  Station,
   Grosse lie, Michigan. 503 pp.

Schwab, D. and D. Beletsky. 1998. Lake Michigan
   Mass Balance Study:  Hydrodynamic Modeling
   Project.   National Oceanic  and Atmospheric
   Administration,   Great  Lakes  Environmental
   Research  Laboratory,  Ann  Arbor,  Michigan.
   NOAATechnical Memorandum ERLGLERL-108,
   53pp.

Thomann, R.V. and J.A.Mueller. 1987. Principles of
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   Row Publishers, New York, New York. 644 pp.

U.S.  Environmental Protection Agency. 1997. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume 1:  Sample  Collection
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   Agency, Great Lakes National Program Office,
   Chicago, Illinois.  EPA/905/R-97/012a, 1,440pp.
                                             285

-------
                                          PART 4

                                        LM2-TOXIC
Appendix 4.4.1 Sample Data Interpolation
for the LMMBP

Xiangsheng Xia
Computer Sciences Corporation
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

Many sample data sets of physical  and chemical
parameters collected for the Lake Michigan Mass
Balance  Project (LMMBP) were often  sparse and
occurred on irregular grids. For modeling purposes,
values of these  parameters were needed on a 5 x 5
km grid.  This presented a problem of using sample
data to estimate or predict values in areas which
were not sampled.   Thus, some   interpolation
mechanisms based on "insufficient"  samples were
needed to bridge the gap between the desired and
the reality world of data collection. Distance square
inverse and natural-neighbor interpolation methods
were carefully studied and applied to sample data
analysis for  this project.

The   distance-weighted-averaging   approach
determines  the estimated values at grid points as
the sum of weighted values of the individual sample
datum. In general, the closer a  datum point to the
grid point to be estimated, the greater influence the
datum  at  that  point  exerts.    It  is a  method
characterized as a global approach.  The distance-
weighted-averaging method is well understood and
widely accepted by scientists in various fields. It is
also easy to implement. The major disadvantage of
this method  has been its tendency to smooth out all
small variations in the relatively small local area.
Therefore, it is not very well suited to find the trend of
samples in small local areas. The distance-weighted-
averaging  interpolation  is  compromised by   its
essentially  one-dimensional nature.  Although the
interpolating  surface  is  smooth,  it  cannot, for
instance, form ridges or domes from sparse data.
Furthermore, distance-weighted-averaging is unable
to infer (or extrapolate) a surface lying above or
below the range of sample  values. In general, the
estimation computed by distance-weighted-averaging
lies between  the maximum and minimum  of the
sample data.

Neighborhood-based  interpolation,  on the  other
hand,  is a local  approach which  utilizes all the
(natural) neighbors of the sample points. The natural-
neighbor method can infer values outside the known
range. It  is unique for a given data configuration and
choice of blending  function parameters.   If used
properly  on dense data sets, neighborhood-based
interpolation can  provide much  richer information
such as rapid changes, ridges, or dams in smaller
areas. However, neighborhood-based interpolation,
in contrast to distance-based methods, is much more
complicated to implement and harder to understand.
In case  an ambiguity or unexpected phenomena
arise from a neighborhood-based interpolation, it may
require a knowledgeable person to make reasonable
interpretation of results.

During the course  of the LMMBP  data analysis
process,  distance  square  inverse  interpolation
combined  with  application   codes  written  in
Interactive  Data  Language  (IDL)  were  used
intensively to find the interpolated values of a 5 x 5
km grid  of Lake Michigan for various parameters,
                                              286

-------
such as polychlorinated biphenyls (PCBs), atrazine,
nutrients, etc. On the other hand, natural-neighbor
interpolation was primarily used for sediment data
analysis where sample locations were relatively
dense.

More details of distance square inverse and natural-
neighbor interpolations are presented in the next two
sections.  Many applications of interpolation have
been developed, including  contour plots, volume-
weighted  averages,   and  others.  These  are
discussed  in Section  4.4.1.4.   Some  problems
applying natural-neighbor interpolation are discussed
in Section 4.4.1.5.

A4.4.1.1   The Distance Square Inverse
Method

The inverse distance  to  a power method is  a
weighted-average interpolation. Data are weighted
during interpolation such  that the influence of one
sample point relative to another declines with
increasing distance from the grid node. Weighting is
assigned  to  data using  a weighting power  that
controls  how the weighting  factor drops off  as
distance from a grid node increases. As  the power
increases, the grid node value approaches the value
of the neighboring data points. The weighting power
determines  how quickly  weighting falls off with
distance from  the  grid  node.   As the  power
approaches zero, the generated surface approaches
a horizontal planar surface through the average of all
observations from the data file.  As  the  power
increases,   the generated surface is a "nearest
neighbor" interpolation, and the  resultant surface
becomes  polygons  which  represent the nearest
observation to the interpolated node. Power values
are usually between one and three to avoid extreme
results.  Distance square inverse is the distance-
weighted method with the power chosen as two.

The smoothing  factor parameter  allows one to
incorporate  an  uncertainty factor associated with
sample data.   The  larger the  smoothing  factor
parameter,   the  less   influence   a   particular
observation has in computing a  neighboring grid.
The smoothing factor for this study was 2.5 (miles).

The equation in the inverse distance square method
is:
                                      (A4.4.1.1)
where

/'  =   runs for all samples

v, -   interpolated value at grid point i,

Cj =   value of sample j,

dy =   distance between grid  point i and sample
       location j,

r0 =   smoothing factor

n  =   total number of samples being considered in
       the interpolation

An IDL code which implements the inverse distance
square  interpolation scheme  was  received from
David Schwab (National Oceanic and Atmospheric
Administration,   Great   Lakes   Environmental
Research Laboratory). This was further developed for
the LMMBP data analysis.

A4.4.1.2     The  Natural-Neighborhood
Method

Natural-neighbor  interpolation  offers  a different
approach to spatial interpolation and extrapolation.
It has good mathematical properties and offers more
flexibility than the distance square inverse method.

All interpolation methods involve, to some extent, the
idea that the value of the interpolated point should
depend more on  data values at nearby data sites
than   at  distant   ones.     In   natural-neighbor
interpolation,  the idea of  neighbors in a  spatial
configuration is formalized in a natural way and made
quantitative, and  the  properties of the  method
depend on an apparently  new geometrical  identity
relating this quantitative measure of neighbors to
position.
                                              287

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Any two  data  are  natural-neighbors if there is a
location or region that is equally close to each of the
pair,  and no other  datum is closer.  Any three or
more data on the plane are natural-neighbors if no
other  datum lies within their circum-circle.  The
spatial relationships determined by a set of natural-
neighbors circles have two common and widely
known graphical representations. These are Voronoi
tessellation (of Voronoi polygons) and  Delaunay
triangulations.  The Voronoi tessellation illustrates
that  each datum has  a  unique  natural-neighbor
region associated with it and is bounded by halfway
interfaces of that datum with its natural-neighbor.
Neighborhood  coordinates  are local coordinates
relating the position of the interpolation point to reach
a  datum  in  the neighborhood  subset.   These
coordinates (weights for the interpolation), ranging
between  zero  and one, are proportional to areas
defined on natural-neighbor regions for each of the
data.   Such coordinates are  superior to distance-
based  coordinates.  Distance-based coordinates
make no allowance for the distances to the other
data;  that is,  distance-based interpolation is not
sensitive to a changing spatial  context.   Finally,
natural-neighbor interpolation is  a linear-weighted
average of natural-neighbor coordinates.  The basic
equation used in natural-neighbor interpolation can
be defined as follows:
    /=1

where

V,  =

k  =


Sj  =
                                      (A4.4.1.2)
interpolated value at grid point i,

number  of  samples  inside  the  natural-
neighborhood of V,

value of sample j,

weight associated with S,
The c-code nngridr, a complete commercial package
of the natural-neighbor algorithm, from David Watson
(Watson, 1994), with some modification by in-house
developers, was used for developing applications of
natural-neighbor  interpolation at the Large Lakes
Research Station (LLRS).
A4.4.1.3  Application

Interpolations, distance square inverse or natural-
neighbor,  were  needed to  build two-dimensional
estimates of a 5 x 5 km grid of Lake Michigan from a
limited number of  samples of various parameters.
Other applications  could then be applied based on
the interpolated grid data.

A4.4.1.3.1  Contouring Plots

Interpolated  grid  data  is   a  list  of  numbers
representing the estimates of physical parameters on
the grid for each grid point.  Contour plots connect
points in the grid having the  same value with lines.
An  incredible amount of information about the data
can be  revealed by contour plots.  These include
plateaus and canyons, trends, the existence and
location of high and low concentrations, etc.

Contour plots are very effective visualization tools for
analyzing data. Contours in this study were created
by  using  IDL  and other tools.   There  were  a
surprising variety of approaches used to generate
contours. The various techniques that were applied
possess their own  advantages and disadvantages.
IDL's  standard CONTOUR  procedure  uses grid
contouring which is the most widely used contouring
technique  (Research   Systems,  Inc.,  1995, see
Section 15-1). CONTOUR generates plots from data
stored in a rectangular array (grid data) which usually
is  generated  by  interpolation  and  extrapolation.
Some other information such as the boundary of
Lake Michigan, sample locations, and the maximum
and minimum  values for   samples  were also
produced.

A4.4.1.3.2   Volume-Weighted  Averaging  With
Formulations

One way to evaluate and validate the performance of
mathematical models is to compare the model output
and the measured  data at the same  time (cruises)
and same  location (segments).  Volume-weighted
average (VWA) is  a  method  to  compute the
estimated field data associated with a segment and
a cruise. Depending on the model and segmentation
scheme used, a segment consists of cells of 5 x 5 km
at certain depth range called a layer. The locations of
cells  associated  with  segments   are  normally
                                              288

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provided  by  segmentation  files.    The  volume
concentration for  one cell  can be computed by
multiplying interpolated concentration of this cell by
its volume. The volume concentration for a segment
is the sum of volume concentration of all cells in this
segment.  And finally, VWA can be computed by
dividing the volume concentration of the segment by
the  total  segment  volume.  The equation  for
computing VWA is :
                                     (A4.4.1.3)
where




V, =

C, =

n  =
       volume-weighted average of segment n,

       volume of cell i,

       concentration associated with cell i

       total number of cells
 Besides the VWAs, statistical  information (mean,
 variance, standard error) is also generated for the
 users.  VWAs were generated by IDL programs
 developed in-house. The interpolated grid field data
 were generated by either distance square inverse or
 natural-neighbor  from samples collected  for the
 LMMBP project.

 A4.4.1.4 Discussion

 It  has  been  observed   and   documented  that
 extrapolations  generated  by  using  the natural-
 neighbor c-code  nngridr could cause  problems.
 Extrapolation sometimes is necessary to estimate
 values for grid points located outside the convex hull,
 which is a polygon bounded   by the  outermost
 sample  data points.   At the beginning of the
 interpolation process of running  nngridr, a very large
 triangle is established which encloses all data being
 used for interpolation.  Then,  a pseudo datum  is
 assigned to each of the three vertices of the triangle.
 Extrapolation, if needed, is  performed based on the
 pseudo  data.  This  process  is  doomed  to  be
 unreliable  due  to  the unpredictable nature  of the
pseudo numbers and the large triangle used in this
process.

This problem can be remedied by adding some extra
reasonable pseudo samples at the corners outside
the gridding data area so all interpolated grid points
will be inside the expanded convex hull.  By doing
this, nngridr is forced to use interpolation, rather than
extrapolation, to calculate estimations based on the
original and pseudo samples. This is a more reliable
estimation process. The choice of pseudo samples,
if necessary, should  be based on experience and
nearby samples.

Another limitation of nngridr is that it can only handle
two-dimensional interpolation. There are occasions
when three-dimensional interpolation is needed. One
example   is  the  sediment  PCB  concentration
estimations to be used for fish uptake. This is much
better represented if the depth of samples could be
utilized   to define  the   neighborhood.    The
neighborhood  becomes a three-dimensional  ball
instead of a two-dimensional circle. Because nngridr
is a relatively large program, there was no easy way
to add a three-dimensional  interpolation.

A4.4.1.5  Steps to Run nngridr

The c-code from David Watson, nngridr, was used to
generate the natural-neighbor interpolation. Often,
nngridr was called within an IDL program to generate
the interpolation.  Sample data were reformatted to
the required IDL format. The interpolation on the 5
km grid  was  then  used  for  data analysis and
visualization (post-process) applications.

Details about  how  to   initialize  and  run  nngridr
together  with  IDL application  programs at LLRS
follow.

1.   Change the c-code

    In  nngridr.c,  comment  out   the  statement
    'InstringO'  right  after   the  statement  'printf
    ("Change parameters or Make the grid? C or M)",
    to prevent the  read option  from  a  terminal.
    Therefore, the c-code nngridrto generate the grid
    is run by using the default option 'M'.
                                              289

-------
2  Initialization

   A. Change the  make  file  and then  use the
      command:  %make -f makefile to generate
      the executable code.

   B. The nngridr is run first to generate the initial
      file and setup parameters (file names, grid
      configurations, etc.).  The  result will  be  in
      nngridr.ini, which can be used for successive
      runs without changing  parameters  again.
      The most important aspect of initialization is
      to generate a two-dimensional grid.   The
      southwest corner with longitude-87.9721 and
      latitude 41.5845 is used as the origin of the
      grid coordinates.  The northeast corner is  at
      longitude -84.7206  and  latitude  46.1069
      which  is the grid coordinate of  (53,  102).
      There  is an option to output the grid south-
      north  or north-south.   The orientation  of
      output should be set from south-to-north.
      Otherwise, the image will be upside down.
      See Watson (1994) for more details.

3. Raw data  files are pre-processed to prepare the
   input data for IDL code.  The formats of data files
   should be the same.

4. The  configuration of segmentation  should be
   stored in a file for the segmentation classification.

5. IDL programs are coded to generate data files
   similar to jdavis.dat by reading the pre-processed
   data. Coordinates are converted from longitude
   and latitude coordinates to 5 km grid coordinates.
6.  Once the jdavis.dat is established, nngridr is run
    by the following commands within IDL programs:

    'SPAWN, 'nngridr', Results, /NOSHELU.

    This creates a  child process  under the Unix
    operating  system  and  stores all  messages
    generated by this code into the character array
    Results.

7   After a successful run (need error checking if run
    fails), the  grid  data should be generated and
    named  as nngridr.grd. This file is called in IDL
    programs to generate contour plots, VWA results,
    and statistics.

8.  For the  Unix   system,   both  jdavis.dat and
    nngridr.grd will be destroyed automatically when
    new ones are created.    For  other operating
    systems,  Microsoft Windows, for example, these
    files need to be deleted.

9.  Green Bay data need to be processed separately
    from open lake data.

References

Research Systems, Incorporated. 1995. IDL User's
    Guide:  Interactive Data Language, Version 4.
    Research  Systems,  Incorporated,  Boulder,
    Colorado.  544 pp.

Watson, D.  1994.   nngridr  An Implementation of
    Natural  Neighbor  Interpolation.   Claremont,
    Australia.  170 pp.
                                              290

-------
                                          PART 4

                                       LM2-TOXIC
Chapter  5.  LM2-Toxic Calibration  and
Confirmation

For diagnosis of system behavior and reasonable
prediction of long-term reactions of a water system,
it is very important that a water quality model is well-
calibrated and confirmed under a conceptually well-
constructed solid and toxic chemical dynamics. Two
of  the main  objectives in developing the Level 2
contaminant transport and fate model (LM2-Toxic)
were to calibrate and confirm the model using the
Lake Michigan Mass Balance  Project  (LMMBP)-
generated data and to apply the model for long-term
forecasts  of  the  polychlorinated  biphenyl (PCB)
concentrations in water and sediment of the lake for
different forcing functions  and  loading  scenarios.
The advantages of calibrating a coarse spatial grid
water quality  model like LM2-Toxic are its efficiency
and quickness.

LM2-Toxic is a coupled mass balance  of organic
carbon solids and toxic chemical (PCBs) dynamics.
Because of  no feedback mechanisms  from  PCB
behavior to organic carbon behavior in the model, the
calibration  of the LM2-Toxic was  done using two
separate calibration stages.  The first stage was the
calibration  of organic carbon  dynamics without
considering any behavior of PCBs in the system and
the second  stage  was the calibration  of  PCB
dynamics  without   adjusting  any  parameters
associated with organic carbon dynamics.

Prior to the  organic carbon and  PCB dynamic
calibrations, the LM2-Toxic was used as a thermal
balance model to calibrate  the  vertical  dispersion
coefficients between  the water  column  layers.  A
detailed description of inputs used in the thermal
balance model can be found in Part 4, Chapter 4,
Section 4.4.1.2.

The data collected for the LMMBP were spatially
averaged  using  a  volume-weighted  averaging
algorithm to generate segment-specific cruise mean
concentrations (Appendix 4.4.1).  The  averaging
algorithm also computed the statistical standard error
that was expressed as  error bars related to  the
mean.    The  cruise  mean  concentrations were
compared with the predicted concentrations from the
LM2-Toxic for  the  vertical dispersion coefficients
calibration, the organic carbon dynamics calibration,
and the PCB dynamics calibration.

4.5.1   Vertical  Dispersion  Coefficients
Calibration

Vertical dispersion coefficients  at the  interfaces
between LM2-Toxic water column layers were very
important to overall water transport in Lake Michigan.
The vertical mixing defined by the vertical dispersion
coefficients moves large amounts of mass vertically
in  the  lake  and is strongly influenced by water
temperature that has a distinct seasonal variation. A
thermal balance model was applied to calibrate the
predefined  vertical dispersion  coefficients.  The
results in Appendix  4.5.1 demonstrate excellent
agreement between observed temperature and the
model simulation for the LMMBP period. The results
from the thermal balance model were also compared
to   the  results  from   the   Princeton   Ocean
Hydrodynamic  Model (POM) (Schwab and Beletsky,
1998) for selected segments (Figure 4.5.1). Both
                                             291

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   30
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                         segment 25
                                        30
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                                                       Temperature (degrees C)
                                                             LM2-Toxic
                   — Model Output
                   • Cruise Mean
               error bars = standard error
                                     30
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    Mar-94 Aug-94  Jan-95   Jun-95  Nov-95   Mar-94  Aug-94  Jan-95  Jun-95  Nov-95   Mar-94  Aug-94  Jan-95  Jun-95 Nov-95

Figure 4.5.1 a.  Comparison between the temporal profiles for temperature results from the LM2-Toxic.
    30
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    30
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models did a good job of simulating temperature in
the lake.

4.5.2     Organic  Carbon   Dynamics
Calibration

Because the movement of particulate organic carbon
(POC) (POC = PDC + BIG, where PDC = particulate
detrital carbon, BIC =  biotic carbon) dominates the
transport and fate of the PCBs in a  natural  water
system, the quality of the calibration of  the organic
carbon sorbent dynamics was very  crucial  for a
successful subsequent calibration of PCB dynamics.
The  calibration strategy for the organic carbon
dynamics was to 1) fix  as  many independent
parameters used in the model as possible based on
literature, field measurements, and analytical results
from the field samples, and 2) adjust the  parameters
considered to be the most uncertain and site-specific
without   using values  exceeding  their   range
constrained by literature.

4.5.2.1 Calibration Process/Procedure

The   procedures  taken  in  the  organic  carbon
dynamics calibration  for the LM2-Toxic were the
following:

1. Fixing segment-specific sediment burial rates (vb)
   and thickness of surficial sediment mixing layer
   based on  analyzed  results from the LMMBP
   sediment core measurements (Bobbins  et al.,
   1999).

2. Fixing the dissolved organic  carbon (DOC)
   diffusion  coefficient  between   the  surficial
   sediment layer and the overlying water column at
   1.73 x 10-4  m2/d (DePinto et al., 1993).

3. Fixing segment-specific  critical  wave  heights
   using Equation 4.3.12.

4. Assigning   initial   segment-specific   settling
   velocities for BIC and PDC based on values from
   the literature (DePinto et al., 1993; Eadie  et al.,
   1990; Baker et al., 1991; Eadie, 1997, Eadie et
   al., 1984; Thomann and Di Toro, 1983).

5. Estimating  segment-specific  empirical   wave
   coefficients (a) using Equation 4.3.11.
6. Computing segment-specific daily resuspension
   rates using procedures described in  Sections
   4.3.4.2.1   (Steady-State   Resuspension
   Calculation) and 4.3.4.2.2  (Empirical Wave-
   Induced Resuspension Calculation).

7. Executing  the LM2-Toxic,  examining  model
   outputs (carbon concentrations), and adjusting
   segment-specific  settling  velocities   and
   resuspension  rates  accordingly by  repeating
   steps 4-6 as necessary.

8. Adjusting   biochemical    organic    carbon
   transformation rates and yield coefficients listed
   in Table 4.4.16.

Most  of  the  organic  carbon   decay  rates
(transformation rates) and yield coefficients were
initially given the same values used in the Green Bay
Mass Balance  Project (GBMBP) and were adjusted
during  the LM2-Toxic  organic carbon  dynamics
calibration. The principal parameters adjusted during
the organic carbon dynamics calibration were rates
related to carbon vertical transport such as settling
and resuspension velocities, carbon decay rates, and
yield coefficients.  The final values  for these rates
had to be consistent with available literature data and
limnological theory.

4.5.2.2 Results and Discussion

The final values for the biochemical transformation
rates and yield coefficients are presented in Table
4.4.16. The carbon decay rates and yield coefficients
shown  in this  table are quite  consistent with the
limnological theory of organic carbon cycling  in a
natural water system. It was expected that the final
carbon decay rates (left-hand side of Equations 4.3.3
  4.3.5)  in pelagic freshwater systems  would
decrease in the  order of BIC,  PDC, and DOC.
Therefore, the  values of substrate saturated decay
rates and Michaelis-Menten half-saturation constants
for the organic carbon would have the  following
sequences:
'Wo- respectively.

The lake-wide concentrations (including Green Bay)
in surface water layers (epilimnion) for DOC, PDC,
                                              293

-------
and BIG could be as high  as  1.8, 0.30, and 0.20
mg/L, respectively. It is favorable and reasonable to
have'the value of Michaelis-Menten half-saturation
constant for each organic carbon state variable close
to double that of  its  concentration.   The final
sediment PDC decay rate used in the LM2-Toxic was
within the range of the value (5.7 x 10'5 d'1 at 20°C)
used in GBMBP (DePinto et ai, 1993) and the value
(0.001  d'1) from other  literature  (Gardiner  et al.,
1984).  The  values of yield coefficients (Y(BIC^PDC),
Y(PDC-DOO in  Table 4.4.16 indicated  less loss to
carbon dioxide (CO2) during the conversion from BIG
to PDC than during the conversion from  PDC to
DOC.

The  model  results  of  organic  carbon dynamics
calibration for  the  LM2-Toxic  are  presented in
Figures 4.5.2 to 4.5.4 for temporal profiles of all 41
water  column segments.   The  complete  set of
calibration plots for the organic carbon including
temporal  profiles in the sediments are provided in
Appendix 4.5.2.

Figure 4.5.2  shows slight temporal variation of DOC
in the main lake and outer Green Bay (see  Figure
4.3.1  for Level 2 segmentation).  This temporal
change of DOC concentration was closely related to
the strong seasonal  variation of PDC concentration
and was  more evident in the epilimnion of the lake.
Other  than the inner Green Bay area, there was
almost no horizontal or vertical spatial variation for
DOC.

The temporal profiles based on the model outputs for
both BIG and PDC (Figures 4.5.3 and 4.5.4) showed
very strong seasonal variation throughout the lake,
especially in  the epilimnion segments. Excluding the
inner shallow water of Green Bay segments where
BIG concentration was controlled by its load from the
Fox River and very localized algal growth, there was
not much horizontal spatial variation for BIG in the
lake. The peaks of the BIG temporal profiles for the
epilimnion segments resulted from an algal bloom in
late spring and early summer.  Compared with the
epilimnion segments, having  a similar temporal
variation,  the  concentration  of  BIG  decreased
dramatically  in the hypolimnion segments.

The temporal profiles of PDC (Figure 4.5.4) indicated
that the main  lake and Green Bay were two very
different  systems.   It appeared that the  PDC
concentration in Green Bay was more dominated by
sediment-water  interactions  (i.e.,   resuspension
events evidenced by the spikes on the plots) than
other components or processes such as tributary
loads, decomposition of BIC, and its own decay. In
the main lake where water segments were larger and
thicker,  there was little evidence of  resuspension
events from the  PDC temporal profiles, with the
exception of a couple of main lake segments at the
end of 1995. Algal primary production and BIG-to-
PDC decay were obvious control processes for PDC
concentration in the  main lake, especially in the
epilimnion.  An almost constant concentration of PDC
(0.12 mg/L)  in  the   large bottom water  column
segments indicated that, without much contribution
from BIC decay to PDC in these segments, there
were persistent and substantial PDC fluxes entering
these segments by settling from the upper water
column  layer,  resuspension from  the  surficial
sediments,   and/or  focusing  process  from the
adjacent shallower area  (segments) in  order to
maintain this concentration of PDC.

Figures 4.5.5a to 4.5.5c show a comparison between
the model  predictions and observed data  (cruise-
segment mean concentration) of DOC, BIC, and PDC
for the  entire calibration  (i.e., the LMMBP  study
period).  In general, there was good  agreement
between the data and model predictions  for  all
organic carbons.  There was a slight overprediction
of BIC concentration indicated by the low slope of the
regression  line.   Though  the  results  from the
regression (Figure 4.5.5b) indicated a poorer quality
of fit to the observed data and overprediction of BIC,
visual  examination  of  temporal profiles  of  BIC
concentration (Figure 4.4.3) suggested the fit was
good for most of the segments.

4.5.3 PCB Dynamics Calibration

Because of no feedback mechanisms from PCB
dynamics to organic carbon dynamics, only a limited
number  of  parameters   associated  with  PCB
dynamics needed to be slightly adjusted.  Due to the
significant influence of organic carbon movement on
the transport and fate of PCBs in Lake Michigan, the
PCB partitioning coefficient became one of the very
crucial parameters for the LM2-Toxic PCB dynamics
calibration.  Mass budget analysis for ZPCBs (see
Chapter 6 for details) based on the outputs of  the
LM-2 Toxic for the two-year LMMBP period indicated
                                              294

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error bars = standard error
Dissolved Organic Carbon (mg/L)
Lake Michigan layers 1 , 2, 3
upper 30 meter
water segments

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Figure 4.5.2. Temporal profiles of DOC in the Lake Michigan water column segments for the organic

carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data.
                                                    295

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Figure 4.5.2. Temporal profiles of DOC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data (Continued).
                                             296

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error bars = standard error
Dissolved Organic Carbon
(mg/L)
Green Bay
water segments

   Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
Figure 4.5.2. Temporal profiles of DOC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data (Continued).
                                            297

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-«. Model Output
* Cruise Mean
error bars = standard error
Blotlc Carbon (mg/L)
Lake Michigan layers 1 , 2, 3
upper 30 meter
water segments




     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.3. Temporal profiles of BIC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data.
                                             298

-------
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30 meters to bottom
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     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
Figure 4.5.3.  Temporal profiles of BIC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the  LM2-Toxic and the LMMBP cruise mean data (Continued).
                                                   299

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                                             —, Model Output
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                                          error bars = standard error
                                                                                          Biotic Carbon (mg/L)
                                                                                               Green Bay
                                                                                             water segments
      Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.3.  Temporal profiles of BIC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data (Continued).
                                                     300

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                                               segment 25
                                                -^ Model Output

                                                » Cruise Mean

                                            error bars = standard error
                                         Participate Detrital Carbon (mg/L)

                                            Lake Michigan layers 1, 2, 3

                                                 upper 30 meter

                                                 water segments
       Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94 Dec94 Jul95 Dec95
Figure 4.5.4.  Temporal profiles of PDC in the Lake Michigan water column segments for the organic

carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data.
                                                       301

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***
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.4. Temporal profiles of PDC In the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data (Continued).
                                              302

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                                                                                         «•% Model Output
                                                                                          » Cruise Mean
                                                                                      error bars = standard error

                                                                                            Participate
                                                                                          Detrital Carbon
                                                                                             (mg/L)
                                                                                            Green Bay
                                                                                          water segments
     Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.4.  Temporal profiles of PDC in the Lake Michigan water column segments for the organic
carbon dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data (Continued).
                                                      303

-------
 3.0


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  1.0
                  1.1548x-0.1961
                   R2 = 0.55
                        DOC
                       (figure a)
    1.0   1.4   1.8   2.2   2.6
           Model outputs mg/L
                                                                               y = 0.9113x +0.0153
                                                                                  R2 = 0.5347
y = 0.5792X + 0.0281
   R2 = 0.2297
                                                0.2
                                           Model outputs mg/L
                          0.2     0.4    0.6
                           Model outputs mg/L
Figure 4.5.5. Observed data versus the LM2-Toxic predictions for DOC, BIC, and PDC for the LMMBP
period.
Michigan.  Enthalpy in Equation 4.3.24 was one of
the volatilization parameters  to  which the  PCB
concentrations in the main lake were very sensitive
to,  and it was slightly adjusted  during the  PCB
dynamics calibration.  In addition to adjustments of
these two parameters, the initial conditions in some
of the Green Bay sediment segments were adjusted
for  some of  the  PCB  congeners.   This  was
considered acceptable because PCB concentrations
initially used in the LM2-Toxic for both the water
column and the sediment segments of Green Bay
were very questionable. Previous studies (Bierman
et al., 1992, DePinto et al, 1993) of the Green Bay
system have shown that strong PCB gradients exist
in both the water column and the sediments.  Small
number of samples  of that  system  would not,
therefore,  be able  to capture  the details of these
gradients.   Also,  the system  is  vulnerable  to
resuspension events that  can contribute to  high
variability in observed  PCB concentrations in the
water column. The initial PCB concentrations for the
Green Bay water column segments were derived
from interpolations based on samples from only two
water stations (GB17 and GB24M).  The initial PCB
concentrations for the Green Bay sediment segments
were  arithmetic averages of samples collected at
only four sediment stations (two Ponar sampling
locations: SD89P and  SD106P; two gravity  core
sampling locations: 95GI and 113GI) during the
LMMBP period.
                                                 The segment-specific cruise mean concentrations for
                                                 each PCB congener were generated using the same
                                                 inverse  distance and volume-weighted averaging
                                                 algorithm (Appendix 4.4.1) based on the 1994-1995
                                                 LMMBP samples collected during the eight cruises.
                                                 These mean concentrations were then used for the
                                                 LM2-Toxic PCB dynamics calibration and comparison
                                                 with the LM2-Toxic outputs.

                                                 4.5.3.1  Calibration Procedures

                                                 The  specific  procedures taken in PCB congener
                                                 dynamics in the lake for the LM2-Toxic were the
                                                 following:

                                                 1.  Fixing the PCB diffusion coefficient between the
                                                    surficial sediment layer and the overlying water
                                                    column at 1.73 x 10/4 nf/d (DePinto et al., 1993).

                                                 2.  Assigning estimated initial partition coefficients
                                                    for  each PCB congener computed by using a
                                                    two-phase partitioning model based on data from
                                                    the LMMBP collected field samples.

                                                 3.  Assigning volatilization-related  parameters such
                                                    as enthalpy and entropy of each PCB congener
                                                    as derived by Bamford et al. (2002).

                                                 4.  Slightly adjusting, within the bounds of literature
                                                    values  and/or acceptable  variation in field
                                                    observations, sediment PCB initial conditions for
                                                    the  Green  Bay sediment  segments, partition
                                              304

-------
   coefficients, and enthalpy for the PCB congeners
   as  necessary  to  improve  the  fit  between
   observed data and model outputs.

4.5.3.2 Results and Discussion

The values for the final set of partition coefficients
(log KVoc,a and Io9 KDOC in Table 4.4.28) for the
LMMBP selected individual  and co-eluting  PCB
congeners were close to the octanol-water partition
coefficient (Kow) calculated  by Hawker and Connell
(1988).  As  shown in  Figure  4.4.15,  the partition
coefficients for some of the low and high chlorinated
PCB congeners were adjusted the most during the
LM2-Toxic PCB  calibration.   Based on the  PCB
dynamics  calibration   procedure  outlined  in  the
previous section along with  the parameters listed in
Tables 4.4.28  and 4.4.29,  the PCB congener
dynamics in  the LM2-Toxic were calibrated for the
two-year LMMBP period. Due to the extremely large
amount of output from the model on a congener
basis, the results and discussion in this section will
be focused on only one PCB congener (PCB28+31)
and  the  sum  of all  the  LMMBP  modeled  PCB
congeners (IPCBs) for the purpose of demonstrating
the calibration outcomes. PCB28+31 had the highest
external loads, and its concentration  in the Lake
Michigan  system was easily double that of the next
closest PCB congener.   The  mass of  IPCBs
accounted for approximately  70-75% of total  PCB
mass.

Figures 4.5.6  and 4.5.7  show  that temporal
calibration profiles of all 41 water column segments
for  PCB28+31  and  IPCBs  (dissolved  phase +
paniculate phase).   Appendix 4.5.3  provides a
complete  set of calibration plots for PCB28+31 and
IPCBs, including temporal profiles in sediments, total
dissolved phase  (unbound  and DOC  bound), and
particulate phase (sorbed to PDC and BIG).

Although complete sets of calibration plots for each
PCB congener are available, it is impossible to put all
of them in this report.

Based  on cruise mean  data and model outputs in
Appendix 4.5.3, PCB concentration in the dissolved
phase was approximately double its concentration in
the particulate phase in the main lake. The temporal
profiles (Figures 4.5.6 and 4.5.7) show some degree
of temporal variation controlled by a combination of
seasonal variation of  external loads, atmospheric
concentration, and sediment resuspension events.
The separate dissolved and particulate PCB temporal
plots  (Appendix  4.5.3)  also  illustrate  that  the
seasonal variation of  particulate  PCBs was  much
more prominent  than the seasonal  variation  of
dissolved PCBs.

The temporal profiles (Figures  4.5.6  and 4.5.7)
indicate  that  there  was  a  slight  longitudinal
concentration  gradient throughout  the  main  lake.
The highest concentrations  were  found in  the
southern segments due to higher  PCB atmospheric
deposition  (dry  + wet)  and  much higher  PCB
atmospheric concentrations observed  in the area
close to Chicago. There was little vertical gradient
found based on main  lake cruise  mean data.  This
indicated that vertical transport  such  as vertical
advective flows,  vertical mixing, and resuspension
strongly influenced the PCB concentrations in Lake
Michigan water column.  On the other hand,  the
model output predicted higher concentrations  in  the
bottom layer of the main lake. Possible explanations
of this  difference between predicted and observed
include: 1) segment-specific resuspension rates for
the depositional area were not setup properly for  the
model, 2) the initial average PCB concentrations in
surficial sediments were too high,  or 3) there should
be a bottom layer with a large pool of resuspended
materials (benthic nepheloid layer, BNL) containing
PCBs added into the model segmentation (Baker et
al, 1991;  Eadie et al., 1984;  Eadie et al.,  1990;
Eadie,  1997).  The BNL, with much higher carbon
and PCB concentrations, could serve  as a buffer
between  the hypolimnion layer  and the surficial
sediment.  The LM2-Toxic PCB dynamics calibration
was conducted  on   the   PCB  congener  level.
Therefore, the calibration was  a  tedious and very
time-consuming task.  The PCB calibration strategy
was to adjust only those parameters that definitely
needed to be adjusted. The PCB concentrations in
the water column, especially the hypolimnion, were
very sensitive to  the  PCB  initial  conditions in  the
sediments.  Sediment samples  collected by box
coring may be biased on the high side due to  the
selection of sites that  could be cored (fine-grained
sediment).   In the deep depositional areas of  the
lake, it is unlikely that resuspension occurs. To avoid
controversy and  excessive effort  on  the model
calibration, the segment-specific resuspension rates
were   not  adjusted  independently  during  the
                                             305

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~. Model Output
» Cruise Mean
error bars = standard error
PCB 28+31 (ng/L)
Lake Michigan layers 1 , 2, 3
upper 30 meter
water segments


      Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95


Figure 4.5.6.  Temporal profiles of PCB28+31 (dissolved phase + particulate phase) in Lake Michigan
water column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean
data.
                                             306

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* * *
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    Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95


Figure 4.5.6.  Temporal profiles of PCB28+31 (dissolved phase + particulate phase) in Lake Michigan
water column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean
data (Continued).
                                              307

-------
    Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95


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                   -s Model Output

                   » Cruise Mean
               error bars = standard error
                  PCB 28+31 (ng/L)

                     Green Bay
                   water segments
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.6.  Temporal profiles of PCB28+31 (dissolved phase + particulate phase) In Lake Michigan
water column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean
data (Continued).
                                                    308

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segment 24
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•~> Model Output
» Cruise Mean
error bars = standard error
IPCB (ng/L)
Lake Michigan layers 1 , 2, 3
upper 30 meter
water segments




    Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.7. Temporal profiles of ZPCBs (dissolved phase + particulate phase) in Lake Michigan water
column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data.
                                            309

-------
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                                                                                     -^ Model Output
                                                                                     • Cruise Mean
                                                                                 error bars = standard error
                                                                                      SPCB (ng/L)
                                                                                       Green Bay
                                                                                     water segments
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
Figure 4.5.7. Temporal profiles of ZPCBs (dissolved phase + particulate phase) in Lake Michigan water
column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data
(Continued).
                                                    310

-------
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Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
•N, Model Output
t Cruise Moan
error bars = standard error
1PCB (ng/L) ^yincm-ru
Lake Michigan layers 4 & 5
30 meters to bottom
water segments

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     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
Figure4.5.7. Temporal profiles of ZPCBs (dissolved phase + participate phase) in Lake Michigan water
column segments for PCB dynamics calibration of the LM2-Toxic and the LMMBP cruise mean data
(Continued).
                                             311

-------
calibration,  and   the  PCB   initial   sediment
concentrations  in the main lake  were kept as the
original results of the interpolation based on surf icial
sediment field data.

Temporal variations of PCBs in Green  Bay were
much different  from those in the main lake.  PCB
concentrations in Green Bay were dominated by the
particulate phase (Appendix 4.5.3), especially for the
water column segments in the inner bay.  Dissolved
PCBs were about  1.5 to 2.0 times higher than the
particulate PCBs for the water column segments in
the main lake.   From visual  examination of the
temporal  profiles  (Figures  4.5.6  and  4.5.7),
calibration results  for Green  Bay segments  were
better than those for the  main lake.   This was
unexpected because observational  data for the
Green Bay water segments were based on only two
water stations  (GB17 and GB24M).  The  PCB
concentrations  in Green  Bay were about 5 to 10
                                                 times higher than that in the main lake.  It appears
                                                 that it was much easier for a water quality model to
                                                 simulate and get a good fit with observed data for a
                                                 chemical  constituent   with   a  much   higher
                                                 concentration than for cases when the concentration
                                                 was close to the detection limit.

                                                 The distribution of PCBs between  dissolved  and
                                                 particulate phases during  the PCB calibration was
                                                 very sensitive to the adjustment of the POC partition
                                                 coefficients (KPOCa).   This distribution  was  not
                                                 sensitive to the DOC partition coefficient due to its
                                                 value being two orders of magnitude  lower, even
                                                 though  the DOC concentration was about 5 to 10
                                                 times higher than the POC concentration in the main
                                                 lake.  Figures 4.5.8a and 4.5.8b show a comparison
                                                 between the cruise mean concentrations and model
                                                 simulation  results for  PCB28+31 and ZPCBs for the
                                                 calibration period. The plots illustrate that there was
              PCB 28+31
          y = 0.9569x + 0.001
              R2 = 0.4847
                                               CD
                                               CO
                                               O
                                                  0.40-
        -i	r
0.01   0.02   0.03   0.04    0.05
     Model outputs ng/L
                                                  0.00
                                                   1.60
                                               I 1.20-
                                               (0
                                               "O

                                               1 0.80-
                                               E
                                                    SPCB
                                            sum of 54 PCB congeners
                                              y = 0.9471x +0.0119
                                                  R2 = 0.6165
                                                       0       0.4      0.8      1.2
                                                                  Model outputs ng/L
                                                                                         1.6
F«r!od 4"5"8'
                      data VerSUS the LM2-Tox'c predictions for PCB28+31 and IPCB for the LMMBP
                                             312

-------
a good correspondence between model simulation
results and  cruise  mean  data.    Most  of  the
overpredictions were for segments in  the  bottom
layer of the main lake.

Table  4.5.1   presents  the   coefficients  (slope,
intercept, and R squares) of the regression equations
based on the results of a comparison between cruise
mean concentration  and the LM2-Toxic simulated
concentration  for  all the LMMBP selected  PCS
congeners.    In  general,  the results from  the
regression for the PCS congeners were reasonably
good.  The results  would be much better if the
original  initial  conditions in  the  Lake  Michigan
sediment segments  for the  PCB congeners were
slightly adjusted  (initial conditions were. adjusted
down to maximum of 20%). The calibration strategy
was to devote only limited  effort  to fine-tune the
calibration and to  adjust as few parameters  as
possible during the calibration.

4.5.4  The LM-2 Toxic Confirmation

As for any mass balance model, the results of the
LM2-Toxic calibration and the outcomes of long-term
model prediction have some degree of  uncertainty.
As the framework of a water quality model becomes
more complex, the credibility of the model does not
necessarily increase.  When the model complexity
increases,  the dimensions  of   the  associated
uncertainties will increase, and the ability to describe
model performance  will also become increasingly
difficult. Other than the uncertainties associated with
the numerical algorithms and equations  used to
describe physical and biochemical processes in the
model, data entered into the model are  subject to a
certain error, and this error propagates into the model
results.    Due to  time constraints,   qualitative
evaluation of the model was the focus for the LM2-
Toxic verification. The following modeling effort was
made for the LM2-Toxic confirmation  in order to
reduce uncertainties associated with water transport,
settling   and resuspension,  and sedimentation.
These processes are crucial to long-term projections
using any water quality model.

4.5.4.1 Mass Balance Checking

It is important to check  the  mass balance on  an
individual  segment  basis  for a   state  variable
simulated in a model. This can ensure that there is
no  excessive mass increase or  decrease in a
segment due to programming error(s) and error(s)
associated with the numerical method used in  the
model.  The task was completed very successfully.
Three organic carbon solids (BIG, PDC, and DOC)
and a conservative tracer (any assumed conservative
tracer) were designated as the only state variables.
With  initial  and  boundary  concentrations of  the
conservative tracer set equal to 1 mg/L  in both  the
water column and sediment segments and with no
external loads, gas exchange, partitioning processes,
or any kinetic processes,  LM2-Toxic was run  for
short-term (two years) and  long-term  (62 years)
simulations.   The results from the short-term  run
showed almost no change in all media. For the long-
term run, an extremely small change was found in
water segments, and a maximum change of 1 % was
found in a couple of the sediment segments. Table
4.5.2 shows the mass balance checking results for
the  long-term  model  run  in  each   segment.
Considering  the small volumes of surficial sediment
segments (average thickness about 1-2  cm), a one
percent change in concentrations over 62 years is an
acceptable variation in terms of conserving mass.

4.5.4.2  Chloride Model

A chloride model was applied as another confirmation
step to ensure that the  overall  water transport
components,  including   both  advection   and
dispersion, used in the LM2-Toxic were  reasonably
accurate.  With the  same model input structure as
the  temperature  balance  model,  chloride was
simulated as a state variable without adjusting any
parameter.    The  resulting  temporal  profiles  in
Appendix 4.3.2 confirmed the credibility of the water
transport used in LM2-Toxic.

4.5.4.3 137Cs and  23»-M0pu Simulation and Results

Based on particle (PDC) net burial in sediments using
Pb-210 core dating results  (Robbins era/., 1999), a
set of settling and resuspension  velocities were
adjusted simultaneously to maintain the net  burial
during the LM2-Toxic carbon dynamics  calibration.
The best-fit values were selected for BIG and PDC.
These rates yielded a rate of cycling of particulate
matter between the sediments and overlying water.
Errors in specifying the  settling and resuspension
rates can have a significant impact on the model
calibrations and the  resulting long-term predictions.
                                              313

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Table 4.5.1. Results of the Regression Between the LM2-Toxic Calibration Results and the Cruise
Mean Data for the LMMBP Selected PCB Congeners
Congener
PCB8+5
PCB15+17
PCB1 6+32
PCB18
PCB26
PCB28+31
PCB33
PCB37+42
PCB44
PCB49
PCB52
PCB56+60
PCB66
PCB70+76
PCB74
PCB77+110
PCB81
PCB87
PCB92+84
PCB89
PCB99
PCB101
PCB118
PCB123+149
PCB1 05+1 32+1 53
PCB151
PCB163+138
PCB170+190
PCB172+197
PCB180
PCB187+182
PCB1 95+208
PCB1 96+203
PCB201
PCB85
PCB146
IPCBs3
Slope
0.9512
0.6072
0.9255
0.6271
0.5238
0.9569
0.4931
0.7625
0.8514
1.1693
0.8729
0.5822
0.8250
0.7159
0.6659
0.5593
0.4079
0.5492
0.4801
0.0825
0.3746
0.8253
0.4403
0.5762
0.7421
0.4629
0.5649
0.5879
0.3501
0.4372
0.7757
0.1696
0.2402
0.0911
0.5099
0.3329
0.9471
Intercept
0.0022
0.0034
0.0122
0.0023
0.0009
0.001
0.0054
0.0041
0.0012
0.0006
0.0042
0.0008
- 0.002
5E-05
0.0008
0.0018
0.0013
0.0020
0.0087
0.0012
0.0055
0.0006
0.0020
0.0011
0.0002
9E-05
0.0028
4E-05
0.0001
8E-05
0.0012
9E-05
0.0007
0.0046
0.0006
0.0013
0.0119
R2
0.1845
0.3995
0.0628
0.4112
0.4188
0.4847
0.2811
0.3332
0.5210
0.3165
0.4145
0.5976
0.5896
0.5146
0.5769
0.4219
0.0160
0.4007
0.1896
0.0029
0.1153
0.4186
0.4540
0.4723
0.6205
0.5125
0.4258
0.3735
0.2309
0.3545
0.2425
0.4053
0.1035
0.0004
0.5194
0.2261
0.6165
aSum of all the LMMBP selected PCB congeners.
                                         314

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Table 4.5.2. Results of the LM2-Toxic Mass Balance Checking for a 62-Year Simulation of an Assumed
Conservative Tracer (Set the Model Initial Conditions and Boundary Concentrations of the
Conservative Trace = 1)
Water Column
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Initial Concentration (mg/L)
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1.00000
1.00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1.00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
1 .00000
0.99959
Concentration at the End of
Simulation (mg/L)
1.00003
1 .00003
1 .00002
1.00002
1.00002
1.00002
1 .00002
1 .00003
1 .00004
1 .00007
1 .00003
1 .00003
1 .00001
1 .00002
1 .00002
1 .00002
1 .00002
1 .00004
1 .00001
1 .00003
1 .00003
1 .00001
1.00001
1 .00002
1 .00002
1 .00002
1 .00002
1 .99999
1 .00003
1.00003
1 .00001
1 .00001
1.00001
1.00002
1 .00001
1 .00002
1 .00002
1 .00000
1 .00000
1 .00001
1 .00001
0.99616
Change (%)
0.003
0.003
0.002
0.002
0.002
0.002
0.002
0.003
0.004
0.007
0.003
0.003
0.001
0.002
0.002
0.002
0.002
0.004
0.001
0.003
0.003
0.001
0.001
0.002
0.002
0.002
0.002
-0.001
0.003
0.003
0.001
0.001
0.001
0.002
0.001
0.002
0.002
0.000
0.000
0.001
0.001
-0.343
                                          315

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Table 4.5.2. Results of the LM2-Toxic Mass Balance Checking for a 62-Year Simulation of an Assumed
Conservative  Tracer (Set the  Model Initial  Conditions  and  Boundary  Concentrations  of the
Conservative Trace = 1) (Continued)
Water Column
Segment
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
~7O
78
79
f\f\
80
81
O^"»
82
o/%
83
O A
84
Initial Concentration (mg/L)
0.99955
0.99944
0.99947
0.99939
0.99958
0.99965
0.99980
0.99979
0.99964
0.99953
0.99963
0.99958
0.99971
0.99979
0.99963
0.99955
0.99962
0.99977
0.99974
0.99979
0.99969
0.99960
0.99922
0.99926
0.99938
0.99950
0.99952
0.99977
0.99980
0.99963
0.99967
0.99964
0.99972
0.99970
0.99943
0.99980
0.99981
0.99982
0.99980
0.99982
0.99982
0.99982
Concentration at the End of
Simulation (mg/L)
0.99496
0.99362
0.99387
0.99064
0.99103
0.98954
0.99263
0.99258
0.99187
0.99359
0.99585
0.99525
0.99772
0.99737
0.99603
0.99330
0.99160
0.99252
0.99215
0.99245
0.99430
0.99566
0.99102
0.99158
0.99318
0.99447
0.99442
0.99356
0.99317
0.99561
0.99563
0.99566
0.99711
0.99688
0.99359
0.99163
0.99284
0.99136
0.99035
0.99137
0.99134
0.99112
Change (%)
-0.459
-0.582
-0.560
-0.876
-0.856
-1.011
-0.717
-0.721
-0.778
-0.594
-0.379
-0.433
-0.200
-0.242
-0.361
-0.626
-0.802
-0.725
-0.759
-0.734
-0.539
-0.394
-0.820
-0.768
-0.621
-0.504
-0.511
-0.621
-0.664
-0.402
-0.404
-0.398
-0.261
-0.282
-0.585
-0.817
-0.697
-0.846
-0.945
-0.845
-0.849
-0.870
                                         316

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Table 4.5.2. Results of the LM2-Toxic Mass Balance Checking for a 62-Year Simulation of an Assumed
Conservative  Tracer (Set the  Model  Initial  Conditions  and  Boundary Concentrations of  the
Conservative Trace = 1) (Continued)
 Water Column
    Segment
Initial Concentration (mg/L)
Concentration at the End of
     Simulation (mg/L)           Change (%)
85
86
87
88
89
90
91
92
93
94
0.99979
0.99979
0.99981
0.99983
0.99983
0.99980
0.99982
0.99979
0.99982
0.99982
0.99032
0.99072
0.99184
0.99389
0.99146
0.98917
0.99063
0.99097
0.99202
0.99233
-0.947
-0.907
-0.798
-0.595
-0.837
-1 .063
-0.920
-0.883
-0.780
0.749
To  confirm the settling and  resuspension rates
selected for the LM2-Toxic, a radionuclide model was
developed.  The radionuclides, 137Cs and 239-24°pu,
were used as state variables in the LM2-Toxic, and
a 46-year hindcast (1950-1995)  simulation  was
executed.   Figure  4.5.9  presents cesium  and
Plutonium lake-wide water  column  concentrations
resulting  from the  radionuclide  model  46-year
hindcast simulation.  The profiles of both 137Cs and
239,240pu jncjjcate that the water column concentration
of the radionuclides decreased at a rate faster than
the  one suggested by the data. There are quite a
few factors that can contribute to this discrepancy.
Among them, loading  history  of  total  suspended
solids, internal primary production history, partitioning
coefficients, coarse segmentation, and the settling
and resuspension rates could have a strong influence
in the level of 137Cs and 239'240pu concentration in the
water column of the lake.

To determine the representativeness of the rate of
sediment-water solids cycling used in the LM2-Toxic,
the  total sediment 137Cs predicted  and observed
inventories were compared.  There was a very good
agreement in total sediment 137Cs inventory for those
sediment segments where  the  sediment core
samples were collected (Figure 4.5.10).  There was
a large discrepancy in the comparison for segment
52.  The model underestimated total sediment 137Cs
inventory in that segment. This underestimation may
                               be caused by the location of segment 52. It is a very
                               narrow  transitional   area  along  the  southeast
                               shoreline of the lake with a steep slope. The LM2-
                               Toxic, with a very coarse spatial resolution for the
                               water column, was unable to predict the radionuclide
                               sediment inventory  in such a  narrow transitional
                               band. In general, the results from the LM2137Cs and
                               239.240pu  mocje|  jmp|y that the combination of the
                               settling, resuspension, and burial rates used in the
                               LM2-Toxic were a  reasonable representation of the
                               rate  of cycling  of particulate matter between the
                               sediments and overlying water column of the lake.

                               4.5.4.4 Long-Term Organic Carbon Simulations

                               For the purpose of providing another dimension of
                               LM2-Toxic confirmation, a 40-year long-term organic
                               carbon simulation was executed to produce the total
                               sediment accumulation over the simulation  period.
                               Using the model, total  sediment thickness  (cm)
                               accumulated over  40 years was  calculated.  It was
                               converted to sediment accumulation rate (cm/year)
                               by dividing  by 40 years and   comparing  to  the
                               measured sediment  accumulation rate.  The three
                               organic  carbons (DOC,  BIG, PDC) were the only
                               state variables in the LM2-Toxic for the simulation.
                               The set of sediment accumulation rates at each box
                               core  sampling location (Robbins et a/., 1999) were
                               interpolated  using  a natural-neighbor interpolation
                               algorithm (Appendix 4.4.1) to generate segment-
                                             317

-------
                      0.0100
                   o
                   Q.
                   c
                   o
c
(D
U
c
O
O
                      0.0075
                      0.0050 •
                   .3  0.0025

                   o
                   J3
                   Q.
                             A. water column average plutonium
                                n	1	1	1	1
                          1950  1955   1960  1965  1970   1975  1980   1985  1990  1995
                   O
                   CL
                    c
                    o
                    O
                    O
                    in
                    CD
                    O
                        0.75
     0.5 .
     0.25 .
                             B. water column average cesium
                          1950  1955   1960  1965   1970   1975  1980   1985  1990  1995
Figure 4.5.9.  Lake-wide average concentrations of (a) 137Cs and (b) 239240Ru computed by the LM2
radionuclide model over 46 years (1950-1995).
                                           50   51   52    60   61

                                            Sediment Segment
                                               62
                                                    63
                                                        70
Figure 4.5.10. Sediment 137Cs inventory comparison between the observed data (see Appendix 4.8 for
details) and the LM2 radionuclide model outputs.
                                               318

-------
specific accumulation rates (cm/year).  Table 4.5.3
presents comparisons between 1) the segment -
specific  sediment  accumulation rates  generated
based on the data (Bobbins et al., 1999) and the
segment-specific accumulation rates computed by
the LM2-Toxic and 2) lake-wide average sediment
accumulation rates generated from the data (Bobbins
et al., 1999) and computed by the LM2-Toxic.  The
discrepancy between the field data and model output
on a lake-wide basis was about 30%. A large portion
of this difference was  contributed  by  sediment
segments under water columns 5 and 6 (segments
65-77) where the bottom geometry is complicated
and by Green Bay sediment segments.  Because
there was only one sediment box  core sample
collected in the sediment segments under water
columns 5 and 6 and no box core sample was taken
in Green Bay, the interpolation of the LMMBP-
generated  accumulation  rates   could  create
substantial uncertainty in the interpolated segment-
specific accumulation  rates  for  the  sediment
segments under water columns 5 and 6 and in Green
Bay.  In general, the results in Table 4.5.3 showed
reasonably good match between the field data and
model predicted accumulation rates, especially  in
areas where a large number of box cores were
collected.

4.5.4.5 PCBHindcast

A hindcast  used as  either  a calibration or  a
confirmation of a mathematical model is considered
an important and preferred approach to assess the
credibility of a model.  It has been used for models
such  as  MICHTOX (Endicott, 2002), LOTOX
(DePinto era/., 2003), HUDTOX (U.S. Environmental
Protection Agency, 2000), and the Delaware River
model (Delaware River Basin Commission, 2003).
To gain confidence  in the prediction of a toxic
chemical model, a PCB  hindcast is conducted to
confirm  the  suitability  of   the  processes
conceptualized in the model and of the associated
parameters used in the model.

A PCB hindcast was not one of the confirmation
components proposed  in  the  original  LMMBP
modeling work plan (U.S.  Environmental Protection
Agency, 1997).  This modeling task was done  in
response to a suggestion from the 2004 peer review
panel (summarized by Rygwelski in Part 7, Appendix
1).   Because LM2-Toxic went through a very
thorough and successful short-term (two-year project
period) calibration and a series of model confirmation
efforts  detailed in the  previous sections, the main
purpose of the LM2-Toxic PCB hindcast simulation
was  to confirm  the  representativeness  of  the
calibration parameters determined from the short-
term calibration and to check the consistency of the
estimated  historical  PCB load with the available
sediment core profiles and historical inventory data.
A description of  data and procedure used for the
LM2-Toxic PCB  hindcast and a discussion of the
results and findings from  the PCB hindcast  are
presented  in the following sections.

4.5.4.5.1   Data  and  Procedure  for the PCB
Hindcast

In order to run  the LM2-Toxic PCB hindcast,  the
following data were required: 1) PCB loading history;
2)   historical   atmospheric  vapor  phase  PCB
concentrations; 3) historical primary productivity; 4)
the date when PCBs started being loaded into Lake
Michigan;   5)   physical  conditions   such   as
temperature,  wind speed, and carbon and PCB
transport  and  kinetic  parameters; and 6)  the
estimated  long-term carbon solid concentrations on
which the resuspension rates should be adjusted to
keep  solid cycling rates balanced (see Section
4.3.4.2.1 - Steady-State Resuspension Calibration
for details).

The minimum data needed to reasonably assess the
performance of the LM2-Toxic PCB hindcast were 1)
continuous long-term  historical annual averaged
water column observed  PCB concentrations;  2)
sediment PCB core profiles representative of the
depositional area, and 3) the sediment total PCB
inventory.

Among the data necessary for a successful PCB
hindcast and  to assess model performance,  PCB
loading  history,    historical  PCB  vapor phase
concentrations, and a reliable estimation of sediment
inventory are  the most critical.  PCB loading history
and  historical PCB vapor  phase  concentrations
control in-flux and out-flux of PCB mass for the Lake
Michigan system. Historical concentrations in the
system  are  controlled by  these fluxes.  When
compared  to  a reliable estimate of sediment  PCB
inventory,  the model generated lake-wide sediment
inventory can provide the confidence that there is no
                                             319

-------
Table 4.5.3. Comparison Between the LMMBP Field-Generated and the LM2-Toxic-Generated Sediment
Accumulation Rates (cm/year)
Segment
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
Field Data3 (cm/year)
0.0000
0.0000
0.0002
0.0058
0.0181
0.0495
0.1135
0.3630
0.2662
0.1444
0.2324
0.0497
0.0466
0.0068
0.0000
0.0036
0.0276
0.1059
0.2213
0.2726
0.2768
0.1847
0.1372
0.0013
0.0032
0.0004
0.0176
0.0543
0.1165
0.1938
0.0000
0.0153
0.0033
0.0054
0.0123
0.0180
0.0496
0.0496
0.0496
0.0496
0.0496
0.0496
0.0496
0.0496
0.0496
Model Output (cm/year)
0.0000
0.0000
0.0000
0.0000
0.0650
0.0900
0.1250
0.3875
0.2900
0.1900
0.1800
0.0000
0.0250
0.0000
0.0000
0.0000
0.1300
0.1900
0.2800
0.3250
0.3100
0.3200
0.0250
0.0000
0.0000
0.0000
0.0000
0.2750
0.2400
0.3000
0.3000
0.3575
0.3250
0.0000
0.0000
0.0000
0.0000
0.0000
0.1000
0.1000
0.1000
0.1000
0.0000
0.0000
0.0000
                                         320

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 Table 4.5.3.   Comparison Between the LMMBP  Field-Generated and the LM2-Toxic-Generated
 Sediment Accumulation Rates (cm/year) (Continued)
           Segment
Field Data3 (cm/year)
Model Output (cm/year)
              87
              88
              89
              90
              91
              92
              93
              94

 Lake-Wide Average
       0.0496
       0.0993
       0.0993
       0.0993
       0.0993
       0.0947
       0.0947
       0.0947

       0.1337
        0.0000
        0.0000
        0.0000
        0.6000
        0.4000
        0.3000
        0.4000
        0.4000

        0.1742
"Sediment segment-specific accumulation rates computed based on sediment accumulation rates at each
 sediment box core sampling location estimated by John Robbins (Bobbins etal., 1999).
significant overestimation or underestimation by the
LM2-Toxic for the  total PCB mass in the  lake
sediments.   The  major challenge for  the PCB
hindcast was that these data are very limited for Lake
Michigan.  The following subsections provide the
references for data sources and a brief description of
each major data set used in the LM2-Toxic PCB
hindcast.

4.5.4.5.1.1  PCB Loading Reconstruction - As is the
case for most large water bodies, there is no long-
term historical PCB load record available for the lake.
Endicott et al. (2005)  and Endicott (2005)  are the
only researchers who  attempted to reconstruct the
long-term PCB  loading history of  Lake Michigan
using an approach  similar to that applied to Lake
Ontario by Mackay (1988) and Gobas et al. (1995).
Endicott (2005) conducted three  55-five year total
PCB hindcast  simulations  using MICHTOX  and
concluded that the results from Scenario B had the
best-fit with the available historical PCB data.  The
year when PCB contamination began was assumed
to be 1940. The PCB loading function of Scenario B
peaked in 1961 and declined after 1963. The rates
of increase in the  loads  including tributary  and
atmospheric loads were the  same as the rates of
decrease.  Based on  observations of the past 25
years, the  rates of increase and decrease were
           defined   as  0.115/year  and  0.0535/year  for
           atmospheric loads and tributary loads, respectively.

           In contrast to the procedure of reconstructing the
           historical PCB total loading  function  used in the
           MICHTOX  (Endicott,  2005),  the  historical  PCB
           loading  estimation used in  the  LM2-Toxic  PCB
           hindcast simulations was done by relating it to the
           results obtained from  the analysis  of  a highly
           resolved sediment core (LM94-15A) collected during
           the LMMBP period (Details are in Part 1, Chapter 7
           of this report).  In doing so, some assumptions and
           professional judgments were made.  One important
           assumption was that the total PCB inventory and the
           shape of the total  PCB profile obtained from the
           sediment core represented the PCB loading history
           from all  sources. Consequently, the following key
           elements that were used in reconstructing a historical
           PCB loading function were derived based on the core
           LM94-15A profile:  1) the starting date of the  PCB
           loading time function (January 01,1949); 2) the year
           in which the PCB loading function reached its  peak
           which was determined to be 1967; and 3) the slopes
           on both the inclining and declining sides of the core
           profile curve were linear. The historical  PCB loading
           time function was then  back-projected based on the
           above elements and  variations  of the  LMMBP
           estimated  loads for 1994  reported by the United
                                             321

-------
States   Geological  Survey  (USGS)  (Hall  and
Robertson, 1998) for tributary loads and the LMMBP
atmospheric working  group (McCarty et al, 2004;
Miller et al., 2001) for atmospheric loads. Details on
the determination of the  key  elements and the
procedure in reconstructing the PCB load history are
further discussed in Part 1, Chapter 7. Figure 4.5.11
shows the final reconstructed total PCB historical
loading function (January 01, 1949 - December 31,
1995) and the  PCB profile from the sediment core
(LM94-15A). The monthly variation pattern in the
reconstructed long-term historical PCB load functions
used in the LM2-Toxic model for each PCB congener
followed the same monthly pattern established by the
LMMBP estimated loads.

4.5.4.5.1.2     PCB   Atmospheric  Vapor   Phase
Concentration  Reconstruction  -  Recent  studies
(Thomann and  Di Toro, 1983; Rodgers et al., 1988;
Jeremiason et al., 1994; DePinto et al., 2003; Part 3
of this report; Part 4.6.2 of this  report) have shown
that the air-water exchange between dissolved PCBs
in surface water and overlaying vapor phase  PCBs is
a very important and possibly the most significant
loss/gain process, especially in recent years,  for
PCBs in  the  Great Lakes region.   The gross
volatilization and absorption fluxes transport large
amounts of PCB mass in and out of the lakes due to
the gradient of  PCB concentrations between the air
and  surface water.   These individual fluxes were
usually greater than the sum of the external loads
(tributary,  atmospheric wet and  dry  deposition).
Therefore,  a  reasonable representation of  a
reconstructed  historical  PCB  atmospheric vapor
phase concentration is critical to the success of the
LM2-Toxic  PCB hindcast for   Lake   Michigan.
Following   a   similar  approach   used   in  the
reconstruction  of the loading  time function, the
starting  date of the PCB vapor phase time function
was January 01, 1949, the year for which the PCB
vapor concentration reached its peak was 1967, and
the slope on the inclining side of the time function is
the same as the one indicated from the core LM94-
15A profile.  The only difference is that the vapor
concentration  on the declining side of the time
function was back-projected based on the LMMBP
(1994-1995) generated PCB vapor concentration and
the decline rate  that was carefully selected via a
thorough review  of research studies conducted on
PCB vapor phase   concentration and  the data
collected within the Lake Michigan watershed for the
25 years prior to 1996.

The  estimation of the  declining rate in the  vapor
phase PCB concentrations is very subjective  to the
data set and  number  of  data  points used  in the
derivation of the rate.  The half-life of the declining
rate  published in the literature ranges from 6 to 20
years for PCB vapor phase concentrations over Lake
Michigan (Hillery et al.,  1997; Schneider et al.,  2001;
and Buehler et al., 2002, 2004). The individual data
sets  used by researchers to  determine the PCB
vapor concentrations declining rate were usually a
subset of the  historical PCB vapor concentrations
measured between 1977  and  2001.  Hillery  et al.
(1997) and  Schneider et al. (2001) calculated  a
declining rate of six-year half-life based on the
measurements collected before 1997, while a rate of
half-life  as high  as 20 years  was  estimated by
Buehler et al. (2002, 2004) using only the data from
the  Integrated Atmospheric Deposition Network
(IADN),  1992-2001. In this study, a declining rate of
0.115/year, which corresponds to a six-year half-life,
was  chosen because the  simulation period of the
LM2-Toxic PCB hindcast  was from 1949 through
1995 and overlapped the post-peak declining period
used by Hillery et al. (1997) and Schneider  et al.
(2000).  Similar to the PCB load reconstruction, the
reconstructed PCB vapor phase concentration time
function  maintains  the  same monthly pattern
observed during the LMMBP period.  Figure 4.5.12
shows the reconstructed historical time function of
total  PCB vapor phase  concentrations (January 01,
1949 - December 31,  1995)  along  with the
reconstructed  total loading  function  for  Lake
Michigan.

4.5.4.5.1.3    Estimation   of   Historical Primary
Productivity  -  PCBs   are  hydrophobic  organic
chemicals and are closely associated with organic
carbon in natural water systems. The movement and
fate of organic carbon mass are very important to the
distribution of  PCBs in the Lake Michigan system.
Therefore,  a  historical  organic carbon loading
function  was  necessary  for  a  LM2-Toxic   PCB
hindcast.  The carbon solid loads to the lake are
primarily from tributaries (external loads) and primary
production of phytoplankton (internal load). Both the
measurements and outputs from the LM3-Eutro for
the two-year project period (1994-1995) indicated
that  the internally-generated organic carbon load
                                              322

-------
tr9000-
CO
0»
"01
                 •Q
                 re
                 _o
                 CD
                 O
                 Q.
7500-
6000-
4500-
3000-
1500-
                      0
                           — Projected historical annual PCS load
                           o Sediment core LMMB94 15A
                        i^ o o o
                                                             280
•240
-200
-160
 120
 80
 40
                                          0
   1940   1950  1960  1970  1980  1990  2000
                    Years

-5*
"5)
c
c
.0
13
"c
o
o
c
o
o
00
o
Q.
in
                                                                 o
                                                                 O
Figure 4.5.11. Reconstructed historical total PCB loading time functions and sediment core LM94-15A
total PCB concentration profiles for Lake Michigan. Note: The total PCB loading time function was
back-projected based on the estimated PCB load for 1994. The last data point of the load time function
represents the estimated PCB load for 1995.

                  ro
                  m
                  o
                  a.
                  3
                  o
                         — Reconstructed PCB load
                          o Reconstructed PCB vapor concentration
                      .5
 9.0-
 7.5-
 6.0-
 4.5-
 3.0-
 1.5-
  9000
 •8000
  7000
 heooo
  5000
  4000
 (-3000
  2000
 MOOO
                                                                   ^
                                                                   ^
                                                                   •a
                                                                   _i
                                                                   O

   0
   1940  1950  1960   1970   1980  1990  2000
                     Years
Figure 4.5.12. Reconstructed total PCB vapor phase concentrations and total PCB loading time
functions for Lake Michigan. Note:  The total PCB loading time function was back-projected based on
the estimated PCB load for 1994. The last data point of the load time function represents the estimated
PCB load for 1995.
                                           323

-------
(primary production) accounted for approximately
90% of the total carbon solid loads for Lake Michigan
(details are in Part 4, Section 4.4.2). Consequently,
it would  be an  adequate  representation  of the
historical carbon solid loading function if the primary
production  history  for  Lake  Michigan could  be
reasonably  reconstructed.  However, there are too
few primary production data for Lake Michigan in the
literature  to permit  creation of an organic carbon
loading history. Due to time constraints, a simple
relationship was established by correlating primary
productivity with available historical total phosphorus
loads (Appendix 4.5.5).

By  establishing a  relationship  between  total
phosphorus  loads  and  primary  productivity,  an
estimation of  historical  primary productivity was
constructed for Lake Michigan using an approach
similar to the  one  by Vollenweider  et  al. (1974).
Figure 4.5.5.1  in   Appendix  4.5.5   shows  the
relationship between total phosphorus loads and lake
area normalized primary productivity. Figure 4.5.13
presents the reconstructed total carbon solids loading
time function used for the LM2-Toxic PCB hindcast
simulations.

The reconstructed  carbon  solids loading history
(Figure 4.5.13) indicated that the annual  average
internal carbon loads for Lake Michigan reached a
peak value of approximately 3.5 x 109 kg/year in
1980 and then decreased to the current level (1994-
1995) of  2.1 x 109  kg/year used in the LM2-Toxic
model. The annual average carbon concentrations
were  expected to  follow the same trend as the
historical carbon solids loading function.

4.5.4.5.1.4  Other Physical and Kinetic Parameters -
In terms of physical processes, one of the  main
assumptions made for the LM2-Toxic PCB hindcast
was that the time functions for water transport, wind
speed, wave heights, and temperature constructed
for the two-year LMMBP period were representative
of annual average conditions (see Part 1, Chapter 4
for  details) that existed  in the lake  for the entire
hindcast  simulation  period.   Values  of  the kinetic
parameters used in the model calibration were kept
the same during the hindcast simulation.

Resuspension  rate  is a function of organic carbon
concentration in the water column, assuming the
other parameters in  Equation 4.3.8 are constant (see
Part 4, Chapter 3 for details).  By using the carbon
concentration data generated during the LMMBP and
the reconstructed carbon solids loading history, the
annual average organic carbon concentrations in the
water column were estimated (Appendix 4.5.5). The
resuspension  rates for each  year throughout the
hindcast simulation period were adjusted with the
estimated annual organic carbon concentrations (see
Section  4.3.4.2.1  -  Steady-State Resuspension
Calibration for details). The adjusted resuspension
time function maintained the same monthly pattern
that was derived from the observations made during
the LMMBP period.

4.5.4.5.1.5    Historical   Water  Column  PCB
Concentrations  and  Sediment  Core Profiles  -
Historical PCB concentrations in the water column
and sediments are essential data to  evaluate the
results of a water quality model hindcast simulation.
The available Lake Michigan water column data are
listed in Table 4.5.4  together with their sources.
Measurements made before  1994 LMMBP were
taken at a  very limited number of  locations, within
short-time periods and at depths less than 10 m in
the water column. Thus they are biased and may not
truly represent annual average water  column PCB
concentrations.

Table 4.5.5 provides the sediment PCB core profiles
with depth and temporal information at three LMMBP
sediment sampling locations  (LM94-015A, LM95-
061 A, and LM95-086A). Two of the sediment cores
(LM95-061A and  LM95-086A) were  taken  within
depositional zones as defined in Figure 4.3.2. Core
LM94-015A was collected in the transitional zone.
Figure 4.5.14 shows the  locations  of the three
sediment cores taken during the LMMBP period. For
more details about these three sediment cores, see
section Part 1, Chapter 7.

4.5.4.5.1.6    PCB Inventory  in  Lake  Michigan
Sediments - An estimate of PCB inventory in the
lake sediments is the  final information needed to
ultimately confirm  the outcomes of the LM2-Toxic
PCB hindcast and the applicability of the  model for
future  predictions.  Table 4.5.6  lists the available
PCB sediment inventory data for Lake Michigan. The
most recent estimate from Eadie and Van  Hoof (see
Part 1, Chapter 7 for details) showed that the PCB
inventory for the lake is about 60,998 kg.
                                              324

-------
                    1940    1950     1960    1970
                                         Years
1980
1990
Figure4.5.13. Reconstructed total organic carbon load (primary production + LMMBP tributary loads)
for Lake Michigan.
Table 4.5.4.  Available Historical Water Column Total PCB Concentrations for Lake Michigan
Year
1976
1979
1980
1980
1980
1981
1986
1991
1992
1993
1994-1995
Concentration
(ng/L)
7.2
2.88
5.66
6.36
1.2
0.28
1.1
0.64
0.424
0.22
0.259
Standard Error/
Deviation (ng/L)
3.1
3.37
1.12
1.3
0.5

0.2
0.43
0.058
0.04
0.172
Depth
(m)

1
1
1
5
1

8

5

Reference
Chambers and Eadie, 1980
Rice eta/., 1982
Rice etal., 1982
Rice etal., 1982
Swackhamer and Armstrong, 1 987
Filkins etal., 1983
Lefkovitz, 1 987
Pearson, 1996
Bicksler, 1996
Anderson, 1999
LMMBP
                                          325

-------
Table 4.5.5. Sediment PCB Concentration Vertical Profiles Analyzed for Three Sediment Box Cores
Taken During the LMMBP
LM94-015A
Interval
cm
0-1
1-2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
22-23
23-24
24-25
25-26
26-27
27-28
28-30
Total PCBs Mid-Interval
ng/g Dry Date
73.7
60.7
67.6
83.7
91.1
102
119
140
148
199
217
230
208
190
222
221
204
142
88.8
71
31.4
23.5
12.5
10.5
9.52
3.58
2.19
1.37
1.62
1994.16
1992.78
1991.16
1989.45
1987.37
1985.17
1983.07
1980.81
1978.61
1976.55
1974.43
1972.21
1969.91
1 967.54
1965.35
1963.31
1961.05
1958.70
1 956.47
1 954.23
1951.82
1949.47
1 947.27
1945.07
1942.78
1940.30
1937.73
1934.83
1930.81
LM95-061A
Interval
cm
0-0.5
0.5-1
1-1.5
1.5-2
2-2.5
2.5-3
3-3.5
3.5-4
4-5
5-6
6-7
7-8
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
20-22
22-24
24-26
26-28
28-30

Total PCBs
ng/g Dry
91.1
111
110
127
117
120
122
136
100
106
103
117
119
117
144
164
159
128
86.9
65
50.4
29.2
17.6
7.02
4.51
4.22
2.62
1.51

Mid-Interval
ng/g Dry
1995.37
1994.78
1994.28
1993.89
1993.37
1992.63
1991.90
1991.12
1989.61
1987.43
1985.08
1982.53
1979.84
1977.08
1 974.34
1971.69
1968.97
1966.30
1963.55
1960.58
1 957.68
1954.65
1951.57
	
	
	
	
	

LM95-086A
Interval
cm
0-0.5
0.5-1
1-1.5
1.5-2
2-2.5
2.5-3
3-3.5
3.5-4
4-5
5-6
6-7
7-8
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16









Total PCBs
ng/g Dry
78.3
64.6
66.6
75.7
77.2
77
80.1
76.6
77.7
102
101
97.2
82
34.6
14.5
7.84
3.54
2.61
1.51
0.893









Mid-Interval
Date
1994.90
1993.30
1991.40
1989.30
1987.10
1984.60
1982.00
1979.00
1974.20
1968.10
1961.90
1955.70
1 949.50
1 942.00
1933.80
1925.00
1916.50
1908.60
1900.50
1891.60









                                         326

-------
Figure 4.5.14. The sampling sites of the sediment box core samples (LM94-15A, LM95-61 A, LM95-87A)
taken during the LMMBP for which vertical PCB concentration profiles were analyzed and available.
Table 4.5.6. Available Inventories of PCBs in Lake Michigan Sediments
System
Lake Michigan
Green Bay
Lake Michigan + Green Bay
Inventory (kg)
75,000
14,565
60,000
Source
Golden et al., 1993
Wisconsin Department of Natural Resources, 2003
Eadie and Van Hoof (personal communication, 2006)
                                          327

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4.5.4.5.2  Results From  the  LM2-Toxic PCB
Hindcast

As mentioned  previously, in order  to  ensure  the
predictability of a water quality model, a long-term
PCB hindcast has conventionally been used for either
confirming a set of key model parameters defined by
the combination of literature sources and short-term
model calibration or directly as a long-term calibration
tool if a short-term  model calibration has not been
conducted.   LM2-Toxic  went  through  a very
comprehensive and   data   intensive  short-term
calibration using LMMBP-generated field data.  The
PCB hindcast for this report is to confirm the model
parameters defined  by the short-term calibration and
to provide  confidence  in  modeling  long-term
predictions.

Results from the LM2-Toxic PCB hindcast presented
and discussed in this section include 1} water column
PCB  concentrations  from  the   hindcast  versus
available  historical  data,   2)   sediment  PCB
concentrations from the  hindcast versus sediment
core  profiles,  3) mass  budget  checking  for  the
hindcast simulation period,  and  4) a comparison
between model-generated PCB sediment inventory
and available sediment inventory estimations made
by other researchers.   In  general,   the  results
demonstrate  the   LM2-Toxic PCB  hindcast  is
reasonably  successful   for  reproducing  PCB
concentrations in both the water column and  the
sediments of Lake Michigan.

The  results  of the LM2-Toxic PCB hindcast  are
presented in  Figures4.5.15through 4.5.17forannual
and monthly average concentrations in the water
column and  annual average concentrations in  the
sediments.  In general, the results from the hindcast
are reasonably good for both the water column and
the sediment relative to the available historical water
column field data and the three  LMMBP sediment
cores.   The  results demonstrate that the model is
able  to generate  the temporal trends  of PCB
concentrations which  closely match the observed
trends.

Some of the historical data derived before and during
1980 (Chambers and Eadie,  1980; Rice  etal., 1982)
are not shown  in Figures 4.5.15 and 4.5.16.  Due to
the limitation of the  analytical method, the objective
of the research studies, and the water depth  (< 1
meter   below   the   water   surface),  the
representativeness of these early measurements for
the lake as a whole is questionable. Taking account
of the possible variation in PCB concentrations with
water depth, the open lake value of 1.2 ng/L ± 0.46
(for surface water only - five meters) for  1980 from
Swackhamer and Armstrong (1987) was considered
to be more appropriate comparison with the lake-
wide  annual or  monthly  average concentration
generated from the model hindcast.

It is important to consider temporal variation when
comparing model results with  historical  data. For
most of the historical data, sampling occurred during
a two-week or less period during the year  (usually
summer).   Compared  to the  annual  lake-wide
average water column concentration in Figure4.5.15,
the monthly lake-wide  average  concentration in
Figure 4.5.16 demonstrates significant variation in
the water column PCB concentrations for any given
year.   The monthly  variation in the  lake PCB
concentrations becomes most intense in the top layer
of the water column (within 10 meter depth)  with the
highest concentrations occurring in the summer
months  (July  to   September)  and  lowest
concentrations in the winter months (December to
February).

The vertical profile of PCB sediment concentrations
assembled based on the LM2-Toxic PCB hindcast
outputs for the depositional area of the lake was
compared to the three sediment core profiles (LM94-
015A, LM95-061A, and LM95-086A) measured and
analyzed for the LMMBP- The  concentration profile
from the model is porosity normalized.  Considering
the fact  that the  model-generated vertical PCB
sediment concentration profile is a lake-wide average
vertical profile and based on the outputs of the model
with a very coarse spatial segmentation,  the model
did a fairly good job of matching the general trend of
the vertical profiles from the three sediment cores
and had the closest match with core LM95-061.

There are certainly unmatched portions between the
model-generated profile and the profiles from the
sediment cores. But it is very important to understand
that the common purpose of a hindcast is to provide
confirmation of the  general conceptualization and
parameterization of a model. The successfulness of
a model hindcast should not  be judged  only on
whether  the  model  output   can   match  field
                                              328

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               3.5i-
LMT-Toxic model (lakewide annual average)
historical data
               3.0 -I
            •   2.0
            u
            §  1.0
            15   0
            o  1940    1950   1960    1970    1980    1990   2000
            4-<
                                        Years

Figure 4.5.15. Annual lake-wide average total PCB water column concentrations from the LM2-Toxic
PCB hindcast simulation.
                    — LMT-Toxic model (lakewide monthly average, top layer)
                    — LMT-Toxic model (lakewide monthly average)
                    • historical data
                                                                 2000
Figure 4.5.16. Monthly lake-wide average total PCB water column concentrations from the LM2-Toxic
PCB hindcast simulation. High concentration in the summer months and low concentration in the
winter months.
                                          329

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                  CM
o  Depositional zone (LM2-Toxic)
A  LM94-15A (core data)
•  LM95-061 (core data)
•  LM95-086 (core data)
$  Depositional zone surfacial sediment average
   (LMMBP)                         	
                  15  1940    1950    1960   1970    1980   1990    2000
                  5                        Years
Figure4.5.17. Annual average total PCB concentration profiles in the sediment depositional zone from
the LM2-Toxic PCB hindcast simulation.
observation at one point or a
temporal profile.
  short portion of the
The  discrepancy between  the LM2-Toxic PCB
hindcast generated sediment profile and the profiles
from the sediment cores becomes most noticeable
for the portion after 1990.  This is the part of the
profiles that represents the unconsolidated portion -
the surficial sediment mixed layer. The explanations
for the discrepancy could be:

1) The  model   does   not   have   adequately
   reconstructed historical physical conditions such
   as wave, water circulation, and temperature time
   functions.  These  time functions  can  dictate
   organic carbon and  PCB concentrations  in the
   lake and  the solids  cycling  rates  such  as
   resuspension, burial, and settling.  For example,
   a severe weather event could  create a  major
   localized resuspension event in the lake and,
   therefore,  could elevate PCB concentrations in
   the local water column and surficial sediments
   along the path impacted by water transport. The
                       2)
elevated concentrations could also remain for a
long time after the major event. The recorded
maximum  wave  heights  from the  southern
National   Oceanic   and  Atmospheric
Administration's (NOAA) buoy 45007 indicate that
a major storm event occurred on September 23,
1989 which induced a maximum wave height of
5.6 meters.   The sediment profile  from core
LM95-061A  shows a  step  increase in PCB
concentrations just after the event.   The step
increase  would very likely be caused  by  re-
depositing  the sediments with   higher PCB
concentrations resuspended from a nearby area.

The unit for the  sediment PCB  concentration
output from the LM2-Toxic is ng/L.  In order to
compare it with the PCB sediment profiles (ng/g
dw) from the cores, porosity is used to convert
the   model-generated    PCB   sediment
concentration from the  unit of ng/L to ng/g dw.
The converted PCB  sediment  concentration is
extremely sensitive to the porosity used in the
conversion.  The  value (0.953) for the porosity
                                             330

-------
   used in the conversion is the average of the
   porosities at the sediment sampling sites located
   within the depositional area of the lake.  The
   range of the  porosities  at these  sediment
   sampling sites is between 0.943 and 0.966.

3)  Some physical-chemical processes such as the
   BNL and its associated transport were not built in
   the LM2-Toxic model. A seasonal persistent BNL
   with  high  total suspended  solid is not  an
   uncommon phenomenon in the Great Lakes and
   has  been  observed  and  documented  by
   numerous researchers (Chambers and Eadie,
   1980, 1981; Eadie  et  al.,  1984; Baker and
   Eisenreich, 1985; Baker ef a/., 1991; Eadie, 1997;
   Hawley, 2003). The BNL is usually formed in a
   large lake such as a Great Lake during summer
   stratification. The thickness of the BNL is from a
   few meters up to 10 meters above the bottom of
   the lake (Chambers and Eadie, 1980,  1981;
   Eadie et al., 1984). Total suspended solids and
   associated  chemicals in both particulate and
   dissolved phases increase exponentially from the
   top to the bottom of the BNL. The suspended
   solids in the hypolimnion  with  less  attached
   pollutant will adsorb more PCBs when the solids
   settle through this layer to the lake bottom. The
   existence of the BNL and the physical-chemical
   processes  associated with  this layer  could
   elevate  the concentration  of  PCBs  in  the
   sediments.   Compared  to  the  hypolimnion,
   samples collected in the BNL during the LMMBP
   (August 1994, August 1995, and September 1995
   cruises)  show a 4 to 20 times higher particulate
   PCB  concentrations  and   1.5  times higher
   dissolved PCB concentrations. Because the BNL
   and the associated processes are not built in the
   LM2-Toxic, the PCB hindcast could underpredict
   PCB concentrations in certain areas of the lake
   sediments.

4) There are  uncertainties associated  with the
   derived  historical  primary  production,   PCB
   loading, and vapor-phase time functions.

5) The uncertainties related to model parameters
   including  calibration  parameters  could  also
   propagate through the model to the  predicted
   sediment PCB concentrations.
Mass budget checking is a necessary step to ensure
that a water quality model does not generate or lose
mass through the entire simulation period. Figure
4.5.18 shows the mass budget of IPCB (sum of 54
PCB congeners for Lake  Michigan during the
simulation period of the LM2-Toxic PCB hindcast
(1949-1995).  The inventories in the mass budget
diagram represent the IPCB masses left in both the
water column and sediment at the end  (last time
step)  of  the  model  simulation.   Each mass
component with an arrow in Figure 4.5.18 indicates
a mass flux in or out of the system during the entire
simulation period. A simple mass budget check was
done based on the numbers in Figure 4.5.18. Table
4.5.7 lists the IPCB inventories, the mass fluxes, and
the calculation of the mass budget checking.  The
result of the mass budget checking shows that there
is only about 0.66% of mass not being accounted for
over the 47 years of the hindcast simulation period.
This small amount of uncounted mass over the entire
hindcast period should not have much of an impact
on the accuracy of PCB concentrations computed by
the LM2-Toxic.

The  most reliable confirmation of the LM2-Toxic
model comes from the comparison between the total
PCB sediment inventory (total PCB = IPCB x 1.1668
= 52,278 kg x 1.1668 =  60,998 kg) calculated from
the  LM2-Toxic   PCB   hindcast  and the  latest
estimation of the total PCB sediment inventory (about
60,000  kg)  provided  by  Eadie  and  Van  Hoof
(personal communication, see Part 1, Chapter 7).
The factor 1.1668 used to convert the IPCBs to the
total PCBs was derived from the LMMBP  data (Part
1,  Chapter  3).   The  latest  sediment  inventory
estimate from Eadie and Van Hoof was based on a
large quantity of information collected during the
LMMBP for Lake Michigan sediment, including more
than  60 box core PCB  sediment profiles and  also
roughly  60 ponar and gravity core samples. A few
previous and most recent studies listed in Table 4.5.6
indicate that the lake-wide sediment inventory is likely
between 60,000 kg and 75,000 kg.  The sediment
PCB inventory (60,988 kg) computed from the LM2-
Toxic PCB hindcast  is within the range provided  in
the literature.
                                             331

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                        loads
                      168048 kg
                              net volatilization
                                 114401 kg
                               water column inventory
                                      692kg
                                                          dispersion
                                                            30kg
             Chicago diversion
                  168kg
                                     net advection
                                  1643 kg (2516-873)
                                 sediment inventory
                                     52278 kg
Figure 4.5.18. IPCB mass budget of Lake Michigan during the period of the LM2-Toxic PCB hindcast
(1949-1995).
Table 4.5.7. Calculations in PCB Mass Budget Checking for the LM2-Toxic PCB Hindcast
 Mass of In-Flux (kg)
         Mass of Out-Flux (kg)
          Inventory (Kg)
 Loads
 Dispersion
16,048   Net Volatilization
   30    Net Advection
         Chicago Diversion
114,401    Water Column
  1,643    Sediment
   168
  692
52,278
 Difference in PCB mass over the hindcast simulation period (47 years) = (Inventory + Mass of out-flux)
 (Mass of in-flux) = (692 + 52,278 + 114,401 + 1,643 + 168) - (168,048 + 30) = 169,182 - 168,078 =
 1,104kg

 Percentage of the difference in PCB mass over the PCB total input into Lake Michigan = difference in
 PCB mass over the hindcast simulation/mass of in-flux =1,104 kg/168,078 kg = 0.657%
                                            332

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Given the adequate level of reconstructed historical
forcing functions including loading and vapor phase
concentrations, the overall results from the LM2-
Toxic PCB hindcast demonstrate that the model is
able  to generate  PCB  concentrations in both the
water column and sediment that compare reasonably
well with the available historical data and estimated
sediment total  PCB inventories.

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   pp.
                                            336

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                  PART 4




                LM2-TOXIC
Appendix 4.5.1. Results From Thermal Balance Model
                    337

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o

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        Segment  7
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                                                                         346

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                                             348

-------
           Appendix 4.5.2



Calibrated Results for Organic Carbons
                349

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                                                       350

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GO
0.4.

0.3.

0.2-

0.1-
                    segment 17
                                              segment 18
segment 19
             segment 26
      0
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95

    Q.5-,
01


-------
0.5

0.4-

0.3-

0.2-

0.1
                     segment 1
                                                   segment 2
 segment 3
 segment 4
     0
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 JuI94 Dec94  Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95


    0.5,
    0.4.

    0.3

    0.2.

    0.1.
                     segment 5
                                                   segment 6
segment 11
segment 12
      0
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95


    0.5-,
T5,

0
0.
    0.4

    0.3

    0.2.

    0.1

     0
                    segment 13
                                                  segment 14
                              segment 16
           Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95
    0.5'
O
O
a.
    0.4-

    0.3

    0.2

    0.1-
                    segment 20
                                                  segment 21
segment 22
segment 23
     0
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94  Jul95 Dec95
    0.4
01  0.3-
o   0.2
                                                  segment 25
                                                                            •s. Model Output
                                                                            « Cruise Mean
                                                                       error bars = standard error
                                                                    Participate Detrital Carbon (mg/L)
                                                                       Lake Michigan layers 1 , 2, 3
                                                                            upper 30 meter
                                                                            water segments
     Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95
                                                           359

-------
    0.5
    0.4-
|>  0.3-


n  0.2
    0.1
                    segment 29
segment 30
segment 31
segment 32
      0
     Jan94  Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95

0.4-
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segment 33


JL
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segment 34


* .
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segment 36



I **
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segment 37




- 	 — I iT'^N^. ^t^^'
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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec

0.5-,
n A

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segment 38





~«>^iJ, . ~ ^ r__^»^
* I * *"~ *
	 1— i i












segment 39




•^. Model Output
*• Cruise Mean
error bars = standard error
Participate Detrital Carbon
(mg/L)
Lake Michigan layers 4 & 5
30 meters to bottom
water segments

•~-, * — :f^**
^*\\ '*"' «








segment 40




i »J»
<***4*~*~**f^****^jr**~*a™"^
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segment 41




T
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O
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0.6-


0.4-


0.2
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1.0


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0.6-


0.4-


0.2-
                               0
segment 8
1.0


0.8-


0.6


0.4-


0.2-
                                                                  0
segment 9
                                                                                                0
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94  Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
    1.0
O
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0.8-


0.6.


0.4


0.2-
      0
                    segment 17
                              1.0
0.8.


0.6.


0.4


0.2
                                               segment 18
                               0
                                                            1.0
            0.8-


            0.6.


            0.4


            0.2.
                                                                  0
                                               segment 19
                                                                                               1.0
            0.8-


            0.6-


            0.4


            0.2-
                                                            segment 26
     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
0.8
3
"& 06
0 0.4.
Q.
0.2-
segment 27
^ *v- «
0.8-
0.6.
0.4-
0.2-
segment 28
0.8
0.6.
0.4-
0.2-
segment 35

-. Model Output
» Cruise Mean
error bars = standard error
Particulate
Detrital Carbon
(mg/L)
Green Bay
water segments
     Jan94 Jul94 Dec94  Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95
                                                            361

-------
10000-1

7500-
0.
£ 5000-
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°- 2500-
n.

segment 42
















segment 43















segment 44















segment 45






     Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95  Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
OOUU-,
7500-
5000-
2500-
n.
segment 46










segment 47










segment 48









segment 49




     Jan94  Jul94 Dec94 Jul95 Dec95Jan94  Jul94  Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95
1UUVJU -
_ 7500-
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,§ 5000-
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segment 50

.
segment 51


segment 52


segment 53

      Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95



  10000-
   7500-
£ 5000
o
Q

°- 2500 -I
                    segment 54


•~> Model Output
Participate
Detrital Carbon
(mg/L)
Lake Michigan
sediment segments


segment 55

segment 56
segment 57
      Jan94 Jul94  Dec94 Jul95 Dec95Jan94 Jul94  Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94  Jul95 Dec95
„ 7500-
_j
^>
§5000-
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segment 58


segment 59

-
segment 60



segment 61

                                                          362

-------
IUIAAJ •
,-. 7500-
I
~ 5000-
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Q
Q.
2500-
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segment 62


segment 63


segment 64


segment 65

Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec£
,-, 7500-
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~ 5000-
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segment 66

.
segment 67


segment 68


segment 69

Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9
innnn
_ 7500-
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£-5000-
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segment 70


segment 71

.
segment 72

-

segment 73

Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9
10000
„ 7500
^
JSODO.
Q
0_
2500-
0-
segment 74


segment 75

Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 J


segment 76

.
segment 77

u!95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 DecS
—• Model Output
Particulate
Detrital Carbon
(mg/L)
Lake Michigan
sediment segments

363

-------
10000.
_ 7500-
O)
E. 5000
0
Q
°- 2500-
n.
segment 78

•
segment 79


segment 80


segment 81

      Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
en


o
Q
uuuu-
7500-
5000-
2500-
n.
segment 82










segment 83










segment 84










segment 85




      Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95

7500-
5000 .
2500.
o.
segment 86









segment 87










segment 88









segment 89





      Jan94 Jul94 Dec94 Jul95 Dec95Jan94 Jul94  Dec94 Jul95 Dec95Jan94  Jul94  Dec94 Jul95 Dec95Jan94  Jul94  Dec94 Jul95 Dec95

„ 7500-
O)
.§ 5000-
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°~ 2500-
0-
segment 90













segment 91













segment 92













segment 93







      Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95
_ 7500-
_i
^>
o5000'
Q
Q.
2500-
0-
segment 94


~» Model Output
Participate
Detrital Carbon
(mg/L)
Green Bay
sediment segments

      Jan94 Jul94 Dec94  Jul95 Dec95
                                                             364

-------
             Appendix 4.5.3



Calibration Results for PCB28+31 and IPCBs
                  365

-------
            PCB28+31 (ng/L) in segment 1
                                              PCB28+31 (ng/L) in segment 2
PCB28+31 (ng/L) in segments
0.05
0.04-
0.03
0.02
0.01
0
Jan
F
0.05
0.04
0.03
0.02
0.01
0
Jan
0.05
0.04
0.03
0.02
0.01
0
— Model Output
» Cruise Mean

T I
v**kri4^\K^^'i4-vv
94 Jul.94 Dec.94 Jul.95 Dec
•CB28+31 (ng/L) in segment 4
— Model Output
* Cruise Mean

*

94 Jul.94 Dec.94 Jul.95 Del
0.0*
0.04
0.03
0.02 -
0.01 -
0
.95 Jan
F
0.05
0.04
0.03
0.02
0.01
0
=-95 Jan
— Model Output
* Cruise Mean


>^f~f**~*~\***iti^~~~/f^$*r*
.94 Jul.94 Dec.94 Jul.95 De
"CB28+31 (ng/L) in segment 5
— Model Output
» Cruise Mean

§
v^—^ 4J-^_«_J-»V-
94 Jul.94 Dec.94 Jul.95 Dec
PCB28+31 (ng/L) in segment 1 1 PCB28+31 (ng/L) in segment 1 2
— Model Output
• Cruise Mean 0.04 -
0.03 -
0.02
-• 	 1 	 1 	 1 	 0
— Model Output
* Cruise Mean

^~Ui4_j_X~

0.04
0.03
0.02
0.01
0
:.9S Ja
F
0.05
0.04
0.03
0.02
0.01
0
95 Jan
F
0.05
0.04
0.03
0.02
0.01
n -
— Model Output
* Cruise Mean

,
w-v-i-*-M~*_^-r*«-.
•^wtw^v-
n.94 Jul.94 Dec.94 Jul.95 Dec
•CB28+31 (ng/L) in segment 6
— Model Output
* Cruise Mean

*
— «
94 Jul.94 Dec.94 Jul.95 Dec.9
>CB28+31 (ng/L) in segment 13
— Model Output
« Cruise Mean

O_u__
^T~
           Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
                                            Jan.94  Jul.94 Dec.94  Jul.95  Dec.95
                                                                               Jan.94  Jul.94 Dec.94  Jul.95  Dec.95
PCB28+31 in dissolved phase (ng/L)
Lake Michigan layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                        366

-------
                 PCB28+31 (ng/L) in segment 14
                      PCB28+31 (ng/L) in segment 15
                                                                                          PCB28+31 (ng/L) In segment 16
             0.05

             0.04

             0.03

             0.02

             0.01
— Model Output
* Cruise Mean
0.04 -
0.03
0.02
0.01
n •
— Model Output
» Cruise Mean


^_^-t_t~w^_^-»-^V

0.04 •
0.03
0.02
0.01
n
— Model Output
* Cruise Mean


^_^t-2^__^»^^*-^_
•
               Jan.94  Jul.94  Dec.94  Jul.95   Dec.95    Jan.94  Jul.94   Dec.94   Jul.95  Dec.95     Jan'94  Jul-94   Dec.94   Jul.95  Dec.95
                  PCB28+31 (ng/L) in segment 20       PCB28+31 (ng/L) in segment 21
                                                                                          PCB28+31 (ng/L) in segment 22
               0.05
               0.04


               0.03


               0.02


               0.01
                                — Model Output
                                » Cruise Mean
                  o.os

                  0.04

                  0.03

                  0.02
                  0.01 •
— Model Output
» Cruise Mean
                 Jan.94  Jul.94  Dec.94  Jul.95   Dec.95  Jan-94   Jul-94  Dec'94  Jul'95  Dec-95
                  PCB28+31 (ng/L) in segment 23
                       PCB28+31 (ng/L) in segment 24
0.05


0.04


0.03


0.02


0.01
                                — Model Output
                                * Cruise Mean
0.04
0.03
0.02
0.01
— Mod el Output
« Cruise Mean

v ***^f~^At^*. j - i *-*V^r
* ^ »
                                                                        0.05


                                                                        0.04


                                                                        0.03


                                                                        0.02


                                                                        0.01
— Model Output
» Cruise Mean
                                                                                         Jan.94   Jul.94  Dec.94  Jul.95   Dec.95
                                                                                           PCB28+31 (ng/L) in segment 25
                                                         0.05


                                                         0.04


                                                         0.03


                                                         0.02


                                                         0.01
                                        - Model Output
                                        • Cruise Mean
                Jan.94  Jul.94   Dec.94   Jul.95  Dec.95    Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
                                                                                         Jan.94  Jul.94   Dec.94  Jul. 95  Dec.95
PCB28+31 in dissolved phase (ng/L)
Lake Michigan layers  1,  2, and 3
Upper 30 m water segments
Error bars = standard error
                                                                 367

-------
     PCB28+31 (ng/L) in segment 29       PCB28+31 (ng/L) In segment 30      PCB28+31 (ng/L) in segment 31        PCB28+31 (ng/L) in segment 32
0.05
0.04
0.03
0.02
0.01
0 -
Jan
F
0.05
0.04
0.03 -
0.02
0.01
0
Jan
f
0.05
0.04
0.03 -
0.02 -
0.01 -
0
— Model Output
» Cruise Mean

^ 	 sSt^J-—^
•* * *{
.94 Jul.94 Dec.94 Jul.95 Dec
CB28+31 (ng/L) in segment 33
— Model Output
» Cruise Mean


* .
.94 Jul.94 Dec.94 Jul.95 De<
CB28+31 (ng/L) In segment 38
— Model Output
* Cruise Mean


TT'Xr
0.05
0.04
0.03
0.02
0.01
0
.95 Jan
F
0.05
0.04
0.03
0.02
0.01 -
0
.95 Jar
F
0.05
0.04
0.03 -
0.02 -
0.01 -
0 -
— Model Output
» Cruise Mean

f I
.94 Jul.94 Dec.94 Jul.95 Dec
•CB28+31 (ng/L) In segment 34
— Model Output
» Cruise Mean


-*^—^~
.94 Jul.94 Dec.94 Jul.95 Dec
'CB28+31 (ng/L) in segment 39
— Model Output
» Cruise Mean


^TT^^^T
0.04 -
0.03
0.02 -
0.01 -
0
.95 Jan
F
0.05 •
0.04
0.03
0.02
0.01
0
.95 Jan
p
0.05-
0.04
0.03
0.02
0.01
0 •
— Model Output
» Cruise Mean

* «
I.94 Jul.94 Dec.94 Jul.95 Dei
'CB28+31 (ng/L) in segment 36
— Model Output
* Cruise Mean


"^^^^r
.94 Jul.94 Dec.94 Jul.95 Dec
CB28+31 (ng/L) in segment 40
— Model Output
« Cruise Mean


» •
0.04
0.03
0.02
0.01
0
:.9S Jar
F
0.05 -
0.04 -
0.03 -
0.02
0.01
0
.95 Jar
F
O.OS
0.04
0.03
0.02
0.01
o -
— Model Output
» Cruise Mean

*
.94 Jul.94 Dec.94 Jul.95 Dec
>CB28+31 (ng/L) in segment 37
— Model Output
* Cruise Mean


**** " '.
.94 Jul.94 Dec.94 Jul.95 Dec
CB28+31 (ng/L) in segment 41
— Model Output
» Cruise Mean


' » * * * «
    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95  Jan.94 Jul.94  Dec.94  Jul.95  Dec.95   Jan.94  Jul.94  Dec.94 Jul.95  Dec.95
PCB28+31 in dissolved phase (ng/L)
Lake Michigan layers 4 and 5
30 m to bottom water segments
Error bars = standard error
                                                          368

-------
      PCB28+31 (ng/L) in segment?
                                                                          PCB28+31 (ng/L) in segment 10
 0.1


0.08


O.OE


0.04


0.02
                   — Model Output
                   * Cruise Mean
 0.1


0.08


0.06


0.04


0.02
— Model Output
» Cruise Mean
U.3
0.4
0.3
0.2

0.1
0 -
— Model Output
» Cruise Mean


*

•
 1


0.8


0.6


0.4
— Model Output
» Cruise Mean
    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95   Jan.94  Jul.94   Dec.94  Jul.95  Dec.95  Jan.94   Jul.94   Dec.94  Jul.95   Dec.96  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95


      PCB28+31 (ng/L) in segment 17        PCB28+31 (ng/L) In segment 18       PCB28+31 (ng/L) in segment 19         PCB28+31 (ng/L) in segment 26
                                                                                                                            — Model Output
                                                                                                                            « Cruise Mean
    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95   Jan.94  Jul.94   Dec.94  Jul.95  Dec.96  Jan.94   Jul.94   Dec.94   Jul.95   Dec.95    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
      PCB28+31 (ng/L) in segment 27        PCB28+31 (ng/L) in segment 28
                                    o.s
                                      PCB28+31 (ng/L) In segment 35
    Jan.94   Jul.94  Dec.94  Jul.95  Dec.95  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95  Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
PCB28+31 in dissolved phase (ng/L)
Green Bay water segments
Error bars = standard error
                                                                  369

-------
0.4i

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CD
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0.


segment 42



""-•x.
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0.1



segment 43



^^^
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0.3^


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n.

segment 44




^^^^fc.-

' ' 	 -~^__


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segment 45




^-i^, 	

— «^_^_^^
~^*~

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„. r\ A n A n A
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segment 46










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segment 47










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segment 48










0.3-


0.2-


0.1-


segment 49





— t 	
" ' 	


Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9
U.4'
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segment 50





	 	 	 	


0.3-
0.2i



0.1-
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segment 51








0.3-
0.2-



0.1-
n.
segment 52

•^-^^
^^~^^^
^ — >^_^
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0.3-
0.2-



0.1-
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segment 53





> 	
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   Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95  Jan94 Jul94 Dec94 Jul95 Dec95  Jan94 Jul94 Dec94 Jul95 Dec95

I0-3'
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segment 54




^*"*»*»fci^
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0.3-
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segment 55





^ ^_

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0.2-



0.1-
Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jar
segment 56






i94 Jul94 DecS4 Jul95 Dec
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0.2-



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segment 57






i94 Jul94 Dec94 Jul95 Dec.
  0.4
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"0.2-]

CO
fM
CO
                   segment 58
                                 0.4
0.3-



0.2-



0.1-

segment 59
— . Model Output
dissolved
PCB 28 + 31 (ng/L)
Lake Michigan
sediment segments


                                                                 0.4
                                                                 0.3-
0.2-
                                                                 0.1-
    °"J	'	   i      •      1    0-J	1      i       .      1   Q|       :                   |    Q|       _      _      ,
   Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94  Jul95 Dec95  Jan94 Jul94  Dec94 Jul95 Dec95  Jan94 Jul94  Dec94 Jul95 Dec95
                                                                                  segment 60
                                                                                                 0.4
0.3-



0.2-



0.1-
                                                                                segment 61
                                                             370

-------
ux-
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segment 62



i94 Jul94 Dec94 Jul95 Dec
segment 66



i94 Jul94 Dec94 Jul95 Dec
segment 70


i94 Jul94 Dec94 Jul95 Dec
segment 74


i94 Jul94 Dec94 Jul95 Dec

0.3-
0.2-
0.1 J
0-
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0.4-1
0.3-

0.2-
0.1-
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0.4-I
0.3
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0.1-
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0.4-
0.3-
0.2-
0.1-
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segment 63






i94 Jul94 Dec94 Jul95 Dec
segment 67






i94 Jul94 Dec94 Jul95 Dec
segment 71




194 Jul94 Dec94 Jul95 Dec
segment 75


194 Jul94 Dec94 Ju



I95 Dec

0.3-
0.2-
0.1-
0-
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0.4-
0.3-

0.2-
0.1-
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0.4n
0.3-
0.2-
0.1-
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0.4-
0.3-
0.2-
0.1-
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segment 64



i94 Jul94 Dec94 Jul95 Dec
segment 68



i94 Jul94 Dec94 Jul95 Dec
segment 72


i94 Jul94 Dec94 Jul95 Dec
segment 76


194 Jul94 Dec94 Jul95 Dec
•x Model Output
dissolved

0.3-
0.2-
0.1-
O-l
95 Jar
0.4-
0.3-

0.2-
0.1-
O-l
95 Jar
0.4-
0.3-
0.2-
0.1-
o-l
95 Jar
0.4-1
0.3-
0.2-
0.1-
o-l
95 Jar
segment 65



i94 Jul94 Dec94 Jul95 DecJ
segment 69



i94 Jul94 Dec94 Jul95 Dec?
segment 73


)94 Jul94 Dec94 Jul95 Dec£
segment 77


i94 Jul94 Dec94 Jul95 Dec<
PCB 28 + 31 (ng/L)
Lake Michigan
sediment segments
371

-------
0.4-]
I0'3'
«0.2-
CO
CM
03
Q-
segment 7Q

U.4-
0.3-
0.2
0.1-
segment 79

U.H-
0.3-
0.2-
0.1-
n.
segment 80

U.T
0.3-
0.2-
0.1-
n.
segment 81

Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 JuISS Dec£
0.4-
i>3-
«0.2-
co
CM
m
oo.1
segment 82

0.3-
0.2
0.1

segment 83

0.3-
0.2-
0.1-
n.
segment 84

0.3-
0.2-
0.1
n.
segment 85

Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9
r\ A r\ A r\ A r\ A
?0.3-
O>
C
"0.2-
co
CM
m
00.1-
0-
segment 86

0.3-
0.2
U.1-
r\.
segment 87

0.3-
0.2-
0.1-
segment 88
0.3-
0.2-
0.1-
segment 89
Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul'95 Dec95 Jan94 Jul'94 Dec94 Jul95 Dec9
|°-3-
« 0.2-
co
CM
CO
00.1-
0-
Ja
0.4-
I0-3
« 0.2
CO
CM
CD
00.1
0-
segment 90
0.3-
0.2-
0.1-
n.
segment 91
^^^_
0.3-
0.2-
0.1-
n
segment 92

0.3-
0.2-
0.1-
segment 93

i94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 JuJ94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9
segment 94


-v Model Output
dissolved
PCB 28 + 31 (ng/L)
Green Bay
sediment segments


Jan94 Jul94  Dec94 Jul95 Dec95
                                                      372

-------
            PCB28+31 (ng/L) in segment 1
PCB28+31 (ng/L) in segment 2
PCB28+31 (ng/L) in segment 3
0.04
0.03
0.02
0.01
0
Jan
0.05
0.04
0.03
0.02 •
0.01
0
Jan
0.05
0.04
0.03
0.02
0.01
0
— Model Output
» Cruise Mean


^M^>^>-
94 Jul.94 Dec.94 Jul.95 Dec
'CB28+31 (ng/L) in segment 4
— Model Output
» Cruise Mean


^-L*~^L*~^-^
0.04
0.03
0.02
0.01
0
.95 Jan
0.05
0.04
— Model Output
» Cruise Mean


Jj-,^^^
.94 Jul.94 Dec.94 Jul.95 Dec
PCB28+31 (ng/L) in segment 5
— Model Output
» Cruise Mean
0.03 -
0.02 -
0.01

0.04 -
O.OJ -
0.02
0.01
0
95 Jar
0.05 -I
0.04
0.03
0.02
0.01 -
" 0
— Model Output
* Cruise Mean


^t-^1^—
.94 Jjl.94 Dec.94 Jul.95 Dec
PCB28+31 (ng/L) in segment 6
— Model Output
» Cruise Mean


-J^— ^_— -r-r-
0 -I 	 r-*-^* 	 r 	 '- 	 r-*-» 	 1
Jan.94 Jul.94 Dec.94 Jul.95 Dec.95
.94 Jul.94 Dec.94 Jul.95 Dec.95
PCB28+31 (ng/L) in segment 11
— Model Output
» Cruise Mean


"-J*H-^-r^
F
0.05
0.04
0.03
0.02
0.01
0 H
•CB28+31 (ng/L) in segment 12
— Model Output
* Cruise Mean


* " "
^- — ^%V' 	 '"IT"""-
F
0.05
0.04
0.03
0.02
0.01 -
0
'CB28+31 (ng/L) in segment 13
— Model Output
» Cruise Mean


<_Ur>-~-r>-
           Jan.94  Jul.94  Dec.94  Jul.95  Dec.95       Jan.94  Jul.94 Dec.94 Jul.95  Dec.96
                                                                                 Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
PCB28+3i in particulate phase (ng/L)
Lake Michigan layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                        373

-------
                                               PCB28+31 (ng/L) in segment 15
PCB28+31 (ng/L) in segment 16
.OS --
.04 -
.03
.02
.01
— Model Output
» Cruise Mean



— Model Output
0.04 - » Cruise Mean 0.04 •
0.03 - 0.03
0.02 - 0.02
0.01
J°an.94 MM Dec.94 J.I.H Dec.95 Ja"-M Jul-94 DeC-94 Jul'9S DeC'9i
f
0.05
0.04 -
0.03
0.02
0.01
0
Jar
O.OS
0.04
0.03
0.02
0.01
0
'CB28+31 (ng/L) in segment 20
— Model Output
» Cruise Mean


•^^^^^
1.94 Jul.94 Dec.94 Jul.95 De
PCB28+31 (ng/L) in segment 23
— Model Output
» Cruise Mean


"— i>T>-^» — rr-

F
0.05
0.04
0.03 -
0.02-
0.01
0
c.95 Jan
1
0.05
0.04
0.03
0.02
0.01
3 0
'CB28+31 (ng/L) in segment 21
— Model Output
« Cruise Mean



.94 Jul.94 Dec.94 Jul.95 Dec
'CB28+31 (ng/L) in segment 24
— Model Output
* Cruise Mean


^__t-« 	 •**_»—• •»-*•**
	 1— * 	 * 	 1 	 1— 	
0.01
0-
Jan
0.0
0.0
— Model Output
* Cruise Mean


• 	 **7^ 	 •-^T^—
94 Jul.94 Dec.94 Jul.95 Dec.95
PCB28+31 (ng/L) in segment 22
— Model Output
4 - » Cruise Mean
0.03-
0.02
0.0
.95 J
O.OE
0.04
0.03
0.02
0.01
0
1 -
an.94 Jul.94 Dec.94 Jul.95 Dec
PCB28+31 (ng/L) in segment 25
— Model Output
* Cruise Mean


^__»j. — .. 	 _^ 	 — K-_

           Jan.94  Jul.94  Dec.94  Jul.95 Dec.95
                                                Jan.94 Jul.94  Dec.94  Jul.95  Dec.95       Jan.94  Jul.94  Dec.94  Jul.95 Dec.95
PCB28+31 in particulate phase (ng/L)
Lake Michigan layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                        374

-------

0.04
0.03
0.02
0.01

— Model Output
» Cruise Mean

\

•kii*—*^! — ,-JJ

0.04
0.03
0.02 •
0.01

— Model Output
* Cruise Mean

I
I
I 	 ^ f ,-f

0.04
0.03 -
0.02 -
0.01 -

— Model Output
» Cruise Mean



* . .[-f-^ ~_ 	

0.04
0.03
0.02-
0.01 -

— Model Output
» Cruise Mean



^__t-t— 4-— _•— — ri—-






O-l 	 1 	 1 	 < 	 • — 1 0-1 	 ^ 	 1 	 1 	 1 0-1 	 r-=— 	 1 	 , 	 : 	 1 0-1 	 r-2 	 , 	 , 	 : 	 1
Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95
PCB28+31 (ng/L) in segment 33 PCB28+31 (ng/L) in segment 34 PCB28+31 (ng/L) in segment 36 PCB28+31 (ng/L) in segment 37

0.04
0.03
0.02
0.01 -

— Model Output
* Cruise Mean



^ -- -*-r*-

0.04
0.03
0.02
0.01

— Model Output
» Cruise Mean



±_iL_ , _ ^___^_

0.04
0.03
0.02 i
0.01 -

— Model Output
» Cruise Mean



t » i

0.04
0.03
0.02
0.01

— Model Output
» Cruise Mean



. -*.n 	 _-!-*-






o ._ 	 ^* 	 , — -.; — , — _: — . o 	 r-* 	 , 	 ^— i — - — 0-- 	 r^—* — , 	 : 	 1 	 0 	 r^-^ — , 	 : 	 , 	
Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.95
PCB28+31 (ng/L) in segment 38 PCB28+31 (ng/L) in segment 39 PCB28+31 (ng/L) in segment 40 PCB28+31 (ng/L) in segment 41

0.04-
0.03 -
0.02 -
0.01 -
0 -
— Model Output
» Cruise Mean





0.04
0.03
0.02
0.01 -
0 -I
— Model Output
* Cruise Mean


	 *-' i 7 	 > 	 -r-i —


0.04
0.03 •
0.02
0.01
0 -I
— Model Output
» Cruise Mean





0.04 •
0.03
0.02 -
0.01 •
0 -t
— Model Output
» Cruise Mean


1 	 *-»T 	 ' 	 * 	 T1 —







   Jan.94  Jul.94  Dec.94 Jul.95  Dec.95  Jan.94 Jul.94  Dec.94  Jul.95  Dec.95  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95   Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
PCB28+31 in particulate phase (ng/L)
Lake Michigan layers 4 and 5
30 m to bottom water segments
Error bars = standard error
                                                         375

-------
    PCB28+31 (ng/L) in segment 7
                                       PCB28+31 (ng/L) in segments
                                                       PCB28+31 (ng/L) in segments
                                                                          PCB28+31 (ng/L) in segment 10
 0.1


 0.08


 0.06


 0.04


 0.02
-Model Output
> Cruise Mean
 0.1


0.08


0.06


0.04


0.02
— Model Output
* Cruise Mean
0.5


0.4


0.3


0.2
— Model Output
* Cruise Mean
  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95   Jan.94  Jul.94  Dec.94  Jul.95  Dec.95 Jan.94  Jul.94  Dec.94  Jul.95  Dec.95  Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
    PCB28+31 (ng/L) in segment 17
                                      PCB28+31 (ng/L) in segment 18
                                                       PCB28+31 (ng/L) in segment 19
                                                                                          PCB28+31 (ng/L) in segment 26
                                                                                                                          — Model Output
                                                                                                                          » Cruise Mean
  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95    jan.M  Ju| 94  Dec94   Ju| 95  Dec.95  Jan.94   Jul.94   Dec.94  Jul.95   Dec.95  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
    PCB28+31 (ng/L) in segment 27
                     PCB28+31 (ng/L) In segment 28
                                        PCB28+31 (ng/L) in segment 35
                                                     — Model Output
                                                     * Cruise Mean
                                     0.1


                                    0.08


                                    0.06


                                    0.04 •


                                    0.02
                                                                      — Model Output
                                                                      » Cruise Mean
  Jan.94  Jul.94  Dec.94  Jul.95  Dec.95    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95  Jan.94   Jul.94  Dec.94  Jul.95  Dec.95
PCB28+31 in particulate phase (ng/L)
Green Bay water segments
Error bars = standard error
                                                                  376

-------
            PCB28+31 (ug/L) in segment 42
                         — Model Output
         Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
                                                    PCB28+31 (ug/L) in segment 43
                                                                 — Model Output
                                                Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
PCB28+31 (fig/L) in segment 44
             — Model Output
                                                                                       Jan.94   Jul.94   Dec.94  Jul.95  Dec.95
             PCB28+31 (ug/L) in segment 45
                          — Model Output
         Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
                                                   PCB28+31 (ug/L) in segment 46
                                                                — Model Output
                                                Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
                                                                                          PCB28+31 (ug/L) in segment 47
                                                                                                        — Model Output
                                                                                       Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
              PCB28+31 (ug/L) in segment 48
                                                     PCB28+31 (ug/L) in segment 49
                                                                                           PCB28+31 (ug/L) in segment 50

— Model Output




4-1

3
2
1


— Model Output


	 " 	 —I



3
2 -
1 -
0 -

— Model Output


—

           Jan.94  Jul.94   Dec.94  Jul.95   Dec.95      Jan.94   Jul.94   Dec.94  Jul.96   Dec.95     Jan.94  Jul.94  Dec.94   Jul.95   Dec.95
           in particulate phase (pg/L)
Lake Michigan sediment segments
                                                                 377

-------
            PCB28+31 (ug/L) in segment 51
                          — Model Output
         Jan.94  Jul.94  Dec.94   Jul.95   Dec.95
                                                      PCB28+31 (ug/L) in segment 52
                                                                   — Model Output
                                                   Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
                                                                                             PCB28+31 (ug/L) in segment S3
                                                                                                           — Model Output
                                                                                          Jan.94  Jul.94  Dec.94   Jul.95  Dec.95
             PCB28+31 (ug/L) in segment 54
                          — Model Output
          Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
                                                        PCB28+31 (ug/L) in segment 55
                                                                     — Model Output
PCB28+31 (ug/L) in segment 56
                                                                                                            — Model Output
                                                     Jan.94   Jul.94   Dec.94   Jul.95   Dec.95     Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
              PCB28+31 (ug/L) in segment 57


3
2
1

— Model Output








Jan.94 Jul.94 Dec.94 Jul.95 Dec.95
PCB28+31 (ug/L) In segment 60

3
2
1
n •
— Model Output









                                                         PCB28+31 (ug/L) in segment 58
                                                                                              PCB28+31 (ug/L) in segment 59
                                                                      — Model Output
                                                                                                            — Model Output
                                                     Jan.94  Jul.94  Dec.94  Jul.95  Dec.95     Jan-94   Jul-94   Dec'94  Jul'95  Dec'95
                                                         PCB28+31 (u.g/L) In segment 61
                                                                      — Model Output
           Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
                                                     Jan.94  Jul.94  Dec.94   Jul.96   Dec.95
PCB28+31 in particulate phase (pg/L)
Lake Michigan sediment segments
                                                                  378

-------
            PCB28+31 (ug/L) in segment 62
                         — Model Output
         Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
                                                       PCB28+31 (ug/L) in segment 63
                                                                     -Model Output
                                                   Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
                                                                                              PCB28+31 (ug/L) in segment 64
                                                                                                           — Model Output
                                                                                           Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
            PCB28+31 (ug/L) in segment 65
                         — Model Output
         Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
                                                        PCB28+31 (ug/L) in segment 66
                                                                                                PCB28+31 (ug/L) in segment 67
— Model Output





3
2
1 •
0 -I
— Model Output



I 	 r- 	 • 	 1 	 1 	 1
                                                    Jan.94  Jul.94  Dec.94   Jul.95  Dec.95        Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
            PCB28+31 (ug/L) in segment 68
                         — Model Output
         Jan.94   Jul.94  Dec.94  Jul.95  Dec.95
                                                        PCB28+31 (ug/L) in segment 69
                                                                     — Model Output
                                                    Jan.94  Jul.94  Dec.94   Jul.95  Dec.95
                                                                                                PCB28+31 (ug/L) in segment 70
                                                                                                             — Model Output
                                                                                            Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
PCB28+31  in particulate phase (M9/L)
Lake Michigan sediment segments
                                                                 379

-------
          PCB28+31 ((ig/L) in segment 71               PCB28+31 (jig/L) in segment 72             PCB28+31 (ug/L) in segment 73
4-1

3
2
1
0

— Model Output




1

3
2
1
0 -

— Model Output






3
2
1


— Model Output




       Jan.94   Jul.94  Dec.94  Jul.9S   Dec.95        Jan.94   Jul.94   Dec.94   Jul.96  Dec.95       Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
          PCB28+31 (ug/L) in segment 74
PCB28+31 (ng/L) in segment 75
                                                                                         PCB28+31 (fig'L) in segment 76

— Model Output




4

3
2
1
n .

— Model Output






3
2
1
0 •

— Model Output




       Jan.94  Jul.94   Dec.94   Jul.95   Dec.95        Jan.94   Jul.94   Dec.94   Jul.95  Dec.95       Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
            PCB28+31 (ug/L) in segment 77
                        — Model Output
        Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
PCB28+31 in participate phase (M9/L)
Lake Michigan sediment segments
                                                               380

-------
             PCB28+31 (ug/L) in segment 78
                          — Model Output
          Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
 PCB28+31 (ug/L) in segment 79
                                                                        - Model Output
PCB28+31 (ug/L) in segment 80
                                                        — Model Output
                                                      Jan.94   Jul.94  Dec.94   Jul.95   Dec.95         Jan.94   Jul.94   Dec.94  Jul.95  Dec.95
            PCB28+31 (ug/L) in segment 81
                         — Model Output
         Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
PCB28+31 (ug/L) in segment 82
             — Model Output
PCB28+31 (ug/L) in segment 83
             — Model Output
                                                      Jan.94   Jul.94   Dec.94  Jul.95   Dec.95         Jan.94   Jul.94   Dec.94  Jul.95  Dec.95
            PCB28+31 (ug/L) in segment 84
                         — Model Output
         Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
 PCB28+31 (ug/L) in segment 85
                                                       2 •
                                                                       — Model Output
                                                                                                   PCB28+31 (ug/L) in segment 86
                                                                                                1 -
                                                                                                                — Model Output
                                                      Jan.94   Jul.94  Dec.94   Jul.95   Dec.95         Jan.94   Jul.94   Dec.94  Jul.95  Dec.95
PCB28+3i in particulate phase (pg/L)
Green Bay sediment segments
                                                                 381

-------
         PCB28+31 (ug/L) In segment 87
      2
                      — Model Output
      Jan.94  Jul.94  Dec.94  Jul.95  Dec.96
PCB28+31 ((ig/L) in segment 88
                                                                 — Model Output
                                                 Jan.94  Jul.94   Dec.94  Jul.95   Dec.95
PCB28+31 (ug/L) in segment 89
                                                        — Model Output
                                                                                           Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
            PCB28+31 (ug/L) in segment 90
                         — Model Output
        Jan.94   Jul.94  Dec.94   Jul.95  Dec.95
                                                      PCB28+31 (ug/L) in segment 91
               — Model Output
                                                   Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
                                          PCB28+31 (ug/L) in segment 92
                                                                                                           — Model Output
                                                                                           Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
           PCB28+31 (ug/L) in segment 93
                        — Model Output
        Jan.94  Jul.94   Dec.94   Jul.95   Dec.95
   PCB28+31 (pg/L) in segment 94
                                                                    — Model Output
                                                   Jan.94   Jul.94  Dec.94   Jul.95  Dec.95
PCB28+31 in participate phase (|jg/L)
Green Bay sediment segments
                                                                 382

-------
U.LD'
5" 0.04
r 0.03-
to
8 0.02-
03
CL 0.01-
Q.
segment
Jan94 Jul94 Dec94 Jul95
005
j 0.04.
X 0.03.
CO
a 0.02.
CO
CL 0.01-
o
1
h

^J^
Dec95 Jan94 Jul94
segment 5
4
Jan94
0 05
j 0.04-
^ 0.03-
co
8 0.02-
m
CL 0.01-
0-
Ja
5 0.04-
~ 0.03-
co
8 0.02-
CD
CL 0.01-
Jul94 Dec94 Jul95

segment 2
r
•^^^^^f^l

segment 3

segment 4
T

Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
segment 6
Dec95Jan94 Jul94
segment 13
j
**
194
•^
1
Jan94
J 0.04-
ra
^ o.o3-
CO
+
8 0.02-
CQ
O
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Jul94 Dec94 Jul95
segment
2
'*
Jul94 Dec94 Jul95


segment 11
V>^T""*r"ASfc
-------
0.05-
j 0.04-
^ 0.03-
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8 0.02-
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5" 0.04-
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a! 0.01-
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segment 29
, I
SfM-rf

•
.
segment 30
t
UVV"~IT*



segment 31

^rr^^r^



segment 32

v_l<-K^t>
i94 Jul94 Dec94 Jul95 Dec95 Jan94 JuJ94 Dec94 Jul95 Dec95 Jan94 JuJ94 Dec94 Jul95 Dec95 Jan94 JuJ94 Dec94 Jui95 Dec!
segment 33


^?-^r~




segment 34


*• »




segment 36

A~_^--''~X\
*»« * , *^




segment 37

~^-^~~*\
*H . *{
i94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 DecS

segment 38
^r>^s-<^



segment 41
** « *»
Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
                                                      384

-------
  0.10
  0.08
                     segment 7



^ 0.06 ]

?     I
3 0.04-1


£ 0-02  W^HKfc*'-^
                                                                                 segment 9
     Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94  Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95


   0.10-,

   0.08 J
                                                                                                               segment 26
     Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95


   0.10,
5 0.08-
"a

" 0.06.
n

S  0.04-I

-------
      PCB28+31 (ug/L) In segment 42
                  — Model Output
  Jsn.94  Jul.94   Dec.94   Jul.95  Dec.95
                                          PCB28+31 (pg/L) In segment 43
                                                                                PCB28+31 (ng/L) in segment 44
                                                                                                                     PCB28+31 (|ig/L)ln segment 45
— Model Output

~— 	 —

3
2
1
0
— Model Output

"^- 	 —

3
2
1 •
0
— Model Output

~~ 	 — 	

      PCB28+31 (u.g/L) in segment 46
                                          PCB28+31 (ug/LJ in segment 47
                                                                                PCB28+31 (u.g/L) In segment 48
                                                                                                                     PCB28+31 (u,g/L) in segment 49

— Model Output




4 •

3 -
2
0

— Model Output






3
2
0

— Model Output






3
2
0

— Model Output



  Jan.94  Jul.94   Dec.94   Jul.95  Dec.95    Jan.94  Jul.94  Dec.94  Jul.95  Dec.95     Jan.94  Jui.fl4   Oec.94   Jul.95  Dec.95    Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
      PCB28+31 (jig/L) in segment 50
                                          PCB28+31 (iig/L) in segment 51
                                                                               PCB28+31 (ng/L) in segment 52
                                                                                                                     PCB28+31 (|ig/L) in segment 53
— Model Output



.94 Jul.94 Doc. 9 4 Jut. 95 De
3
2 -
1 -
0 -
.95 Jar
— Model Output



.94 Jul.94 Dec.94 Jul.95 Dec
3
2
1
0
.95 Jar
— Model Output

~^~- 	
.94 Jul.94 Dec ,94 Jul.95 Dec
3
2
1
0
.95 Jan
— Model Output


.94 Jul.94 Dec.94 Jul.BS Dec
      PCB28+31 (|tg/L) In segment 54
                                          PCB28+31 (ug/L) in segment 55
                                                                               PCB28+31 (|ig/L) in segment 56
                                                                                                                     PCB28+31 (|ig/L) in segment 57
— Model Output


-~ 	


3
2
1 -

— Model Output




— Model Output





3 -
2
1 -

— Model Output




   Jan.94  Jul.94   Dec.94   Jul.95  Dec.95    Jan.94   Jul.94  Dec.94  Jul.95  Dec.95     Jan.94  Jul.94   Dec.94  Jul.95  Dec.95    Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
      PCB28+31 (ttS/L) in segment 58
                                          PCB28+31 |ug/l_) in segment 59
                                                                                PCB28+31 (u.g/L) in segment 60

— Model Output



4 •

3
2
1

— Model Output



' ' 	 • 	 \ 0 -1 	 — 	 r- 	 , 	 T 	 , 	 1
.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan.94 Jul.94 Dec.94 Jul.BS De<
4 -I

3
2 -
1
0 -I
.95 Jan

— Model Output



.84 Jul.94 Dec.94 Jul,95 Dec
                                                                                                                     PCB28+31 (|ig/L) In segment 61
                                                                                                                                  -Model Output
                                                                                                                 Jan.94   Jul.94  Dec.94  Jul.95   Osc.95
PCB28+3i (dissolved + particulate) (ug/L)
Lake  Michigan sediment segments
                                                                        386

-------
                 PCB28+31 (fig/L) in segment 62           PCB28+31 (ug/L) in segment 63
                                      PCB28+31 (ug/L) in segment 64
— Model Output




4
3
2
1
0 -I
— Model Output



	 , 	 , 	 , 	 *

3
2
1
0 -I
— Model Output




              Jan.94  Jul.94   Dec.94  Jul.95  Dec.95    Jan.94  Jul.94  Dec.94   Jul.96  Dec.95     Jan.94   Jjl.94   Dec.94  Jul.95  Dec.95
                 PCB28+31 (ug/L) in segment 65
PCB28+31 (jig/L) in segment 66
                                                                                            PCB28+31 (ug/L) In segment 67

— Model Output






3
2
1


— Model Output



4 i

3 J
2 -
1
• i • i 0 j

— Model Output




              Jan.94   Jul.94   Dec.94   Jul.95   Dec.95    Jan.94  Jul.94  Dec.94   Jul.95   Dec.95    Jan.94  Jul.94  Dec.94   Jul.95   Dec.95
                PCB28+31 ((ig/L) in segment 68
PCB28+31 (ug/L) in segment 69
                                                                                            PCB28+31 (uglL) in segment 70

— Model Output




4 -]

3
2
1
0 J

— Model Output






3
2
1


— Model Output




             Jan.94  Jul.94   Dec.94  Jul.95  Dec.95     Jan.94   Jul.94   Dec.94   Jul.95   Dec.95
                                                                                         Jan.94   Jul.94   Dec.94  Jul.95  Dec.95
PCB28+31  (dissolved + particulate) (|jg/L)
Lake Michigan sediment segments
                                                                 387

-------
                 PCB28+31 
-------
              PCB28+31 (|ig/L) in segment 78
PCB28+31 (ug/L) in segment 79
PCB28+31 (ug/L) in segment 80
— Model Output




3
2
1
n •
— Model Output




3
2
1
n
— Model Output




           Jan.94  Jul.94   Dec.94  Jul.95  Dec.95    Jan.94   Jul.94   Dec.94   Jul.95   Dec.95   Jan.94  Jul.94  Dec.94   Jul.95   Dec.95
              PCB28+31 (ug/L) in segment 81
PCB28+31 (ug/L) in segment 82
PCB28+31 (|ig/L) in segment 83
                           — Model Output
— Model Output





3
2 -
1 -

— Model Output




           Jan.94   Jul.94   Dec.94  Jul.95   Dec.95    Jan.94   Jul.94   Dec.94  Jul.95   Dec.95   Jan.94  Jul.94  Dec.94   Jul.95   Dec.95
              PCB28+31 (ug/L) in segment 84
                                                   PCB28+31 (|ig/L) in segment 85
                                    PCB28+31 (ug/L) in segment 86
           3
— Model Output





3
2
1
— Model Output





3
2
1
0 •
— Model Output



           Jan.94   Jul.94   Dec.94  Jul.95   Dec.95   Jan.94   Jul.94   Dec.94   Jul.95   Dec.95   Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
      8+3i (dissolved + particulate) (|jg/L)
Green Bay sediment segments
                                                                 389

-------
             PCB28+31 (ng/U) in segment 87
          2
                         — Model Output
                                                    PCB28+31 (ng/U) in segment 88
                                                                 — Model Output
                                         PCB28+31 (ug/L) in segment 89
                                                      — Model Output
         Jan.94  Jul.94   Dec.94   Jul.9B   Dec.95        Jan.94  Jul.94   Dec.94  Jul.95  Dec.96        Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
            PCB28+31 (ng/L) in segment 90
                         — Model Output
PCB28+31 (jig/L) in segment 91
                                                                 — Model Output
         Jan.94  Jul.94   Dec.94   Jul.95   Dec.95       Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
                                                                                            PCB28+31 (ug/L) In segment 92
                                                                                                         — Model Output
                                                                                        Jan.94   Jul.94   Dec.94  Jul.95   Dec.95
             PCB28+31 (|ig/L) in segment 93
PCB28+31 (ng'L) in segment 94
— Model Output




3
2
1
n
— Model Output



         Jan.94  Jul.94   Dec.94  Jul.95   Dec.95       Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
PCB2B+31 (dissolved + participate) (|jg/L)
Green Bay sediment segments
                                                                390

-------
              ZPCB (ng/L) in segment 1
                                                  ZPCB (ng/L) in segment 2
                                      ZPCB (ng/L) in segment 3
        0.2
                        — Model Output
                        * Cruise Mean
           — Model Output
           » Cruise Mean
                                                                                  0.4
            — Model Output
            « Cruise Mean
         Jan-94  Jul-94  Dec-94  Jul-95   Dec-95     Jan-94  Jul-94  Dec-94  Jul-95  Dec-95      Jan-94  Jul-94  Dec-94  Jul-96  Dec-95
             EPCB (ng/L) in segment 4
ZPCB (ng/L) in segment 5
ZPCB (ng/L) in segment 6
                        — Model Output
                        » Cruise Mean
           — Model Output
           » Cruise Mean
                                                                                 0.3
           — Model Output
           » Cruise Mean
         Jan-94  Jul-94   Dec-94   Jul-95  Dec-95     Jan-94  Jul-94   Dec-94  Jul-95  Dec-95     Jan-94  Jul-94   Dec-94  Jul-95  Dec-95
            ZPCB (ng/L) in segment 11
ZPCB (ng/L) in segment 12
ZPCB (ng/L) in segment 13


0.3
0.2


0 1 -
D -J
— Model Output
» Cruise Mean

*
^ *
*\— "'^•s^ *
^"~ ' * — »^^^~^«j*^™ ~w ""^
* *


0.3
0.2


0 1
n •
— Model Output
» Cruise Mean


. » *
y 	 _ * *
^^ 11^ ' ^-> — ~~ , -JT/1



0.3
0.2


01 -
a -
— Model Output
» Cruise Mean


»
L ^ *
^ ^ — 	 A ^ - - t { ^
*
        Jan-94  Jul-94  Dec-94  Jul-95   Dec-95      Jan-94  Jul-94   Dec-94  Jul-95  Dec-95     Jan-94  Jul-94  Dec-94   Jul-95  Dec-95
IPCBs in dissolved phase (ng/L)
Lake Michigan  layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                               391

-------
             ZPCB (ng/L) in segment 14
        0.2
                        — Model Output
                        • Cruise Mean
                                                    ZPCB (ng/L) in segment 15
                                               0.4
                                               0.2
                                               0.1
— Model Output
» Cruise Mean
         Jan-94  Jul-94  Dec-94  Jul-95  Dec-95        Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                              ZPCB (ng/L) in segment 16
                                                                                        0.1
— Model Output
» Cruise Mean
                                                                                         Jan-94  Jul-94   Dec-94  Jul-95  Dec-95
              ZPCB (ng/L) in segment 20
         0.4
                         — Model Output
                         « Cruise Mean
                                                     ZPCB (ng/L) in segment 21
                                               0.1
— Model Output
» Cruise Mean
          Jan-94  Jul-94  Dec-94  Jul-95   Dec-95       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                              ZPCB (ng/L) in segment 22
                                                                                        0.4
— Model Output
» Cruise Mean
                                                                                         Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
              ZPCB (ng/L) in segment 23
         0.4
         0.2
                         — Model Output
                          » Cruise Mean
                                                     ZPCB (ng/L) in segment 24
 — Model Output
 » Cruise Mean
                              ZPCB (ng/L) in segment 25
— Model Output
» Cruise Mean
          Jan-94  Jul-94  Dec-94  Jul-95   Dec-95        Jan-94  Jul-94  Dec-94  Jul-95   Dec-95        Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in dissolved phase (ng/L)
Lake Michigan layers 1,  2, and 3
Upper 30 m water segments
Error bars = standard error
                                                                392

-------
     ZPCB (ng/L) in segment 29
                                  ZPCB (ng/L) in segment 30
ZPCB (ng/L) in segment 31
ZPCB (ng/L) in segment 32
0.4
0.3
0.2

0.1

— Model Output
* Cruise Mean
,
1 1 *

0.4 -i
0.3
0.2

0.1 •

— Model Output
« Cruise Mean


-r^^ .

0.3
0.2

0.1 V

— Model Output
» Cruise Mean
^
» ^
^ 	 *" ""*>•>•* — ^~ 	 %^v

0.3
0.2

0.1

— Model Output
« Cruise Mean

4 *
•- — ' "^V^- 	 — -"v^





Jan-94 Jul-94 Dec-94 Jul-95 Dec-95Jan.94 Ju|_94 Dec-94 Jul-95 Dec-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) in segment 33 _ ZPCB <"9'L) in segment 34 ^^ ^^ ^ ^ «,nmonf 1K ZPCB ("3"-) in segment 37
0.4
(M

07-


ni -

— Model Output
• Cruise Mean

»

_^-**» *
— /* V—v^. — — 'V-'
»* « »
Jan-94 Jul-94 Dec-94 Jul-95 Dec
0.3

0.2


0.1 •
0 •
— Model Output
» Cruise Mean

»

-j^J**, *
"" —\^ \r~~.r** — ^-^^V<«.

0.4
0.3

0.2


0.1
95Jan-94 Jul-94 Dec-94 Jul-95 Dec-95 °
Ja

— Model Output
» Cruise Mean

»

>^^V^-. -^^~~v_

^ »

1-94 Jul-94 Dec-94 Jul-95 De
0.3 -

0.2 -


0.1 -

-95Ja«
ZPCB (ng/L) in segment 38 ZPCB (ng/L) in segment 39 ZPCB (ng/L) in segment 40 04
0.4
0.3
0.2
0.1
0
— Model Output
» Cruise Mean

^
^r^~^^



0.2
0.1
0
— Model Output
» Cruise Mean

»
^ » * »

0.4
0.3
0.2
0.1
n
— Model Output
* Cruise Mean

«
. . » *

0.3

0.2
0.1
0
— Model Output
» Cruise Mean


s^*^~. ^—^
/• ^V^ _-tr^ *^^









-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) in segment 41
— Model Output
» Cruise Mean

*
,_. r » v^.x. ,,,.'*"-^^^v
^ *







  Jan-94  Jul-94  Dec-94  Jul-95  Dec-95Jan-94  Jul'94  Dec-94  Jul-95  Dec-95  Jan_94  Ju|-94  Dec-94 Jul-95  Dec-95
                                                                                          Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in dissolved phase (ng/L)
Lake Michigan layers 4 and 5
30 m to bottom water segments
Error bars = standard error
                                                         393

-------
       EPCB (ng/L) in segment?
                                        EPCB (ng/L) in segment 8
                                   EPCB (ng/L) in segments
                                       EPCB (ng/L) in segment 10
 0.4
                  — Model Output
                  » Cruise Mean
                                  0.4
                                  0.2
            — Model Output
            » Cruise Mean
                                                                   3.2
                                                                   2.4
                                                                   0.8
                 - Model Output
                 > Cruise Mean
                                                                                                     3.2
                                                                                                     2.4
                                                                                                     1.6
— Model Output
• Cruise Mean
  Jan-94  JUI94  Dec-94  Jul-95   Dec-95  Ja"-94   Jul'94  Dec-94  Ju|-95  Dec-96 Jan-94  Jul-94  Dec-94  Jul-95  Dec-96 Jan-94  Jul-94  Dec-94  Jul-95  Dee-95



      EPCB (ng/L) in segment 17           EPCB '" segment 18
                                  EPCB (ng/L) In segment 19           £PCB ("a'1-)in segment 26
                                                               0.8
                 — Model Output
                 « Cruise Mean
            — Model Output
            » Cruise Mean
                                  0.2
                                                                                                     0.2
  Jan-94  Jul-94  Dec-94   Jul-95  Dec-95

      EPCB (ng/L) in segment 27
4  Jul-94   Dec-94   Jul-95  Dec-95

 EPCB (ng/L) in segment 28
Jan-94  Jul-94   Dec-94  Jul-95   Dec-95


      EPCB (ng/L) in segment 35
 0.8 -
                 — Model Output
                 « Cruise Mean
             — Model Output
             * Cruise Mean
                                                                    0.8
                                                                    0.6
                 — Model Output
                 » Cruise Mean
                                                                       WWi»+**JW*^
  Jan-94  Jul-94  Dec-94   Jul-95  Dec-95  Jan-94  Jul-94  Dec-94  Jul-95   Dec-95  Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in dissolved phase (ng/L)
Green  Bay water segments
Error bars = standard error
                                                  — Model Output
                                                  » Cruise Mean
                                                                                                             Jul'94
                                                                                                                           Ju''9i  Dec.95
                                                                 394

-------
          EPCB (ng/L) In segment 42
                     — Model Output
                                               EPCB (ng/L) In segment 43
                                         1.8


                                         1.2 -


                                         0.8
                                                          - Model Output
                                                                                  EPCB (ng/L) in segment 44
                                                                                             — Model Output
                                                                                                                      IPCB (ng/L) in segment 45
                                                                                                                                 — Model Output
      Jan-94  Jul-94   Dec-94  Jul-95   Dec-95      Jan-94  Jut-94  Dcc-94  Jul-95  Dec-95     Jan-B4   Jul-94  Dec-94   Jul-05  Dec-95     Jan-94  Jul-94  Dcc-94   Jul-95  Dec-B5
          EPCB (ng/L) in segment 46
                     — Model Output
      Jan-94  Jul-64  Dec-94  Jul-95  D«c-95
                                                IPCB (ng/L) in segment 47
                                                                                  IPCB (ng/L) in segment 48
                                                                                                                      ZPCB (ng/L) in segment 49

.4
.8
.2
.6
0 -


— Model Output




3 -I
2.4
1.8
1.2
0.8
0 -i


— Model Output




3 1
2.4 •
1.8
1.2
0.6 •
0


— Model Output

— — 	


           EPCB (ng/L) in segment 50
                                               EPCB (ng/L) (n segment 51
2.4
1.8
1.2


— Model Output




2.4 •
1.8 •
1.2 -


— Model Output




                                                                                  EPCB (ng/L) in segment 52
                                                                                             — Model Output
                                                                       •95     Jan-94   Jul-94  De
                                                                                                                      EPCB (ng/L) in segment 53
                                                                                                                                 — Model Output
           EPCB (ng/L) in segment 54
                                              EPCB (ng/L) in segment 55
                                                                                  EPCB (ng/L) in segment 58
                                                                                                                      EPCB (ng/L) in segment 57

2.4
1.8
1.2
D.6


— Model Output

^_^^
	


2.4
1.8
1.2
0.6


— Model Output


^^— — — __


2.4
1.8
1.2
0.6 •


— Model Output





2.4
i.a
1.2
0.8


— Model Oulput




          EPCB (ng/L) in segment 58
                                              EPCB (ng/L) in segment 59
                                                                                  EPCB (ng/L) in segment 60
                                                                                                                      EPCB (ng/L) In segment 61

— Model Output





2.4
1.8
1.2
0.6


— Model Output





2.4
1.8
1.2
0.6


— Model Output





2.4
1.8 -
1.2
0.6


— Model Output




SPCBs in dissolved phase (ng/L)
Lake Michigan sediment segments
                                                                      395

-------
                                                                ZPCB (ng/L) in segment 64
                                                                                                IPCB (ng/L) in segment 65
3
2.4
1.8
1.2
0.6
0
Ja
3
2.4
1.8
1.2
0.6
O-l
Jan
3
2.4
1.8
1.2
0.6

— Model Output



1-94 Jul-94 Dec-94 Jul-95 De
ZPCB (ng/L) in segment 66
— Model Output



-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 70
— Model Output



0 I 	 1 	 , 	 , 	 1
Jan-94 Jul-94 Dec-94 Jul-96 Dec
3 •
2.4 •
1.8
1.2
0.6
0 -I
ZPCB (ng/L) in segment 74
— Model Output



	 1 	 1 	 1- 	 1
3
2.4
1.8
1.2
0.6
0
c-99an
3
2.4
1.8
1.2
0.6
0
-95 Ja
3
2.4-
1.8
1.2
0.6
0 -I
.95 Jar
3 -
2.4-
1.8
1.2-
0.6
0-
— Model Output



3
2.4-
1.8
1.2 -
0.6

— Model Output



	 T_ 	 , 	 , 	 1 o 4- 	 1 	 1 	 1 	
-94 Jul-94 Dec-94 Jul-95 Dec-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 67
— Model Output



3 •
2.4
1.8
1.2
0.6
ZPCB (ng/L) in segment 68
— Model Output



•1 	 1 	 , 	 1 	 1 OH 	 1 	 1 	 	 1 	 1
n-94 Jul-94 Dec-94 Jul-95 Dec-96 Jan-94 Jul-94 Dec-94 Jul-95 Dec
EPCB (ng/L) in segment 71
— Model Output



3 -
2.4-
1.8
1.2 -
0.6 -
i i 0
-94 Jul-94 Dec-94 Jul-95 Dec-95 Jar
ZPCB (ng/L) in segment 75
— Model Output




3
2.4
1.8
1.2
0.6
0
3
2.4
1.8
1.2
0.6
0
,-95 •>*
3
2.4
1.8
1.2
0.6
0
95 J.

— Model Output







1-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) in segment 69
— Model Output







in-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) in segment 72 ZPCB ("9"-) '" segment 73
3 ., 	 ,
— Model Output



-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 76
— Model Output




2,4
1.8
1.2
0.6
— Model Output







0 • 	 1 	 1 	 1 	 1
.95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-95
3 -
2.4
1.8 -
1.2
0.6
0 -I-
ZPCB (ng/L) in segment 77
— Model Output



	 1 	 1 	 1 	 '




 Jan-94  Jul-94  Dec-94  Jul-95  Dec-95 J'"'M  JU'"94  DeC"94  J"''95  DeC'95  Jan-94  Jul-94  Dec-94  Jul-95  Dec-95  Jan-94   Jul-94   Dec-94  Jul-95  Dec-95

ZPCBs in dissolved phase (ng/L)
Lake Michigan sediment segments
                                                          396

-------
           IPCB (ng/L) in segment 78
 IPCB (ng/L) In segment 79
 IPCB (ng/L) In segment SO

2.4
1.8
1.2
U.b

— Model Output




2.4
1.8
1.2
0.6
n -

— Model Output




2.4-
1.8
1.2
0.6


— Model Output




       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95      Jan-94   Jul-94  Dec-94  Jul-95   Dec-95      Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
           ZPCB (ng/L) in segment 81
 IPCB (ng/L) in segment 82
IPCB (ng/L) in segment 83

2.4
1.8
1.2


— Model Output




2.4
1.8
1.2

D

— Model Output





2.4
1.8
1.2
OR


— Model Output




       Jan-94   Jul-94  Dec-94  Jul-95  Dec-95      Jan-94   Jul-94  Dec-94  Jul-95   Dec-95      Jan-94   Jul-94  Dec-94  Jul-95   Dec-95
           IPCB (ng/L) in segment 84
IPCB (ng/L) in segment 85
                                      IPCB (ng/L) in segment 86

2.4
1.8
1.2-
0.6-
n -

— Model Output





2.4
1.8
1.2
OB
o -

— Model Output





2.4
1.8
1.2
o.e
n .

— Model Output



       Jan-94   Jul-94  Dec-94  Jul-96  Dec-95     Jan-94  Jul-94   Dec-94  Jul-95  Dec-95      Jan-94  Jul-94   Dec-94   Jul-95  Dec-95
ZPCBs in dissolved phase (ng/L)
Green Bay sediment  segments
                                                                397

-------
                ZPCB (ng/L) in segment 87
                                                     ZPCB (ng/L) in segment 88
                                            ZPCB (ng/L) in segment 89
3

2.4
1.8
1.2
0.8

— Model Output




                                                 3


                                                2.4


                                                1.8


                                                1.2


                                                0.6
                 — Model Output
                                       2.4


                                       1.8


                                       1.2


                                       0.6
           — Model Output
           Jan-94  Jul-94   Dec-94   Jul-95  Dec-96     Jan-94   Jul-94  Dec-94  Jul-95  Dec-95      Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
               ZPCB (ng/L) in segment 90
          2.4


          1.8


          1.2


          0.6
                          — Model Output
     ZPCB (ng/L) in segment 91
2.4


1.8


1.2


0.6
                 — Model Output
ZPCB (ng/L) In segment 92

2.4 •
1.8
1.2
0.6
n -

— Model Output



           Jan-94   Jul-94  Dec-94  Jul-95   Dec-96      Jan-94   Jul-94  Dec-94  Jul-95  Dec-95       Jan-94   Jul-94   Dec-94  Jul-95  Dec-95
               ZPCB (ng/L) in segment 93

2.4
1.8
1.2
0.6
0

— Model Output



                                                     ZPCB (ng/L) in segment 94

2.4 -
1.8 -
1.2
0.6
n

— Model Output



           Jan-94  Jul-94  Dec-94  Jul-96  Dec-96      Jan-94  Jul-94  Dec-94  Jul-9S  Dec-95
ZPCBs in dissolved phase (ng/L)
Green  Bay sediment segments
                                                                 398

-------
               IPCB (ng/L) in segment 1
— Model Output
» Cruise Mean
          Jan-94  Jul-94   Dec-94   Jul-95  Dec-95
                              ZPCB (ng/L) In segment 2
                                                  0.3-
                                                  0.2 •
— Model Output
» Cruise Mean
                                                   Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                             ZPCB (ng/L) in segment 3
                                                                                          0.3
                                                                                                           — Model Output
                                                                                                           » Cruise Mean
                                                                                           Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
              IPCB (ng/L) in segment 4
        0.1
                        — Model Output
                        * Cruise Mean
         Jan-94  Jul-94  Dec-94  Jul-95   Dec-96
                                                       IPCB (ng/L) in segment 5
                                                 0.2 •
                                        — Model Output
                                        » Cruise Mean
                                                  Jan-94  Jul-94  Dec-94  Jul-95   Dec-95
                                                                      ZPCB (ng/L) in segment 6
                                         — Model Output
                                         » Cruise Mean
                                                                                           Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
              ZPCB (ng/L) in segment 11
                         — Model Output
                         » Cruise Mean
          Jan-94  Jul-94   Dec-94  Jul-95  Dec-95
                            ZPCB (ng/L) in segment 12
                                                0.1
                                       — Model Output
                                       » Cruise Mean
                                                 Jan-94  Jul-94   Dec-94   Jul-95  Dec-95
                             ZPCB (ng/L) in segment 13
                                        — Model Output
                                        • Cruise Mean
                                                                                           Jan-94   Jul-94  Dec-94  Jul-95   Dec-95
IPCBs in particulate phase (ng/L)
Lake Michigan layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                               399

-------
              ZPCB (ng/L) in segment 14
                                                     ZPCB (ng/L) in segment 15
                                        ZPCB (ng/L) in segment 16
         0.3-
                         — Model Output
                         » Cruise Mean
                                                0.2
            — Model Output
            » Cruise Mean
                                                                                        0.2
           — Model Output
           * Cruise Mean
                                                                                                   • *          » »
          Jan-94  Jul-94  Dec-94   Jul-9S  Dec-95        Jan-94  Jul-94  Dec-94  Jul-95  Dec-95        Jan-94   Jul-94  Dec-94  Jul-95  Dec-95
             IPCB (ng/L) in segment 20
ZPCB (ng/L) in segment 21
                                                                                             ZPCB (ng/L) in segment 22
        0.4
                        — Model Output
                        » Cruise Mean
                                                0.1
           — Model Output
           » Cruise Mean
           — Model Output
           » Cruise Mean
         Jan-94  Jul-94  Dec-94  Jul-95   Dec-95        Jan-94  Jul-94  Dec-94  Jul-95  Dec-95        Jan-94   Jul-94  Dec-94  Jul-95   Dec-95
              ZPCB (ng/L) in segment 23
  ZPCB (ng/L) in segment 24
ZPCB (ng/L) in segment 25
         0.3
         0.2
                         — Model Output
                         * Cruise Mean
             — Model Output
             » Cruise Mean
                                                                                        0.2
                                                                                        0.1
            — Model Output
            » Cruise Mean
          Jan-94  Jul-94  Dec-94  Jul-96  Dec-95         Jan-94  Jul-94  Dec-94  Jul-96  Dec-95       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in particulate phase (ng/L)
Lake Michigan layers 1, 2, and 3
Upper 30 m water segments
Error bars = standard error
                                                                400

-------
      IPCB (ng/L) in segment 29
ZPCB (ng/L) in segment 30
ZPCB (ng/L) in segment 31
                                                                                                IPCB (ng/L) in segment 32
0.3
0.2
0.1
0
Ja
0.4
0.3
0.2
0.1
0
Jan
0.4
0.3
0.2
0.1
0
— Model Output
* Cruise Mean
*
i^]U>>-^-*--*A>_Jk_^J«J
1-94 Jul-94 Dec-94 Jul-95 De
ZPCB (ng/L) in segment 33
— Model Output
* Cruise Mean

^T-^-^
-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 38
— Model Output
* Cruise Mean


<— *-*-*-. ^^—rr~
0.3
0.2
0.1
0
c-95 Ja
0.4
0.3
0.2
0.1
0
-95 Jar
0.4
0.3
0.2
0.1 •
n
— Model Output
* Cruise Mean


1-94 Jul-94 Dec-94 Jul-95 De
ZPCB (ng/L) in segment 34
— Model Output
* Cruise Mean

* »
1-94 Jul-94 Dec-94 Jul-95 Del
ZPCB (ng/L) in segment 39
— Model Output
» Cruise Mean


	 *-»-5~7~- . 	 -r^
0.3
0.2
0.1
0-
c-95 Jar
0.4
0.3 •
0.2
0.1
0
:-95 Jan
0.4
0.3
0.2
0.1 -
n -
— Model Output
* Cruise Mean

^^^-r-
-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 36
— Model Output
» Cruise Mean

• t . *
-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (ng/L) in segment 40
— Model Output
* Cruise Mean


* » * *
0.4
0.3
0.2
0.1
0
-95 Jar
0.4
0.3
0.2
0.1
0
.95 Jan
0.4
0.3
0.2
0.1 -
0 -
— Model Output
» Cruise Mean

•



-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) In segment 37
— Model Output
* Cruise Mean

^^^-



-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (ng/L) in segment 41
— Model Output
* Cruise Mean


*~* » ^ *- «—-




  Jan-94  Jul-94  Dec-94  Jul-95  Dec-95 Jan-94  Jul-94  Dec-94  Jul-95  Dec-95  Jan-94  Jul-94  Dec-94 Jul-95  Dec-95 Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in particulate phase (ng/L)
Lake Michigan layers 4 and 5
30 m to bottom water segments
Error bars = standard error
                                                         401

-------
       EPCB (ng/L) in segment 7
  ZPCB (ng/L) in segment 8
                                                                          IPCB (ng/L) in segment 9
                                                                                                            IPCB (ng/L) in segment 10
 0.4
                  — Model Output
                  » Cruise Mean
                                  0.8
                                  0.6
             — Model Output
             * Cruise Mean
                                                                    2.4
             — Model Output
             » Cruise Mean
  Jan-94  Jul-94   Dec-94   Jul-95  Dec-95 Jan.g4   Ju|_94  Dec-94  Jul-95   Dec-95



      IPCB (ng/L) in segment 17
                 — Model Output
                 « Cruise Mean
ZPCB (ng/L) in segment 18           ZPCB ("3"-) in segment 19
                                                               0.8


                                                                0
                              Jan-94   Jul-94  Dec-94   Jul-95  Dec-95Jan-94  Jul'94  Dec-94  Jul-95  Dec-95


                                                                     IPCB (ng/L) in segment 26
                                                                   2.4
                                              — Model Output
                                              « Cruise Mean
                                                                                                      0.8
                                                                                                      0.6
                                                                                                      0.4
                                                                                                      0.2-
                                              — Model Output
                                              « Cruise Mean
                                                                    isn (M  ini CM   n*>f 
-------
             ZPCB (Jg/L) In segment 42
                        — Model Output
                                                   ZPCB (JglL) in segment 43
                                          ZPCB (Og/L) in segment 44
30

24

18 -

12 -
                                                              — Model Output
                                                                                  24


                                                                                  18


                                                                                  12
— Model Output
         Jan-94  Jul-94  DeC-94  Jul-95  Dec-95     Jan-94  Jul-94  Dec-94  Jul-95   Dec-95    Jan-94  Jul-94   Dec-94  Jul-95   Dec-9S
             ZPCB (Og/L) in segment 45
                         — Model Output
         Jan-94   Jul-94  Dec-94  Jul-95  Dec-95
                                                   ZPCB (Og/L) in segment 46
                                           ZPCB (Og/L) in segment 47
                                              24


                                              18


                                              12


                                              6
                                                              — Model Output
                                     30


                                     24


                                     18


                                     12


                                      6
— Model Output
                                              Jan-94  Jul-94  Dec-94  Jul-95   Dec-95
                                                                                   Jan-94   Jul-94  Dec-94  Jul-95  Dec-95
             ZPCB (Og/L) in segment 48
                                                   ZPCB (Og/L) in segment 49
                                          ZPCB (Og/L) in segment 50

24
18
12
6
0

— Model Output

	 	


30 -,
24
18
12 -
6


— Model Output

~- 	 .



24 •
18
12
6
0 -

— Model Output

' 	 	 	


        Jan-94  Jul-94   Dec-94   Jul-95   Dec-95     Jan-94   Jul-94  Dec-94  Jul-95  Dec-95     Jan-94  Jul-94   Dec-94   Jul-95   Dec-95
IPCBs in particulate phase (pg/L)
Lake Michigan sediment segments
                                                                403

-------
             EPCB (Og/L) in segment 51
                                             EPCB (Og/L) In segment 52
IPCB (Og/L) in segment 53
30
24
18
12
6
0
Jan
30
24
18
12
e
0
Ja
— Model Output


	 ' 	
.94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (Og/L) in segment 54
— Model Output



n-94 Jul-94 Dec-94 Jul-95 De
EPCB (Og/L) in segment 57
30 -
24
18 •
12
6
— Model Output


Jan-94 Jul-94 Dec-94 Jul-95 Dec
30
24
18
12
6
0
EPCB (Og/L) in segment 60
— Model Output



30
24
18
12
6
0
-95 Jar
30 -
24
18
12
6 -
0 -
c-95 Jar
30
24-
18
12
6
0
95 Jan
30
24
18
12
6
0
— Model Output

^— -— _^

-94 Jul-94 Dec-94 Jul-95 Dec
EPCB (Og/L) in segment 55
— Model Output


	 	 	 ^
-94 Jul-94 Dec-94 Jul-95 Dec
EPCB (Og/L) in segment 58
— Model Output



94 Jul-94 Dec-94 Jul-95 Dec
EPCB (Og/L) in segment 61
— Model Output

	 	
24
18 -
12
6
0 -I
-95 Jan
30-
24
18
12 -
6
0
-95 Jan
30
24
18
12
6
0
95 Ja
— Model Output








-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (Og/L) in segment 56
— Model Output







-94 Jul-94 Dec-94 Jul-95 Dec-95
EPCB (Og/L) in segment 59
— Model Output






n-94 Jul-94 Dec-94 Jul-95 Dec-95



         Jan-94  Jul-94  Dec-94  Jul-95  Dec-95    Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
ZPCBs in particulate phase (M9/L)
Lake Michigan sediment segments
                                                       404

-------
                                            ZPCB (Og/L) in segment 63
                                                                               IPCB (Og/L) in segment 64

30-
24
18
12
E
0
Ja
— Model Output


	 . 	 	

1-94 Jul-94 Dec-94 Jul-95 De
24
18
12
6
0
c-95 Ja
— Model Output




24
18
12
6
0
<-94 Jul-94 Dec-94 Jul-95 Dec-95 Jar

— Model Output










-94 Jul-94 Dec-94 Jul-95 Dec-95
ZPCB (Og/L) in segment 65 .. ^B (Og/L) in segment 66 EPCB (Jg/L) in segment 67
30
24
18
12
6
— Model Output



24
18
12
6
0
O-l 	 , 	 1 	 1 	 1 Ja
Jan-94 Jul-94 Dec-94 Jul-95 Dec-95
— Model Output



1-94 Jul-94 Dec-94 Jul-95 De
24
18
12
G
— Model Output







0 -I 	 1 	 1 	 1 	 1
;-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-95
IPCBOalL) in segment 68 IPCB (VD in segment 69 ZPCB  In segment 70
30 -
24
18
12-
6
0
— Model Output



, , ,
24
18
12
6 -
0 -t
— Model Output




24
18
12
6

— Model Output



1






       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                                       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                                                                           Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
ZPCBs in paniculate phase (M9/L)
Lake Michigan sediment segments
                                                        405

-------
                                                ZPCB (Jg/L) in segment 72
                                                                                ZPCB (JgIL) in segment 73
30
24
18
12
6
0
Jan
30 -
24 -
18 -
12 -
6
0
Jai
— Model Output



30 -I 	
- Model Output M . ~ Model Output
18 18
12 • 12 "
6 • 6 "
0 	 , 	 1 	 1 	 1 " '
-94 Jul-94 Dec-94 Jul-95 Dec-95 Jan-94 Jul-94 Dec- u - ec-
ZPCB (Jg/L) in segment 74
— Model Output



IPCB (Jg/L) in segment 75 £pCB ^ |p segment 76
30
— Model Output
24- —Model Output
18 18
12 12
6
Jan-94 Jul-94 Dec-94 Jul-95 Dec-95 ° .
-94 Jul-94 Dec-94 Jul-96 Dec-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-95
IPCB ()glL) in segment 77
24-
18
12
e
n
— Model Output






           Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in particulate phase (pg/L)
Lake Michigan sediment segments
                                                   406

-------
                                           IPCB (jg/L) in segment 79
                                                                            ZPCB ()g/L) in segment 80

30-
24
18
6
— Model Output

24 •
18
12
6 -
— Model Output

24
18
12
6
0

— Model Output
lan 04 ini Qd nor- QA ini oft nA<- aE Jan-94 Jul-94 Dec-94 Jul-95 Dec
Jan-94 Jul-94 Dec-94 Jul-95 Dec-95 Jan-94 Jul-94 Dec-94 Jul-96 Dec-95
ZPCB(Jg/L) in segment 81 EPCB (JgIL) in segment 82 ZPCB()glL) in segment 83
24
18
12
e
Jan
JO
24
18
12
6
n
— Model Output
-94 Jul-94 Dec-94 Jul-95 Dec
ZPCB (Jg/L) in segment 84
— Model Output

24 •
18
12
6
— Model Output
-95 Jan-94 Jul-94 Dec-94 Jul-95 Dec
EPCB (jg/L) in segment 85

24
18
12
6

— Model Output
' 	 r 	 • 	 1 	 1 	 1
24 •
18
12
6
0 -
-95 Jan-
30
24
18
12
6
0
— Model Output

94 Jul-94 Dec-94 Jul-95 Dec-9
ZPCB (JglL) in segment 86
— Model Output


     Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
                                       Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in particulate phase (pg/L)
Green Bay sediment segments
                                                      407

-------
            ZPCB (jg/L) in segment 87
ZPCB (Jg/L) In segment 88
                                                                                       ZPCB (JgIL) in segment 89
30 -I

24 -
18

E

— Model Output




30 T

24
18
12
6

— Model Output

— 	 —- ^__



24

18 -
12-
6 -
0 -

-Model Output

^— ^__
	 --— .

0 iii UH 	 , 	 , 	 , 	 1
Jan-94 Jul-94 Dec-94 Jul-95 Dec-95 Jan.94 Ju|.94 Dec.94 ju(.95 De(;.95 Jan-94 Jul-94 Dec-94 Jul-95 Dec-S
ZPCB (J g/L) in segment 90 ZPCB () g/L) in segment 91
30
24
18 -
1?
6
A
	 — Model Output
-_



24
18
12
6
n
— Model Output
""--— ^____^^



24
18
12
E
0
ZPCB (JgIL) in segment 92
— Model Output



	 1 	 i 	 1 	
        Jan-94  Jul-94  Dec-94  Jul-95   Dec-95        Jan-94   Jul-94  Dec-94  Jul-95  Dec-95
                                                                                    Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
             ZPCB (Jg/L) in segment 93
  ZPCB (JgIL) In segment 94

24
18
12
6 •

— Model Output








Jan-94 Jul-94 Dec-94 Jul-96 Dec-95

24
18 •
12 -
6
n •

— Model Output




                                               Jan-94  Jul-94  Dec-94  Jul-95  Dec-95
IPCBs in participate phase (pg/L)
Green Bay sediment segments
                                                           408

-------
U.O-

0.4-
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segment 1



» »
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segment 4



4
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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95
05

0.4.
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segment 5



»
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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95

0.4-
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segment 20

»
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segment 21

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segment 22


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segment 23

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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Ju!94 Dec94 Jul95 Dec95 Jan94 Ju)94 Dec94 Jul95 Dec95
n **
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segment 24


»
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segment 25



*^~«vs *
^ — ^V; — -> — ^v-







•^ Model Output
» Cruise Mean
error bars = standard error
SPCB (ng/L)
Lake Michigan layers 1 , 2, 3
upper 30 meter
water segments







Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
                                                     409

-------
0.5-1

0.4-
1 0.3-
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0 0.2-
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0.1-

segment 29


it *
ilijUt- ii »Ju[ 1 __*JLJ
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segment 30


*
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segment 31


*
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segment 32


^ ,
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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec'
n ^

0.4.
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segment 33




** *S — "frlfSn
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segment 34


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segment 37


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Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec£
«*. Model Output
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0.4.
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segment 38



^-**^^N^ —
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-cament 30 error bars = standard error tinmpnt 4D
,cgmont30 vPCB (ng/L) segment 40
Lake Mich gan layers 4 & 5
30 meters to bottom
water segments
^^T^^ 	
-^"^ ^^^^^ ^^^~ ' ~ '*
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-

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-

segment 41



^-**~\
f^^ *w_ ^**^^^X
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f • *
• » »

Jan94 Jul94 Dec94 Jul95 Dec95 Jan94  Jul94  Deo94 Jul95 Dec95 Jan94  Jul94  Dec94 Jul95 Dec95 Jan94 Jul94 Dec94 Jul95 Dec9S
                                                      410

-------
m
o
a.
   1.0'
   0.8-
   0.6
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   0.2
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                    segment 7
   »


Vv\wW^*s^
                                                           2.4-
                                                           1.6-
                                                           0.8-
                                                                             segment 9
    Jan94 Jul94 Dec94 Jul95 Deo95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94 Dec94 Jul95 Dec95

                                                                                        1.0-
    Jan94 Jul94 Dec94 Jul95  Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94  Jul94 Dec94 Jul95 Dec95
I
03

W
                                                                                                «>» Model Output
                                                                                                * Cruise Mean
                                                                                            error bars = standard error
                                                                                                 SPCB (ng/L)
                                                                                                  Green Bay
                                                                                                water segments
    Jan94 Jul94 Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95 Jan94 Jul94  Dec94 Jul95 Dec95
                                                       411

-------
           ZPCB (jig/L) in segment 42
                                             ZPCB (ug/L) in segment 43
ZPCB (ug/L) in segment 44
30 -
24 -
18
12
6
0
Jar
30
24
18 -
12
6
0 -
Jar
30
24
— Model Output

• 	 ^
^ 	



1.94 Jul.94 Dec.94 Jul.95 De
ZPCB (ug/L) in segment 45
— Model Output


^^^ 	 ' 	
.94 Jul.94 Dec.94 Jul.95 Dec
ZPCB (ug/L) in segment 48
-
18
12
6
0
30 -
24 -
18 -
12
6
0
;.9S Jai
30 -
24 -
18 •
12
6
0
.95 Jan

— Model Output
30
24
18
12
6
0
— Model Output

	
---
— Model Output
18
12 	

.94 Jul.94 Dec.94 Jul.95 Dec. 95 Jan.94 Jul.94 Dec.94 Jul.95 Dec
ZPCB (ug/L) in segment 46 ZPCB (ug/L) in segment 47
— Model Output


	 ' 	 • 	
— Model Output
18
12
6
94 Jul.94 Dec.94 Jul.95 Dec. 95 Jan.94 Jul.94 Dec.94 Jul.95 Dec.9
ZPCB (ug/L) in segment 49 IPCB (" 9"-) in segment 50
*n
— Model Output

	 ' 	 .

— Model Output
18
12 	 _____________^
6
0 	 1 	 1 	 1 	
       Jan.94  Jul.94  Dec.94  Jul.95  Dec.95      Jan.94  Jul.94  Dec.94  Jul.95  Dec.95      Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
IPCBs (dissolved + particulate) (|jg/L)
Lake Michigan sediment segments
                                                         412

-------
              ZPCB (ug/L) in segment 51
                          — Model Output
ZPCB (ng/L) in segment 52
                                                                  -Model Output
                                                                                             ZPCB (|ig/L) in segment 53
                                  30


                                  24


                                  18


                                  12 •
                                                                                                         — Model Output
         Jan.94  Jul.94   Dec.94  Jul.95   Dec.95       Jan.94   Jul.94   Dec.94  Jul.95   Dec.95      Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
               ZPCB (ug/L) in segment 54
                                                     ZPCB (ug/L) in segment 55
                                       ZPCB (ug/L) in segment 56
30-i
24
18
12
6 •
0 -I

— Model Output


"^ 	 ' 	 	


24
18 -
12
6


— Model Output


	 	 _______

24
18
12
6


— Model Output




           Jan.94  Jul.94  Dec.94  Jul.96  Dec.95
                                                Jan.94  Jul.94  Dec.94   Jul.95  Dec.95       Jan.94  Jul.94  Dec.94   Jul.95  Dec.95
               ZPCB (ug/L) in segment 57
                                                      ZPCB (ug/L) in segment 58
                                      ZPCB (ug/L) in segment 59
30 -I
24
18
12
6
0 -I

— Model Output





24
18
12
6
0

— Model Output





24
18
12
6
0

— Model Output




           Jan.94  Jul.94  Dec 94  Jul.95  Dec.95      Jan.94   Jul.94   Dec.94  Jul.95   Dec.95     Jan.94  Jul.94   Dec.94  Jul.95  Dec.95
                ZPCB (ug/L) in segment 60
 ZPCB (ug/L) in segment 61

24
18
12
ft

— Model Output




24
18
12
6
n

— Model Output



           Jan.94   Jul.94  Dec.94   Jul.95   Dec.95     Jan.94  Jul.94  Dec.94   Jul.95  Dec.96
iPCBs (dissolved + participate) (|jg/L)
Lake Michigan sediment segments
                                                                 413

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              ZPCB (fig/L) in segment 62
1

24
18
12
b

— Model Output




                                                      ZPCB (|ig/L) in segment 63
                                                                                            ZPCB (fig/L) in segment 64
30 -1
24
18
12
6
n -

— Model Output





24
18
12
6 •
0 -I

— Model Output




         Jan.94   Jul.94  Dec.94  Jul.95  Dec.95        Jan.94  Jul.94  Dec.94  Jul.95  Dec.95
                                                                                       Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
              ZPCB ((ig/L) in segment 65
                                                     ZPCB (jig/I) in segment 66
                                                                                            EPCB (|ig/L) in segment 67

— Model Output





24
18
12 -
6
0 -I

— Model Output




30 -I
24
18
12
6
0 -I

— Model Output




          Jan.94   Jul.94  Dec.94  Jul.95   Dec.95
                                                Jan.94  Jul.94   Dec.94  Jul.95   Dec.95      Jan 94   Ju, 94
                                                                                                            Ju| 95
              ZPCB (fjg'L) in segment 68
                                                    ZPCB (ug/L) in segment 69
                                                                                          ZPCB (|ig/L) in segment 70
30 -I
24
18
12 •
6
0 -

— Model Output




30 -I
24
18
12
6
0

— Model Output



I- 	 1 	 1 	 1 	 1

24
18
12-
6
0

— Model Output




          Jan.94   Jul.94  Dec.94  Jul.95   Dec.95      Jan.94   Jul.94  Dec.94  Jul.95  Dec.95     Jan.94   Jul.94  Dec.94   Jul.95  Dec.95
ZPCBs (dissolved + particulate) (|jg/L)
Lake Michigan sediment segments
                                                                414

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           IPCB (ug/L) in segment 71
IPCB (ng/L) in segment 72
IPCB (»ig/L) in segment 73
24
18
12
6
0
Jan
30
24
18
12
6
0
Ja
— Model Output



.94 Jul.94 Dec.94 Jul.95 Dec
ZPCB (ug/L) in segment 74
— Model Output
-
-

24
18
12
6
0 -
.95 Jar
30 -
24
18 -
12 -
6
— Model Output



.94 Jul.94 Dec.94 Jul.95 Dec
ZPCB (ug/L) in segment 75
— Model Output



n.94 Jul.94 Dec.94 Jul.95 Dec.95 Jan-94 Jul-94 Dec 94 Jul'95 De<
ZPCB (ug/L) in segment 77
24
18
12
6
n -
— Model Output




24
18
12
6
0 -\
.96 Jar
30
24
18
12
6
0
.95 J




— Model Output




.94 Jul.94 Dec.94 Jul.96 Dec.95



•
•
ZPCB (ug/L) in segment 76
— Model Output







in. 94 Jul.94 Dec.94 Jul.95 Dec.95








       Jan.94 Jul.94  Dec.94  Jul.95  Dec.95
IPCBs (dissolved + particulate) (pg/L)
Lake Michigan sediment segments
                                                     415

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          ZPCB (ug/L) in segment 78
                                                   IPCB (ug/L) in segment 79
                                             ZPCB (ug/L) in segment 80
3D

24
18
6 •


— Model Output




JO
24 •
18
12
6
n .

— Model Output



                                                                                                           — Model Output
     Jan.94  Jul.94   Dec.94  Jul.95   Dec.95         Jan.94   Jul.94  Dec.94  Jul.9S  Dec.95
                                                                                          Jan.94   Jul.94  Dec.94  Jul.95  Dec.95
           ZPCB (ug/L) in segment 81
                      — Model Output
                                                   ZPCB (M9/L) in segment 82
                                            ZPCB (ug/L) In segment 83

24
18
12
6

— Model Output



       Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
                                               Jan.94   Jul.94  Dec.94  Jul.95  Dec.95

24
18
12
6
ft

— Model Output



                                                                                          Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
           ZPCB (ug/L) in segment 84
      30
      24


      18 •


      12


       6
                       — Model Output
ZPCB (ug/L) in segment 85
           — Model Output
       Jan.94   Jul.94  Dec.94  Jul.95   Dec.95       Jan.94  Jul.94   Dec.94   Jul.95  Dec.95
                                                                                              ZPCB (ug/L) in segment 86

24
18
12
G
n -

— Model Output



                                                                                         Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
IPCBs (dissolved + participate)  (Mg/L)
Green Bay sediment segments
                                                                 416

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            ZPCB (ug/L) in segment 87

24
18
17
6


— Model Output




                                                      ZPCB (fig/L) in segment 88
                                                 30
                                                 24


                                                 18


                                                 12


                                                  6 •
                                                                 — Model Output
        Jan.94   Jul.94  Dec.94  Jul.96  Dec.95
                                                 Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
                                                                                               ZPCB (ng/L) in segment 89
                                                                                          Jan.94  Jul.94   Dec.94  Jul,95   Dec.95
             ZPCB (ug/L) in segment 90
                                                      ZPCB (fig/L) in segment 91
                        — Model Output
         Jan.94   Jul.94  Dec.94  Jul.95   Dec.95
                                                 Jan.94  Jul.94  Dec.94  Jul.96   Dec.95
ZPCB (ug/L) in segment 92

24-
18
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— Model Output




                                                                                          Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
        24-


        18


        12


         6 -
             ZPCB (|ig/L) in segment 93
                        — Model Output
                                                       ZPCB (ug/L) in segment 94

24
18
12
6


— Model Output




         Jan.94  Jul.94  Dec.94  Jul.95   Dec.95
                                                  Jan.94   Jul.94   Dec.94   Jul.95  Dec.95
ZPCBs (dissolved +  particulate)  (pg/L)
Green Bay sediment segments
                                                                417

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         Appendix 4.5.4



Simulation Results From Chloride
              418

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                                 423

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                                  425

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                                          PART 4

                                        LM2-TOXIC
Appendix 4.5.5.  Primary Production for
the LM2-Toxic

Amy Anstead
Welso Federal Services, LLC
Large Lakes Research Station
9311 Groh Road
Grosse lie, Michigan 48138

The LM2-Toxic hindcast model (1940-1995) relies on
external measurements or calculations to provide the
internally produced (primary production) carbon
loads. There have been very few studies published
that estimated lake-wide primary productivity for Lake
Michigan (Fee, 1973). It is, therefore, difficult to put
together a   primary  productivity  history  from
1940-1995  as required  for  the  model  hindcast.
However,  historical total phosphorus loads and
concentration  values  were  available,   and  a
relationship between total phosphorus and primary
productivity   was  used  to  estimate   primary
productivity.  Lake Michigan total phosphorus loads
were estimated from 1974-1991 by the International
Joint Commission (IJC) (Great Lakes Water Quality
Board, 1989; Pauer era/., 2006), and for 1994-1995
as part  of  Lake Michigan Mass Balance Project
(LMMBP) (described in Part 2).  Prior to 1974, there
were only a few reports published that documented
measured total phosphorus loads (Patalas,  1972;
Lee 1974).  However, total phosphorus loads from
1800-1970  were estimated  using  a model that
incorporated phosphorus sources and sinks for the
Great Lakes (Chapra, 1977).  There were reliable
lake-wide total phosphorus concentration data from
1976 to  present and measurements dating back to
the 1950s (Risley and Fuller, 1965; Rockwell et al,
1980).  In most cases the data prior to 1976 were
location specific, and it was difficult to extrapolate the
results  to obtain a  representative  lake-wide total
phosphorus concentration.

Modeled loads from 1800-1850 (Chapra, 1977) were
higher than measured total phosphorus loads from
1994-95  (Figure 4.5.5.1).   It was curious  that
measured total phosphorus loads in recent times of
higher anthropogenic phosphorus input (1994-1995)
were lower than modeled total phosphorus loads
during  the pre-Western civilization period (1800-
1850).  There seemed to be a disconnect between
the measured loads of recent times and the loads
modeled  for the 1800s. The model  simulation was
recreated and extended until 2000. As expected, the
model overpredicted total phosphorus loads when
compared with the measured data (Figure 4.5.5.1).
Improvements were made to the model parameters
which resulted in a better fit to the measured data
from 1974-1995 (Figure 4.5.5.1). Modifications to the
model  included decreasing  phosphorus export
coefficients for land use, decreasing per capita
detergent  phosphorus  export  and  decreasing
atmospheric phosphorus loads.   All modifications
made fell within reported literature values for the
Great Lakes system (Reckhow and Simpson, 1980;
Rast and Lee, 1983; U.S. Environmental Protection
Agency,  1976). The model also simulated annual
average   surface  water   total  phosphorus
concentration for Lake  Michigan by simply dividing
modeled  total phosphorus load by lake volume.
Modeled  total phosphorus concentration compared
well to measured total phosphorus concentration
values  for Lake Michigan (Rockwell et al., 1980;
Risley and Fuller,1965; Beeton and Moffett, 1964;
                                             426

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         16000
                       original model
                       modified model
                   n   measured TP loads from IJC and LMMBP
             1800
1825
1975     2000
Figure 4.5.5.1.  Model data versus measured total phosphorus loads.
Holland,1969;  Rousar and Beeton,  1973) (Figure
4.5.5.2).  The adjusted model output provided a
complete  annual  average   total  phosphorus
concentration history from which primary productivity
was calculated.

Vollenweider etal. (1974) established a relationship
between  total  phosphorus  load  and   primary
productivity but it was limited by few values from a
time when phosphorus loads and primary productivity
were at their highest in Lake Michigan's history. The
relationship was not as strong for years with  lower
total phosphorus  loads.  We established a total
phosphorus-primary productivity relationship (Figure
4.5.5.3) from the output of a 1976-1995 hindcast of
the  eutrophication  model,   LM3-Eutro.    This
relationship was used to calculate lake-wide annual
primary   productivity   from   1940-1995   Total
phosphorus concentration  history.   Final annual
primary production (as organic carbon) was provided
as a spreadsheet for incorporation into LM2-Toxic.
                           References

                           Beeton,  A.M. and J.W.  Moffett.   1964.   Lake
                               Michigan Chemical Data,  1954-55,  1960-61.
                               U.S.  Department of the Interior, U.S. Fish and
                               Wildlife  Service, Ann  Arbor,  Michigan.  Data
                               Report 6.

                           Chapra, S.C. 1977. Total Phosphorus Model for the
                               Great Lakes. J. Environ. Engin., 103(EE2):147-
                               161.

                           Fee, E.J.  1973. A  Numerical Model for Determining
                               Primary  Production and Its  Application to Lake
                               Michigan.    J.  Fish.  Res.  Board  Canada,
                               30(10): 1447-1468.

                           Great Lakes Water Quality Board.  1989.  1987
                               Report on Great Lakes Water Quality, Appendix
                               B:    Great  Lakes Surveillance,  Volume  I.
                               International Joint Commission, Windsor, Ontario,
                               Canada. 287 pp.
                                             427

-------
        o


        c
        o
        c
        
-------
Holland, R.E.  1969.  Seasonal Fluctuations of Lake
   Michigan   Diatoms.     Limnol.  Oceanogr.,
   14(3):423-436.

Lee, G.F. 1974.  Phosphorus, Water Quality and
   Eutrophication of Lake Michigan.  Proceedings of
   the Fourth Session of the 1972  Conference  on
   Pollution of Lake Michigan and Its Tributary Basin
   by Illinois,  Indiana,  Michigan, and Wisconsin.
   U.S. Environmental Protection Agency, Region V,
   Chicago, Illinois.

Patalas,  K.  1972.  Crustacean Plankton and the
   Eutrophication of St. Lawrence Great Lakes.  J.
   Fish. Res. Board Canada, 29(11): 1451-1462.

Pauer, J.J., A.M. Anstead, K.W. Taunt, W. Melendez,
   and R.G. Kreis, Jr.  2006.  The  Lake Michigan
   Eutrophication  Model,   LM3-Eutro:    Model
   Development and Calibration.   Submitted for
   publication to the Canadian J. Fish. Aquat. Sci.
Rast, W. and G.F. Lee.   1983.
   Estimates  for  Lakes.    J.
   109(2):502-517.
Nutrient Loading
Environ.  Engin.,
Reckhow, K.H. and J.T.  Simpson.   1980.   A
   Procedure Using Modeling and Error Analysis for
   the Prediction of Lake Phosphorus Concentration
   From Land Use Information.  Canadian J. Fish
   Aquat. Sci., 37(9): 1439-1448.

Risley,  C. and  F.  Fuller.   1965.    Chemical
   Characteristics   of   Lake   Michigan.    In:
   Proceedings of the Eighth Conference on Great
   Lakes Research,  pp.  168-174.   Great  Lakes
   Research Division, The University of Michigan,
   Ann Arbor, Michigan.
Rockwell, D.C.,  C.V. Marion, M.F.  Palmer,  D.S.
   DeVault.andR.J.Bowden. 1980. Environmental
   Trends in Lake Michigan.  In:  R.C. Loehr,  C.S.
   Martin,  and  W.   Rast  (Eds.),  Phosphorus
   Management Strategies for Lakes, Chapter 5, pp.
   91-132.    Ann  Arbor   Science   Publishers,
   Incorporated, Ann Arbor, Michigan.

Rousar, D.C. and A.M. Beeton. 1973. Distribution of
   Phosphorus,   Silica,   Chlorophyll  a,   and
   Conductivity in Lake Michigan and Green  Bay.
   In:     E. McCoy  (Ed.),  Transactions of  the
   Wisconsin Academy  of  Sciences, Arts,  and
   Letters, pp.  117-140.   University of Wisconsin,
   Madison, Wisconsin.

U.S.  Environmental  Protection  Agency.   1976.
   Areawide Assessment Procedures Manuals.
   Volume   2.    U.S.  Environmental Protection
   Agency,   Municipal  Environmental  Research
   Laboratory, Cincinnati, Ohio. EPA/600/9-76/014,
   427 pp.

Vollenweider, R.A., M. Munawar, and P. Stadelmann.
   1974.  A Comparative Review of Phytoplankton
   and Primary Production in the Laurentian Great
   Lakes. J. Fish. Res. Board Canada, 31 (5):739-
   762.
                                            429

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                                         PART 4

                                       LM2-TOXIC
Chapter  6.   The LM2-Toxic  Application
and Interpretation

One of the important goals during the development of
the LM2-Toxic was  to  quantitatively  understand
polychlorinated biphenyl  (PCB) dynamics (i.e.,
transport and  fate of PCBs)  in the  Lake Michigan
system and delineate the relationship between PCB
external loads and its concentrations in the system.
For the following discussion, Lake Michigan refers to
the main part of the lake  (main lake)  only, excluding
Green  Bay. Any references  to the  Lake Michigan
system should be considered to include both Green
Bay and Lake Michigan.  Lake Michigan system =
Lake Michigan + Green Bay.   Main lake = Lake
Michigan.  After calibration of organic carbon and
PCB congener dynamics and model confirmation, the
LM2-Toxic was used as a  mass budget diagnostic
tool to  identify the critical contaminant sources and
sinks  and key environmental  processes in Lake
Michigan and Green Bay.   The model was also
applied for forecasting the long-term responses of the
Lake  Michigan  system to  a variety of  forcing
functions and load reduction scenarios for PCBs.
The mass budget analysis and  the long-term
forecasts under the specified load reductions were
intended to provide information  useful in making
management  decisions for  the Lake Michigan
system. Long-term PCB exposure  concentrations
predicted from the LM2-Toxic for various  forcing
functions and load reduction scenarios were provided
to LM Food Chain as forcing functions  to compute
PCB concentrations in fish tissue.
4.6.1    Conversion  of PCB  Congener
Results to Total PCBs

LM2-Toxic is a PCB congener-based model. It was
developed to compute the concentrations, total mass,
and mass movement (fluxes) of 54 PCB congeners
in each compartment of the Lake Michigan system.
These 54 PCB congeners account for about 63% to
85% of the total PCB mass in the various media (see
Table 3.1.1 for the list of ratios between total PCBs
and the summed modeling congeners in all media).
There were enormous amounts of information related
to the model inputs and outputs from the model on a
basis of each PCB congener. For the efficiency and
effectiveness of presenting the input information and
results from the model, and for the convenience of
the reviewers  and readers,  all  numbers in this
chapter are presented as total PCBs.  The model
outputs for each Lake Michigan  Balance Project
(LMMBP) selected PCB congener were first summed
as IPCBs, and the IPCBs was then converted to
total PCBs using  a regression between total PCBs
and IPCBs. The regression analysis  for different
media was done by Computer Sciences Corporation
(CSC)/Large  Lakes  Research   Station  (LLRS)
personnel and is detailed in Part 1,  Chapter 3 of this
report. The regression equations used in this chapter
for the specified  media are listed in  Table 4.6.1.
More information on regression equations for all
media can be found in Table 1.3.1.

4.6.2 Mass Budget Diagnosis of the LM2-
Toxic for the LMMBP Period

A mass budget diagnostic tool was developed within
the LM2-Toxic in order to quantitatively analyze the
                                            430

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Table 4.6.1.  Regression Equations Used for Converting ZPCBs to Total PCBs for the LM2-Toxic
Results
 Media
  Regression Equation
 Dissolved Water
 Particulate Water
 Dissolved + Particulate Water
 Surficial Sediment
  y = 1.2738x + 0.0268
  y = 1.2251 x + 0.0051
  y = 1.2427x + 0.0347
  y = 1.1668x + 0.6125
0.9413
0.9992
0.9829
0.9970
behavior of PCBs in the Lake Michigan system. This
tool has the ability to estimate very detailed PCB
mass fluxes in the lake, mass inventories in different
compartments of the lake, phase distributions, and
contaminant  residence times   in  the  system.
Therefore, the results of the mass budget diagnosis
were used to demonstrate the most significant PCB
sources and sinks and to identify key environmental
processes in the Lake Michigan system.

A mass budget diagnosis was performed for each
selected PCB congener modeled in the LM2-Toxic
for the two-year LMMBP period  (1994-1995).  The
final  results of the  mass  budget diagnosis are
presented as the annual total PCBs only. Figures
4.6.1 and  4.6.2 provide a summary of the results of
the total PCB mass budget diagnosis and analysis in
Lake Michigan and Green Bay. Figure 4.6.1 depicts
the masses transported and inventories for the entire
Lake  Michigan system that includes Green Bay.
Figure 4.6.2 depicts  the mass transported  and
inventories for Green Bay separately from the main
lake.  Table 4.6.2 lists more detailed results of the
total PCB  mass budget analysis, including total PCB
mass distributions in different phases and residence
times in the system.  The diagrams and table also
give  an indication of the importance  for   each
environmental  process conceptualized  in the Lake
Michigan  system. The unit  of the annual average
mass fluxes (average of the two-year LMMBP period
- 1994-1995)  in the mass budget diagrams is in
kg/year. The mass inventories in the diagrams for
both water column and surficial sediment (0-4 cm)
are the average mass at any time over the two-year
LMMBP period and in units of kg. Due  to seasonal
variations  in the concentration of both the water
column and the surficial sediments in  the  Lake
Michigan system, the numbers for inventories can be
different on any given day in the LMMBP period. The
average mass of total PCBs in the water column of
the Lake Michigan system during 1994-1995 was
1,216 kg. About 30% (370 kg) of the total PCB mass
in  the water column was in the particulate phase
(particulate detrital carbon [PDC] bounded + biotic
carbon [BIG] bounded).  Dissolved phase (dissolved
organic carbon  [DOC]  bounded +  unbounded)
accounted for approximately 70% (846 kg) of the
average mass of total PCBs in the water column.
The average mass of total PCBs in the surficial
sediments (0-4 cm) during the LMMBP period was
13,085 kg, and virtually all of the mass in the surficial
sediment was bound to PDC. Based on the volumes
of  the water  column (4.8148  x 1012  m3)  and the
surficial sediment layer (1.0871 x 109 m3) of the Lake
Michigan system, the average concentration of total
PCBs in the water column was 0.253 ng/L, and the
average concentration of total PCBs in the surficial
sediment   layer  was   12,037   ng/L.    These
concentrations were consistent with  the  average
concentrations (0.259 ng/L for the water column and
650-25,000 ng/L for the  surficial  sediment layer)
derived from the LMMBP field data.

Compared with Lake Michigan,  the total PCB mass
distributions in dissolved and particulate phases were
quite different in the Green Bay water column. The
inventories of particulate and  dissolved PCBs in
Green Bay were  almost  equal.  The  higher PCB
mass in the particulate phase in Green Bay was due
to the dominant tributary load from the Fox River. In
the river, the particulate PCB concentrations were
much higher than the dissolved PCB concentrations.
In  Lake Michigan, the particulate PCBs were less
than half of the dissolved PCBs in its water column.
                                             431

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             volatilization
                   3439
       gas absorption
                1507
resuspension
    1393
          m
 export to
Lake Huron
    12
   atmospheric
    deposition
       980
  Chicago,
   River {
   export /
     8
                                                                 water column    =1216
                                                                 active sediment  = 13,085
                                                                 (0-4 cm interval)
             sediment
                 burial
                 1284
      monitored and unmonitored
          tributary loading
      (Lake Michigan watershed)
Figure 4.6.1. Mass budget average for 1994-1995 total PCBs in the Lake Michigan system (including
Green Bay). Unit of the masses transported (arrows) is in kg/year.
                                            432

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                                                                                    input from
                                                                                  Lake-Huron
                                                                                           4
                        Green Bay
                       volatilization
                              721
Green Bay
gas absorption
111
         Green Bay
         monitored and
         unmonitored
         tributary loading
         230  C
                                                   Green Bay
                                                   resuspension

                                                 i453
                                                   -rr-r  •= J'
                                                            Green Bay
                                                            sediment burial
                                                            68
      settling i
         355 •
atmospheric
deposition
267
                                                    Green Bay Mass Budget
          main lake
        volatilization
              2718
            Green Bay
              export
               128
                      mam lake
                      gas absorption
                      1395
    atmospheric
    deposition
    714
                                                export to
                                               Lake Huron
                                                   12
                                resuspension
                                    939
                                                                     PCB Inventory kg
  export via
   Chicago
  Diversion
                                  Main Lake:
                                  water column = 1165
                                  active sediment = 10380
                                  (0-4 cm interval)
                                          mam lake monitored and
                                        Linmonitored tributary loading
                                         (Lake Michigan watershed
                                           excluding Green Bay)
                                                   151
                                  Green Bay:
                                  water column = 51
                                  active sediment = 2704
                                  (0-4 cm interval)
                 sediment
                    burial
                    1216
Figure 4.6.2.  1994-1995 total PCB Lake Michigan and Green Bay mass budget (averaged). Unit of the
masses transported are in kg/year.
                                              433

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Table 4.6.2. Results of Total PCB Mass Budget Analysis for Lake Michigan and Green Bay (Fluxes are
Annual Averages of the Two-Year LMMBP -1994-1995; Inventories are the Average Inventories of the
Two-Year LMMBP Period)
 Mass Budget Component
Lake Michigan
 + Green Bay
Lake Michigan   Green Bay
 Fluxes (kg/year)

 Total Loads
    (Monitored Tributary)
    (Unmonitored Tributary)
    (Atmospheric Dry)
    (Atmospheric Wet)
 Settling
    (PDC Bounded)
    (BIG Bounded)
 Resuspension
 Burial (Sedimentation)
 Diffusion
 Absorption
 Gross Volatilization
 Net Volatilization
 Input From Lake Huron
 Export to Lake Huron
 Net Output to Lake Huron
 Chicago Diversion
 Net Flux From Green Bay to Lake Michigan

 Inventory (kg)

 Water Column
    (PDC Bounded)
    (BIG Bounded)
    (DOC Bounded)
    (Unbounded)
 Surficial Sediment (0-4 cm)

 Residence Time in Water Column (Days)
 Residence Time in Sediment (Days)
    1362
     348
     33
     767
     214
    1136
    1076
     61
    1393
    1284
     154
    1507
    3439
    1932
      4
     12
      8
      1
     128
    1216
     297
     73
     36
     810
    13085

     97
    1688
     865
     124
      26
     514
     200
     782
     764
      17
     939
     1216
     137
     1395
     2718
     1323
      4
      12
      8
      1
     128
     1165
     277
      67
      35
     786
    10380

     121
     1653
 496
 224
  7
 253
 13
 355
 311
 44
 453
 68
 16
 111
 721
 610
 128
 51
 19
 6
  1
 24
2704

 17
1837
Note: The fluxes represent the masses transported across the total area of an interface between adjacent
compartments of the Lake Michigan system. Residence time for the water column was calculated by dividing
the annual average inventory in the compartment by the annual  total output (sum of gross volatilization,
settling, export to Lake Huron, and Chicago diversion) from the water column. Residence time for the surficial
sediment layer was derived  by dividing the annual averaged sediment inventory by the sum of the losses
(burial, resuspension, and diffusion).
                                            434

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The information on the individual fluxes of total PCBs
in Figures 4.61 and 4.6.2 and Table  4.6.2 on an
annual average basis for the LMMBP period provide
further quantitative diagnosis on the importance of
each source, sink,  and  the  key  environmental
process in the Lake Michigan system.  The single
largest flux leaving the lake was gross  volatilization
(3,439 kg/year). This flux was countered by the flux
from gas  absorption (1,507 kg/year) as the largest
source to  the lake.  The air-water exchange was the
most important  process for the  Lake  Michigan
system. It accounted for the largest net loss (1,932
kg/year) from the Lake Michigan system of which
31.6%  was from  Green  Bay   (610  kg/year).
Resuspension (1,393 kg/year) was a major flux into
the water  column of the Lake Michigan system.  This
was offset by the flux from settling (1,136 kg/year).
The processes  associated  with  the interaction
between  the water  column  and the surficial
sediments (resuspension and  settling) were  very
important processes in the Lake Michigan system.
The results of these processes led to the second
largest  net source (257 kg/year) next to the  total
external load (1,362 kg/year) for the water column of
the Lake  Michigan system.  About 40% of this net
gain from the sediment-water  interactions  was
contributed from Green Bay (98 kg/year). Green Bay
received  more than one-third (496  kg/year) of the
total external load.  The principal loss in the surficial
sediment  layer was burial (1,284 kg/year). The flux
contributed by diffusion from the surficial sediment
layer to the water column was 154 kg/year. The high
value for the mass transported by the diffusion  from
the surficial sediment layer was not unexpected. The
reason for the high value could lie in the selection of
the diffusion coefficient used in the LM2-Toxic (1.73
x 10'4 m2/day) (DePinto et al.,  1993) for sediment-
water diffusion  process.    Compared  with   the
coefficient used  in the level 1  model (1.8  x 10'5
m2/day, Table 3.3.2) and the coefficient defined
under low-flow conditions for river sediment (1.5 x
10'4 m2/day) (Ortiz et al., 2004), the coefficient used
in the LM2-Toxic was a bit higher.   This  could be
another reason for the higher PCB concentrations for
the bottom water segments output from the model
during the LM2-Toxic PCB dynamics calibration (see
Chapter 5 for details).

For the Lake Michigan system, the total external  PCB
'oad (monitored  tributary, unmonitored tributary,
atmospheric dry, and atmospheric wet loads) and
input from Lake Huron was 1366 kg/year. The total
output or loss was equal to 3,229 kg/year due to net
volatilization, sediment burial, export to Lake Huron,
and Chicago diversion.  Therefore, there was  a net
loss of 1,863 kg/year of total PCBs from the entire
Lake Michigan system. This indicated that both the
water column and the surficial sediment layer of the
lake were not at steady-state during the LMMBP
period.  By examining the mass fluxes of total PCBs
in the water column alone, the annual average total
gain (1,777 kg/year) during the project period was the
sum  of  total  external  load,  net  resuspension
(resuspension flux - settling flux) from the surficial
sediment layer, diffusion from the surficial sediment
layer, and input  from Lake  Huron.  The annual
averaged total loss for the water column during the
same period was 1,945 kg/year and was equal to the
sum of net volatilization, export to  Lake  Huron and
Chicago  diversion.    Thus the  water  column
experienced a total net annual loss of 168 kg/year.
The annual average net export of total PCBs from
Green Bay  to  Lake  Michigan  was  equal to 128
kg/year during 1994-1995. The number is very close
to the value (122.3 kg/year) estimated from the  1989
Green Bay Mass Balance Project (GBMBP) (DePinto
et al., 1993). A net loss of 1,695 kg PCB per year
from sediment was due to burial below the surficial
sediment (1,284 kg/year); net resuspension to the
water column (257 kg/year = resuspension flux 1,393
kg/year - settling flux 1,136 kg/year); and diffusion
(154 kg/year) from the surficial sediment layer to the
water column.

Residence time for the water column was calculated
by  dividing  the  annual  average  inventory in the
compartment by  the annual  total  output  (sum of
gross volatilization, settling, export to Lake Huron,
and Chicago  diversion)  to the  water  column.
Therefore, the water column total PCB residence
times for Lake Michigan including Green Bay,  Lake
Michigan  only,   and  Green   Bay  only   were
approximately 97,  121, and 17 days, respectively.
Similarly, the residence time for the surficial sediment
layer was derived by dividing the annual averaged
sediment inventory by the sum of the losses (burial,
resuspension,  and diffusion).  Thus the total  PCB
residence times  in the surficial sediment layer for
Lake Michigan including Green Bay, Lake Michigan
only, and Green Bay only were about 1,688, 1,653,
and 1,837 days, respectively.  Thus, PCBs reside in
                                             435

-------
the surficial sediment layer much longer than in the
water column of Lake Michigan.

4.6.3  LM2-Toxic  Application  for Long-
Term Forecast and Sensitivity Scenarios

Predictions of  long-term  PCB  dynamics under a
variety of external forcing conditions  were  made
using  LM2-Toxic for  seven  PCB forecast and
sensitivity scenarios.  The model-predicted  PCB
concentrations  were  then used  as  the  time-
dependent exposure concentrations in the LM Food
Chain to calculate PCB concentrations in lake trout.
The  simulation period for each scenario was  62
years, starting on January 1, 1994 and ending  on
December 31,  2055.

All scenarios used the same LMMBP-generated field
data as input for the first two years (1994-1995) of
the simulations.  Then, each scenario began  on
January 1, 1996 and ran  for a  period of 60 years.
The observed  PCB total load for the LMMBP period
(1994-1995) was adjusted  upward by a factor of 1.98.
The adjusted PCB load is consistent with the  1994
total load used in the PCB  hindcast (see 1.7.3 of this
report for details  on  the derivation  of  the  PCB
hindcast  loading function)  and  is a  reasonable
estimate when considering the possibility of missing
atmospheric loads during the LMMBP  period (see
 1.7.3 of this report for a detailed discussion).

The  seven PCB forecast  and sensitivity scenarios
were:

A) Constant Conditions -  The measured PCB loads
   (tributary load  plus atmospheric dry and wet
   deposition) for the  LMMBP period (1994-1995),
   but adjusted upward by a factor of 1.98.  The
   adjusted  loads followed   the same  spatial
   distribution  and  monthly  variation patterns
   established by the LMMBP measured PCB loads.
   The adjusted loadings,  the  1994-1995 vapor-
   phase concentration,  Lake  Huron boundary
   conditions,  and all other forcing functions  as
   observed  in 1994  and 1995 were  repeated
   throughout the simulation  period.   Sediment
   burial  was active as  well  as  all other model
   processes.
B) Continued Recovery (Fast) - This was the same
   as Scenario "A", but atmospheric components
   (vapor  phase  concentration,  wet  and  dry
   deposition) declined with  a six-year  half-life
   (Hillery etal., 1997; Schneider et at, 2001), and
   tributary  loads declined with a 13-year half-life
   (Endicott, 2005; Marti  and  Armstrong, 1990).
   The  boundary conditions   at  the  Straits  of
   Mackinac declined  at a  rate of 0.17/year (a four-
   year half-life) (Schneider et al., 2001). These
   rates were applied  starting on January 1,1996.

C) Continued Recovery (Slow) - This was the same
   as Scenario "A", but atmospheric components
   (vapor  phase  concentration,  wet  and dry
   deposition) declined with  a 20-year  half-life
   (Buehler  etal., 2002) and tributary loads declined
   with a 13-year half-life.  The boundary conditions
   at the Straits of Mackinac declined with a  four-
   year half-life.  These rates were applied starting
   on January 1,1996.

D) No Atmospheric Deposition - This was the same
   as Scenario "A", but starting on January 1,1996,
   the atmospheric loads (dry and wet deposition)
   were set  to zero.  All other forcing functions as
   observed in the LMMBP period were repeated
   throughout the simulation period.

E) No Tributary Loadings - This was the same as
   Scenario "A", but starting on January 1,1996, all
   tributary loads were set  to zero. All other forcing
   functions as observed in the LMMBP period were
   repeated throughout the simulation period.

F) Lakewide Sediment  Cleanup - This was the
   same as  Scenario "A", but starting on January 1,
   1996, the lake-wide sediment PCB concentration
   was  instantaneously set to zero.   All other
   sediment properties remained as existed prior to
   sediment clean-up. All other forcing functions as
   observed in the LMMBP period and processes
   were repeated throughout the simulation period.

G) No  Atmospheric Deposition and No Tributary
   Loadings - The loading cuts of Scenarios "D" and
   "E" were combined. All other forcing functions as
   observed in the LMMBP period were repeated
   throughout the simulation period.
                                              436

-------
The results of the above seven scenarios will be
presented in  two separate groups.   These are
forecast  scenarios  (Scenarios A,  B,  and C)  and
sensitivity or engineering scenarios (Scenarios D, E,
F, and G).

4.6.4    Results  of  the   Forecast  and
Sensitivity Scenarios and Discussion

Figures 4.6.3 and 4.6.4 show the annual and monthly
average  long-term responses of total PCBs in the
water column of the Lake Michigan system for the
seven forecast and sensitivity scenarios. Compared
to the annual lake-wide  total PCB concentrations
(Figures  4.6.3a and 4.6.3b), the monthly lake-wide
total PCB concentrations show  a  much  wider
variation with  high concentrations in  the summer
months and low concentrations in the winter months
(Figures  4.6.4a and 4.6.4b).

The model results were compared to measured data
and water quality criteria.  The lake-wide average
total PCB concentration (0.259 ± 0.172 ng/L) for the
LMMBP  period (1994-1995) was based on 298 field
measurements from various water depths at 41 water
column  sampling   stations.    The  total   PCB
concentration (0.165 ± 0.029 ng/L) from the Episodic
Events-Great  Lakes  Experiment (EEGLE)  project
represents the  average  PCB  concentration for
southern Lake Michigan in 2000 (Miller, 2003). The
focus of EEGLE was to  investigate  the potential
impact of major sediment resuspension events on
persistent organic pollutants (POPs) in southern Lake
Michigan. Field sampling for EEGLE was conducted
in 1998, 1999,  and 2000.   The  average  PCB
concentration for 2000 from the EEGLE project was
a better representation than the earlier years for the
open-water concentration  of PCBs in  the lake and
was used for post-audit comparisons to the results of
the LM2-Toxic PCB long-term forecast and sensitivity
scenarios.  USEPA water quality criteria for the
protection of  wildlife  is 0.12  ng/L  and for  human
health is  0.026 ng/L; which is a human cancer value
(HCV) that is still under review and  development
(U.S. Environmental Protection Agency, 2005; U.S.
Environmental Protection Agency, 1997).

Scenario A - Constant Conditions serves  as the
upper bound  of the range of possibilities resulting
from the specified  PCB  forecast  and sensitivity
scenarios. The long-term response to the Constant
Conditions Scenario  clearly  demonstrated  that,
during the LMMBP period (1994-1995), the  Lake
Michigan system was not at steady-state with respect
to the 1994-1995 loads, vapor phase concentrations,
and the level of sediment total PCB inventory. As the
mass budget  analysis indicated  in  the  previous
section and in Table 4.6.2, the mass losses from net
volatilization and  sediment burial were the major
contributors  to   the  decline   of  total   PCB
concentrations in  both the water column and the
surficial sediment  layer.   The spatially averaged
steady-state value for the water column under this
scenario was about 0.145  ng/L and was reached
around 2024. This value represents a 44% reduction
in the annual average  concentration (0.259 ng/L) in
the water column for the  LMMBP period.   The
steady-state concentration of the Constant Condition
Scenario will still be approximately 20% higher  than
the most recent  (U.S.  Environmental Protection
Agency, 2005) USEPA water quality criteria for the
protection and wildlife and  five to six times higher
than  the  USEPA water  quality  criteria for  the
protection of human  health  in the  Great  Lakes
system  (U.S.  Environmental  Protection  Agency,
1997).

Among  the forecast scenarios, the  outcome  from
Scenario A  (Constant Conditions) may not be a
realistic prediction of long-term PCB concentrations
in the lake.  Because  PCB  production was phased
out in the 1970s, PCB  inputs into the Lake Michigan
system  through the atmosphere  (via dry and wet
deposition, and absorption of vapor  phase)  and
tributaries have been decreasing significantly due to
regulatory policies and remediation efforts made by
federal  and  state agencies.   Therefore,   it  is
reasonable to assume that PCB inputs into the lake
should continue decreasing  under current regulatory
policies and clean-up efforts. The decline rates used
in Scenarios B and C  for PCB  inputs from the
atmosphere  and   tributaries  were  the result  of
analyzing  observed data collected for the past 25
years. These rates were subject to a certain degree
of uncertainty (see Section  1.7.2). The variation of
the  estimated  decline  rates  for  atmospheric
components (dry   and wet deposition and vapor
phase concentration) was quite large with half-lives
ranging from 6 to 20 years.  It appears that the rate
of decline decreased with the addition of more recent
data  (Hillery et al.,  1997; Simcik  et al., 1999;
Schneider era/., 2001; Buehler et al., 2002, 2004).
                                             437

-------
                      0.30-
       Constant conditions - scenario A
       Continue recovery (slow) - scenario C
       Continue recovery (fast) - scenario B
       LMMBP data -1994-95; EEGLE data 2000
       EPA water quality criteria for protection of
       wildlife (2005)
       EPA water quality criteria for protection of
       human health (1997)
                         1994    2004   2014    2024    2034    2044    2054
                                               year

Figure 4.6.3a.  Annual long-term responses to total PCB concentrations in the water column of Lake
Michigan for the forecast scenarios and USEPA water quality criteria for the protection of wildlife (U.S.
Environmental Protection Agency, 2005) and human health (U.S. Environmental Protection Agency,
2997) in the Great Lakes system.
                       0.30
                       0.25
                    ~  0.20
                    §  0.151
                    c
                    o
                    0  0.10-
                    DO
                    O
                    °-  0.05-
       Lakewide sediment cleanup - scenario F
       No tributary loadings - scenario E
       No atmospheric deposition - scenario D
       No atmospheric deposition & tributary
       loadings - scenario G
       LMMBP data -1994-95; EEGLE data 2000
                         1994
2004
                                        2014
2024
year
                      2034
                                                              2044
2054
Figure 4.6.3b.  Annual long-term responses to total PCB concentrations in the water column of Lake
Michigan for the sensitivity scenarios.
                                                438

-------
                    0.30
              ~— Constant conditions - scenario A
              	 Continue recovery (slow) - scenario C
              	 Continue recovery (fast) - scenario B
                »   LMMBP data -1994-95; EEGLE data 2000
              —  EPA water quality criteria for protection of
                   wildlife (2005)
              	 EPA water quality criteria for protection of
                   human health (1997)
                    0.00 -I
                       1994
            2004
                                                             2044
                                                                    2054
Figure 4.6.4a. Monthly long-term responses to total PCB concentrations in the water column of Lake
Michigan for the forecast scenarios and USEPA water quality criteria for the protection of wildlife (U.S.
Environmental Protection Agency, 2005) and human health (U.S. Environmental Protection Agency,
1997) in the Great Lakes system.
                      0.30-
                      0.25
CD
o
C
o
o
CD
O
a.
43
"o
                      0.20
                      0.15-
                      0.10-
                      0.05-
                        1994
                   No atmospheric deposition - scenario D
                   No tributary loadings - scenario E
                   Lakewide sediment cleanup - scenario F
                   No atmospheric deposition & tributary
                   loadings - scenario G
                   LMMBP data -1994-95; EEGLE data 2000
                                          2044
2054
Figure 4.6.4b. Monthly long-term responses to total PCB concentrations in the water column of Lake
Michigan for the sensitivity scenarios
                                               439

-------
The purpose of forecast Scenarios B and C was to
provide bounds for predicted long-term PCB water
column concentrations by assuming six-year half-life
and  20-year   half-life  decline  rates  in   PCB
atmospheric components, respectively.

For Scenario B (Fast Continued Recovery Scenario),
it takes about five years (starting January 1,1996) for
PCB concentrations in the water column to meet the
USEPA water quality criteria  for the protection of
wildlife (U.S. Environmental Protection Agency, 2005)
and more than two decades to reach the USEPA
water  quality criteria for the  protection of  human
health (U.S. Environmental Protection Agency, 1997)
in the Lake Michigan system.  The water column
PCB concentrations predicted in Scenario C (Slow
Continued  Recovery Scenario) declined at a much
slower speed.  The model results indicated  that it
would take about 12 years for the water column PCB
concentrations in the lake to reach the USEPA water
quality criteria for the  protection  of wildlife  in the
Great Lakes (U.S. Environmental Protection Agency,
2005).   Figure 4.6.3a also shows that  the  PCB
concentrations  in the  water column reached the
USEPA water quality criteria for the protection of
human  health  (U.S.   Environmental   Protection
Agency, 1997) around 2046 (five decades  after
1996). In both forecast Scenarios B and C, the PCB
inventory in the surficial active sediment layer plays
an important role in sustaining the water column PCB
concentrations.

It is important to point out that the decline rate used
in Scenarios B and C may not necessarily be realistic
rates for the Great Lakes in the future.  There is a
chance  that the  actual  rate  could  be slower,
especially  if  there  is  no  further  regulatory and
remediation actions taken to reduce the PCB sources
from the atmosphere of the entire Lake  Michigan
watershed.

The sensitivity Scenarios  D, E,  F,  and G were
designed for the purpose of demonstrating how
sensitive the  long-term  responses  of the  Lake
Michigan   system  could   be  by  hypothetically
eliminating  atmospheric deposition (dry and wet),
tributary loads,  total sediment inventory, and total
external loads (dry and wet atmospheric deposition,
and tributary loads altogether), respectively, starting
on January 1, 1996. It is very important to mention
that for these sensitivity scenarios, the  PCB vapor
phase concentrations were kept the same as the
LMMBP (1994-1995) measured concentrations.

Long-term PCB concentrations in the water column
were more sensitive to the atmospheric deposition
(dry and wet) than the load from tributaries (Figure
4.6.3b).  The steady-state concentration predicted
from Scenario E (No Tributary Loadings) was 0.131
ng/L  which  was  equivalent to less than  a  10%
decrease in the steady-state concentration (0.145
ng/L) from Scenario A (Constant Conditions).  The
steady-state concentration predicted from Scenario
D (No Atmospheric Deposition) experienced a much
larger drop  to 0.094 ng/L, with a 35% reduction
compared  to  the  steady-state  concentration of
Scenario A.  When eliminating both atmospheric
deposition (wet and dry) and tributary load (Scenario
G), the steady-state concentration decreased to
0.080 ng/L. By eliminating PCB total inventory in the
lake sediments, starting on January 1,1996, the PCB
concentration in the water column experienced a
steep drop initially and then gradually increased and
reached a steady-state concentration of 0.145 ng/L.
Notice that this value was the same as the  one
predicted from Scenario A (Constant Condition).

It should be emphasized that Scenarios D, E, F, and
G are hypothetical and not realistic. Because LM2-
Toxic was not coupled with an air quality model to
dynamically   compute   PCB   vapor  phase
concentration, the atmospheric concentrations in the
sensitivity scenarios were kept constant as measured
during the 1994-1995 LMMBP period. In reality, the
PCB  water  column concentrations should   be
significantly  lower  than   the  steady-state
concentrations resulting from these four sensitivity
scenarios.  As  demonstrated in Figure 4.6.1, the
gross volatilization flux is the largest flux moving PCB
mass out of the lake.  The decrease in the water
column PCB concentrations after the actions taken
for these four sensitivity scenarios would reduce the
gross volatilization fluxes.  As a result, the PCB vapor
phase concentration  over  the watershed should
decrease  accordingly.   This  could lead  to  the
reduction of PCB absorption flux to the lake  and
further reduce the PCB water column concentrations.

Figure 4.6.5 shows the long-term responses of PCB
concentrations in  the lake sediments to the seven
forecast and sensitivity scenarios.  In general, the
PCB  concentrations  in  the  sediments  followed
                                              440

-------
                  Constant conditions - scenario A
                  Continue recovery (slow) - scenario C
                  Continue recovery (fast) - scenario B
                                   2044   2054
Figure 4.6.5a.  Annual  long-term responses to
total PCB concentrations in the surf icial sediment
of Lake Michigan for the forecast scenarios.
 O>
'10000-


 8000


 6000-


 4000-


 2000
	 No atmospheric deposition - scenario D
— No tributary loadings - scenario E
132 Lakewide sediment cleanup - scenario F
— No atmospheric deposition & tributary loadings
  scenario G
     0-1-
     1994   2004  2014   2024   2034   2044   2054
                      Year

Figure 4.6.5b.  Annual long-term responses to
total PCB concentrations in the surf icial sediment
of Lake Michigan for the sensitivity scenarios.
similar long-term trends predicted  for  the  water
column.    The  steady-state   PCB  sediment
concentration  predicted from  Scenario  E  (No
Tributary Loads) was 1,347 ng/L which was 18% less
than the steady-state PCB sediment concentration
(1,665 ng/L) from Scenario A (Constant Conditions).
The  steady-state  concentration  predicted   from
Scenario D (No Atmospheric Deposition   Wet and
Dry) was 909 ng/L which was 45% less than the
concentration from Scenario A.
A few  differences were  noted when comparing
responses of PCBs in sediment with the long-term
responses of PCBs in the water column. First, there
was no apparent seasonal variation in the long-term
temporal   profiles  of   the  sediment  PCB
concentrations.  The large PCB inventory and the
mixing processes within the surficial sediment layer
may have been factors smoothing out any seasonal
variations caused by atmospheric components and
tributary loads. Secondly, the long-term responses
of  PCB concentrations in  sediment were  more
sensitive than those in the water  column for  the
sensitivity Scenarios D (No Tributary Loads), E (No
Atmospheric Depositions), and G (No Tributary and
Atmospheric Loads). LM2-Toxic was developed for
simulating 54 PCB congeners.  Based on the data
analysis results for PCBs in different media (see Part
1.6 for details), the heavier PCB congeners (higher
molecular weight) were more abundant in tributary
loads and atmospheric deposition (dry and wet) than
in the vapor phase.  In the water column, more than
two-thirds of PCB mass was in the dissolved phase,
and PCB concentrations were significantly influenced
by  PCB vapor phase concentration.  Unlike in  the
water column, particulate PCBs in the lake sediments
were the dominant phase,  while PCBs in  the
dissolved  phase were  negligible.   In addition,  the
heavier PCB congeners were more  abundant in the
sediments due to their higher partitioning coefficients.
The larger  percentage  of heavy congeners  in
tributary loads and atmospheric deposition and  the
strong influence of vapor PCB concentrations on the
water column PCB  concentrations might be  the
factors making the PCB concentrations in sediment
more sensitive to the tributary loads and atmospheric
deposition than in the water column.

Reducing the PCB vapor phase concentration was
critical to the level of long-term PCB  concentration in
the Lake Michigan system.  Both Figures 4.6.3 and
4.6.5 (Scenarios B and C compared to Scenario A)
demonstrate that the long-term response of PCBs in
the lake system  is very sensitive  to PCB vapor
concentrations.  Figure 4.6.1 also  shows that  the
volatilization flux and absorption flux are the number
one and the number two fluxes for Lake Michigan.
Again, the future declining rates for PCB atmospheric
components (including vapor concentration, wet and
dry deposition) may be slower than  the rate used in
Scenario C if there is no action taken in the future to
continue reducing PCB vapor concentrations and
                                             441

-------
particulate deposition from the atmosphere of the
Lake Michigan watershed.

4.5.5  Results Provided for the LM Food
Chain Model

The exposure concentrations generated from LM2-
Toxic for the seven scenarios were provided as
forcing functions to the LM Food Chain model to
predict the long-term concentration changes for PCB
congeners in lake trout tissue.   Sets of exposure
concentrations were generated for the  Saugatuck
biota box, segment 2, the Sturgeon Bay biota box,
and segment 3 (see Figure 5.4.1  in Part 5 LM Food
Chain for the locations of the biota zones).   Each
data set contained water column dissolved PCB
concentrations (ng/L), water column particulate PCB
concentrations  (ng/g organic carbon),  sediment
dissolved PCB concentrations (ng/L), and sediment
particulate   PCB  concentrations  (ng/g   organic
carbon).  Because there were multiple LM2-Toxic
sediment segments under each biota box and water
column segment, the sediment PCB concentrations
provided to the LM Food Chain were computed using
area-weighted  averaging  based on the segment-
specific concentrations generated by the LM2-Toxic.

Field data  for water column  hypolimnetic total
particulate PCB concentrations for the  Saugatuck
biota box was higher than for the much larger
hypolimnetic level 2 segments (Segments 21,30,37)
within which the Saugatuck  biota box  resided.
Therefore,  a factor  of  1.5  was  used to  scale
    hypolimnetic water column total particulate PCB
    concentrations from the larger level 2 segments
    (Segments 21,30,37) to the hypolimnetic Saugatuck
    biota box.  This factor was calculated based upon the
    LMMBP data collected from hypolimnetic sampling
    locations near the biota box and sampling locations
    within hypolimnetic segments 21, 30, and 37 as a
    whole (Table 4.6.3).

    References

    Buehler, S.S., I. Basu, and R.A. Hites. 2002. Gas-
        Phase  Polychlorinated   Biphenyl  and
        Hexachlorocyclohexane Concentrations Near the
        Great Lakes:  A Historical Perspective. Environ.
        Sci. Technol., 36(23):5051-5056.

    Buehler,  S.S., I.  Basu, and  R.A.  Hites.  2004.
        Causes of Variability  in Pesticide and PCB
        Concentrations  in Air  Near the  Great  Lakes.
        Environ. Sci. Technol., 38(2):414-422.

    DePinto,  J.V.,  R.  Raghunathan, P. Sierzenga, X.
        Zhang, V.J. Bierman, Jr., P.W. Rodgers, andT.C.
        Young.  1993.  Recalibration of  GBTOX:  An
        Integrated Exposure Model for Toxic Chemicals
        in Green Bay, Lake Michigan. Final Report. U.S.
        Environmental Protection  Agency,  Office  of
        Research and Development, ERL-Duluth, Large
        Lakes Research Station, Grosse  lie, Michigan.
        132pp.
Table 4.6.3. Mean and Median Particulate PCBs/Organic Carbon and Field Data and Scaling Factor for
Hypolimnetic Level 2, Segments 21, 30, and 37, and for Saugatuck Biota Box Hypolimnion
 Field Data
Segments 20, 29, 36
   (Hypolimnion)
 Saugatuck Biota
Box, Hypolimnion   Factor
 Mean Particulate PCBs/Organic Carbon
 (ngPCB/mgC)

 Median Particulate PCBs/Organic Carbon
 (ngPCB/mgC)
       0.560
       0.509
      0.817
      0.747
1.5
1.5
                                             442

-------
Endicott, D.D.  2005.  2002 Lake Michigan Mass
   Balance Project: Modeling Total PCBs Using the
   MICHTOX Model.  In:   R. Rossmann (Ed.),
   MICHTOX:      A   Mass  Balance  and
   Bioaccumulation Model for Toxic Chemicals in
   Lake Michigan, Part  2.   U.S.  Environmental
   Protection Agency, Office of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse He,  Michigan.
   EPA/600/R-05/158, 140 pp.

Hillery, B.L, I. Basu, C.W. Sweet, and R.A. Hites.
   1997. Temporal and Spatial Trends in a Long-
   Term Study of Gas-Phase PCB Concentrations
   Near the Great Lakes. Environ. Sci.  Technol.,
Marti,   E.A.  and   D.E.   Armstrong.     1990.
   Polychlorinated  Biphenyls  in  Lake  Michigan
   Tributaries. J. Great Lakes Res., 16(3):396-405.

Miller,  S.M.   2003.  The Effects of Large-Scale
   Episodic Sediment Resuspension on Persistent
   Organic Pollutants in Southern Lake Michigan.
   Ph.D. Thesis, The University of Iowa, Iowa City,
   Iowa. 194pp.

Ortiz, E., R.G. Luthy, D.A. Dzombak, and J.R. Smith.
   2004.  Release  of Polychlorinated Biphenyls
   From River Sediment to Water Under Low-Flow
   Conditions: Laboratory Assessment. J. Environ.
   Engin., 130(2):126-135.
Schneider, A.R., H.M. Stapleton, J. Cornwell, and
   J.E.  Baker.   2001.   Recent Declines in PAH,
   PCB, and Toxaphene Levels in the Northern
   Great  Lakes  as  Determined   From  High
   Resolution  Sediment Cores.   Environ.  Sci.
   Technol., 35(19):3809-3815.

Simcik, M.F., I. Basu, C.W. Sweet, and R.A. Hites.
   1999. Temperature Dependence and Temporal
   Trends of Polychlorinated Biphenyl Congeners in
   the Great Lakes Atmosphere.   Environ.  Sci.
   Technol., 33(12)1991-1995.

U.S.   Environmental  Protection  Agency.   1997.
   Revocation  of  the  Polychlorinated  Biphenyl
   Human  Health  Criteria  in the  Water Quality
   Guidance for the Great Lakes System. Federal
   Register, October 9, 1997, Volume 62, Number
   196.  [DOCID:fr09oc97-9].   From the Federal
   Register   Online   via  GPO   Access
   [wais.access.gpo.gov].

U.S. Environmental Protection Agency. 2005. Water
   Quality Guidance for  the Great Lakes System.
   Code of Federal Regulations, Title 40, Volume
   21, Chapter 1,  Part  132.  Http://www.access.
   gpo.gov/nara/.
                                           443

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                                           PART 4

                                        LM2-TOXIC
Chapter 7. LM2-Toxic Sensitivity Analysis

Sensitivity analysis is a very efficient tool that can
semi-quantitatively demonstrate the uncertainties in
the outputs from  a water quality  model.   These
uncertainties in the model outputs could result from
the uncertainties associated with the forcing time
functions;  the water and sediment transport; the
numerical algorithms used in  the model;  or the
parameters  for  the chemical  and  biochemical
processes  defined  in  the  model.  This  chapter
summarizes the results of a  series of sensitivity
analyses  completed only for  the  forcing  time
functions  such  as  primary  production  and
polychlorinated  biphenyl (PCB)  loads.  The  PCB
atmospheric components (PCB vapor concentration,
dry and wet deposition) and tributary PCB load half-
lives are discussed in Part 4,  Chapters 3  and 6.
Model sensitivity analyses were performed over both
a short-term (two years, 1994-1995) and long-term
(62 years -1994 to 2055) periods.

4.7.7  Primary Production Sensitivity

Due to the affinity of PCBs for organic carbon, the
internal organic  carbon load (primary  production) is
very important in understanding the transport and
fate of PCBs in the Lake Michigan system.  The
internal organic  carbon load used in the LM2-Toxic
was generated by the eutrophication model, LM3-
Eutro (See Part 2 for details).  Because there are
uncertainties  associated  with  the  LM3-Eutro
generated internal organic carbon load, the variations
in  the  load on the LM2-Toxic  PCB  model output
concentrations (including solids  dissolved organic
carbon  (DOC),   biotic carbon (BIG), paniculate
detrital carbon (PDC),  and PCBs) were explored.
The results of the sensitivity analysis for PCBs are
illustrated with the PCB28+31 congener pair because it
is the most abundant PCB congener pair in Lake
Michigan.  In LM2-Toxic, this congener  pair is
modeled as a single state variable.  The internal
organic carbon load generated from the LM3-Eutro
for the LMMBP period  (1994-1995) was increased
50% for  one analysis  and decreased  50% for a
second  analysis.   The model simulations  for the
analyses were conducted for both a short-term (two-
year period:  1994 and 1995) period and a long-term
(62-year  period:  1994-2055)  periods.  The results
from the sensitivity analysis were  compared to the
results from the  LM2-Toxic model base runs (i.e.,
calibration run for the 1994-1995 period  and long-
term  Constant  Condition  Scenario,  see Part 4,
Chapters 4 and 6 for detailed descriptions of both of
these base runs).

Below is a summary of the results from the sensitivity
analysis:

1.  As shown in Figures 4.7.1  and 4.7.2,  a 50%
   increase or decrease in the primary production
   has   a   noticeable   effect  on   the  solid
   concentrations  (DOC,  BIG,  PDC) in the water
   column compared to the base run concentrations
   for both  short-term and long-term simulations.
   Table 4.7.1 lists annual average concentrations
   for the water  column carbon solids and the
   percentage change of the water column carbon
   solids concentrations  due to the increase and
   decrease in the primary production for both short-
   term  and long-term  simulations.    Primary
   production has significant and almost instant
                                              444

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                      Jan-94  May-94  Aug-94  Dec-94  Apr-95  Aug-95 Dec-95

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lakewide average
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BIC concentration (mg/L)
lakewide average
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1994 2004 2014 2024 2034 2044 2054
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1994 2004 2014 2024 2034 2044 2054
year
Figure 4.7.2.  Long-term (1994-2055) variations of lake-wide (Green Bay included) organic carbon
concentrations for ± 50% primary production changes without adjusting settling and resuspension
rates.
                                            446

-------
Table 4.7.1. Annual Average Concentrations of Water Column Carbon Solids and Annual Average
Change in Percentage for Water Column Carbon Solids Concentrations Resulting From the LM2-Toxic
Model Runs for Both the Short-Term (1994-1995) and the Long-Term (1994-2055) Simulations With 50%
Increase and 50% Decrease of the LM3-Eutro Produced Primary Production
Carbon Solids
(Short-Term
Simulations)
DOC
BIG
PDC
Concentration
(mg/L) From
Original Base
1.48
0.034
0.129
Concentration
(mg/L) From
the Run With
50% Reduction
1.42
0.025
0.095
Annual
Average
Percentage
(%) Change
-4
-27
-26
Concentration
(mg/L) From
the Run With
50% Increase
1.55
0.042
0.159
Annual
Average
Percentage
(%) Change
5
23
23
 Carbon Solids (Long-Term Simulations)
DOC
BIG
PDC
1.23
0.034
0.129
0.96
0.025
0.088
-22
-27
-32
1.47
0.042
0.162
20
23
26
    impact on the level of BIG and PDC concentration
    in the water column.  Because of much larger
    initial  DOC inventory in  the  lake  and slower
    degradation process from  PDC  to DOC, the
    impact on DOC concentration  in the lake due to
    the changes of primary production  was not as
    evident as on BIG and PDC in the early portion of
    the long-term simulation period.

 2.  Figure 4.7.3 shows that, compared with the base
    run, there is very little difference in the PCB water
    column concentrations generated from the runs
    with 50%  decrease  and  a 50%  increase in
    primary production for the short-term simulation.
    Table 4.7.2 provides detailed information on
    individual mass fluxes in Lake Michigan and PCB
    inventories of  both water column and surficial
    sediment for the two-year (1994-1995) sensitivity
    analyses. The decrease  and  increase in the
    settling PCB mass flux due to the 50% decrease
    and the 50% increase in primary production were
    compensated  by the  increase and decrease in
    the gross volatilization mass flux, respectively.
    This keeps the PCB water column inventories
    predicted from these two runs very close to the
    inventory generated from the short-term  base
    runs.
3. The long-term, steady-state PCB concentrations
   in the water column was not significantly different
   from the base run concentrations (Figure 4.7.4).
   Interestingly, PCB concentrations from the long-
   term sensitivity runs started out almost identical
   and deviated with each other toward the end of
   the simulation.  Table 4.7.3 provides  detailed
   information on  individual PCB mass fluxes in
   Lake Michigan and inventories of both the water
   column and the surficial sediment for the last two
   years  of  the  long-term  (62-years)  model
   simulation. Similar to the short-term  simulation,
   PCB settling and net  volatilization fluxes were
   affected  the  most  by the increase and  the
   decrease  of  the  primary production.   The
   increase or decrease of the settling fluxes due to
   the increase or decrease in primary production
   was countered by the decrease or increase of the
   net volatilization fluxes.

In conclusion, the sensitivity  analyses illustrate that,
under  the   1994-1995  PCB  loading/boundary
conditions/other forcing functions, a 50%  increase or
decrease in primary production generated from the
LM3-Eutro does not have a  significant influence on
PCB concentrations in Lake Michigan for both short-
                                             447

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                                          base run - constant condition
                                          decrease primary production by 50%
                       1994    2004
                      2014
2024
year
2034   2044   2054
Figure 4.7.4.  Long-term (1994-2055) variations of lake-wide (Green Bay included) PCB28+31 (dissolved
+ particulate) concentrations for ± 50% primary production changes without adjusting settling and
resuspension rates.
Table 4.7.3. PCB28+31 Mass Fluxes and Inventories for Lake Michigan System Results From the LM2-
Toxic Sensitivity Analysis on Primary Production For the Last Two Years of the Long-Term (62-Year
Period: 1994-2055) Simulations
 PCB Mass Fluxes, kg/(Two Years) and
 Inventories, kg
                         Original Base Run   50% Reduction   50% Increase
Loads
Settling
Resuspension
Burial
Water Column Inventory
Sediment Inventory
Diffusion
Absorption
Gross Volatilization
Net Volatilization
Export to Lake Huron
Chicago Diversion
113.97
43.80
25.17
21.98
33.78
130.74
0.52
263.07
355.33
92.26
0.69
0.07
113.97
32.47
23.42
22.39
31.21
133.13
0.70
263.07
365.30
102.23
0.58
0.06
113.97
51.94
24.68
21.81
35.71
129.71
0.46
263.07
346.47
83.40
0.77
0.07
 Mass Gain/Loss in Water Column
                                                2.84
                                                  2.73
                                     2.93
                                            449

-------
term and long-term forecast scenarios.  Table 4.7.4
lists the model-generated average PCB inventories
resulting from the sensitivity runs for both the water
column  and  the surficial sediment layer and the
percentage change of the average PCB inventories
in  both  compartments  due to the  increase and
decrease in primary production.  The inventories are
the average PCB inventories for the two-year period
of the short-term (1994-1995) simulation and the last
two-year period of the long-term simulation.  The
largest percentage change of PCB inventories due to
the 50% increase or decrease in primary production
goes to the PCB inventory in the water column at the
end of the long-term  simulation  period and is in the
range of 5-8%. The impact on PCB inventory of the
surficial sediment layer due to the changes of primary
production is very small for both the short-term and
the long-term periods.

4.7.2  PCB Loads Sensitivity

The variation in the outcomes of  PCB concentrations
in both the water column and sediment from the LM2-
Toxic  model  can  be very significant  due to the
uncertainty of PCB  loads used as input.  The
uncertainty of the LMMBP-generated  PCB loads
could  be  due to sampling  approach, analytical
method, interpolation algorithm  used for estimating
the loads,  and loads  that were  missed  or not
considered.

There  is evidence  (Wethington and Hornbuckle,
2005)  that  an additional  input  of  PCBs  was
contributed from the local  Milwaukee atmosphere
through vapor-water exchange and wet and dry
deposition to Lake  Michigan that were not accounted
for in the LM2-Toxic model. The  combined additional
PCB source from the Milwaukee regional atmosphere
was  estimated to be at least 120 kg per year
(Wethington  and Hornbuckle, 2005). It  is possible
that loads in other areas of the basin could  have
been missed, such as Green Bay.

Another potential unaccounted  PCB source to the
lake  is the  load  associated with  very large
atmospheric particles.  These are  particles with a
diameter greater than 10 urn and settling velocities
greater  than 7.4 cm  s'1.  Although there is some
disagreement among experts in the field regarding
the magnitude of PCB loads to the lake via the large
particles, studies indicate that PCB dry deposition
associated with large particles could be a significant
PCB source to the lake (Miller era/., 2001; Franz et
al., 1998; Holsen et al.,  1991).  The annual PCB
inputs  from the atmosphere through the coarse
particles could be in a range of 320 kg/year to 5,500
kg/year (data provided from the LMMBP atmospheric
working group;  Wethington  and Hornbuckle, 2005;
Franz era/., 1998; and Holsen et al. 1991) during the
period  of  1989-1995.  However, the science and
technique is not  well-developed enough to make
reliable over-lake estimates of these fluxes. Much of
the uncertainty in measuring large particle fluxes
comes from the difficulty in quantifying how far these
large particles travel from their source to the lake.

Model runs were designed to evaluate the impact of
potential missing loads on  the model  outputs.  A
sensitivity analysis was completed for the Milwaukee
load by adding 120 kg/year of PCB load into segment
1 in the LM2-Toxic model.   Additional simulations
were run to gain insight into how the model would
respond to increasing the total PCB load (tributary
load +  atmospheric load) by 50% and  100%.  The
results  from  the sensitivity analysis  were  then
compared with the LM2-Toxic model long-term (62
years) base run results (Figure 4.7.5). Compared to
the steady-state concentration from the long-term
base run, the simulation showed total PCBs in the
water  column  increased less  than 5% for the
suggested 120 kg/year missing PCB source from the
Milwaukee atmosphere.  An increase of 15% and
30% was found for the simulations  where the  PCB
load was increased by 50% and 100%, respectively.
The amount of increase in total PCB concentrations
was much less during the first five years of the
simulation than during the steady-state period.  This
indicates that, under the current conditions in the
Lake Michigan system, the LMMBP-generated PCB
loads were not the dominant PCB flux controlling the
concentration of PCBs in the lake.  When the load
was doubled, the PCB concentrations  in the water
column in the first few years only increased about
15%.    Part 4,  Chapter 6 provides  quantitative
analyses and in-depth discussions on the critical
sources and sinks and  important environmental
processes for PCBs in Lake Michigan.
                                              450

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Table 4.7.4. PCB28+31 Average Inventories of Water Column and Surficial Sediment Results From the
LM2-Toxic Simulations for the Primary Production Sensitivity Analysis, and Changes in Percentage
for These Inventories Compared to the Inventories From the Original Base Runs
Simulations)
Water Column
Surficial Sediment
PCBs (Long-
Term
Simulations)
Water Column
Surficial Sediment
Inventory
Original Base
70.79
808.76

33.78
130.74
Inventory (kg) From
Reduction in
Primary Production
69.67
797.63

31.21
133.13
Percent (%)
Change
-1.6
-1.4

-7.6
1.8
Inventory (kg) From
the Run With 50%
Increase in Primary
Production
71.41
819.53

35.71
129.71
Percentage
(%) Change
0.88
1.3

5.7
-0.79
              0.30
            ^0.25-
           w 0.20-
           03
            8 o.isH
            c
            o
            o
           CD 0.1 OH
           O
           0.
           I 0.05 -\
100% more total PCB load
50% more total PCB load
120 kg/yr more atmospheric load from Milwaukee
base run - constant condition
LMMBP data -1994-95; EEGLE data 2000
           •<- '-} v V V V V" V" V* V"
0
1994
                         2004
      2014
2024
year
2034
2044     2054
Figure 4.7.5. Sensitivity analysis of the LM2-Toxic predictions to varying PCB loads. Note: LMMBP
data - 1994-1995 = 0.259 ± 0.172 ng/L; EEGLE data 2000 = 0.165 ± 0.029 ng/L.  The model output
concentrations and field data in this graph are lake-wide average concentrations.
                                         451

-------
References

Franz, T.P., SJ. Eisenreich, and T.M. Holsen. 1998.
   Dry  Deposition of Paniculate Polychlorinated
   Biphenyls and Polycyclic Aromatic Hydrocarbons
   to Lake  Michigan.    Environ.  Sci.  Technol.,
   32(23):3681 -3688.

Holsen, T.M., K.E. Noll,  S. Liu, and W. Lee.  1991.
   Dry Deposition' of Polychlorinated  Biphenyls in
   Urban Areas. Environ. Sci. Technol., 25(6):1075-
   1081.
Miller,  S.M.,  M.L. Green, J.V. DePinto,  and K.C.
    Hornbuckle.   2001.   Results  From  the Lake
    Michigan  Mass Balance Study: Concentrations
    and  Fluxes   of  Atmospheric  Polychlorinated
    Biphenyls and frans-Nonachlor.  Environ. Sci
    Technol.,  35(2):278-285.

Wethington,  D.M. and K.C.  Hornbuckle.  2005.
    Milwaukee,  Wl, as a Source  of Atmospheric
    PCBs to Lake Michigan.  Environ. Sci. Technol
    39(1):57-63.
                                             452

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                                         PARTS
                                   LM FOOD CHAIN
                                          Xin Zhang
                                 Welso Federal Services, LLC
                                 Large Lakes Research Station
                                       9311 Groh Road
                                  Grosse lie, Michigan 48138
Chapter 1.  Executive Summary

This part of the report documents the food web
bioaccumulation  model  developed for the Lake
Michigan  Mass Balance Project  (LMMBP).  The
model established dynamic relationships between
polychlorinated biphenyl (PCB) concentrations in the
exposure environments and result PCB levels in the
fish food webs of Lake Michigan.  The primary
objective  of this  work was to provide practical
modeling tools to predict toxic  PCB levels  in lake
trout and coho salmon in response to projected water
quality improvements for the lake.

The model was based upon available theory and data
characterizing the bioaccumulation of toxic chemicals
in fish and other aquatic organisms.  A detailed
description of the model development is provided in
the document.  Extensive collections of data on lake
trout and coho salmon food webs in Lake Michigan
were conducted to facilitate refinements  of model
parameters  to  site-specific conditions.    Forty
congeners  or  congener  groups  of  PCBs were
targeted  for the model calibration or parameter
refinements.  These  PCB congeners represented
toxic  chemicals  covering  a  wide   range  of
hydrophobicity.
The food web model was calibrated with PCB data
collected in 1994 and 1995 for three lake trout food
webs  at Sturgeon  Bay,  Sheboygan  Reef, and
Saugatuck. The lake trout sub-populations in these
three biota zones were believed to be  appropriate
representations of lake trout  in  Lake Michigan.
Model calibration was also performed for a lake-wide
coho salmon food web. During the model calibration,
model  parameters were  refined  to  achieve an
adequate agreement between model calculationsand
observed PCB data for a food web.  In this study, the
focus  of  model calibration was not limited to top
predators nor to toxics with certain hydrophobicity.
The model parameters were systematically optimized
for all  species at various trophic levels and for PCB
congeners of a wide range of hydrophobicity. Extra
care was taken to ensure  the  refined parameter
values  were consistent with the hydrophobicity of
individual  PCB congeners and with  the  trophic
position of individual species. Satisfactory calibration
results were achieved for the lake trout food webs at
Sturgeon Bay and Saugatuck.  Although no formal
validation  was   possible  due   to   additional
requirements of large amount  of  PCB congener-
specific field data, the calibrated food web models for
Sturgeon Bay  and Saugatuck  were confirmed in
some  degree by the  identical values of calibrated
parameters between these two models.
                                            453

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The availability of a complete account of observed
data for each food web made this model calibration
probably the most thorough process among similar
efforts. Although PCB concentrations in lake trout or
coho  salmon  was  the  endpoint  of  the model
computation  and  the  focus  of  most  model
applications, we believe that the food web model with
parameters "fine-tuned" for  species at all trophic
levels can be used to target any desirable species in
the food web with a high degree of confidence. Also,
the food web model  was capable of assimilating
toxics with various hydrophobicities.  In fact, no food
web  model intended to simulate  as  many  toxic
chemicals  with  diverse hydrophobicity has been
previously developed.

We believe that the  food web  model capable of
simulating congener-based PCB dynamics in fish
food webs provided a useful tool for the development
of more  effective  load  reduction  plans  or  total
maximum daily loads (TMDLs) targeted to priority
PCB congeners, instead of traditional category-based
load reduction plans targeted to various contaminant
sources.  The results of the PCB congener-based
model simulations also help to  better understand
toxic chemical behavior in food webs.

The calibrated food web model was used to perform
several model simulations for PCBs in lake trout food
webs at Sturgeon Bay and Saugatuck. These model
simulations depicted dynamic responses of individual
PCB congeners in the food webs to different PCB
exposure  input.    Hypothetical  long-term  PCB
exposure  scenarios  for the  food  webs  at the
Sturgeon  Bay  and Saugatuck biota  zones  were
generated by the  water quality  model LM2-Toxic
corresponding to different management choices for
the reduction  of PCBs  into  the Lake  Michigan
ecosystem.

For each  long-term  PCB exposure input, similar
model responses were observed for these two biota
zones. As an example,  the temporal responses of
individual  PCB  congener-based  concentrations in
adult lake trout at Sturgeon  Bay associated with
constant external PCB loadings are presented for
discussion. The expected total PCB concentrations
in  adult lake trout at Saugatuck in response to
various hypothetical PCB exposure inputs are also
presented.  Given the exposure PCB concentration
time functions provided by the LM2-Toxic, the model
simulations   suggested  that  without  further
intervention, the total PCB concentrations in adult
lake trout (5.5 year-old) was expected to  reach the
target level of 0.075 ppm in 2026 for Saugatuck biota
zone and in 2032 for Sturgeon Bay biota zone.
                                             454

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                                          PARTS
                                    LM  FOOD CHAIN
Chapter 2. Recommendations

Through the  course  of  the development  and
calibration of the food web models, every effort was
made to make full use of available information and to
make necessary simplifications and assumptions to
achieve a balanced progress without sacrificing the
overall quality in the food web modeling. The models
described in this part was believed to represent the
best available knowledge for the  management of
congener-specific  PCB  contaminants  in  Lake
Michigan food webs.  However, additional works are
needed to further improve the validation and to test
the applicability of the models to other hydrophobic
chemicals.  There are also  research  needs that we
believe are  essential for the improvement of the
performance of the fish models.

5.2.7 Additional Model Validations

Additional model  validation should  be  conducted
once sufficient new field data sets become available.
One of the additional model validation would be to
test the validity of the models for PCB concentrations
in Lake Michigan food webs after 1994-1995. This
exercise   would   include  calculating   PCB
concentrations  in fish  using  the  new  field data
collections  of PCB  concentrations  in  water  and
sediment, comparing the model calculated data with
actual measured PCB concentrations in fish,  and
evaluating model performance in reproducing future
PCB concentrations in Lake Michigan fish.

Apparently, model validation is a continuous process.
Each validation  exercise  only addresses  model
performance under a  set of specific conditions or to
a  type  of  application.     For   example,   the
aforementioned validation exercise, once achieved,
indicates that the models can be used as a practical
tool  to make quantitative prognoses about PCB
contaminants in particular Lake Michigan food webs.

In order to test  models'  applicability to  other
chemicals, model validation can also be extended to
frans-nonachlor  in  Lake  Michigan.   There  are
adequate field data sets available to carry out this
validation study.  The values of chemical-specific
model  parameters  for  frans-nonachlor  can  be
deduced from the correlations between parameter
values and log Kow values of the contaminants, which
were established based on model calibrations for
PCB congeners.

5.2.2  Model Applications

It is desirable to examine the capability of the fish
models to reproduce archived fish PCB data for Lake
Michigan. This can be done with the reconstruction
of historical PCB concentrations  in  water  and
sediments by a fate and transport model (such as the
LM2-Toxic) based on dated sediment core profiles for
PCBs.  Model estimates of past PCB concentrations
in various fish species can then be made from the
reconstructed concentrations in water and sediments.
Once completed satisfactorily, the  results  of this
model study may provide a better baseline for PCB
load reduction analysis.
                                             455

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5.2.3 Future Improvements

The following were  some  of the  research  needs
identified to be important for the improvement of the
model quality.

A. Additional  studies   for  the  refinement  of
   bioenergetic information and growth data for
   species in lower trophic levels of the food webs,
   such as zooplankton, Mysis, deepwater sculpin,
   and slimy sculpin in Lake Michigan.

B. Field measurement of the moisture contents of
   zooplankton, Mysis,  and Diporeia at each biota
   zone in Lake Michigan. These data will help
   reduce   model   uncertainty  associated  with
   erroneous estimates of  water content  while
   converting dry weight-based data to wet weight-
   based.

C. More  studies  on  correlations between the
   chemical assimilation efficiency and Kow values of
   the chemicals, and on species dependency of the
   chemical assimilation efficiency.

D. Investigation of possible PCB metabolism in Lake
   Michigan fish, and the kinetics of the processes.

E. More data collections for possible refinement of
   the   model   descriptions  for  fish  dietary
   compositions in Lake Michigan.
                                             456

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                                          PARTS
                                    LM FOOD CHAIN
Chapter 3.  Model Description

A food web bioaccumulation model usually consists
of a series of submodels characterizing chemical
bioaccumulation in each of the aquatic organisms in
the food web.  The individual submodels are linked
together through feeding interactions among the
organisms. The food web model (LM Food Chain)
constructed for the  Lake Michigan Mass Balance
Project (LMMBP)  is based  on  Version 5.2  of the
computer model FDCHAIN  which was originally
developed by HydroQual, Inc., Mahwah, New Jersey.
The original model and its early  versions have been
previously applied in numerous projects including the
Green Bay Mass Balance  Food  Chain Modeling
project (Connolly et al., 1992). Several additions
and modifications have been made to enhance the
performance of the food  web model.  They include
the introduction of a multi-compartment approach to
better accommodate the spatially variable conditions
in Lake Michigan, refinements of certain parameters
to reflect advancements of knowledge in related
disciplines, the incorporation of a new submodel for
chemical bioaccumulation of benthic invertebrates,
and the integration of alternative modeling equations
for species-specific parameters  that are not readily
obtainable. The following is a detailed description of
submodels used in the LM Food Chain for simulating
organic chemicals in individual fish and in organisms
of lower trophic levels of fish food webs.

5.3.1  Chemical Bioaccumulation in Fish

The model is a set of equations derived using the
principle of  mass  conservation.   It is  generally
accepted  that  the primary processes of chemical
exchange  between  a  fish  and  its  exposure
environment are: 1) chemical uptake from water, 2)
chemical uptake from food sources,  3)  chemical
elimination due to respiration and excretion, and 4)
chemical concentration reduction by growth dilution
(Figure  5.3.1).     The  submodel  for  chemical
bioaccumulation in fish can then be derived based on
a simple mass balance equation for chemicals in the
fish.  The general form of the mass balance equation
is well-defined.   The rate of change in  chemical
concentration in a fish (dCF/dt) is equal to the sum of
the relevant chemical fluxes into and out of the fish.
dCF/dt = F

where
w
                Fp - F0  - Fg
                            (5.3.1)
dCp/dt =  chemical increment in fish per unit time
          (ug/kg/day)

Fw    =  flux of  chemical  uptake  from  water
          (ug/kg/day)

Fp    =  flux of chemical uptake from prey items
          (Mg/kg/day)

Fe    -  flux of chemical elimination via respiration
          (ug/kg/day)

Fg    =  flux of chemical  reduction by  growth
          dilution (ug/kg/day)

In some cases, other chemical fluxes, such as flux
associated with the chemical elimination through
metabolism transformation in the organism, may also
need to be included in Equation 5.3.1.  In this study,
                                             457

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                                                              dilution by growth
          uptake from prey
                \
                 F
                                                                     elimination
                uptake from water           (via respiration and excretion)

Figure 5.3.1.  Primary chemical exchange processes between a fish and its environment.
we  assumed that  metabolism transformation  of
polychlorinated biphenyl  (PCB) contaminants was
negligible (Gobas, 1993; Stapleton etal., 2001; U.S.
Environmental Protection Agency,  1999).

After construction of the mathematical description for
each of the chemical fluxes in the mass balance
equation, the chemical concentration in the fish CF
(ug-chem/kg-body) at time t + At was then calculated
by numerical integration:
          = CF(0  + [dCF(t)/dt]
(5.3.2)
To predict chemical bioaccumulation for top predator
fish, the mass  balance  equation  was repeatedly
applied to organisms at each trophic level to simulate
chemical biomagnification from forage species to top
predators.

Several methods have been developed to describe
chemical dynamics in  fish  and to estimate  related
chemical fluxes in the mass balance equation. In this
food web  bioaccumulation model,  the  chemical
dynamics were described  based on fish bioenergetics
(Lantry and Stewart, 1993; Rudstam, 1989; Rudstam
ef a/.,  1994; Stewart et ai, 1983; Stewart and
Binkowski, 1986).  The mathematical equation used
to estimate the chemical fluxes in the mass balance
equation are described below.
         5.3.1.1 Chemical Uptake From Water

         The chemical flux entering an organism from water
         via gill ventilation (Fw) is expressed as a product of
         the fish's ventilation rate and the dissolved chemical
         concentration  in  water.   The  extent to  which
         chemicals that enter the gill compartment by gill
         ventilation and are actually absorbed by the fish is
         usually  expressed by  the  chemical gill  transfer
         coefficient, Ec, which is included in Fw.
= E. •  AC.
(5.3.3)
         where
         Ec =  chemical gill transfer coefficient

         Kv =  gill ventilation rate (L-water/kg-fish/day)

         Cw -  dissolved chemical  concentration in water
               (ug-chem/L-water)

         The gill ventilation rate of a fish (Kv) is dependent on
         the amount of oxygen required by the fish to sustain
         its normal respiration (R0) and the oxygen content in
         the water that passes through the gill membrane.

                      •  [O2])                     (5.3.4)
                                              458

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where

R0   =  rate of oxygen uptake from water, or fish
        respiration rate (mg-O2/kg-fish/day)

E0   =  oxygen gill transfer coefficient

[OJ =  oxygen content in water (mg-O2/L-water)

Similarly, the  oxygen  gill  transfer coefficient,  E0
reflects the extent to which oxygen that enters the gill
compartment by gill ventilation is actually absorbed
by the fish. The value of R0, which is expressed in
terms of  oxygen  consumption,  can usually  be
calculated using a bioenergetics model (Hewett and
Johnson, 1989). Oxygen content in water [O2] was
estimated as a function of water temperature based
on  an empirical equation for oxygen saturation in
water (Greenberg etal., 1992).

Substituting  Equation 5.3.4 into Equation 5.3.3, the
chemical flux via gill uptake  from  water (FJ then
follows as
 Fw = (EC/E0)  •  (R0/[02])  •  Cw

 5.3.1.2 Chemical Uptake From Prey
                                         (5.3.5)
The chemical flux absorbed by fish from diet (Fp) via
the gastrointestinal tract is expressed using the food
ingestion  rate  of the  fish  (Kf) and  chemical
concentration  in its diet (Cp).  The extent to which
chemicals in the diet  are actually absorbed by the
fish can be expressed by the chemical assimilation
efficiency a, which is included in F .
 FP =
where
                                         (5.3.6)
 Of  =  chemical assimilation efficiency

 K,  =  food ingestion rate (g-prey/g-body/day)

 Cp  =  chemical concentration in prey (ug-chem/g-
       food)

 The chemical concentration in the diet (Cp) is based
 on diet composition and chemical content in each
 prey component.   The  food  ingestion rate  is
 determined by an energy balance. The energy intake
from food sources is equal to the energy expenditure
of the fish for respiration and growth:

(K, • Dp)  •  p = R •  DF + G •  DF       (5.3.7)

where

Dp =  energy density of prey (kJ/kg-prey)

DF =  energy density of the fish (kJ/kg-body)

R =  fish  respiration rate (kg-fish/kg-body/day)

G =  fish  growth rate (kg-fish/kg-body/day)

/? =  fraction of ingested energy that is assimilated

R can usually be calculated using a fish bioenergetics
model (Hewett and Johnson, 1989).   G can  be
estimated by individual fish weight-age relationships.
The  energy density (DF and Dp) can be estimated
from the lipid and protein content of the fish and prey.
Substituting Equation 5.3.7  into Equation 5.3.6, the
flux of chemical uptake v/afood consumption, Fp, can
be formulated as follows:
                                                                 (DF/Dp) •  (fl+G)  •  Cp
                                                   5.3.1.3 Chemical Elimination Via Gills

                                                   The flux of chemicals eliminated by a fish via the gills
                                                   is expressed  as a product of gill elimination rate
                                                   constant, Ke,  and chemical  concentrations in the
                                                   organism, CF:
CF
                                         (5.3.9)
                                                  where
                                                  Ke =  gill elimination rate constant (1/day)

                                                  CF =  chemical concentration in  organism  (ug-
                                                         chem/kg-body)

                                                  Because the elimination is,  in essence, the reverse
                                                  process  of  gill  uptake,  the  gill  elimination  rate
                                                  constant can  be  related to the gill uptake  rate
                                                  constant.  If we view the  ratio of gill uptake and
                                                  elimination rate constants as the chemical partition
                                                  coefficient between the body  tissue and aqueous
                                               459

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phases of the organism, the gill elimination rate
constant can then be derived as
                        fL •  n)        (5.3.10)

where

Ec =   chemical gill transfer coefficient

Kv =   gill ventilation rate (L-water/kg-body/day)

p  =   aqueous phase density of the organism (kg/L)

fa  =   non-lipid fraction of the fish

fL  =   lipid fraction of the fish

n  =   chemical  partition coefficient between lipid
       and non-lipid phases of the organism

Substitution of Equation 5.3.10 into Equation 5.3.9
yields an equation for estimating the flux of chemicals
eliminated from the fish via gill ventilation:
= CF  • (Ec • Kv)
                              /i. • n)    (5.3.11)
For most organic chemicals, gill elimination is a major
mechanism of chemical discharge from fish (Gobas
ef a/., 1989).   Fecal elimination and excretion of
chemicals are not specifically modeled in this mass
balance equation. Their contribution can be viewed
as having been factored  into the food of chemical
assimilation efficiency and gill transfer coefficient.

5.3.1.4  Chemical Dilution by Growth

Fish growth results is an increase of the fish volume
and a reduction of chemical  mass per fish volume.
The equivalent flux of chemical loss due to  fish
growth (Fg) is expressed as  a product of the  fish
growth rate (G) and chemical concentration  in the
fish (CF).

Fg = G • CF                          (5.3.12)

where

G  =  growth rate of organism (1/day)

CF =  chemical concentration in fish (ug-chem/kg-
       body)
The fish growth rate (G) was estimated based on fish
weight-age relationships established for each fish
species.

5.3.2  Chemical Bioaccumulation in the
Base of Food Webs

The aquatic species at the base of the Lake Michigan
food web are zooplankton (pelagic) and Diporeia
(benthic). The modeled equations discussed above
for individual fish can not be applied to zooplankton
and Diporeia due to the lack of  species-specific
bioenergetics  data.    Alternative  submodels are
needed for chemical  bioaccumulation in the base of
the food webs.

5.3.2.1     Chemical   Bioaccumulation  in
Zooplankton

Zooplankton in the Lake Michigan food webs are a
mixture of a wide variety of species.  The species
composition of the zooplankton is not fixed.  It varies
with season depending on the optimal temperature
for  the growth  of individual  species.   It is also
dependent on prey selections of its predators in a
given food web.  At this stage, it is unfeasible to
develop a kinetic submodel for this species group
due to the lack of appropriate  information.

For simplicity, a  steady-state model was adapted in
our food web models to calculate concentrations in
Lake   Michigan   zooplankton.   In this  chemical
bioaccumulation  submodel,   zooplankton  were
assumed to be  a homogeneous  pseudo-species.
Under steady-state, the chemical mass  balance
Equation 5.3.1 can then be expressed as

FW  +  FP - Fe ~ F9 = °                  (5.3.13)

The parameters in this equation  have the same
definition as those in the fish submodel.  Substituting
Equations  5.3.3,  5.3.6,  5.3.9, and  5.3.12 into
Equation  5.3.13,  the  chemical  concentration in
zooplankton  Cz  can then be calculated  by the
following equation.
                                                                                 G)   (5.3.14)
                                               460

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5.3.2.2 Chemical Bioaccumulation in Diporeia

There are several experimental studies on chemical
uptake from sediments by Diporeia (Landrum, 1989;
Landrum etal., 1985).  Because most of the studies
were  conducted  under   controlled  laboratory
conditions,  the  kinetics  of  chemical  exchange
between Diporeia and its environment derived from
these studies can not be readily transformed into a
kinetic model applicable to a real system. The lack
of information on site-specific growth data and the
difficulty in  characterizing the  surface  sediment
portion that is actively selected by Diporeia as a food
source also hindered the development of a kinetic
model for chemical bioaccumulation in Diporeia.

The submodel  for  chemical bioaccumulation in
Diporeia used in this food web model was based on
a published steady-state model for benthic animals.
This model,  introduced by Morrison et al.  (1996),
assumes that under a steady-state condition the total
chemical intake flux from water (Uw) and food (Ud) by
a benthic animal equals the total chemical elimination
flux from the animal via gill (Dw), faeces (D(), and
metabolism (DJ:

Uw+Ud=Dw+ Df + Dm               (5.3.15)

For detrivores, this assumption yields the equation:

(V g = [Ew • Gw • (fw/fs) +  Ed • Gd • (fd/fs)
       DSd-OCd
I[EW-GW
                                -3)
      + vb'Kn,'Ktw\                   (5.3.16)

where

fb  = chemical fugacity in benthos, Pa

fs  = chemical fugacity in sediment, Pa

fa  = chemical  fugacity  in  diet (sediment  or
      suspended particles), Pa
Gw  =  gill ventilation rate, L/day

Ed   =  chemical assimilation efficiency from diet

Gd   =  food ingestion rate, L (wet volume)/day

DSd =  density of diet (wet),  kg/L

OCd =  organic carbon  fraction of diet  on  wet
        weight base

Koc  =  organic carbon-water partition coefficient,
        L7kg

cr   =  organic carbon assimilation efficiency

/?   =  fraction of ingested diet absorbed

Vb   =  volume of benthic animal, L

km   =  chemical metabolic transformation rate in
        benthic animal, 1/day

Kbw  =  benthos-water   partition  coefficient   of
        chemicals, L/L

With mathematical manipulation and necessary  unit
conversion, the chemical fugacity terms (fb/fs), (fw/fs)
and (fyfs) in this model equation can be replaced by
some more readily available chemical parameters.
The  submodel for chemical  bioaccumulation in
benthic animals can then be expressed as:


CB - (EW-GW-CW  + Ed- GD-CD) • Lb-Kow/

   /[EW-GW-1000 + Ed-(1  -a)(1 - P)

   •
-------
Wb   =  body weight of fresh benthic animal, gram

Lb   =  lipid fraction in fresh benthic animal

5.3.3  Model  Description  of  Exposure
Environment

Calculations  in the  submodels  discussed  above
require  information  which   characterizes  the
environmental conditions for individual organisms,
such as environmental temperature, oxygen content,
and  the contaminant levels in  water (for  pelagic
species) and sediment (for benthic species).  These
data are essential for application of a  food  web
bioaccumulation model.

However,  among all existing aquatic food  web
models the environmental condition of a food web is
typically defined with a single spatial compartment.
This makes no distinction of preferred living condition
among  individual organisms  and implies  that ail
organisms  in  a  food  web  live in  an  uniform
environment.   This  simplified  model approach  is
adequate for food webs in shallow and small water
bodies where gradients are relatively small, and thus
the  exposure  environments are expected to be
similar among organisms in different trophic levels on
a seasonal basis. However, for food webs in a large
aquatic  system, such as Lake Michigan, the single
spatial compartment approach for defining exposure
environment of a  food web may not be adequate.

In Lake Michigan,  the  spatial  variation in water
temperature can  be substantial, especially during
summer stratification (Ayers, 1962; Brandt et al.,
1991; Carr,  1973; Sommers ef al.,  1981).  As a
result, organisms in the lake are exposed to different
temperatures depending on individual temperature
preferences (Brandt et al., 1980; Otto et al., 1976).
Species living  in  surface  water  are exposed  to a
temperature that varies dramatically from season to
season. Species  living in deep water are exposed to
a relatively  stable  temperature.  There are  also
species that prefer different environments at different
life  stages.  The exposure temperatures of these
species are  expected to vary  by age (Lantry and
Stewart, 1993; Stewart and Binkowski, 1986).  It is
therefore,  possible for  a  food  web  to consist  of
predators  and prey that  have  different exposure
temperatures.  It appears that existing food  web
model frameworks are not adequately formulated to
accommodate the differential exposure temperatures
among organisms in Lake Michigan food webs.

To better  represent the exposure environment for
each component of a food web and thus, to reduce
the associated uncertainties in model estimates, a
multi-compartment approach was introduced in the
food web model framework. Unlike the original single
compartment modeling approach which models the
exposure condition as a homogeneous one for the
whole food web, the multi-compartment approach
allows  modelers to define the exposure conditions
individually for each organism with separate spatial
compartments.  Each compartment can be assigned
organism-specific parameters which  reflect the
environmental condition of the preferred location of
the associated organism.  The temporal variation of
the preferred  location of the  organism  can be
represented  by the corresponding change in the
parameters of the compartment over time.  Figure
5.3.2 provides the conceptual diagrams for both the
original single compartment approach and the new
multi-compartment modeling  approach.   For the
modified model approach, the differential exposure
temperatures among the  organisms in a food web
can be easily described by defining each organism
with an independent spatial compartment.

References

Ayers,  J.C.    1962.    Great  Lakes Water, Their
   Circulation   and   Physical   and  Chemical
   Characteristics.   In: H.J. Pincus (Ed.),  Great
   Lakes  Basin.  American Association for the
   Advancement of Science, Washington, D.C.

Brafield, A.E. and M.J. Llewellyn.   1982.  Animal
   Energetics.   Blackie and Son, Ltd., Glasgow,
   Scotland. 168 pp.

Brandt, S.B., J.J.  Magnuson, and  L.B. Crowder.
   1980.  Thermal Habitat Partitioning by Fishes in
   Lake Michigan.  Canadian J. Fish. Aquat. Sci.,
   37(7): 1557-1564.

Brandt, S.B., D.M. Mason, E.V. Patrick, R.L. Argyle,
   L. Wells, P.A. Unger, and D.J. Stewart.  1991.
   Acoustic Measures of the Abundance and Size of
   Pelagic Planktivores in Lake Michigan. Canadian
   J. Fish. Aquat. Sci., 48(5):894-908.
                                             462

-------
  forage fish 1

  zooplankton
                 predator fish
forage fish 2
forage fish 3

phytoplankton
       D = Ta(t)
  Homogeneous compartment
  with an average seasonal
  temperature profile for whole
  food web
           benthos
A. Single compartment approach for exposure temperature in
existing food chain models.
  Separate compartments
  with individualized
  seasonal temperature
  profile for each food
  web component
 B. Multi-compartment approach for exposure temperature in food
 chain models.

Figure  5.3.2.  Comparison   of   modeling
approaches for exposure temperatures in food
web models.
Carr, J.F.,  J.W. Moffett, and J.E. Gannon.  1973.
   Thermal Characteristics of Lake Michigan, 1954-
   1955.   U.S.  Bureau of Sport Fisheries  and
   Wildlife, Washington, D.C. Technical Paper 69,
   143pp.

Connolly, J.P., T.F. Parkerton, J.D. Quadrini,  S.T.
   Taylor,  and A.J. Turmann. 1992. Development
   and Application of PCBs in the Green Bay, Lake
   Michigan Walleye  and Brown Trout  and Their
   Food Webs.  Report to the U.S. Environmental
   Protection Agency,  Office of  Research  and
   Development,   ERL-Duluth,   Large  Lakes
   Research Station, Grosse He, Michigan.  300 pp.
Gobas,  F.A.P.C., K.E. Clark,  W.Y. Shiu,  and D.
   Mackay.     1989.     Bioaccumulation  of
   Polybrominated  Benzenes  and Biphenyls and
   Related Superhydrophobic Chemicals in Fish:
   Role  of Bioavailability and Fecal  Elimination.
   Environ. Toxicol. Chem., 8(3):231-247

Gobas, F.A.P.C. 1993. A Model for Predicting the
   Bioaccumulation   of   Hydrophobic   Organic
   Chemicals in Aquatic Food-Webs: Application to
   Lake Ontario.  Ecol. Model., 69(1/2):1-17.

Greenberg, A.E., L.S. Clesceri, and  A.D.  Eaton
   (Eds.).    1992.    Standard Methods for the
   Examination  of  Water and Wastewater, 18th
   Edition.  American Public  Health Association,
   Washington, D.C.  982 pp.

Hewett,  S.W.  and B.L. Johnson.  1989.  A General
   Bioenergetics  Model  for  Fishes.   American
   Fisheries Society Symposium, 6:206-208.

Landrum,  P.F.,  B.E.  Eadie,  W.R.  Faust,  N.R.
   Morehead, and M.J. McCormick.   1985.   The
   Role  of Sediment  in  the  Bioaccumulation of
   Benzo(a)pyrene  by the Amphipod Pontoporeia
   hoyi.   In:  M.  Cooke  and A.J.  Dennis (Eds.),
   Polynuclear   Aromatic   Hydrocarbons:
   Mechanisms, Methods and Metabolism, Eighth
   International Symposium, pp. 799-812. Batteile
   Press, Columbus, Ohio.

Landrum,   P.F.     1989.     Bioavailability   and
   Toxicokinetics    of   Polycyclic   Aromatic
   Hydrocarbons Sorbed to  Sediments for the
   Amphipod,  Pontoporeia  hoyi.   Environ.  Sci.
   Technol., 23(5):588-595.

Lantry, B.F. and D.J. Stewart.  1993.  Ecological
   Energetics of Rainbow Smelt in the Laurentian
   Great Lakes: An Interlake Comparison. Trans.
   Amer. Fish. Soc., 122(5):951-976.

Morrison, H.A., F.A.P.C. Gobas, R. Lazar, and G.D.
   Haffner. 1996. Development and Verification of
   a   Bioaccumulation   Model  for   Organic
   Contaminants in Benthic Invertebrates. Environ.
   Sci. Technol., 30(11):3377-3384.
                                             463

-------
Otto,  R.G., M.A.  Kitchel, and J.O. Rice.   1976.
   Lethal and Preferred Temperatures of the Alewife
   (Alosa pseudoharengus)  in  Lake Michigan.
   Trans. Amer. Fish. Soc.,  105(1):96-106.

Rudstam, L.G.  1989.  A Bioenergetic Model  for
   Mysis Growth  and Consumption Applied to a
   Baltic Population of Mysis mixta.   J. Plankton
   Res., 11(5):971-983.

Rudstam, L.G., P.P.  Binkowski,  and M.A.  Miller.
   1994. A Bioenergetic Model for Analysis of Food
   Consumption  Patterns  by  Bloater  in  Lake
   Michigan.  Trans. Amer. Fish. Soc.,  123(3):344-
   357.

Sommers, L.M., C. Thompson, S. Tainter, L.  Lin,
   T.W. Colucci, and J.M. Lipsey.  1981.  Fish in
   Lake  Michigan.  Michigan Sea Grant Advisory
   Program, Ann Arbor, Michigan. 38 pp.
Stapleton, H.M., R.J. Letcher, and J.E. Baker. 2001.
   PCB Metabolism in a Freshwater Fish. Environ
   Sci. Technol., 35(12):4747-4752.

Stewart, D.J., D. Weininger, D.V. Rottiers, and T.A.
   Edsall.   1983.   An Energetics Model for Lake
   Trout, Salvelinus namaycush: Application to the
   Lake  Michigan Population.  Canadian J. Fish.
   Aquat. Sci., 40(6):681-698.

Stewart, D.J. and F.P  Binkowski. 1986.  Dynamics
   of Consumption and Food Conversion by Lake
   Michigan  Alewives:  An  Energetics-Modeling
   Synthesis.  Trans. Amer. Fish. Soc., 115(5):643-
   661.

U.S.   Environmental Protection  Agency.   1999.
   Polychlorinated  Biphenyls  (PCBs)   Update:
   Impact on Fish Advisories.  U.S. Environmental
   Protection Agency, Office of Water, Washington,
   D.C. 7pp.
                                              464

-------
                                         PARTS
                                   LM FOOD CHAIN
Chapter   4.     Description   of   Data,
Constants,   and   Other   Information
Necessary to Run Model
5.4.7  Chemical
Contaminants
Properties  of  PCB
Polychlorinated  biphenyls  (PCBs)   have  been
recognized as significant environmental contaminants
since 1966 (Mullin et ai, 1984).  Their impact is
particularly evident  in  the  Great  Lakes  basin
(Neidermeyer and Mickey, 1976; Hesselberg et ai,
1990; Oliver et ai, 1989; Eisenreich et ai, 1989). In
this modeling  project, 40 PCB  congeners or  co-
eluters were targeted for simulation of their individual
bioaccumulation by fish in the lake. Most of the PCB
congeners were selected for their abundance and
bioaccumulative tendency in  the lake  ecosystem.
Other PCB congeners were included to make  the
targeted PCB  group  cover the full range  of PCB
hydrophobicity, and thus, a  better representative
subset of all existing 209 PCB congeners.

Hydrophobicity of a PCB congener is measured by its
octanol-water partition coefficient (Kow) which is  the
most  important  chemical   property  governing
bioaccumulation  of  the  congener in  organisms.
Another  important chemical  property  involved in
modeling PCB  contaminants is the organic carbon
partition coefficient (Koc) whose value can often be
correlated to that of Kow.  In this work, the following
empirical relationship (Eadie et ai, 1990) was used:
      = 1-94 + 0.72 logK0.
                  (5.4.1)
The targeted PCB congeners or co-eluter congeners
are listed in Table 5.4.1  with their octanol-water
partition  coefficients Kow.   The values of  Kow are
those of Hawker and Connell (1988). The molecular
weight (MW) for each PCB congener is also listed for
additional reference.

5.4.2  Site-Specific Data

5.4.2.1 Fish Food Web Structures

The structure of a food web shows how individual
organisms in the food web are related to each other
through feeding interactions. This dietary information
is  necessary for establishing appropriate linkages
among individual submodels of a food web model
and  is  important  to the accurate simulation of
chemical bioaccumulation in the food web.

The fish food webs of interest are those of two top
predators in Lake Michigan, lake trout  and coho
salmon.  These two species were selected for their
important economic value.  It is desirable to have a
better  understanding of the present and future
concentrations of PCB contaminants in these two fish
populations with the help of model simulations.

5.4.2.1.1 Lake Trout Food Web

It is believed that the lake trout in Lake Michigan are
represented by three subpopulations at Sturgeon
Bay, Sheboygan Reef, and Saugatuck (Figure 5.4.1).
Movements  of  lake trout  in  Lake Michigan are
believed  to  be considerably  restricted in range
(Brown  et  ai,  1981).   Each  of the lake trout
subpopulations  has  a  site-specific  food web
structure.
                                            465

-------
Table 5.4.1. Targeted PCB Congeners and Their K0
Congener

4
2,3
2,4'
3,4
3,4'
4,4'
2,4',4
2,2', 3
2,4,6
2,2', 5
2,3', 5
2,4,4'
2,4', 5
2',3,4
3,4,4'
2,2',3,4
2,2', 3,5'
2,24,5'
2,2',5,5'
2,3,3',41
2,3,4,4'
2,3',4,4'
2,3',4',5
2',3,4,5
2,4,4',5
3,3', 4,4
2,3>3',4',6
3,4,4',5
2,2', 3,4,5'
2,2', 3,3', 6
2,2', 3,5,5'
2,2',3,4,61
2,2',3,4,4'
2,2', 4,4, 5
2,2', 4,5,5'
2,3',4,4',5
2',3)4,41,5
2,2'I3,4',5',6
2,3,3', 4,4
2,2', 3,3', 4,6'
2,2',4,4,5,5'
2,2',3,5,5'J6
2,2',3,4,4I,5'
2,3,3',4',5,6
2,21,3,4',5,51
2,21,3,31,4,41,5
IUPAC
0
3
5
8
12
13
15
17
16
32
18
26
28
31
33
37
42
44
49
52
56
60
66
70
76
74
77
110
81
87
84
92
89
85
99
101
118
123
149
105
132
153
151
138
163
146
170
Homolog
0
1
2
2
2
2
2
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
5
4
5
5
5
5
5
5
5
5
5
6
5
6
6
6
6
6
6
7
Molecular Weight
154
188
223
223
223
223
223
257
257
257
257
257
257
257
257
257
292
292
292
292
292
292
292
292
292
292
292
326
292
326
326
326
326
326
326
326
326
326
361
326
361
361
361
361
361
361
395
log Kow
4.09
4.69
4.97
5.07
5.22
5.29
5.3
5.25
5.16
5.44
5.24
5.66
5.67
5.67
5.6
5.83
5.76
5.75
5.85
5.84
6.11
6.11
6.2
6.2
6.13
6.2
6.36
6.48
6.36
6.29
6.04
6.35
6.07
6.3
6.39
6.38
6.74
6.74
6.67
6.65
6.58
6.92
6.64
6.83
6.99
6.89
7.27
                                         466

-------
 Table 5.4.1. Targeted PCB Congeners and Their Kow (Continued)
 Congener
IUPAC
Homolog    Molecular Weight
logK0
2,3,3',4)41,5,61
2,2', 3,3', 4,5,5'
2,2', 3,3', 4,4', 6,6'
2,2', 3,4,4', 5,5'
2,2, 3,4,4', 5,&
2,2, 3,4', 5,5', 6
2,2', 3,3', 4,4', 5,6
2,2',3,3',4,5,51,6,6I
2,21,3,3',4,4',5',6
2>2',3,4>4',5,5',6
2,21,3,31,41,5,5'>6
190
172
197
180
182
187
195
208
196
203
201
7
7
8
7
7
7
8
9
8
8
8
395
395
430
395
395
395
430
464
430
430
430
7.46
7.33
7.3
7.36
7.2
7.17
7.56
7.71
7.65
7.65
7.62
Figure 5.4.1. Biota zones in Lake Michigan.
                                         467

-------
For each lake trout subpopulation, the food web was
constructed using dietary data compiled from field
sampling of  lake trout and associated  forage fish
population.   Lake  trout (Salvelinus namaycush)
were caught at the three locations during the spring,
summer, and fall of 1994 and 1995.   They were
primarily captured via gill  netting at depths ranging
from 9  to 40  m.  A  minor portion of trout was
captured by  bottom trawling.  Bottom trawling was
used at depths of 10 to 50 m to obtain  forage fish.
Prey fish included alewife (Alosa pseudoharengus),
rainbow   smelt   (Osmerus  mordax),  bloater
(Coregonus hoyi), slimy sculpin (Cottus cognatus),
and deepwater sculpin (Myoxocephalus thompsoni).
The  diets of  lake  trout and  forage  fish  were
determined by stomach analysis following a standard
operating  procedure  established   for  the  Lake
Michigan  Mass  Balance Project  (LMMBP)  (U.S.
Environmental Protection Agency, 1997a). For lake
trout, the diet components were further classified into
age classes.

The organisms in the base of Lake Michigan fish food
webs are zooplankton, Mysis, and  Diporeia.  Their
dietary  information  was  obtained  from  literature
sources. Mysis are reported to feed  on zooplankton,
phytoplankton, and  "fresh"  detrital  material at the
sediment surface and suspended in the water column
(Beeton  and Bowers,  1982; Grossnickle,  1982).
Zooplankton are believed to feed  on  organic-rich
particles, mainly  phytoplankton in the water column
(Peters and  Downing, 1984). Diporeia are reported
to feed on relatively "fresh" detrital  material at the
sediment surface (Evans etal., 1990; Gardner etal.,
1990;  Johnson,  1987;  Lydy and  Landrum,  1993;
Marzolf,   1965;   Quigley,   1988;   Quigley  and
Vanderploeg, 1991).

Annual average  dietary data for  lake trout and its
forage populations in the three biota zones of the
lake are summarized in Tables 5.4.2a through 5.4.7.
These data were used to construct a complete food
web structure for each  of the three  lake  trout
populations in Lake Michigan.

5.4.2.1.2  Co/70 Salmon Food Web

The coho salmon in Lake Michigan are believed to
move around large portions of the  lake during the
fish's lifetime (Patriarche, 1980). They were modeled
as a single  lake-wide  population.  The dietary
information of the coho salmon was compiled from
field  sampling.    Coho  salmon  (Oncorhynchus
kisutch) were sampled from  angler's catches at
various locations of the lake from May to November
in 1994 and April to November in 1995.

The diet of coho salmon was determined by stomach
analysis following a standard operating procedure
established for the LMMBP (Elliott etal., 1996; Elliott
and Holey,  1998;  U.S. Environmental Protection
Agency, 1997a).   The prey species were  further
classified  into  age  classes.    The  results are
presented in Table 5.4.8.

Due to their extensive movement, coho salmon in the
lake may encounter site-specific forage populations
in different regions. This means that a given forage
species in  the coho  salmon diet may belong to
different subpopulations. The forage fish may have
a location-dependent dietary history. Therefore, the
food web structure below the top trophic level can
vary with the movement of coho salmon. In order to
construct an accurate food web  structure for coho
salmon in Lake Michigan, information on its migration
pattern and  food  web structures of its  forage
populations  in related locations is needed. The
migration pattern of the coho salmon was established
based on a general index of fish density, catch-per-
unit-of-effort (CPE), in various locations on a monthly
basis.  In  general, the fish aggregate in southern
Lake Michigan during spring  and travel  to the
southwestern region of the lake in summer.  In the
late summer and early autumn,  most of the coho
salmon are found in the northeastern region of the
lake.  They move back to the southeastern region
during the winter.  However, dietary information for
forage fish  in  these locations  were  not  readily
available. Therefore, it was not possible to construct
a comprehensive food web structure for coho salmon
that reflects the seasonal or spatial variation of its
forage food webs.

The most complete dietary information for forage fish
was  that  collected  from   the  Sturgeon Bay,
Sheboygan  Reef, and Saugatuck lake trout biota
zones (Tables 5.4.3  through 5.4.7).  In this study,
these dietary data were used to construct three local
food web structures for the coho salmon by linking
each of them with the dietary  data  of the coho
salmon as presented in Table 5.4.8.
                                              468

-------
Table 5.4.2a. Annual Dietary Composition of Lake Trout at Saugatuck (1994-1995)
Lake
Trout
Age
Age1




Age 2

Age3



Age 4


Age 5


Age 6




Age?



Age 8




Age 9



Age 10




Forage
Fish
Age
Age1
Age 2
Age3
Age 4
Age 5
Age 1
Age 2
Age 1
Age 2
Age 3
Age 4
Age 2
Age 3
Age 4
Age 2
Age3
Age 4
Age 2
Age 3
Age 4
Age 5
Age 6
Age 3
Age 4
Age 5
Age 6
Age 3
Age 4
Age 5
Age 6
Age 7
Age 4
Age 5
Age 6
Age 7
Age 2
Age 3
Age 4
Age5
Age 6
Alewife Rainbow Bloater Slimy Deepwater Diporeia Mysis
Smelt Sculpin Sculpin
20 20
20
20

20
35
5 20 40
10 20 10
20
30
10
5 25 10
10 25
25
5
10 20 15
10 40
10 5
20
10 5 20

20 10
15
15 30
10
30
10
20 15
20
20 10
5
20
30
20 10
20
10
10
15 15 10
10
30
                                          469

-------
Table 5.4.2a. Annual Dietary Composition of Lake Trout at Saugatuck (1994-1995) (Continued)
Lake
Trout
Age
Age 11




Age 12






Table 5.4.2b.
Lake
Trout
Age
Age1
Age 2
Age 3

Age 4




Age 5





Age 6



Forage
Fish
Age
Age 3
Age 4
Age 5
Age 6
Age?
Age1
Age 2
Age 3
Age 4
Age 5
Age 6
Age 7
Annual
Forage
Fish
Age
Age 1
Age1
Age1
Age 2
Age 1
Age 2
Age 3
Age 4
Age 5
Age 1
Age 2
Age 3
Age 4
Age 5
Age 6
Age 2
Age 3
Age 4
Age 5

Alewife Rainbow Bloater Slimy Deepwater Diporeia
Smelt Sculpin Sculpin
10
30
25
10
25
5

10

20 15
10 30
10
Dietary Composition of Lake Trout at Sheboygan Reef (1994-1995)

Alewife Rainbow Bloater Slimy Deepwater Diporeia
Smelt Sculpin Sculpin
85 15
80 10 5
55

20
20 10
10
10
10
20
15
15
10 20

10
30 10
20
10
10 20

Mysis















Mysis


5

45

20




10








                                         470

-------
Table 5.4.2b. Annual Dietary Composition of Lake Trout at Sheboygan Reef (1994-1995)
(Continued)
Lake
Trout
Age
Age?



Age8



Age 9




Age 10




Age 11




Age 12





Forage
Fish
Age
Age 2
Age3
Age 4
Age 5
Age 2
Age 3
Age 4
Age 5
Age 2
Age3
Age 4
Age 5
Age 6
Age 2
Age 3
Age 4
Age 5
Age 6
Age 2
AgeS
Age 4
Age 5
Age 6
Age 2
AgeS
Age 4
Age 5
Age 6
Age 7
Alewife
35
25
10
15
20
5
20
15
10
15
30
20

5
20

40

5

20

20
10
10
15
10
10

Rainbow Bloater Slimy Deepwater Diporeia Mysis
Smelt Sculpin Sculpin


15



20
20



10
15


15
10
10


15
20
20



20

25
                                         471

-------
Table 5.4.2c. Annual Dietary Composition of Lake Trout at Sturgeon Bay (1994-1995)
Lake
Trout
Age
Age1
Age 2
Age 3


Age 4



Age 5



Age 6






Age?





Age 8





Age 9




Forage
Fish
Age
Age 1
Age 1
Age1
Age 2
Age 3
Age1
Age 2
AgeS
Age 4
Age1
Age 2
Age 3
Age 4
Age1
Age 2
Age 3
Age 4
Age 5
Age 6
Age?
Age 2
Age3
Age 4
Age 5
Age 6
Age?
Age 2
Age 3
Age 4
Age5
Age 6
Age?
Age3
Age 4
Age5
Age 6
Age?

Alewife

85
80
45
10

30

10
10
30

15
10
10

20


30
15
30
20
20
10

10
10
20
25

10
5
10
30

20
10

Rainbow Bloater Slimy Deepwater Diporeia Mysis
Smelt Sculpin Sculpin
15
10 55
5
5 10
5 20

20
30


15
15
15
5

10


10

5
5




15


5
10

10

10 10


472

-------
Table 5.4.2c. Annual Dietary Composition of Lake Trout at Sturgeon Bay (1994-1995) (Continued)
Lake
Trout
Age
Age 10
Age 11
Age 12
Forage
Fish Alewife Rainbow Bloater Slimy Deepwater
Age Smelt Sculpin Sculpin
Age 2 5
Age 3 15 5
Age 4 20
Age 5 25 5 5
Age 6
Age 7 20
Age 2 5
Age 3 1 5
Age 4 20
Age 5 35 5
Age 6
Age 7 20
Age 2 15
Age 3 25
Age 4 10
Age 5 25 25
Diporeia Mysis

Table 5.4.3. Dietary Composition of Alewife in Lake Michigan (1994-1995)

Small:
Age 1-2
Large:
Age 3-7
Saugatuck Sturgeon Bay
Prey (0-<75m) (0-~100m)
Fish Length < 120 mm
Diporeia 10 45
Mysis
Zooplankton 90 55
Fish Length > 120 mm
Diporeia 10 75
Mysis
Zooplankton 90 25
Sheboygan Reef
(50 - 75 m)
40
60
20
50
30
                                         473

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Table 5.4.4. Dietary Composition of Bloater in Lake Michigan (1994-1995)

Small:
Age 1-3
Large:
Age 4-7
Prey
Fish Length <= 160 mm
Diporeia
Mysis
Zooplankton
Fish Length (g) > 160 mm
Diporeia
Mysis
Zooplankton
Saugatuck
(0 - < 75 m)

80
20

75
25
Sturgeon Bay
(0-~100m)

100

70
30
Sheboygan Reef
(50 - 75 m)

35
35
30

25
75
Table 5.4.5. Dietary Composition of Rainbow Smelt in Lake Michigan (1994-1995)
                         Prey
                     Saugatuck
                     (0 - < 75 m)
             Sturgeon Bay
             (0-~100m)
            Sheboygan Reef
              (50 - 75 m)
 All Ages
Diporeia
Mysis
Zooplankton
                                           65
                                           35
                  10
                  90
                  60
                  40
Table 5.4.6. Dietary Composition of Slimy Sculpin in Lake Michigan (1994-1995)
                         Prey
                     Saugatuck
                     (0 - < 75 m)
             Sturgeon Bay
             (0-~ 100m)
            Sheboygan Reef
              (50 - 75 m)
 All Ages
Diporeia
Mysis
90
10
80
20
90
10
                                           474

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Table 5.4.7. Dietary Composition of Deepwater Sculpin in Lake Michigan (1994-1995)
                          Prey
                      Saugatuck
                      (0 - < 75 m)
              Sturgeon Bay
              (0-~100m)
            Sheboygan Reef
               (50 - 75 m)
 All Ages
Diporeia
Mysis
70
30
45
55
80
20
Table 5.4.8.  Dietary Composition of Coho Salmon in Lake Michigan (1994-1995)
Coho
Salmon Age
Age 1

Age 2






Forage Fish
Age
Age 1
Age 2
Age 1
Age 2
Age3
Age 4
Age 5
Age 6
Age 7
Alewife Rainbow Smelt Bloater Diporeia Mysis
40 10 10
40
25
10
20 5
20
10

10
5.4.2.2 Fish Growth Rates

At a given body weight, W, fish growth rate, G, can
be written as:
                            where
 G = (dw/dt)/W

 where
(dw/dt)  =  the derivative of fish weight W with
           respect to fish age t

With a set of weight-age data of a fish available, the
average value for the fish growth rate for a given
period of time can then be estimated by the following
equation:
                                       (5.4.3)
                             W1  =  fish weight (g) at age t, (day)

                             W0  =  fish weight (g) at age t0 (day)
                    (5.4.2)    G   =  fish average growth rate during age t0 to t1
                            The weight-age data for fish species in the food webs
                            were obtained  from  field  sampling conducted in
                            1994-1995 by the Great Lakes National Program
                            Office (GLNPO) for the LMMBP. The methods offish
                            collection are described in Section 4.2.1.  Each fish
                            was weighed to the nearest gram. The lake trout and
                            coho salmon  were aged based on either decoding
                            the information on a  coded-wire tag (if  found) or
                            enumeration of annuli  on scales in conjunction with
                            use of fin clip information.  More details on the fish
                            aging procedure can  be found in  Lake Michigan
                            Mass Balance Study Methods Compendium  (U.S.
                                            475

-------
Environmental  Protection  Agency,  1997a)  and
Madenjian et al. (1998a, 1999).  Forage fish were
aged based on lengths and weights taken from the
literature, and compared to the length and weight
data collected for each of the fish species in this
study.

A general relationship between age and weight for
each fish was established through regression of the
large  amount  of  field  data.   The age-weight
relationships for the lake trout in three biota zones,
the migratory coho salmon,  and their forage fish
populations are  presented in Tables 5.4.9a through
5.4.9c.   Age-weight relationships for forage fish
exhibit no regional variation, and a lake-wide average
was obtained for each forage species.  The results in
Tables 5.4.9a,  5.4.9b,  and  5.4.9c were used to
estimate fish growth rates in the food web models.

The weight-age relationship for Mysis was estimated
based on information from literature sources (Brafield
and Llewellyn, 1962; Pothoven et al., 2000).  The
results are presented in Table 5.4.9d.

A constant value of 0.10 (1/day) was adapted as the
average  growth  rate for zooplankton in  the  lake
(Connolly et al., 1992).

5.4.2.3 Energy Density of Food Web Components

In a bioenergetics-based food web model,  energy
balance is the basis for estimating chemical fluxes
between  fish and its prey species.  It is, therefore,
important to have a good knowledge  of the energy
content of the fish and its prey items.

Energy densities,  D, of all fish species in this study
were estimated  based on lipid and protein fractions
in individual organisms (Lucas, 1996).
D = 35.5 fL + 20.08 fpr
(5.4.4)
The terms fL and fpr are lipid and protein fractions in
the fish body, respectively. The energy equivalents
of lipid components (kj/g) is 35.5, and the energy
equivalents of protein components (kJ/g)  is 20.08.
The standard value of energy equivalent for protein
is 23.4 kJ/g-protein  (Cho  et al., 1982).   It was
adjusted  to  a lower  value of 20.08  kJ/g-protein
because  after digestion, a portion of energy  in the
assimilated protein is lost by nitrogenous excretion
and is not available for further respiration.  Energy
contributions from other body components of a fish,
such as carbohydrates, are negligible (Diana, 1995).

Fish  lipid  content was  analyzed by extracting
homogenized fish composite with 100 mL of 90/10
(v:v) petroleum ether/ethyl acetate. The extract was
then evaporated and  the  residue was  weighed as
extractable lipid.  Detailed procedures for fish lipid
separation  and determination are available in the
Lake   Michigan  Mass  Balance Study  Methods
Compendium   (U.S.   Environmental   Protection
Agency, 1997b) and Madenjian  et al (2000).  The
values of protein fraction in the lake  trout, coho
salmon, and the other fish were compiled from or
estimated based on various literature sources (Flath
and Diana, 1985; Foltz and Norden, 1977; Gardner
et al., 1985; Rottiers and Tucker, 1982; Schindler ef
al.,  1971; Vijverberg and Frank, 1976). The lipid and
protein fractions used  for estimating energy content
for all organisms in this study are compiled in Tables
5.4.1 Oa through 5.4.1 Oh.

5.4.2.4  Exposure Conditions

Environmental conditions to which fish are exposed
play an important  part  in  determining  chemical
exchange fluxes between a fish and its environment.
Among the model parameters which characterize the
environmental conditions for food webs, contaminant
levels in water and sediment have direct influence on
the  contaminant level in exposed fish food webs, and
temperature and oxygen  content of the  exposure
environment regulate  the  chemical kinetics in fish
food webs.

Due  to  the variation  in Lake Michigan water
characteristics, the exposure condition is different
among fish food webs in different biota zones.  To
facilitate model calculations for  fish food webs at
Sturgeon Bay,  Sheboygan  Reef, and  Saugatuck,
exposure information  for each of these three biota
zones was  required.   Exposure data used are
summarized here.  All data for the  LMMBP are
available upon request to the GLNPO.
                                               476

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Table 5.4.9a. Average Weight-Age Relationships for Lake Trout in Lake Michigan (1994-1995)
Sheboygan Reef
Age Weight (g)
1 20
2 128
3 244
4 490
5 900
6 1378
7 1900
8 2600
9 3400
10 4000
1 1 4400
12 4700
13 4900
14 5200
Saugatuck
Weight (g)
90
180
550
1100
2050
2850
3400
4000
4500
5400
6500
6900
7100
7100
Sturgeon Bay
Weight (g)
98
120
350
800
1500
2700
.3200
3700
4400
5000
5500
5600
5800
6000
Table 5.4.9b. Average Weight-Age Relationships for Coho Salmon in Lake Michigan (1994-1995)
Age Day
1 90
122
152
183
214
244
274
304
335
366
2 30
60
90
121
151
183
214
244
274
304
Weight (g)
30
80
140
220
322
450
620
878
880
885
890
895
900
1400
1850
2190
2450
2670
2860
3050
                                          477

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Table 5.4.9c. Average Weight-Age Relationships of Forage Fish in Lake Michigan (1994-1995)
Age
1
2
3
4
5
6
7
8
9
10
11
Alewife
Weight (g)
3
15
27
37
45
50
53
55



Bloater
Weight (g)
3.7
12
26
38
50
65
88
110



Rainbow Smelt
Weight (g)
5.3
8
13
19
22
25
28
30
32
34

Slimy Sculpin
Weight (g)
0.6
1.2
2.2
4.6
8.4
10
10.6




Deepwater Sculpin
Weight (g)
0.6
1.8
3.5
7
13
19
24
29
34
38
40
Table 5.4.9d. Estimated Weight-Age Relationships of Mysis in Lake Michigan
        Month
       Weight (g-wet)
        Sturgeon Bay
 Weight (g-wet)
Sheboygan Reef
Weight (g-wet)
  Saugatuck
0 0.00019
4 0.00194
8 0.00893
12 0.01691
16 0.03336
0.00001
0.00061
0.00330
0.00910
0.01860
0.00001
0.00095
0.00537
0.01706
0.04123
Table 5.4.10a. Average Lipid and Protein Fractions (%) of Lake Trout in Lake Michigan (1994-1995)
       Age
Sheboygan Reef    Sturgeon Bay
         Saugatuck
     Protein %
1 2.3
2 3.66
3 7.9
4 9.36
5 12.48
6 15.56
7 18.6
8 19.36
9 19.34
10 19.1
1 1 20.73
12 22.4
13 20.2
14 20.1
15
4.8
4.68
9.21
11.81
17.04
18.3
19.13
20.52
20.15
22.63
22.5
20.53
20.9
21.4
22.4
2.3 17.37
3.66
7.13
9.52
14.77
18.96
21.05
18.56
19.12
20.68
22
23
21.7
19.7
30.6
                                           478

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Table 5.4.1 Ob. Average Lipid and Protein Fractions (%) of Coho Salmon in Lake Michigan (1994-1995)




         Age                   Day                  Lipid %               Protein %
1 90
122
152
183
214
244
274
304
335
366
2 30
60
90
121
151
183
214
244
274
304
5.14 20.00
5.25
5.37
5.54
5.75
6.01
6.36
6.90
6.90
6.91
6.92
6.93
6.94
7.98
8.91
9.61
10.15
10.61
11.00
11.39
Table 5.4.1 Oc. Average Lipid and Protein Fractions (%) of Alewife in Lake Michigan (1994-1995)




       Age         Sheboygan Reef      Saugatuck       Sturgeon Bay       Protein %
1 7.2
2 8.5
3 9
4 10.5
5 11.5
6 12
7 12.2
8 12.5
5.5
5.5
6
7.5
9
10
11
12
4
6
6
6
6
6
6
6
16.7







                                           479

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Table 5.4.1 Od.  Average Lipid and Protein Fractions (%) of Bloater in Lake Michigan (1994-1995)
Age Sheboygan Reef
1 5
2 5.5
3 8
4 11
5 12
6 12.5
7 13
8 13.5
Saugatuck
4
4.5
5.5
6.5
7.5
8.5
10.5
11
Sturgeon Bay
5
7
8.5
9.5
12.5
13.5
14.5
15.5
Protein %
16.3







Table 5.4.1 Oe. Average Lipid and Protein Fractions (%) of Rainbow Smelt in Lake Michigan (1994-1995)
Age Sheboygan Reef
1 4.4
2 4.4
3 4.4
4 4.4
5 4.4
6 4.4
7 4.4
8 4.4
9 4.4
10 4.4
Saugatuck
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
Sturgeon Bay
3
3
3
3
3
3
3
3
3
3
Protein %
16.9









Table 5.4.1 Of. Average Lipid and Protein Fractions (%) of Slimy Sculpin in Lake Michigan (1994-1995)
Age Sheboygan Reef
1 6.4
2 6.5
3 6.6
4 6.8
5 7.1
6 7.2
7 7.3
Saugatuck
3.5
4
4.5
5
5.2
5.2
5.2
Sturgeon Bay
8
8.1
8.2
8.3
8.4
8.5
8.5
Protein %
15.9






                                            480

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Table 5.4.1 Og. Average Lipid and Protein Fractions (%) of Deepwater Sculpin in Lake Michigan (1994-
1995)
       Age
Sheboygan Reef
Saugatuck
Sturgeon Bay
Protein %
1
2
3
4
5
6
7
8
9
10
11
8.8
8.9
9
9.1
9.4
9.7
9.9
10.1
10.3
10.5
10.6
2
3
4
5
5.5
6
7
7.2
7.2
7.5
7.5
7 14.4
7.1
7.2
7.3
7.5
7.7
7.8
7.9
8
8.1
8.2
Table 5.4.1 Oh.  Average Lipid and Protein Fractions (%) of Zooplankton, Mysis, and Diporeia in Lake
Michigan (1994-1995)
      Species
Sheboygan Reef
Saugatuck
Sturgeon Bay
Protein %
Zooplankton
Mysis
Diporeia
2.91
2.31
3.21
2.79
1.61
1.66
1.57
2.9
4.48
7.1
7
10
5.4.2,4.1 PCB Concentrations in Water

Lake Michigan water and particulate samples were
collected at several stations within the Sturgeon Bay,
Sheboygan  Reef,  and  Saugatuck biota  zones.
Information   regarding  the  sampling  stations,
collection procedures,  sample  preparation, and
methods for PCB analysis are available in detail (U.S.
Environmental Protection Agency, 1997a, 1997b).
The  organic carbon  fraction in  the  suspended
particles was  also  analyzed.    The  analysis
procedures  can  also  be  found  in  the   above
documents.

No temporal variation  of PCB concentrations was
found for samples collected  during 1994 and 1995.
PCB concentrations in suspended particles were
organic carbon normalized.  There was substantial
variation of  PCB  concentrations  in  suspended
                            particles  among samples collected from different
                            water depths.  No substantial vertical variation was
                            found for PCBs in the dissolved form.  PCBs in
                            suspended particles were divided into those collected
                            at depth < 20 m and those collected at depth > 20 m.
                            For this study,  it was assumed that the fish food
                            webs  were   exposed  to  particulate   PCB
                            concentrations in the deeper layer.  Median values
                            for  dissolved  PCBs and  those associated with
                            suspended particles were used for model calibration.
                            The PCB concentrations in the water column of the
                            three biota zones are given in Table 5.4.11.

                            5.4.2.4.2 PCB Concentrations in Sediment

                            Sediment sampling was not specifically conducted
                            within  the  three biota  zones.  Sediment  PCB
                            concentrations  in  the  three  biota zones were
                                             481

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Table 5.4.11. PCB Concentrations in Lake Michigan Water Column (1994-1995)
Sturgeon Bay
PCB
Congeners
3
8+5
12
13
15+17
16
32
18
26
28+31
33
37+42
44
49
52
56+60
66
70+76
74
77+110
81
87
92+84
89
85
99
101
118
123+149
132+153+105
151
163+138
146
170+190
172+197
180
1 87+1 82
208+1 95
196+203
201
Dissolved
(ng/L)
0
0
0.002831
0.001163
0.003063
0
0
0.00333
0.000941
0.008012
0.004408
0.008967
0.003189
0.002259
0.005627
0.00134
0.001664
0.002179
0.00103
0.00291
7.68E-05
0.00227
0.005722
0.00068
0.000507
0.006156
0.001328
0.001236
0.000705
0.000724
0
0.002134
0.00059
4.74E-05
0
0
0.002588
4.34E-05
3.28E-05
0.000168
Particulate
(ng/g-OC)
0
0
0
0.63374
4.02012
1.00157
1 .37860
3.57836
0.22132
12.42481
2.28478
14.5969
5.38999
3.96632
9.48455
7.20351
18.39126
7.46113
4.18880
13.79423
1.52913
4.43503
15.83466
0.15860
4.76618
25.2633
10.76926
10.49375
6.59078
18.7597
2.11833
20.59195
5.19236
2.54427
1 .03453
1.91204
4.91753
0.88921
1 .50532
3.05836
Sheboygan Reef
Dissolved
(ng/L)
0
0
0.002265
0.00122
0.002608
0
0
0.00377
0.001258
0.007067
0.005054
0.009517
0.002878
0.002054
0.005518
0.001344
0.001893
0.0021
0.001039
0.002586
0
0.002572
0.007226
0
0.000569
0.004236
0.00278
0.001156
0.000862
0.000958
0
0.002948
0.000583
7.36E-05
0
5.13E-05
0.000984
0
2.75E-05
7.34E-05
Particulate
(ng/g-OC)
0
0
6.94990
2.09185
7.54759
1 .56798
1 .58024
5.47443
0.37498
17.84289
3.4361 1
15.35747
7.30135
6.86181
16.12783
13.76338
29.53261
16.42939
5.84207
28.8021 1
2.09813
8.03297
32.15896
0
8.63774
36.02048
17.31631
19.16489
13.43283
31.21532
3.88177
37.87159
7.64438
5.30883
1.85133
5.94020
7.07428
1.91721
3.83510
6.59986
Saugatuck
Dissolved
(ng/L)
0
0
0.003126
0.0009
0.004061
0.001473
0
0.004623
0.001582
0.009846
0.006045
0.008866
0.00581
0.003302
0.008475
0.00198
0.002783
0.003036
0.001371
0.004342
0.000147
0.002373
0.01356
0
0.000681
0.004228
0.004522
0.001713
0.001331
0.001451
0
0.002877
0.000572
0.000131
0
0
0.000683
0
0
0.00018
Particulate
(ng/g-OC)
0
0
0
2.11576
11.95844
3.22824
4.22044
10.4442
2.96423
55.58153
9.97024
16.86476
20.89396
14.05582
35.36909
30.47388
63.51781
33.74864
14.10166
49.83354
2.60703
13.37302
74.07366
1 .59772
13.30061
49.72043
34.31707
35.9058
21 .52096
58.52275
6.37438
55.57444
9.26933
8.24745
2.81659
18.31716
12.63331
2.42335
5.10087
9.01875
                                          482

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estimated based  on samples collected at several
nearby stations.  These stations were selected for
their closeness to a specific biota zone in distance,
depth, and sediment characteristics.   Because
organic carbon normalized sediment PCB  data
showed limited horizontal variation, the estimate of
sediment PCB exposure by using data from nearby
stations was appropriate. Information regarding the
sampling  stations,  collection procedures, sample
preparation, and  methods for  PCB analysis are
available in detail (U.S. Environmental  Protection
Agency,  1997a, 1997b).  Organic carbon and dry
fraction of  sediment samples were also analyzed.
The analysis procedures can also be found in the
above documents.

Sediment  data analysis  revealed  no  significant
temporal variation in PCB concentrations for samples
collected during 1994 and 1995. PCB concentrations
in sediment  were  organic  carbon  normalized.
Median values for PCBs in sediment carbon  were
used for model calculations.  The concentrations of
PCBs dissolved  in  sediment  pore  water   were
estimated based  on measured  PCB  data, organic
carbon content, dry fraction in the sediment samples,
and organic carbon-water partition coefficients for
individual PCB congeners.   The results of  PCB
concentrations in the sediment solids and pore water
for the three biota zones are given in Table 5.4.12.

5.4.2.4,3  Exposure Temperature

Lake Michigan is a vast water body with a volume of
4,920 km3. It has  a surface area of 57,800 km2, and
its deepest point is 282 m (Coordinating Committee
on Great Lakes Basic Hydraulic and Hydrologic Data,
1992). Physical characteristics of the lake vary with
region and depth (Environment Canada  and  U.S.
Environmental Protection Agency, 1997).  To better
reflect this reality, the model was constructed to
simulate the exposure environment for each species,
rather than as a whole for all species in a food  web.

The   prevailing  annual   cycles   of  exposure
temperature for a lake-wide coho salmon population
and for three lake trout and their forage populations
at Sturgeon Bay,  Sheboygan Reef, and Saugatuck
were established and are presented in Figures 5.4.2a
through 5.4.2c. The results were compiled based on
site-specific information, such   as  annual water
temperature profiles (U.S. Environmental Protection
Agency,  1995), species optimal temperature and
depth at different life  stages  (Otto  et al.,  1976;
Peterson et al., 1979; Stewart era/., 1983; Wismer
and  Christie, 1987; Wells,  1968), prey availability
(Crowder and Crawford, 1984; Eck and Wells, 1986;
Janssen  and Brandt,  1980),   spawning  season
(Janssen  and  Brandt, 1980),  and  spawn site
preference (Jude et al.,  1986;  Rice, 1985).  For
simplicity, the exposure temperatures for different
age groups in certain species were aggregated and
average annual temperature cycles were determined
for the species.  The seasonal variation of surface
water temperatures (U.S. Environmental Protection
Agency,  1995) in  the lake is also presented in the
first  panel of Figures 5.4.2a    5.4.2b and Figure
5.4.2c for reference.

5.4.2.4.4 Oxygen Concentration in Water

The  oxygen concentration in water that organisms
vent through their gill membranes was determined by
water temperature.  In this study, the dissolved
oxygen   content  in  water  [O2]  was  estimated
according to an empirical correlation between oxygen
solubility  (mg/L) and water temperature (Greenberg
etal., 1992).

Ln[O2] =  -139.34411  +  (1.575701  x 105/T)

       -  (6.642308 x107/T*) +  (1.2438  x ID10/!3)

       -  (8.621949 x 1011/74)             (545)

where

7   =  temperature (°K)

5.4.3   Physiological Data of  Fish  and
Other Organisms

5.4.3.1 Species-Specific Respiration Rates

In the bioenergetics-based food web model (LM Food
Chain), fish respiration (or metabolism) rate is a key
model parameter which determines the dynamics of
chemical   uptake  from water  and  food.   Fish
respiration  rate  is  dependent  on  fish  weight,
temperature, and degree of fish activity. For most of
the fish species in the Lake Michigan food webs, an
extensive study of respiration as a function of weight,
                                             483

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Table 5.4.12. PCB Concentrations in Lake Michigan Surface Sediment (1994-1995)
Sturgeon Bay
PCB
Congeners
3
8+5
12
13
15+17
16
32
18
26
28+31
33
37+42
44
49
52
56+60
66
70+76
74
77+110
81
87
92+84
89
85
99
101
118
123+149
132+153+105
151
163+138
146
170+190
172+197
180
187+182
208+195
1 96+203
201
Pore Water
(ng/L)
0
0.0279054
0.0014341
0.0013447
0.0096751
0.0038924
0.0024807
0.0071207
0.0026656
0.049642
0.0125176
0.009989
0.0149115
0.0078881
0.0174969
0.0183856
0.0446862
0.0180978
0.0075816
0.0175742
0.0004445
0.0049453
0.0089757
0.000158
0.00761
0.0065556
0.0116518
0.0104703
0.0034083
0.0133833
0.0011542
0.0099996
0.0014001
0.0009791
0.0002736
0.0017814
0.0010107
0.0001999
0.0006348
0.0006772
Particle
(ng/g-OC)
0
10.00643
0.71635
0.75432
5.29398
1.76031
1.41452
3.67665
2.76099
52.27209
1 1 .73799
13.00778
17.92995
11.19489
24.42368
40.15014
113.2876
43.29467
19.2216
69.76274
1.46919
14.55518
22.56819
0.3229
22.77225
22.77379
39.81201
64.97851
19.95935
79.90554
6.06862
82.25939
11.14243
17.12468
4.51562
30.90044
13.11631
5.46997
17.80964
18.07668
Sheboygan Reef
Pore Water
(ng/L)
0
0.0382893
0
0.0029074
0.0252991
0.0074487
0.0026843
0.018433
0.0035926
0.067541
0.0149408
0.019252
0.023162
0.0098061
0.0227162
0.0281371
0.05182
0.0239207
0.007947
0.026113
0.0006704
0.0062292
0.0178462
0.0011103
0.00995
0.007798
0.0138112
0.0130125
0.0040488
0.0151232
0.0012538
0.0127299
0.0015557
0.0009686
0.0002758
0.0018309
0.000956
0.000171
0.0005772
0.0004869
Particle
(ng/g-OC)
0
13.72811
0
1 .63073
13.84212
3.36831
1 .53063
9.51698
3.72098
71.12439
14.00947
25.29948
27.84947
13,91655
31 .70843
61 .44742
131.3768
57.22649
20.14776
102.5709
2.21588
18.33357
44.87101
2.26910
29.77426
27.0894
47.18955
80.75468
23.71038
95.42261
6.59256
104.7201
12.38059
16.94218
4.53335
31 .75848
12.40633
4.67938
16.19255
12.99829
Saugatuck
Pore Water
(ng/L)
0
0.0908558
0
0.0061499
0.0681406
0.0171774
0.0277008
0.0504009
0.0167924
0.2105729
0.0549273
0.061323
0.06441 97
0.0308258
0.0629656
0.0645118
0.1180745
0.0613122
0.0219209
0.0514251
0.0018093
0.0162384
0.0322442
0.0019127
0.0154561
0.0150233
0.029209
0.0212667
0.0083891
0.02301 93
0.0026042
0.0209239
0.0028539
0.001 6554
0.0005621
0.0030391
0.0016946
0.0002984
0.0011243
0.0012951
Particle
(ng/g-OC)
0
32.58473
0
3.44944
37.28868
7.76701
15.80006
26.02673
17.39422
221.7646
51.50962
80.17273
77.46354
43.75034
87.89781
140.8908
299.3598
146.6866
55.5777
200.6624
5.98041
47.79412
81 .07549
3.90920
46.2521
52.19077
99.80335
131.9822
49.12844
145.2468
13.69295
172.1294
22.71165
28.95361
9.23983
52.71641
21 .99273
8.16719
31 .54036
34.57076
                                          484

-------
    25-i

    20-

    15-
   1
    10-

     5c
     o
	surface temperature
	trout age class O - 1
 O trout age class 2-15
                                                      bloater age class 0
                                                     - bloater age class 1-7
                                                      mysis
 25 T
 20-

 15-

' 10-
                                               5 x— x— x— x-
                                                                            D
                                                                        o       a
                                                                                    a
                                                                                                      x
      Jan Feb Mar Apr May Jun  Jul  Aug Sep  Oct  Nov  Dec     Jan  Feb  Mar Apr  May Jun Jul  Aug Sep Oct Nov Dec

16 -i

14 -
12-
o 10 -
in
£ 8-
Ol
* s-

2 -
n

A alewife age class O - 2
	 alewife age class 3 - 1O

/"





A A A
A
\ A
\


12-i

10 -
8-
o
1 6-
O)
CD
^ 4C1

i
2 -
n

-"•* — deepwater sculpin
O slimy sculpin

/ ^
/ ^^
0 O O O O O O \
^ \
V. 	 ,/~Y—I— r^i f~{ 	 . - - 	 - 	 	 - -r


      Jan  Feb  Mar  Apr May Jun  Jul Aug  Sep  Oct  Nov  Dec     Jan  Feb  Mar Apr  May Jun Jul  Aug Sep Oct Nov Dec
Figure  5.4.2a. Typical annual  cycles of  exposure temperature for  Lake Michigan food webs at
Saugatuck and Sturgeon Bay.
     25 -r

     20-

     15-

     lo-

      se
   surface temperature
	 trout age class 0-1
O  trout age class 2-15
           I    I    I    I	1	1	1	1	1	1—
      Jan Feb  Mar  Apr May Jun  Jul Aug Sep Oct Nov Dec
                                                     — bloater age class 0
                                                     D   bloater age class 1-7
                                                        mysis
                                                          Jan Feb Mar Apr May Jun  Jul  Aug  Sep  Oct  Nov  Dec
               1	1	1	1	1	1	1	r
      Jan  Feb  Mar Apr May Jun  Jul  Aug Sep Oct Nov Dec
                                                      T	1	1	1	1	1	1	1	r
                                                 Jan  Feb  Mar Apr May Jun  Jul Aug Sep Oct
                                                                                                  Nov  Dec
Figure 5.4.2b. Typical  annual cycles  of exposure  temperature for  Lake Michigan food web at
Sheboygan Reef.
                                                   485

-------
                      25
                      20-
                    O
                    
                    ra
                       10H
                       5-
                               surface temperature

                               coho salmon
                           —i	1	1	1	1	1	1	'	1    i
                        Jan  Feb  Mar Apr May Jun  Jul Aug Sep Oct Nov Dec
Figure 5.4.2c. Typical annual cycles of exposure temperature for coho salmon in Lake Michigan.
temperature, and swimming speed was conducted,
and results were reported (Lantry and Stewart, 1993;
Rudstam, 1989; Rudstam etal., 1994; Stewart etal.,
1983; Stewart and Binkowski, 1986). In general, a
fish's daily respiration rate,  in g-O2/day, can  be
formulated as:
R=
jPr. &vu
                                         (5.4.6)
where a, P, p, v  are species-specific empirical
constants, W is weight, and U is the swimming speed
of the fish, in cm/s.

For a given aquatic species, the swimming speed
can be expressed as a function of body weight and
water temperature:
U = co
           e
            *7"
(5.4.7)
where co,  5,  cj)  are  species-specific  empirical
constants.

The  values   of  the   species-specific  empirical
constants used to estimate the respiration rate were
collected from literature sources (Lantry and Stewart,
1993; Rudstam, 1989; Rudstam etal., 1994; Stewart
et al., 1983; Stewart and Binkowski, 1986) and are
listed in  Table 5.4.13.  For slimy and  deepwater
sculpin, there was insufficient information available to
generate species-specific respiration rates.  As an
alternative, their  respiration rates  were estimated
using the generalized fish respiration equation. The
constants used for the calculation of their respiration
rates were also given in the table.
         In this study,  a value of 13.56 kJ/g-O2 (Elliott and
         Davison, 1975; Brafield and Llewellyn, 1982; Crisp,
         1984) was used as the respiratory energy equivalent,
         or oxycalorific coefficient, for converting oxygen
         respiration to energy utilized by fish.

         For zooplankton, a simple equation was used to
         estimate its respiration, in kJ/gwet/day, as a function
         of water temperature (Connolly et al., 1992):
                                                  fl=0.60epr
                                                   (5.4.8)
                                       5.4.3.2 Respiration  Rates Adjusted for Specific
                                       Dynamic Action (SDA)

                                       The respiration rate  estimated with Equation 5.4.6
                                       represents the average energy requirement for the
                                       resting metabolism of a fish. It has been reported
                                       that there is  an increase in respiration rate for a
                                       recently fed fish  (Kayser,  1963).  The  additional
                                       respiration activity is often  referred to as Specific
                                       Dynamic Action (SDA).   The  origin  of  the extra
                                       respiration is  believed  to  be due to the energy
                                       necessary for the digestion of ingested foods, the
                                       absorption of nutrients, the deaminization of amino
                                       acids,  and  the  synthesis  of   the  products  of
                                       nitrogenous excretion.   In homothermic animals, it
                                       has been  shown that SDA  represents 30% of the
                                       caloric content of the ingested protein, 13% for a
                                       lipid, and 5% for a carbohydrate (Lucas, 1996). Due
                                       to the difficulty in experimentally discriminating SDA
                                       from   additional  respiration    associated  with
                                       excitement and activity with feeding, different SDA
                                               486

-------
Table 5.4.13.  Bioenergetic Parameters of Lake Michigan Fishes
Parameter
a
(gOj/gwet/day)
P
P
0)
5
4>
V
Mysis
0.00182
-0.161
0.0752
0
0
0
0
Slimy
Sculpin
0.043*
-0.3
0.03
1.19
0.32
0.045
0.0176
Deepwater
Sculpin
0.043*
-0.3
0.03
1.19
0.32
0.045
0.0176
Alewife
0.00367
-0.2152
0.0548
5.78
-0.045
0.149
0.03
Rainbow
Smelt
0.0027
-0.216
0.036
0
0
0
0
Bloater
0.0018
-0.12
0.047
7.23
0.25
0
0.025
Lake Trout
0.00463
-0.295
0.059
11.7
0.05
0.0405
0.0232
Coho
Salmon
0.00264
-0.217
0.06818
9.7
0.13
0.0405
0.0234
*With a unit of gwet/gwet/day.
values were cited in the literature that ranged from
9% to 20% of the energy contained in the  diet
(Jobling, 1981).

In this study, the SDA is modeled as a portion  of a
fish's dietary ingestion. The respiration rate adjusted
for SDA can then be written as:
                             The  final  respiration  rate,  in  kJ/day, was  then
                             estimated as:
  SDA

where
(RSDA + G • D,)
                                        (5.4.9)
RSDA =  SDA adjusted respiration rate, g-O2/day

R   =  resting  respiration  rate  calculated with
        empirical equations, g-O2/day

Qox  =  respiratory energy equivalent or oxycalorific
        coefficient, kJ/g-O2

SDA =  fraction  of  assimilated energy  spent  on
        specific dynamic action

G   =  fish growth rate, 1/day

DI   =  energy density of the fish
                     -G- D,)/(1 -SDA)   (5.4.10)

5.4.4  Calibrated Model Parameters

There are several constants and variables in the
model's equation whose values are either not readily
available  or inconclusive.    Their  values  were
determined through model calibration to site-specific
conditions. The calibrated parameters include food
assimilation efficiency  (P) for each species or age
group,  the chemical assimilation efficiency (a) for
each species or age group for each PCB congener,
the chemical relative gill transfer coefficient (EyEJ
for each  species  (or  age group) for each  PCB
congener, and  the fraction of ingested energy for
SDA for each species or age group.

An acceptable value range for each of the calibrated
model parameters and its  general trend for  PCB
congeners or species in different trophic levels was
established based on information from the literature
and experience gained in previous modeling work.
Depending  upon   species  and  its  diet,  food
assimilation efficiency has a value ranging from 0.05
to 0.85 (Brocksen era/., 1 968; Brocksen and Brugge,
1 974; Elliott, 1 976; Averett, 1 969).  The value for the
                                              487

-------
chemical assimilation efficiency can vary from 0.2 to
0.8 and is reported to be correlated with the Kow value
for the  chemical  (Gobas, 1988).   The chemical
relative gill transfer coefficient (E,/E0) ranges from 0.1
to 1.0 and is also believed to be related to Kow for the
chemical (McKim et a/., 1985). Energy fraction for
SDA has a  value ranging from 0.00 to 0.20.  These
data were used to guide our model calibrations for
appropriate parameterization.

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                                             489

-------
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   Reader.    1999.    Spatial  Patterns  in  PCB
   Concentrations of Lake Michigan Lake Trout. J.
   Great Lakes Res., 25(1):149-159.

Madenjian, C.P., R.F. Elliott,  T.J. DeSorcie, R.M.
   Stedman,  D.V. O'Connor, and D.V.  Rottiers.
   2000.  Lipid Concentrations in Lake Michigan
   Fishes: Seasonal,  Spatial,  Ontogenetic, and
   Long-Term Trends.   J.  Great Lakes Res.,
   26(4):427-444.

Marzolf, G.R.  1965.   Substrate Relations  of the
   Burrowing Amphipod Pontoporeia affinis in Lake
   Michigan.  Ecology, 46(5):579-592.

McKim, J., P. Schmeider, and G. Veith.  1985.
   Absorption  Dynamics  of Organic  Chemical
   Transport  Across  Trout  Gills  as  Related  to
   Octanol-Water  Partition Coefficient.   Toxicol.
   Appl. Pharmacol., 77(1):1-10.

Mullin,  M.D.,  C.M.  Pochini, S.  McGrindle,  M.
   Romkes, S.H. Safe, and L.M.  Safe. 1984. High-
   Resolution  PCB  Analysis   Synthesis  and
   Chromatographic  Properties  of All 209 PCB
   Congeners.  Environ. Sci.  Technol., 18(6):468-
   476.
Neidermeyer,  W.J.  and  J.J.  Hickey.    1976,
   Chronology of Organochlorine Compounds in
   Lake Michigan Fish, 1929-1966. Pest. Monit. J.
   10(3):92-95.

Norstrom,  R.J.,  A.E.  McKinnon,  and  A.S.W.
   deFreitas. 1976. Bioenergetics-Based Model for
   Pollutant Accumulation by  Fish.  Simulation of
   PCB and  Methylmercury  Residue Levels in
   Ottawa River Yellow Perch (Perca flavescens).
   J. Fish. Res. Board Canada, 33(2):248-267.

Oliver, B.G.,  M.N.  Charlton,  and R.W. Durham.
   1989.     Distribution,   Redistribution,  and
   Geochronology  of   Polychlorinated  Biphenyl
   Congeners and Other Chlorinated Hydrocarbons
   in  Lake Ontario Sediments.   Environ. Sci.
   Technol., 23(2):200-208.

Patriarche, M.H.  1980.  Movement and Harvest of
   Coho  Salmon  in Lake Michigan, 1978-1979.
   Michigan  Department of  Natural Resources,
   Lansing, Michigan.  Fisheries Research Report
   1889.

Peters, R.H. and  J.A. Downing.  1984.  Empirical
   Analysis of Zooplankton  Filtering and Feeding
   Rates.  Limnol. Oceanogr., 29(4):763-784.

Peterson,  R.H., A.M. Sutterlin, and J.L. Metcalfe.
   1979.   Temperature  Preference of  Several
   Species of Salmo and Salvelinus and Some of
   Their Hybrids.   J. Fish. Res. Board Canada,
   36(9): 1137-1140.

Pothoven, S.A., G.L. Fahnenstiel, H.A. Vanderploeg,
   and M. Luttenton. 2000. Population Dynamics of
   Mysis  relicta in Southeastern Lake Michigan,
   1995-1998. J. Great Lakes Res., 26(4):357-365.

Quigley, M.A.  1988. Gut Fullness of the Deposit-
   Feeder  Amphipod,  Pontoporeia  hoyi,   in
   Southeastern  Lake  Michigan.   J. Great Lakes
   Res., 14(2):178-187.

Quigley,  M.A.  and  H.A.  Vanderploeg.    1991-
   Ingestion of Live Filamentous Diatoms by the
   Great  Lakes Amphipod,  Diporeia  sp.: A Case
   Study  of the  Limited Value of Gut Contents
   Analysis. Hydrobiologia, 223(1 ):141-148.
                                              490

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Rice, J.A.  1985. Mechanisms that Regulate Survival
   of  Larval  Bloater  Coregonus  hoyi in  Lake
   Michigan.   Ph.D. Dissertation,  University of
   Wisconsin, Madison, Wisconsin.

Rottiers, D.V. and R.M. Tucker. 1982.  Proximate
   Composition and Caloric Content of Eight Lake
   Michigan Fishes. U.S. Department of the Interior,
   U.S. Fish and Wildlife Service, Washington, D.C.
   Technical Paper 108, 8 pp.

Rudstam, L.G.  1989.  A Bioenergetic Model for
   Mysis Growth and Consumption Applied to  a
   Baltic  Population  of Mysis mixta.  J. Plankton
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Rudstam, L.G.,  P.P. Binkowski, and M.A.  Miller.
   1994.  A Bioenergetic Model for Analysis of Food
   Consumption  Patterns  by  Bloater  in  Lake
   Michigan. Trans. Amer. Fish. Soc., 123(3):344-
   357.

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   Seasonal  Calorific   Values  of   Freshwater
   Zooplankton, as  Determined with a  Philipson
   Bomb Calorimeter Modified for Small  Samples.
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Stewart, D.J., D. Weininger, D.V. Rottiers, and T.A.
   Edsall. 1983.  An Energetics Model for Lake
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   Michigan  Alewives:  An   Energetics-Modeling
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   Compilation. Great Lakes Fishery Commission,
   Ann Arbor, Michigan.  Special Publication 87-3,
   165pp.
                                             491

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                                           PARTS
                                     LM FOOD CHAIN
Chapters.  Calibration

5.5.1  Introduction

Calibration  is  a  step  of  model  development
necessary  for  accurate  parameterization   and
simulation.   Before a food  web  bioaccumulation
model is used to predict future contamination levels
in fish  or to address other related environmental
issues,  it needs to be calibrated  to refine certain
species- and  chemical-specific parameters to site-
specific conditions. The extensive collection in 1994
and  1995   of   data   on   congener-specific
polychlorinated biphenyl (PCB) concentrations in fish
food webs and in water  and sediment  of Lake
Michigan provided an excellent data set for model
calibration.

5.5.2 Description of Process

The food web model was calibrated with site-specific
conditions for lake trout in three biota zones and for
a  lake-wide   coho  salmon  population.     The
calibrations were conducted for 40 PCB congeners or
co-eluters individually. For each lake trout food web,
the 1994-1995 measured data of PCBs in water and
sediment, and temperature profiles in the associated
biota zone, were used as model inputs.  They were
assumed to be representative of life-long average
exposure condition. This assumption is appropriate
because there  are  no  congener-specific  PCB
exposure concentrations available prior to 1994, the
decline in PCB concentrations in the lake has slowed
down in recent years, and post-exposure input has a
limited impact on the model output for a recent date.
The dynamic food web model was run continuously
until a steady-state was reached for model outputs.
The obtained model outputs were considered to be
the model estimates of PCB concentrations in the
fish food web in response to the exposure inputs.
The model predicted  concentrations of  individual
PCB congeners were then compared to the observed
PCB concentrations in the biota zone for species in
each trophic level of  the  food  web.  During the
calibration process, selected parameters  (i.e., food
assimilation  efficiency (P), chemical  assimilation
efficiency (a), chemical relative gill transfer coefficient
(Ec/Eo),  and specific dynamic action (SDA)) were
adjusted to  improve  agreement  between model
results and measured PCB data for the food web.
The adjustments of the calibrated parameters were
constrained within the limits defined by the accepted
range of the parameters. Starting at the  bottom of
the food web, parameter adjustment and refinement
was conducted  for each  species to  identify the
optimal combination of the parameters which yielded
the best agreement between model results and field
data for all PCB congeners.   This process was
repeated for all trophic levels in the food web.

The resulting calibrated  parameters were  then
examined for all  species across trophic levels to
ensure that the parameter values among trophic
levels and  among chemical hydrophobicities  were
internally consistent and that their trends over trophic
levels and hydrophobicities were in agreement with
those reported  in the  literature.  If necessary, the
calibration  process was repeated by altering the
optimal   combination   of   parameters  until  the
calibrated  parameters  agreed  with  generally
accepted trends.
                                              492

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Similar calibrations were done for PCBs in the Lake
Michigan coho salmon.     Given  the  available
information,  it  was  not  possible  to construct a
dynamic food web structure to reflect variable diets of
the migratory coho salmon in the lake.   The coho
salmon model was calibrated with three fixed food
web structures. They were constructed by combining
an average dietary composition of coho salmon with
one of the forage food web structures from the three
lake trout biota zones.

The model calibration described in this chapter was
based on observed PCB data at a single point in time
(1994-1995).   This model calibration focused  on
individual PCB congeners (rather than total PCBs
alone) in all age classes of the top predator as well
as their  entire supporting forage  base.  The use of
constant exposure history, as represented by the
1994-1995  field  data for  PCBs  in  water and
sediment,  in  this  model   calibration  was  an
appropriate approximation. Our model test indicated
that, within  a certain range, the variation  in past
exposure concentrations had  only minor impacts on
the model output for current  contaminant levels in
fish.  The  uncertainty in the  model  calibration
associated with the constant exposure history was
well below the uncertainty from other sources, such
as variability in food web structures and  PCB field
data.

5.5.3 Calibration Results

The parameter values that generated  the best
agreement between modeled and  measured PCB
data were considered to be the best estimates of the
calibrated parameter set for modeling PCBs in each
food web. The calibrated  results for the Diporeia
submodel are listed in Table 5.5.1. Other calibrated
parameter values for each lake trout food web are
given in Tables 5.5.2 and 5.5.3.   A range of values
was given in the tables for the chemical assimilation
efficiencies of fish and Mysis.  They were treated as
functions of  hydrophobicity of individual  PCB
congeners.    The  correlation   of  the  chemical
assimilation  efficiency a to the hydrophobicity  (or
Kow) of a PCB congener was adopted from the work
ofGobasefa/. (1988):
For
            < 6:  a =0.5
 For logKow > 6:  -1 = 5.3 (± 1 .5) • 1(r8 • Kow
                  a
                     + 2.3 (±0.3)        (5.5.1)
This relationship was selected because it offered the
best overall calibration  results for congeners  with
different hydrophobicity.

There  are  considerable variations in the reported
values of chemical assimilation efficiency at a given
logKow value (Buckman  et a/., 2004; Gobas  et a/.,
1988; Muir and Yarechewski, 1988; Niimi and Oliver,
1983; Stapleton et at., 2004; Thomann era/., 1992).
There are also indications that chemical  assimilation
efficiency may be a function of species (Gobas era/.,
1988;  Muir and Yarechewski,  1988).   However,
adequate information was not available to support
derivation  of  a   species-specific  assimilation
efficiency.  For simplicity, they were assumed to be
independent  of  the  species.     The  chemical
assimilation efficiency used in this study are at the
low end of the literature reported values (Buckman et
al., 2004; Gobas et a/., 1988; Stapleton era/., 2004).
We  believe that  the lower values  may  better
represent   chemical  assimilation  in  the   real
environment. This is because chemical assimilation
efficiencies  were   mostly  estimated  based  on
laboratory studies using manufactured fish  foods
spiked with contaminants. The contaminants coated
on the foods are likely to be more susceptible to
digestion and thus more available for absorption by
fish than contaminants accumulated naturally by prey
species in the lake. Therefore, the actual chemical
assimilation efficiencies  for  species  in  the  real
environment may be lower than what were reported.

Table 5.5.4 gives the calibrated parameters values
for  coho salmon which yielded  the  best  overall
agreement  between modeled and observed data for
all three supporting forage food webs.

The calibrated value for a particular model parameter
is apparently related to other parameter  values.  For
example, the estimated value of the food  assimilation
efficiency, p, is largely influenced by  our selection of
the chemical assimilation efficiency, a. If higher than
those expressed by Equation 5.5.1, the calibrated
values of the food assimilation efficiency listed in
                                              493

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Table 5.5.1.  Calibrated Parameter Values for Diporeia Submodel
 Parameter
Calibrated Value
 Water ventilation rate across the respiratory surface, Gw (L/day)
 Food ingestion rate, Gd (g-dry/day)
 Fraction of food absorbed, p
 Organic carbon assimilation efficiency, a
 Chemical assimilation efficiency from diet, Ed
 Chemical assimilation efficiency from water, Ew
    6.0E-03
    1.8E-04
      5%
      46%
      0.72
      0.60
Table 5.5.2.  Calibrated Model Parameters for PCBs in the Sturgeon Bay and Saugatuck Lake Trout
Food Webs

Zooplankton
Mysis
Deepwater Sculpin
Slimy Sculpin
Bloater (Age 1 -3)
Bloater (Age 4-7)
Alewife (Age 1 -2)
Alewife (Age 3-7)
Rainbow Smelt
Lake Trout (Age 1 -4)
Lake Trout (Age 5- 12)
Chemical
Assimilation
Efficiency (a)
0.15
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
Food
Assimilation
Efficiency (P)
0.60
0.80
0.60
0.65
0.25
0.40
0.90
0.40
0.60
0.40
0.20
Chemical Relative
Gill Transfer
Coefficient (E.7EJ
0.7
1.0
0.7
0.5
0.4
0.4
0.7
0.5
0.4
0.6
0.6
Energy Fraction for
Specific Dynamic
Action (SDA)

0.18
0.15
0.15
0.18
0.18
0.00
0.18
0.00
0.15
0.18
Table 5.5.3.  Calibrated Model Parameters for PCBs in the Sheboygan Reef Lake Trout Food Web

Zooplankton
Mysis
Deepwater Sculpin
Slimy Sculpin
Bloater (Age 1-3)
Bloater (Age 4-7)
Alewife (Age 1 -2)
Alewife (Age 3-7)
Rainbow Smelt
Lake Trout (Age 1 -4)
Lake Trout (Age 5-1 2)
Chemical
Assimilation
Efficiency (a)
0.15
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
0.50-0.22
Food
Assimilation
Efficiency (P)
0.35
0.90
0.45
0.50
0.20
0.35
0.90
0.40
0.55
0.45
0.25
Chemical Relative
Gill Transfer
Coefficient (£,/£„)
1.0
1.0
0.8
0.7
0.3
0.4
0.7
0.5
0.3
0.7
0.8
Energy Fraction for
Specific Dynamic
Action (SDA)

0.15
0.18
0.15
0.18
0.18
0.00
0.15
0.00
0.18
0.18
                                            494

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Table 5.5.4. Calibrated Model Parameters for PCBs in Lake Michigan Coho Salmon

Coho Salmon (Age 1 )
Coho Salmon (Age 2)
Chemical
Assimilation
Efficiency (a)
0.3
0.6
Food
Assimilation
Efficiency ((3)
0.8
0.6
Chemical Relative
Gill Transfer
Coefficient (£,/£„)
1
0.5
Energy Fraction for
Specific Dynamic
Action (SDA)
0.18
0.18
Tables 5.5.2 and 5.5.3 would have to be adjusted
upward.  Therefore, the value of a parameter in the
tables can not be viewed or used independent of
those of other parameters.

Model parameterization  is also influenced by the
quality of  the available field  data.   For model
calibrations conducted with limited field data that
include only a few chemicals and an incomplete food
web  (species  or   age   classes),   model
parameterization can  be  biased  toward  certain
species or chemical properties with which it was
calibrated.   With the  help of  the extensive  data
collection, which covers a large number of chemicals
with a wide range of hydrophobicities and a more
complete account of species and age classes of a
food web, the calibration results in Tables 5.5.1 -5.5.4
is believed to be less biased and more applicable to
a wide range of chemical  contaminants and  food
webs in the lake.

5.5.4  Field Data  for  PCBs in Fish  and
Their Comparisons to  Calibrated Model
Outputs

Except coho salmon, Lake Michigan fish samples
were collected in three biota zones in 1994 and 1995.
Phytoplankton and zooplankton were collected for
the same time periods in  the biota zones.  Plankton
samples were collected by pumping and separating
into phytoplankton and zooplankton  (< 102 urn and >
102 urn, respectively).  Coho salmon samples were
collected from various locations in  1994 and 1995.
Information  regarding   the   sampling   stations,
collection  procedures,  sample preparation,  and
methods  for PCB analysis are available in detail
(U.S. Environmental Protection Agency, 1997a,b).
For lake trout, samples were further classified into
age classes. The method for age classification is
available from Lake Michigan Mass Balance Study
Methods   Compendium   (U.S.   Environmental
Protection Agency,  1997a) and  Madenjian et al.
(1998a,b, 1999).

For lake trout and  its  forage species, PCB data
exhibited no temporal variation over the two-year
period of 1994-1995. Median values for congener-
based PCB concentrations in each age class or size
class  of a species were calculated for each  biota
zone.   For coho salmon, PCB   data  showed
considerable temporal variation due to their  rapid
growth.    Whole  lake  median  values  for  the
concentrations of individual PCB congeners in coho
salmon  for different seasons (size  class)  were
estimated. The resultant values of the observed PCB
concentrations  in  Lake  Michigan  fish,  Diporeia,
Mysis, and zooplankton  in 1994-1995 are presented
in Appendix 5.5.1.  This comprehensive PCB data set
made the Lake Michigan food web model calibration
probably the most complete and systematic in terms
of the completeness of  the food web structure and
the  range   of   hydrophobicities   of  chemical
contaminants,  among reported model studies for
chemical bioaccumulation in a food web.

The agreement between  simulated model outputs
and observed field data is an important measure of
the quality of the simulated  food web  model.
Appendix  5.5.2  illustrates  overall   comparison
between calibrated model  results  and  observed
concentrations of  individual PCB  congeners for all
species  in  the three lake  trout  food  webs.  To
facilitate the comparison, the measured PCB data for
zooplankton, Mysis, and Diporeia were converted to
wet-weight  basis.   A  dry fraction  of 15% was
assumed for zooplankton and Mysis, and 20% for
Diporeia in  the lake.  Each data  point  in the plots
denotes the model result for  an individual  PCB
                                             495

-------
congener and the corresponding field measurement.
For forage fish and Mysis, observed PCB data were
reported  for  composite  samples of several age
classes.  The maximum and minimum age classes
included in the composite samples were identified.
An  average value of the model results for the
encompassed age classes was used to represent the
model estimate for the PCB  concentration  in the
composite  sample and  was  compared with the
observed composite data.  For example, bloater (>
160 mm) at Sturgeon Bay represents a composite
sample of bloater with age classes ranging from four
to seven years old.  Therefore,  in Appendix 5.5.1,
each measured  PCB congener concentration for
bloater (> 160 mm) was plotted against an average
value of modeled concentration for age four through
age seven bloaters.  The solid  line in each  of the
figures in Appendix 5.5.2 indicates the position of the
"perfect match" between the model simulation and
the observed data.

Among lake trout food webs in the three biota zones,
calibration results for Sturgeon Bay and Saugatuck
agree with the observed data reasonably well for
most species from zooplankton to the top predator,
as demonstrated by the strong positive correlations
between modeled and measured  PCB congener
concentrations. The results indicate that the quality
of model simulations increases with the trophic level
of modeled species.  This observation is consistent
with the fact that the field measurements for PCBs in
highly contaminated fish  species  are usually less
variable and better defined than the field PCB data
for less contaminated forage fish and invertebrates in
lower trophic levels. The apparent model biases for
overestimating   or  underestimating   PCBs   in
zooplankton, Mysis, and  Diporeia may be attributed
to possible errors in the presumed values for water
content in these invertebrates used for converting
dry-weight based PCB data to wet-weight  based
values.

Overall  the model yielded a satisfactory result for
congener-specific PCBs  in the top predator - lake
trout.  For forage species which is not  specifically
targeted in most previous model studies, the model
results could be improved by adjusting the chemical
assimilation efficiency individually for each species.
However,  we  decided   to  limit  the  parameter
adjustment to minimize  the  risk  of  turning the
calibration  into   a  mere  curve-fitting  exercise.
Considering the large  variability in the measured
congener-specific PCB data for water, sediment, and
organisms which were used either as exposure input
or for the comparison  to the model output of the
calibration and considering the constraints imposed
on   congener-specific   model   parameters,  the
agreement between the calibrated  and measured
congener-specific PCB data shown in Appendix 5.5.2
are remarkable.

The calibrated parameter values which result in good
fits for fish PCB data at Saugatuck and Sturgeon Bay
did not yield good model results for Sheboygan Reef
fishes in comparison with the observed data.  In
order to improve the  agreement between  model
results  and the observed fish  PCB data for the
Sheboygan  Reef biota zone, a  different set of
calibrated parameter values was  required.  The
parameter   values  calibrated  specifically  for
Sheboygan Reef are given in Table 5.5.3.  After the
additional  parameter  refinement,  satisfactory
agreement was obtained between the simulated and
observed PCB concentrations for the food web at
Sheboygan Reef (Appendix 5.5.2).

For the lake-wide coho salmon, there  were  three
calibrated model results  associated with different
forage food web inputs.   With a common  set of
calibrated model parameters (Table 5.5.4), each of
the calibrated model results agreed  reasonably with
the observed data for coho salmon.  As an example,
calibrated results associated with  the  Saugatuck
forage food web are compared in Figure 5.5.1 with
the observed PCB data for coho salmon at different
life stages.  The figure shows that except for the
second  year coho salmon in  spring  (April-May), the
calibrated model results agree reasonably well with
the observed PCB data for  coho salmon over the
season. The discrepancies for the second year coho
salmon  in  spring  probably result from  a mis-
characterization  of the coho salmon growth curve
(Table 5.4.9b). Due to large variability in fish weight
at a given age and a gap in  weight data collection,
the  estimated  weight-age  relationship may not
properly reflect the fish growth curve in the early days
of two year-old fish.  The resulting growth rate may
be smaller than what was actually the case. A small
growth  rate indicates a slow dilution process for
chemicals in fish, which results in a  build-up of
chemicals in the fish and, consequently, a model
overestimate of chemicals in  fish (see Figure 5.7.5).
                                              496

-------
     0.018
     0.016
                     o hatchery coho salmon
                                                      0.025
         0  0.002     0.006    0.010    0.014
                   measured PCB (ug/g-wet)
                                             0.018
                                                                 0.005    0.010    0.015   0.020   0.025
                                                                    measured PCB (ug/g-wet)
      0.06
®   0.05

•g   004-
:t
m   0.03
O
£   0.02
s.
I   °01
      0
             o
                              o age 2 coho salmon
                                (April - May)
                                                       0.08
          0    0.01   0.02   0.03    0.04   0.05
                   measured PCB (pg/g-wet)
                                             0.06
                                                   m  0.04
                                                   a.
                                                      01
                                                         0.02
                                                               o age 2 coho salmon (June
                                                                      o
                                                                   0.02       0.04       0.06
                                                                    measured PCB (ug/g-wet)
0.08
    0.12

1   0.10-
61
1   0.08-1

§   0.06-j
a.
|   0.04-1

o   0.02-
                              o age 2 coho salmon
                                (July - October)
               0.02   0.04   0.06   0.08   0.10
                   measured PCB (ug/g-wet)
                                             0.12
Figure 5.5.1.  Agreement between modeled and observed fish PCB concentrations in coho salmon
using Saugatuck food web (1994 and 1995).
                                                 497

-------
Further refinement of the weight-age relationship for
the fish may help reduce the discrepancies between
modeled and observed spring PCB data for two year-
old coho salmon.

Appendix 5.5.2 shows  that the calibrated models
overestimated the concentrations of most PCB
congeners in young lake trout, specifically one and
two year-old lake trout at Sturgeon Bay and one and
three year-old lake trout at Saugatuck. It is possible
to improve the agreement for these young age
classes  if model parameters  were allowed to be
adjusted  independently for individual  age classes.
However,  in this  study, model parameters  were
defined to be species-specific  or assigned to age
groups (young or adult) of a species.  Therefore, they
were  not individually refined for each age class.
Rather, they were optimized systematically for all age
classes or combined age classes of a species. The
restriction of  excessive parameter calibrations  is
important to reduce the risk of the calibration process
being a mere curve-fitting exercise. The discrepancy
between the modeled and the observed PCB data for
the young lake trout does not necessarily indicate the
model's limitation. In fact, this discrepancy may be
attributed to the difference  in  the environmental
condition between model simulated and the real one
occupied by the young lake trout.  Lake trout is a
stocked species in  Lake Michigan (Holey et al.,
1995).   Before  it  was  exposed  to  the  lake
environment and associated food webs, it was reared
in hatchery facilities around the lake  (Peck,  1979;
Rybicki,  1990)  and  was exposed  to a  controlled
environment  and  food.    It  is  likely   that the
manufactured  fish foods  used in the  hatchery
facilities  were  less  PCB-contaminated  than the
natural food items used in the food  web models.
Therefore, the stocked young lake trout should have
lower PCB concentrations than that estimated by the
food web model. An incorporation of the exposure
environment in hatchery facilities  into the current
model framework may improve the calibrations for
the young lake trout.  Until then, higher predicted
PCB levels in the young lake trout are  expected.

A similar argument can be made for the calibration
results of young  coho  salmon, another stocked
species.  Figure 5.5.1 shows that, with exception of
few PCB congener data, the model results for young
coho  salmon  were  generally higher  than the
observed ones for most PCB congeners.
In order to evaluate the agreement between modeled
and observed data in relation to the hydrophobicity of
individual PCB congeners, an individual comparison
was made for each PCB congener. As an example,
the PCB congener data for all age classes of the lake
trout at Saugatuck are illustrated in Figure 5.5.2. The
results indicate that the calibrated model performed
equally well for  PCB congeners over a range of
different hydrophobicities (log Kow ranges from 5.6 to
7.71).  For all PCB congeners,  the modeled and
observed data agreed well, taking into consideration
of the uncertainty associated with the measured PCB
data for individual congeners.

No  comparison   could  be  made of the  current
calibration to other modeling studies in terms of
model performance. No similar modeling attempt
has been reported to reproduce congener-specific
PCB data for an entire  aquatic food web.   Most
previous calibrations were focused on total PCBs
only and were usually performed for adult predators
without consideration of model  results for forage
species.  While  current  calibrations yielded  good
agreements  between the simulated and observed
congener-specific  PCB   concentrations,  it  is
interesting to see how well the  calibrated models
perform in terms  of the total PCB concentrations,
Modeled total PCB data in this study were estimated
by  summing  model  results for  individual  PCB
congeners and scaling the sum based on the ratio of
total PCBs to the sum of the targeted congeners from
the 1994-1995 observed data. For Saugatuck lake
trout, the ratio was 1.369. Figure 5.5.3 illustrates the
comparison between modeled and observed total
PCB data  for all age  classes of  lake trout  at
Saugatuck. The result indicates  that the calibrated
food web model reproduces total PCB concentrations
in the lake trout and its bioaccumulation trend for the
age classes reasonably well.

References

Buckman, A.H.,  S.B.  Brown, P.F. Hoekstra, K.R.
   Solomon, and AT. Fish. 2004. Toxicokinetics of
   Three   Polychlorinated  Biphenyl   Technical
   Mixtures  in   Rainbow Trout  (Onchorynchus
   mykiss).  Environ. Technol. Chem., 23(7):1725-
   1736.
                                              498

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Figure 5.5.2. Individual comparison between modeled and observed data for PCB congeners in lake

trout at Saugatuck (1994 and 1995).
                                                   499

-------

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Figure 5.5.3. Comparison between modeled and observed total PCBs for lake trout at Saugatuck (1994
and 1995).
Gobas,  F.A.P.C.,  D.C.G. Muir,  and  D.  Mackay.
   1988. Dynamics of Dietary Bioaccumulation and
   Faecal  Elimination  of Hydrophobic  Organic
   Chemicals in Fish.  Chemosphere, 17(5):943-
   962.

Holey, M.E., R.W. Rybicki, G.W. Eck,  E.H. Brown,
   Jr., J.E. Marsden,  D.S. Lavis, M.L. Toneys, T.N.
   Trudeau, and  R.M.  Horrall.  1995.   Progress
   Toward Lake Trout Restoration in Lake Michigan.
   J. Great Lakes Res., 21 (Suppl. 1 ):128-151.

Madenjian, C.P., T.J. DeSorcie, and R.M. Stedman.
   1998a.  Ontogenic and Spatial Patterns in Diet
   and  Growth of Lake Trout  in Lake Michigan.
   Trans. Amer. Fish. Soc., 127(2):236-252.

Madenjian, C.P., R.J. Hesselberg, T.J. DeSorcie, L.J.
   Schmidt,  R.M. Stedman,  R.T.  Quintal,  L.J.
   Begnoche, and D.R. Passino-Reader. 1998b.
   Estimate of  Net Trophic Transfer  Efficiency of
   PCBs to Lake  Michigan Lake Trout From Their
   Prey. Environ. Sci. Technol., 32(7):886-891.
Madenjian, C.P., T.J. DeSorcie, R.M. Stedman, E.H.
   Brown, Jr.,  G.W.  Eck,  L.J.  Schmidt,  R.J.
   Hesselberg, S.M.  Chernyak, and D.R. Passino-
   Reader.    1999.    Spatial  Patterns in  PCB
   Concentrations of Lake Michigan Lake Trout. J.
   Great Lakes Res., 25(1 ):149-159.

Muir, D.C.G. and A.L. Yarechewski.  1988.  Dietary
   Accumulation  of  Four  Chlorinated  Dioxin
   Congeners  by Rainbow  Trout  and Fathead
   Minnows. Environ. Toxicol. Chem., 7(3):227-236.

Niimi, A.J. and B.G. Oliver. 1983.  Biological  Half-
   Lives  of  Polychlorinated   Biphenyl  (PCB)
   Congeners in Whole fish and Muscle of Rainbow
   Trout  (Salmo gairdneri).   Canadian J.  Fish.
   Aquat. Sci., 40(9): 1388-1394.

Peck.J.W. 1979.  Utilization of Traditional Spawning
   Reefs by Hatchery Lake Trout in the Upper Great
   Lakes.     Michigan   Department  of  Natural
   Resources,  Lansing,   Michigan.    Fisheries
   Research Report  Number 1871, 33 pp.
                                              500

-------
Rybicki, R.W. 1990. Growth, Survival, and Straying
   of Three  Lake Trout Strains Stocked  in  the
   Refuge of Northern Lake Michigan.  Michigan
   Department  of Natural  Resources, Charlevoix.
   Michigan. Fisheries  Research Report Number
   1977.

Stapleton, H.M., R.J. Letcher, J. Li, and J.E. Baker.
   2004.  Dietary Accumulation and Metabolism of
   Potybrominated Diphenyl Ethers by Juvenile Carp
   (Cyprinus carpto).    Environ.  Toxicol.  Chem..
   23(81:1929-1946.

Thomann,  R.V.,  J.P. Connolly, and T. Parkerton.
   1992. An Equilibrium Model of Organic Chemical
   Accumulation  in  Aquatic Food  Webs With
   Sediment Interaction. Environ. Toxicol. Chem.,
   11(51:615-629.
U.S. Environmental Protection Agency. 1997a. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium,  Volume 1:  Sample  Collection
   Techniques.   U.S.  Environmental  Protection
   Agency, Great Lakes National Program Office.
   Chicago. Illinois. EPA905 R-97,012a. 1.440pp.

U.S. Environmental Protection Agency. 1997b. Lake
   Michigan Mass Balance Study (LMMB) Methods
   Compendium, Volume 2: Organic and Mercury
   Sample  Analysis   Techniques.     U.S.
   Environmental  Protection Agency, Great Lakes
   National  Program  Office.  Chicago.  Illinois.
   EPA'905.'R-97/012b. 532 pp.
                                            501

-------
           PARTS




       LM FOOD CHAIN
Appendix 5.5.1. PCB Concentrations
             502

-------
  PCB Concentrations (pg/gdry) in Lake Michigan Zooplankton (1994 and 1995)
Congener
003
8+5
012
013
15+17
16+32
018
026
018
031
033
037
042
044
049
052
56+60
066
70+76
074
77+110
081
084
085
087
089
092
099
101
105+132
118
123+149
163+138
146
151
153
170+190
172
180
187+182
208+195
196
197
201
203
Sturgeon Bay
942.42
0.00
0.00
0.00
346.18
611.41
428.48
1162.99
0.00
780.54
155.28
627.83
177.23
1551.78
1361.32
3184.89
140.42
2494.88
1819.84
485.56
4734.04
526.94
138.86
1196.67
1172.30
0.00
1717.47
2236.80
3555.53
945.97
1848.85
2055.80
5421.44
604.57
705.24
4141.67
1370.12
0.00
2233.74
1355.95
271 .63
561.42
0.00
859.29
755.51
Sheboygan Reef
4596.10
0.00
0.00
36.50
505.07
14.10
879.95
3091.17
202.80
1008.82
144.44
1055.40
196.84
2167.43
1865.86
6015.69
393.11
4837.90
2449.22
891.89
7091 .83
640.49
209.04
1693.86
1530.19
114.54
4379.51
4110.96
7212.97
575.56
4981 .37
4760.39
10186.65
2325.00
1630.44
10988.05
2069.29
89.13
7918.56
3872.70
768.08
1627.29
0.00
2013.64
2126.44
Saugatuck
2524.99
0.00
20.63
37.72
1701.43
533.16
1532.19
3287.49
275.03
2932.27
557.12
729.66
344.48
3387.58
3048.96
9735.53
430.87
5282.59
4070.87
2982.48
8637.94
929.34
569.02
1576.04
2107.52
1.98
4713.77
5052.15
9913.42
1200.71
5604.17
6475.89
13291.16
1961.19
2229.45
9058.01
2704.17
0.00
6411.65
3372.63
476.79
1231.23
6.23
2227.83
1421.58
Total PCBs (ng/gdry)
79.76
164.96
                                       186.57
                                     503

-------
      PCB Concentrations (pg/gdry) in Lake Michigan Mys/s(1994 and 1995)
Congener
003
8+5
012
013
15+17
16+32
018
026
018
031
033
037
042
044
049
052
56+60
066
70+76
074
77+110
081
084
085
087
089
092
099
101
105+132
118
123+149
163+138
146
151
153
170+190
172
180
187+182
208+195
196
197
201
203
Sturgeon Bay
295.99
0.00
0.00
0.00
864.95
247.21
317.42
2999.53
2556.78
2962.47
572.50
642.45
900.57
2114.48
1396.40
2338.15
3878.33
17962.44
7397.18
5483.80
16483.66
1978.49
431.90
5453.35
2871.70
690.72
5534.60
8409.94
10566.23
3981 .38
12965.63
6600.91
23085.36
6185.89
1450.94
18171.41
3114.04
1653.80
9439.54
6134.45
598.11
1273.98
143.04
2629.61
1686.18
Sheboygan Reef
1442.10
0.00
0.00
0.00
764.41
365.10
335.53
2396.32
1844.79
2460.94
437.38
390.94
498.41
1423.97
1001.56
1432.18
3459.49
12566.99
5921.41
3204.08
15121.06
1797.28
462.87
4998.50
2445.71
1253.79
4544.90
7174.27
8639.79
3512.08
12239.49
5732.46
19870.08
4776.05
1198.93
15615.26
2729.24
961.35
7593.51
5843.48
577.48
1164.65
104.25
2469.69
153
Saugatuck
2776.39
0.00
0.00
7.83
727.53
676.49
387.07
3763.90
3874.53
3804.77
768.72
538.32
1219.20
2141.67
1385.91
2443.17
5055.25
15158.91
8394.57
4788.27
18968.39
1554.18
568.36
6336.71
3263.37
1422.84
6474.88
9223.89
10362.89
5012.26
13760.94
7126.46
25270.07
6196.97
1543.19
18526.82
4091.89
1845.96
10890.27
7583.53
950.25
2111.79
168.82
3660.18
2578.83
Total PCBs (ng/gdry)
268.55
231.50
304.20
                                      504

-------
    PCB Concentrations (pg/gdry) in Lake Michigan Diporeia (1994 and 1995)
Congener
003
8+5
012
013
15+17
16+32
018
026
018
031
033
037
042
044
049
052
56+60
066
70+76
074
77+110
081
084
085
087
089
092
099
101
105+132
118
123+149
163+138
146
151
153
170+190
172
180
187+182
208+195
196
197
201
203
Sturgeon Bay
3906.66
0.00
189.56
43.98
1770.95
1307.43
1409.46
7416.87
3395.46
4529.39
375.32
1809.82
1294.33
4256.19
3808.59
10389.60
5368.74
16343.52
7109.30
5273.06
22583.42
2503.48
3197.15
6488.84
4386.27
1377.35
7507.20
9411.39
16886.93
5656.26
15147.07
8145.97
23232.90
5766.45
3775.24
18210.67
4153.52
2213.68
12962.69
6036.73
1713.80
2536.71
261.67
5337.86
3508.22
Sheboygan Reef
5658.22
0.00
0.00
22.03
2020.00
1339.92
1624.00
10941.37
3496.20
4809.64
1191.56
1732.81
1711.93
4520.00
4495.64
12587.27
4930.76
18348.06
7900.30
4219.65
27073.80
2486.35
3343.36
7345.93
4808.38
2314.04
7997.86
11629.65
20088.19
8086.64
15025.20
10093.62
31096.81
7260.52
4523.58
23022.24
7085.55
3342.80
16248.07
8097.15
2549.43
3680.19
363.58
7118.87
4486.55
Saugatuck
3962.63
0.00
62.00
0.00
1768.21
718.87
1782.47
10387.19
1071.94
4855.98
1175.22
2324.90
1912.89
5510.35
5355.30
14273.38
1209.98
16440.11
9109.38
4824.45
23745.34
2144.60
3077.91
5647.89
4888.14
884.02
8354.76
10301.72
19176.48
2835.04
13256.51
11363.05
29498.80
6785.31
4541.13
20568.66
7736.04
3414.31
18870.57
8591.93
2502.72
4064.65
305.39
7602.03
5058.51
Total RGBs (ng/gdry)
334.12
438.93
458.97
                                     505

-------
   PCB Concentrations (pg/gwet) in Sturgeon Bay Forage Fish (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Alewife
(< 120 mm)
0.000
10.430
0.000
0.000
0.000
2.030
5.229
2.540
1.466
0.371
14.853
10.112
2.747
3.210
6.199
3.156
7.973
5.786
0.056
8.933
8.511
2.377
4.952
1.313
2.491
0.809
4.646
2.716
0.240
1.752
0.084
1.959
0.142
Alewife
(> 120 mm)
1 1 .309
19.914
4.674
5.844
10.254
1 1 .293
29.891
12.458
8.869
1.595
22.095
37.137
10.889
14.144
20.015
14.297
26.990
25.380
0.176
29.602
38.504
7.384
13.634
3.212
5.596
2.401
13.806
8.151
0.666
4.951
0.223
5.169
0.296
Bloater
(<160mm)
4.610
0.000
6.327
0.000
6.392
9.505
26.145
11.314
-6.745
1.283
18.612
44.757
10.867
15.814
22.667
14.575
28.566
25.432
0.198
37.441
47.722
9.073
11.978
3.860
7.643
3.447
19.987
1 1 .201
1.021
7.438
0.319
6.677
0.531
Bloater
(> 160 mm)
1 1 .290
0.000
5.516
0.000
7.248
12.013
31.444
14.319
8.675
1.772
20.619
54.165
14.158
20.217
26.797
18.511
38.224
33.211
0.308
46.953
52.778
10.518
15.353
4.453
8.045
3.597
21 .033
11.706
1.221
7.788
0.327
6.638
0.545
Deepwater
Sculpin
3.865
0.000
0.000
0.000
3.587
5.964
17.164
2.057
4.268
0.273
12.972
10.183
10.934
12.301
1.791
14.156
1.879
25.661
0.164
44.284
39.733
0.965
1.088
0.870
7.527
0.702
20.044
0.696
1.075
6.710
0.341
1.674
0.417
Slimy
Sculpin
3.750
0.000
0.000
0.000
1.504
6.902
20.223
4.867
5.411
0.589
21 .998
30.950
11.030
13.387
13.398
12.552
14.950
22.083
0.109
35.290
38.979
6.368
3.506
2.932
6.423
2.710
13.790
5.999
0.988
5.650
0.260
4.050
0.406
Rainbow
Smelt
3.897
0.000
0.000
0.000
1.681
5.929
14.597
6.381
3.869
0.561
20.743
23.046
7.338
8.558
9.145
9.082
13.985
15.399
0.117
25.625
32.502
6.106
7.258
1.490
2.648
1.392
8.513
6.541
0.374
2.532
0.159
2.389
0.181
Total PCBs
172.116
560.804
                                  575.407
733.247
314.139
417.064  315.647
                                       506

-------
 PCB Concentrations (ng/gwet) in Sheboygan Reef Forage Fish (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Alewife
(<120mm)
3.256
0.000
0.000
3.794
7.518
4.101
11.592
5.386
2.222
0.258
22.356
18.842
3.998
5.708
10.598
6.140
13.830
8.954
0.114
12.590
18.392
3.655
6.245
1.525
3.283
1.074
5.879
3.330
0.310
2.326
0.089
2.373
0.131
Alewife
(> 120 mm)
5.826
17.442
4.725
0.000
7.477
10.283
30.267
12.757
8.767
1.014
25.641
39.098
10.782
14.739
22.411
14.964
28.398
23.092
0.251
30.020
40.612
8.056
13.816
3.123
6.858
2.643
14.764
8.193
0.756
5.615
0.226
5.539
0.317
Bloater
(<160mm)
5.176
0.000
0.000
0.000
5.680
8.582
22.764
1 1 .334
5.688
1.369
32.846
54.979
14.992
19.385
29.371
19.847
35.807
35.099
0.308
53.335
73.856
15.614
16.067
5.699
13.007
5.204
29.506
14.603
1.623
1 1 .622
0.462
10.145
0.708
Bloater
(> 160 mm)
9.765
0.000
0.000
0.000
4.368
13.409
35.035
17.053
8.894
1.980
31 .378
62.057
19.016
26.322
24.681
26.596
42.061
45.496
0.284
62.731
84.649
17.237
17.228
4.268
10.441
4.516
27.837
18.631
1.249
9.774
0.508
8.146
0.602
Deepwater
Sculpin
0.000
0.000
0.000
0.000
0.000
9.164
25.382
2.514
5.477
0.309
22.663
15.595
14.039
18.989
2.941
25.039
2.737
44.167
0.460
65.938
68.900
0.805
1.661
1.223
14.640
0.836
32.047
0.630
1.909
11.016
0.490
2.093
0.661
Slimy
Sculpin
0.000
0.000
0.000
0.000
1.013
5.943
17.985
4.395
3.853
0.448
26.423
32.187
10.972
16.932
14.064
15.741
13.300
28.068
0.187
39.911
52.639
8.398
3.357
2.748
9.105
3.151
19.841
6.081
1.225
7.622
0.285
4.599
0.501
Rainbow
Smelt
0.000
0.000
0.000
0.000
0.000
5.223
13.927
5.845
3.333
0.543
22.723
24.521
6.759
9.077
9.083
9.552
15.578
16.827
0.224
22.113
30.566
6.086
7.115
1.337
3.100
1.438
8.622
6.368
0.346
2.720
0.137
2.521
0.163
Total PCBs    220.814     546.978    748.376     831.049     501.386    419.502    300.601
                                      507

-------
PCB
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Concentrations (ng/gwet) in Saugatuck Forage Fish (1994 and 1995)
Alewife
(<120mm)
8.369
12.556
0.000
0.000
5.064
4.031
11.127
5.283
2.762
0.843
21.428
19.299
4.345
6.106
11.825
4.641
13.259
9.053
0.060
12.061
16.115
3.638
7.161
1.928
2.478
1.005
5.795
3.209
0.269
2.153
0.098
2.328
0.130
Alewife
(> 120mm)
9.774
23.427
0.000
12.753
10.687
9.631
25.752
1 1 .572
7.322
1.493
24.871
35.866
9.506
13.533
21 .757
13.427
26.335
23.145
0.169
30.510
40.579
7.868
15.348
3.534
5.940
2.545
16.046
10.416
0.713
5.531
0.229
5.925
0.279
Bloater
(< 160 mm)
3.847
0.000
0.000
0.000
16.495
6.094
16.980
9.069
4.327
1.033
22.140
43.896
9.436
14.445
23.211
13.123
24.681
24.234
0.201
37.195
49.265
10.892
13.537
4.619
9.344
3.632
22.774
12.581
1.255
9.494
0.387
8.416
0.635
Bloater
(> 1 60 mm)
8.098
0.000
0.000
0.000
1 1 .790
11.164
30.477
18.093
9.070
1.633
28.286
63.825
12.142
23.084
28.569
18.116
36.576
31.442
0.255
51 .392
67.268
13.072
18.804
5.717
1 1 .285
4.637
31 .759
16.100
1.514
10.981
0.507
9.230
0.612
Deepwater
Sculpin
0.000
0.000
0.000
0.000
0.000
4.857
13.539
1.205
3.879
0.198
17.683
5.713
8.920
12.242
1.106
14.779
1.076
26.089
0.132
42.229
40.831
0.321
1.044
0.593
13.140
0.365
33.473
0.293
2.331
12.955
0.482
1.224
0.498
Slimy
Sculpin
0.000
0.000
0.000
0.000
8.412
3.654
10.335
3.387
2.999
0.343
21.236
24.274
5.737
8.725
9.976
8.350
8.987
13.556
0.110
24.553
30.389
5.365
2.983
2.913
6.654
2.518
17.227
4.641
1.102
6.902
0.229
4.152
0.400
Rainbow
Smelt
4.240
0.000
0.000
0.000
1.777
4.761
11.979
5.760
3.151
0.593
18.614
19.974
5.141
7.006
7.163
7.826
11.775
14.153
0.084
18.611
24.013
4.155
6.525
1.213
2.594
1.072
9.206
5.999
0.307
2.541
0.122
2.252
0.141
Total PCBs
287.310
573.372
615.045
873.001
                                                                 365.842
376.329
291.917
                                               508

-------
   PCB Concentrations (ng/gwet) in Sturgeon Bay Lake Trout (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age1
0.000
0.000
1 8.226
13.725
2.865
4.367
14.793
4.675
4.097
0.266
29.668
18.243
7.875
10.914
8.835
9.559
12.077
17.712
0.094
24.818
30.316
5.660
5.013
1.784
5.711
2.126
12.361
4.102
0.686
4.655
0.150
3.556
0.278
Age 2
0.000
0.000
0.000
0.000
0.000
5.724
17.145
5.936
4.797
0.000
31 .048
27.508
8.563
11.486
13.159
8.991
14.841
16.302
0.208
22.648
29.042
5.411
5.752
1.680
4.696
2.475
13.768
4.351
0.625
4.004
0.069
3.189
0.243
Age3
10.680
0.000
0.000
5.046
13.777
13.488
41 .804
13.636
10.716
1.371
24.925
60.967
19.108
25.777
30.762
24.529
35.922
38.407
0.300
68.139
99.208
17.116
20.771
6.443
12.011
5.097
28.648
17.432
1.579
10.452
0.501
10.328
0.564
Age 4
17.650
0.000
2.880
14.347
22.469
17.320
50.468
22.306
17.430
2.445
36.077
82.217
24.593
30.332
36.979
30.827
45.971
53.027
0.584
82.362
94.453
20.982
26.291
8.044
14.740
6.625
38.273
19.547
1.989
13.647
0.793
12.528
0.771
Age5
22.534
0.000
5.227
13.267
0.000
25.674
72.777
29.391
20.678
2.605
54.880
126.234
34.299
49.448
55.655
48.503
70.921
87.062
0.722
119.892
168.222
30.064
37.733
10.979
23.259
8.950
55.861
32.642
2.608
19.335
0.984
17.812
1.101
Age 6
41.786
32.120
18.855
20.095
27.100
40.163
110.836
45.679
30.696
5.245
49.721
160.033
50.831
66.624
77.678
59.322
106.324
103.727
1.064
165.469
214.542
43.110
54.963
15.430
29.841
12.034
75.209
42.033
3.594
26.749
1.234
24.681
1.423
Age 7
46.792
42.462
24.149
39.700
42.695
39.780
118.203
47.853
34.171
4.702
70.747
168.053
51.816
74.340
79.291
66.525
109.363
123.433
1.306
181.435
220.792
47.982
57.888
16.537
35.128
12.254
80.560
43.520
3.884
29.878
1.252
24.434
1.363
Total PCBs    349.583
364.790
884.714
1287.150
1742.295
2248.412    2478.275
                                      509

-------
    PCB Concentrations (ng/gwet) in Sturgeon Bay Lake Trout (1994 and 1995)
                                 (Continued)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age 8
66.954
63.982
33.195
37.845
64.537
55.385
186.923
76.679
50.498
6.685
75.729
245.107
57.672
94.898
113.195
79.250
144.145
142.990
1.401
206.942
260.991
56.778
74.369
21 .626
39.045
16.711
102.704
62.193
4.410
35.589
1.692
33.695
1.824
Age 9
77.598
34.107
49.237
41.842
58.491
82.393
255.298
105.075
70.697
6.907
78.939
277.331
109.100
125.154
151.327
99.385
176.551
168.539
1.715
247.985
281 .228
69.026
81.397
21.294
38.363
20.770
115.630
68.694
4.982
34.609
1.708
32.718
1.815
Age 10
1 1 1 .520
36.697
50.701
40.070
92.921
99.851
264.540
110.659
78.345
12.407
100.210
390.988
102.649
156.480
193.178
140.080
227.895
240.937
1.792
320.117
407.877
84.552
117.008
30.955
64.018
28.197
149.134
91.501
8.105
56.418
2.338
51.387
2.758
Age 11
99.443
75.689
32.234
51.421
82.763
137.322
386.921
152.905
107.210
13.600
140.067
416.209
141.713
209.150
267.999
165.916
309.951
276.779
2.359
320.940
398.335
80.844
127.702
34.259
70.959
33.870
186.764
95.639
7.937
62.819
2.813
58.205
2.785
Age 12
107.946
53.131
28.188
62.538
87.828
73.866
234.730
95.558
66.468
10.319
96.935
284.160
89.222
151.254
160.833
133.617
232.517
226.950
1.702
304.475
304.516
90.964
113.001
32.845
55.435
26.544
156.371
71.585
7.043
51.484
2.245
48.042
2.443
Total PCBs         3395.8         4267.0         5190.4         6416.8        5024.9
                                     510

-------
  PCB Concentrations (ng/gwet) in Sheboygan Reef Lake Trout (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age3
2.349
0.000
3.051
0.000
0.000
7.959
22.084
8.479
5.341
0.727
29.304
37.496
12.537
14.628
16.199
15.756
20.635
28.838
0.206
45.226
59.090
6.424
8.984
2.774
8.053
3.109
19.163
7.342
1.205
7.677
0.375
4.954
0.453
Age 4
6.491
0.000
0.000
0.000
4.723
1 1 .579
29.180
1 1 .284
7.176
0.869
33.709
49.209
19.777
25.622
20.698
22.053
25.731
42.093
0.270
60.176
75.884
11.309
10.106
3.524
15.352
5.254
36.244
9.645
1.721
12.473
0.540
7.097
0.552
Age 5
8.572
0.000
0.000
0.000
10.966
20.051
52.964
18.908
13.591
1.706
42.660
85.739
28.877
40.493
41.807
35.820
46.707
61.520
0.550
97.252
128.908
20.558
21.734
6.856
21.165
7.616
49.873
20.146
2.812
19.801
0.782
12.938
1.019
Age 6
13.126
0.000
0.000
14.172
12.785
20.911
57.596
26.067
15.090
2.401
49.367
104.224
30.582
40.355
51.197
34.615
64.792
60.930
0.655
87.272
116.502
25.583
27.628
7.979
18.992
7.988
48.246
23.297
2.438
17.086
0.780
14.318
0.924
Age?
21 .200
17.724
0.000
6.689
22.029
27.984
82.542
33.795
21.299
3.278
57.814
133.213
37.123
50.249
61.600
57.052
82.438
100.543
1.069
143.787
180.747
37.545
42.637
13.019
28.705
1 1 .342
74.927
33.271
3.542
26.200
1.251
21 .804
1.378
Age8
23.931
13.163
23.109
9.919
23.444
32.555
90.674
37.338
22.533
3.318
70.299
165.941
42.600
71.108
77.654
75.080
109.961
140.992
1.471
190.680
237.563
49.172
62.837
17.667
37.889
14.512
86.163
45.150
4.914
34.354
1.505
30.457
1.856
Total PCBs      544.1        749.0      1300.1       1329.4       2011.4       2662.7
                                     511

-------
  PCB Concentrations (ng/gwet) in Sheboygan Reef Lake Trout (1994 and 1995)
                                (Continued)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
1 32+1 53
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age 9
27.117
14.768
17.491
14.438
34.453
39.777
118.889
49.819
30.417
4.220
70.967
196.518
38.322
83.588
102.514
85.615
123.866
145.182
1.521
215.050
261.633
59.306
76.345
20.782
39.775
16.805
100.669
56.780
5.210
36.921
1.643
33.076
2.148
Age 10
38.287
1 1 .304
33.339
23.805
49.661
56.678
164.240
70.778
44.126
7.203
87.231
258.625
44.303
109.036
144.378
1 1 1 .966
204.392
182.151
1.666
233.549
334.204
74.663
108.299
28.133
53.027
23.309
128.123
75.421
7.259
49.995
1.948
46.398
2.865
Age 11
59.381
23.388
42.762
31.731
62.762
73.976
222.135
86.727
57.365
8.449
109.337
314.461
67.224
144.470
168.091
170.353
217.835
303.263
2.902
385.133
478.044
109.645
131.518
36.344
82.994
29.654
207.344
85.610
10.952
77.609
3.471
64.124
3.829
Age 12
50.904
7.998
31.584
28.917
51 .654
65.390
186.011
71.353
50.362
7.179
97.456
266.142
47.548
123.786
138.410
145.071
177.645
236.468
2.261
333.006
414.488
82.588
109.975
32.945
83.730
22.048
173.536
74.777
11.943
74.577
2.697
45.504
3.766
Total PCBs
2885.367
3847.910
                                                   5291.523
4320.339
                                     512

-------
     PCB Concentrations (ng/gwet) In Saugatuck Lake Trout (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age1
0.000
0.000
0.000
0.000
0.000
2.407
6.976
2.972
2.101
0.420
15.370
15.212
3.366
4.767
7.864
4.060
8,261
8.237
0.038
15.417
18.143
3.369
4.445
2.005
3.444
1.455
8.902
3.628
0.518
3.647
0.000
2.746
0.282
Age 2
0.000
0.000
0.000
0.000
54.702
10.121
30.022
12.541
8.940
1.597
34.205
65.689
18.363
22.423
32.706
21.851
35.693
41.458
0.192
66.905
87.664
16.388
17.108
7.014
13.279
5.627
35.430
16.926
1.730
1 1 .663
0.727
9.588
0.600
Age3
0.000
0.000
0.000
0.000
15.423
10.335
28.322
13.400
8.828
1.209
28.866
60.687
16.325
20.386
32.377
17.916
35.403
33.447
0.195
56.234
71.280
13.260
1 9.775
6.813
13.325
5,426
35.959
17.716
1.735
12.134
0.632
8.852
0.573
Age 4
12.689
0.000
7.271
0.000
21.443
16.764
50.218
20.900
14.090
1.798
33.756
98.227
27.565
34.339
45.768
34.405
52.105
65.537
0.348
104.088
131.670
22.683
29.044
10.232
18.845
8.320
51 .095
27.286
2.472
17.917
0.945
14.221
0.873
Age5
25.357
60.046
18.186
12.937
28.489
25.861
75.783
32.136
20.651
3.538
40.463
133.026
39.935
49.952
64.437
48.238
74.116
89.691
0.448
146.448
186.316
32.292
41.290
14.458
24.501
10.427
68.493
32.853
3.321
23.482
1.156
18.886
1.162
Age 6
54.836
149.814
26.458
23.683
42.646
41.315
128.176
57.787
35.901
5.481
63.679
228.572
62.355
79.670
117.938
68.026
134.279
127.994
0.733
192.622
255.139
52.359
71.993
22.469
36.805
15.972
95.069
67.401
4.614
34.081
1.664
31.062
1.718
Total PCBs       175.474     1006.024      854.026      1325.389     2048.679     3169.660
                                      513

-------
     PCB Concentrations (ng/gwet) in Saugatuck Lake Trout (1994 and 1995)
                                  (Continued)
Congener
28+31
33
44
49
52
56+60
66
70+76
74
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Age?
41.441
0.000
27.384
24.918
53.326
41 .852
134.351
55.184
40.012
5.054
62.337
223.314
59.930
92.361
117.717
90.825
121.589
159.552
0.959
277.072
350.735
52.772
80.649
27.012
55.392
19.716
142.617
81.900
7.168
51.628
2.424
39.138
2.316
Age8
58.278
79.286
38.107
54.032
97.743
65.226
186.428
85.751
50.604
6.480
89.576
315.128
72.836
117.029
175.219
108.533
181.002
182.133
1.659
276.537
371 .566
82.009
107.123
33.280
55.922
25.493
156.647
76.951
7.541
55.952
2.386
49.023
2.662
Age 9
94.278
45.341
60.763
66.320
113.608
86.046
296.478
132.429
85.699
9.185
115.261
440.209
84.631
158.320
241.748
129.450
238.491
222.638
1.817
351.994
461 .226
93.598
125.935
43.959
68.048
30.099
179.478
102.800
9.519
66.377
2.816
57.490
3.202
Age 10
89.884
24.287
57.841
63.576
133.449
86.744
293.366
124.580
84.646
9.300
124.845
483.935
85.427
177.869
275.204
144.524
271.898
263.991
2.146
380.232
532.781
119.568
163.525
48.187
95.684
38.397
249.146
124.901
13.455
99.183
3.763
73.102
4.315
Age 11
71 .240
68.407
0.000
46.492
89.570
106.207
326.176
126.473
91 .269
9.469
132.524
467.124
67.642
226.828
251 .779
273.178
273.766
396.848
1.067
487.895
651.971
116.769
168.020
53.570
133.525
39.965
315.883
162.844
18.328
124.932
5.345
81.521
5.277
Age 12
74.058
32.485
85.738
68.946
110.241
97.381
291.395
127.506
83.357
11.042
119.929
450.639
87.862
191.150
245.095
225.447
261.772
318.105
2.647
438.150
547.906
136.812
176.019
55.721
113.024
39.942
276.035
164.270
15.683
108.448
4.608
80.992
4.428
Total PCBs
3530.741
4430.807
                                    5958.251
                                   6542.673
                                   7476.621
6541.367
                                      514

-------
  PCB Concentrations (ng/gwet) in Lake Michigan Coho Salmon (1994 and 1995)
Congener
28+31
33
44
49
52
56+60
66
70+76
074
77
81+87
89+84+92
85
99
101
105
110
118
123
132+153
163+138
146
149
151
170+190
172
180
187+182
195
203+196
197
201
208
Hatchery
0.000
0.000
0.000
0.000
0.000
0.831
4.261
3.380
2.179
0.000
15.588
9.848
1.986
3.236
7.712
2.426
7.763
6.489
0.000
5.912
7.482
1.148
2.887
1.015
0.831
0.278
2.231
2.239
0.098
0.717
0.013
0.670
0.060
Yearling
0.000
0.000
0.000
0.000
0.000
1.955
5.117
2.828
1.311
0.264
13.926
10.935
2.855
3.688
6.708
3.166
6.956
5.805
0.034
11.521
15.237
2.778
4.611
1.305
2.131
1.013
6.447
3.229
0.328
2.383
0.115
2.358
0.133
Age 2
(April-May)
7.456
0.000
0.000
0.000
5.398
3.726
1 1 .520
5.557
3.305
0.632
16.780
18.090
4.998
6.318
10.768
5.716
13.981
10.452
0.105
14.538
20.484
2.877
6.049
1.589
3.522
1.250
7.808
4.324
0.417
2.920
0.123
2.724
0.168
Age 2
(June)
17.375
15.158
10.925
2.036
17.627
9.774
32.110
13.159
9.079
1.436
24.814
50.450
14.463
18.208
28.445
16.229
34.774
32.501
0.331
45.758
66.101
14.276
18.890
5.109
9.169
3.738
21 .549
12.225
1.043
8.102
0.330
7.455
0.431
Age 2
(July-Oct.)
17.334
35.219
11.268
13.136
18.419
17.444
57.477
24.125
16.652
2.594
34.594
95.721
21 .408
34.905
52.062
30.831
59.180
60.284
0.427
86.036
122.707
23.141
33.776
9.346
16.075
7.271
41.654
24.890
2.038
15.307
0.614
15.002
0.667
Age 2
(November)
18.672
15.184
9.721
8.569
15.121
13.042
42.305
18.233
11.621
2.370
28.300
64.203
18.322
24.366
35.947
21.356
41.925
40.341
0.246
60.727
87.200
13.169
21.454
5.672
10.634
4.724
25.801
17.066
1.144
8.816
0.366
8.712
0.403
Total PCBs      112.920      170.560      279.089      690.301     1379.036      926.867
                                     515

-------
                     PARTS

                LM FOOD CHAIN
Appendix 5.5.2. Agreement Between Modeled and Observed
                PCB Concentrations
                       516

-------
Agreement Between Modeled and Observed PCB Concentrations in Zooplankton,

              Mysis, and D/pore/aat Sturgeon Bay (1994 and 1995).
                        0.0010
                                  0.0002  0.0004 0.0006  0.0008 0.0010
                                     measured PCB (pg/g-wet)
                      •E?
                      O>


                      m
                      O
                      0.

                      T3
                      5?
                      0)
                      •o
                      O
                         0.004
                         0.003
0.002
0.001
                                    0.001     0.002    0,003

                                     measured PCB (ug/g-wet)
                                   0.004
                      •5?
                      O)
                      n.

                      CO
                      O
                      DL.
                      Q)
                      73
                      O
                                   0.002     0.004     0.006

                                     measured PCB (pg/g-wet)
                                   0.008
                                        517

-------
Agreement Between Modeled and Observed Fish PCB Concentrations in Forage
                          Fish at Sturgeon Bay (1994 and 1995)
           O Slimy Sculpin
             at Sturgeon Bay
              0.01   0.02   0.03   0.04   0.05
                   measured PCB (ug/g-wet)
     0.03
                                O alewife (<120)
                                  at Sturgeon Bay
                    0.01          0.02
                   measured PCB (ug/g-wet)
                                       0.03
0.06

0.05
   o>  0 04-
            O bloater (<160mm)
              at Sturgeon Bay
              0.01   0.02   0.03   0.04   0.05
                   measured PCB (ug/g-wet)
                                        0.06
      0.04
            O rainbow smelt
              at Sturgeon Bay
                 0.01      0.02      0.03
                  measured PCB (ug/g-wet)
                                        0.04
                                             I
                                             j

                                             co
                                                   *
                                                   
                                                   O
    0.06

    0.05

    0.04

    0.03

    0.02

    0.01
O deepwater sculpin
  at Sturgeon Bay
                                                           0
                                        0.06
       0    0.01    0.02   0.03   0.04   0.05   0.06
                 measured PCB (pg/g-wet)
                                             I
                                             CD
                                             O
                                             a.

                                             I
                                             120mm)
                                                              at Sturgeon Bay
                                                                               0
                                                                         0
                                                                  0
                                                                      n
                                                                       0
                                                                           0
            0.01    0,02   0.03   0.04   0.05   0.06
                 measured PCB (ug/g-wet)
I
-S1
en

CO
                                                            O bloater (>160mm)
                                                              at Sturgeon Bay
           0.01   0.02  0.03  0.04  0.05  0.06  0.07
                 measured PCB (ug/g-wet)
                                               518

-------
Agreement Between  Modeled and Observed Fish  PCB Concentrations in Lake Trout
                                  at Sturgeon  Bay (1994 and  1995).
                                           0.12
              0.01 0.02 0.03 0,04 0.05 0.06
              measured PCB (ug/g-wet)
                                            0.02 0.04 0.06  0.08 0.10 0.12
                                            measured PCB (|jg/g-wet)
                                                                              0.12
                                                                            f 0.10
                                       0.02 0.04 0.06  0.08  0.10 0.12
                                        measured PCB (pg/g-wet)
                                           0.20
                                                                              0.30
                 0.04    0.08    0.12
               measured PCB (ug/g-wet)
                                             0.04  0.08  0.12  0.16  0.20
                                             measured PCB (ug/g-wet)
                                    0  0.05 0.10 0.15 0.20 0.25 0.30
                                        measured PCB (ug/g-wet)
        0.40
     f 0.35
     1 0.30
     lo.25
     g 0.20
     | 0.15
     "5 0.10
     1 0.05
          0
                0.10   0.20   0.30    0.40
              measured PCB (ug/g-wet)
                                      0.5

                                    S 04
                                    jji
                                    D>
                                    3 03
                                    O
                                    Q- 0.2
                                    1
                                    § 0.1
0   0.1    0.2   0.3  0.4   0.5
    measured PCB (ug/g-wet)
                                 0.5

                              Z  0.4
                              •51
                              o>
                              ^  0.3
                              GO
                              S-  0.2
                                                                                 0.1   0.2   0.3   0.4   0.5
                                                                                 measured PC B (ug/g-wet)
8
a.
         0.6
         0.5
         0.4

         °-3
         0-2
         0.1
          0
           0   0.1  0.2  0.3  0.4  0.5
              measured PCB (ug/g-wet)
                              0.6
0.7
06

04
0.3
0.2
0.1
   0.1 0.2 0.3  0.4 0.5 0.6
    measured PCB (ug/g-wet)
                                                                       0.7
                              CO
                              O
                              Q_
                              •o
0.8

0.6

0.4

0.2
                                            0.2    0.4     0.6
                                          measured PCB (pg/g-wet)
                                                                                                          0.8
                                                      519

-------
Agreement Between Modeled and Observed PCB Concentrations in Zooplankton,
             Mysis, and Diporeia at Sheboygan Reef (1994 and 1995)
                        0.0035
                                                zooplankton at
                                                Sheboygan Reef
                             0  0.0005     0.0015     0.0025
                                     measured PCB (pg/g-wet)
0.0035
                         0.005
                                                Mysis at
                                                Sheboygan Reef
                                  0.001  0.002   0.003  0.004   0.005
                                     measured PCB ((jg/g-wet)
                               diporeia at
                               Sheboygan Reef
                             0  0.001     0.003     0.005
                                     measured PCB (ug/g-wet)
0.007
                                         520

-------
Agreement Between Modeled and Observed Fish  PCB Concentrations in Forage
                        Fish at Sheboygan Reef (1994 and 1995)
   0.08
         slimy sculpin
         at Sheboygan Reef
                                                   0.10
               0.02      0.04      0.06
                measured PCB (ug/g-wet)
                                           0,08
   0.04
                               alewife (<120)
                               at Sheboygan Reef
               0.01      O.02       0.03
                measured PCB (ug/g-wet)
                                           0.04
          bloater (<160mm)
          at Sheboygan Reef
               0.02      0.04      O.06
                measured PCB (ug/g-wet)
    0.05
             0.01     0.02    O.O3     O.O4
                measured PCB (ug/g-wet)
                                           0.08
                                           0.05
i
                                                   0.08
                                                   0.06
                                                   0.04
                                                1
                                                   0.02
                                                   0.08
            0.02     0.04    0.06    0.08
               measured PCB (ug/g-wet)
                                         0.10
                              alewife (>120mm)
                              at Sheboygan Reef
              0.02      0.04      0.06
               measured PCB (ug/g-wet)
                                         0.08
         bloater (>160mm)
         at Sheboygan Reef
            0.02     0.04    0.06    0.08
               measured PCB (ug/g-wet)
                                                                                          0.1O
                                              521

-------
Agreement Between Modeled and Observed Fish PCB Concentrations in Lake Trout
                              at Sheboygan Reef (1994 and 1995)
                                                                          0 0.01   0.03  0.05  0.07  0.09
                                                                             measured PCB (ug/g-wet)
         0.01   0.03  0.05  0.07   0.09
           measured PCB (pg/g-wet)
                                                                      0.25
                                     0.04   0.08   0.12   0.16
                                     measured PCB (ug/g-wet)
    0.05 0.10  0.15  0.20 0.25
    measured PCB (ug/g-wet)
                                      0.3
       0       0.1      0.2      0.3
           measured PCB (ug/g-wet)
                                    A)
                                      0.2
                                   CO
                                   O
                                   CL
                                    O)
                                    E
                                      0.1    0.2    0.3   0.4
                                    measured PCB (ug/g-wet)
                                                              035
                                                            | 0.30
                                                            -S»0.25
                                                             D)
                                                            ~0.20
                                                            £ 0.15
                                                            | 0.10
                                                            | 0.05
                                                                 0
0 0.05    0.15    0.25   0,35
     measured PCB (ug/g-wet)
I 0.05  015  0.25   0.35
   measured PCB (ug/g-wet)
                              0.45
                                          0.05    0.15    0.25    0.35
                                            measured PCB (pg/g-wet)
                                                                      0.35
                                                                    | 0.30
                                                                      0.25
                                                                      0.10
                                                                     I 0.05
                                                                        0
  005    0.15    025    0.35
    measured PCB (ug/g-wet)
                                                  522

-------
Agreement Between Modeled and Observed PCB Concentrations in Zooplankton,
                Mysis, and D/pore/aat Saugatuck (1994 and 1995)
                                      0.0010      0.0020      0.0030
                                      measured PCB (ug/g-wet)
                          0.006
                              0   0.001  0.002  0.003 0.004 0.005 0.006
                                      measured PCB (ug/g-wet)
                         0.007

                       ijf 0.006

                       -g 0.005
                       3.
                       m 0.004
                       O
                         0.003 -j
                       o
                       E
                         0.002
                         0.001

                            0
                              0   0.001 0.002 0.003 0.004 0.005 0.006 0.007
                                     measured PCB (ug/g-wei)
                                        523

-------
Agreement Between Modeled and Observed PCB Concentrations in Forage Fish at
                                 Saugatuck (1994 and 1995)
                                                     0.06
                0.01    0.02    0.03    0.04
                   measured PCB (ug/g-wet)
0.05
            0.01   0.02   0.03   0.04   0.05
                measured PCB (ug/g-wet)
                                                                                    0.06
       0.03
    0)


    I
    m
    O
    a.
    O
0.02-
       0.01
   0.06

i" 0.05
   0.04
                                                     alewife (>120mm)
                                                     at Saugatuck
                                                         O
                                                                O
                                                                       O
                                                                     _Q
                     0.01         0,02
                   measured PCB (pg/g-wet)
                                      0.03
                0.01    0.02   0.03   0.04   0.05
                     measured PCB (ug/g-wet)
                                          0.06
                                                     0.10-,
            bloater (<160mm)
            at Saugatuck
              0.01  0.02  0.03  0.04   0.05  0.06
                   measured PCB (ug/g-wet)
0.07
                                                        0.02    0.04    0.06     0.08
                                                           measured PCB (pg/g-wet)
                                          0.10
       0.05
                0.01    0.02    0.03    0.04
                   measured PCB (ug/g-wet)
                                      0,05
                                               524

-------
Agreement Between  Modeled and Observed PCB Concentrations in Lake Trout at
                                    Saugatuck (1994 and 1995)
     0.05
                                         0.10
            0.01  0.02  0.03  0.04 0.05
            measured PCB (ug/g-wet)
                                            0.10
               0.02  0.04  0.06  0.08  0.10
               measured PCB (ug/g-wet)
                                    0.02  0.04  0.06  0.08  0.10
                                    measured PCB (pg/g-wet)
     0.15
   2 0.10
   CO
   o
   CL
     0.05
                                         0.25
               0.05
                        0.10
                                0.15
            measured PCB (pg/g-wet)
               0.05  0.10  0.15  0.20  0.25
                measured PCB (ug/g-wet)
                                            0.35
                                         I" 0.30
                                         -0-25

                                            0,20
                                            0.15
                                            0.10
                                            0.05
                                              0
                                0  0.05 0.10 0.15 0.20 025 0.30 035
                                    measured PCB (ug'g-wet)
            0.1   0.2  0,3   0.4
            measured PCB (ug/g-wet)
0.5
                                          0.6
                                          0.5
                                      I
         0.4
                                      S  0.3
                                       CD
                                       E
         0.2
         0.1
0.1  0.2  0.3  0.4  0.5
measured PCB (ug/g-wet)
                                                                     0.6
                           •5"
                           O)
                           ^.
                           CD
                           O
                           n_
0.6
0.5
0.4
0.3
0.2
                                            0.1
      0.1   0.2  0.3  0,4  0.5  0.6
        measured PCB (ug/g-wet)
      0.7
      0.6
      0.5
      0.4
      0.3
      0.2
      0.1
           0.1 0.2 0.3 0.4 0.5  0.6  0.7
            measured PCB (pg/g-wet)
           0  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
               measured PCB (ug/g-wet)
                                                                             0.8
                                            0.6
                                         99 0.4
                                         I
                                            0.2
                                      0.2     0.4    0.6    0.8
                                    measured PCB (pg/g-wet)
                                                    525

-------
                                           PARTS
                                     LM  FOOD CHAIN
Chapter 6.  Model Verification

5.6.1  Introduction

Model validation represents an independent test of a
model's  ability  to  reproduce  measured  PCB
concentrations in Lake  Michigan fish. Ideally, a
calibrated model should be subsequently validated
with another  set of  observed  data  that  are
independent of those used in the model calibration
process.  Additional sample collection and analysis
for PCB concentrations in the Lake Michigan system
is currently underway. Once completed, this work
will provide a new PCB data set necessary for the
validation of the food web models.

In   this  study,  no  particular  procedures  were
performed to validate the calibrated food web models
due to the limited data currently available.  In fact,
subsequent validation of a calibrated bioaccumulation
model  is  rare because the necessary field data are
not readily available.

5.6.2  Model Applicability to Other Sites

An  alternative of model  evaluation  is to test  the
applicability of the calibrated models to other sites.
For the lake trout food web models, this  test was
possible  thanks to the extensive collection of PCB
data at  the  three biota zones for this  project.
Because  the lake trout food webs consist of the same
predator  and prey species  among the three biota
zones, the model parameters required for simulating
bioaccumulation in the food webs should be identical
at  these three  biota zones.   Therefore,  model
parameters calibrated for one biota  zone  could be
applied  to model food  web  bioaccumulation  at
another biota zone.  The agreement between the
model results and the observed PCB data for the
second biota zone could  then be evaluated for the
verification of the calibrated  model parameters.
Apparently, a properly calibrated model parameter
set should be able to generate good fits for both biota
zones. This exercise could serve as an independent
test of how well the calibrated model represents fish
bioaccumulation   under  different   environmental
characteristics and food web structures.

The calibration results presented in  Chapter 5 show
that the lake trout food webs at Sturgeon Bay and
Saugatuck share a common set of parameter values
(Table 5.5.2).  The identical parameter values for
Sturgeon  Bay and Saugatuck suggested that the
model parameters calibrated for Sturgeon Bay could
be satisfactorily applied to model the Saugatuck food
web, and vice-versa.   In other words, the model
parameters (Table 5.5.2) calibrated with observed
data for Sturgeon Bay have been in effect verified
with the observed data for the Saugatuck biota zone.
Or conversely, the model parameters can be viewed
as having been calibrated with the  Saugatuck data
and verified by the observed data for the Sturgeon
Bay.

It should be pointed out that the identical calibrated
parameter values obtained for the Sturgeon Bay and
Saugatuck food  webs were not achieved under a
"blind test".  In this sense, the applicability to both
Sturgeon  Bay and Saugatuck food webs did not
necessarily  constitute  a strict  validation of the
calibrated models. This  applicability, however, did
provide us  with  a certain  confidence  about the
model's performance at these two biota zones for its
                                              526

-------
intended purpose, which was to establish quantitative
linkage between PCB levels in fish and exposure.

The food web model calibrated for Sheboygan Reef
had a unique set of parameter values (see Table
5.5.3). This indicated that the calibrated model for
this biota zone could  not be  validated  with the
observed data from Sturgeon Bay or Saugatuck. The
reasons  for this discrepancy were  unclear.   The
mismatch in sampling locations for the observed PCB
data for  forage  species  and  other food  web
components  may  be one  explanation.   Due  to
difficulty in collecting fish samples at Sheboygan
Reef, the forage fish samples were collected near
Port Washington instead of from the biota zone. It is
possible that the observed PCB data for the forage
fish   species   did   not  represent  the  actual
contamination levels at Sheboygan Reef. Therefore,
the food web model calibrated with the data may not
be optimized properly.
                                             527

-------
                                           PARTS
                                     LM  FOOD CHAIN
Chapter  7.
Uncertainty
Model  Sensitivity   and
5.7.7  Introduction

Sensitivity analysis was conducted  to study  how
variations in different model parameters affect the
model estimates for polychlorinated biphenyls (PCBs)
in a fish food web. The purpose of this analysis was
to determine which  model  parameters  had the
greatest  influence on model simulations  for PCB
bioaccumulation in the food web.  The results were
very useful for guiding the calibration of the models.
In fact, sensitivity analysis was performed repeatedly
during the  model calibration  through the trophic
levels and for each of the calibrated parameters in
order to properly direct the calibration effort.

No model uncertainty analysis has been performed
to estimate the uncertainty in the model output due to
the variability in model parameters and other input
variables. There are many uncertain aspects in a
model that affect the model output.  For a well-
calibrated model, uncertainties in calibrated model
parameters have a very limited effect on the overall
model uncertainty. This is because the influences of
uncertainty in individual model parameters has been
cancelled out by the model calibration process. The
effect of a change in one parameter can be balanced
by  appropriate  adjustment  to  the  other   (i.e.,
compensating effects).  Therefore, for a calibrated
model, the major sources of model uncertainty are
likely from other aspects, such as the quality of the
field data used to guide the model calibration, the
simplified model representations of fish  behavior,
food web structure and environmental conditions,
and approximations made during model construction.
It is difficult to adequately quantify these sources of
uncertainty.   In addition, the  reliability of model
results for chemical bioaccumulation is associated
with  individual model applications and, to a large
extent, is predominated by the uncertainty in the
input of exposure chemical concentrations whose
reliability is  often not adequately  defined.  Any
applications of a model beyond the domain for which
the model is  calibrated will also likely increase the
uncertainty associated with the model results. It is,
therefore, not an easy task to perform an uncertainty
analysis to define the expected distribution of model
outputs for the fish models.

In this study, model sensitivity to selected parameters
and  input variables were quantitatively assessed in
terms of their relative impacts on the output of the
model. The results are presented in this chapter to
illustrate the  potential range of uncertainty of the
model in association with different model parameters
and  input variables.   In addition, by identifying the
most sensitive input variables, the results can also be
useful  to guide the effort  for effective reduction of
model  uncertainty.

5.7.2  Sensitivity Analysis

Model  outputs for PCB bioaccumulation in the lake
trout food web  at Saugatuck, Lake Michigan, were
used as  an example  for the model sensitivity
analysis.  A total of seven input parameters were
assessed for their influence  on model outputs for
PCBs in the top predators of the food web. These
included four calibrated model parameters:
                                              528

-------
• Chemical assimilation efficiency (a);

• Food assimilation efficiency (P);

• Chemical relative gill transfer coefficient (E,/E0);

• The fraction of ingested  energy  for specific
  dynamic action (SDA);

and three other input variables:

• Fish growth  rate;

• Octanol-water partition coefficient K,^,;

• Fish diet.

 Most of these parameter inputs are species-specific.
 For simplicity, sensitivity simulations were performed
 for input parameters associated with the top predator
 (lake trout) only. This is because the involvement of
 input parameters for species in lower trophic levels
 complicates the sensitivity analysis.  Due to feeding
 interactions,  an input parameter  for a species  in
 lower trophic levels will impact not only the  model
 outputs for that particular species but also the model
 outputs for species in  upper trophic levels.  The
 quantification of this  type of across trophic levels
 propagation  and aggregation of  the impact of a
 parameter is strongly dependent  on the feeding
 relationships between the species and its predators.
 In order to properly isolate the impacts of individual
 parameters on  model output, all parameters in lower
 trophic levels were fixed at their nominal values and
 the parameters associated with lake trout only were
 adjusted for the sensitivity simulations.

 The calibrated food  web model  for lake trout at
 Saugatuck was used to conduct  model sensitivity
 analysis.  The analysis was performed by running the
 model with the modified values  of a tested input
 parameter to   calculate  the  steady-state  PCB
 concentrations  in the fish, while the other parameters
 were  fixed  at their calibrated   values.    PCB
 concentration changes in the lake trout (age four)
 relative to the calibrated model results are used to
 illustrate the  sensitivity of modeled PCB data to the
 change of a particular input parameter.
5.7.2.1  Chemical Assimilation Efficiency (a)

The  chemical  assimilation  efficiency for fish  is
believed to  be  correlated  to  the octanol-water
partition coefficient (KoJ of a  chemical. In this study,
the  correlation  between   chemical  assimilation
efficiency and the K^ value reported by Gobas et al.
(1988) was used in the calibrated model. The values
estimated by this correlation are  generally at the
lower end of the reported chemical assimilation data.
For sensitivity  analysis, the chemical assimilation
efficiency of the lake trout for all PCB congeners was
increased by 20%.   This adjustment of parameter
input  essentially increases the chemical intake of a
fish by 20%. The result of  a sensitivity simulation
indicates that, except for a few low K^, congeners,
PCBs in lake trout increase by 20% as a result of a
similar increase in chemical  assimilation efficiency.
Figure 5.7.1 reflects the  proportional increase  in
computed PCBs in lake trout responding to a 20%
increase in the  values  of  chemical assimilation
efficiency.

5.7.2.2  Food Assimilation  Efficiency (3)

The calibrated values of food assimilation efficiency
for lake trout are related to selection of the values of
chemical assimilation efficiency (a).  For sensitivity
simulations,  the  value of  the food assimilation
efficiency for young lake  trout (ages  1-4)  was
adjusted upward from  0.40  to 0.50.  The value  of
food assimilation efficiency has a direct impact on the
amount of food or chemical intake by a fish.  The
increase in the food assimilation efficiency from 0.40
to  0.50 will result in  a proportional reduction in the
amount of food intake necessary to meet the fish's
energy demand. A reduced  food intake, 80% of the
original   (0.40/0.50  =  0.80),  translates  into  a
correspondingly lower dietary chemical intake by the
fish.  The sensitivity simulation indicates that for high
KOW PCB congeners,  their concentrations in the lake
trout reduced by 20% as suggested by the expected
reduction in chemical uptake by dietary route.  The
concentration reduction for low  Kow PCB congeners
in  lake trout were not as pronounced as for high K^.,,
PCB congeners (Figure 5.7.2).   For low K^ PCBs,
the dietary route of  PCB intake by a fish is not as
dominant as for high Kow PCBs.
                                               529

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Figure 5.7.1. Sensitivity of PCBs in lake trout (age four) to chemical assimilation efficiency presented
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in Lake Michigan presented as ratios of model outputs with modified food assimilation efficiency to
model outputs with the calibrated food assimilation efficiency.
                                                530

-------
5.7.2.3    Chemical   Relative  Gill   Transfer
Coefficient
5.7.2.4   The  Fraction of Ingested  Energy for
Specific Dynamic Action (SDA)
The value of the chemical relative (to oxygen) gill
transfer coefficient  (E^EJ used  in the model for
young lake trout  (ages  1-4)  is 0.60.   For the
sensitivity analysis, this value was increased to 0.80.
The corresponding changes in  model results were
greatest (up to 1 6% decrease)  for PCB congeners
with lower Kow values.  The model results for highly
chlorinated PCB congeners were less sensitive to the
variation  in  the chemical  relative  gill  transfer
coefficient (Ec/EJ. For PCB congeners with chlorine
numbers greater than seven, essentially no changes
were observed in response to the increase  in this
parameter (see Figure  5.7.3).  The increase in this
parameter  raises  the  elimination  rate  of  PCBs
through gill ventilation.  The results of this sensitivity
analysis suggests that  the chemical elimination via
gill ventilation is an insignificant loss mechanism
relative to growth dilution for highly chlorinated PCB
congeners.
The calibrated value of the SDA for young lake trout
(ages 1-4) is 0.15. For the sensitivity analysis, this
value was  reduced  to  0.10.   The  downward
adjustment of  this  parameter  corresponded  to
roughly  a 5% reduction  in chemical  dietary uptake
rate based on Equation 5.7.1 (see Part 5, Chapter 4,
Section  5.4.3.2).   The results of  the  sensitivity
simulation  show  that   lower  chlorinated  PCB
congeners are not sensitive to changes in SDA. For
highly chlorinated PCB  congeners,  a  reduction  in
SDA from  0.15 to 0.10  produces about  a 5%
reduction in modeled PCB concentration  in the fish
(see Figure 5.7.4).  This  result suggests that for
these highly  chlorinated  congeners,  the dietary
uptake  may be the dominant route for chemical
accumulation  in fish.

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Figure 5.7.3. Effect of changes in chemical relative gill transfer coefficient (E,JE0) on the computed
PCB data for lake trout in Lake Michigan presented as ratios of model outputs with modified chemical
relative gill transfer coefficient to model outputs with the calibrated chemical relative gill transfer
coefficient.
                                              531

-------
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Figure 5.7.4. Effect of changes in SDA on the computed PCB data for lake trout in Lake Michigan
presented as  ratios of model outputs with modified SDA parameter to model outputs with the
calibrated SDA value.
5.7.2.5 Fish Growth Rate

Fish growth rates  in this model were calculated
based on the age-weight relationships presented in
Tables 5.4.9a-d (Part 5, Chapter 4).  For young lake
trout at  Saugatuck, the  average growth rate was
estimated  to be  about   0.0018  day"1  based  on
Equation 5.7.2  (see Part 5,  Chapter 4, Section
5.4.2.2). A sensitivity simulation was performed for
this parameter by adjusting the lake trout growth rate
for all age classes from  the current growth rate to
zero.  Figure 5.7.5 shows that the modeled PCB
concentrations for lake trout (age 4) were sensitive to
the changes in this parameter. A reduction to zero in
the fish growth rate resulted in significant increases
in modeled PCB concentrations for higher chlorinated
PCB congeners and moderate decreases for lower
chlorinated PCB congeners.
                                        (5.7.2)
The reduction in fish growth has two opposite effects
on the modeling of chemical bioaccumulation in fish.
It lowers the  estimate for chemical clearance (via
growth dilution) from the fish, which would result in a
higher model estimate for chemical bioaccumulation.
On the other hand, the reduction in fish growth also
cuts back the fish's energy demand in response to a
slow growth,  which would result in a lower model
estimate for fish's  food intake  and  associated
chemical uptake. The net effect of a zero fish growth
rate  on  the  overall   model  output  for  PCB
bioaccumulation will depend on the individual PCB
congeners.

The  elevated  PCB levels in  the zero-growth fish
suggested that fish growth is  an important  route of
elimination (via dilution) for highly chlorinated PCB
congeners.  For lower chlorinated PCB congeners,
fish growth plays a minor role in the overall clearance
of PCBs from  the fish  in  comparison with other
elimination processes. The net effect of a zero fish
growth is thus dominated  by the reduction in energy
demand  and consequently  the  lower chemical
                                              532

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Figure 5.7.5. Sensitivity of PCBs in lake trout (age four) to fish growth rate presented as the ratios of
model outputs with zero lake trout growth rate to model outputs with field estimated growth rate.
uptake.  As a result,  the  model generates  lower
estimates for PCB concentrations in the zero growth
fish.

5.7.2.6  Octanol-Water Partition Coefficient Kow

Reported Kow values for PCB congeners vary in the
literature (Hawker and Connell, 1988; Li et a/., 2003;
Schenker et a/., 2005; Miller et a/., 1984; Woodburn
etai, 1984). For a given PCB congener, a variation
of up to 0.5 logarithm unit is not uncommon among
reported data of the Kow.  Kow values used in the
calibrations of  the  food  web  model  are  those
published by Hawker and Connell (1988). Eight PCB
congeners  with  different  hydrophobicity  were
selected for sensitivity analysis.  Model  sensitivity
simulations were conducted by reducing the log Kow
value for each PCB  congener by 5%. Figure 5.7.6
indicates that PCB estimates for lake trout (age four)
are  very sensitive to  log  Kow values.  For  lower
chlorinated congeners,  model estimated   PCB
concentrations  decrease  to about  'one-third  in
response to a 5% reduction in log Kow values. The
reduction in model  predictions is a result of an
increased gill elimination rate caused by the reduced
l°9  Kow values.  The  model sensitivity becomes
                   smaller with higher chlorinated PCB congeners. This
                   is consistent with the fact that gill elimination is a less
                   important route for higher chlorinated congeners than
                   lower chlorinated congeners.

                   5.7.2.7  Fish Diet

                   There are considerable variations in dietary data for
                   fish  in Lake Michigan.  The dietary compositions
                   used in the calibrated food web models were the
                   average values over a sampling time period of two
                   years (1994 and 1995). For lake trout at Saugatuck,
                   the main dietary components are alewife, rainbow
                   smelt, and bloater  (Table  5.4.2a).    Sensitivity
                   simulations were  run  by  changing the  dietary
                   composition from their original diet to 50% rainbow
                   smelt and  50% alewife for all age classes of lake
                   trout. The  results of the sensitivity analyses for four
                   year-old lake trout are presented as an example in
                   Figure 5.5.7. Except for a few lower chlorinated PCB
                   congeners, a  10% to  20%  drop in modeled PCB
                   concentrations occurred in response to the dietary
                   change.
                                              533

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'- \\ ^
hi H
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       OQ
                                                              CQT-T-^-T-T-^^^

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                                                                                        CM CM CM
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                      O ^
                      Q. °

                      CD CD


                      35 g

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Figure 5.7.7.  Sensitivity of PCBs in lake trout (age four) to fish diet presented as ratios of model

outputs with modified fish diet to model outputs with field estimated fish diet.
                                               534

-------
Alteration of fish diet had a direct impact on model
estimates for dietary PCB flux into the fish. A shift to
a less contaminated diet will  result in a lower PCB
uptake by the fish. Because the diet of lake trout
varies with age classes, a dietary shift to rainbow
smelt and alewife exclusively will likely have different
results for different age classes of the lake trout in
terms of modeled PCB concentrations. For lake trout
at a age  class   whose  original  prey is  less
contaminated than rainbow  smelt or  alewife,  the
above  dietary  shift will result  in a  higher  model
estimate of PCBs in the fish.  It should be noted that
for the four-year-old lake trout, model results are not
only  influenced by its own dietary change but also by
the dietary changes in younger age classes because
the initial  PCB level in an older fish is  determined by
the PCB  levels in the fish while in previous age
classes.

The  hypothetical dietary composition for sensitivity
analysis was chosen based on the observation that
Diporeia is disappearing in Lake Michigan (Landrum
ef a/., 2000; Nalepa et a/., 1998).  This event will
inevitably affect the population and availability of
deepwater sculpin, slimy sculpin, and bloater that
prey on Diporeia as a primary food source. However,
this sensitivity simulation is by no means a prediction
as to what  will happen to the  lake  trout PCBs in
association  with the disappearance  of  Diporeia in
Lake Michigan because it is not possible to predict
the alternative  diet of a fish in response to changes
in prey community composition. We may be able to
estimate  the  relative  abundances  of  potential
alternative  food  items with the  depletion  of a
preferred food  source.  However, food selectivity of
a fish is not only a function of prey populations but
also  determined  by other factors, such as food
preference  and  the  ability  to actively  select a
favorable food.

References

Gobas, F.A.P.C.,  D.C.G.  Muir, and  D.  Mackay.
   1988. Dynamics of Dietary Bioaccumulation and
   Faecal  Elimination of  Hydrophobic Organic
   Chemicals  in Fish.  Chemosphere, 17(5):943-
   962.
Hawker, D.W. and D.W. Connell.  1988.  Octanol-
   Water Partition Coefficients of Polychlorinated
   Biphenyl Congeners.   Environ.  Sci. Technol.,
   22(4):382-387.

Landrum, P.F., D.C. Gossiaux, T.F. Nalepa, and D.L.
   Fanslow.  2000.  Evaluation of Lake Michigan
   Sediment for Causes of  the Disappearance of
   Diporeia spp.  in  Southern Lake Michigan.  J.
   Great Lakes Res., 26(4):402-407.

Li, N.Q., F. Wania, Y.D. Lei, and G.L. Daly. 2003. A
   Comprehensive  and   Critical   Compilation,
   Evaluation, and Selection of Physical-Chemicai
   Property  Data  for  Selected   Polychlorinated
   Biphenyls. J. Phys. Chem. Ref Data, 32(4):1545-
   1590.

Miller, M.M., S. Ghodbane, S.P. Wasik, Y.B. Tewari,
   and D.E. Martire.  1984.  Aqueous Solubilities,
   Octanol/Water  Partition  Coefficients,   and
   Entropies of Melting of Chlorinated Benzenes and
   Biphenyls. J. Chem. Engin. Data, 29(2):184-190.

Nalepa, T.F., D.J. Hartson, D.L. Fanslow, G.A. Lang,
   and S.J. Lozano.   1998.  Declines in  Benthic
   Macroinvertebrate Populations in Southern Lake
   Michigan, 1980-1993.  Canadian J. Fish. Aquat.
   Sci., 55(11):2402-2413.

Schenker, U., M.  MacLeod,  M. Scheringer, and K.
   Hungerbuhler. 2005. Improving Data Quality for
   Environmental Fate Models:  A Least-Squares
   Adjustment   Procedure  for  Harmonizing
   Physicochemical   Properties   for   Organic
   Compounds.     Environ.  Sci.   Technol.,
   39(21 ):8434-8441.

Woodburn, K.B., W.J. Doucette, and A.W. Andren.
   1984.   Generator  Column Determination  of
   Octanol/Water Partition Coefficients for Selected
   Polychlorinated Biphenyl  Congeners.  Environ.
   Sci. Technol., 18(6):457-459.
                                              535

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                                          PARTS
                                    LM FOOD CHAIN
Chapter 8. Model Application

5.8.1 Introduction

The fish bioaccgmulation models provide a dynamic
linkage between polychlorinated  biphenyl  (PCB)
levels in fish tissue and PCB concentrations in their
exposure environment. They are valuable tools for
helping us to obtain a quantitative understanding of
the bioaccumulation and trophic transfer of PCBs in
the lake.  The bioaccumulation  models can be
applied to estimate PCB levels in the fish given the
input of exposure concentrations in the water and
surface sediment. Besides this common application,
the models can also be used to derive estimates of
PCB concentrations  in the exposure environment
based on observed fish PCB data. For example, fish
bioaccumulation models  have  been  applied,  in
conjunction  with fate and transport  models,  to
reconstruct   the time   history  of  contaminant
concentrations  in the environment (Gobas  et a/.,
1995; DePintoefa/,,2003).

In this chapter, the focus of  model application is
primarily on simulating future PCB levels in lake trout
on the basis of projected exposure concentrations in
sediment and overlying water that were provided by
the LM2-Toxic model (see Part 4).

5.8.2   Simulation of Fish PCB Levels
Based on Hypothetical Exposure Inputs

The  food  chain  bioaccumulation  model was
developed as a component of an integrated series of
the Lake Michigan Mass Balance models. One of the
main objectives of the models was to evaluate the
impact of  PCB  load  reduction strategies on PCB
concentrations in the Lake Michigan ecosystem.
Several PCB load reduction scenarios were selected
for model analyses. The PCB concentrations in water
and  sediment associated  with each  of the load
reduction scenarios were estimated by the LM2-Toxic
model (Part 4, Chapter 6).  These predicted future
environmental concentrations  provided a basis for
estimating corresponding fish PCB levels using the
fish bioaccumulation models.

5.8.2.1 Exposure Concentration Inputs Used for
Model Simulations

Environmental concentrations  are the  most  critical
input when  models  are  applied to  deduce the
resulting PCB levels in fish.   It  cannot be over-
emphasized  that it is exposure input data  which
"drive" the  models because, to  a first approximation,
fish PCB levels are proportional to the concentrations
in its exposure environment.

Site-specific   PCB  concentrations  in water and
sediment were provided by the LM2-Toxic model for
the  Saugatuck  and  Sturgeon Bay biota  zones.
Regional average PCB environmental concentrations
were also  provided for two large areas denoted as
segment 2 and segment 3  in the LM2-Toxic  model,
respectively.  Segment 2 is the southeastern  part of
Lake Michigan  surrounding the  Saugatuck biota
zone, and  segment 3 is the northwestern part of the
lake surrounding the Sturgeon Bay biota zone (see
Figure 4.3.1).

For each site, a total of seven hypothetical scenarios
of  long-term  (1994-2055)   PCB  environmental
concentrations were provided  as exposure input to
                                             536

-------
the food chain models. These were generated by
the LM2-Toxic model as a quantitative prediction of
environmental concentrations under various PCB
load  reduction scenarios for the  Lake Michigan
ecosystem. As described in Part 4 (Chapter 6) of this
document, the seven PCB load reduction scenarios
were:

A) Constant Conditions - The measured PCB loads
   (tributary load  plus atmospheric dry and wet
   deposition) for the LMMBP period (1994-1995),
   but adjusted upward by a factor of 1.98.  The
   adjusted  loads  followed  the  same   spatial
   distribution  and   monthly variation  patterns
   established by the LMMBP measured PCB loads.
   The adjusted loadings, the 1994-1995  vapor-
   phase concentration,  Lake  Huron  boundary
   conditions,  and all other forcing functions as
   observed  in 1994 and  1995  were repeated
   throughout  the  simulation period.   Sediment
   burial  was  active as well as all other  model
   processes.

 B) Continued Recovery (Fast) - This was the same
   as  Scenario "A", but  atmospheric components
   (vapor  phase   concentration,   wet  and dry
   deposition) declined  with  a  six-year  half-life
   (Hillery era/., 1997; Schneider etat., 2001), and
   tributary loads declined with a 13-year half-life
   (Endicott,  2005;  Marti and Armstrong,  1990).
   The boundary  conditions at  the   Straits  of
   Mackinac declined at a rate of 0.17/year (a four-
   year half-life) (Schneider et al.,  2001).  These
   rates were applied starting on  January 1,  1996.

 C) Continued Recovery (Slow) - This was the same
   as  Scenario "A",  but  atmospheric components
   (vapor  phase  concentration,   wet  and dry
   deposition) declined  with  a 20-year half-life
   (Buehler era/., 2002) and tributary loads declined
   with a 13-year half-life. The boundary  conditions
   at the Straits of Mackinac declined with a four-
   year half-life.  These  rates applied starting on
   January 1, 1996.

 D) No Atmospheric Deposition - This was the same
   as Scenario "A", but starting on January 1,1996,
   the atmospheric loads (dry and wet deposition)
   were set to zero.  All other forcing functions as
   observed in the  LMMBP period were repeated
   throughout the simulation period.
E)  No Tributary Loadings - This was the same as
    Scenario "A", but starting on January 1,1996, all
    tributary loads were set to zero. All other forcing
    functions as observed in the LMMBP period were
    repeated throughout the simulation period.

F)  Lakewide Sediment Cleanup -  This  was the
    same as Scenario "A", but starting on January 1,
    1996, the lake-wide sediment PCB concentration
    was instantaneously  set  to zero.   All other
    sediment properties remained as existed prior to
    sediment clean-up. All other forcing functions as
    observed  in the LMMBP period and processes
    were repeated throughout the simulation period.

G)  No Atmospheric Deposition and No Tributary
    Loadings - The loading cuts of Scenarios "D" and
    "E" were combined. All other forcing functions as
    observed  in the LMMBP period were repeated
    throughout the simulation period.

Each of the  seven long-term PCB concentration
scenarios  consisted of a set  of congener-specific
PCB concentrations in the water column and surface
sediment as functions of time. As an example, some
congener-specific PCB exposure concentration data
for  Sturgeon Bay corresponding to Scenario  A
(continuation of current PCB loading) are presented
in Figures 5.8.1a and 5.8.1b) as provided by the
water quality model (LM2-Toxic).

5.8.2.2  Responses of Fish  Models to Different
Exposure Inputs

The food web models calibrated for Saugatuck and
Sturgeon Bay lake trout food webs were used for the
modeling exercise  in this chapter. The  Saugatuck
model was applied to simulate fish bioaccumulation
in  response  to  exposure  concentrations  for
Saugatuck and segment 2. The Sturgeon Bay model
was applied to simulate  fish  bioaccumulations  in
response to exposure  concentrations for Sturgeon
Bay and  segment 3.   We believe  that  these
bioaccumulation models can reasonably represent
PCB concentrations in fish as a function of exposure
concentrations  in  water and  sediment for  the
calibrated biota zones. For the large segments (two
and three) beyond the sites for which the models
were calibrated, the reliability of the models becomes
uncertain.
                                             537

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                               participate water PCB concentration (ng/g Organic Carbon)
Figure 5.8.1 a. PCB congener-specific exposure concentrations at Sturgeon Bay predicted by LM2-
Toxic for Scenario A - PCBs in suspended particles of the water column.
                                              538

-------
                                  sediment PCB concentration (ng/g Organic Carbon)
          _^o -- ro co -P». oi o>  _».o
                             -k _». K> ro
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Figure 5.8.1 b.  PCB congener-specific exposure concentrations at Sturgeon Bay predicted by LM2-

Toxic for Scenario A - PCBs in the surface sediment.
                                               539

-------
The  extrapolative  applications  of  the calibrated
models to segments 2 and 3 were carried out on
assumptions  that  the  average  environmental
concentrations  in   large  segments  were  good
representations of the exposure conditions for fish in
the segments, and that the food web structures used
in  the models were still representative beyond the
specific biota zones.

However, caution should be taken while interpreting
these model outputs because assumptions may  or
may not be valid.  For example, there is evidence
that lake trout are usually congregated in nearshore
areas (Rybicki and Keller, 1978;  Schmalz et al.,
2002). This implies that lake trout caught in any area
of Lake Michigan may  have actually spent a large
portion of their living history in nearshore areas and
were  exposed to PCB  concentrations found there.
Therefore, interpretation of the segment-specific fish
model results need to take the actual home range of
fish into consideration.
                                           A  fundamental  assumption  of  the  fish  model
                                           application is that the food web  structure, related
                                           biological   parameters  (such  as  growth  and
                                           consumption   rates   for   each   species)  and
                                           environmental  elements  (such   as   annual
                                           temperature) will  remain the same over the entire
                                           time period of the model simulations. Aside from this
                                           assumption, the reliability of the fish model estimates
                                           is primarily dependent on the quality of model inputs
                                           for PCB concentrations in water and sediment (see
                                           Part 4, Chapter 6).

                                           Fish model  simulations were initiated  with the
                                           assumption that PCBs in fish at day  zero of the
                                           model simulations (January 1,1994) were at steady-
                                           state with the exposure concentration. The temporal
                                           trend of PCB concentrations in lake trout food web
                                           species were estimated in response  to  different
                                           hypothetical long-term exposure concentrations at
                                           Saugatuck and  Sturgeon   Bay,  segment  2 and
                                           segment 3.  The simulation  results in terms of total
                                           PCBs in an adult lake trout  (age 5.5) are presented
                                           in Figures 5.8.2 and 5.8.3.
                      Lake Trout-age 5.5 at Sturgeon Bay
                                                           Lake Trout-age 5.5 at Segment 3
                           scenario A o historical data
                           scenario C A LMMBP data
                           scenario B
                                                          2000   2010    2020   2030    2040    2050
                                                                       year
                        Lake Trout-age 5.5 at Saugatuck
                                                           Lake Trout-age 5.5 at Segment 2
                           scenario A o historical data
                         a*= scenario C A LMMBP data
                           scenario B
0
1990   2000    2010   2020    2030    2040   2050   1990
                   year
                                                          2000
                                                                 2010
                                                                       2020
                                                                       year
                                                                              2030   2040   2050
Figure 5.8.2. Total PCB concentrations of the lake trout in response to the exposure concentration
inputs associated with various loading scenarios.
                                               540

-------
        3-
      CD
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      £L
  2-
        0
        3-
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        2
        n
                     Lake Trout-age 5.5 at Sturgeon Bay
                                                            Lake Trout-age 5.5 at Segment 3
                                                  0
        1990   2000   2010   2020    2030   2040    2050   1990    2000   2010   2020   2030    2040   2050
                          year                                        year
        4
                       Lake Trout-age 5.5 at Saugatuck
                                                            Lake Trout-age 5.5 at Segment 2
        0
        1990   2000   2010    2020   2030   2040   2050   1990   2000   2010   2020    2030    2040   2050
                           year                                       Vear


Figure 5.8.3. Total PCB concentrations of the lake trout in response to the exposure concentration
inputs associated with various loading scenarios.
The model  estimates  of  total  PCB values  were
calculated based on the sum of fish model results for
individual PCB congeners. The age 5.5 lake trout
represents the average of five and six year-old lake
trout and was selected for illustration because of the
availability of long-term observed PCB data for the
lake trout at similar ages.  The observed total PCB
data were  also included in the  figures for the
Saugatuck and Sturgeon Bay biota zones.

The results  show  that the fish model  simulations
made for Sturgeon Bay and segment 3 have very
similar outputs.   This is  because environmental
exposure concentrations for these two  sites  were
almost identical,  and the model for these two sites
used the same food web.

The results also show that the fish model outputs for
Saugatuck and segment 2 were different. Because
the model simulations for these two sites used the
same food  web structure, the difference can be
                                            attributed to the different exposure concentrations
                                            used for their simulations.

                                            The results further  indicate that, under the same
                                            loading scenario, projected PCB levels in lake trout
                                            declined at a much faster rate  at Saugatuck and
                                            segment 2 than at  Sturgeon Bay and segment 3.
                                            The  difference in  rates of  decline in   fish  PCB
                                            concentration was a result of similar declining trends
                                            in the PCB exposure concentrations used as input for
                                            fish model simulations.

                                            Assuming exposure inputs from the LM2-Toxic model
                                            are reasonable depictions  of future environmental
                                            PCB concentrations in Lake Michigan under different
                                            PCB load  reduction  scenarios,  the  fish  models
                                            predicted that total  PCB concentrations  in age 5.5
                                            lake trout will  level off in response to constant
                                            external loading in 2040 (Scenario A) at all four sites.
                                            The  estimated  steady-state values of  total PCB
                                            concentrations in lake trout are expected to be 0.84
                                            ug/g-wet for Sturgeon Bay and segment 3, and 0.77
                                              541

-------
ug/g-wet  and 0.56  ug/g-wet  for  Saugatuck  and
segment  2, respectively.   The higher final value
estimated for  segment  3  (0.84  (jg/g-wet) in
comparison  to  segment  2  (0.56  ug/g-wet) is
consistent with observations (LMMBP data) that the
lake trout  food web at  Sturgeon  Bay  has  a
substantially higher bioaccumulation capacity than
the one at Saugatuck.

Based on the exposure input data provided by the
LM2-Toxic model, the fish models further suggested
that for the fast recovery scenario  (Scenario B), the
targeted total PCB concentration for fish (0.075 ppm,
see Appendix 3.4.1)  would be achieved in about
2036 at Sturgeon Bay and segment 3, and 2033 and
2030 at Saugatuck and segment 2, respectively. All
other PCB reduction  scenarios do not achieve the
targeted PCB levels in the lake trout within the time
period of  the model simulations (2055).

5.8.2.3 Discussion

It should  be noted that the temporal trend  of total
PCBs in  lake trout for each load scenario (Figures
5.8.2  and  5.8.3) was the  result  of  combined
contributions from the fish model results for individual
PCB congeners. For each of the PCB congeners,
the concentration temporal trend  in the fish  was
largely a reflection  of the time  functions of the
exposure concentrations for water and sediment, as
shown in  Figures 5.8.1 a and 5.8.1b.

The temporal trends of total PCB  concentrations in
water and sediment for various PCB load  scenarios
were illustrated in Part 4 of this report (Figures 4.6.3
and 4.6.5).  These lake-wide  average data are a
good  representation   of  the  site-specific
environmental total PCB concentrations used for the
food web bioaccumulation model simulations.

As expected, the model results illustrate that future
PCB levels in fish are closely related to the projected
environmental concentrations. For example, the total
PCB concentrations in age 5.5 lake trout associated
with the Scenario B (Fast Continued Recovery) are
the lowest of all scenarios at the end  of the model
simulation (Figure 5.8.2). This result is similar to that
for the modeled total PCB  concentrations in water
and sediment (Figures 4.6.3 and 4.6.5).
Similarly, the dramatic decline at the early stage of
the model simulation in fish PCB levels associated
with the Scenario F (Sediment Cleanup)  (Figure
5.8.3) is the result of the parallel trends in the total
PCB concentrations for water and sediment (Figures
4.6.3 and 4.6.5). Among the PCB load reduction
scenarios, the sediment cleanup scenario appears to
have  the   most  immediate   impact  on  PCB
concentrations   in   water   and  sediment  and
consequently on PCB levels in the fish.

For the total  PCB concentrations in the fish at the
Sturgeon Bay and Saugatuck biota zones, the model
generated temporal trends for all scenarios appears
to be in line with the expected trend inferred from the
field data.   Comparing  Scenarios B  and  C, the
Continued Recovery (Fast) (Scenario B) appears to
fit the field data better at both locations.  Additional
field data are needed to confirm this observation.

It should be emphasized that the model results are
the product of the  model, its structure, and the
assumptions  made.  Because the results  of fish
bioaccumulation models  are highly dependent on
exposure concentrations, the model results should be
interpreted in light of uncertainty in the exposure
predictions (Part 4, Chapter 6).

References

DePinto, J.V., W.M. Larson, J. Kaur, and J. Atkinson.
   2003.   LOTOX2  Model Documentation   In
   Support of  Development of Load Reduction
   Strategies and a TMDL for PCBs in Lake Ontario.
   Submitted to New  England Interstate Water
   Pollution Control Commission, Boott Mills South,
   Lowell, Massachusetts. 122 pp.

DeVault, D.S., W.A. Willford, RJ. Hesselberg, D.A.
   Nortrupt, E.G.S. Roundberg, A.K. Alwan, and C.
   Bautista.  1986.  Contaminant Trends  in  Lake
   Trout (Salvelinus namaycush) From the Upper
   Great Lakes.  Arch.  Environ. Contam. Toxicol.,
   15(4):349-356.

Gobas,  F.A.P.C., M.N. Z'Graggen, and X.  Zhang.
   1995.  Time Response  of  the  Lake  Ontario
   Ecosystem  to Virtual  Elimination of  PCBs.
   Environ. Sci. Technol., 29(8):2038-2046.
                                              542

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Golden, K.A.,  C.S.  Wong, J.D. Jeremiason, S.J.
   Eisenreich,  G.  Sanders,  J.  Hallgren,  D.L.
   Swackhamer, D.R. Engstrom, and D.T.  Long.
   1993. Accumulation and Preliminary Inventory of
   Organochlorines in Great  Lakes Sediments.
   Water Sci. Technol., 29(8-9):19-31.

Rybicki, R.W. and M. Keller.  1978. The Lake Trout
   Resource in Michigan Waters of Lake Michigan,
   1970-1976.   Michigan Department of Natural
   Resources,   Lansing,  Michigan.     Fisheries
   Research Report Number 1863.
Schmalz,  P.J.,  M.J. Hansen, M.E.  Holey,  PC.
   McKee, and M.L. Toneys.  2002.  Lake Trout
   Movements  in  Northwestern  Lake  Michigan.
   North Amer. J. Fish.  Mgt., 22(3):737-749.

Schneider, A.R.,  H.M. Stapleton, J. Cornwell,  and
   J.E. Baker. 2001. Recent Declines in PAH, PCB,
   and Toxaphene  Levels in the  Northern Great
   Lakes as Determined  from High  Resolution
   Sediment Cores.  Environ.   Sci.  Technol.,
   35(19):3809-3815.
                                             543

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                                          PARTS
                       COMPARISON OF MODEL RESULTS
                                       Timothy J. Feist
                                  Welso Federal Services, LLC
                                             and
                                     Kenneth R. Rygwelski
                              U.S. Environmental Protection Agency
                              Office of Research and Development
                  National Health and Environmental Effects Research Laboratory
                                 Mid-Continent Ecology Division
                       Large Lakes and Rivers Forecasting Research Branch
                                 Large Lakes Research Station
                                       9311 Groh Road
                                  Grosse lie, Michigan 48138
6.1. Summary

Three levels of models were  used or developed
during the Lake Michigan  Mass  Balance Project
(LMMBP).    The   toxic  fate   and  transport/-
bioaccumulation portion of  the project included a
previously-developed  model (MICHTOX) for the
coarse scale Level  1 and newly-developed models
(LM2-Toxic and LM Food  Chain) for the middle
resolution Level 2. MICHTOX was used to assist in
the development of the sampling program and  to
provide   a  screening-level  assessment of  the
polychlorinated biphenyl (PCB) data. The LM2-Toxic
and  LM  Food Chain  models  (LM  models)  were
developed during the LMMBP as part of a suite of
integrated mass balance models.  While similar in
function,  the  Level  1  and  Level  2 models  had
different development histories and capabilities, and
a comparison of model results was useful to evaluate
suitability for  potential future uses.  This chapter
provides  an examination of the similarities  and
differences between the models and compares the
results of  scenario predictions from both sets of
models.

The  LM  models  provided  a  higher resolution
evaluation of PCB dynamics on a spatial, chemical,
and biological scale than MICHTOX.  The higher
resolution  of   the  LM   models  included
hydrodynamically modeled water transport, smaller
water quality segments, congener versus homolog-
level modeling, and a more detailed, data-based food
web  structure.  While both sets of models used
similar kinetics and forcing functions, the LM models
were more fully calibrated to process data that were
not available during the earlier development of the
MICHTOX model. The higher resolution and more
thorough calibration should allow the LM models to
provide better representation of  system processes
and better predictions of the effects of future loading
changes.  These features of the  LM models should
also allow them to be used with minimal re-calibration
for modeling localized areas of  the lake that may
have   different  congener  composition,   carbon
production, or lake trout diets.
                                             544

-------
The comparison of model results demonstrated that,
while differences in model parameterization resulted
in different flux rates for important processes in PCB
cycling in Lake Michigan, the annual net changes in
water column concentrations  were similar.  The
different flux rates resulted in different steady-state
concentration  predictions  under  a  hypothetical
constant loading condition scenario. However, under
a scenario with declining loading trends based on
scientific literature, predicted concentrations in water
and fish from both sets of models converged and the
rate of decline was more important  to the model
results than differences in model parameterization.

6.2  Comparison of Models

The LMMBP included three levels of models (Figure
6.1).    For  the  PCB  contaminant  evaluation,
MICHTOX represented the simpler, coarse resolution
models. MICHTOX is comprised of two submodels:
a toxics fate and transport submodel, and a food
chain bioaccumulation submodel. LM2-Toxic and LM
Food Chain (LM models) represented the higher
resolution Level 2 models.  This  section discusses
the similarities and differences in the models.
                                              6.2.1 Model Similarities

                                              The toxics models were similar in a number of ways.
                                              The MICHTOX fate and transport submodel and the
                                              LM2-Toxic model were both based upon the United
                                              States Environmental Protection Agency (USEPA)
                                              WASP4 toxics model (Ambrose et al., 1988) and
                                              used similar approaches for modeling toxic fate and
                                              transport.   While  MICHTOX still  possessed the
                                              general   structure  of  WASP4,  the  LM2-Toxic
                                              computer code was completely new. MICHTOX and
                                              LM2-Toxic contained similar kinetics  and mass
                                              transport functions. Both models included advection,
                                              dispersion, diffusion, settling  and  resuspension  of
                                              toxics bound to particles, and deep sediment burial.
                                              The air/water exchange functions were also similar in
                                              the models.   Both  models used  the  1994-1995
                                              LMMBP data to develop forcing functions for tributary
                                              loads, atmospheric  deposition, and atmospheric
                                              vapor concentrations, although LM2-Toxic simulated
                                              individual PCB congeners while MICHTOX simulated
                                              total PCBs divided  into two homologs.
     model type
                     Level 1 models
Level 2 models
Level 3 models
   Hydrodynamic and
       load models
                                                                                 POM
                                                                                advective/
                                                                                dispersive
                                                                                transport and
                                                                                bottom
                                                                                .shear stress
     Eutrophication/
   sorbent dynamics
                                                              internal carbon
                                                              aggregated to
                                                              Le"vel2
                                                              transport
                                                             aggregated
                                                              to Level 2
   Contaminant
transport and fate
        Food web
    bioaccumulation
 Figure 6.1.  Supporting models and links for MICHTOX and the LM models.
                                              545

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The food chain bioaccumulation models were also
conceptually similar.   The MICHTOX food chain
submodel and the LM Food Chain model were both
based upon the concept of mass conservation and
used similar kinetic processes, including uptake,
elimination, and  concentration  reduction  through
growth.  Both models used the LMMBP data set for
estimates of fish and invertebrate weight, growth, and
initial concentrations.

6.2.2 Model Differences

The primary differences in the models involved the
level of resolution and the degree of calibration. The
LM models provided a higher resolution evaluation of
PCB dynamics  with  regard  to  spatial  scales,
hydrodynamic scales, kinetic processes, PCB forms,
and biological components.  The LM models were
also rigorously calibrated using extensive field and
process data, while MICHTOX was developed at a
time when few PCB data were available.

LM2-Toxic was developed using a higher resolution
spatial grid than  MICHTOX; this provided a more
accurate  representation  of  spatially-dependent
processes and of the effects of spatial variability in
loads and concentrations.  The  LM2-Toxic water
quality grid was composed of five vertical segments
and 41 total water segments (Figure 4.3.1), while the
MICHTOX grid used two vertical segments and nine
total water segments (Figure 3.3.2). LM2-Toxic was
also  much  more  highly  resolved  in sediment
segments having 53 sediment segments divided into
non-depositional, transitional, and depositional zones
(Figure  4.3.2).    MICHTOX  used  six sediment
segments (Figure  3.3.2),  with  the  area  of the
segments adjusted to represent sediment focusing of
contaminants.

The hydrodynamic processes in the models also
used   different   resolutions.   LM2-Toxic  used
hydrodynamic  predictions  from  the  fine-scaled
(44,042 cell) Great Lakes version of  the Princeton
Ocean  Model  (GL-POM) aggregated  to  the 41-
segment LM2-Toxic grid.  Horizontal and vertical
flows were obtained from the GL-POM  hydrodynamic
model   and  dispersion  coefficients   from  the
temperature model (see Section 4.5.1  in Part  4,
Chapter 5 for details).  MICHTOX used externally-
specified estimates of advective and  dispersive
exchanges between its nine segments.
The LM  models also contained higher  resolution
water  quality  kinetics and PCB  forms.   They
simulated over 30 state variables (34 in LM2-Toxic,
40 in LM-Food Chain) representing PCB congeners
or co-eluting congeners.  MICHTOX modeled total
PCBs in water, sediment, and the food web as two
homologs.   LM2-Toxic  contained  three  carbon
classes for partitioning of toxics: biotic carbon (BIC),
particulate  detrital carbon  (PDC),  and  dissolved
organic carbon (DOC).   MICHTOX used a single
solids class with a seasonally-specified fraction of
organic carbon along with DOC. LM2-Toxic used
dynamic  phytoplankton  carbon  production loads
estimated  using  the LM3-Eutro   model,  while
MICHTOX carbon loads were based upon a steady-
state solids balance  and  seasonal organic  carbon
fraction calculated from historical data.

In addition to containing  more detail in PCB state
variables, LM Food Chain was more detailed in its
representation of the food chain than the MICHTOX
food chain submodel. The LM Food Chain model
was based  on the  LMMBP data  and included
phytoplankton, three  invertebrates,  and six fish
species.  The MICHTOX food chain submodel used
an idealized food chain that included phytoplankton,
two invertebrates,  and two fish species.

The models also differ in the degree of calibration
applied to them.  LM2-Toxic and LM Food  Chain
were thoroughly calibrated to the LMMBP data set,
and the LM2-Toxic was confirmed against a sediment
core derived PCB hindcast loading  estimate.   In
addition, the LM models had process data available
to reduce degrees of freedom during calibration, such
as  particulate settling velocities, sediment  mixing
zone  thicknesses, and  complete fish diet data.
MICHTOX was never fully calibrated against a PCB
data set. It was calibrated to solids transport and
plutonium data, validated to the small amount of PCB
data  available  when the  model  was  originally
developed, and later compared to three hypothetical
hindcast loading scenarios and the LMMBP data set
(Endicott et a/., 2005;  Endicott, 2005). At the time of
development of MICHTOX, there were little process
data available against which to constrain the model
parameterization.
                                              546

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6.3 Comparison of Model Results
6.3.1     Comparison
Analyses
of  Mass  Budget
Mass budget analyses were conducted  for PCB
simulation  results  for the  1994-1995  LMMBP
sampling period from both the MICHTOX fate and
transport submodel and LM2-Toxic (Part 3, Chapter
3, Section 3.3.3.3 and Part 4, Chapter 6, Section
4.6.2).  For this and  the  following comparisons,
results from the LM models for individual congeners
were summed and then converted to total PCB
concentrations (Part 4, Chapter 6, Section 4.6.1) for
comparison with results from the MICHTOX model.

There were a number of similarities in the results
from both models. They both demonstrated a net
loss of PCBs from the system.  The net loss  was
calculated as the sum of fluxes out of the system
minus the sum of fluxes into the system, or (sediment
burial + gross volatilization + export to Lake Huron +
Chicago diversion)  (tributary loads + atmospheric
deposition +  gas absorption +  input  from  Lake
Huron). Predicted net losses from the system were
2,673 kg/year for MICHTOX and 1,863 kg/year for
the LM2-Toxic. The net loss from the system means
that, under measured 1994-1995 loads, the system
was not at steady-state and observed concentrations
would decline. For the water column only, predicted
net losses of total PCBs were similar for both models:
182 kg/year for MICHTOX and 159 kg/year for LM2-
Toxic.

Both models show that gross volatilization,  gas
absorption, resuspension, and settling are significant
mass transfer rate processes  in the Lake Michigan
system (Figure 6.2).  Net volatilization of PCBs  was
the largest flux process. Resuspension of PCBs was
greater than settling for both models, which means
there was  a net movement of PCBs from  the
sediment to the water column. Export of PCBs to
Lake Huron or through the  Chicago diversion  was
negligible, and was not shown on Figure 6.2.

The results in Figure 6.2 highlight the differences in
parameterization of the models. PCB resuspension
and settling fluxes were much greater for MICHTOX
than for LM2-Toxic. Volatilization and gas absorption
fluxes were also higher in MICHTOX. MICHTOX was
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                       total PCB mass balance analyses results, 1994-
                       1995.
                       predicting more PCBs moving from the sediment to
                       the water column  than LM2-Toxic  through  net
                       resuspension and diffusion, but was also removing
                       more  PCBs from the water  column through  net
                       volatilization. This resulted in the net change in PCB
                       mass in the water column being similar between the
                       models.  Net resuspension fluxes and diffusion in
                       MICHTOX were as large of a PCB  source to  the
                       water column as the external loads. They were less
                       than a third of the external loads for the LM2-Toxic.
                       LM2-Toxic has a greater sediment burial loss from
                       the system, but the magnitude is small compared to
                       net volatilization losses.

                       6.3.2   Comparison of  Model Forecast
                       Scenarios

                       Results for the model forecast  scenarios  were
                       compared from both the fate and transport models
                       and the food chain/bioaccumulation  models.  For
                       comparison purposes,  the  LM2-Toxic  segment
                       results were volume-weighted  and  averaged to
                       match   the  MICHTOX  segmentation.     The
                       bioaccumulation  model results  provided only an
                       approximate comparison between models because
                       the MICHTOX food  chain  submodel  lake trout
                       concentrations were modeled on a  segment-wide
                                            547

-------
basis and the LM Food Chain results were for the
biota boxes used for model calibration.

A comparison of the Constant Conditions Scenario
results showed  that  MICHTOX  predicted  lower
concentrations in the epilimnion  than  LM2-Toxic
(Figure 6.3). This agreed with the model comparison
to   1994-1995  data   in  which   MICHTOX
underpredicted  epilimnion  concentrations (Part  4,
Chapter 3).  The models contained the same general
processes and  used the same forcing functions,
therefore, differences in predicted concentrations
were primarily due to model parameterization and
resolution differences.  For the  southern  Lake
Michigan   model   segments,   hypolimnetic
concentrations reached comparable concentrations
at steady-state even though MICHTOX  had higher
annual average concentrations initially.  While both
models used the same initial conditions, MICHTOX
had higher sediment PCB resuspension rates which
resulted  in  the  higher  initial  annual  average
                                             hypolimnetic water column concentrations shown on
                                             the plot. For the central Lake Michigan hypolimnetic
                                             segments, MICHTOX predicted lower concentrations
                                             than LM2-Toxic.

                                             Sediment  concentration  predictions were slightly
                                             higher for MICHTOX than for the LM2-Toxic model
                                             (Figure 6.4). The initial sediment concentrations on
                                             a ng/L basis were significantly different between the
                                             models. While both models used initial conditions
                                             based  upon   measured   1994-1995   PCB
                                             concentrations (ng PCB/g sediment), LM2-Toxicalso
                                             used  measured porosity from the 1994-1995 data
                                             while  MICHTOX used  the  same  porosity  and
                                             sediment density used  during  the initial  model
                                             development.

                                             The water concentration results from the Continued
                                             Recovery - Fast Scenario were  also  compared
                                             (Figure 6.5).  For both sections of Lake  Michigan,
     0.5
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  .9  0.3 -
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     0.4-
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     0.2-
0.1-
            Southern Lake Michigan
                Epilimnion
                                                    0.5
                                                    0.4-
                                            .9  0.3-
                                                    0.2-
                                            O  0.1
       2000  2010   2020  2030   2040  2050   2060
            Southern Lake Michigan
                Hypolimnion
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      1990  2000   2010  2020   2030  2040   2050   2060
                                                    0.4-
                                                 °  0.3-
                                                    0.2-
                                               0.1 •
                                                       Central Lake Michigan
                                                           Epilimnion
          2000  2010   2020   2030  2040  2050   2060
                                                       Central Lake Michigan
                                                           Hypolimnion
                                                1990   2000  2010  2020   2030  2040   2050  2060
Figure 6.3.  Comparison of model output annual average total PCB water concentrations for the
Constant Conditions Scenario.
                                              548

-------
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                             Sediments
                      	MICHTOX segment 11
                      —=— LM2 segments 42-55
                                                   25,000-
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                                                 .9 15,000-
                                                   10.000-
                                                 CQ
                                                 £  5,000-
                                                           0-1
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      Sediments
	MICHTOX segment 12
-— LM2 segments 56-64
     1990   2000   2010  2020   2030  2040   2050   2060
                                                       1990   2000   2010   2020   2030   2040   2050   2060
Figure 6.4.  Comparison of model output annual average total PCB sediment concentrations for the
Constant Conditions Scenario.
    0.5
    0.4-
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    0.2-
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0.1-
            Southern Lake Michigan
                 Epilimnion
                                                          0.5
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     1990   2000  2010   2020   2030   2040   2050  2060
            Southern Lake Michigan
                Hypolimnion
                                                         0.2-
                                                      0.1-
                                                  CQ
                                                  O
                                                  9=
                                                               Central Lake Michigan
                                                                   Epilimnion
                                                          0.4-
                                                          0.3-
                                                          0.2-
   0.1 -
     1990   2000  2010   2020   2030   2040   2050  2060
                                                           0-
                                                           1990
                                                           1990   2000   2010   2020   2030   2040   2050   2060
                                                               Central Lake Michigan
                                                                   Hypolimnion
                                                             2000
                                                                   —i	1—
                                                                    2010   2020
                             —I	1	1	
                              2030   2040   2050   2060
Figure 6.5.  Comparison of model output annual  average total PCB water concentrations for the
Continued Recovery - Fast Scenario.
                                                    549

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the model predictions  converged over time to  low
concentrations. In the Constant Conditions Scenario
results,  differences  in   model  parameterization
resulted  in  differences  in   long-term  predicted
concentrations. For scenarios in which the external
forcing functions declined over time, the differences
in predicted concentrations were still present, but the
magnitude of the difference was small compared to
the overall decline in concentrations.  Thus for long-
term predictions,  the  rate  of decline  was more
influential  than differences  between the  model
parameters.

The bioaccumulation models were also compared for
the Constant Conditions Scenario and the Continued
Recovery  - Fast  Scenario.   For  the   Constant
Conditions Scenario, MICHTOX predicted lower total
PCB concentrations than LM  Food Chain in the 5-6
year-old lake trout at both locations (Figure 6.6). The
predicted concentration differences were larger for
the central Lake Michigan/Sturgeon Bay results than
the southern  Lake Michigan/Saugatuck  results.
Differences in lake trout concentrations predicted by
the models were a function  of both the exposure
concentrations predicted by the water quality models
and factors  affecting bioaccumulation in  the food
chain models.

The results from the bioaccumulation models for the
Continued Recovery - Fast  Scenario (Figure 6.7)
                              were similar to the results from the fate and transport
                              models.  PCB concentrations declined over time to
                              low concentrations, with the difference in predicted
                              concentrations  between  the  models  becoming
                              smaller over time.  For Saugatuck,  the LM  Food
                              Chain model predicted that the 0.075  ug/g lake trout
                              PCB concentration target would be achieved in 2033,
                              and the MICHTOX food chain submodel predicted it
                              would be achieved in 2025.  For Sturgeon Bay, the
                              LM Food Chain predicted a much slower decline than
                              the MICHTOX food chain submodel, with the target
                              being reached in 2036 and 2018, respectively. The
                              difference in time required  to achieve the target
                              concentration was primarily due to  the delay in the
                              start of the concentration decline  in  the LM  Food
                              Chain predictions.

                              References

                              Ambrose,  R.B., T.A. Wool, J.P. Connolly, and R.W.
                                 Schanz.  1988.  WASP4, a Hydrodynamic and
                                 Water Quality  Model  - Model  Theory, User's
                                 Manual  and  Programmer's  Guide.    U.S.
                                 Environmental   Protection  Agency,  Office of
                                 Research  and  Development,   Environmental
                                 Research    Laboratory,   Athens,   Georgia.
                                 EPA/600/3-87/039, 297 pp.
   3.0
   2.5-
 ~ 2.0-

 o
 9: 1.5-1
   1.0-
   0.5-
• MICHTOX - Southern Lake Michigan
• LM Food Chain
 Saugatuck biota box
                                                   3.0
                                                   2.5-
                             w  2'°"
                             CO
                             O
                             e=  1.5-j
                                                   1.0-
                                                   0.5-
    0
    1990  2000   2010  2020   2030   2040  2050  2060
— MICHTOX - Central Lake Michigan
""LM Food Chain
 Sturgeon Bay biota box
                                 1990   2000  2010  2020   2030   2040  2050  2060
Figure 6.6. Comparison of the bioaccumulation model annual average total PCB concentration results
for the Constant Conditions Scenario.
                                               550

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                   MICHTOX - Southern Lake Michigan
                   LM Food Chain
                   Saugatuck biota box
                  MICHTOX - Central Lake Michigan
                  LM Food Chain
                  Sturgeon Bay biota box
    1990  2000  2010   2020   2030  2040   2050  2060
   1990  2000   2010  2020   2030  2040   2050  2060
Figure 6.7. Comparison of the bioaccumulation model annual average total PCB concentration results
for the Continued Recovery - Fast Scenario.
Endicott, D.D.  2005.  2002 Lake Michigan Mass
   Balance Project: Modeling Total PCBs Using the
   MICHTOX  Model.   In:   R.  Rossmann  (Ed.),
   MICHTOX:     A   Mass   Balance   and
   Bioaccumulation  Model for Toxic Chemicals in
   Lake Michigan,  Part 2.   U.S.  Environmental
   Protection  Agency, Office  of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research  Station, Grosse  He, Michigan.
   EPA/600/R-05/158, 140 pp.
Endicott,  D.D., W.L. Richardson, and D.J.  Kandt.
   2005.  1992 MICHTOX: A Mass Balance and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan.   In:   R.  Rossmann  (Ed.),
   MICHTOX:     A   Mass   Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake  Michigan,  Part  1.   U.S.  Environmental
   Protection  Agency, Office  of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes Research Station, Grosse  lie, Michigan.
   EPA/600/R-05/158, 140 pp.
                                             551

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                                         PART?

                                     APPENDICES
Appendix  7.1    Lake  Michigan  Mass
Balance  Project  (LMMBP)   PCB   Peer
Review Report

Kenneth R. Rygwelski
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects
  Research Laboratory
Mid-Continent Ecology Division
Large Lakes and Rivers Forecasting Research
  Branch
Large Lakes Research Station
9311 Groh Road
Grosse He, Michigan 48138

7.1.1 Executive Summary

The United States Environmental Protection Agency
(USEPA), National Health and Environmental Effects
Laboratory  (NHEERL),   Mid-Continent   Ecology
Division at Grosse lie, Michigan in cooperation with
the USEPA Great Lakes National Program Office
(GLNPO), conducted a  polychlorinated biphenyl
(PCB) transport and fate  mass balance  modeling
study  of PCBs in  Lake  Michigan  to  determine
strategies for managing and remediating this toxic
chemical in the lake basin.  Some specific programs
that this effort support  include the  Lake Michigan
Lake-wide Management Plan (LaMP) and the Great
Lakes Water Quality Agreement (GLWQA). Within
the ecosystem approach, the Lake Michigan Mass
Balance Project (LMMBP) models account for the
sources,  sinks, transport, fate,  and food chain
bioaccumulation of PCBs.  The calibrated  models
offer an opportunity for running various PCB load
reduction scenarios to get an insight on the effects to
the lake  ecosystem.   Model forecasting of  PCB
concentrations in lake trout is one of the primary end-
points of the investigation as  it relates to  both
ecosystem  and  human   health.    In  addition,
demonstration of a whole lake Total Maximum Daily
Load (TMDL) process to yield a  desired target PCB
concentrations in lake trout has been achieved. A
significant factor that  differentiates this study  from
other PCB transport and fate modeling projects is
that PCBs were modeled as single PCB congeners to
predict  total  PCBs.     Also,  a  high-resolution
hydrodynamic  model  was  applied   and  a
eutrophication model  was used to generate the
primary  productivity solids in this system where
autochthonous solids  production is significant and
plays an important role in describing PCB transport.
Mass balance estimates indicate that the lake system
is losing approximately 2,000 kg/year of PCBs. Also,
the bioaccumulation model predicts that the target
level for unrestricted consumption in lake trout (0.075
ppm for whole fish) was forecasted to be achieved for
five to six year-old lake trout between the years 2025
and 2035.

The main sampling  activity  for the project  was
conducted in 1994 through 1995; however, a  PCB
screening-level  model  called   MICHTOX  was
developed and running PCB simulations before the
LMMBP began.  This model was developed to gain
an initial  insight into the PCB transport and fate in
Lake Michigan  including its biota.  Later on, the
MICHTOX model was run again using the newer data
collected from the LMMBP.  The more advanced
PCB and support models included a hydrodynamic
model called Princeton  Ocean Model (POM), a
eutrophication model  called LM3-Eutro, and  a 41
                                             552

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segment PCB model called LM2-Toxic.  The output
from the LM2-Toxic was used to define the exposure
concentration for the bioaccumulation model called
LM-Food Chain.  Development of a high resolution
PCB  model  (LM3-Toxic)  is  proceeding,  and  is
discussed in the Modelers' Comments section.

On July 27 - 28, 2004, peer reviewers representing
modelers in  academia, research,  and the USEPA,
convened at the Crowne Plaza Hotel in Romulus,
Michigan to review the LMMBP PCB models (Figure
1). Prior to this review, a June 2004 draft copy of
"Results of the Lake Michigan Mass Balance Project:
PCBs Modeling Report" prepared by the Large Lakes
and  Rivers  Forecasting Research  Branch,  Mid-
Continent Ecology Division,  NHEERL, Office  of
Research and  Development  (ORD),  USEPA  at
Grosse lie, Michigan was provided to each of the
peer reviewers.  In general, the review panel agrees
that the model construct (spatial, temporal, process
resolution) and application  is consistent with the
problem  definition  for  which  the  model  was
developed and for  the  available resources.   In
addition to providing a comprehensive review of the
model, the panel also provided detailed suggestions
for future model improvements. Most of the panel's
comments were  captured at the  meeting and are
identified as "consensus" comments.  James Martin
provided additional post-meeting comments  (see
Section 7.1.5), and the modelers' responses to his
questions follow the  responses to the consensus
comment section.   In  addressing the  review
comments, we had to carefully consider where to
apply available resources and prioritize actions that
help  to  ensure  model  integrity.   Hopefully  our
responses reflect this balance.  Responses identify
actions that have been taken, are on-going, or will be
conducted in the future. The USEPA wishes to thank
the panel for their willingness  to participate in this
review and for their constructive comments.

7.1.2 LMMBP Peer Review  Panel

Robert B. Ambrose, Jr., P.E.
Environmental Engineer
Ecosystems Research Division
National Exposure Research Laboratory
U.S.  Environmental Protection Agency
Office of Research and Development
960 College Station Rd.
Athens, Georgia  30605
Voice:  706-355-8229; Fax:706-355-8104
ambrose.robert@epa.gov

Joel E. Baker, Ph.D.
Professor
Chesapeake Biological Laboratory
P.O. Box 38
2108 Fowler Laboratory
University of Maryland
1 Williams Street
Solomons, Maryland 20688-0038
Voice:  410-326-7205
baker@cbl.umces.edu

Ken Drouillard, Ph.D.
Assistant Professor
Great Lakes Institute for Environmental Research
Biological Sciences Department
401 Sunset Avenue
Windsor, Ontario Canada  N9B 3P4
Voice:  519-253-3000 Ext. 4744
kgd@uwindsor.ca

Barry Lesht, Ph.D.
Department of Energy, Acting Director of
    Environmental  Research Division
Argonne National Laboratory
9700 S. Cass Avenue
Argonne Illinois  60439
Voice:  630-252-4208 Fax: 630-252-2959
bmlesht@anl.gov

James L Martin, Ph.D., P.E.
Professor and Kelly Gene Cook, Sr. Chair in Civil
    Engineering
Department of Civil Engineering
Mississippi State University
P.O. Box 9546
222 Walker Engineering Building
Mississippi State, Mississippi  39762-9546
Voice: (662) 325-7194; Fax: (662) 325-7189
jmartin@engr.msstate.edu

7.7.3    LMMBP PCB  Charge   to  Peer
Reviewers

The members of the Peer Review Panel have been
assembled by the USEPA GLNPO because they are
experts in multimedia mass balance modeling and
have expertise  in  one or more  of the multimedia
                                            553

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Table 7.1.1. Agenda - Lake Michigan Mass Balance PCB Modeling Peer Review, Crowne Plaza Hotel,
8000 Merriman Road, Romulus, Michigan 48174 at Detroit Metropolitan Airport, Romulus, Michigan,
July 27 and 28, 2004
 Time
Tuesday's Agenda
Speaker
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11:1 Oam
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8:00 am
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12:00 pm
Welcome/Introductions and Project Goals/Objectives/Uses
Agenda Overviews/Previous Reviews
Charge to Peer Review Panel
PCB Background and History
PCB QA Report
PCB Data Summary/Representativeness
Questions and Discussion
Break
Modeling Introduction/Overview
Atmospheric Load Modeling
Questions and Discussions
Tributary Load Modeling
Questions and Discussion
Lunch
MICHTOX Level 1 Modeling
Questions and Discussion
Hydrodynamic Modeling and POM to WASP Linkage
Questions and Discussion
Break
Eutrophication Modeling - Autochthonous Carbon Production
Questions and Discussion
PCB Fate and Transport Modeling
Questions and Discussion
Food Chain Bioaccumulation Modeling
Questions and Discussion
Remaining Issues/Wednesday's Agenda
Adjourn for the Day
Wednesday's Agenda
Comparisons of Models
Questions and Discussion
Future Plans and Applications
Questions and Discussion
Summary of Peer Review Panel Recommendations
Break
Wrap-Up Session Final Discussion and Debriefing by
Reviewers
Adjourn
P. Horvatin
J. Keough
G. Warren
R. Kreis
L. Blume
R. Rossmann


R.. Kreis
D. Hornbuckle/J. DePinto

D. Hall


D. Endicott

D. Beletsky


J. Pauer

Xiaomi Zhang

Xin Zhang

R. Kreis

Speaker
K. Rygwelski

R. Kreis

R. Kreis




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aspects of this modeling approach. Panel Reviewers
are  expected  to  provide an objective,  unbiased
review of the  Lake Michigan Mass Balance PCB
Modeling: science, best modeling practices, conduct,
and  supporting  components.     Panel   Review
comments should be verbally summarized at the end
of the review  and then  provided  in written form,
cognizant of constraints  in data availability, staff,
and financial resources associated with the project.
Written comments on the format and content of the
project  documentation   can  be  provided,  if
appropriate.

7.1.3.1 Overall Multimedia Ecosystem Modeling
Approach

Does  the  suite  of  models  applied,  including
atmospheric   and   tributary   load  calculation
models/methodologies,   hydrodynamics   model,
autochthonous solids (eutrophication) model, water
column and sediment transport and fate models, and
food chain bioaccumulation  model, represent an
integrated approach to ecosystem modeling?  Are
these models, in combination, state-of-the-art? What
are the strengths and weaknesses of the overall
approach?

7.1.3.2 Overall Model Performance

Overall, how  well does the model  suite represent
physical, chemical and biological processes?  How
consistent  are   the  modeling  concepts   and
assumptions with current scientific knowledge?  Are
the processes being depicted at  the  spatial  and
temporal scales appropriate/adequate for the issues
being addressed and data availability? Overall, how
well  are  transport,  exchange,  and  partitioning
processes for  PCBs accounted for? Are the food
web, trophic structure, and processes which affect
bioaccumulation represented accurately?  Overall,
how well is food chain bioaccumulation of  PCBs in
Lake Michigan represented?  Are model algorithms
used to describe processes appropriate (complexity
versus simplification)?   Have  the  data been
adequately and fully utilized in the modeling? What
are the strengths, weaknesses, and  uncertainties of
the overall modeling performance?
7.1.3.3 Suitability for Management

In terms of their  predictive capability  related  to
transport, fate, and bioaccumulation of PCBs in lake
trout, is the suite of models and application sufficient
to evaluate and guide potential PCB load reduction
strategies for Lake Michigan? What are anticipated
modeling    strengths   and  weaknesses   for
management uses?

7.7.4    Modelers'  Responses  to Peer
Review Comments

1.  How do you reconcile the difference in peak PCB
   production  versus peak loads for  the hindcast
   run?

Modelers'  Response -  The explanation  for this
difference  is  not  readily  apparent.    A  similar
difference has  been noted for Lake Ontario (Gobas
et a/., 1995).  Peak production occurred in 1970.
Gobas et al. (1995) found the best overall agreement
between   observed  and  predicted  totaf  PCB
concentrations  in  water,   sediment,  and  biota
occurred when peak loading was assumed to occur
in 1961.   One would not expect peak  loading  to
necessarily  occur in  the  same  year  as  peak
production. Much of the PCBs produced were used
in transformers and other sealed sources which
would not have an impact on the environment until
product failure occurred.  It is believed that most of
the significant  loading of PCBs  to Lake Michigan
came from PCBs that were used for other purposes.
The use of these  PCBs in the basin does not appear
to coincide with production. Within the basin, the first
noted use of PCBs was at Waukegan, Illinois in 1948
when Outboard   Marine Corporation purchased
hydraulic fluid  with PCBs.  From the mid-1950s to
mid-1960s, PCBs from deinking were loaded to the
Kalamazoo River. In the  1950s, PCBs were used in
the Green Bay area for production of PCB-coated
carbonless copy  papers. These discharges to the
Fox River peaked in 1969-1970.  The  use of PCBs
for these papers  was phased-out in 1971-1972.  In
the  1960s,   industrial   PCBs  were  loaded  to
Sheboygan Harbor. Thus it appears that PCB use in
the basin began  in 1948 and ended in 1972.  The
loadings over time from these uses of PCBs is not
currently documented; however, it appears that PCB
loadings to the Lake Michigan basin do not coincide
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with production or sales figures. This discussion will
be included in Part 1, Chapter 7 of the report.

2.  POM: Extend the hydrodynamic record from two
   years to ten years.

Modelers' Response -  Currently,  funding  is not
available to  extend the  POM  modeling from two
years to ten years.  When  preparing to conduct
forecasts,  we  were  concerned   about   how
representative POM (1994 and 1995) results would
be when repeated along with  water  temperature,
velocities, and dispersion coefficients for LM2-Toxic
(PCB) and LM3-Eutro (nutrients and carbon) model
runs extending beyond the two-year LMMBP period.
The POM model used lake conditions and forcing
functions present March 31,1982 through November
20, 1983 for calibration purposes.  Comparing lake
and atmospheric conditions such as wave height, air
temperature,   lake  levels,   tributary  flows,  and
precipitation for  the 1982-1983  and the LMMBP
period 1994-1995 with the historical record, we found
that neither  of the two-year periods  were at any
extreme from  means.   Based  on this review, we
believe that the two years of hydrodynamic modeling
fairly  represent  average lake  conditions.    More
discussion on the representativeness of the 1994-
1995 period can be found in Part 1-Introduction and
Chapter 4 of the report.

3.  LM3-Eutro:   Why  does the model not predict
   dissolved silicon beyond 0.7? Was  the code
   checked for possible errors? Identify Green Bay
   stations on the model versus observed plots.

Modelers' Response - This  question is  related to
Figure 2.5.3 in the June 2004 draft copy of "Results
of  the Lake Michigan Mass Balance Project: PCBs
Modeling Report." Please note that the axes in this
figure were incorrectly labeled. The abscissa axis
should be labeled "model results", and the ordinate
axis should be labeled "field data." The dissolved
silica output from the model was examined carefully.
Although it appears that the model does not predict
values higher than 0.7 mg/L (Figure 2.5.3), closer
inspection of the model output  reveals that the
majority of the predicted values  are relatively evenly
distributed between 0.4 and  0.76 mg/L, with a few
values as high as 0.78 mg/L. A limitation of the LM3-
Eutro model was the absence of  a fully-developed
sediment   submodel   that  reflected  seasonal
variations.     User-defined   soluble   reactive
phosphorus,   ammonia,   dissolved   silica,   and
dissolved organic carbon sediment fluxes were used
to provide an  estimate of the  sediment feedback.
However, these fluxes were constant values in space
and time and were selected to provide a reasonable
estimate of annual averages. Due to this limitation,
the model underestimated the silica build-up at the
bottom of the lake during the late summer months
caused  by the slow decay of the biogenic silica,
which settled to the bottom during the spring and
early  summer  diatom blooms,  and its  potential
resuspension.  It is  believed that this is the major
reason why model output values are less that 0.8
mg/L whereas several field values are well above 1
mg/L.  There is  little difference between observed
silica concentrations in Green Bay and the open lake
with large seasonal  variations at both locations.  In
Lake Michigan, the observed silica range is between
2.1  and 0.04 mg/L, while in Green  Bay it ranges
between 1.58 and 0.13 mg/L.

4. LM2-Toxic:  Run the model with a conservative
   tracer and check that mass balances (set initial
   conditions and boundary concentration = 1).

Modelers' Response - This test was completed very
successfully.   By  setting  initial  and  boundary
concentrations of an assumed conservative tracer
equal to 1  mg/L in both the  water column and
sediment segments with  no  external  load, no gas
exchange, and no partitioning  process, the model
was run for a short-term simulation (two years) and
a long-term simulation (60 years). The results from
the model runs show no change for the two-year
model run in all  media.  For the long-term run, an
extremely small change (0.001 %) was found in water
column  segments with roughly a 0.5% change in
sediment segments.

5. LM2-Toxic: Consider adding subsurface benthic
   layers below the surficial layer. It is likely that
   higher PCB concentrations  reside in the deeper
   layers.  Look at historical data.

Modelers'  Response  -  There  are subsurface
sediment layers (called ghost layers) defined in the
current   LM2-Toxic   segmentation.    A quasi-
Lagrangian framework is  used to allow a moving
sediment-water  interface.   There   is no mass
exchange between the mixed surficial sediment layer
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and subsurface sediment  layer,  and between two
adjacent  subsurface  layers through  mixing  or
diffusion processes. The cores that demonstrate the
high PCB concentrations in deeper layers are found
in the depositional  zones  where the potential for
resuspension is minimal. Furthermore, mass transfer
via diffusion between deeper layers is likely to be
minimal for hydrophobic PCBs  (see response to
comment  14).  A detailed  description of the semi-
Lagrangian sediment bed option is detailed in Part 4,
Chapter 3, Section 4.3.4.2.3 of the report and IPX
2.74 documentation (Velleux et at., 2000).

6.  LM2-Toxic: Recheck assumptions on scenarios
   with attenuation rates for tributary loads and
   wet/dry atmospheric deposition.  Check Mites'
   data  against   assumed  decline   in   vapor
   concentration.

Modelers'  Response - The half-life of the PCB
decline in tributary and vapor phase loadings were
assumed   to  be   12.5  years  and  six  years,
respectively,   in   our  model  runs  for  "natural
attenuation."  These rates are consistent with the
PCB tributary and atmospheric  loading rates of
decline calculated and used by  other researchers
(Velleux and Endicott, 1994; Endicott, 2002; Marti
and Armstrong, 1990; Hillery et a/., 1997;  Schneider
etal., 2001). Further examination of additional data
has not revealed any change to these assumptions.
See Part  3, Chapter 3 of the report and Endicott
(2005)  for documentation  of materials used for
hindcasts and forecasts for MICHTOX  and LM2-
Toxic. Eventually, Attachment 4 will become a stand-
alone ORD publication.  See Part 4, Chapter 6,
Paragraph 4.6.3 for documentation of sources of
information used for forecasts for the LM2-Toxic. A
detailed description of the uncertainties that would
have an impact on hindcast and forecast choices will
be detailed in a  revision of the report  in Part 1,
Chapter 7.

7. LM2-Toxic: Compare model projections to water
   data post-1998 (southern Lake Michigan).

Modelers' Response - Post-1998 data for southern
Lake Michigan have been located.  These data will
be compared with the model's long-term projections
as part of  the model verification.  The results of the
comparison will be detailed in a revised edition of the
report. Additional verification of the model will occur
after data collected in 2005 are available.

8.   LM2-Toxic:  The Panel recommended that a
     Monte Carlo uncertainty analysis be performed
     using a steady-state version of the model.

Modelers'  Response - This is certainly  a valid
suggestion. However,  given the complexity of the
model and the number of solids (three solids) and
PCB congeners (54 congeners) simulated in the
model, it could be very costly and time-consuming to
do  the  recommended  Monte  Carlo   uncertainty
analysis on even a few selected critical parameters
used in a  steady-state version of the  model.  In
addition  to the uncertainties associated with the
parameters defined by chemical and  biochemical
processes  conceptualized  in   the  model, water
transport, solid  cycling  rates, numerical algorithms
used in the model, and data input into the model are
all  subject to  a  certain  error,  and  this  error
propagates in the model results. The uncertainties
associated with these errors could be much greater
than the ones only related to the chemical-specific
parameters. See Part 4, Chapter 5, Section 4.5.4 for
details  on the  tasks  conducted to  reduce the
uncertainties  caused by water transport and solid
cycling rates.  If computing resources and manpower
become available, this issue will be addressed in the
future.

9.   LM2-Toxic:   In regards  to  solids dynamics
     (radioisotope calibration), the Panel requested
     that the Modelers' examine the decline rate and
     add more recent data.

Modelers' Response - We will examine the decline
rate and add more recent data in  the future as
available.

10.  Peer  Review Panel:   LM2-Toxic/Eutro:  How
     sensitive  is  the  PCB  model  to  primary
     productivity  changes  versus  sediment net
     resuspension changes?

Modelers'  Response - The suggested sensitivity
analysis  has been thoroughly investigated. For a
50% increase or decrease in  primary production
corresponding to the primary production generated
from LM3-Eutro for the  1994-1995 period, the LM2-
Toxic model was tested for both a short-term (the
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two-year calibration)  period and a long-term (62-
year)  period.   The  results  from the  sensitivity
analyses were compared to the results from the LM2-
Toxic model base runs (i.e. 1994-1995 calibration run
and long-term Constant Condition Scenario).  See
Part 4, Chapters 5 and 6 for detailed descriptions of
both of these base runs.  The general results from
the tests are: 1) The  solids concentrations (DOC -
Dissolved Organic Carbon, BIG - Biotic Carbon, and
PDC -  Particulate Detrital Carbon)  in the water
column have a substantial deviation from the base
run concentrations for both the short-term calibration
and the long-term scenario simulations; and 2) the
total (particulate plus dissolved) PCB concentrations
in the water column has a noticeable difference from
the base  run  concentrations  for the short-term
calibration simulation, but  it has very little difference
for the base run concentrations for the long-term
simulation.  The results from the sensitivity analyses
suggest   that,  under  the   1994-1995   PCB
loading/boundary conditions/other forcing functions,
the influence of primary  production  on the PCB
concentrations in  the water column  is very small,
especially for long-term forecast scenarios.   The
details on the procedures  used to conduct the test
and the associated results will be presented in the
revised edition of the  report.

11.  LM2-Toxic/POM:  The model did not consider
     ice cover in  various processes (volatilization,
     resuspension, etc). Perhaps test the affect with
     a sensitivity analysis.

Modelers' Response -The POM model was applied
to Lake  Michigan by  the National  Oceanic and
Atmospheric Administration  (NOAA)-Great  Lakes
Environmental  Research Laboratory (GLERL). The
current version does not include ice cover algorithms.
However, in the absence of an ice model, both POM
and LM2 were run with the water temperature steady
at 2°C from the period January  1,  1994 through
March 31, 1994. Lake  Michigan ice cover for 1982
and 1994 were greater than the mean and median
whereas 1983 and 1995  were less than the mean
and median. None  of the four years (1982-1983
hydrodynamic  model calibration years and 1994-
1995  LMMBP  years) represented an extreme  of
mean  daily ice  cover.   There is  an  in-depth
discussion  on  historical  ice  cover data for Lake
Michigan in Part 1, Chapter 4 of the report.
Both  NOAA  and Large  Lakes Research Station
(LLRS) staff agree that ice cover algorithms in POM
would be worthwhile additions to the model. During
most winters, Lake Michigan ice cover occurs most
often in the nearshore areas only.  LM2-Toxic could
utilize ice cover predictions from POM by indicating
the fraction of  the surface segment area that is
covered during certain times.  However, the coarse
grid structure of LM2-Toxic could  not be used to
predict the impact of ice cover in specific small
regions of the lake, such as nearshore zones. At this
time,  we do  not have the in-house expertise to
develop a revised POM that addresses ice cover;
however, when a revised POM is  made available
from GLERL that incorporates these algorithms, we
could incorporate this version into our Level 3 models
where segmentation  resolution is  fine  enough to
better deal with year-to-year  and within-year ice
cover variations. GLERL is planning to incorporate
ice cover algorithms in POM for application to Lake
Erie.

The effect of  ice cover on PCB mass fluxes across
the air-water  interface through gas absorption and
gross volatilization is likely to be small because our
calculations predict that  these PCB mass fluxes
decrease   substantially  with  a  decrease  in
temperature.  However, it is recognized that ice cover
could  affect  both particulate  settling  rates and
sediment resuspension fluxes of PCBs in certain time
periods in a year in the nearshore regions. It is our
opinion that ice  cover most likely will not have a
substantial  impact on the long-term results from the
LM2-Toxic model, because ice cover does not affect
the total inventory of PCBs  in the lake system.
However, for short-term predictions, ice cover would
be expected to impact the model predictions.

12.  LM2-Toxic/Food  Chain:  Investigate congener
     patterns in air, water, fish, and sediment. How
     do these compare?

Modelers' Response - The PCB patterns of multiple
media will be compared to determine similarities and
differences within and among media. This technique
is commonly referred to as PCB fingerprinting or PCB
signature recognition and  has had mixed success in
the  past.    This  recommendation  has  minor
implications to the modeling; however, it is a data
analysis tool and has merit for data presentation and
interpretation purposes. The relative percent of total
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RGBs  represented  by  each  congener  will  be
computed and then expressed as  a cumulative
frequency plot for comparative purposes. These will
represent data for an  entire study period, will by
tested with both mean and median values, and will
be a composite expression of seasonal and spatial
data.  In addition, selected evaluation of  pattern
recognition using the LMMBP data set can be found
in Kuehl  (2002) and McCarty etal. (2004).

Fingerprints  will be calculated for sediment, water
column (dissolved and particulate), vapor phase, wet
and dry atmospheric deposition, and age 5-6 year-old
lake trout signatures  from the Saugatuck biota site.
Atmospheric signatures will be based on a subset of
all congeners  because  vapor phase data  were
computed by Keri Hornbuckle for the study, and over-
lake  concentrations  were only calculated  for the
congeners that are being modeled at Grosse He. In
addition,  PCB  patterns  associated  with  water
discharging  from the  Kalamazoo  River  near the
Saugatuck biota site and other selected tributaries
will be compared/contrasted to the lake water. These
results will be presented in Part 1, Chapter 6 of the
report.

13.  LM2-Toxic:  Consider the missing 120  kg/year
     total PCB contribution  from Milwaukee  (sum
     vapor/wet/dry);  how sensitive is the PCB model
     to atmospheric  and tributary loads?

Modelers' Response - The  issue, documented in
Wethington  and Hornbuckle, 2005, of an additional
PCB load from the Milwaukee area through vapor
exchange, wet deposition, and dry deposition to Lake
Michigan was  not included  in our  model.   The
additional PCB load from  the atmosphere  was
estimated to be  at  least 120  kg  per year.   The
sensitivity analysis for the  Milwaukee load  was
performed by adding a 120 kg/year PCB load into
segment 1  in our model.   The results  from the
sensitivity analysis were then compared to results
from the LM2-Toxic model long-term (62 years) base
run (Constant Conditions Scenario).  The steady-
state concentrations  from this simulation show an
increase  of  less  than  5%  in the  steady-state
concentration compared to the original long-term
base run.  The details on the Milwaukee  loading
sensitivity analysis and the impact of the external
PCB load changes and vapor phase concentrations
to the projected level  of PCB concentrations in Lake
Michigan will be discussed in the revised report.  It
should be  noted  that additional data will always
continue  to become available, and this  is such a
case.

Another potential missing load to the lake is that load
associated with very large particles greater than 10
um.  Although experts disagree on the magnitude of
the PCB load to the lake via large particles, various
scientists indicate that PCB dry deposition associated
with large particles could be a significant PCB source
to the lake  (Miller et a/., 2001; Franz et a/., 1998;
Holsen, 1991). Currently, it is not possible to make
reliable estimates  of these fluxes to the lake. The
uncertainty in these flux estimates is associated with
the uncertainty of how far these large particles travel
from their sources.

Simulations were  run  to gain insight into how the
model would respond  to increasing  the total PCB
load  (load from all tributaries + atmospheric load to
the entire lake) by 50% and 100%. The results from
these sensitivity analyses were then compared with
those from the LM2-Toxic model long-term (62 years)
base run  (Constant  Conditions  Scenario).  The
steady-state concentrations from this simulation
show an increase of less than 10% and 25%,
respectively, to the steady-state concentration from
the long-term base run.

14.  LM2-Toxic:  Investigate PCB  diffusion from
     deeper sediment layers, relative to sediment
     resuspension.

Modelers' Response - Because of very large PCB
partition coefficients, most  of the PCB mass in the
sediment is associated with the particulate phase.
Therefore, relative to  sediment resuspension, the
PCB mass moved by PCB diffusion between deeper
sediment layers through pore water is trivial. Adding
this  process  into the  current  semi-Lagrangian
scheme for  sediment transport  in the LM2-Toxic
model would require considerable effort and time to
modify the code, calibrate the model, and analyze the
output.

15.  LM2-Toxic/LM Food  Chain:    Conduct  a
     hindcast for PCBs which will be further used in
     the LM2 Food Chain  hindcast; possibly select
     five congeners.
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Modelers' Response - We agree with the review
panel on the importance of this task.   Although it
requires significant resources, time and effort, this is
an effort that is certainly worth doing.  This task is
currently underway  and  all  of the  selected  PCB
congeners  (54)  will be  simulated  in  the model
hindcast.  The  LM  Food Chain model will be run
using the output of the LM2-Toxic hindcast that was
run to describe the exposure history.  The approach,
inputs, model outputs,  and interpretation of the
results will be presented in the revised report.

16.  LM2-Toxic/POM:    Examine  the  effects  of
     changing  the   horizontal  grid structure  to
     evaluate translating POM output to the  LM2
     grid.

Modelers' Response -  This  is  a  valuable  and
interesting suggestion.  However, it would require
considerable effort and expertise to accomplish the
task. It would require extramural personnel such as
David  Schwab  of NOAA-GLERL to adapt POM
output to the new grid structure.  Considering the
resources and time that would be involved in this
task, it cannot be done in the foreseeable future.

17.  LM-Food  Chain:   Calibrate over declining
     exposure concentrations rather than constant
     exposure  history.   To  facilitate  this effort,
     consider hindcasting five PCB congeners.

Modelers' Response - Due to the lack of  credible
PCB congener-specific exposure history data, the
measured data for  PCBs in water  and sediment
(1994-1995) was  assumed to be representative of
life-long average  exposure concentrations for the
food  web  and  was,  therefore,  used  in model
calibration  simulations.    Model  calibration  over
declining exposure concentrations will be attempted
once the temporal profiles of congener-based  PCB
concentrations  in  water  and sediment  become
available from the LM2-Toxic hindcast.

Theoretically, calibrations over declining exposure
concentrations should yield better results than that
conducted over a constant exposure history because
PCB loads to the Great Lakes have been  and will
likely  continue  to decline toward a steady-state.
However, it  is difficult to accurately determine the
rates of decline for exposure concentrations in the
various media.   The  lake trout,  as well as  coho
salmon food webs in Lake Michigan, are exposed to
PCBs associated with both the water and sediment.
Therefore, model calibrations for their food webs
require information  on temporal variations of PCB
concentrations in both the water and sediment over
the exposure history. However, the temporal trends
of PCB concentrations decline are usually reported
for total PCBs only. Congener-specific PCB data are
rarely available.  For total PCBs, the quality of the
estimated  exposure  decline   rates  is  usually
questionable due to the often considerable variability
and uncertainty of the measured total PCB data in
the water and sediment. Therefore, with limited data
availability and poor data quality, a reconstructed
declining exposure history is not necessarily a better
representation of the actual exposure condition than
the constant exposure assumption.  If one can
assume  that the  PCB concentrations  in  Lake
Michigan system is currently declining at a very small
rate, then model calibrations using current congener-
specific PCB data as average life-long exposure may
be  a  more  desirable  alternative than  model
calibrations over declining exposure concentrations
for total PCBs or a limited number of congeners.

18.  LM  Food Chain:   Concern on using specific
     dynamic action (SDA) versus activity cost
     (respiration). Recommend using activity cost
     for  calibration.   This  question  will  be re-
     formulated in the written review.

Modelers' Response - In the  LM Food Chain model,
activity cost  for each  species  is estimated  as  a
function  of temperature  based  on bioenergetic
equations and is not "refined" during calibrations. We
chose to adjust only SDA with the hope to minimize
the risk of turning  calibration  into  a  curve-fitting
exercise.

19.  LM Food Chain: Explain why 5.5 year-old trout
     data are so variable at Saugatuck.

Modelers'  Response - It is not uncommon for fish to
have variable PCB levels among individuals of the
same age class  and  among  age  classes.  The
variability  of PCB levels among individual fish can
have a direct impact  on the uncertainty interval
associated  with  the  measured   PCB  data   in
composites.  The variability  of  an individual  fish's
PCB concentrations may be attributed to, among
other things, the differences in body  size,  health
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condition, feeding skill, dietary preference among
individual fish, exposure variances due to their spatial
feeding range, and  analytical chemistry variability.
These individual differences are likely to have direct
impact on the growth rate and the amount of dietary
PCB intake of individual fish  and subsequently on
fish's PCB bioaccumulation.

The trend for the USEPA historical Saugatuck lake
trout PCB concentration data is clearly downward.
Similarly, the within-year  variability represented by
95% confidence intervals for these observations also
demonstrates a decreasing trend in time. Data prior
to  1981  clearly have  much  more within-year
uncertainty than data collected  after  that date, and
the concentration means for these earlier data show
greater year-to-year differences  than  do later
composites.  A cursory examination of the historical
PCB  lake  trout composite  concentration data
compared against available mean fish length, fish
weight, % lipids, and %  males in the composites
revealed no distinct relationships for the  years
examined.

Although a cursory examination of the historical PCB
lake trout composite concentration data collected at
Saugatuck compared against  mean fish weight and
length showed no distinct relationship, some of the
within-year variability of composites could be further
attributed to the fact that the monitoring  samples
were not collected for a particular age  class.  Rather,
the lake trout samples were collected and classified
as600-700 mm size class.  For many of the historical
600-700 mm lake trout samples, their age  classes
are uncertain. Based on accurate age classification
for the 1994-1995 lake trout samples, the 600-700
mm size class can be roughly associated with 5 and
5 year age classes.    However,  this size-age
correlation may not necessarily be applicable for lake
trout samples collected in other years.   In  other
words, the monitoring data for adult lake  trout at
aaugatuck over the years  represent PCB levels in a
range of age classes of lake trout.  In our report,
Jese monitoring data were labeled and plotted as
 LBs in  5.5  year-old   lake   trout  merely  for
 onvenience in comparing with age-specific modeled
£B data for lake trout. To demonstrate the range of
  ,  predicted concentrations in lake trout, model
        e 4> 5> 6-and 7 year-old lake trout will be
     with the Saugatuck historical lake trout data.
This graphic will appear in the revised draft of the
report.

Dietary preference is likely a very important aspect in
evaluating long-term trend data.  Food web changes
have and are occurring in Lake Michigan based upon
past and present disparate  reports on  the topic.
However, the 1994-1995 diet study results suggest a
general  consistency with  known lake  trout  diet
preferences in the past. Although these are typically
dominated by alewife, bloater, sculpin, and smelt,
there may be a greater trend toward bottom-dwelling
bloater and sculpin than during the general evidence
in the past two decades.

The year-to-year differences in mean concentrations
and the within-year variability observed in lake trout
could also possibly be related to variable exposure
resulting from meteorological and physical factors.
These factors have the potential to have direct and
indirect impacts on the food web and  exposure
gradients within the feeding range of Saugatuck lake
trout.  A primary factor is  PCB loading  events
associated with high flow from the Kalamazoo River
that discharges at Saugatuck. The Kalamazoo River
has a history of  PCB contamination (see table that
follows in response to 1.20). Also, periodic low lake
level  events have the potential to  reduce PCB
exposure to the lake trout food chain in certain zones
which could be  reflected in periodic low-level PCB
body burden results.  In a period from the mid-1960s
to the late-1980s, the lake levels were at near record
lows and near record highs, respectively.

Much of the analytical chemistry data on fish prior to
1983  was performed using  packed  column  gas
chromatography  (GC).      Dichlorodiphenyl-
dichloroethylene (DDE) often co-extracted with the
PCBs and was very difficult to analytically separate
from PCBs  on  the  packed-column GC  (Michael
Mullin, personal communication). If the concentration
of DDE  was significant, and separation  was not
complete, then a positive PCB bias results in the
measurement.     Some  measurements  were
performed where sample extracts were analyzed
using joint GC and mass spectrophotometry.  This
combined analytical  system improved the ability to
separate the DDE  from the  PCBs.  Most  of the
analytical work performed post-1983 was done using
capillary GC which significantly improved separation
of DDE from PCBs.
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20.  LM-Food    Chain:      Develop   a   time
     line/chronology  of regulatory  and  remedial
     actions,   relative  to  the   fish  monitoring
     record/trend at Saugatuck.

Modelers' Response - This has been done and will
be found in the revised edition of the report in Part 1,
Chapter  5.  Where appropriate,  dates have been
added to figures in that  chapter.  The table used to
summarize the time-line is shown in Table 7.1.2.
                            21.  LM-Food Chain: Provide an estimate of model
                                error on the fish long-term monitoring trend.

                           Modelers' Response — There are many sources that
                           contribute to model errors. They include conceptual
                           errors and/or omissions, errors in parameterization,
                           uncharacterized system variability, and errors in data
                           used for calibrations. In addition, the quality of model
                           prediction of the  long-term fish monitoring trend
Table 7.1.2.  Significant Dates in the History of PCBs in the Lake Michigan Basin
         Date
Event
          1865
          1881
          1914
          1927
          1935
       1948-1971

          1954

  Mid-1950s to Mid-1960s
       1959-1972

          1960s
       1969-1970
          1970

       1971-1972

          1973
          1975
          1977
          1984
          1985
       1989-1990
          1991
       1991-1992

          1998
       1997-1999

       1997-1998
       1998-1999
       1994-2000
         20002
First PCB-like chemical discovered
First PCBs synthesized
Measurable amounts of PCBs found in bird feathers
PCBs first manufactured at Anniston, Alabama
PCBs manufactured at Anniston, Alabama and Sauget, Illinois
Outboard Marine Corporation at Waukegan, Illinois purchase eight million gallons of
     hydraulic fluid with PCBs
Appleton Paper Company began using PCBs as PCB-coated carbonless copy paper
     with discharges to the Fox River
PCBs loaded to Kalamazoo River from deinking
Outboard Marine Corporation at Waukegan, Illinois used hydraulic fluid with PCBs for
     die-casting
PCBs used by Tecumseh Products Company loaded Sheboygan River
Paper company discharges of PCBs to Fox River peaked
PCB production peaked at 85 million pounds and huge contamination noted at Sauget,
     Illinois plant
Appleton Paper Company and NCR Corporation phased-out PCB use. Recycling of
     carbonless paper had occurred for several decades
U.S. Food and Drug Administration (USFDA) establish 5 ppm PCB tolerance level in fish
124,000 cans of salmon from Lake Michigan seized because of PCBs
PCB production ends
USFDA lowered PCB tolerance level in fish to 2 ppm
Commercial fishing for carp and other valuable species outlawed on Green Bay
Sheboygan Harbor PCB Remediation
U.S. Department of Health and Human Services label PCBs as possible carcinogen
Waukegan Harbor PCB remediation (1 million pounds PCBs) completed for this action
    in  1992. Additional work  is planned.
The eight Great Lakes States agreed on a "Great Lakes Protocol for Fish Consumption
    Advisories" that lowered the regional standard from the USFDA commercial
    standard of 2 ppm down to 0.05 ppm
Kalamazoo River sediment PCB remediation on Bryant Mill Pond (20,000 pounds of
    PCBs). Additional work is planned
Milwaukee River PCB remediation
Upper Fox River deposit N (17,000 cubic yards) and sediment management units 56
    and 57 dredging partially  completed. Additional work  is planned on the Fox River
    system
Manistique Harbor PCB remediation (141,000 cubic yards)
Possibly begin Grand Calumet River PCB remediation
                                                562

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depends on the availability of a realistic projection of
future  exposure  concentrations  in  water  and
sediment for the food web. Not all errors from these
sources can be quantified. In the final report, we will
provide an estimate of model error associated with
model  parameterization  and  calibration with an
emphasis on error associated with a potential shift in
food web structures.

22.  LM Food Chain: Conduct a prey sensitivity
     analysis for  lake trout.

Modelers'  Response -  We will  be testing  the
sensitivity of the model to changes in the food web
structure. In addition to a prey sensitivity analysis for
lake trout  at Saugatuck, model sensitivity  to fish
growth rate, lipid  content, and temperature (among
others) will also provided in the revised edition of the
report.

23.  Enhance data presentations of the project data
     to provide regional  and open lake/nearshore
     differences and gradients for multiple media.

Modelers'  Response -  We  agree  that  data
presentation is an important aspect of this project to
aid in the understanding of modeling results.  PCB
data used for the modeling will appear in the revised
edition of the report in Part 1, Chapter 5, "PCBs in
the Lake Michigan Ecosystem."

24.  Provide  CDs of presentations to the Peer
     Review Panel.

Modelers' Response - All presentations given at the
peer review were provided to the panel members in
electronic form after the review.

25.  Provide CD copies of draft modeling  report,
     appendices, and attachments (those available
     electronically) to the Peer Review Panel.

Modelers' Response — The Draft Modeling Report,
Appendices, and Attachments were provided to the
panel in bound hard-copy in  late  June 2004 for
review purposes.  CD/electronic copies of the report
and associated materials were provided to the  peer
review panel after the review.
7.1.5   Modelers' Responses to Specific
Comments Made by Peer Review Panel
Member - James Martin

Note: Some of Dr. Martin's comments were identical
to those listed in the consensus comments above
and were, therefore, not repeated in this section.

1. MICHTOX:  Continue to  maintain the Level  1
   model, particularly for comparison  with Level  2
   predictions.

Modelers'  Response -  While  we appreciate  the
reviewer's interest, it would be difficult to continue to
maintain the MICHTOX PCB model for purposes of
comparing future  Level  2 predictions  because of
current resource limitations. Furthermore, it is rather
difficult to make a direct comparison of MICHTOX to
Level 2 because the construct of the two models is
so different. We plan, however,  to continue with the
development  of  a  Level 3  PCB transport/fate/-
bioaccumulation model which would offer much more
spatial  resolution  and would incorporate SEDZL
sediment resuspension  velocities  along with  the
QUICKEST-ULTIMATE sediment algorithms into the
framework. The Level 3 model should; therefore, be
very useful for comparison  to  the Level 2 model
predictions.

2. MICHTOX, LM2, and LM3: Explore incorporating
   specific algorithms, such as the  steady-state
   algorithm (as exists in MICHTOX), with the Level
   2 and potentially Level 3 models.

Modelers' Responses - A steady-state version would
be helpful if  Monte Carlo type simulations were
performed to help understand model uncertainty. For
the LM2 and LM3 PCB  congener-level models,  a
Monte Carlo type uncertainty analysis presents many
challenges as described in our response to comment
8 above. However, for our other two LMMBP toxic
chemicals of interest (mercury and frans-nonachlor),
a  Monte Carlo type  simulation   would be more
feasible due to the fewer number of state variables.
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3.  MICHTOX/LM2:     Both   models  predicted
   remarkably  similar  changes  in  total   PCB
   concentrations  over time  in  the  long-term
   projections.  However,  there were differences
   between the two models such as in the rates of
   settling/resuspension   used  and   in   the
   characterization of the sediment bed. As a result,
   the two  models predicted  similar results for
   somewhat dissimilar reasons.  It would  be of
   interest to further investigate factors leading to
   the similarity in predictions,  which may provide
   some  additional  insights  as  to the factors
   controlling PCBs in Lake Michigan.

Modelers'   Responses  -   In  Ken  Rygwelski's
PowerPoint presentation at the peer review titled, A
Comparison of Lake Michigan Mass Balance Project
(LMMBP)   Polychlorinated  Biphenyl  Models:
MICHTOX versus LM2-Toxic and LM Food Chain, a
graphic was presented that displayed the whole lake
total PCB concentration projections for the  two
models. It is noteworthy that at the beginning of the
runs depicted, January 1,1994, MICHTOX starts out
higherthan LM2-Toxic. The reason is that MICHTOX
concentrations  at  that   time   represent   the
concentrations  predicted   from  the  MICHTOX
hindcast  whereas  the concentrations form  LM2
represent observed lake concentrations at that time.
Also, note that in the same presentation MICHTOX
was losing approximately 2,958 kg/year and LM2-
Toxic was losing 2,043 kg/year. Although MICHTOX
was losing more PCBs per year than LM2-Toxic, it
started out with a  higher lake inventory of PCBs in
the water, which can explain, in part, why the two
models predict similar concentrations.  The construct
of these two models is rather different in a number of
ways, and this makes comparisons difficult. Some of
these  differences,  however,  were  most  likely
overcome through the calibration process of the two
models.   A  discussion on this  topic of model
comparability between MICHTOX and LM2-Toxic will
appear in the revised report.

The two food chain models were also very close to
predicting when the 5.5 year-old  lake trout at the
Saugatuck  biota  zone  would reach  the target
consumption criteria of 0.075 ppm PCBs in whole
fish.  MICHTOX predicted year  2025  and LM Food
Chain predicted the year 2026.  A major difference
between  these  two  model  constructs  is  that
MICHTOX is composed of four members in the food
web whereas  LM Food Chain has  10 members.
Also, MICHTOX is based on two PCB homologs and
LM Food Chain is PCB congener-based. It is likely
that much of the similarities in  the  predictions of
these two models is due to calibration and the use of
the same rate of decline for PCB loads for  natural
attenuation.

4.  POM/LM2/LM3:  Continue development of the
    linked POM and Levels 2/3 models.

Modelers' Response - Currently, neither in-house
expertise  nor   funding  exists  to  further   the
development of POM  for Lake Michigan.  We do
recognize; however, that simulating ice cover and
incorporating finer  spacial resolution  for  some
nearshore "hot spot" areas could be described better
with a POM upgrade.

In terms of upgrading  Level 3  models, we are
currently working on upgrading the coupled LM3-
Eutro and LM3-Toxic (PCB) model. This near-term
goal includes resuspension velocities from SEDZL
into the coupled model. The QUICKEST-ULTIMATE
algorithm will be implemented in the 10 sediment
layers.   The model has two particle classes: fine-
grained  inorganic fraction and fine-grained organic
fraction.     Refractory   organic   carbon,  total
phosphorus, total nitrogen, and  total silica  will be
associated with the particulate resuspension  flux as
well as the PCB modeled congeners.  With this new
construct, a LM3 hindcast from 1960 to 1995 will be
run for both the Eutro and Toxic components of the
coupled model.  A long-term goal includes adding
sediment  diagenesis   and  dissolved oxygen
algorithms to LM3 Eutro.

LM3-Eco is an enhanced version of LM3-Eutro and
will  eventually  include  Bythothrephes,   Mysis,
carnivorous zooplankton, herbivorous zooplankton,
diatoms, and green algae (Phase 1), and additional
state  variables  including  Dreissena, Diporeia,
nitrogen-fixing  blue-green algae, and non-nitrogen-
fixing  blue-green algae  (Phase  2).  At this time,
Phase 1  of LM3-Eco is in the model calibration stage.
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5.  Provide documentation of the POM application
   and  testing,  particularly with  regard  to an
   assessment of the applicability of the model to
   the transport of PCBs and other water quality
   constituents. Perhaps include it as an appendix
   to the modeling report.

Modelers' Response - See Schwab and Beletsky
(1998) and  Beletsky and Schwab (2001).  These
documents   are   available  at  http://www.gler!.
noaa.gov/pubs/techrept.html   and  http://www.
glerl.noaa.gov/pubs/fulltext/2001/20010008.pdf,
respectively.    LLRS has electronic and  hardcopy
forms of these documents in our library.

6. Investigate  potential  linkages issues between
   POM and with SEDZL.

Modelers'  Response  -  SEDZL  has  its  own
hydrodynamic model and is not at all connected with
POM; therefore, all  of  the hydrodynamic  forcing
functions are input into  the SEDZL model.  The
Donelan parametric wind wave model applied to Lake
Michigan by David Schwab of NOAA-GLERL is a
stand-alone model  which is run before SEDZL is run,
and the output of the wave model becomes input for
the SEDZL model.

7. Investigate  assumptions/limitations  of using a
   sigma  grid,  particularly  in   resolving  both
   nearshore and  open  lake issues. One potential
   limitation to the POM model construct (relative to
   this  application)  is  related  to the coordinate
   system used in the vertical dimension (a sigma
   grid). A sigma grid requires a constant number of
   vertical  layers  throughout  the model domain
   (beneath each  of the 5 km horizontal grid cells
   (the number of vertical layers was variously cited
   as from  15  to 20 in the modeling report, which
   should be corrected).  This use of the sigma grid
   may impact the ability of the model to resolve
   vertical gradients, particularly in deeper sections
   of the lake while  still sufficiently capturing
   nearshore circulation patterns. In addition, sigma
   grids may produce artificial horizontal transport
   patterns. While there are numerical schemes for
   compensating for this, I am not aware that they
   have been  implemented in POM or that any
   sigma transport tests have been conducted for an
   application such as Lake Michigan.
Modelers' Response - The actual number of sigma
layers  throughout the POM construct is 19 water
layers. The citations mentioning 15 or 20 layers will
be corrected in the revised edition of the report.

The potential  problem  of  using  the sigma  grid
structure for POM is that an extra term is introduced
in the  horizontal  gradient terms that can  lead to
artificial vertical diffusion of heat and momentum,
particularly in areas of large topographic gradients as
was described in Schwab and Beletsky (1998).  To
help minimize this affect, the 5 km gridded depths
were slightly smoothed by adjusting the depths to
ensure  that the  relative depth  change between
adjacent grid squares was less than 0.5 while still
preserving the volume of the original grid.

The model did not perform as well in the thermocline
area as it performed near the surface.  The simulated
thermocline was too diffuse.  To study the effect of
vertical  resolution on  the  vertical  temperature
gradients, a model run  with 39 sigma levels  was
conducted.  NOAA also ran the model with zero
horizontal diffusion to test for artificial diffusion along
sigma   surfaces.     In  both  cases,  very  little
improvement was noticed in model results.  On the
other hand, experiments with an one-dimensional
version of the model showed that the Mellor-Yamada
scheme  can provide  a  sharp  thermocline that is
sensitive to the choice of extinction coefficient which
posses significant spatial and temporal variability in
large lakes but was kept constant in the calculations
because at the  time of generating the model runs,
temporal data on the extinction coefficients were not
available.  In Schwab and Beletsky,  1998, NOAA
mentions that  a  2 km  grid structure  or  higher
resolution would likely improve the results, but would
likely push computer resources beyond current limits
for  the hydrodynamic model and associated  LM3
water quality models.

8.  In  addition  to spatial  averaging,  there  was
    apparently   time-averaging  of  hydrodynamic
    predictions as well, allowing a daily time-scale for
    the LM2-Toxic model. The procedures  used to
    average the hydrodynamic predictions, and tests
    conducted   to determine  the impact  of  that
    averaging, should be documented.

Modelers' Response - Schwab and Beletsky (1998)
indicate that aggregated average surface heat flux
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(on an  hourly time  scale) and average  vertical
temperature profiles (on a six hour time scale) were
computed for each of the 10 LM2 surface segments
and the 41 segments, respectively, for both the 1982-
1983 and 1994-1995 periods. In addition, horizontal
and vertical inter-segment transports averaged over
one-day and six-day intervals were computed for the
10 column LM2 grid with five vertical layers:  layer
one, 0-10 m; layer two, 10-20 m; layer three, 20-30
m; layer four, 30-50 m; and layer five, 50 m-bottom.
They do not discuss any tests run to determine the
impact of averaging. A discussion on how these data
were used in LM2 can be found in the report in Part
4, Chapter 4,  Sections  4.4.1.2 and  4.4.1.3.  All
programs and  data sets associated with the LM2
aggregation are  on the Final  Report CD received
from NOAA-GLERL.

9.   LM3-Eutro: Table 2.4.6 lists the two "types" of
     data  but  does   not  describe  how  the
     transformations were made.

Modelers' Response - The relationship between the
variables measured in the field and state variables
used in the model can be found in Appendix 2.4.1 of
the report.

10.  LM3-Eutro: Table 1.1.2 does not indicate that
     zooplankton were  a  measured  parameter,
     although  it  is a model state variable and the
     text indicate that  zooplankton  data  were
     collected (Part 2, Chapter 4, Section 2.4.2.2.4).

Modelers' Response - As part of the  introductory
material, Table 1.1.2 was only intended to offer a
general overview of the major types of data collected.
For example, some sub-parameters of PCBs such as
a listing of all of the congeners measured, or the fact
that dissolved  and particulate were measured are
missing from the table. However, zooplankton is a
major biological and will be included in the table.  A
comment will  be  added  where  Table  1.1.2  is
referenced in the text explaining that for a complete
listing of parameters measured, the reader should
see Part 1, Chapter 3 of  the report. For modeled
parameters, the reader  should  see  individual
chapters on MICHTOX, MICHTOX Food Chain, LM2-
Toxic, LM Food Chain, or LM3-Eutro modeling in the
report.
11.   LM3-Eutro: While I agree that the expansion of
     variables to include dissolved organic and labile
     and refractory particulate organic forms allows
     for  more  realistic description (which is an
     increasingly common practice)  there are no
     established  protocols  for  measuring these
     forms. Therefore, it must have been necessary
     to make assumptions regarding, for example,
     the partitioning of particulates among labile and
     refractory forms. Those assumptions should be
     described in  the  report,  and perhaps some
     sensitivity analyses performed as to the impact
     of differing assumptions on model predictions.
     The  assumptions regarding  the  split  were
     indicated (Part 2, Chapter 4, Section 2.4.1.1)
     for atmospheric loads, but not for other loading
     sources that this  reviewer could find.
Modelers' Response - The LM3-Eutro model has
labile and refractory state variables for particulate
nitrogen and phosphorus whereas particulate silica is
in the refractory form only.  Since nitrogen was not a
limited nutrient in the model, the evaluation of the two
particulate  nutrient forms  focuses  on  phosphorus
only.

Total phosphorus, dissolved phosphorus, and soluble
reactive phosphorus (SRP) were measured in the
water column of Lake Michigan.   The labile and
refractory forms of particulate phosphorus can  be
calculated based on equations described in Appendix
2.4.1.    For  initial  lake conditions,  particulate
phosphorus was evenly split between the labile and
refractory forms.  Somewhat different fractions of
particulate phosphorus were used for the labile and
refractory forms in tributary and atmospheric loads
(e.g. the tributary particulate was 0.55 labile and 0.45
refractory - see Part 2, Chapter 4).

The  mineralization  rates  for the  two particulate
phosphorus forms used  in the model were very
similar; therefore, no significant differences would be
expected when different fractions of these forms are
used in the  model. This was confirmed when several
model sensitivity  simulations were performed  by
varying  the  initial   lake  condition  and  loading
percentages between 25% and 75% for the two
particulate state variables, and in all cases the results
were virtually the same.
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12.   LM3-Eutro:   In  Part 2,  Chapter 4, Section
     2.4.4.2,  it  is stated that laboratory primary
     production rates were used to verify the overall
     production  rates  in  the  model.    These
     comparisons  should  be  included in  the
     modeling report.

Modelers' Response - A comparison of the model-
predicted  versus  laboratory-measured   primary
production rates can  be found  in  Figure  2.5.2.
However, there was no reference to this figure in Part
2, Chapter 4, Section 2.4.4.2. (there was a  reference
to the graph in Part 2, Chapter 5, Section 2.5.2). The
text will be updated to  include a reference to the
figure in Part 2, Chapter 4, Section 2.4.4.2, and the
figure will be moved.

13.  LM3-Eutro:   The characterization  of non-
     diatoms versus diatoms is a useful breakdown.
     Since blue-greens were  the dominant  algae
     (see  page 99), some  additional explanation
     would be  worthwhile  as  to  how  nitrogen
     limitation was computed for these algae.

Modelers'  Response  -  Blue-green  algae  was
present in  Lake Michigan  in  large   numbers.
However, because of their very small size, they made
up less than 6% of the total phytoplankton carbon
mass.  For this reason, we lumped this group in the
model as part of the "non-diatom" algae group and
we assumed that phosphorus, rather than nitrogen,
was the limiting nutrient. The corresponding section
in the revised edition of the report will be updated to
provide a better and more detailed explanation.

14.  LM3-Eutro/LM3-Toxic: Continue to develop the
     Eutro Model, for  both  linkages to the  Toxic
     Model as well as for use related to addressing
     conventional pollution in Lake Michigan and its
     tributaries/embayments.

Modelers' Response — We do plan to continue to
develop the  coupled  LM3-Eutro and LM3-Toxic
(PCB)  model.  SEDZL provides  us with a time-
variable resuspension velocity  which we will use in
LM3-Toxic (PCB). Current plans include the addition
of particulate resuspension processes to LM3-Eutro
including particulate forms of nutrients and  refractory
organic carbon. Eventually, we will add diagenesis to
the sediment  compartment  and  algorithms  to
compute dissolved oxygen.  See our response to
Number 4 above for additional details.

15.  LM3-Eutro: Explore and document methods to
     relate measurable field data to model input
     values  (e.g., refractory  particulate  organic
     matter).

Modelers' Response  - This question has been
answered in Number 11 above.

16.  Conduct additional calibration (e.g., to nitrogen
     series) as  an additional test of the  model's
     performance and if the model may be used to
     address questions in the future with regard to
     conventional pollution.

Modelers' Response - Because  nitrogen does not
drive this model, relatively little time and effort was
spent on  the calibration of  the  different nitrogen
species and was, therefore, not included in the June
2004 draft copy of the report.  However, nitrogen will
be  fully   calibrated  in  future  modeling   efforts
especially  when addressing  lake nutrient  and
phytoplankton (chlorophyll-a) issues.

17.  LM3-Eutro:   The  comparisons  of  model
     predictions and  field  data  were somewhat
     limited  in  Part  2,  Chapter  5.   Additional
     comparisons   should  be   provided,   both
     graphical  and   statistical,   between  model
     predictions and observed data. Comparisons
     should be  provided if  possible for all state
     variables.   For example, no comparisons are
     presently provided for nitrogen species.

Modelers' Response - In the revised edition of the
report, Part 2, Chapter 5 (Calibration) was expanded
and updated to include additional  graphical and
statistical results of the calibration process.

18.  LM3-Eutro/LM3 - Toxic:  Presently, the LM2-
     and LM3-Eutro codes specify sediment fluxes
     as  zero order  rates,  which is  a common
     practice.   However,  there are  models  of
     sediment  diagenesis  that  allow  prediction,
     rather than description, of those rates. While
     probably not critical in the context of using the
     Eutro predictions for input to the toxic model,
     incorporation of a sediment diagenesis model
     may be worthwhile should the LM3-Eutro model
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     be used in the future to assess eutrophication
     related management questions.

Modelers' Response - Sediment diagenesis will be
added to LM3-Eutro.  See "Modelers Response" to
Number  4  above  for  additional   planned
enhancements of LM3-Eutro.

19.  LM3-Eutro/POM:  The  linkage of the POM
     model with the LM3-Eutro grid was only briefly
     discussed.     The   incorporation   of  the
     QUICKEST-ULTIMATE routines from the U.S.
     Army Corps of Engineers CE-QUAL-ICM model
     should provide a suitable numerical framework
     for that linkage.  However,  the  linkage  of
     hydrodynamic and water quality models, even
     using  a one-to-one spatial grid, is not a trivial
     task.   For  example, because  of differing
     solution schemes, mass imbalances can occur
     which, if not properly treated, can accumulate
     and impact long-term model  predictions.  As
     such testing is required to ensure that water
     and constituent mass are conserved globally
     and locally in the linked water quality model.
     This  testing needs  to  be documented and
     should be included  in  the  modeling  report,
     perhaps as an appendix.

Modelers' Response - All of these topics and issues
were covered in four reports written by Ray Chapman
associated  with the U.S. Army Corps Of Engineers,
Waterways Experiment Station which will be included
in an updated version of the LM3 User Guide (Settles
et al. 2002). This updated LM3 User Guide will be
included as a new appendix in the revised report.

20.  LM3-Eutro: The section of the report (Chapter
     1) dealing with the calibration of the LM2-Eutro
     and LM3-Eutro was somewhat confusing, with
     regard to which model was calibrated against
     existing data.

Modelers' Response - The LM3-Eutro model is a
Level 3 model with 44,042 (5x5 km2) segments -
there is no  LM2-Eutro model.  However, as part of
the post-processing, the model was collapsed to a
Level 2 grid, similar to the LM2-Toxic  framework.
This enabled a comparison of field data with model
output on the Level 3 grid and on the Level 2 grid.
Part 2,  Chapter 5 of the report (Calibration) was
updated to better explain the calibration of LM3-Eutro
on the Level 2 and Level 3 segmentation schemes.

21.  LM2/POM:  An overlay grid, such as between
     the POM model and LM2-Eutro and LM2-Toxic
     is often more problematic than using a one-to-
     one spatial grid (between a hydrodynamic and
     a water quality model). In this application, it
     was suggested that linkage problems did occur,
     resulting  in the necessity of adding  "water
     balancing flows" (Part 4, Chapter 3, Section
     4.3.3).  Adding water-balancing flows is not an
     uncommon practice in linking three-dimensional
     hydrodynamic  and  water  quality  models.
     Typically those flows are small but without them
     water volume  imbalances accumulate over
     time.   However,   it  was indicated  during
     presentations that in this study not including the
     "balancing" flows resulted  in water volumes
     going to zero in some water quality segments
     (in Green Bay). This is indicative of a linkage
     problem that should be further investigated. In
     addition, the approach used to compute vertical
     exchanges (Equation  4.4.1) should not have
     been applicable if vertical flows (gross not net)
     were included with the hydrodynamic linkage.
     It is suggested that additional testing  of the
     linkage be conducted and documented within
     the modeling report, perhaps as an appendix.

Modelers'  Response - LM2-Eutro, referred to in
sentence one, does not exist. Primary productivity is
estimated by LM3-Eutro in space and time, and this
information is exported to LM2-Toxic (PCB).  The
overlay between LM3-Eutro and POM is a one-to-one
spatial grid.

We  agree  with   Dr.  Martin's  comments  and
suggestion on the linkage between POM and LM2.
The linkage problem (mass of water is not balanced
in individual segment basis in LM2-Toxic model) was
identified during the period of testing the linkage, and
the water balancing flow  was introduced to correct
the imbalance.  This problem was noted in  a very
small segment volume in Green Bay and after a run
time  of 70 years.  See Part 4, Chapter 3, Section
4.3.3 of the report for additional discussion on this
issue. The results of the test will be included as an
appendix in the revised report.   NOAA-GLERL
performed the linkage calculations between POM
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and LM2, so any further investigation would need to
be referred to them.

Dr. Martin is  also  correct on the  applicability of
vertical exchanges. The vertical flows provided by
NOAA based on POM outputs are the net vertical
flows; therefore, the vertical exchange process must
be added in the overall water transport field used in
LM2-Toxic model.     Additional  discussion  and
description of  the  method  of calculating vertical
exchange   coefficients   and  calibration   are
documented in Part 4, Chapter 3, Section 4.3.3  and
Part 4, Chapter 4, Section 4.4.1 in the report.

22.  LM2-Toxic: As indicated in  Part 2, Chapter 6,
     Section  4.6.2, the flux  contributed by  the
     diffusive  term from the  sediment bed was
     unexpectedly large relative to the resuspension
     flux. This may have been due to the relatively
     large specified diffusion coefficient used relative
     to  the Level  1  model.  In addition,  it was
     indicated  in Part 4, Chapter 6, Section 4.6.2
     that the  total  PCB  residence  time for Lake
     Michigan were on the order of 100 days. This
     estimate seems  low to this reviewer.  It would
     be interesting to see how  this compares to
     predictions from a Level 3  model which may
     more realistically estimate vertical exchanges in
     layers isolated from the water surface.  The
     Level 3 model  could be used to determine if the
     rapid removal  may be in part an artifact of the
     modeling approach used in the Level 2 studies.
     As an example, given the rates of settling used,
     surface particles would require approximately
     one year to reach  the bottom, while with a
     single vertical-box model it would be assumed
     that  vertical   transport   is  on  average
     instantaneous.

 Modelers' Response  - We agree with  Dr. Martin
 regarding the  high flux contributed  by diffusion
 between the water column  and  surficial sediment
 layer. We plan to  investigate  model responses in
 both the water column  and sediment to various mass
 fluxes  across  the sediment-water interface by
 changing the   diffusion  coefficient  and/or mixing
 length  between the  water column and  surficial
 sediment as identified in Part 4, Chapter 2  of the
 report. There is further discussion on the diffusion
 coefficient  used  in LM2-Toxic  model  in Part 4,
 Chapter 6, Section  4.6.2  of the report.  This also is
one of the recommendations in Chapter 2 of Part 4.
Like Dr. Martin, we also noticed that the total PCB
residence time for Lake Michigan is relatively low and
will do the comparison with the LM3 model when
these results become available.

23.  LM2-Toxic: Apply the model to refine whole-
     lake estimates of PCB concentrations.

Modelers' Response-Whole lake, volume-weighted
average concentrations of total PCBs in the lake can
be found in Part 4, Chapter 6, Section 4.6.4 of the
report for various load reduction scenarios, including
the "No-Effects Action."

24.  LM2-Toxic: Extend the modeling framework to
     include other contaminants  of concern (e.g.,
     mercury).

Modelers' Response - We will extend the modeling
framework to include other contaminants such  as
mercury and frans-nonachlor and believe the LM2-
Toxic model would make an excellent and easy-to-
use  screening  and  diagnostic  tool for  helping
management  personnel  and  policy  makers  to
understand the key processes controlling the level of
the contaminant of  interest in the water system.

25.  LM2-Toxic: The comparisons of measured and
     simulated  concentrations seem reasonable.
     However,  since differences occur between
     factors controlling PCBs in Lake Michigan and
     Green Bay, the results for these two systems
     should be reported separately.

Modelers' Response - For LM2-Toxic, most field
measured data were interpolated separately for the
two systems.  We also reported the two systems
together and separately for model calibration results
and mass budget diagnosis. See Part 4, Chapters 5
and 6 of the report  for details.

26.  LM2-Toxic/LM3-Toxic:   The sediment  bed
     model seems reasonable.   However, some
     additional  clarification of the semi-lagrangian
     method for simulating the sediment bed (Part 4,
     Chapter 3, Section 4.3.4.2.3) would be useful.
     In addition, the present  construct does not
     allow for the tracking of materials buried out of
     the layer,  or  perhaps entrained into the layer
     from deeper contaminated sediments.  Some
                                              569

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     additional  development  of  the   sediment
     algorithms would be useful for the Level  2
     model and for incorporation into the Level  3
     framework where it may be more important with
     regard to near-shore issues).

Modelers' Response - There is a detailed description
on  the  semi-Lagrangian  method  for  simulating
sediment bed in the IPX model user manual (Velleux
et a/., 2000). An algorithm for tracking masses and
fluxes of both solids and PCBs in the sediment bed,
including deeper  sediments,  has  already been
implemented in the LM2-Toxic model codes. We will
consider additional development of LM2 sediment
algorithms for  incorporation  into LM3 as another
option. Also, see "Modelers Response" to Number 4
above for additional enhancements planned for the
sediment bed model.

27.   LM  Food Chain:  Continue to develop and
     refine the food chain model.

Modelers'   Response  - Additional   calibration
simulations will be run with reconstructed historical
exposure PCB concentrations in water and sediment
as  inputs.   The  new calibration results  will be
compared with those from the constant exposure
assumption.   The results will  be  provided and
discussed in the report.  Further development and
refinement  of the  model  will be carried out when
additional  data for the   lake trout  food web  at
Sheboygan and for the  coho salmon  population
become available.

28.   LM Food Chain: Extend the calibration period
     to an evaluation of historical loadings and/or a
     period encompassing all available data (not just
     the 1994-1995 data set).

Modelers' Response -  A new set of  calibration
procedures  will  be  performed  using  estimated
temporal  profiles  of historical PCB   exposure
concentrations in  water  and sediment  as model
inputs. All currently available field PCB  monitoring
data for fish in Lake Michigan will be compiled and
used in the calibration. The results will be provided
in the final  report.  However, the credibility of the
calibration results  will be impaired  by the lack of
historical information regarding food web structures
and dietary shift, age-specific PCB data for lake trout,
PCB data for forage fish, PCB compositional change,
and congener-specific PCB values.

29.  LM Food Chain:  Use the model along with any
     revisions made  to  the  LM2-Toxic  to refine
     estimates  of  future  trends  in  fish  PCB
     concentrations.

Modelers' Response - As revised PCB exposure
scenarios  in the water and sediment provided by
LM2-Toxic becomes available, new model projections
of future trends in lake trout PCB concentrations will
be made and reported in the final report.

30.  LM Food Chain:  Initiate extending the model
     (and data  analysis) to other pollutants of
     concern (e.g., mercury).

Modelers'  Response - It  has been  our intention to
expand  the model to  other pollutants  of  concern,
including application to other organic chemicals such
as frans-nonachlor and mercury.

31.  LM Food  Chain/LM3-Eutro:  Perhaps some
     more direct coupling of the eutrophication and
     food chain model could be considered in future
     applications  to  aid  in addressing questions
     regarding  impacts of changes in  food chain
     structure  on uptake  of  PCBs   and other
     toxicants.

Modelers' Response-This recommendation was not
addressed in  the  report.  The possibility  of direct
coupling of the eutrophication and food chain models
could be explored in future applications. However,
the current state  of  understanding regarding the
mechanism of fish dietary selection/adaption does
not permit prediction of changes in  food chain
structures  with   eutrophication  data.     Further
investigation is required on this topic before attempts
are made to couple the food web  model with the
eutrophication model to address PCB uptake issues.

32.  LM3-Toxic/LM2-Toxic: Continue to develop the
     LM-3 model  in order to test against the LM2-
     Toxic predictions to estimate the  potential
     impact of a more physically realistic model on
     lake-wide PCB impacts.

Modelers' Response - We do plan to further develop
the LM3 models to compare to LM2-Toxic.  See
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"Modelers' Response"  to comments  in Number 4
above. Comparison of the models on a lake-wide
basis, such as volume-weighted averages, will be
performed.

33.  LM3-Toxic:   Continue to develop the LM3
     Model in order to  aid in addressing nearshore
     impacts which can not be addressed using the
     LM2 structure.

Modelers' Response -  We will continue to develop
LM3-Toxic and LM3-Eutro/Eco to address nearshore
impacts as best as can be accomplished within the
limitations of the 5 km grid structure. Much finer
space scales would  need to be implemented  in a
model, however, in order to model specific harbors
associated with an  Area of  Concern or tributary
mouths. In those cases, a finer scaled model would
need to be constructed, and LM3 could be used to
provide the boundary condition for this finer-scaled
model construct.

34.  LM3-Toxic: Continue to develop and test the
     linkage between the POM and  LM3 models
     (both Eutro and  Toxic),  such as testing to
     ensure that mass  conservation is maintained.

Modelers' Response - A mass conservation test was
performed several years ago on LM3-Toxic using a
conservative tracer.  During the test, all loadings
were shut off. All lake  cell  concentrations were
equal.  The model was run for two years, and no
noticeable  change  was  detected   in   the
concentrations.

35.  LM3-Toxic/SEDZL:    Continue  to  explore
     linkages or incorporation of SEDZL routines in
     the Level 3 models. This linkage may be of
     particular importance in evaluating nearshore
     trends and issues.

Modelers' Response - We will continue to pursue
linking SEDZL output to Level 3 models as described
in the "Modelers' Response" to Number 4 above.

If we decide to further  develop the linkage beyond
the current construct, we would likely choose SEDZL-
J as it is now being promoted by the experts as the
better model to use versus SEDZL. SEDZL-J utilizes
SED-Flume data and also allows for non-constant
vertical sediment profile data. SED-Flume measures
the total erosion rate on actual sediment cores and
includes  both  the  rate  at  which sediments are
transferred to the water column (resuspension), but
also the bed-load rate. SEDZL-J includes bed load
and bed armoring whereas SEDZL does not.  SED-
Flume data as well as bulk density profiles have been
collected on Lake Michigan cores by the University of
California  at Santa Barbara.  A  project  to  apply
SEDZL-J  was  proposed,  but funding  was not
available.  Of course, if bedload, armoring, and non-
constant vertical sediment profile data are not issues,
then there does not seem to be a great advantage of
SEDZL-J  over SEDZL.   A potential  limitation  of
applying   either  SEDZL   or  SEDZL-J's,   two-
dimensional  models to  deeper  portions of Lake
Michigan during stratification exists.  At this time, a
three-dimensional, SEDZL-J, is not available.

The extent of this impact on our resuspension rate
estimates is not known.

References

Beletsky, D. and D. J. Schwab.   2001.  Modeling
   Circulation  and  Thermal  Structure  in  Lake
   Michigan:  Annual   Cycle  and   Interannual
   Variability.  J. Geophy.  Res., 106(C9): 19745-
   19771.

Endicott, D.D.  2005.  2002 Lake Michigan Mass
   Balance Project: Modeling Total PCBs  Using the
   MICHTOX  Model.   In:   R.  Rossmann  (Ed.),
   MICHTOX:     A  Mass   Balance   and
   Bioaccumulation Model for Toxic Chemicals in
   Lake Michigan, Part  2.   U.S.  Environmental
   Protection  Agency,  Office of Research and
   Development, National Health and Environmental
   Effects Research Laboratory, MED-Duluth, Large
   Lakes  Research Station, Grosse lie,  Michigan.
   EPA/600/R-05/158, 140 pp.

Franz, T.P., S.J. Eisenreich, and T.M. Holson. 1998.
   Dry  Deposition of Particulate Polychlorinated
   Biphenyls and Polycyclic Aromatic Hydrocarbons
   to Lake Michigan.   Environ.  Sci.  Technol.,
   32(23):3681-3688.

Gobas,  F.A.P.C., M.N. Z'Graggen,  and X.  Zhang.
   1995.   Time Response of  the  Lake Ontario
   Ecosystem  to  Virtual  Elimination  of   PCBs.
   Environ. Sci. Technol., 29(8):2038-2046.
                                             571

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Hillery, B.R., I. Basu, C.W. Sweet, and R.A. Hites.
   1997.  Temporal and  Spatial  Trends in Long-
   Term Study of Gas-Phase PCB Concentrations
   near the Great Lakes.  Environ. Sci. Technol.,
Holsen, T.M., K.E. Noll, S. Liu, and W. Lee.  1991.
   Dry Deposition of Polychlorinated Biphenyls in
   Urban Areas. Environ. Sci. Technol. 25(6):1075-
   1081.

Kuehl, M.  2002.  Polychlorinated Biphenyl (PCB)
   Congener  Patterns in Lake Michigan  Mass
   Balance Study Biota. Master's Thesis, University
   of Wisconsin, Green Bay, Wisconsin.  120 pp.

Marti,  E.A.   and  D.E.  Armstrong.      1990.
   Polychlorinated  Biphenyls  in Lake  Michigan
   Tributaries. J. Great Lakes Res., 16(3):396-405.

McCarty, H.B., J. Schofield, K. Miller, R.N. Brent, P.
   Van Hoof, and B. Eadie.  2004.  Results of the
   Lake  Michigan   Mass   Balance  Study:
   Polychlorinated Biphyenyls and frans-Nonachlor
   Data  Report.  U.S. Environmental  Protection
   Agency, Great Lakes Program Office, Chicago,
   Illinois. EPA-905/R-01/01 1 , 289 pp.

Miller, S.M., M.L. Green,  J.V.  DePinto,  and  K.C.
   Hornbuckle.  2001.   Results From  the Lake
   Michigan Mass Balance Study:  Concentration
   and  Fluxes  of  Atmospheric  Polychlorinated
   Biphenyis and frans-Nonachlor.   Environ. Sci.
   Technol., 35(2):278-285.

Schneider, A.R., H.M.  Stapleton, J. Cornwell, and
   J.E.  Baker.  2001.  Recent Declines in  PAH,
   PCB, and  Toxaphene Levels in  the Northern
   Great  Lakes  as   Determined  From   High
   Resolution Sediment  Cores.   Environ.  Sci.
   Technol., 35(19):3809-3815.
Schwab, D. and D. Beletsky.  1998. Lake Michigan
   Mass Balance  Study: Hydrodynamic Modeling
   Project.   National Oceanic and  Atmospheric
   Administration,   Great  Lakes  Environmental
   Research  Laboratory, Ann Arbor, Michigan.
   Technical Memorandum ERLGLERL-108,53 pp.

Settles, M., W. Melendez, and J. Pauer. 2002. LM3:
   The Lake  Michigan  Mass  Balance  Model.
   Internal Report.  U.S.  Environmental Protection
   Agency, Office  of Research and Development,
   National   Health  and  Environmental  Effects
   Research Laboratory, MED-Duluth, Large Lakes
   Research Station, Grosse lie, Michigan. 203 pp.

Velleux, M. and D.D. Endicott. 1994. Development
   of a Mass Balance Model for  Estimating PCB
   Export from the Lower Fox River to Green Bay.
   J. Great Lakes Res., 20(2):416-434.

Velleux, M., S. Westenbroek, J. Ruppel, M. Settles,
   and D.D. Endicott. 2000. A User's Guide to IPX,
   the  In-Place Pollutant Export  Water  Quality
   Modeling  Framework, Version 2.7.4.   U.S.
   Environmental  Protection Agency, Office  of
   Research and Development, National Health and
   Environmental  Effects Research   Laboratory,
   MED-Duluth, Large Lakes Research  Station,
   Grosse Me, Michigan. 179 pp.

Wethington,  D.M.  and K.C.  Hornbuckle.  2005.
   Milwaukee,  Wl, as a Source  of  Atmospheric
   PCBs to Lake Michigan.  Environ. Sci. Technol.,
   39(1 ):57-63.
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                                         PART?

                                     APPENDICES
Appendix 7.2   Comments  as  Received
From  Dr.  James  Martin  Peer  Review
Summary:  Lake Michigan Mass Balance
Project

James L. Martin
Department of Civil Engineering
Mississippi State University
Mississippi State, Mississippi 39762

7.2.1 General Comments

These comments are based upon a review of the
draft (June 24, 2004) documentation "Results of the
Lake  Michigan  Mass  Balance  Project: PCBs
Modeling Report" as well as materials presented and
discussed at  the  July 27-28, 2004  Peer  Review
Workshop held in Romulus, Michigan.  The charge of
the peer review was to focus on and  address three
major  categories  each   with  subcomponents,
considering strengths and weaknesses:

1.  Overall   Multimedia   Ecosystem   Modeling
   Approach
2.  Overall Model Performance
3.  Suitability for Management

Each of these topics are discussed below followed by
a summary of recommendations for continued and
future development.
7.2.1.1 Overall Multi-Media Ecosystem Modeling
Approach

The overall multi-media and mass balance approach
is a necessity to a system like Lake Michigan where
both the biota concentrations  are an end-point for
management decisions and biota impact the PCB
cycling (since such a large component of the organic
solids are of biotic origin).  It  is also necessary to
include the hydrodynamics of Lake Michigan, since
hydrodynamics impacts contaminant transport.  For
the analysis  of loadings, consideration of  loadings
from all media (tributaries, the atmosphere, etc.) is
also essential. The overall multi-media approach as
implemented in the Lake  Michigan Mass  Balance
Project included  all  of these  components.   The
specific components and their relative importance will
shift as issues progress from whole-lake to nearshore
areas and from PCBs to other contaminants such as
mercury. However, the framework developed for the
Lake Michigan mass balance studies will provide a
suitable base  for the extension  of  the overall
approach into other areas and  to other chemicals of
concern.

7.2.1.2 Overall Model Performance

The model(s) is (are) considered by this reviewer to
adequately representthephysical/biological/chemical
processes impacting  PCB concentrations in  the
water, sediment, and biota of  Lake Michigan. The
present construct  is  considered  limited  in  its
applicability to whole lake issues.   However, the
extension of the framework to  the LM3 level should
allow for the modeling system to be used to address
nearshore issues as well.   The modeling framework
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is also suitable as a basis for the development of
models  of other chemicals  of concern, such as
mercury.

7.2.1.3 Suitability for Management

An  assessment of the suitability of the modeling
framework for management requires first delineating
the specific management questions that framework
will be asked to address. The strength of the studies
completed to date is that they provide a framework
that can be used  in the contributing toward the
"weight-of-evidence"  with  regard to the  relative
importance  of loading sources  on the average
concentrations of  PCBs in the lake as well as in
estimating the time required for natural recovery in
the water column and biota.  To the extent that this
weight-of-evidence relates to the management goals
for the Lake, the modeling framework is suitable.  It
integrates the present  understanding  of  factors
impacting PCBs in Lake Michigan. While the spatial
segmentation of  the  LM2-Toxic  model is more
detailed than that of the Level 1 models, the structure
is  perhaps best  suitable for refining whole  Lake
estimates  of  PCB  concentrations  rather  than
predicting local variations.

However, there are a number of issues, such as
nearshore and tributary issues,  that the present
model Level 2 models cannot address. For example,
the LM2-Toxic model  cannot be used to address
issues related to specific Areas of Concern, other
than as a lake-wide average.  It is expected that the
LM3-Toxic model, when completed, would be more
suitable for  addressing local  variations in  PCB
concentrations and exposure.

An  additional major strength of the study is  serving
as a framework for the evaluation of available data
and in the planning of future data  (and modeling)
efforts.    For example,  analyses designed to
determine  model  uncertainty provide  not  only
information concerning predictive uncertainty but can
guide monitoring efforts to reduce that uncertainty.
An iterative program of model development and data
collection may provide for the most efficient use of
limited resources,  particularly given the reasonably
long time frame before some  of the issues (such as
the Total Maximum Daily  Load (TMDL) for  Lake
Ontario) need to be  addressed.   This iterative
approach  to the collection and  analysis of data,
exemplified by the Levels 1 and 2 studies and leading
to Level 3 is clearly an effective means of organizing
all of the myriad efforts and parties involved in the
collection and analysis of data. The participants are
to be commended  on the demonstrated efficacy of
the use of the mass balance approach in the design
of the PCB study.

7.2.2  Specific Recommendations

7.2.2.1 POM and Linkages

1. Continue development of the linked POM and
   Level 2 and Level 3 models.

2. Provide documentation of the POM application,
   perhaps as an appendix to the modeling report.

3. Assist  in developing ice cover algorithms and
   linkages with water quality model.

4. Investigate potential linkages issues between
   POM and with SEDZL.

5. Investigate assumptions/limitations of using a
   sigma   grid,   particularly  in  resolving  both
   nearshore and open-lake issues.

7.2.2.2 LM2-Eutro and LM3-Eutro

1. Continue to develop the Eutro model, for both
   linkages to the Toxic model as well as for use
   related  to addressing conventional pollution in
   Lake Michigan and its tributaries/embayments.

2. Explore  and   document  methods to  relate
   measurable field data to model input values (e.g.,
   refractory particulate organic matter).

3. Conduct additional calibration (e.g., to nitrogen
   series)  as  an  additional  test of the model's
   performance and if the model may be used to
   address questions in the future with  regard to
   conventional pollution.

4. Consider including a sediment diagenesis model
   if Eutro will be used in the future to address
   management questions related to conventional
   pollution.
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5.  Consider exploring a more direct linkage with the
   food chain model, to address potential changes
   in the food chain structure.

7.2.2.3 Level 1  Model

1.  Continue  to maintain  the  Level  1  model,
   particularly  for   comparison  with  Level  2
   predictions.

2.  Explore incorporating specific algorithms, such as
   the steady-state algorithm, with the Level 2 and
   potentially Level 3 models.

7.2.2.4 LM2-Toxic

1. Test the linkage with the POM model by running
   conservative tracer test to insure that a mass
   balance is maintained.

2. Revisit and  refine sediment component of the
   model (e.g.,  number of layers).

3. Extend the calibration period to an evaluation of
   historical loadings and/or a period encompassing
   all available  data  (not just the 1994-1995 data
   set).

4. Investigate why similar predictions were obtained
   to those from the Level 1 for what appears to be
   dissimilar reasons   (differences  in  settling
   velocities, diffusion rates, bed thickness, etc.).

5. Apply the model to refine whole-lake estimates of
   PCB concentrations.

6. Extend the modeling framework to include other
   contaminants of concern (e.g., mercury).

7.2.2.5 LM Food Chain

 1. Continue to  develop and refine the food chain
   model.

2. Extend the calibration period to an evaluation of
   historical loadings and/or a period encompassing
   all available  data  (not just the 1994-1995 data
   set).

3. Consider investigating a more direct linkage with
   biotic models (such as LM3-Eutro).
4.  Use the model along with any revisions made to
    LM2-Toxic to refine estimates of future trends in
    fish PCB concentrations.

5.  Initiate extending the model (and data analysis)
    to other pollutants of concern (e.g., mercury).

7.2.2.6  LM3-Toxic

1.  Continue to develop the LM3 model in order to
    test   against  the   LM2-Toxic  predictions  to
    estimate the potential impact of a more physically
    realistic model on lake-wide PCB impacts.

2.  Continue to develop the LM3 model in order to
    aid in addressing nearshore impacts which can
    not be addressed using the LM2 structure.

3.  Continue to develop and test the linkage between
    the  POM and LM3 models  (both  Eutro  and
    Toxic),  such as testing  to ensure  that mass
    conservation is maintained.

4.  Continue to explore linkages or incorporation of
    SEDZL  routines in the Level 3  models.  This
    linkage  may  be of particular  importance in
    evaluating nearshore trends and issues.

7.2.3 Specific Comments

What follows  are  some  specific comments  and
observations  regarding  each  of  the  modeling
components.   Some of these comments request
clarification of specific assumptions and  methods
used in the development of the models.  While the
documentation provided was extensive, there were
specific areas identified where additional information
and/or clarification would be helpful.

7.2.3.1  Hydrodynamics and POM Linkage

The hydrodynamic model used in this application for
LM3-Eutro,  and  planned for LM3-Toxic,  is  the
Princeton Ocean Model (POM). POM is widely used
and accepted and is similar  in construct to several
other hydrodynamic models in common usage (e.g.,
the ECOM model, which was  based largely on POM,
and EFDC).   However, there was  no  information
provided in the  subject modeling report as to the
model  application  and  testing  of  the  model,
particularly with regard to an assessment of the
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applicability of the model to the transport of PCBs
and other water quality constituents. The information
provided  was  limited  to a brief presentation  by
D'mitry Beletsky and David Schwab. As such, there
was not sufficient information presented to assess
the application or performance of the hydrodynamic
model. Since the hydrodynamic model is a critical
component of the Levels  2 and  3 studies, it is
suggested  that  documentation   of  the   model
application be provided in the PCB modeling report,
perhaps as an appendix.

One potential limitation to the POM model construct
(relative  to  this  application)  is  related  to  the
coordinate system used in the vertical dimension (a
sigma  grid).   A sigma grid requires  a constant
number of vertical  layers throughout the  model
domain (beneath each of the 5 km horizontal grid
cells (the number of vertical layers was variously
cited as from 15 to 20 in the modeling report, which
should be corrected). This use of the sigma grid may
impact the ability of the model to resolve  vertical
gradients, particularly in deeper sections of the lake
while still sufficiently capturing nearshore circulation
patterns.  In addition, sigma grids may  produce
artificial horizontal transport patterns.  While there
are numerical schemes for compensating for this,  I
am not aware that they have been implemented in
POM or that any sigma transport tests have been
conducted for an application such as Lake Michigan.

A second potential limitation of the POM model is its
present inability to  predict ice cover.   From the
presentations, it was suggested that ice algorithms
will be added to the model and it is recommended
that the incorporation of ice algorithms be pursued.

The linkage  of the POM model with the LM3-Eutro
grid was only briefly discussed. The incorporation of
the QUICKEST-ULTIMATE routines from the U.S.
Army Corps of Engineers CE-QUAL-ICM  model
should provide a suitable numerical framework for
that linkage.  However, the  linkage of hydrodynamic
and water quality models, even using a one-to-one
spatial grid,  is not  a  trivial task.  For example,
because  of  differing  solution schemes,  mass
imbalances can occur which, if not properly treated,
can  accumulate  and impact  long-term   model
predictions.  As such,  testing is required to ensure
that water and constituent mass  are conserved
globally and locally in the linked water quality model.
This testing needs to be documented and should be
included  in  the  modeling report, perhaps  as an
appendix.

An overlay grid, such as  between the POM model
and  LM2-Eutro  and   LM2-Toxic is  often more
problematic  than  using a one-to-one spatial  grid
(between a hydrodynamic and water quality model).
In this application,  it was suggested  that linkage
problems  did occur resulting in the necessity of
adding "water balancing flows" (Part 4, Chapter 3,
Section 4.3.3). Adding  water-balancing flows is not
an uncommon practice  in linking three-dimensional
hydrodynamic and water  quality models. Typically
those flows are small but without them water volume
imbalances accumulate over time. However, it  was
indicated during presentations that in this study, not
including the "balancing" flows resulted in water
volumes  going to  zero in  some  water quality
segments (in Green Bay).  This is indicative of a
linkage problem that should be further investigated.
In addition, the approach used to compute vertical
exchanges (Equation 4.1.1) should not have been
applicable if vertical flows (gross not net) were
included  with  the   hydrodynamic linkage.   It is
suggested that additional testing of the linkage be
conducted and documented  within  the modeling
report, perhaps as an appendix.

In addition to spatial averaging, there was apparently
time-averaging of hydrodynamic predictions as well,
allowing a daily time-scale for the LM2-Toxic model.
The procedures used to average the hydrodynamic
predictions, and tests conducted to  determine the
impact of that averaging, should be documented.

In general, the linkage of  POM with the LM models
represents an advancement and provides additional
capabilities  that  should be  continued  to  be
developed. For example, the coupled model should
more accurately predict the transport patterns in the
lake, which  are  always of  questionable accuracy
when based on purely descriptive techniques. In
addition,  the coupled model may be more readily
applied to predict conditions in more localized areas,
such as nearshore, and to predict conditions (such as
extreme   events)   that  cannot  be   adequately
characterized using  a descriptive approach.
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7.2.3.2 LM2-Eutro and LM3-Eutro

The eutrophication model is an important component
of the overall multi-media modeling approach.  The
addition of an eutrophication model is of particular
importance for Lake Michigan due to the reported
large fraction of total sorbents that are of biotic origin
(reported to be 90 percent of the total organic carbon
load to the  lake, Part 4, Chapter 3, Section 4.3.4).
The overall  structure of the eutrophication model
seems reasonable, and seems comparable to other
eutrophication   models.    The  majority  of  the
comments provided below are related to clarifications
that would be helpful in the modeling report.

Some additional description is needed as to how the
field data (as listed in Table 1.1.2) were converted to
model input or used for model comparisons. Table
2.4.6  lists the two "types"  of  data  but  does not
describe how the transformations were made. Table
 1.1.2  does not  indicate  that zooplankton  were a
measured parameter, although it is a  model state
variable and the text indicate that zooplankton data
were collected (Part 2, Chapter 4, Section 2.4.2.2.4).
Also, while I agree that the expansion of variables to
include dissolved organic and labile and  refractory
particulate organic  forms allows for more  realistic
description   (which  is  an  increasingly  common
practice) there  are no  established  protocols  for
measuring  these forms.   Therefore, it must have
been necessary to make assumptions regarding, for
example, the partitioning of particulates among labile
and refractory forms. Those assumptions  should be
described in the report, and perhaps some  sensitivity
analyses performed as to the impact of differing
assumptions on model predictions. The assumptions
 regarding the split were indicated (Part 2, Chapter 4,
 Section 2.4.1.1) for atmospheric loads, but not for
other loading sources that this reviewer could find.

 In Part 2, Chapter 4, Section 2.4.4.2, it is stated that
 laboratory primary  production rates were used  to
verify the  overall  production rates in the model.
These comparisons  should be  included  in  the
 modeling report.

The characterization of non-diatoms versus diatoms
 is a useful breakdown. Since blue-greens were the
dominant algae, some additional explanation would
 be worthwhile as to how nitrogen  limitation  was
computed for these algae.
The section of the report (Chapter 1) dealing with the
calibration of the LM2-Eutro and LM3-Eutro was
somewhat confusing, with regard to which model was
calibrated against existing data.

The comparisons of model predictions and field data
were  somewhat limited in  Chapter 5.   Additional
comparisons should be provided, both graphical and
statistical, between model predictions and observed
data.  Comparisons should be provided if possible for
all state variables. For example, no comparisons are
presently provided for nitrogen species.

Presently, the LM2-Eutro and the LM3-Eutro codes
specify sediment fluxes as zero order rates, which is
a common practice. However, there are models of
sediment diagenesis that allow prediction, rather than
description, of those rates. While probably not critical
in the context of using the Eutro predictions for input
to  the Toxic model,  incorporation  of a  sediment
diagenesis  model  may be worthwhile  should the
LM3-Eutro model be used  in the future to assess
eutrophication-related management questions.

In   general,  the  linkage  of the Lake  Michigan
eutrophication and toxicant models represents an
advancement and provides additional capabilities that
should be continued to be developed. This reviewer
considers the existing eutrophication model construct
sufficient for its intended use, to provide biotic solids
for the toxicant model.  However, the eutrophication
model is also considered important in its own right,
and should have applicability in addressing questions
regarding conventional pollutants in  Lake Michigan.
In addition, perhaps some more direct coupling of the
eutrophication and food  chain model  could  be
considered in future applications, to aid in addressing
questions regarding impacts of changes in food chain
structure on uptake of PCBs and other toxicants.

7.2.3.3 Level 1 Models

The inclusion of the Level  1 model in the modeling
report and  presentations,  and  the contrasting of
model construct and  predictions  with the  Level 2
model, was  considered by  this reviewer to be very
useful.  First, the Level 1 model and its predictions
were  useful  in  providing  insights  into factors
impacting PCBs in Lake Michigan and addressing
interim  management  questions.   In addition, the
Level  1  modeling studies  illustrated  what this
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reviewer considers to be one of the best uses of
models and modeling studies: to first aid in mining
and  interpreting available  data,  to  then identify
deficiencies   in  available   data   and  modeling
approaches, and finally to aid in planning additional
studies and model refinements.

One area that perhaps deserves further investigation
is the similarity in predictions of the Level 1 and Level
2 models.  Both models predicted remarkably similar
changes in total  PCB concentrations over time in the
long-term  projections.    However,  there  were
differences between the two models such as  in the
rates of  settling/resuspension  used  and in  the
characterization of the sediment bed. As a result, the
two models predicted similar results for somewhat
dissimilar reasons. It would be of interest to further
investigate factors  leading to  the similarity  in
predictions,  which may  provide  some  additional
insights as  to  factors controlling  PCBs in  Lake
Michigan.

There were some capabilities of the Level 1 model
which should be considered for incorporation into the
Level 2 model.  One such capability is the steady-
state solution. A goal in future studies, as expressed
during the presentations, was to assess uncertainty
in the Level 2 model.  Uncertainty is most commonly
assessed  using  steady-state rather than dynamic
predictions.   The long simulation  time required to
achieve steady-state predictions in the dynamic Level
2  model  may  preclude  conducting uncertainty
analyses.     Incorporating   steady-state   solution
techniques in the Level 2 (and ultimately the Level 3
model) would facilitate the analysis.

7.2.3.4 LM2-Toxic

The Level 2-Toxic model represents an advancement
over its predecessor, the Level  1  model.  These
advancements not only  include  simulation of PCB
congeners,  but  improvements  in transformation
kinetics, such as volatilization. A number of  these
improvements resulted from Level 2 investigations,
and the study serves as a very good example of the
benefits achieved through the iterative development
and refinements of models

The coupling of the POM predictions to the Level 2
model  seems a reasonable approach.  However,
using a 1-1 grid  rather than a course-grid overlaying
a fine-grid hydrodynamic model is  a preferable
approach, which is the approach planned for the
Level 3 model. Several recommendations regarding
testing   of   the   linkage  between  the   POM
hydrodynamic model and both the LM2-Toxic and
LM3-Toxic model  were discussed in a  previous
section.

With regards to solids transport, the approach used
for   computing  sediment  resuspension  seems
reasonable.   However, it is  hoped  that a  more
detailed sediment model (SEDZL, which was part of
the original plan) can be incorporated into the  Level
3 framework.  The settling velocities used also seem
reasonable but are lower than those  used in the
Level 1  study.  Since the estimated resuspension
velocities  will vary with the settling velocities, the
rates used are also  presumably  lower than those
used in the Level 1  studies. Since the projections of
the two  models were  remarkably similar, some
additional investigation as to why similar predictions
were obtained using  dissimilar   rates would be
worthwhile.

The  sediment bed  model  seems   reasonable.
However, some additional clarification  of the semi-
Lagrangian method for simulating the sediment bed
(Part 4,  Chapter 3, Section  4.3.4.2.3) would be
useful.  In addition, the present construct does not
allow for the tracking of materials buried out of the
layer, or perhaps entrained into the layer from deeper
contaminated  sediments.    Some  additional
development  of the sediment algorithms would be
useful for the Level 2 model and for incorporation into
the  Level 3  framework (where  it may  be  more
important with regard to nearshore issues).

As indicated in Part 4, Chapter 6, Section 4.6.2, the
flux  contributed by  the diffusive term  from the
sediment bed was unexpectedly large relative to the
resuspension flux.  This may have been due to the
relatively large  specified diffusion coefficient  used
relative to the Level 1 model.  As suggested above,
some additional testing is recommended to compute
and  compare factors causing predicted  variations
between the Level 1 and Level 2 studies, and the
diffusive fluxes should be considered in that testing.
In addition, it was indicated in Part 4, Chapter 6,
Section 4.6.2 that the total PCB residence time for
Lake Michigan were on the order of 100 days. This
estimate seems low to this reviewer.  It would be
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interesting to see how this compares to predictions
from a Level 3 model which may more realistically
estimate vertical exchanges in layers isolated from
the water surface.  The Level 3 model could be used
to determine if the rapid removal may be in part an
artifact of the modeling approach used in the Level 2
studies. As an example, given the rates of settling
used, surface particles would require approximately
one year to reach the bottom, while  with a single
vertical-box model it would be assumed that vertical
transport is on-average instantaneous.

The  comparisons  of measured and  simulated
concentrations seem  reasonable.  However, since
differences occur between factors controlling PCBs
in Lake Michigan  and Green Bay, the results for
these two systems should be reported separately.

One limitation to the Level 2 application, and to many
similar studies, was the limited time-scale to which
the model was applied. The model was applied and
calibrated using data  from the 1994 and 1995 field
studies.  Given the time  scale of changes in  Lake
Michigan PCB concentrations, this  period is not
sufficient to test the model against long-term trends
in  the  PCB  concentrations  for  Lake Michigan.
Similarly to the  Level  1  studies,  it would  be
worthwhile as an additional test to run the model with
estimated historical loadings and for comparison to
all existing data, including data  from this sampling
period. Such an application would provide additional
testing of the robustness of the  model, particularly
since the models intended use is in the projection of
long-term trends in PCB concentrations for Lake
Michigan.

7.2.3.5  LM Food Chain

The food chain model used in this study was based
upon  what I consider to be a widely accepted
approach and which I consider adequate for  the
purposes of this study.  However, my experience in
food chain modeling is limited and  dated so I would
defer to others  with  more  recent experience to
evaluate this component of the model.  As with  the
LM2-Toxic model, the application of the model to a
longer period  of  record  is  recommended  as  an
additional test of the model.
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EPA600/R-04/167          Dec. 2006
                              c.3
Results of the Lake Michigan Mass Balance
Project: polychlorinated biphenyls
modeling report
              US EPA
        MID-CONTINENT
      ECOLOGY DIVISION
   LABORATORY LIBRARY
           DULUTH, MN
                                   DEMCO

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United States
Environmental Protection
Agency

Office of Research and Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300


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