United States
Environmental Protection
Agency
The Lake Michigan Mass
Balance Project
Quality Assurance Plan for
Mathematical Modeling
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EPA/600/R-04/018
March 2004
The Lake Michigan Mass Balance Project
Quality Assurance Plan for
Mathematical Modeling
by
The Modeling Workgroup
Edited by
William L. Richardson
Douglas D. Endicott
Russell G. Kreis, Jr.
Kenneth R. Rygwelski
Community-Based Science Support Staff
Large Lakes Research Station
Grosse He, Michigan 48138
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division-Duluth
Large Lakes Research Station
Grosse lie, Michigan 48138
Recycled/Recyclable
Printed with vegetable-based ink on
paper that contains a minimum of
50% post-consumer fiber content
processed chlorine free.
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Notice
The information in this document has been obtained primarily through funding by the U.S. 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 U.S. 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 Office of Research and Development (ORD)
in producing mathematical models that account for the input, fate, and food chain bioaccumulation of certain chemicals
in the lake. This approach has been used in the past to develop target loads for phosphorus in controlling eutrophication.
During an intensive study of Green Bay, it proved to be a reliable and effective means of providing a basic scientific
understanding of the ecosystem, mass fluxes, and chemical and biological processes. The approach also proved to be
an efficient means of organizing the project and aiding decision-makers in choosing among alternative management
options. By focusing federal, state, local and academic efforts and resources on a common goal, much more was
accomplished than if these entities acted independently.
This approach requires all monitoring and field research be coordinated and common methodologies used. The product
will then be a consistent and reliable database of information that will be accessible by project participants and the public.
Data for the LMMBP were collected during 1994 and 1995 and are now being compiled according to specified quality
assurance/quality control (QA/QC) requirements.
The means to synthesize and interpret this information needs similar scrutiny. This quality assurance project plan (QAPP)
for mathematical modeling provides the basic procedures that all aspects of model development and application will
follow. It attempts to follow guidance provided by the USEPA and other agencies in assuring that the scientific theory
is implemented accurately and completely by model computer code. It requires modelers to specify the theory and
processes included in the models and requires that they document their work.
This QAPP also provides for a scientific review process using an interdisciplinary panel of scientists and experts that will
review model theory and application on a continuing basis. The purpose is to ensure that decisions based on the modeling
efforts are reliable and scientifically credible.
This plan 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 Workplan (USEPA, 1997a) and the Modeling Workplan (USEPA, 1995a, and
other documents cited herein). The Modeling Workplan is included as an appendix to this report. Also, readers can
access project information via the GLNPO WEB page, http://www.epa.gov/glnpo/lmmb/.
in
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Abstract
This report documents the quality assurance process for the development and application of Lake Michigan Mass Balance
models. The scope includes the overall modeling framework as well as the specific submodels that are linked to form
a comprehensive synthesis of physical, chemical, and biological processes of Lake Michigan.
The models cited in this report include hydrodynamic, sediment transport, eutrophication, transport chemical fate, and
food chain bioaccumulation. In addition, the report includes the quality assurance (QA) process for the development of
atmospheric models used to describe the emission of atrazine from the agricultural portion of the watershed and its
transport and deposition to the lake. It also includes the QA process for the estimation of tributary and atmospheric loads
for atrazine, polychlorinated biphenyls (PCBs), frans-nonachlor (TNC), and mercury.
This report does not include the QA process for field collection and laboratory analyses. These are covered in separate
documents (USEPA, 1997b,c,d,e).
With the ever increasing costs of environmental regulation and remediation, the reliance on scientific interpretation of
information, and the need to forecast future impacts, USEPA is placing more emphasis on the quality and credibility of
the synthesis process and tools. The Agency has issued several documents covering broad requirements of the
development and use of mathematical models and these are used in the formulation of the plan for Lake Michigan.
Because this guidance is new and somewhat limited, this QAPP is a prototype for this process which includes a suite of
linked, multi-media models which together form an ecosystem approach.
In the final analysis, the quality of the work and the reliability and credibility of the models will be determined not only
by the issuance of a QA plan, but by the desire and integrity of the project personnel. History has shown the
mathematical models of Great Lakes water quality to be reliable in predicting future events and determining regulatory
and remedial strategies that have been successful. The Lake Michigan modeling efforts build on this long history of
model development by the ORD's Great Lakes Modeling Program at Grosse lie, Michigan, the Modeling Program at
Research Triangle Park, North Carolina, the experience of the National Oceanic and Atmospheric Administration
(NOAA) Great Lakes hydrodynamic modeling program at the Great Lakes Environmental Research Laboratory (GLERL)
in Ann Arbor, Michigan, and the Modeling Program of the U.S. Army Corps of Engineers (USACOE) at Waterways
Experiment Station (WES), Vicksburg, Mississippi. In addition, it relies upon the experience and knowledge of other
federal, private, and academic organizations.
IV
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Contents
Foreword iii
Abstract . iv
Figures vii
Acronyms and Abbreviations viii
Acknowledgments x
Chapter 1. Introduction 1
Background 1
General Considerations for Modeling Quality Assurance 2
Basis of Great Lakes Modeling Quality Assurance . . 3
Background of Air Quality Modeling 5
Chapter 2. Common Quality Assurance Topics as Applied to All Project Models 7
Modeling Quality Objectives and Acceptance Criteria 7
Project Description 8
Scope, Purpose, Objectives .... 8
Products and Timetable 11
Project Personnel 11
Key Support Facilities and Services 11
Community-Based Science Support Staff, Large Lakes Research Station,
Grosse He, Michigan 11
National Exposure Research Laboratory, Atmospheric Modeling Division,
Research Triangle Park, North Carolina 12
NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan 12
Wisconsin Department of Natural Resources, Madison, Wisconsin 13
U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi 13
The University of Michigan, Air Quality Laboratory, Ann Arbor, Michigan 13
Modeling Approach 14
Water Models 14
Air Models 15
Mercury Emissions Inventory 16
Quality Control 17
Data Control 18
Data Quality Assessment 18
Database Tracking 18
Model and Input/Output File Tracking 18
Record Keeping 18
Data Usage 19
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Peer Review • 19
References for Chapters 1 and 2 19
Chapter 3. Individual Model Quality Assurance Plans 24
Computational Transport 24
Hydrodynamic Model of Lake Michigan . . 24
Wind Wave Model for Lake Michigan . 26
Sediment and Contaminant Transport/SEDZL 28
Hydrodynamic Model Linkage with WASP-IPX 32
Mass Balance Water Quality Models 33
General Considerations for All Mass Balance Water Quality Models . . 33
Phytoplankton Solids/Eutrophication Model 37
Atrazine Model Water Quality Model 41
Mercury Model 44
PCB/TNC Model 45
Bioaccumulation and Ecosystem Models 49
Food-Chain Model for PCBs and TNC in Lake Michigan 49
Ecosystem Model 54
Load Computations Models and Estimation Methodology 58
Terrestrial Emissions and Atmospheric Fate and Transport Estimates for Atrazine
and Mercury 58
Emissions of Agricultural Use of Atrazine from Soil (ORTECH Soil Emissions
Model) 59
Mercury Emissions Inventory 64
Generation of Driving Meteorological Conditions (MM5-PX) 65
CMAQ 76
Tributary Loading 77
PCB Tributary Loading Models 77
Atmospheric Loading for Mercury 80
Atmospheric Loadings of PCBs, TNC, and Atrazine 88
Appendices
A. Lake Michigan Mass Balance Project: Modeling Workplan 102
B. Lake Michigan Mass Balance Project: Modeler's Curriculum Vitae 140
C. Revision Code System 218
D. Project Approvals 221
E. Model Development and Progress 224
F. Lake Michigan Mass Balance Project Committees, Workgroups, and Personnel 225
G. Quality Systems and Implementation Plan (QSD?) 226
VI
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Figures
1. Lake Michigan Mass Balance Project Workplan Diagram 2
2. Lake Michigan Modeling Framework . 9
3. Loads to Lake Michigan 10
4. Relationship Between Mass Balance Models 34
5. Phytoplankton and Detrital Carbon Dynamics in Lake Michigan 39
6. Lake Michigan Lake Trout Food Web Spatially and Temporally Variable: Age Dependent 50
7. MM5 Modeling System 65
VII
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Acronyms and Abbreviations
AMD Atmospheric Modeling Division
AQSM Air quality simulation models
AREAL Atmospheric Research and Exposure Assessment Laboratory
CAAA-90 Clean Air Act Amendments of 1990
CBSSS Community-Based Science Support Staff
CEAM Center for Exposure and Assessment Modeling
CGEIC Canadian Global Emissions Interpretation Centre
CILER Cooperative Institute for Limnology and Ecosystem Research
CTF Contaminant transport and fate model
CWA Clean Water Act
DOC Dissolved organic carbon
EMP Enhanced Monitoring Program
BSD Eutrophication sorbent dynamics model
GBMBS Green Bay Mass Balance Study
GLERL Great Lakes Environmental Research Laboratory
GLNPO Great Lakes National Program Office
GLWQA Great Lakes Water Quality Agreement
Hg Mercury
HOC Hydrophobic organic chemicals
IJC International Joint Commission
LaMPs Lake-wide Management Plans
LLRS Large Lakes Research Station
LMMBP Lake Michigan Mass Balance Project
MED-Duluth Mid-Continent Ecology Division-Duluth
MM5 Mesoscale Meteorological Model
NCAR National Center for Atmospheric Research
NERL National Exposure Research Laboratory
NHEERL National Health and Environmental Effects Research Laboratory
NOAA National Oceanic and Atmospheric Administration
NWS National Weather Service
ORD Office of Research and Development
PCB Polychlorinated biphenyls
POC Particulate organic carbon
POM Princeton Ocean Model
PSU Penn State University
QA Quality assurance
QAPP Quality assurance project plan
QC Quality control
QSIP Quality systems and implementation plan
vin
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RADM
RAPs
RCS
RDMQ
RPM
SUNY
TNC
UCSB
UMAQL
USACOE
USCG
USDA
USEPA
USGS
WASP
WDNR
WES
Regional Acid Deposition Model
Remedian Action Plans
Revision Control System
Research Data Management and Quality Control System
Regional Particulate Model
State University of New York
rra«s-nonachlor
University of California-Santa Barbara
University of Michigan Air Quality Laboratory
United States Army Corps of Engineers
United States Coast Guard
United States Department of Agriculture
United States Environmental Protection Agency
United States Geological Survey
Water Quality Simulation Program
Wisconsin Department of Natural Resources
Waterways Experiment Station
IX
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Chapter 1
Introduction
A primary component of the Lake Michigan Mass Balance
Project (LMMBP) involves the development and
application of mathematical models relating the sources of
chemicals to their concentration in air, water, sediment,
and biota. Models integrate the complex transport and fate
processes involved in determining mass balances of
important chemicals and predicting future conditions
under a variety of alternative management scenarios.
Because of the economic and environmental consequences
of pending decisions, care must be taken to ensure the
quality, dependability, accuracy, and scientific credibility
of all aspects of the project. This quality assurance (QA)
plan for the modeling aspects of the project will help
ensure that these goals are achieved.
The Modeling Workgroup members prepared this plan
under the direction of the Chairperson, William L.
Richardson, P.E., Environmental Engineer, U.S.
Environmental Protection Agency (USEPA), Office of
Research and Development (ORD), National Health and
Environmental Effects Research Laboratory (NHEERL),
Mid-Continent Ecology Division-Duluth (MED-Duluth),
Community-Based Science Support Staff (CBSSS), Large
Lakes Research Station (LLRS), Grosse He, Michigan.
Guidance for the preparation of the plan has been obtained
from several sources including:
Quality Assurance Guidelines for Modeling
Development and Application Projects: A Policy
Statement. Environmental Protection Agency, ERL-
Duluth. November 1991.
Reducing Uncertainty in Mass Balance Models of
Toxics in the Great Lakes-Lake Ontario Case Study.
Great Lakes Program, State University of New York
at Buffalo. February 1993.
Agency Guidance for Conducting External Peer
Review of Environmental Regulatory Modeling.
Agency Task Force on Environmental Regulatory
Modeling. July 15, 1994.
National Exposure Research Laboratory Position
Paper on Multimedia Modeling, Third Draft.
USEPA, NERL. May 19, 1995.
Standard Practice for Evaluating Mathematical
Models for the Environmental Fate of Chemicals.
ASTM, Designation: E 978-92.
Background
The LMMBP was initiated by the USEPA Great Lakes
National Program Office (GLNPO) cooperation with the
USEPA/ORD and other federal and state agencies. The
project was initiated in response to regulatory mandates
contained in the Great Lakes Water Quality Agreement
(GLWQA) between the United States and Canada and
federal legislation that requires the development of
"Remedial Action Plans" (RAPs) and "Lake-wide
Management Plans" (LaMPs). The purpose is to restore
and maintain the chemical, physical, and biological
integrity of the waters of the Great Lakes Basin ecosystem.
USEPA also intends that the LaMP process serves as the
basis for the development of State Water Quality
Management Plans. This project also has implications and
applications to the Great Lakes Binational Toxics Strategy
(Virtual Elimination Strategy) and the Great Waters
Program.
The primary goal of the LaMP is to develop a sound
scientific base of information to guide future toxic load
reduction efforts at the federal, state, tribal, and local
levels. Objectives include: (1) identification of relative
loading rates of critical pollutants from major sources to
the Lake Michigan Basin; (2) to evaluate relative loading
rates by media (tributaries, atmospheric deposition,
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contaminated sediments) to establish a baseline loading
estimate to gauge future progress and load reductions; (3)
develop the predictive ability to determine the
environmental benefits of specific load reduction scenarios
and the time to realize those benefits; and (4) improve our
understanding of key environmental processes which
govern the cycling, dynamics, and availability of
contaminants within relatively closed ecosystems. These
objectives are consistent with those of the LMMBP and
with the need for multi-media mathematical modeling in
an ecosystem approach. The primary pollutants of
concern are polychlorinated biphenyls (PCBs), trans-
nonachlor (TNC), atrazine, and mercury. These
contaminants have different sources, environmental
behaviors, modes of action, and pose different threats to
the ecosystem's food web as well as wetlands, wildlife and
fisheries. The target species for this investigation are lake
trout and coho salmon; however, the supporting food
chains of each of the primary species require examination.
In addition, the project was to be synchronized with the
States' "Enhanced Monitoring Program." A series of
preliminary meetings was held to discuss the need and
organization of the project. A committee structure was
developed and implemented. Under the direction of a
Steering Committee and Technical Coordinating
Committee, a detailed workplan was prepared (USEPA,
1997a). The Modeling Workgroup prepared a Modeling
Workplan (USEPA, 1995a) which guided the project
design. A Program QA Plan (USEPA, 1997b) was
prepared for the project but did not directly include QA for
mathematical modeling. Subsequent QA audits of the
project determined the need for a specific modeling QA
plan.
In addition to the project workplan, modeling workplan
and QA plan, a methods compendium (USEPA,
1997c,d,e), data administrative plan (USEPA, 1995b), and
data reporting format (USEPA, 1991 f) have been prepared
(access these documents via the GLNPO web page:
http:/www.epa.gov/glnpo/lmmb/). The project planning
scheme is shown in Figure 1.
These provide the documentation infrastructure for QA of
field, laboratory, data, and database management aspects
which support project information being utilized by the
models. Because the documents are available elsewhere,
these aspects will only be summarized in this report.
Lake Michigan Mass Balance Project
Proje
ct Workplan Plan
LMMB QA Management Plan
Methods Compendium
Data Administrative Plan
Data Reporting Formats
Modeling Workplan
Modeling QA Plan
Figure 1. Lake Michigan Mass Balance Project Workplan
Diagram.
General Considerations for Modeling Quality
Assurance
Traditionally, scientific and engineering philosophy and
ethics profess a high regard for QA and quality control
(QC). Within USEPA and ORD, quality of science has
been a primary and over-riding consideration in project
planning and execution. But within the regulatory context
of USEPA, meeting deadlines is also important and there
are always judgements made on the trade-offs between
quality (primarily in terms of thoroughness and
complexity) and timeliness. When quality is sacrificed in
lieu of timeliness, there may be severe consequences. The
space shuttle Challenger disaster exemplifies this well.
The approach for this plan attempts to attain a balance
without sacrificing scientific credibility, accuracy, and
thoroughness. The challenge is to assemble the necessary
scientific/modeling experts, to determine the best mix of
modeling theory and approaches, to use the most current
modeling computer programs, and to modify and apply
these to the scientific and management issues confronting
Lake Michigan. This must be done with resource
constraints and with a common sense approach to meeting
project timeliness.
There are limitations with the level or detail of modeling
that can be accomplished within the budgets available
For example, it would be desirable and preferable to
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develop and apply a three-dimensional model of sediment
transport. This was the original intent but, because of
budgetary constraints and personnel limitations, this was
changed and a compromise was made to use an existing
two-dimensional model to help understand sediment
transport processes. Although this is not perfect, it will
reflect state-of-the-art modeling for the Great Lakes.
Credibility will be achieved by carefully qualifying model
results, defining the uncertainty of the calculations, and
ensuring that the models are correct within the appropriate
time and space scales.
It should be understood that the modeling efforts for this
project build upon a rich history of Great Lakes research
conducted by the USEPA and its partners. This history
adds considerable credibility to the Lake Michigan
modeling endeavors. Much of this history has led to the
approaches that are being used to ensure quality for the
present project. These include the following factors:
1. Qualified personnel including education, training,
experience, expertise, integrity, and publication
record.
2. Infrastructure including laboratories and offices,
computers, software tools, supporting administrative
staff and progressive and supportive management.
3. Adequate extramural research budgets for acquisition
of expertise beyond that of the in-house research
staff.
4. The administrative means to include extramural
researchers and contractors via cooperative
agreements and contracts including the ability to
build coordinated teams and partnerships directed at
answering relevant scientific and management
questions.
5. Interaction within the scientific and engineering
communities at scientific meetings and workshops
and through publications in journals to ensure the
utility of most currently accepted scientific theory.
6. Professional engineering judgement.
7. Computer programming support to implement the
theory into computer code.
8. Verification of computer code and calculations.
9. Evaluating and reporting uncertainties of calculations
and stating assumptions, qualifications, and caveats
which could affect research application to regulatory
problem-solving.
10. Peer review of research including theoretical
construct, computational methodology,
appropriateness of application, assumptions, and
interpretations.
11. Common sense and hard work.
These factors are incorporated into this QA Plan for Lake
Michigan.
Basis of Great Lakes Modeling Quality
Assurance
The Lake Michigan models build on over two decades of
modeling research, conducted by USEPA, ORD, and its
cooperators. In 1971, the International Field Year on the
Great Lakes was initiated as an interagency endeavor to
investigate the physical, chemical, and biological status of
Lake Ontario. The first calibrated, eutrophication model
for a Great Lake resulted (Thomann and Di Toro, 1975).
A series of projects was conducted by the ORD Great
Lakes Modeling Program at the Grosse He Facility (LLRS)
from 1973 through about 1980 in response to the research
requirements of the U.S. Canada GLWQA with direction
from the International Joint Commission (IJC). Field
studies were conducted on Lake Erie, Lake Huron, and
Lake Michigan that provided baseline observations and
input information to construct eutrophication models for
each of those lakes and for Saginaw Bay (Bierman and
Richardson, 1976; Richardson, 1976; Di Toro and
Matystik, 1980; Rodgers and Salisbury, 1981; Bierman et
al., 1984; Bierman and Mcllroy, 1986; Bierman and
Dolan, 1986).
In 1977 toxic chemicals became a primary concern for the
Great Lakes. USEPA/ORD responded with the
development of the first PCB models for the Great Lakes
(Richardson, et al., 1983; and Connolly, 1984; Thomann
and Di Toro, 1984). Related to the special needs of the
Great Lakes, ORD developed the capability of analyzing
PCB congeners at ultra trace levels.
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These experimental and modeling endeavors were carried
out by teams of investigators under the direction of
USEPA principal investigators and project officers. Much
of the pioneering modeling research was done by a group
of environmental engineers at Manhattan College,
Riverdale, New York. Expertise grew over the past 25
years and as a result, a Great Lakes "modeling
community" emerged including scientists and engineers
from a number of universities, government research
laboratories, and private consulting firms.
The Great Lakes modeling community's efforts included
fate and bioaccumulation modeling of PCBs and other
toxic chemicals for the Great Lakes (Thomann and
Connolly, 1984), modeling of toxicity for the Raisin River
and Detroit River (Di Toro et al, 1985a,b, 1986, 1988),
screening level modeling for PCBs, tetrachlorodibenzo-p-
dioxin (TCDD), atrazine, and other chemicals for Lake
Michigan, Green Bay, and Lake Ontario (Martin et al.,
1989; Endicott etal., 1991, 1992).
These efforts culminated in the development and
application of toxic chemical models for the lower Fox
River and Green Bay. During this study, GLNPO
requested ORD to lead the modeling efforts. The field and
laboratory efforts were designed according to the
modeling requirements. This project could be viewed as
the definitive Great Lakes modeling effort to date
(Connolly et al., 1992; Bierman et al, 1992; DePinto et
al, 1993; HydroQual, 1995; Martin et al, 1995; Lick et
al, 1995; Velleux et al, 1995, 1996; Richardson et al,
1997). This effort demonstrated the feasibility of the mass
balance modeling approach in a large embayment. QA for
modeling was not a formal requirement within this study;
however, an on-going peer review process whereby
modelers presented their research plans, interim results
and final results at meetings, workshops, and scientific
conferences during the entire project provided more than
sufficient scrutiny to assure a credible product in the end.
Several approaches can be taken to examine model
credibility including calibration to observed data,
verification of predicted or historical conditions over time,
and paleolimnological methods. Another approach
includes checking the validity of different models in
response to the same problem. This has been part of the
history of Great Lakes modeling research. For example,
in the development of target loads for phosphorus under
the GLWQA, a number of models were developed and
applied for Lake Erie at various levels of spatial and
chemical resolution. Comparison of model predictions
provided at least one test of model credibility (Di Toro et
al, 1987; Bierman and Dolan, 1986).
Another case involved the development and application of
a model for toxic chemicals including dioxin in Lake
Ontario. Insufficient data were available for model
calibration so two models were used to gain credibility.
These models had been developed independently by two
modeling groups, unique theoretical constructs, and
different computer programs and solution techniques. The
final predictions of chemical concentration made by these
different models were nonetheless comparable. During
this project, the model computer programs and input data
sets were provided to an independent review panel. This
panel reviewed the model constructs, input data sets, and
re-ran the models to reproduce results before submitting
their assessments.
In a limited number of cases, models developed over 20
years ago have been post-audited and serve as a form of
model verification and validation (Di Toro and Connolly,
1980; Di Toro et al, 1987; Zahakos et al, 1993; Chapra
and Sonzogni, 1979; Lesht et al, 1991; Bierman and
Dolan, 1986; Bierman et al, 1984). Annual loading
estimates over the validation period were used in the
models to simulate concentrations over the same time
period. Although results vary somewhat, good agreement
between model predictions and field data are generally
observed. The agreement between predicted and observed
concentrations indicate the predictive capabilities of the
models and their known certainty.
Until recently, formal QA plans were not required for
Great Lakes model development or application. Even so,
the model theory and computer programs developed have
been used successfully throughout the world for
investigations of many important pathways. So lack of a
QA plan and auditing process does not imply models are
less credible nor does the inclusion of a QA plan
necessarily ensure that models are correct. In the final
analysis, model credibility depends on many factors and a
QA plan will help ensure these factors are taken into
consideration in a formal, logical manner.
Preparation of this QA plan has required considerable
effort but in the long run should save time by reducing
errors, minimizing corrections and reanalysis, and
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reducing the need for continual explanations and
justifications. The process also allows the modelers to
present their views concerning resources required and to
clarify expectations.
Much of the background work was done before the formal
QA document was begun. A Modeling Workplan
(USEPA, 1995a) was developed by the modeling staff at
Grosse He and the modeling workgroup including staff
from the National Exposure Research Laboratory (NERL)
in Research Triangle Park, North Carolina. This plan,
which included specification of the Lake Michigan
Enhanced Monitoring Program, was submitted to the
Technical Coordination Committee for use in developing
the overall project workplan. Although a formal QA plan
had not been in place by the commencement of the project,
considerable work had been done that logically belongs to
this plan.
Background of Air Quality Modeling
When concern over air quality developed in the United
States and Canada several decades ago, the problem
appeared to consist essentially of excessive local
concentrations of common pollutants such as sulfur
dioxide, particulates, carbon monoxide, and ozone. Air
quality is now recognized as a much more complex
problem or group of problems that span many pollutants
having media-specific behaviors over very large
geographic areas.
The role of atmospheric transport and deposition to the
Great Lakes basin has been addressed under several
modeling constructs, including mass balance models. In
principle, the complex movements of pollutants through
different parts of the environment can be described
through a mass-balanced model. In practice, however, the
data requirements needed to make reasonable estimates of
the many processes involved are large, and sufficient data
for these calculations usually are not available.
Uncertainties are substantial even with the best available
data on atmospheric and non-atmospheric inputs. The
LMMBP will seek to reduce uncertainty in the
atmospheric component of the mass balance by employing
mathematical models of atmospheric transport and
deposition, to provide estimates for spatial and temporal
gaps in actual monitoring databases, and to test hypotheses
about characterizations of atmospheric transformations
and removal.
Air Quality Simulation Models (AQSMs) are frequently
used to characterize the emission, transport, and deposition
of hazardous air pollutants over large geographic areas.
These models incorporate fairly extensive source emission
inventories and meteorological databases (e.g., wind
fields, temperature, mixing height) and apply the collected
data to simulated processes such as dispersion,
transformation, and deposition. The models are run to
generate estimates of pollutant concentrations and
deposition rates over a spatial and temporal pattern.
The mathematical relationships between emissions and
concentration (or deposition) are typically nonlinear, due
to the influences of the atmospheric transport, chemical
and physical transformation, and deposition processes.
Therefore, one cannot extrapolate, based on measurements
alone, the quantitative relationship between changes in
emissions and changes in atmospheric concentrations (or
deposition). AQSMs attempt to account for the nonlinear
physical and chemical processes influencing atmospheric
concentrations deposition.
Development of AQSMs started in the late 1970's. The
Urban Airshed Model (UAM; Scheffe and Morris, 1993)
followed by the Regional Oxidant Model (ROM; Lamb,
1983) provided Eulerian-based models for ozone, the
former for urban and the latter for regional scale.
Strategies for State Implementation Plans (SIPs) used
ROM to provide boundary conditions for UAM
simulations. Attention to acid deposition issues was
addressed in the 1980's with the development and
evaluation of regional acid deposition models such as the
Regional Acid Deposition Model (RADM; Chang et al.,
1987), the Acid Deposition and Oxidant Model (ADOM;
Venkatram et al., 1988) and the Sulfur Transport and
Emission Model (STEM; Carmichael e? al., 1986). Other
major modeling systems included the Regional Lagrangian
Modeling of Air Pollution model (RELMAP; Eder et al.,
1986), a Lagrangian framework system, and semi-
empirical and statistical models. Models of this period
were designed to address specific air pollution issues, such
as ozone or acid deposition. Thus, flexibility to deal with
other issues such as particulate matter or toxics was very
limited. With the passage of the Clean Air Act
Amendments of 1990 (CAAA-90), a wide range of
additional issues was identified including visibility, and
fine- and coarse-particles, as well as indirect exposure to
toxic pollutants such as heavy metals, semi-volatile
organic species, and nutrient deposition to water bodies.
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In the 1990's, the USEPA embarked upon the development
of an advanced modeling framework to meet the
challenges posed by the CAAA-90. The Models-3
framework has been designed for holistic environmental
modeling utilizing state-of-science representation of
atmospheric processes in a high performance computing
environment. Descriptions of Models-3 can be found in
Novak et al. (1998) and Byun et al. (1998). The science
components in Models-3 are called the Community Multi-
scale Air Quality (CMAQ) system and are described
briefly in Ching et al. (1998). The Models-3/CMAQ
system is designed as a multi-pollutant, multi-scale
Eulerian framework air quality and atmospheric deposition
modeling system. It contains state-of-science
parameterizations of atmospheric processes affecting
transport, transformation, and deposition of such
pollutants as ozone, paniculate matter, airborne toxics, and
acidic and nutrient pollutant species. It is the new
modeling system that will be further enhanced and applied
to address the specific areas of concern for the LMMBP.
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Chapter 2
Common Quality Assurance Topics as Applied to All Project Models
Because the LMMBP modeling framework consists of a
series of linked models, general QA aspects that apply to
all of the models will be presented first. Aspects of each
model will then be presented individually in Chapter 3.
Also, because many of these models are under
development, it is impossible to provide all of the detailed
information immediately. Rather, as models are
developed, tested, and applied, the information will be
updated as future addenda to this report.
The general and specific details that follow are presented
in the format suggested by modeling QA guidelines
(USEPA, 1991). Some additional sections are included
that fulfill more recent Agency requirements.
Modeling Quality Objectives and Acceptance
Criteria
Before a model is used for remedial guidance and/or
regulatory purposes, there needs to be some agreement
between the expectations of the managers who will be
using the model and the model developers. Managers need
to be versed in the science of modeling natural systems.
They should realize that simulating natural phenomena,
unlike controlled systems like electrical or mechanical
systems, is very difficult because of the inherent
variability and ever changing biological structures.
Modelers have the responsibility of not only attempting to
make the models reliable, but to state unequivocally their
assumptions and uncertainties. This is usually done by
providing the most probable answer(s) along with
uncertainty brackets which provide the probability that the
actual answer is contained within a range. The decision-
maker must determine whether to use the model with the
uncertainties and caveats provided, or to provide
additional resources to refine the results.
There is an attempt within this document to help managers
determine the degree to which the models will be
calibrated to field data. This constitutes the project
acceptance criteria and reflects what can practically be
done with the resources commitments. Basically, the
criteria for accepting the modeling results lies in the ability
to simulate measured concentrations of materials in water,
sediment, and biota during the field collection period. If
this is done within the statistical range required, then the
model(s) can be used to extrapolate these concentrations
in space and time. Model validation is beyond the scope
of the project. Validation is defined as the process by
which model predictions are compared to measurements
made at some future time. This may ultimately be done,
but has not been included (by management) as a
requirement. Modelers attempt to use whatever data are
available and many of the model simulate historical data.
This should be viewed as an additional rationale for model
acceptance.
The modeling quality objectives are incorporated into the
LMMBP's "data quality objectives (DQO)" in the overall
QA plan (USEPA, 1997b):
"After following the DQO process, LMMBP
Study managers agreed that the overall LMMBP
Study DQO was to develop a model capable of
calculating pollutant concentrations in Lake
Michigan to within a factor of two of observed
concentrations in the water column and target
fish species. Study managers also agreed to
accept an uncertainty level for each input to the
model that is within 20-30% of the mean at the
95% confidence interval."
The DQO was developed by members of the Technical
Coordinating Committee and participating government
employees. Discussions were held between the QA
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managers and government modelers. The QAPP was
approved by the Executive Steering Committee. Names
and organizations of participating personnel are listed in
Appendix F.
This statement is interpreted by the modelers to mean that
the project's monitoring, surveillance, and analytical
programs were established in an attempt to define all
model forcing functions and state variables within 30% of
the actual values. The modeling objective is to simulate
the average water quality within plus or minus two
standard errors of the mean (by cruise/segment average).
Plus or minus two standard errors means that there is 95%
confidence that the actual mean falls within this range.
The range should be within 30% of the mean if sampling
and analysis design is correct. This is the goal for all state
variables for all segments by cruise or collection group.
The data means and standard errors will be computed
using statistical interpolation/extrapolation techniques
such as found in contouring or kriging algorithms.
In addition, model simulations will attempt to reproduce
the statistical distribution properties of the data. This will
be evaluated by comparing cumulative frequency
distribution plots of data to frequency distribution plots
from comparable model predictions.
Prediction bias will be minimized by calibration, the
process of parameter optimization seeking to minimize
residuals (the difference between calculated and measured
concentrations), without violating constraints imposed by
scientific observations and principles. Methods of
calculating or estimating loadings or other forcing
functions may be refined, if necessary, but no calibration
of forcing functions will be allowed. The goal for bias
reduction is to remove any apparent spatial or temporal
trends in residuals. Practically, this means that residuals
are uncorrelated and reduced to the magnitude of analytic
or replication errors.
The uncertainty of model predictions will be estimated
using a two-step procedure. The parameter variance-
covariance matrix resulting from calibration will be
estimated; then, this matrix will be applied to generate
exceedence levels for model predictions using Monte
Carlo methods. While it is not possible to make a priori
estimates of prediction uncertainty, the goal is 95%
exceedence limits within a factor of two of the predicted
toxic chemical concentrations in water and top predator
fish over the duration of the calibrated period (1994-95).
Project Description
Scope, Purpose, Objectives
The project description including scope, purpose, and
objectives is provided in the project workplan (USEPA,
1997a), in the modeling workplan (USEPA, 1995a), and
are summarized in Chapter 1 of this report. Rather than
repeating the details, the Modeling Workplan is included
here as Appendix A. It should be noted that the Workplan
continues to be revised as the feasibility of various aspects
of the project are determined. Another source of general
project information can be accessed on the GLNPO Web
site: http://www.epa.gov/glnpo/lmmb/.
In summary, the primary purpose of modeling is to provide
the scientific basis for understanding the sources,
transport, fate, and bioaccumulation of toxic chemicals in
Lake Michigan. Once a scientifically sound suite of
models are developed, they can be used to forecast future
in-lake chemical concentrations under alternative
management scenarios. For example, the models will be
used to forecast the concentration of PCBs in lake trout.
In addition, the models will be capable of discerning the
internal and external sources of toxic chemicals in broad
categories - tributary, atmosphere, and sediment.
Specifically, four toxic chemicals are being studied:
mercury, PCBs, atrazine, and TNC. The modeling
framework includes transport, fate, and bioaccumulation
(Figure 2).
The models are being developed and applied at different
levels of scale and uncertainty. The first of these has been
the development of screening level models. These models
attempt to assemble all present knowledge for a given
chemical and assess the problem in broad space and time
scales. The screening models have been useful in project
design by helping define important gaps in knowledge and
understanding and directing process research and
surveillance efforts to acquire the most useful information
to reduce uncertainties. Screening models have been
developed for PCBs and atrazine (Endicott et al, 1992;
Rygwelskirffl/., 1997). The primary caveat for screening
models is that they are not necessarily well-calibrated to
field data (or calibrated at all) and that what data exist may
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Computational
Transport
Mass
Balance
Bioaccumulation
Collapsed Grid
Transport Model
Food Web
Bioaccumulation
Model
Figure 2. Lake Michigan Modeling Framework
originate from a variety of sources with inconsistent
methodology and quality control. Therefore, these models
are not to be used for regulatory decision-making but
rather to help in directing research and surveillance
efforts, and for, perhaps, providing a basis for scientific
and management debate.
The second level of model involves the refinement of the
spatial and temporal scales for transport and chemical fate,
and biological processes. A medium-resolution
segmentation scheme was developed by dividing the lake
into 10 horizontal surface segments with five vertical
layers and a comparable sediment bed segmentation. At
this level of resolution, it will be feasible to calibrate
model processes and to begin reliable lake-wide, long-term
simulations for management purposes. These level-two
models are conceptually similar to screening models
except with greater spatial/temporal resolution and with a
greater degree of reliability because they are based on
calibration using verified field data and loading/forcing
function estimates.
At the highest level of resolution, a hydrodynamic model
is being applied to simulate the three-dimensional
temperature and current structure of the lake. This
information, required for water quality modeling, cannot
be measured at the necessary spatial and temporal
resolution. Hydrodynamic simulations will be performed
on a five-kilometer square horizontal grid. A sediment
transport model will also be applied at the five kilometer
resolution to predict particle transport fluxes due to
shoreline erosion, wave-and-current-driven resuspension,
and particle settling. Results from both the hydrodynamic
and sediment transport models will be used as input to the
mass balance models, including eutrophication and
contaminant transport and fate.
There are two reasons for pursuing different model levels
in the Lake Michigan project. First, modeling at different
levels of resolution and process detail yields valuable
insight regarding, for example, the trade-off between
model complexity and reliability. Modeling is always
somewhat experimental, and different "level" approaches
will maximize the opportunity for experimentation. This
approach has been endorsed by prominent Great Lakes
water quality modelers (Mackay and Bierman, 1993) and
should lead to a more accurate final modeling product.
The second, more practical reason, is that lower-resolution
and -complexity models can provide interim results before
the higher resolution/complexity models are completed.
This is because model programs are available for lower
level application, while development continues on the
higher level model programs. In addition, development
and application proceeds more rapidly using the lower
resolution/complexity models, due to factors such as easier
input processing, error checking, and calibration, less
computational requirements and, lesser user training to
gain proficiency.
As mentioned above, all model simulation results will be
compared to measurements obtained from the project data
collection program. QA in the context of field collection
of samples and analytical chemistry, physical, and
biological measurements is a very important aspect of this
project as well as being a requirement by the Agency. The
data requirements for modeling as specified in the
Modeling Workplan, Appendix A, have been incorporated
into the Project Workplan (USEPA, 1997a) and in the
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field and laboratory program. A QA plan (USEPA,
1997b) and methods compendium (USEPA, 1997c,d,e)
have been prepared and are being implemented for these
aspects of the project.
Four primary contaminants were elected for examination
in this study: PCBs, TNC, atrazine, and mercury. The
Project Workplan (USEPA, 1997a) provides the overall
sample design and media targeted for collection. In
summary, these contaminants have been measured in air,
tributaries, water, sediment, and biota (atrazine not
measured in biota). Nutrients, such as various forms of
phosphorus, nitrogen, and silica have also been measured
for appropriate media. Additionally, conventional
parameters (e.g., chloride, temperature, chlorophyll a,
organic carbon, etc.) were measured in water samples.
Associated studies of sediments, sediment traps, and
radiated sediment cores have also been conducted for
model calibration procedures.
Target fish species for LMMBP are lake trout and coho
salmon. In each case, the supporting food chains of each
species also has been examined. Lake trout were collected
in south, central, and northern parts of the lake along with
forage fish (bloater chub, alewife, smelt, and sculpin).
Zooplankton, phytoplankton, and benthic invertebrates
were also collected as the lower food. Coho salmon have
been collected according to their seasonal migration
pattern in the lake. For these samples, the
bioaccumulative contaminants have been analyzed.
Supporting data such as age, weight, length, percent lipid,
percent moisture, etc., have also been collected. Gut
content studies on target and forage fish have been
conducted to examine seasonal and temporal food web
relationships.
In all cases, the partners responsible for collection and
analysis have provided workplans, QA plans, and standard
operating procedures (SOPs) for each aspect in accordance
with the Project Workplan (USEPA, 1997a) and the
Project QA Plan (USEPA, 1997b). These have been
reviewed and approved by Project QA Management.
Additionally, field sampling methodologies are found in
USEPA, 1997c, and laboratory analysis procedures are
contained within USEPA, 1997d, and USEPA, 1997c.
Quality assurance audits and reviews of the resultant data
from the LMMBP are discussed later in this document.
Resources limited the number of samples collected and
analyses that could be performed so model evaluation will
include estimates of uncertainty. Uncertainty is also a
function of what is known about the processes governing
the transport, fate, and bioaccumulation of each chemical.
More is known for PCBs as this chemical has been the
subject of intensive research and modeling efforts in the
past. Less is known about the other chemicals,
particularly mercury. A first attempt will be made to
balance mass for the total mercury in water and sediment.
Refinements to incorporate more of the mercury species
and fate processes, as well as examination of some
modeling for bioaccumulation in the food chain, will be
made as time permits, but it is expected that the mercury
model development and application will extend beyond the
time frame of the LMMBP.
Receptor-oriented
Approach
(Hornbuckle et. al)
-^ Source-oriented
_L Model Approach
"~ (Cooter et. al)
Atmospheric Transport
and Deposition
Model (CMAQ)
Atmospheric
Loadings
Calculations
Figure 3. Loads to Lake Michigan
Atmospheric modeling will take two approaches. One will
be 'receptor-oriented'; the other will be 'source-oriented'.
In the former, estimates of the loads of PCBs, atrazine,
TNC, mercury to Lake Michigan will be made by
interpolating atmospheric concentration data across the
lake. In the 'source-oriented' method, emissions of
mercury and atrazine from sources will be estimated and
their deposition to the Great Lakes modeled.
10
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Products and Timetable
The products from the modeling efforts will include a final
modeling project report including executive summary for
each of the contaminants of interest, individual model
reports by each modeler or model team, and
documentation of models and other computer programs.
In addition, scientific journal publications will be
produced. Modeling products will be completed in
sequence as project data and loads become available and
the modeling process proceeds to an appropriate end point.
The preliminary sequence is atra/ine, PCBs/TNC, and
mercury. Priority will be placed on the management needs
of GLNPO and cooperating regulatory agencies and
modeling reports will be dovetailed with the overall
project reporting schedule which is being determined.
Also, presentations of model results will be made during
and after the project. Management presentations will be
made at the request of GLNPO and other USEPA and state
officials. Scientific presentations will be made at meetings
such as the Society of Environmental Toxicology and
Chemistry (SETAC) and the International Association for
Great Lakes Research (IAGLR).
The original project timetable is provided in the project
workplan (USEPA, 1997a). At this time (November 26,
1997), project database development has been somewhat
slower than anticipated and modelers have only received
limited project data. Generally, model results will be
ready for review for the first contaminant of interest in
about one year after release of project data including
submission of atmospheric and tributary loadings. After
two years, a draft project report should be ready for
review. It is anticipated that the formal modeling aspects
of the project will be completed near the end of FY-2000.
Journal articles and presentations at scientific meetings
will likely occur during and after the project.
Project Personnel
Modeling personnel are located at three primary
participating laboratories:
1. The Office of Research and Development, National
Health and Environmental Effects Research
Laboratory, Mid-Continent Ecology Division-Duluth,
Community-Based Science Support Staff, Large
Lakes Research Station, Grosse lie, Michigan.
2. National Oceanic and Atmospheric Administration,
Air Resource Laboratory, Atmospheric Sciences
Modeling Division, Research Triangle Park, North
Carolina (under Interagency Agreement (IAG) with
USEPA, National Exposure Research Laboratory).
3. National Oceanic and Atmospheric Administration,
Great Lakes Environmental Research Laboratory,
Ann Arbor, Michigan (under LAG with CBSSS).
4. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
In addition, the Modeling Workgroup includes personnel
from state government agencies, Canadian Global
Emissions Interpretation Centre (CGEIC), Mississauga,
Ontario, Canada; the consulting firm, Limno-Tech, Inc.,
Ann Arbor, Michigan; Gerald Keeler, University of
Michigan; Keri Hornbuckle and Joseph DePinto, State
University of New York (SUNY) at Buffalo; and Steven
Eisenreich, Rutgers University. The USEPA-Grosse lie
group includes on-site contractor modelers and
programmers from SoBran, Inc., PAI/SAIC and OAO
Corporation.
Vitae for all primary modelers and support personnel are
included in Appendix B. All primary modelers have
considerable training and experience in their areas of
expertise. Many are regarded as international experts and
have excellent publication records. An important note is
that the water modelers have spent most of their careers
working on various aspects of the Great Lakes and
understanding and modeling Great Lakes phenomena.
Key Support Facilities and Services
Community-Based Science Support Staff, Large
Lakes Research Station, Grosse lie, Michigan
This research facility located on Grosse He, Michigan
houses state-of-the-art computer and laboratory equipment.
Modelers use PCs (with Pentium processors) and
Macintoshes (power PC processors). They access several
on-site UNIX-based workstations via Ethernet. These
include two DECAlpha servers, two DECAlpha
workstations, a Sun SparclO workstation, two Sun Sparc2
workstations, and a Silicon Graphics workstation. In
addition, they are linked via Tl connection to the Internet
to other agency computers including the Cray
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supercomputer in Bay City, Michigan. Two RAID disk
arrays provide 50 GB of local data storage. Model code
development is supported by an on-site contractor
programming staff from OAO Corporation.
Grosse He computer software resources include
FORTRAN compilers and debugging tools. Other
software includes: Oracle database, ArcInfo/ArcView
GIS, AVS 2- and 3-b visualization, Excel/QuattroPro
spreadsheets, WordPerfect wordprocessing, and IDL. A
"modeling support" system is being developed which will
expedite development of models and analysis and
visualization results. Model code is managed using the
Revision Control System (RCS) (Appendix C).
National Exposure Research Laboratory,
Atmospheric Modeling Division (AMD),
Research Triangle Park, North Carolina
The computer/networking infrastructure available to AMD
modelers provides interoperability and connectivity to
allow transparent access to distributed high-performance
computational resources from the user's desktop. The
distributed resources appear to the user to be a single
computing environment with data accessible via the
facilities of Network Information Service (NIS), Network
File System (NFS), and Automount. The user desktop
hardware is typically composed of Sun workstations, Sun
Spare 10/40, Sun Spare 20/50, and Sun UltraSparc H's. -
although a few users have chosen Macintoshes (Power PC
processors) or PCs (with Pentium Processors). In addition
to the desktop computing capabilities, more powerful
servers are also transparently available to all modelers: a
general interactive server (Sun UltraSparc II with creator
3D graphics), a model execution server (DEC AlphaServer
2100 with 4-21064 CPUs (190 Mhz)), 512 Mbytes
memory, 50 GB disk, anonymous FTP server, primary and
backup Network Information Servers, file and e-mail
server - (Sun Spare 10/40), application server (Sun
UltraSparc II), OSF application server (DEC AlphaServer
2100 with 2-21064 CPUs (275 Mhz)), archive server
(200+ GB disk and 300 GB near-line tape storage), Single
Instruction Multiple Data array computer (4096 processor
MasPar with DEC-station front-end), visualization server
(SGI Indigo-2 with Extreme graphics subsystem), a public
access server (Sun Spare 10/40). In addition to the local
computing infrastructure, the modelers have access to
USEPA' s National Environmental Supercomputing Center
(NESC) in Bay City, Michigan via T3 connection. Model
code development is done in-house with some contract
programming support from OAO Corporation. A
modeling framework development, Models-3, is done by
contract systems development system from Science
Applications International Corporation (SAIC).
AMD computer software resources include D, C++,
JAVA, FORTRAN 77 & 90, Basic, and Perl compilers;
SAS and National Center for Atmospheric Research
(NCAR) graphics libraries, Digital Extended Math
Library, and NetCDF libraries, Parallel Virtual Machine
(PVM), KAP optimizer parallel computing tools; Oracle
and ObjectStore data management systems; AVS, NCSA
Collage, Fis5D, Package for Analysis and Visualization of
Environmental Data (PAVE) visualization packages; SAS,
Arc/Info, and Mathematics; Lotus 123, WordPerfect,
LaTEX; WABI and SoftWindows emulation environment
for Microsoft Windows; HTML publishing and Internet
access tools; Kermit, FTP, TN3270, x3270 communication
tools. Model code is currently managed using SCCS, but
a transition is underway to CVS for code management.
NOAA, Great Lakes Environmental Research
Laboratory (GLERL), Ann Arbor, Michigan
The computer facilities at GLERL are being used for the
hydrodynamic and wind wave modeling components of the
Lake Michigan Mass Balance Modeling Program.
Modelers at GLERL have access to UNIX workstations
(HP C160, HP 715/100) and an HP K200 SMP computer
with 4 PA-RISC 7200 100 Mhz processors and 256 Mb of
shared memory. Over 50 GB of disk space is available for
intermediate storage of model results. A DAT/DDS-2 tape
backup system allows for long-term storage of large data
sets and CDR equipment is available for permanent
storage of intermediate size data sets on CDROM. All
machines are connected to the Internet via GLERL's Tl
connection through the Merit Network.
Software in use for the LMMBP program at GLERL,
includes HP's FORTRAN compiler with support for
parallel processing on SMP machines, IDL for data
analysis and visualization, CorelDraw for presentation
graphics, and various wordprocessing and spreadsheet
programs. Computer animations of model output in the
FLC animation file format can be created and displayed on
workstations and PCs using public domain software.
GLERL programs for wind interpolation, wind wave
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calculations, and three-dimensional circulation modeling
use a common programming framework developed at
GLERL and use a machine-independent industry standard
for compact storage of numerical output (XDR format).
Wisconsin Department of Natural Resources,
Madison, Wisconsin
The computing facilities located at the Wisconsin
Department of Natural Resources (WDNR) headquarters
office in Madison, Wisconsin are being used for tributary
model development. Modelers at WDNR have access to
a DECAlpha Station 500 UNIX workstation running a 500
Mhz, EV4 Alpha processor with 256 Mb of RAM and 10
GB of disk storage. A 4 mm DAT tape system allows for
file backup and long-term storage. This platform is
connected to the internet through WDNR's Tl connection.
Additional in-house computing facilities include
Windows-based Intel platforms (80486 and Pentium n
processors) and Apple Macintosh personal computers.
WDNR computer software resources include FORTRAN
compilers and debugging tools. Other software includes:
ArcInfo/ArcView GIS, Excel, and Quattro spreadsheets,
and Word and WordPerfect wordprocessing. Model code
will be managed using the RCS.
U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi
Modelers at Waterway Experiment Station (WES) use
PCs, a DECAlpha workstation, and a Silicon Graphics
ESfDY workstation.
The modeling team communicates via the Internet for e-
mail and for transferring data sets and code. Monthly
teleconferences are held to review project status and
discuss important issues.
The University of Michigan, Air Quality
Laboratory, Ann Arbor, Michigan
The University of Michigan, Air Quality Laboratory
(UMAQL) is serviced by two major computing resources
centers on the campus of the University of Michigan: the
Computer Aided Engineering Network (CAEN) and the
Information Technology Division (ITD). CAEN supports
more than three thousand workstations, personal
computers, and specialized research computers. Among
these computers are those that may be found in CAEN's
engineering labs, including SUN and Hewlett-Packard
workstations and Apple Macintosh and IBM-compatible
personal computers. The CAEN also houses the Center
for Parallel Computing which contains IBM, Convex and
Kendall Square Research parallel supercomputers. The
ITD provides computing services to the remainder of the
University of Michigan campus.
The University of Michigan is directly connected to two
regional Internet providers, MichNet and CICNet.
MichNet is a network administered by Merit Network,
Inc., which connects Michigan educational institutions to
a backbone network service provided by MCI. CICNet is
a network that connects several Midwest educational
institutions together. Together, these networks provide the
University of Michigan's connectivity to the outside
world.
The University of Michigan's computing facilities provide
state-of-the-art support for the UMAQL and other research
interests on campus. Among the services available for this
project are the Advanced Visualization Laboratory and the
ITD Videoconferencing Service. The Advanced
Visualization Laboratory (AVL) at the University of
Michigan is designed to facilitate the analysis and display
of scientific data and imagery. The AVL provides
resources that allow users to easily work with both video
and computer based images and to be able to save and
display those images in a variety of formats (video,
computer, color prints, and color slides).
Computer resources within the UMAQL include a SUN
SparcStation/10 for ingestion, display, analysis, and
archive of real-time meteorological data from the National
Weather Service (NWS) and NOAA. The UMAQL
houses one SUN SparcStation IPC (with one gigabyte
local storage capacity, plus a 750 megabyte external
storage device), one SUN SparcStation/20 (with a 4.2
gigabyte external storage device), and one SUN
UltraSparc 167MHz workstation. Finally, the UMAQL
owns an Exabyte 8505 high density tape drive, which is
needed to read the WSR-88D radar data which will be
used to compute wet-deposition estimates for the project.
The UMAQL software library contains all of the necessary
tools to carry out the tasks as described above. Basic
statistical analyses will be carried out using the SAS 6.12
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Statistical Software Package. This package will allow for
sophisticated multivariate correlation analyses, as well as
the use of various hypothesis testing approaches. The
UMAQL software library also contains the most recent
version of the Regional Atmospheric Modeling Systems
(RAMS), Version 3B. Further, a Lagrangian particle
dispersion model and airmass trajectory model (HY-
SPLIT) has been recently updated and will be available for
this project.
In addition, the University of Michigan has a site license
for the NCAR Graphic software package, which will allow
for the detailed graphical presentation of the deposition
model output. Also, advanced data display software
available at the University of Michigan will allow for the
presentation of transport simulations in an animated, three-
dimensional format.
Modeling Approach
The general approach for developing models for large
aquatic systems is described in the Green Bay Final Report
(Richardson et al., 1997). This approach has been
followed during the initial phases of the Lake Michigan
project:
1. Determine specific management questions.
2. Define the appropriate modeling framework needed
to address these questions.
3. Propose alternative modeling/project designs for
management review for narrow range of expectations
and costs.
4. Using historical data and current modeling theory,
construct a preliminary screening model to test the
sensitivity of various model components.
5. Perform statistical analyses of historical data to
determine optimal sampling designs.
6. Make specific sampling design recommendations.
7. Maintain a continuing dialog with other committees
on technical issues.
8. Work with investigators who collect and analyzed
samples to conduct a "data quality assessment" to
evaluate project data. Evaluate data replicates and
other QA notations to determine appropriate
interpretation of data.
9. Develop and test the final models. Testing includes
comparison of calculated concentrations to field data
and adjusting model parameters within appropriate
and justifiable ranges to obtain a fit within
plus/minus one standard error of data mean.
10. Provide answers to specific management questions.
11. Document models and results.
Steps 1 through 6 have been completed and step 8
continues. Step 9, develop and test final models, is
presented in detail in the Modeling Workplan (Appendix
A). The Lake Michigan "Model" will embody a set of
linked submodels. The submodels are depicted in Figure
2 and include:
Water Models
1. Computational Transport Models. These models,
which predict physical motion and transport in the
lake in response to gravitational and frictional forces
(primarily wind), are applied on a common 5 km
square horizontal grid for Lake Michigan. They
include:
A. The hydrodynamic model (Princeton Ocean
Model (POM)) solves the equations of
continuity, momentum, and energy balance to
predict three-dimensional velocity,
dispersivity, and temperature distributions in
the lake. The prediction of water motion by
the hydrodynamic model serves as the
transport foundation for all mass balance
simulations.
B. The surface wave model (GLERL/Donelan
Wave Model) predicts the height, period, and
direction of surface waves based upon
momentum balance. Surface waves are
important forcing functions for sediment
resuspension, and also influence the rate of
chemical exchange between water and air.
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C. The sediment transport model (SEDZL)
predicts the settling, resuspension, and
deposition of coarse, medium, and fine-grained
sediments based upon the coupling of
hydrodynamic and mass balance computations.
Vertical particle transport fluxes predicted by
SEDZL will be used as input to the
contaminant transport and fate model,
specifically, as sediment resuspension fluxes
and settling velocities.
2. Mass Balance Models. These models predict the
concentrations of chemical constituents in water
column and sediment, based upon mass balance
equations using a common three-dimensional
segmentation and computational framework. They
integrate loading estimates for the atmosphere and
tributaries, initial conditions in sediment and water
column, physical transport, and chemical-specific
kinetic processes. They include:
A. The eutrophication/sorbent dynamics model
integrates organic carbon primary productivity
and transformation processes, based upon a
eutrophication modeling framework, with
sediment transport fluxes, to predict the
transport and transformation of particulate
organic carbon (POC). POC is the primary
sorbent phase for hydrophobic organic
chemicals in aquatic ecosystems, therefore, its
simulation in the mass balance framework for
Lake Michigan has been enhanced.
B. The contaminant transport and fate model
predicts toxic chemical concentrations in the
water column and sediment. This model
shares the computational and many conceptual
features with the eutrophication/sorbent
dynamics model. The toxic chemicals are
added as state variables, which partition
between aqueous and several operational
sorbent phases in each spatial compartment.
Also added are volatile exchange between
water and air, and chemical-specific
transformation processes. The contaminant
transport and fate model will also be used to
predict bioavailable chemical concentrations to
be used as exposure input to the food web
bioaccumulation model.
3. Bioaccumulation and Ecosystem Models
A. Food Web Bioaccumulation Model: The food
web model simulates the bioaccumulation of
toxic chemicals leading to the prediction of
chemical concentrations in lake trout and coho
salmon. The model is based upon a single-
component chemical mass balance for a fish.
B. The Ecosystem Model will build on the
existing Great Lakes eutrophication models
and incorporate more biological detail. This
will be done to reinforce the understanding of
ecosystem modification impacts on energy and
chemical cycling. Because this is a recent
addition to the project, details for this work
will be incorporated as they become more
clear.
Air Models
Atmospheric fluxes of toxic chemicals over the large
surface areas of the Great Lakes and Lake Michigan, in
particular, are major contributors to the mass balance. The
screening model calculations done using the MICHTOX
model (Endicott et ai, 1992) indicate that over the long-
term atmospheric fluxes to Lake Michigan will eventually
control PCB concentrations in lake trout. Although the
original intent of the project was to develop source-
receptor models for each contaminant, it was determined
that insufficient information exists for the sources of PCB s
and TNC. Therefore, the atmospheric modeling efforts
will focus on atrazine as the sources are known and data
and models for source estimation exist. Also, there will be
sufficient data for mercury to at least make an attempt to
model this chemical. Loadings for PCBs and TNC will be
estimates from interpolation of field measurements. In the
long-term, it should be understood that atmospheric vapor
phase PCB concentration over the lake may determine the
eventual concentration in lake trout. MICHTOX screening
results indicate that if the vapor phase concentration
remain at the present estimated levels of 0.24 ng/m3, the
lake trout concentration will reach a steady-state
concentration of 1 mg/kg. So in the long run it will be
important to determine the sources (global, regional, and
local) of PCBs if a rational control program is to be
determined and instituted. If sufficient source information
becomes available in the future, then coupling PCB air-
water models might be attempted to simulate the bi-
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directional transfer and feedback of contaminant mass
balances for air and water. Again, for this project the
focus of the air modeling will be atrazine and mercury.
The original intent for the project was to develop a
coupled air-water model. However, it was determined at
the Detroit, Michigan, Air/Water Workshop in June 1995,
that because of the time scale differences between air and
water processes, this was not necessary. Rather the air
models will be run independently and the output will be
input to the water models. Because water quality models
usually span time frames of seasons to years and air
models simulate periods of days or weeks, the air models
will be used to simulate important depositional periods.
The climatology and statistical methods will then be used
to estimate atmospheric inputs on the scale of seasons to
years. The model outputs include wet and dry deposition
contaminant fluxes and near surface atmospheric
concentration. These will be used to define input
atmospheric loads and gradient for gas exchange for the
water quality models. The specific air models include:
1. Regional Particulate Model (RPM): An engineering
version of the RPM model adapted for atrazine will
simulate transport above the watershed and lake, the
gas/particle partitioning and transformation of
atrazine in the atmosphere, and the significant
deposition and exchange processes with the
watershed and lake.
2. Regional Acid Deposition Model (RADM):
Simulations will be used to determine the total
particulate mass loadings and particle size
distribution which affect the behavior of particulate
atrazine.
3. Penn State University (PSU)/NCAR Mesoscale
Model-Generation 5 (MM5): Generates diagnostic
simulations of wind temperature, humidity, cloud
cover, and other meteorological variables. This
technique continually corrects certain model
variables toward observed values during the
simulation to control errors. MM5 results are used
in the RADM and RPM models.
Mercury Emissions Inventory
An inventory of anthropogenic sources of atmospheric
mercury has been developed and described in USEPA's
Mercury Study Report to Congress as mandated in Section
112(n)(l)(B) of the Clean Air Act, as amended in 1990.
This inventory accounts for a variety of industrial,
commercial, and residential source types within all 50
states of the United States. It has been subjected to
rigorous peer review both inside and outside USEPA and
has been judged to accurately describe the total mass and
spatial distribution of mercury emitted to the atmosphere
from anthropogenic sources in the U.S. This emission
inventory has been used to support regional-scale
atmospheric mercury deposition modeling, the results of
which are also described in USEPA's Mercury Study
Report to Congress. This regional-scale modeling showed
that, in addition to total mass, the chemical and physical
forms of mercury emissions are important in determining
the patterns and intensity of mercury deposition to the
surface. Studies of the chemical and physical forms of
mercury emissions from various source types are currently
ongoing.
Atmospheric mercury emissions from natural sources and
from anthropogenically contaminated soils and water
bodies are not as well understood as are the current direct
anthropogenic emissions to air. It can be reasonably
assumed that these natural and recycled emissions are
mostly in the form of elemental mercury gas due to the
relatively high vapor pressure of elemental mercury versus
its oxidized compounds. However, the total mass of
natural and recycled mercury emissions and the spatial
distribution of those emissions are not confidently known
at this time. It may be possible to model natural and
recycled mercury in the form of a global-scale background
concentration if it can be determined that no such
emissions are significantly concentrated near Lake
Michigan.
Anthropogenic emissions of mercury from sources in
Canada are currently being surveyed by Canadian federal
and provincial governments and preliminary inventories
from this effort are now available. An accurate emission
inventory for Canada including chemical and physical
form definitions will be required for an accurate modeling
assessment of total mercury deposition to Lake Michigan.
Emissions of mercury from anthropogenic sources in
Mexico and more distant countries might be adequately
accounted for by the global-scale background
concentration also used to account for natural and recycled
emissions. It is generally thought that oxidized mercury
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emissions will mostly deposit to the surface or convert to
the elemental form within the transport distance from
Mexico to Lake Michigan. Atmospheric mixing of the
remaining mercury from these distant anthropogenic
sources could make their mercury plumes
indistinguishable from global-scale emissions. We
currently do not have a complete understanding of the
global-scale transport of atmospheric mercury. Thus, the
concept of a nearly constant global background
concentration of elemental mercury gas may be invalid.
However, in the absence of comprehensive emission
inventories for all industrial nations and global-scale
atmospheric models to use them, we are forced to employ
some form of background concentration or constant
boundary influx concentration in our modeling of
atmospheric mercury deposition to Lake Michigan.
Quality Control
Quality control is defined as the process by which QA is
implemented. All project modelers will conform to the
following guidelines:
1. All modeling activities including data interpretation,
load calculations or other related computational
activities are subject to audit and/or peer review so
careful written and electronic records should be kept
for all aspects of model development and
application.
2. Written rationale will be provided for selection of
models or versions of models like WASP4 or
WASP-IPX, SEDZL, etc.
3. As modeling computer programs are modified, the
code will be checked and a written record made as
to how the code is known to work (i.e., hand
calculation checks, checks against other models,
etc.). This should include input and output, if
appropriate or results of external calculations used
to confirm code.
4. If historical data are used, a written record on where
this was obtained and any information on its quality
will be maintained. A written record on where this
information is located on a computer or server will
be maintained.
5. If new theory is incorporated into the model
framework, references for the theory and how it is
implemented in any computer code will be
documented.
6. All new and modified computer codes will be
documented. This should include internal
documentation, as revision notes in program
headers, and external documentation, in user's
guides and supplements.
Audits of each modelers work will be conducted
periodically by the Agency QA auditing team, the project
QA officer, MED-Duluth QA officer or one or more of
their designees.
Modelers will be asked to provide verbal status reports of
their work at the monthly modeling workgroup
teleconferences. Finally, detailed modeling
documentation will be made available to members of the
Science Review Panel (see peer review section below) as
necessary.
The ability of computer code to represent model theory
accurately will be assured by following rigorous
programming protocols including documentation within
code. Specific tests will be required of all models and
revisions to ensure that fundamental operations are
verified. These include continuity and mass conservation
checks. These also include testing of numerical stability
and convergence properties of model code algorithms, if
appropriate. Model results will be generally checked by
comparing results to those obtained by other models and
by comparison to manual calculations. Visualization of
model results will assist in determining whether model
simulations are realistic. Model calculations will be
compared to actual field data. If adjustments to model
parameters have to be made to obtain a "fit" to the data,
modelers will provide a rigorous explanation and
justification that must agree with scientific knowledge and
with process rates within reasonable ranges as found in the
literature.
Models will be deemed acceptable when they are able to
simulate field data within plus/minus one standard error.
The standard error will be determined by accepted
statistical methods by stratifying data appropriately in time
and space. For cases in which model predictions do not
match the spatial/temporal resolution of data, the
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appropriate averaging of either data or predictions will be
determined and justified. The appropriate scales will be
determined by the modeling team in consultation with the
Science Review Panel.
Data Quality
Data Quality Assessment
Both project-generated and non-project-generated data will
be used for model development and calibration. The QA
procedures for project-generated data and database
development have been discussed in this document and
elsewhere. All analytical data for the model's target
analytes and most supporting data will have been verified
through the RDMQ process before release to the modelers.
The Project QA Plan (USEPA, 1997b) provides the QA
program and process, organizational structure, data quality
objectives, implementation of the QA Program and
information management guidelines for the LMMBP. The
process calls for approved workplans, SOPs, and QA plans
for each aspect of field collections and laboratory
procedures. Rigorous examination of precision, accuracy,
completeness, representativeness, detectability, and
comparability is and will be conducted on project-
generated data by QA managers. These will not only
include examination of the data itself but also technical
systems audits, data quality audits, management systems
reviews, and performance evaluations. Project-generated
data will be verified and validated using the RDMQ
process which controls measurement uncertainty,
evaluates data, and flags or codes data against various
criteria. This portion of the QA process is also associated
with final database construction. The final database
repository for the LMMBP will be Oracle and will contain
all formatted, verified, and validated project-generated
data with associated information (USEPA, 1995b, 1997f).
Modelers will cross-check the data for bias, outliers,
normality, completeness, precision, accuracy, and any
other potential problems. Determinations will also be
made using best professional judgement as to selecting
field replicates in different situations.
Non-project-generated data may be obtained from either
published or unpublished sources. The published data
(including those from gray literature) will have had some
degree or form of peer review. Certainly there is a wide
range of review quality from journal to journal. However,
given that some degree of review has been performed,
databases are often obtained directly from authors or from
on-line databases. These are generally examined by
modelers as part of a data quality assessment. In the case
of databases that have not been published, these databases
are also examined in light of a data quality assessment.
Database Tracking
A database tracking system has been instituted by the
CBSSS, Grosse He, for modeling systems. This system
employs a single contract person for data being received.
One contact person logs in routine information about the
data and coordinates its use. The process provides
updated versions if changes occur from the GLNPO
database. The second component of tracking involves
versions which have been assessed and completed for
modeling purposes. The datasets are X-Y-Z set for model
input (see below).
Model and Input/Output File Tracking
A system for tracking models, input files, and output files
has been developed by CBSSS, Grosse lie. This system is
referred to as "RCS" During model calibration and
testing, various versions of each were used to examine
model performance. This system coordinates the version
of each model, input, and output files so that any can be
recalled, run, or examined. Associated documentation of
these aspects are also developed as part of the tracking and
modeling system.
Record Keeping
All records including modelers notebooks and electronic
files will be maintained according to Agency standards as
defined by the USEPA Office of Information Resources
Management (http://www.USEPA.gov/irmoli) Federal
Information Processing Standards (FIPS),
http://www.nist.gov/itl/div879/pubs and professional
standards like ANSI/IEEE Standard 730-1989 for Software
Quality Assurance Plans.
These laboratory notebooks and electronic files will be
maintained by each modeler and turned over to the
laboratory QA officer upon completion of the project.
Electronic files containing documentation of model
testing, calibration, and validation will be maintained by
18
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each modeler and transferred to a central project archive
as designated by the QA official.
Data Usage
When a great amount of data are collected for various
media such as in this study, a considerable number of
autonomous results will be reported and/or published.
However, this data have been specifically collected and
analyzed for the purposes of the LMMBP, and
mathematical modeling. Undoubtedly other uses will be
attempted but data have not been specifically obtained for
these purposes. Modelers will use the data for establishing
relationships and associations, defining processes and
quantifying process rates, and checking existing model
input files, relationships, and rates.
The above aspects pertain to model calibration and testing.
This is an interactive process and requires considerable
data. A final step in the calibration process is to determine
the agreement between the observed and computed
conditions.
Peer Review
Peer review is an essential component to any successful
and credible scientific/modeling endeavor. Model
development and application is a very complex process
and there are many debatable issues and many approaches
that could be taken. Peer reviews provide an objective
means to arrive at scientific consensus on a number of
these issues as well as providing judgements on scientific
credibility.
USEPA has provided guidance for conducting peer review
of environmental regulatory modeling projects (USEPA,
1994). This guidance acknowledges the utility of peer
reviews for all phases of the modeling work from planning
through application. The Agency policy also points out
that the guidance does not directly address models
developed for reasons other than to support regulatory
decision-making. Therefore, research models developed
for and used exclusively within a research program should
receive peer review by scientific colleagues, senior
scientists, managers, and by reviewers for refereed
journals. If the research model evolves to a point that
decisions may be made as a result of its use, then a formal
Agency peer review would be appropriate, if not
mandatory. Because the LMMBP is being conducted in
support of the Lake Michigan Lake-wide Management
Plan, all aspects of the modeling are deemed to require
peer review.
Agency guidance offers three mechanisms for
accomplishing external peer review:
1. Using an ad hoc technical panel of at least three
scientists;
2. Using an established external peer review
mechanism such as the Science Advisory Board or
Science Advisory Panel; or
3. Holding a technical workshop.
Further guidance is provided for determining when and by
what mechanisms to initiate an external peer reviews and
how to document them. The guidance does not appear to
take into consideration the use of multiple models, as
being done for Lake Michigan. So when referring to "the
model" it is assumed that it applies to the entire modeling
framework provided in the LMMBP Modeling Workplan.
The LMMBP Modeling Workplan was reviewed
externally, but not by a formal peer review panel. It was
incorporated into the project workplan which was
distributed to a large number of experts and to the public.
Comments were received and adjustments were made
accordingly. Before any substantial modeling efforts
begin, a peer review panel should be selected and a review
convened.
All aspects of Lake Michigan model development and
application will be reviewed by a "Science Review Panel"
The panel will consist of well-known scientists and
engineers who have experience in developing and applying
models but who have no direct contact with the project.
This will ensure objectivity and avoid any conflict of
interest. The panel will meet at least semi-annually and
more frequently if needed. The initial review should be
scheduled for February 1998.
References for Chapters 1 and 2
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Uncertainty in Mass Balance Models of Toxics in the
Great Lakes-Lake Ontario Case Study. Great Lakes
Program, State University of New York at Buffalo,
Buffalo, New York. 318pp.
Thomann, R.V. and D.M. Di Toro. March 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 He, Michigan. EPA-660/3-75-005,178pp.
Thomann, R.V. and J.P. Connolly. March 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.
Thomann, R.V. and D.M. Di Toro. May 1984. Physico-
Chemical Model of Toxic Substances in the Great Lakes.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. EPA-600/S3-84-
050, 7 pp.
USEPA. 1991. Quality Assurance Guidelines for
Modeling Development and Application Projects: A
Policy Statement. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth,
Duluth, Minnesota.
22
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USEPA. 1994. Agency Guidance for Conducting
External Peer Review of Environmental Regulatory
Modeling. U.S. Environmental Protection Agency,
Agency Task Force on Environmental Regulatory
Modeling, Washington, D.C.
USEPA. 1995a. Lake Michigan Mass Balance Project:
Modeling Work Plan (Draft). U.S. Environmental
Protection Agency, National Health and Environmental
Effects Research Laboratory, Mid-Continent Ecology
Division-Duluth, Large Lakes Research Station, Grosse
He, Michigan.
USEPA. 1995b. Lake Michigan Project Data
Administration Plan. U.S. Environmental Protection
Agency, Great Lakes National Program Office, Chicago,
Illinois.
USEPA. 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.
USEPA. 1997b. Enhanced Monitoring Program Quality
Assurance Program Plan. U.S. Environmental Protection
Agency, Great Lakes National Program Office, Chicago,
niinois. EPA-905/R-97-017.
USEPA. 1997c. Lake Michigan Mass Balance Study
(LMMB) Methods Compendium, Volume I: Sample
Collection Techniques. U.S. Environmental Protection
Agency, Great Lakes National Program Office, Chicago,
Illinois. EPA-905/R-97-012a, 1,440pp.
USEPA. 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, niinois. EPA-905/R-97-012b.
USEPA. 1997e. Lake Michigan Mass Balance Study
(LMMB) Methods Compendium, Volume 3: Metals,
Conventionals, Radiochemistry, and Biomonitoring
Sample Analysis Techniques. U.S. Environmental
Protection Agency, Great Lakes National Program Office,
Chicago, Illinois. EPA-905/R-97-012c.
USEPA. 1997f. Lake Michigan Mass Balance Data
Reporting Format. U.S. Environmental Protection
Agency, Great Lakes National Program Office, Chicago,
Illinois.
Velleux, M., D. Endicott, J. Steur, S. Jaegar, and D.
Patterson. 1995. Long-Term Simulation of PCB Export
from the Fox River, Green Bay. J. Great Lakes Res.,
21(3):359-372.
Velleux, M., J. Gailani, and D. Endicott. 1996.
Screening-Level Approach for Estimating Contaminant
Export From Tributaries. J. Environ. Engin., 122(6):503-
514.
Venkatram, A., P. Karamchandani, and P. Misra. 1988.
Testing a Comprehensive Acid Deposition Model. Atmos.
Environ., 22:737-747.
Zahakos, H.A., J.P. Connolly, and D.M. Di Toro. 1993.
Lake Erie Eutrophication Model Post Audit 1980-1990.
Report to the U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 28 pp.
23
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Chapter 3
Individual Model Quality Assurance Plans
Computational Transport
Hydrodynamic Model of Lake Michigan
Project Officer: Ronald Rossmann and Douglas Endicott,
USEPA, LLRS
Principal Modeler: David J. Scwab, NOAA
Support Modeler: Dmitry Beletsky, Cooperative Institute
for Limnology and Ecosystems Research (CILER)
A. Model Description
1. Background Information The numerical
circulation model used in this task is a three-
dimensional ocean circulation model developed at
NOAA's Geophysical Fluid Dynamics Laboratory
at Princeton University for coastal ocean
applications by Blumberg and Mellor (1987) and
subsequently adapted for Great Lakes use at
GLERL (Schwab and Bedford, 1994; O'Connor
and Schwab, 1994). The model is driven by time-
dependent surface boundary conditions for wind
stress and heat flux. The physical parameters
predicted by the model are the three-dimensional
velocity distributions, the temperature field, and
the free surface water level. The main features of
the model are:
Fully three-dimensional nonlinear Navier-
Stokes equations
Flux form of equations
Boussinesq and hydrostatic approximations
Free upper surface with barotropic (external)
mode
Baroclinic (internal) mode
Turbulence model for vertical mixing
2.
Terrain following vertical coordinates
(-coordinate)
Generalized orthogonal horizontal coordinates
Smagorinsky horizontal diffusion
Leapfrog (centered in space and time)
Implicit scheme for vertical mixing
Arakawa-C staggered grid
FORTRAN code optimized for vectorization.
The terrain following vertical coordinate system
(sigma-coordinate) replaces the vertical
coordinate, z, with a normalized vertical
coordinate, sigma = z/d, where d is the local
depth. The advantage of this system is that in the
transformed coordinate system, the bottom
corresponds to a uniform value of the vertical
coordinate (sigma = -1), thus simplifying the
governing transport and continuity equations. The
disadvantage is that an extra term is introduced in
the pressure gradient involving the gradient of
bottom topography. As Haney (1991) has shown,
the truncation error in the finite difference
representation of this term can be considerable
near steep topography. We have been careful in
the design of numerical grids for the Great Lakes
to minimize these problems. Although the current
version of the model can incorporate a curvilinear,
coastline-following coordinate system, this feature
is not used in the Great Lakes version. We felt
that the additional complication of a curvilinear
coordinate system in the interpolation and analysis
of model results were not justified by the potential
for increased accuracy in the hydrodynamic
model.
Data Quality - Two data sources will be used to
calibrate the Lake Michigan model. Heat flux and
momentum flux forcing functions will be
24
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estimated from the NWS observations and buoy
data from 1982-83. This data has been used
successfully in the USACOE Wave Information
Study for the Great Lakes (Hubertz et al, 1991).
Model results for the 1982-83 simulation will be
calibrated against currents and temperatures
measured by GLERL during the 1982-83 field
program. These data were collected with state-of-
the-art oceanographic instrumentation from an
extensive deployment array and then edited and
analyzed at GLERL (Gottlieb et al., 1989).
Meteorological data for the 1994 simulation will
be obtained from the NWS stations described
above as well as additional marine observations
from U.S. Coast Guard (USCG) stations and ships
of opportunity in Lake Michigan. These data are
routinely collected and quality-controlled at the
Cleveland Weather Service Forecast Office. In
addition, data from several meteorological stations
in the LMMBP air sampling network around Lake
Michigan will be used. The QA/QC procedures
for these data are described in the air sampling
network plan.
B. Model Development
1. Code Development and Maintenance - The code
used in the hydrodynamic circulation model is
based on the FORTRAN code of the POM as
described in Mellor (1996). The adaptations
made for application to the Great Lakes are
described in Schwab and Bedford (1994) and
within the code itself.
2. Model Documentation - A complete description of
the model equations, underlying assumptions,
boundary conditions, and numerical methods is
contained in Mellor (1996). A practical
operator's guide for the Princeton model was
compiled by O'Connor (1991) and was used
extensively in the development of the Great Lakes
version of the model. The scientific basis for
adaptations of the model to the Great Lakes is
described in Schwab and Bedford (1994) and
O'Connor and Schwab (1994).
3. Code Verification Hydrodynamic modeling
codes are typically verified with tests against
analytic solutions and by sensitivity analysis. The
code used in this task has been tested for several
analytical cases including external and internal
seiches, logarithmic boundary layer, horizontal
and vertical diffusion, thermal structure
development, and barotropic wind-driven
circulation (O'Connor and Schwab, 1994; Schwab
et al., 1994). All tests indicate the model is coded
correctly.
4. Code Documentation - The FORTRAN code for
the model comprises approximately 4000 lines of
code and comments. The code has been
developed over a period of 10 years at Princeton
and adheres to modern programming techniques
and standards. In addition to extensive internal
documentation, a comprehensive user's guide is
available (Mellor, 1996) as well as an operator's
manual (O'Connor, 1991). Documentation of
specific adaptations made for the Great Lakes
version are described by Schwab and Bedford
(1994).
5. Model Calibration/Validation and Uncertainty
The POM has been used extensively for coastal
and estuarine applications, including the Middle
Atlantic Bight, the South Atlantic Bight, the
California Shelf, the Santa Barbara Channel, and
New York Harbor. The Great Lakes version is
used operationally in the Great Lakes Forecasting
System (Bedford and Schwab, 1990; Schwab and
Bedford, 1994) for Lake Erie. Extensive
validation tests with observed currents, water
level fluctuations, and surface temperature
distributions have been carried out in the
development of the Great Lakes Forecasting
System Model validation against 1982-83 current
and water temperature measurements in Lake
Michigan is also a part of this task.
C. References
Bedford, K.W. and D.J. Schwab. 1990. Preparation
of Real-Time Great Lakes Forecasts. Cray Channels.
Summer 1990, pp. 14-17.
25
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Blumberg, A.F. and G.L. Mellor. 1987. A
Description of a Three-Dimensional Coastal Ocean
Circulation Model. In N.S. Heaps (Ed.), Three
Dimensional Coastal Ocean Models, Coastal and
Estuarine Sciences, pp. 1-16. American Geophysical
Union, Washington, D.C.
Gottlieb, E.S., J.H. Saylor, and G.S. Miller. 1989.
Currents and Temperatures Observed in Lake
Michigan from June 1982 to July 1983. National
Oceanographic and Atmospheric Administration,
Great Lakes Environmental Research Laboratory, Ann
Arbor, Michigan. NOAA Technical Memorandum
ERL-GLERL-71,45pp.
Haney, R.L. 1991. On the Pressure Gradient Force
Over Steep Topography in Sigma Coordinate Ocean
Models. J. Phys. Oceanogr., 21:610-619.
Hubertz, J.M., D.B. Driver, and R.D. Reinhard. 1991.
Hindcast Wave Information for the Great Lakes: Lake
Michigan. Coastal Engineering Research Center, U.S.
Army Corps of Engineers, WES Report 24, 472 pp.
Mellor, G.L. 1996. User's Guide for a Three-
Dimensional, Primitive Equation, Numerical Ocean
Model. Atmospheric and Oceanic Sciences
Department, Princeton University, New Jersey. 35 pp.
O'Connor, W.P. 1991. A User's Manual for the
Princeton Numerical Ocean Model. Institute for
Naval Oceanography, Stennis Space Center,
Mississippi. Report SP-5, 69 pp.
O'Connor, W.P. and D.J.Schwab. 1994. Sensitivity
of Great Lakes Forecasting System Nowcasts to
Meteorological Fields and Model Parameters. In
Proceedings of the Third International Conference on
Estuarine and Coastal Modeling, pp. 149-157. ASCE
Waterway, Port, Coastal and Ocean Division.
Schwab, D.J. and K.W. Bedford. 1994. Initial
Implementation of the Great Lakes Forecasting
System: A Real-Time System for Predicting Lake
Circulation and Thermal Structure. Water Pollut. Res.
J. Canada, 29(2/3):203-220.
Schwab, D.J., W.P. O'Connor, and G.L. Mellor.
1994. On the Net Cyclonic Circulation in Large
Stratified Lakes. J. Phys. Oceanogr., 25:1516-1520.
Win d Wave Model for Lake Michigan
Project Officer: Ronald Rossmann and Douglas Endicott,
USEPA, LLRS
Principal Modeler: David J. Schwab, NOAA
Support Modeler: Dmitry Beletsky, CILER
A. Model Description
1. Background Information The wind wave model
used in this task is the GLERL/Donelan
parametric wind wave model developed by
Schwab et al. (1984a,b). This is a numerical
finite-difference solution to the two-dimensional
wave momentum conservation equation. The
wave energy spectrum is parameterized at each
point on a rectilinear computational grid in terms
of total wave energy, peak energy period, and
predominant wave direction. Momentum is
transferred from the wind to the waves using
Donelan's (1979) formulation which depends on
the difference between the phase velocity of the
waves and the local wind velocity.
The principal assumptions of the model are:
Equipartition of kinetic and potential wave
energy
Waves propagate according to deep water
theory
Wave directional spreading follows a cosine
squared law
The JONSWAP (Hasselman et al, 1973)
spectral shape is used
The wave spectrum equilibrium range
parameter follows the JONSWAP empirical
dependence on nondimensional fetch
Only actively generated waves are considered.
The "fossil" wave field discussed in Schwab et
al. (1994a) is not employed.
This model has been successfully applied to Lake
Erie (Schwab et al, 1984a) and Lake Michigan
(Liu et al, 1984), as well as the Baltic Sea and
several other lakes and embayments around the
26
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world. The NWS has used this model for routine
lake wave forecasting on all five Great Lakes
since 1992 (Johnson etal., 1992).
2. Model Parameters and How They Will Be
Specified - The empirical relations between wave
energy, wave period, and nondimensional fetch
resulting from the JONSWAP experiment
(Hasselmann et al., 1973) were developed
independently of the model and will not be
adjusted for calibration. The parameterization of
the momentum transfer from the wind to the
waves (Donelan, 1979) includes an empirical
constant specifying the percentage of wind stress
retained by the waves which can be adjusted for
different types of wind input (i.e., ship reports,
shore stations, buoys, etc.). This parameter can
vary slightly depending on the particular
combination of types of wind reports and lake
geometry for a particular application. We have
adjusted this parameter to optimize the
comparison between wave model predictions and
wave observations from NDBC buoys in Lake
Michigan during the study years (1982-83 and
1994-95).
Meteorological data to supply wind forcing for the
1982-83 and 1994-95 simulations were obtained
from the NWS weather stations and buoys as well
as additional marine observations from the USCG
stations and ships of opportunity in Lake
Michigan. These data are routinely collected and
quality-controlled at the Cleveland Weather
Service Forecast Office. In addition, data from
several meteorological stations in the LMMBP air
sampling network around Lake Michigan were
used. The QA/QC procedures for these data are
described in the air sampling network plan.
B. Model Development
1. Code Development and Maintenance - The code
used in the wind wave model is based on the
FORTRAN code of Schwab et al. (1984b). The
adaptations made for applications to the LMMBP
are described above and within the code itself.
2. Model Documentation - A complete description of
the model equations, underlying assumptions,
boundary conditions, and numerical methods is
contained in Schwab et al (1984a, 1986). The
original source code for the model is presented in
Schwab et al. (1984b). Additional documentation
of adaptations particular to the LMMBP will be
described in the final project report and in the
source code itself.
3. Code Verification Hydrodynamic modeling
codes are typically verified with tests against
analytic solutions and by sensitivity analyses. The
code used in this task has been tested for several
idealized cases including purely fetch-limited
conditions, duration-limited conditions, and
several tests of directional divergence, in various
geometries (Schwab et al., 1984a). All tests
indicate the model is coded correctly.
4. Code Documentation The FORTRAN code for
the model comprises approximately 1200 lines of
code and comments. The code has been
developed over a period of 10 years at GLERL
and adheres to modern programming techniques
and standards. In addition to extensive internal
documentation, a user's guide is available
(Schwab et al., 1984b). Documentation of
specific adaptations made for the LMMBP are
described in the code itself and in the final project
report.
5. Model Calibration/Validation and Uncertainty -
The GLERL/Donelan Wave Model has been used
extensively for Great Lakes applications. Schwab
et al. (1984a) compared wave model results to
wave measurements from an instrumented tower
in Lake Erie and found root mean square
differences on the order of 0.2 m for wave height
and 1 sec for wave period. Liu et al. (1984)
showed a high correlation between model results
and lake-wide synoptic wave height measurements
from an airborne laser altimeter in Lake Michigan.
The GLERL/Donelan model is also used
operationally by the NWS (Johnson et al., 1992)
and has proven to be highly accurate when wind
forecasts are accurate.
Model calibration against wave buoy
measurements in 1982-83 (NDBC 45002 and
45007) and model validation against wave buoy
27
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measurements in 1994-95 (NDBC 45002,45007,
and 45010) in Lake Michigan will also be carried
out as part of this task.
C. References
Donelan, M.A. 1979. On the Fraction of Wind
Momentum Retained by Waves. In J.C. Nihoul
(Ed.), Marine Forecasting, Predictability and
Modeling in Ocean Hydrodynamics. Elsevier
Publications, Amsterdam, The Netherlands.
Hasselmann, K., T.P. Barnett, E. Bouws, H. Carlson,
D.E. Cartwright, K. Enke, J.A. Ewing, H. Gienapp,
D.E. Hasselmann, P. Kruseman, A. Merrburg, P.
Muller, D.J. Olbers, K. Richter, W. Sell, and H.
Walden. 1973. Measurements of Wind-Wave Growth
and Swell Decay During the Joint North Sea Wave
Project (JONSWAP). Dtsch. Hydrogr. Z., A12,95 pp.
Johnson, F.R., D.E. Boyce, J.A. Bunn, and J.L.
Partain. 1992. In Search of the Perfect Wave A
New Method to Forecast Waves on the Great Lakes.
National Oceanographic and Atmospheric
Administration National Weather Service, Eastern
Region, Silver Spring, Maryland. Technical
Attachment No. 92-9A, 12 pp.
Liu, P.C., D.J. Schwab, and J.R. Bennett. 1984.
Comparison of a Two-Dimensional Wave Prediction
Model with Synoptic Measurements in Lake
Michigan. J. Phys. Oceanogr., 14(9):1514-1518.
Schwab, D.J., J.R. Bennett, P.C. Liu, and M.A.
Donelan. 1984a. Application of a Simple Numerical
Wave Model to Lake Erie. J. Geophys. Res.,
89(C3):3586-3592.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1984b.
A Two-Dimensional Lake Wave Prediction System.
National Oceanographic and Atmospheric
Administration, Great Lakes Environmental Research
Laboratory, Ann Arbor, Michigan. Technical
Memorandum ERL-GLERL-51, 70 pp.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1986. A
Two-Dimensional Lake Wave Prediction System.
Environ. Software, l(l):4-7.
Sediment and Contaminant Transport/SEDZL
Principal Investigator: Douglas Endicott, USEPA, LLRS
Contract Support Programmer: Michael Settles, OAO
Corporation
Project Advisor: Joseph Gailani, USACOE
A. Model Description
1. Background Information - The numerical
sediment transport model developed at the
University of California at Santa Barbara (UCSB),
Department of Mechanical and Environmental
Engineering by Ziegler and Lick (1986) and
subsequently refined for use on the Great Lakes at
UCSB and LLRS (USEPA, 1997). SEDZL
couples vertically-integrated hydrodynamic and
sediment transport equations in the water column
to a three-dimensional, time-dependent model of
the sediment bed. Transport of three different
sediment size-classes can be modeled including
fine-grained, cohesive sediments which flocculate
during settling. These particles are modeled as
the medium size-class. All size-classes can be
deposited to and eroded from the sediment bed.
The sediment dynamics incorporated into SEDZL
are based on valid laboratory and field studies
concerning the deposition and resuspension of
fine-grained, cohesive sediments (Fukuda and
Lick, 1980; Lee etal., 1981; Lick, 1992; Tsai and
Lick, 1987; Burban et al, 1990; Xu, 1991). A
brief review of the sediment dynamics used in the
model will now be presented; a more detailed
discussion can be found in Gailani et al., 1993,
1994.
The medium size-class flocculation and laboratory
results have been used to construct an
approximate flocculation model (Burban et al,
1990). The flocculation model estimates the floe
size as a nonlinear function of particle
concentration and shear stress. Once floe size has
been predicted, then the settling rate of medium
size-class sediments is calculated.
The resuspension properties of fine-grained
cohesive sediments differ significantly from
noncohesive sediments, i.e., sand. Both size-
classes are important for sediment transport in
28
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Lake Michigan. Noncohesive sediment will be
resuspended at a constant rate if the sediment bed
is subjected to a uniform shear stress greater than
a critical value. As long as there is a supply of
noncohesive sediment, resuspension will occur.
However, laboratory and field experiments clearly
indicate that only a finite amount of fine-grained,
cohesive sediment can be resuspended under the
same conditions (Tsai and Lick, 1987). Based
upon these experimental results, the total amount
of sediment which can be resuspended at a
particular bottom shear stress can be calculated.
SEDZL incorporates a realistic model of the
sediment bed structure, which is necessary if the
transport processes are to be modeled properly.
The sediment bed is three-dimensional, with
vertical layers representing post-deposition age
(tj) and increasing compaction with depth. The
effects of compaction on resuspension are
accounted for by td, which increases with depth in
the sediment bed. The critical shear stress also
increases with depth until td > 1 day after which
time it is assumed to be constant. Experimental
results have shown that compaction effects begin
to become negligible for td > 6 to 7 days.
A volume integral method was used to derive
finite difference equations which are used to
numerically solve the vertically-integrated Navier-
Stokes and sediment transport equations (Ziegler
and Lick, 1986). The equations are solved
explicitly, using two time levels. Interior and
boundary point equations are second-order
accurate, conservative mass and momentum both
globally and locally, and boundary conditions are
treated correctly. A unique feature of this model
is its successful treatment of open boundary
conditions (Lick etal., 1987).
Vertically integrated hydrodynamic and sediment
transport equations have been used in SEDZL in
order to simplify the numerical analysis (Ziegler
and Lick, 1986) and complexity of the model.
These equations are valid approximations for
situations where the water is relatively shallow
and where the vertical stratification of the water
column is weak. These assumptions limit the
application of SEDZL to situations where there
are no significant vertical gradients in either the
sediment concentration or the horizontal velocity.
SEDZL has also been applied to water bodies
where these assumptions have not been strictly
satisfied, and the limitations of the model have
been evaluated under these circumstances (Lick et
al., 1994; Wang et al, 1996). Application of
SEDZL in Lake Michigan clearly falls in this
latter category; the procedures in place to evaluate
and ensure model credibility are discussed below
(4. Model Calibration/Confirmation and
Uncertainty).
2. Model Parameters and Input Data To run
SEDZL, the following parameters and input data
must be provided: system bathymetry and
boundary geometry, sediment loading rates, wind
and wave boundary conditions, hydrodynamic
parameters (eddy viscosity, bottom friction
coefficient, Nikuradse number), sediment
transport parameters (eddy diffusivity, settling
velocities), sediment bed properties (critical shear
stresses, resuspension flux parameters, and rates
of compaction), and initial size-class distribution.
SEDZL will use the same bathymetry and
boundary geometry data for Lake Michigan as is
being used by POM. The boundary geometry
must be slightly modified to accommodate
differences in the treatment of some shoreline
features by the two models. These include small
islands, small embayments, and narrow
peninsulas. Wind and wave boundary conditions
are input as temporally- and spatially-varying
data, again based upon the same forcing functions
used by POM. Wind fields will be adjusted for
the effects of winter ice-cover, using data
generated by the National Ice Center, Defense
Mapping Agency. Other hydrodynamic and
sediment transport parameters are calculated using
the methods documented in the SEDZL User's
Manual (USEPA, 1997).
Sediment bed properties will be estimated from
resuspension tests conducted on sediment samples
collected in Lake Michigan (Taylor, 1996) and
other locations in the Great Lakes (McNeil et al.,
1996). To estimate the variation in resuspension
properties in sediment, both spatially and with
29
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depth, these parameters will be correlated to
measured properties including bulk density and
grain size (Roberts et al., 1997). In situ testing of
resuspension properties using a submersible,
bottom-resting flume (Hawley, 1991) will provide
additional data for sediment bed parameterization.
Sediment loading rates will be estimated for
sources including shoreline and bluff erosion,
tributary loads, and atmospheric deposition.
Shoreline and bluff erosion rates will be estimated
using the data of Monteith and Sonzogni (1976)
and Colman and Foster (1994). Tributary and
atmospheric loading estimates are being provided
by other LMMBP investigators, as discussed
elsewhere in this Modeling QA Plan. Significant
sediment loadings will be input as temporal
forcing functions to SEDZL.
3. Data Quality - All data used by this project were
collected and manager under strict QA/QC
guidance as documented in several project-related
reports and described above. See "General
Considerations" above.
B. Model Development and Maintenance
1. Code Development and Maintenance The
computer program used to model sediment
transport is based on the SEDZL model developed
by Ziegler and Lick (1986). Refinement of
SEDZL for use on the Great Lakes and for this
Project are documented in the User's Guide
(USEPA, 1997). The code is written in
FORTRAN and follows modern programming
conventions. The 27,000-line SEDZL program is
stored in 42 FORTRAN files and 92 common
block files. Program compilation and linking are
controlled using a Makefile. Development and
maintenance of the SEDZL program is managed
using the RCS operating on Digital UNIX
workstations. RCS forces strict revision control;
supports check-out, locking, and check-in of
individual program files for development; and
maintains history and documentation on all
changes made to each program and common
(include) file.
2. Model and Code Documentation - A User's Guide
for SEDZL (USEPA, 1997) is maintained at
LLRS. All functional changes made to the model
program are incorporated into periodic revision of
the User's Guide. Internal documentation is also
maintained in the header comments of each
program subroutine.
3. Code Verification - The SEDZL model has been
verified using several approaches, including
numerical testing and tests against analytic
solutions (Ziegler and Lick, 1986). The
operation of the SEDZL model has also been
verified through application to at least 10
different water bodies, which have collectively
tested all aspects of model performance.
Input/output data for simulations in several of
these systems have been maintained as
"benchmark" tests which are rerun to confirm
model performance after code modification. In
this study, SEDZL will also be tested by
comparison of vertically-integrated velocity and
sediment bed shear stress predictions, to
comparable predictions made by POM. In
addition, SEDZL sediment resuspension fluxes
will be compared to predictions generated by
sediment transport models employed by the
USACOE-WES, Coastal Engineering Research
Center.
4. Model Calibration/Confirmation and Uncertainty
- Specific data-collection efforts were supported
by the LMMBP for calibration and confirmation
of sediment transport predictions. These
included shipboard sampling of suspended solids
and vertical temperature and transmissivity
profiles, vertical sequencing-collection sediment
trap deployments (to measure bi-weekly particle
settling fluxes), deployments of instrument
arrays to measure vertical water column profiles
of velocity, temperature, and transmissivity,
sediment coring and radiometric analyses (to
measure the particle burial flux in the sediment
bed, the rate of vertical mixing, and the local
sediment focusing factor), and additional
physical (i.e., grain size distribution, water
content) and chemical analyses of surficial (0-1
cm) sediments collected at -180 locations
throughout Lake Michigan. In situ and
30
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laboratory testing of sediment bed resuspension
properties using flume devices have already been
mentioned, as have the source of data for forcing
functions (including wind stresses, surface
waves, and solids loading).
The principal calibration variables in SEDZL are
the particle settling velocities and the sediment
resuspension parameters (critical shear stress and
resuspension parameter) which vary in three-
dimensions within the sediment bed. Initial
estimates for these parameters will be based
upon settling velocities calculated from the
sediment traps, and resuspension parameters
calculated from the flume experiments. Spatial
distribution of resuspension parameters will be
estimated using sediment grain size and water
content as correlating variables. SEDZL
calibration will also be based upon
parameterization from previous applications (as
described in the User's Manual) as well as
parameterization used in other models applied to
lakes and coastal oceans.
Model predictions will be confirmed in both
water column and sediment bed. In the water
column, the spatial and temporal distribution of
suspended solids concentrations will be the
principal confirmation variable. Predicted and
measured settling fluxes will also be compared.
In the sediment bed, the predicted rate and
distribution of solids accumulation will be
compared to the sedimentation rates based upon
core analyses.
Uncertainty in SEDZL prediction of
resuspension fluxes is an important issue, since
we intend to use the resuspension fluxes as
forcing functions in the contaminant transport
and fate model. The two major components of
uncertainty are expected to be errors arising from
use of vertically-integrated hydrodynamics to
compute bed shear stresses, and uncertainty (due
to lack of sufficient measurements) in the
parameterization of sediment resuspension
properties. The first component of uncertainty
will be evaluated by comparing SEDZL shear
stress predictions to those based upon POM,
which computes the three-dimensional
distribution of lake currents. This comparison, in
terms of residual shear stress, can then be
translated into a resuspension flux error. The
second component of uncertainty, the
parameterization of sediment bed resuspension
properties, will be estimated by treating the
parameters as variables in a Monte Carlo
analysis..
C. References
Burban, P.Y., Y.-J. Xu, J. McNeil, and W. Lick.
1990. Settling Speeds of Floes in Fresh Water and
Seawater. J. Geophys. Res., 95:18213-18220.
Colman, S.M. and D.S. Foster. 1994. A Sediment
Budget for Southern Lake Michigan: Source and Sink
Models for Different Time Intervals. J. Great Lakes
Res., 20(l):215-228.
Fukuda, M. and W.J. Lick. 1980. The Entrainment
of Cohesive Sediments in Fresh Water. J. Geophys.
Res., 85:2813-2824.
Gailani, J., K. Pickens, W. Lick, C.K. Ziegler, and D.
Endicott. June 1993. Sediment and Contaminant
Transport in the Buffalo River. Presented at the 36th
Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin. June 4-10, 1993.
Gailani, J.Z., W. Lick, M.K. Pickens, C.K. Ziegler,
and D.D. Endicott. March 1994. Sediment and
Contaminant Transport in the Buffalo River. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 54 pp.
Hawley, N. 1991. Preliminary Observations of
Sediment Erosion from a Bottom Resting Flume. J.
Great Lakes Res., 17(3):361-367.
Lee, D.Y., W.J. Lick, and W.W. Kang. 1981. The
Entrainment and Deposition of Fine-Grained
Sediments. J. Great Lakes Res., 7(3):224-233.
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Lick, W.J., K. Ziegler, and C.-H. Tsai. 1987.
Resuspension, Deposition, and Transport of Fine-
Grained Sediments in Rivers and Near-Shore Areas.
Report to the U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth,
Large Lakes Research Station, Grosse He, Michigan.
94pp.
Lick,W. 1992. The Importance of Large Events. In
Reducing Uncertainty in Mass Balance Models of
Toxic Chemicals in the Great Lakes - Lake Ontario
Case Study, Great Lakes Program, State University
of New York at Buffalo, Buffalo, New York. Donald
W. Rennie Memorial Monograph Series, Great Lakes
Monograph No. 4, pp. 286-307.
Lick, W., J. Lick, and C.K. Ziegler. 1994. The
Resuspension and Transport of Fine-Grained
Sediments in Lake Erie. J. Great Lakes Res., 20:599-
612.
McNeil, J., C. Taylor, and W. Lick. 1996.
Measurements of the Erosion of Undisturbed Bottom
Sediments With Depth. J. Hydraul. Engin.,
122(6) :316-324.
Monteith, T.J. and W.C. Sonzogni. 1976. U.S. Great
Lakes Shoreline Erosion Loadings. Great Lakes
Basin Commission, Ann Arbor, Michigan.
Roberts, J., R. Jepsen, and W. Lick. 1997. Effects of
Bulk Density and Particle Size on Sediment Erosion
Rates. Presented at the 40th Conference on Great
Lakes Research, International Association for Great
Lakes Research, Buffalo State College and
University of Buffalo, Buffalo, New York. June 1-5,
1997.
Taylor, C.L. 1996. Erosion Properties of Great
Lakes Sediments. M.S. Thesis, University of
California, Santa Barbara, California. 101 pp.
Tsai, C.-H. and W. Lick. 1987. Resuspension of
Sediments from Long Island Sound. Water Sci.
Technol., 21(6/7): 155-184.
USEPA. 1997. User's Manual for SEDZL: A Two-
Dimensional Hydrodynamic, Sediment Transport,
and Contaminant Transport Model. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 126
pp.
Wang, K.P., Z. Chroneer, and W. Lick. 1996.
Sediment Transport in a Thermally Stratified Bay. In
- Estuarine and Coastal Modeling, Proceedings of the
Fourth International Conference, pp. 466-477.
Xu, Y.-J. 1991. Transport Properties of Fine-
Grained Sediments. Ph.D. Thesis, University of
California, Santa Barbara, California.
Ziegler, O.K. and W. Lick. 1986. A Numerical
Model of the Resuspension, Deposition, and
Transport of Fine-Grained Sediments in Shallow
Water. Report to the U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse Be,
Michigan. 179 pp.
Hydrodynamic Model Linkage with WASP-IPX
Personnel and contractors at the USACOE-WES have
been assisting USEPA personnel in implementing a
higher-order transport algorithm in their water quality
model (IPX) and developing linkage software between
the POM and IPX.
The model design for the LMMBP is based on a linked
submodel approach, which, in part, includes
hydrodynamics, sediment transport, eutrophication, and
contaminants. It is the desire of the USEPA that the
hydrodynamic model POM and water quality model IPX
linkage task follows the work performed by WQCMB,
EL on the Chesapeake Bay Eutrophication Study. The
objectives of this research project are to: (1) develop and
implement a processor subroutine in the POM
hydrodynamic model to provide input geometry, flow and
diffusion data for the IPX water quality model; (2)
implement and test an ICM-type transport scheme in IPX;
(3) perform linkage testing on simplified and prototype
grids; and (4) document the linkage methodology and
develop a user" s guide. The objective of Task 3 has been
to implement and test ICM transport within IPX. A
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detailed description of the ICM transport methodology
including all input and output operations is presented in
Cerco and Cole (1995).
Linkage and Quality Assurance Testing
The testing of the linkage methodology was performed
utilizing a 20 x 20 x 10 test grid. The POM grid
coordinate and depth file and IPX-MT (IPX-Modified
Transport) map file are the same as those used during
Task 2. The POM hydrodynamic output file was read by
IPX-MT and a volume balance was performed. The
comparison of POM and IPX-MT grid cell volumes were
identical within machine accuracy. ULTIMATE
QUICKEST mass conservation testing was performed
within IPX-MT. Specifically, uniform concentration and
spot dump mass conservation tests were performed. The
first set of mass conservation tests utilized a one hour
time step in POM with no time averaging performed.
This resulted in a two hour hydrodynamic update interval
in EPX-MT. Subsequent mass conservation tests were
performed utilizing six hour average POM flow data,
which resulted in a twelve hour hydrodynamic data
update in IPX-MT. During all tests, mass conservation
was maintained within machine accuracy.
References
Cerco, C.F. and T. Cole. 1995. A User's Guide to the
Ce-QUAL-ICM Three-Dimensional Eutrophication
Model, Release Version 1.0. U.S. Army Waterways
Experiment Station, Vicksburg, Mississippi. Technical
Report EL-95-15.
Mass Balance Water Quality Models
General Considerations for All Mass Balance
Water Quality Models
Project Officer: Douglas Endicott, Kenneth Rygwelski,
and William Richardson, USEPA, LLRS
Principal Modelers: Douglas Endicott, Solids and PCBs;
Kenneth Rygwelski, Atrazine and Mercury; William
Richardson, TNC, Atrazine and Eutrophication
Support Modelers: Xiaomi Zhang, SoBran, Inc.,
Transport, Solids and General Water Quality; James
Pauer, SoBran, Inc., Eutrophication; Victor Bierman,
Limno-Tech, Ecosystem
A. Model Descriptions
A series of mass balance models are being developed
and applied at the USEPA CBSSS. These are
generally referred to as water quality models and
utilize the same basic transport fields based upon
hydrodynamic and sediment transport simulations.
They are dissimilar as they are used to model
different chemicals and, therefore, diverge somewhat
in their fate processes. The four toxic chemicals are:
atrazine, PCBs, mercury and TNC. PCBs and TNC
use the same fate model but use separate fate process
rates.
These models build upon a specified transport
regime. This is being developed independently
(described in Chapter 2, above). A special project
with the USACOE-WES was initiated to assist in
translating the hydrodynamic model velocity and
dispersion field into appropriate input as a forcing
function to the water quality models. In a similar
fashion, sediment resuspension fluxes predicted by
the sediment transport model will be translated into
resuspension rates for bed sediments in the mass
balance models. This, together with specifications of
external loading of solids and settling velocities for
biotic and abiotic particle classes, will establish a
mass balance for solids.
While the solids mass balance is a requirement, it is
not sufficient to fully describe the transport of
particulate chemicals. It is also necessary to simulate
the dynamics of the sorptive phase, which is
generally agreed to be organic carbon. Organic
carbon is non-conservative, with primary production,
transformation, and loss all occurring in the lake.
The dynamics of organic carbon sorbents are
modeled within a eutrophication model framework.
After the solids/sorbent mass balance model is
constructed and calibrated, work can be done to
develop the models for toxic chemicals. The
appropriate processes governing the fate of each
chemical will be considered.
The sections below cover information that apply to
all of mass balance models. Specifics will be noted
as necessary for later sections describing the QA
plans for individual models.
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Background Information The Lake Michigan
Water Quality models are based on the approach
provided in the general USEPA-supported water
quality model, Water Analysis Simulation
Program (WASP). WASP has a long history and
has been developed, applied, and refined over the
past 25 years. It was originally developed by
Dominic Di Toro, Manhattan College, who
received support from USEPA, ORD (Di Toro et
al., 1983). Modelers at the USEPA LLRS began
using the original version of WASP in the mid-
1970s. At that time it only ran via support of
Manhattan College staff on the New York
University (NYU) computer. HydroScience,
Inc., an environmental consulting firm also had
a proprietary version of the model and was
applying it to water bodies throughout the world.
Because WASP was difficult to operate
remotely, LLRS contracted with HydroScience to
formalize the code, document it, and implement
it on the USEPA DEC-POP-11/45 computer at
Grosse He, Michigan. The user manual was
widely distributed and the source code was
transferred to the USEPA, Athens, Georgia
laboratory where it became a public domain
USEPA-supported model at Center for Exposure
and Assessment Modeling (CEAM).
The original WASP models were developed to
simulate water quality state-variables for
dissolved oxygen and eutrophication. In the late
1970's, hybrids of WASP were developed by
Thomann, Di Toro, and Richardson for
simulations of solids and partitioned toxic
chemicals including PCBs. The Manhattan
College version became known as WASTOX and
the USEPA version combining the WASP
chassis with EXAMS processes became known
as ToxiWASP. In the mid-1980's, a project was
funded by ORD to consolidate the best of the
these two versions into WASP-4. Since then
CEAM has revised the model further into
WASP-5. Documentation and user manuals are
available for all of these versions and the
CEAM- supported versions with documentation
can be obtained from the Internet via their
homepage, http://www.epa.gov/CEAM/
ceamhome.htm.
Mass Balance
Models
TNC Mercury Atrazine
Figure 4. Relationship Between Mass Balance Models.
While WASP (and its derivatives) has been the
primary water quality model employed by
USEPA and their cooperators, this framework is
being substantially modified to incorporate
transport solution algorithms from the USACOE
QUAL-ICM model. QUAL-ICM has been
applied by the WES as a eutrophication model in
numerous studies, most notably Chesapeake Bay
and Los Angeles/Long Beach Harbors. QUAL-
ICM is being incorporated in the Lake Michigan
mass balance models for two reasons. First, the
QUICKEST/ULTIMATE (Leonard, 1979,1991)
method for solution of the advective/dispersive
transport components of the mass balance
equation removes restrictions on segmentation
geometry and solution time-step size. These are
quite problematic when applying WASP at the
high resolution intended for this project. Second,
the QUAL-ICM model already incorporates
procedures to read hydrodynamic model-
generated advective and dispersive transport
fields, as well as the necessary mapping
translation between gridded and unstructured
segmentation models.
Mass balance models require specification of
segment geometry; advective and dispersive
transport; boundary concentration for state
34
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variables; point and diffuse source loads; kinetic
parameters; constants, and time functions; and
initial conditions. These input data, together
with the general mass balance equations and the
specific chemical kinetics equations, uniquely
define a special set of water quality equations.
These are numerically integrated as the
simulation proceeds in time. At user-specified
print intervals, values of selected state variables
are saved for subsequent evaluation in
visualization and statistical post-processor
programs.
Advective and dispersive transport fields are
required for the transport sub-model. These will
be specified using input from the hydrodynamic
model, POM, with translation provided by
USACOE-WES. Once specified, transport of
temperature will be used to check the validity.
In addition to the loads for solids, settling and
resuspension rates to and from bottom sediments
must be specified. These will be estimated at
fine scale using a sediment transport model,
SEDZL (see above).
2. Data Quality - All target model analyte (mercury,
PCB congeners, TNC, and atrazine) and most
supporting analytical and in-field data were
collected and analyzed in compliance with an
USEPA approved QAPP. Louis Blume, GLNPO
QA Manager, and a team of QC Coordinators
with specific knowledge, hands-on experience
and training in the analysis of the target analytes
verified the data using a software package
developed by Environment Canada. The
program is called Research Data Management
and Quality Control System (RDMQ) and runs
on a SAS-based platform. The requirements for
the data precision, accuracy, representativeness,
comparability, completeness and sensitivity
contained in each researcher's approved QAPP
are programmed into RDMQ. When the
requirements are not met, the data are flagged
and brought to the attention of the QC
Coordinator for resolution with the researcher.
RDMQ also will allow for reconciliation of field
collection and sample analysis information. The
QC data reported by the researchers such as lab,
trip and field blank contents, lab and field
duplicate results, matrix and surrogate spike
recoveries, reference material results and
calibration check data are assessed during the
RDMQ verification and the QC Coordinator
determines if any noncompliant data affects the
project data. An additional code is added to any
affected data by the QC Coordinator if in their
assessment the data are biased high, biased low,
or invalid. No values are censored before release
to the modelers, they are only flagged.
B. Model Development
1. Code Development and Maintenance - The basis
for the water quality models will be "IPX-MT"
(modified transport) which incorporates QUAL-
ICM advective-dispersive transport with GBTOX
organic carbon sorbent dynamics and IPX solids
transport. These latter models were both
versions of WASP4 developed during the Green
Bay Mass Balance Study (GBMBS) Each of
these models has been checked and documented
(Velleux et al., 1993). The Lake Michigan mass
balance models will use IPX-MT as the initial
chassis. The code will be modified to include
those processes included in GBTOX, a mass
balance model developed and applied for Green
Bay (Bierman etal, 1992; DePinto etal., 1993).
Each chemical-specific model will contain a
unique set of processes in addition to the normal
transport and solids submodels.
The code used in the mass balance water quality
model is written in FORTRAN. Coding is done
using standard programming practices and all
code changes are rigorously checked and
debugged. Development and production code is
maintained at LLRS in the RCS.
2. Model Documentation - A complete description
of the model equations, underlying assumptions,
boundary conditions, and numerical methods are
contained in several user manuals for WASP (Di
Toro et al., 1983; Ambrose et al, 1993) and
QUAL-ICM (Cerco and Cole, 1995). The
revised Lake Michigan mass balance models will
be documented in a final report.
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3. Code Verification The modeler may make the
code changes or may delegate this to a
programmer. For either case, the modeler is
ultimately responsible for the code changes and
should double check all algorithms. The Digital
UNIX FORTRAN compiler provides a variety of
source code error checking, which is also
invoked during model code development and
testing.
Water modeling codes are typically verified with
tests against analytic solutions, against results
from previous versions, and by sensitivity
analyses. Verification of any code is achieved
over the long run by distributing it to other users
for application at other sites. Users are asked to
notify LLRS of any bugs found. The ideal
situation for code verification would be for
independent programmers and modelers to
thoroughly review the equations and codes. This
may be achieved in part by the peer review
process.
4. Code Documentation Modelers and
programmers continuously document their work
within computer programs and in their project
notebooks. Information in this documentation
include a description of the change, date of
change, and name of person making the change.
As model computer programs are developed, user
manuals will be prepared as the formal
documentation.
5. Model Calibration/Verification and Uncertainty -
The general validity of mass balance models can
be judged according to their track record of
simulating measured conditions and predicting
future conditions. Several "post-audit" studies
have been done for the eutrophication models (Di
Toro et al., 1987; Bierman and Dolan, 1986) and
one post-audit was conducted for the Saginaw
Bay PCB model (Endicott and Kandt, 1994). A
post-audit was performed by comparing
predicted concentration to independent
measurements. The post-audit studies, although
not perfect, show a reasonable level of credibility
for mathematical models.
The Lake Michigan mass balance models will be
calibrated by comparing computed concentration
for appropriate spatial segments to appropriately
averaged field data. The model will be
considered calibrated when the calculated
concentration representing spatially averages in
time compare within one standard error of the
data volume weighted average by cruise.
Once comparable to field data, the model will be
valid within the error constraints specified for the
calibration period. However, the question of
uncertainty remains for the predicted future
concentration. For the predictions, the model
will be run for various scenarios of inputs,
boundary conditions, and process rates bracketed
in terms of extreme expectations and probability
distributions. The results will be provided in
terms of prediction means and exceedence limits.
Model results will also be qualified according to
the any explicit and implied assumptions made in
developing or applying the model. It is expected
that the "science review panel" will also provide
caveats for the model results. Managers will
have to decide whether or not to use the model
results and whether or not to conduct additional
research to improve the models. This is a
continuing process.
C. References
Ambrose, R.B., T.A. Wool, and J.L. Martin. 1993.
The Water Quality Analysis Simulation Program,
WASP5 Part A: Model Documentation. U.S.
Environmental Protection Agency, Office of
Research and Development, Center for Exposure and
Assessment Modeling, Athens, Georgia.
Bierman, V.J. Jr. and D.M. Dolan. 1986. Modeling
of Phytoplankton in Saginaw Bay: n. Post-Audit
Phase. J. Environ. Engin., 112(2):415-429.
36
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Bierman, V.J., Jr., J.V. DePinto, T.C. Young, P.W.
Rodgers, S.C. Martin, and R. Raghunathan.
September 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.
Cerco, C.F. and T. Cole. 1995. User's Guide to the
CE-QUAL-ICM Three-Dimensional Eutrophication
Model. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Technical Report EL-95-15.
DePinto, J.V., R. Raghunathan, P. Sierzenga, X.
Zhang, V.J. Bierman, Jr., P.W. Rodgers, and T.C.
Young. December 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 He, Michigan. 132
pp.
Di Toro, D.M., J.J. Fitzpatrick, and R.V. Thomann.
May 1983. Documentation for Water Quality
Analysis Simulation Program (WASP) and Model
Verification Program (MVP). U.S. Environmental
Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research
Station, Grosse He, Michigan. EPA-600/3-81-004,
145 pp.
Di Toro, D.M., N.A. Thomas, C.E. Herdendorf, R.P.
Winfield, and J.P. Connolly. 1987. A Post Audit of
a Lake Erie Eutrophication Model. J. Great Lakes
Res., 13(4): 801-825.
Endicott, D.D. and D.J. Kandt. 1994. Assessment
and Remediation of Contaminated Sediments
Remedial Action Modeling (ARCS/RAM) Work
Group Far Field Models for Buffalo and Saginaw
Rivers and Food Chain Bioaccumulation Model for
Saginaw River/Bay. U.S. Environmental Prection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse He,
Michigan. 126 pp.
Leonard, B.P. 1979. A Stable and Accurate
Convective Modelling Procedure B ased on Upstream
Interpolation. Comp. Methods Appl. Mechan.
Engin., 19:59-98.
Leonard, B.P. 1991. The ULTIMATE Conservative
Difference Scheme Applied to Unsteady One-
Dimensional Advection. Comp. Methods Appl.
Mechan. Engin., 88:17-74.
Velleux, M.L., J. Gailani, F. Mitchell, and D.
Endicott. October 1993. In-Place Pollutant Export
Model (IPX): User's Guide and Description of
Modifications Beyond TOXI4LFR. Report to the
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan, 3 pp.
Phytoplankton Solids/Eutrophication Model
Principal Investigators: William Richardson and Douglas
Endicott, USEPA, LLRS
Contract Support Modeler: James Pauer, SoBran, Inc.
A. Model Description
1. Background Information Modeling
eutrophication and phytoplankton solids in lakes
are a complex issue which can be approached on
many levels. The extent and complexity of the
modeling exercise depends on the time and
manpower resources available, and the quality
and quantity of the data. Historical
eutrophication models range from simplistic
empirical models, e.g., predicting phytoplankton
chlorophyll as a function of the total phosphorus
concentration (Dillon and Rigler, 1974) to "state-
of-the-art", multi-class, multi-segmentation
models with sophisticated kinetic and transport
processes (Bierman and McHroy, 1986; Cerco
and Cole, 1995).
Several models have been developed for the
Great Lakes, including Lake Michigan and Green
Bay. These are mass balance models (Lake-1,
originally developed in the mid-70's by the
Manhattan Group, Thomann et al., 1975 and
applied to many Great Lakes Systems, Di Toro
and Connolly, 1980; Rodgers and Salisbury,
37
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1981) which fundamentally calculated
concentrations of phytoplankton and zooplankton
biomass as a function of lake nutrient levels.
The MICH1 model (Rodgers and Salisbury,
1981) was developed specifically to describe
eutrophication and phytoplankton production in
Lake Michigan and was verified with an
extensive water quality database from 1976-77
(which is now available in STORET). These
models reflected the state of eutrophication
theory at the time, and were published in the
primary literature for review by the scientific
community. In recent years, several advances
have been made in eutrophication modeling such
as variable phosphorus stoichiometry, internal
nutrient pool kinetics, sophisticated sediment
submodels, incorporation of multi-phyto- and
zooplankton classes as well as zebra mussels.
These improvements have been incorporated into
models such as WASP5 (Ambrose et al, 1993),
The Saginaw Bay Multi Class Model (Bierman
and Mcllroy, 1986), and CE-QUAL (Cerco and
Cole, 1995) which has been applied to several
systems, including the Great Lakes.
2. Model Equations, Systems, and Parameters
Modeling eutrophication involves estimating
biomass (phyto- and zooplankton) as a function
of nutrients which are present in the lake in
different domains (water column and sediments),
oxidation states, and forms (particulate or
dissolved). The systems include diatoms, other
algae, zooplankton, soluble reactive phosphate,
particulate and dissolved organic phosphate,
ammonium, nitrate, particulate and dissolved
nitrogen, dissolved and biogenic silica.
Equations have been formulated to describe the
biochemical transformation reaction between the
different systems. Phytoplankton production can
be modeled based on traditional growth kinetics
which is dependent on nutrient levels, light
intensity, temperature, and water turbidity
(Thomann et al., 1975; Chapra, 1997). The
nutrient dependency is usually modeled
according to Monod kinetics, a semi-empirical
equation (Monod, 1942) applied to a multitude
of lake and river eutrophication models over
many decades (Thomann et al., 1975; Cerco and
Cole, 1995). Predation and mineralization are
described according to "commonly accepted"
eutrophication theories (Di Toro and Connolly,
1980), which have stood the test of time.
Since several equations are used to describe the
nutrient/plankton interactions, a large number of
model parameters have to be estimated, including
rate coefficients for algal growth and death,
predation by zooplankton, and mineralization of
organic nitrogen and phosphorus. Coefficient
values are also required to accommodate and
describe temperature and light interactions,
sediment diagenesis and transport such as
settling and resuspension. A complete list of
model parameters is too large to include here.
These details are contained in the model and
code documentation (Thomann et al., 1975; Di
Toro and Connolly, 1980; Rodgers and
Salisbury, 1981; Ambrose etal., 1993) as well as
textbooks and other documents (Chapra, 1997;
Thomann and Mueller, 1987; Bowie etal., 1985)
as listed in the references below and are available
for inspection at the LLRS.
3. Data Quality - Historical data are obtained from
several sources, primarily STORET. STORET
contains all of the GLNPO's historical data for
Lake Michigan. Records extend back to 1961.
The quality of this information is, to a large
extent, unknown. However, all historical data
will be screened for reasonableness before use.
If questions arise, attempts will be made to
contact the originating laboratory. The final
model calibration and verification will be done
using the 1994-95 project data which will pass
through an intense QA/QC protocol. A
limitation of the eutrophication modeling design
is the limited number of specific laboratory and
field studies done to estimate the large number of
model coefficients. It is, therefore, necessary to
depend on literature values for the different
parameters. Coefficients will be obtained mainly
from historical studies performed in Lake
Michigan and the other Great Lakes. Care will
be taken to selectively use coefficient values
from credible sources such as NOAA, GLERL
and University of Michigan. Further refinement
of these coefficients will be done during the
calibration process. This process will be
38
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documented in detail in the accompanied report
for the LMMBP modeling study.
B. Model Development
1. Code Development and Maintenance The
development of a eutrophication model for Lake
Michigan will take place in phases. The first
phase will begin by resurrecting historical
eutrophication models for Lake Michigan and
Green Bay, as described in the Background
Section. These historical models were generally
developed as tools to predict the phytoplankton
standing crop (as chlorophyll a) and its impact
on water quality in terms of transparency
(aesthetics), dissolved oxygen, taste and odor.
The shift of the emphasis of this modeling
project is to estimate the autochthonous
phytoplankton solids, expressed as organic
carbon, which will be used in a sorbent dynamic
model for hydrophobic toxic chemicals. Figure
5 shows a diagram of the dynamics of the
phytoplankton and detrital carbon in the lake. In
brief, the extent of growth of the phytoplankton
and subsequent phytoplankton solids
concentration is a function of the nutrient levels
and is mediated by meteorological conditions,
such as temperature and solar radiation, as well
as water turbidity. In addition, it is affected by
rates of settling of the phytoplankton species,
higher predation by the zooplankton, and
sediment-water interactions. Code modifications
will be done to adapt the models to incorporate
this shift of emphasis, as well as to be
specifically used for modeling eutrophication in
Lake Michigan.
Water
Temperature
Ligh4
Phosphorus
Silica
Nitrogen
(C02)
Growth
Sediments
Higher Predation
Zooplankton
(carbon)
Grazing
"Non-diatoms"
(carbon)
Diatoms
(carbon)
Settling
Die-off/Decay
Particulate detrital
decay
Dissolved organic
carbon
Figure 5. Phytoplankton and Detrital Carbon Dynamics in Lake Michigan.
39
-------
The next phase will be the development of a
model (or modification of an existing
framework) which incorporate recent "state-
of-the-art" eutrophication kinetics, transport
and water-sediment interactions. Two
frameworks are considered: the existing IPX
framework originally developed at the
USEPA LLRS for modeling toxic chemicals
in the Fox River (Velleux et al, 1994), and the
CE-QUAL-ICM framework developed by the
USCOE (Cerco and Cole, 1995). Proposed
features of the model will be including the
simulation of multiple phyto- and zooplankton
species, hydrodynamics on a small grid size
and sediment-water interactions. Both models
have a few limitations and further
development and code modifications will be
performed. The eutrophication model will be
applied to both the 41 water column
segmentation scheme (as used by some of the
other models) and the ultimate higher order
multi-segmentation grid. It is foreseen that
the 41 segmentation model will be based on
the CE-QUAL framework, while the IPX
model will be used for the multi-segmentation
model. Output from the two models will be
compared, which will improve the credibility
of both frameworks. Code development will
be done using the RCS code management tool
and all changes to the code will be
documented as much as possible within the
code, as well as in a subsequent report or
paper. Specifics for any new model(s) or
modifications will be incorporated into this
QA plan as they are finalized.
2. Model Documentation - The calibrated, verified,
and tested model(s) will be documented as a
technical report and/or scientific paper. This
will include the description of the basic
assumptions, fundamental equations, and model
coefficients. In addition, all
changes/improvements to the model framework
will be documented in detail.
3. Model Validation and Uncertainty Analysis
The models will be validated during (a) the
development and testing period, as well as (b)
verification of the final code using field (project)
data.
(a) The modifications to the models will be
tested against the original equations. In
addition, output from the modified models
will be compared to the original or similar
models.
(b) The models will be calibrated using a field
data set and adjustments will be made to the
model to "fit-the-data". An independent
data set (both sets probably using the 1994-
95 project data) will be used to verify the
model. Uncertainty analysis is an important
issue when modeling eutrophication since
there are so many degrees of freedom
(independent coefficients that have to be
estimated). A number of techniques are
available to determine the sensitivity of
these parameters on the overall model
output, and the uncertainty and error
involved (e.g., Monte Carlo analysis). A
suitable technique(s) will be selected and
used to evaluate the model.
C. References
Ambrose, R.B., T.A. Wool, and J.L. Martin. 1993.
The Water Quality Analysis Simulation Program,
WASPS Part A: Model Documentation. U.S.
Environmental Protection Agency, Office of
Research and Development, Center for Exposure and
Assessment Modeling, Athens, Georgia.
Bierman, V.J., Jr. and L.M. Mcllroy. 1986. User
Manual for Two-Dimensional Multi-Class
Phytoplankton Model with Internal Nutrient Pool
Kinetics. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth,
Large Lakes Research Station, Grosse He, Michigan.
EPA-600/3-86-061, 149 pp.
40
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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 D.C.E.
Chamberlain. 1985. Rates, Constants and Kinetic
Formulations in Surface Water Quality Modeling
(2nd Edition). U.S. Environmental Protection
Agency, Office of Research and Development,
Center for Exposure and Assessment Modeling,
Athens, Georgia. EPA-600/3-85-040.
Cerco, C.F. and T. Cole. 1995. User's Guide to the
CE-QUAL-ICM Three-Dimensional Eutrophication
Model. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Chapra, S.C. 1997. Surface Water Quality
Modeling. McGraw-Hill Publishers Companies, Inc.,
New York, New York.
Dillon, P.J. and F.H. Rigler. 1974. The Phosphorus-
Chlorophyll Relationship in Lakes. Limnol.
Oceanogr., 19(4):767-773.
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. EPA-600/3-80-065, 97 pp.
Monod, J. 1942. Recherches sur la Croissance des
Cultures Bacteriennes. Paris, France.
Rodgers, P.W. and D.K. Salisbury. 1981. 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. Winfield, and D.J.
O'Connor. 1975. Mathematical Modeling of
Phytoplankton in Lake Ontario. 1. Model
Development and Verification. U.S. Environmental
Protection Agency, Office of Research and
Development, ERL-Corvallis, Large Lakes Research
Station, Grosse He, Michigan. EPA-660/3-75-005,
178 pp.
Thomann, R.V. and J.A. Mueller. 1987. Principles
of Surface Water Quality Modeling and Control.
Harper and Row Publishers, New York, New York.
Velleux, M., J. Gailani, and D. Endicott. 1994. A
User's Manual to IPX, The In-Place Pollutant Export
Water Quality Modeling Framework. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 194
pp.
Atrazine Water Quality Model
Principal Modelers: Kenneth R. Rygwelski and William
L. Richardson, USEPA, LLRS
A. Model Description
1. Background Information As a precursor to
atrazine models for the LMMBP, a screening-
level WASP-based mass balance model (Endicott
et al., 1992) was developed to gain an initial
insight to the chemical's behavior in the basin.
This screening model utilized historical data that
pre-dates the LMMBP data set. The results of
this screening-level model strongly suggest that
atrazine is not degrading in the large, cold,
oligotrophic waters of Lake Michigan, and this
conclusion is similar to some other lakes reported
in the literature. While volatilization, associated
with solids, and kinetic degradation are probably
active in this lake, their overall affect on
transport and fate of atrazine is suspected to be
minimal according to a literature review. Lake
Michigan MICHTOX runs from 1964 to 1993
indicate that the lake is steadily increasing in
atrazine concentration. Model results fall within
a range of +/- one standard deviation about the
mean of the field data from 1991 and 1992.
Ongoing modeling efforts will address atrazine
transport and fate in Lake Michigan utilizing all
of the atrazine data associated with the LMMBP
including atrazine degradation products,
deisopropylatrazine and deethylatrazine. This
high resolution model will include processes
such as volatilization and, perhaps, association
with solids. WASP-based models will be used.
41
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2. Model Parameters - A generic description of the
types of model parameters to be used by the Lake
Michigan Project models was described in above
and are applicable to the atrazine models. The
screening-level atrazine model described above
and a thorough literature search on atrazine
processing in freshwater helped in the QA
process by identifying parameters that are
important to the model predictions. Tributary
loadings of atrazine and the degradation
products, deethy latrazine and
deisopropylatrazine are very significant
parameters to the model. Also, loadings in the
form of wet precipitation (rain and snow) are
important. Parameters associated with processes
such as volatilization, association with solids,
accumulation in biota, and kinetics in-situ
degradation are of much less importance in
model predictions, because they are likely to be
minor processes.
Model parameters that are unique to the atrazine
model are watershed-type information:
Watershed Export Percentage, hydrologic soil
type; atrazine application rates to corn by county,
by year in the basin; county size; fraction of
county in the Lake Michigan basin; and corn
acreage by county. Also, total annual atrazine
usage in the United States is used to estimate
historical loadings of atrazine in Lake Michigan.
3. Data Quality Atrazine, and its metabolites
deethylatrazine and deisopropylatrazine will be
measured in the lake and tributaries by
Eisenreich and Rutgers. Hites and Ilora of
Indiana University and Sweet of Illinois State
Water Survey will measure atrazine in the air
vapor phase, wet precipitation, and dry
particulate (air). Both total and dissolved forms
will be measured. All of these data will be
collected under USEPA QAPP, and will be QA-
reviewed by Louis Blume of USEPA GLNPO
with assistance from contractor staff. A QA-
review data software package called RDMQ will
assist in the QA review process according to
requirements of the approved QAPP's associated
with each parameter. RDMQ is owned by
Environment Canada, Atmospheric Environment
Service, Ontario, Canada. RDMQ runs under the
SAS software system that allows: data
visualization, corrects data (e.g., blanks, etc.),
user-defined outlier checking, auditable trail of
data changes, system-generated reports
documenting the data quality flags, and handles
checks on blanks, lab QC samples, matrix spike
samples, duplicate samples, splits, composites,
detection limit, etc. Only data that has passed
GLNPO's data review process will be used in the
atrazine model.
Some of the data that will be used in the models
will not be processed by RDMQ, because the
data were collected prior to the Lake Michigan
Project by other researchers. For instance,
Watershed Export Percentages were obtained
from peer-re vie wed journal articles. Total annual
United States atrazine usage was obtained from
Arnold Aspelin, USEPA, Biological and
Economics Analysis Division. Soil hydrologic
types were obtained from William Battalin of
USGS. Data on corn acreage, application rates
of atrazine by county, and other agricultural data
were obtained from Bruce Kirshner, UC.
B. Model Development
1. Code Development and Maintenance - The code
used in the atrazine model is based on the
FORTRAN code of the WASP-IPX model as
described by Velleux etal. 1994. The adaptation
to Lake Michigan is based on both WASP-IPX
and GBTOX used for the GBMBS (Bierman et
al., 1992).and subsequent incorporation of
QUAL-ICM advective-dispersive transport
solution.
2. Model Documentation The basic model
equations, underlying assumptions, and
numerical methods are documented in the
WASP-IPX model documentation, the GBTOX
report (Bierman et al., 1992), and the QUAL-
ICM user's guide (Cerco and Cole, 1995).
Modifications for the revision for Lake Michigan
will be included in an updated documentation
report. Interim documentation will be
maintained within computer program code and
the programmer's notebooks and electronic files.
42
-------
3. Code Verification - See "General Considerations
for All Mass Balance Water Quality Models"
above.
4. Code Documentation See "General
Considerations for All Mass Balance Water
Quality Models" above.
5. Model Calibration/Validation and Uncertainty -
See "General Considerations for All Mass
Balance Water Quality Models" above for
information on this topic. Also, the following
text highlights some other aspects of model
calibration/validation for atrazine modeling in
Lake Michigan.
As mentioned earlier in this section, a screening
level model of atrazine was developed for Lake
Michigan based on historical data from the
literature. This model helped identify a model
approach and provided some insight into
processes that may be important when the
atrazine model based on Lake Michigan project
data are used. The screening model identified
tributary loadings and precipitation loadings as
being the most important in terms of impacting
lake concentrations. Within the tributary load
estimates, one of the most important factors was
the Watershed Export Percentage.
Also, the screening model results suggest that
kinetic decay, association with solids, and
volatilization are not important in Lake
Michigan, because the model was able to predict
lake concentrations with export of mass
associated with flows out as the only major
operative loss mechanism. The processes that
control most of the model output will also be
those that will have the most effect on overall
model uncertainty.
Tributary loadings for 1995 will be estimated by
both actual measurement of flows and
concentrations of atrazine at the mouths of major
tributaries leading to Lake Michigan. Also,
tributary loadings to the lake will be estimated
based on algorithms that utilize information such
as total annual United States usage and
Watershed Export Percentages. Comparing these
two results helps verify loadings data.
Three estimates of precipitation loadings should
be available in the project: actual measured
fluxes based on a sampling program; fluxes
predicted by an air model component of the
project; and estimates based on total annual
usage. Data from all three of these estimates will
be compared and help in the model validation
process.
C. References
Bierman, V.J., Jr., J.V. DePinto, T.C. Young, P.W.
Rodgers, S.C. Martin, and R. Raghunathan.
September 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.
Cerco, C.F. and T. Cole. 1995. User's Guide to the
CE-QUAL-ICM Three-Dimensional Eutrophication
Model. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Endicott, D.D., W.L. Richardson, and D.J. Kandt.
1992. MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in Lake
Michigan. Internal Draft Report. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 183
pp.
Velleux, M., J. Gailani, and D. Endicott. 1994. A
User1 s Manual to IPX, The In-Place Pollutant Export
Water Quality Modeling Framework. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 194
pp.
43
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Mercury Model
Principal Modeler: Kenneth R. Rygwelski, USEPA,
LLRS
A. Model Description
1. Background Information Ongoing modeling
efforts will address mercury transport and fate in
Lake Michigan utilizing all of the mercury data
associated with the LMMBP. A literature review
has been completed on mercury cycling in lakes,
and as a result of this investigation, the Lake
Michigan mercury model will likely include
processes such as volatilization of Hg(0);
tributary loads of Hg(2+) and methyl mercury;
and atmospheric inputs of Hg(2+). Processes
involving Hg(2+) and methyl mercury with biotic
and abiotic solids will be important. The Hg(2+)
and methyl mercury species form complexes
with a number of anions that are present in the
water, and those that are especially important in
Lake Michigan are neutral complexes with the
chloride and hydroxyl ions. The relative
abundance of these inorganic anions is important
in predicting the overall mix of the complexed
mercury species. This prediction is
accomplished using an equilibrium speciation
model such as MINTEQA2
(ftp://ftp.epa.gov/epa.ceam/ wwwhtml/
minteq.htm). It is important to understand the
composition of the mercury complexes in Lake
Michigan because the overall observed chemical
properties of mercury is dictated by this
composition. For example, the overall octanol-
water partition coefficient for Hg(2+) is strongly
dependent on the actual complexes of Hg(2+)
present in the lake. The octanol-water partition
coefficient (Kow) for Hg(Cl)2 is 3.33, whereas the
Kow for Hg(OH)2 is 0.05. Studies have shown
that mercury uptake by diatoms is a function of
the overall Kow of the particular mercury species
that is present. Higher overall Kow's result in
higher uptake (Mason et ai, 1996).
2. Model Parameters - A generic description of the
types of model parameters to be used by the Lake
Michigan Project models was described above
and are applicable to the mercury models. In
addition, data on the various forms of mercury
Hg(2+), Hg(0), and methyl mercury will be
needed. Chloride concentrations and pH of the
lake water will also be required to assess
complexation of the various mercury species.
3. Data Quality - Total and dissolved mercury will
be measured in the lake and tributaries by Mason
of University of Maryland and Hurley of
University of Wisconsin, Water Chemistry
Laboratory, respectively. Dr. Gerald Keeler of
the University of Michigan will be providing
data on mercury in precipitation (wet and dry)
and vapor phase concentrations. Rossmann of
USEPA LLRS, will be measuring mercury in
sediments. All of these data were collected
under USEPA QAPP and will be QA-reviewed
by Louis Blume of USEPA GLNPO with
assistance from contractor staff. A QA-review
data software package called RDMQ will assist
in the QA review process according to
requirements of the approved QAPP's associated
with each parameter. RDMQ is owned by
Environment Canada, Atmospheric Environment
Service, Ontario, Canada. RDMQ runs under the
SAS software system that allows data
visualization, corrects data (e.g., blanks, etc.),
user-defined outlier checking, auditable trail of
data changes, system-generated reports
documenting the data quality flags, and handles
checks on blanks, lab QC samples, matrix spike
samples, duplicate samples, splits, composites,
detection limit, etc. Only data that has passed
GLNPO's data review process will be used in the
mercury model.
B. Model Development
1. Code Development and Maintenance - The code
used in the mercury model is based on the
FORTRAN code of the WASP-IPX model as
described in Velleux et ai, 1994. The
adaptations to Lake Michigan is based on both
WASP-IPX, GBTOX used for the GBMBS
(Bierman et al., 1992) and subsequent
incorporation of QUAL-ICM advective-
dispersive transport solution.
44
-------
2. Model Documentation The basic model
equations, underlying assumptions, and
numerical methods are documented in the
WASP-IPX model documentation, the GBTOX
report (Bierman et al., 1992) and the QUAL-ICM
user's guide (Cerco and Cole, 1995).
Modifications for the revision for Lake Michigan
will be included in an updated documentation
report. Interim documentation will be
maintained within computer program code and
the programmer's notebooks and electronic files.
3. Code Verification - See "General Considerations
for All Mass Balance Water Quality Models"
above.
4. Code Documentation See "General
Considerations for All Mass Balance Water
Quality Models" above.
5. Model Calibration/Validation and Uncertainty -
See "General Considerations for All Mass
Balance Water Quality Models" above. Also, the
following text highlights some other aspects of
model calibration/validation for mercury
modeling in Lake Michigan.
In order to gain an initial insight to mercury
cycling in Lake Michigan, a screening-level
model will be developed. This screening model
will include volatilization, association with
solids, and mass gain due to precipitation and
tributary loadings. Mass loss with flow out of
Lake Michigan will also be included. This
model will have low spatial and temporal
resolution. MINTEQA2 will be used outside of
the construct of the mass balance model to gain
an understanding on the composition of the
various mercury species complexes on a range of
expected pH and chloride concentrations in the
lake. Hopefully, this screening-level exercise
will identify important factors controlling model
predictions. Since very little mercury speciation
was analyzed, assumptions on likely predominant
species in the various model components will
need to be made. In addition, the significance of
methylation/demethylation reaction rates for
mercury in water, suspended solids, and bed
sediments must be evaluated.
C. References
Bierman, V.J., Jr., J.V. DePinto, T.C. Young, P.W.
Rodgers, S.C. Martin, and R. Raghunathan.
September 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 lie, Michigan. 381
pp.
Cerco, C.F. and T. Cole. 1995. User's Guide to the
CE-QUAL-ICM Three-Dimensional Eutrophication
Model. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Mason, R.P., J.R. Reinfelder, and F.M.M. Morel.
1996. Uptake, Toxicity, Trophic Transfer of
Mercury in a Coastal Diatom. Environ. Sci.
Technol., 30:1835-1845.
Velleux, M., J. Gailani, and D. Endicott. 1994. A
User's Manual to IPX, The In-Place Pollutant Export
Water Quality Modeling Framework. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 194
pp.
PCB/TNC Model
Principal Modeler: Douglas Endicott and William
Richardson, USEPA, LLRS
Support Modeler: Xiaomi Zhang, SoBran, Inc.
A. Model Description
1. Background Information PCBs have been the
subject of considerable research since their
discovery in the Great Lakes ecosystem in the
early 1970's. The first models of toxic chemical
transport and fate were developed for the Great
Lakes by Thomann and Di Toro (1983). This
framework was also applied to Saginaw Bay
(Richardson et al., 1983) which was the first
attempt to calibrate a model to a synoptically
collected dataset for PCBs. A screening level
model was developed for PCBs and other toxic
45
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chemicals in Lake Michigan to provide insights
on program design and research needs (Endicott
etal., 1992). A major step in improving the PCB
modeling framework was taken during the
GBMBS. The basic WASP transport and fate
framework was revised to include more detailed
processes involving particulate fractions
(Bierman et al. 1992, DePinto et al., 1993a).
This model is referred to as GBTOX. In the
same project, WASP4 was also modified to
improve the simulation of sediment transport,
based upon process research and modeling of
settling and especially resuspension processes in
the Fox River (Velleux et al., 1994). This model
was named IPX. As described previously in this
Plan, the transport and fate model for toxic
chemicals in Lake Michigan will be based upon
a combination of features taken from these
models. Each of these models has been
developed to simulate the transport and fate of
PCBs, which serve as model chemicals for a
class of semi-volatile, hydrophobic toxic
chemicals which also includes TNC.
2. Model State Variables and Parameters PCBs
will be modeled as 34 individual congener peaks
(half of which comprise two coeluting
congeners). These congeners were selected based
upon their detection across all or most media
sampled; they are listed in Table 1. In a review
of preliminary data from the Project, the 34
congener peak concentration sum was found to
be greater than 50% of the total PCB
concentration in air vapor, precipitation, lake
water, suspended solids, sediment,
phytoplankton, and lake trout. Therefore, we feel
confident that model predictions of total PCB
concentration can safely be extrapolated from the
congener-specific results. The list of congeners
selected for modeling may be revised, as
necessary upon examination of the full database.
Other than the PCB congener and TNC toxic
chemical state variables, the model will also
include three organic carbon sorbents: biotic
carbon (BIC), particulate detrital carbon (PDC),
and dissolved carbon (DOC). The autochthonous
(internal) loading of BIC and DOC are derived
from the eutrophication model simulation.
Table 1. PCB Congener Peaks Selected for Transport
and Fate (Mass Balance) and Bioaccumulation Modeling.
IUPAC
Homolog
Comment
3
6+5
12+13
15+17
16+32
18
26
31
33
37
44
49
52
56+60
66
70+76
74
77+110
81
84+92
99
101
118
123+149
132+153
151
163+138
170+190
172+197
180
182+187
195+208
196+203
201
Mono
di
di
di/tri
tri
tri
tri
tri
tri
tri
tetra
tetra
tetra
tetra
tetra
tetra
tetra
tetra/penta
tetra
penta
penta
penta
penta
penta/hexa
hexa
hexa
hexa
hepta
hepta/octa
hepta
hepta
octa/nona
octa
octa
mono-ortho
coplanar
coplanar
coplanar
mono-ortho
mono-ortho
46
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External loading of toxic chemicals, categorized
as tributary loading, atmospheric wet deposition,
and dry particle deposition, as well as external
loading of organic carbon sorbents, will be
estimated from the project data by the LMMBP
Atmospheric Modeling and Loading
Workgroups. Lake boundary and initial
condition concentrations will be computed from
project data, and will be verified by model
calibration results. Atmospheric vapor-phase
boundary conditions will be calculated by the
Atmospheric Modeling Workgroup.
Transport parameterization includes specification
of advective and dispersive water column
transport, pore water diffusion, vertical particle
transport, and sediment bioturbation. Advective
and dispersive transport will be based upon
results of hydrodynamic model simulations. This
input will be confirmed using conservative tracer
and temperature simulations. Particle transport
parameters include settling and resuspension
velocities. Particle-class specific settling
velocities will be calculated from sediment trap
data, while resuspension velocities will be based
upon resuspension flux simulations from
SEDZL. Sediment bioturbation will be
calibrated to radionuclide profiles measured in
sediment cores. Sediment core data will also
provide particle burial rates, which will be used
to confirm the rates of burial independently
computed by the model as the difference between
settling and resuspension.
Parameters used to describe the dynamics of the
organic carbon sorbents include the rates and
yield of organic carbon transformation between
state variables (including temperature
dependence) in both water column and sediment,
the rates of organic carbon mineralization, and
the diffusion rates for DOC within the sediment
and at the sediment-water interface. A general
strategy for calibration of these parameters was
developed during the GBMBS (DePinto et al.,
1993b). If possible, however, these parameters
will be coupled to the corresponding parameters
within the eutrophication model simulation.
Chemical-specific processes include partitioning
between aqueous and organic carbon sorbents,
and volatile exchange between the surface water
and atmosphere. The model will describe
chemical partitioning between dissolved and
particulate sorbent compartments, including
multiple particle types, using an organic carbon-
based equilibrium assumption. Both local
equilibrium and first-order kinetic partitioning
process descriptions will be tested in the model.
Upon the recommendation of the Atmospheric
Modeling Group, the volatilization formulation
described by Hornbuckleef al. (1995,1997) will
be applied. Forcing functions from the
hydrodynamic model input will be used to
compute local volatilization rates in the transport
and fate model. Henry's constant for each
chemical will be based upon literature review,
and will be adjusted for surface water
temperature. Chemical transformation by biotic
or abiotic reactions, is assumed to be negligible
for PCBs and TNC.
Rates will be specified initially from literature
values and previous modeling studies. They may
be adjusted during model calibration. The
specific parameters and detailed description are
contained in the references listed below.
3. Data Quality - The data used will extracted from
the project database which will have been
thoroughly checked as described in the general
section above. Initial estimates for model
parameters will be obtained from the literature as
well as prior modeling applications. Parameter
values adjusted during calibration must pass a
test for reasonableness, including falling within
a range of "probable" values.
B. Model Development
1. Code Development and Maintenance - IPX-MT
is coded in ANSI standard FORTRAN 77, with
subroutines and common variable blocks stored
in separate source and include files. UNIX
Makefile is maintained for program compilation.
The FDCHAIN source code and all associated
files are maintained using the Digital UNIX
47
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RCS. Code modifications to the model will be
done in-house at the LLRS.
2. Model Documentation - Model documentation is
provided in a series of reports and publications
cited above. A User's Guide, based upon
Velleux and Endicott (1994), is maintained at
LLRS. As the model program is revised and
modified, updated documentation is added to the
User's Guide.
3. Code Verification - Code changes are carefully
done according to appropriate process
information. Codes are checked and results will
be compared to hand calculations. Modifications
made to the model will be verified by first testing
against results from the original version to ensure
proper function of the code. Testing will then
verify the performance of new or revised model
features. Details of testing performed on model
revisions will be recorded and retained within
modeler notebooks.
4. Code Documentation IPX-MT code has been
internally documented, by its original developers
and by programmers and modelers at LLRS. The
history of revisions to the model code is
maintained, both as chronological entries within
the header comments of each file and within
RCS. Details will also be retained within project
modeler/programmer notebooks.
5. Model Calibration/Validation and Uncertainty -
Comparison of observed and predicted chemical
concentrations in water, suspended solids, and
sediment serves as the basis for model
calibration and confirmation. These comparisons
will include two- and three-dimensional
visualization of concentration predictions and
residuals, as well as conventional calibration
plots of predictions and residuals as functions of
time and depth. Model goodness-of-fit will be
evaluated for individual observations as well as
for spatial averages of data comparable to model
segmentation.
The database and modeling design are
constrained so that the primary chemical-specific
process requiring parameter calibration is
partitioning. Initial values of chemical-specific
organic carbon partition coefficients (Koc) will be
based upon averages calculated from the data.
At the same time, variation in Koc due to
explanatory variables such as season, depth,
organic carbon source and composition, and
disequilibria, will be evaluated. This information
will guide refinement of partitioning parameters
during model calibration.
Once comparable to field data, the model will be
valid within the error constraints specified. The
question of uncertainty will remain for the
predicted future concentrations. For the
predictions the model will be run for various
scenarios of inputs, boundary conditions, and
process rates bracketed in terms of extreme
expectations and probability distributions. The
results will be provided in terms of confidence
levels about the most probable.
Model results will also be qualified as all models
are simplifications of the real system and contain
many explicit and implied assumptions. It is
expected that the "science review panel" will
also provide caveats for the model results and
include recommendations for future work to
reduce uncertainty. Managers will have to
decide whether or not to use the model results
and whether or not to conduct research to
improve the models. This is a continuing
process.
C. References
Bierman, V.J., Jr., J.V. DePinto, T.C. Young, P.W.
Rodgers, S.C. Martin, and R. Raghunathan.
September 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 lie, Michigan. 381
pp.
48
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DePinto, J.V., R. Raghunathan, P. Sierzenga, X.
Zhang, V.J. Bierman, Jr., P.W. Rodgers, and T.C.
Young. December 1993a. RecalibrationofGBTOX:
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. 132
pp.
DePinto, J.V., R. Raghunathan, V.J. Bierman, Jr.,
P.W. Rodgers, S. Hinz, and T.C. Young. 1993b.
Development and Calibration of an Organic Carbon
Based Sorbent Dynamics Model (GBOCS) for the
Green Bay Mass Balance Study. Presented at the
36th Conference on Great Lakes Research,
International Association for Great Lakes Research,
St. Norbert College, DePere, Wisconsin. June 4-10,
1993.
Endicott, D.D., W.L. Richardson, and DJ. Kandt.
1992. MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in Lake
Michigan. Internal Draft Report. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 183
pp.
Hornbuckle, K.C., C.W. Sweet, R.F. Pearson, D.L.
Swackhamer, and S.J. Eisenreich. 1995. Assessing
Annual Water-Air Fluxes of Polychlorinated
Biphenyls in Lake Michigan. Environ. Sci. Technol.,
29:869-877.
Hornbuckle, K.C., J.V. DePinto, S.J. Eisenreich, and
J.E. Baker. 1997. Atmospheric Deposition of PCBs,
Trans-Nonachlor, Atrazine, Nitrogen and Phosphorus
to Lake Michigan. Third-Quarter Report from the
Atmospheric Modeling Group of the Lake Michigan
Mass Balance Project. July 8, 1997.
Richardson, W.L., V.E. Smith, and R. Wethington.
1983. Dynamic Mass Balance of PCB and
Suspended Solids in Saginaw Bay~A Case Study. In
- D. Mackay, S. Patterson, and S.J. Eisenreich (Eds.),
Physical Behavior of PCBs in the Great Lakes, pp.
329-366. Ann Arbor Science Publishers, Ann Arbor,
Michigan.
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.
Velleux, M.L. 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.
Bioaccumulation and Ecosystem Models
Food-Chain Model for PCBs and TNC in Lake
Michigan
Principal Modeler: Douglas Endicott, USEPA, LLRS
Support Modeler: Xin Zhang, PAIVSoBran, Inc.
A. Model Description
1. Background Information The food web
bioaccumulation model predicts chemical
concentrations in biota in response to chemical
concentrations in water and sediment.
Bioaccumulation in Lake Michigan lake trout
and coho salmon will be modeled using an age-
class model for hydrophobic organic chemical
bioaccumulation in aquatic food webs,
FDCHAIN. The formulation of this model
follows the developments of Nordstrom et al.
(1976), Weininger (1978), Thomann and
Connolly (1984), Thomann (1978) and Connolly
etal. (1992). Food web bioaccumulation models
have been successfully applied for PCBs and
other hydrophobic organic carbon (HOCs) in
several large-scale aquatic ecosystems including
Lake Michigan (Thomann and Connolly, 1984),
New Bedford Harbor (Connolly, 1991) and, most
recently, fortheGBMBS (Connolly etal.,1992).
The model developed for that project, FDCHN,
will be adapted for use in Lake Michigan.
For Lake Michigan, bioaccumulation of PCB
congeners and TNC will be modeled for lake
trout and coho salmon food webs. Food web
bioaccumulation will be simulated for sub-
populations of lake trout in three distinct biotic
zones. The general structure of the lake trout
food web in Lake Michigan is shown in Figure 6.
49
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Herbivorous
Zooplankton
Mysis
Phytoplankton
Figure 6. Lake Michigan Lake Trout Food Web Spatially and Temporally Variable: Age Dependent.
In each zone, different food webs support lake
trout, including benthic and pelagic food web
linkages. Biotic zones are defined by the
approximately 50-mile range of movement of
lake trout. The coho salmon, in comparison, is
strictly pelagic. Although the coho food web is
simpler, the bioaccumulation simulation must
account for significant migration over the two
year lifetime of this stocked salmonid in Lake
Michigan.
2. Assumptions FDCHN is a time-variable,
population-based age class model, incorporating
realistic descriptions of bioenergetic,
trophodynamic, and toxicokineticprocesses. The
general features of FDCHN are well-suited to a
modeling application such as the LMMBP. The
general form of the bioaccumulation equation
equates 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 include 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 modeling objective here), the
bioaccumulation mass balance is repeatedly
applied to animals at each trophic level to
simulate chemical biomagnification from
primary and secondary producers, through forage
species to top predators. Chemical
biotransformation (metabolism), an additional
loss mechanism, is apparently negligible for most
PCB congeners in fish, and will be neglected for
this application. Other assumptions made by this
model include:
a. Only freely-dissolved chemical is
bioavailable: Thus, particulate and
colloidal (DOC) chemical phases are not
available for uptake by biota, unless they
are ingested.
b. Lipids are the storage reservoir: Other
tissues are only important in determining
rates of chemical transfer within the
organism. The model only accounts for
50
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HOC accumulation in a single lipid
compartment.
c. The BCF (bioaccumulation factor) and
lipid-water partition coefficient are
correlated to the octanol-water partition
coefficient, Kow. For HOCs up to about log
Kow of 6.0 to 6.5, they are approximately
equal. Such a correlation is not apparent
for chemicals with higher Kow values. Why
such "super-hydrophobic" chemicals do not
follow the bioconcentration behavior of
other HOCs has not been resolved.
However, most of the abundant PCB
congeners in Lake Michigan have log Kow
values less than 6.5, therefore this
uncertainty is not a major problem.
3. Model Parameters The data requirements to
support a scientifically defensible, state-of-the-
art food web bioaccumulation model are
exhaustive. Yet, because the LMMBP was
designed with a modeling objective, it supported
many of these data requirements. These data
have been categorized as follows:
Biota (collection and analysis): The collection,
characterization, and contaminant analyses of
samples of all species selected to represent the
Lake Michigan food web. Biota sampling was
designed to capture the trends in chemical
concentrations in fish and lower food web
organisms, including variations due to age,
spatial distribution, and season, for each species
modeled in the food web. Food webs were
sampled for sub-populations of lake trout in three
distinct biotic zones. A lake-wide grid sampling
design was required to sample coho salmon.
Four collection seasons were established for fish:
spring, summer, and fall of 1994, and spring of
1995. For each fish species, five replicate
composite samples were formed and analyzed for
fish collected in each zone and collection season.
For lake trout and coho salmon, composites were
formed according to age, while for forage species
the composites were based upon size.
To define suitable initial conditions for the
model, and to allow model testing over longer
than the two year duration of the mass balance
project, historical biomonitoring data will be
used. Total and Aroclor PCB concentrations
have been monitored in Lake Michigan lake trout
and coho salmon since 1972, by USEPA and the
States. Several studies have confirmed the
analytical comparability of the historical and
mass balance data, at least for total PCB.
Mysis and Diporeia were sampled in biota zones,
as well as sediment sampling locations, using a
benthic sled. Phytoplankton and zooplankton
were sampled by filtration at the 41 water quality
monitoring stations. Samples of suspended
particulate matter passing a 100 micron nominal
pore size glass fiber filters were operationally
defined as phytoplankton, while those trapped by
the filter were defined as zooplankton.
Toxicokinetic parameters: The toxicokinetic
parameters of the bioaccumulation model define
the rates of chemical uptake from water,
excretion from the organism, and transfer from
the diet. In general, these parameters are a
function of both the contaminant and the
organism. Estimates of these parameters based
upon laboratory data are quite variable, and
establish only broad limits to guide model
calibration. These parameters include the uptake
rate from water, the excretion rate, and the
chemical (dietary) assimilation efficiency.
Bioenergetics: Biological attributes of each
organism that affect bioaccumulation, including
rates of growth (wet weight and lipid),
consumption, and respiration. Bioenergetic data
required for bioaccumulation modeling includes
rates of growth (wet weight and lipid),
consumption, and respiration for each species as
a function of age or weight, temperature, season,
and biota zones. For the Lake Michigan
application, growth rates were based upon
regression of age and weight data. The length,
weight, and age of each fish collected during the
project was determined, allowing direct
estimation of seasonal growth rates for all
species. The lipid content [g(lipid)/g(wet) body
weight] was also determined for all fish collected
in the project. Time functions of lipid content
51
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were constructed for each species, incorporating
both age-dependence and seasonal variation
observed in the lipid data. The lipid time series
were then input to the bioaccumulation model.
Respiration rates were calculated from a standard
allometric relationship, dependent on weight and
temperature, which was fitted to laboratory data
for each species. Species-specific data for SDA
(specific dynamic action) were also included in
the respiration rates.
Ecological characterization: Data defining the
Lake Michigan ecosystem in terms of food web
structure and species migration. Food web
structure: Accurate characterization of the diet
of each fish modeled in the food web is
important, because chemical transfer associated
with food ingestion is the primary route for
hydrophobic chemical accumulation. Fish diets
are determined by analyzing stomach (gut)
contents, and this data is then generalized in
terms of the fraction (by weight) of each prey
species consumed. For lake trout and coho
salmon, prey species and size were determined as
a function of predator size and age. Spatial and
seasonal variation in fish diets were also factored
into the model parameterization of food web
structure. These sources of information were
used to construct food web structures for each
biota zone.
Fish movement and migration: Migration may
be defined as the movement of fish between
habitats suitable for feeding, reproduction, and
refuge in periods of unfavorable conditions. The
movement between habitats is strongly
influenced by the diel pattern of light and dark,
the annual temperature and photoperiod cycles
and the age and sex of the fish. In addition, the
habitats suitable for feeding and refuge may be
different depending upon life stage. The general
migration patterns of individual fish species have
been determined from tagging studies. These
sources of information were used to define
species-specific migration patterns for the model.
B. Model Development
1. Code Development and Maintenance The
FDCHATN model follows the developments of
Nordstrom et al. (1976), Weininger (1978),
Thomann and Connolly (1984), Thomann (1978)
and Connolly et al. (1992). Version 5.0 of
FDCHN, developed by Manhattan College and
HydroQual, Inc. (HydroQual, 1996) for the
USEPA GBMBS, will be adapted for use in Lake
Michigan. FDCHAIN is coded in ANSI standard
FORTRAN 77, with subroutines and common
variable blocks stored in separate source and
include files. A UNIX Makefile is maintained
for program compilation. The FDCHAIN source
code and all associated files are maintained using
the Digital UNIX RCS.
2. Model Documentation - Model documentation is
provided in a series of reports and publications
cited above. A User's Guide, based upon the
1996 HydroQual report, is maintained at LLRS.
As FDCHAIN is revised and modified, updated
documentation is added to the User's Guide.
3. Code Verification FDCHAIN has been tested
through it's application in a number of projects,
as described above. Modifications made to
FDCHAIN will be verified by first testing
against results from the original version to ensure
proper function of the code. Testing will then
verify the performance of new or revised model
features. This will consist of comparisons of
intermediate and final model results to hand (or
spreadsheet) calculations over several integration
time steps. "Extreme case" scenarios will be
selected for these tests, to amplify errors and
maximize the likelihood of their detection.
4. Code Documentation - FDCHAIN code has been
internally documented, by its original developers
and by programmers and modelers at LLRS. The
history of revisions to the FDCHAIN code is
maintained, both as chronological entries within
the header comments of each file and within
RCS.
5. Model Calibration/Confirmation and Uncertainty
- Comparison of observed and predicted species-
52
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specific chemical concentrations serves as the
basis for bioaccumulation model calibration and
confirmation. Comparisons will include
chemical concentration variation between age
classes, across trophic levels, and between
seasons and biota zones, as well as comparisons
based upon standard data transformations such as
bioaccumulation factors and predator-to-prey
contaminant ratios. Toxicokinetic parameters,
which are most often adjusted to calibrate the
model, will be treated as constants, or varied
according to hydrophobicity of the chemical and
trophic level of the organism. Such a systematic
approach to toxicokinetic parameterization will
be sought, in order to reduce degrees of freedom
in the calibration of the model.
Our experience with past food web
bioaccumulation modeling projects, especially in
the GBMBS, suggests that even with a good
database for model calibration, large
uncertainties in model predictions may result due
to unexplained variability and
overparameterization in the model. We propose
to evaluate and estimate bioaccumulation model
uncertainty using the Bayesian Monte Carlo
(BMC) (Dilks et al, 1992) method. BMC
generates estimates of model uncertainty that are
unaffected by parameter covariance, a factor that
causes traditional Monte Carlo analysis to
significantly inflate model uncertainty.
It should be recognized that FDCHN, and in fact,
all current food web bioaccumulation models, is
not predictive in terms of the dynamics of the
food web itself. In other words, the food web
structure is described as model input. FDCHN
does not predict changing forage composition,
trophic status in response to nutrients, exotic
species invasion, or fisheries management. Yet
such factors have been demonstrated to alter
food web structures in the Great Lakes, and these
changes have been suggested to affect
bioaccumulation in top predators including
salmonids. To address the sensitivity of
bioaccumulation predictions to food web
dynamics, the SIMPLE model (Jones, et al.,
1993), a bioenergetic model for fish population
dynamics in the Great Lakes, will be used to
construct scenarios for food web change that will
then be tested in FDCHN. Such testing will
demonstrate the sensitivity of bioaccumulation
predictions to food web dynamics in comparison
to changes in contaminant concentrations in fish
due to reducing exposure concentrations.
C. References
Connolly, J.P. 1991. Application of a Food Chain
Model to Poly chlorinated Biphenyl Contamination of
the Lobster and Winter Flounder Food Chains in
New Bedford Harbor. Environ. Sci. Technol.,
15(4):760-770.
Connolly, J.P., T.F. Parkerton, J.D. Quadrini, S.T.
Taylor, and A.J. Thumann. 1992. Development and
Application of Model of PCBs in the Green Bay,
Lake Michigan Walleye and Brown Trout and Their
Food Webs. Project Report. U.S. Environmental
Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research
Station, Grosse lie, Michigan.
Dilks, D.W., R.P. Canale, and P.G. Meier. 1992.
Development of Bayesian Monte Carlo Techniques
for Water Quality Model Uncertainty. Ecol.
Modelling, 62:149-162.
HydroQual, Inc. 1996. Green Bay Food Chain
Model Documentation. HydroQual, Inc., Mahwah,
New Jersey.
Jones, M.L., J.F. Koonce, and R. O'Gorman. 1993.
Sustainability of Hatchery-Dependent Salmonine
Fisheries in Lake Ontario: The Conflict Between
Predator Demand and Prey Supply. Transact. Amer.
Fisher. Soc., 122:1002-1018.
Nordstrom, R.J., A.E. McKinnon, and A.S.W.
DeFreitas. 1976. A Bioenergetics Based Model for
Pollutant Accumulation in Fish: Simulation of PCB
and Methylmercury Residue Levels in Ottawa River
Yellow Perch (Percaflavescens). J. Fish. Res. Bd.
Canada, 33:248-267.
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Thomann, R.V. April 1978. Size Dependent Model
of Hazardous Substances in Aquatic Food Chains.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. EPA-
600/3-78-036, 40 pp.
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 He, Michigan.
EPA-600/S3-84-026, 3 pp.
Weininger, D. 1978. Accumulation of PCBs by
Lake Trout in Lake Michigan. Department of Water
Chemistry, University of Wisconsin, Madison,
Wisconsin. 232 pp.
Ecosystem Model
Project Officers: Glenn Warren, USEPA, GLNPO;
Russell Kreis, Jr., USEPA, LLRS
Principal Modeler: Victor J. Bierman, Jr., Limno-Tech,
Inc.
A. Model Description
1. Background Information - This model will build
upon and enhance the Phytoplankton
Solids/Eutrophication Model (PSEM) in the
LMMBP. Consistent with the approach used in
the GBMBS (Bierman et al., 1992; DePinto et
al., 1993), the contaminant transport and fate
models in the LMMBP will include explicit
representation of sorbent dynamics in terms of
particulate and dissolved organic carbon. An
important component in the mass balance cycle
for organic carbon is internal loading due to
autochthonous (phytoplankton) production.
Consequently, the mass balance model for
sorbent dynamics must also include a
eutrophication model for generating internal
organic carbon loadings due to primary
production.
The PSEM in the LMMBP will be based on
historical eutrophication models for the Great
Lakes and on recent "state-of-the-art"
eutrophication kinetics, transport and water-
sediment interactions. These models will be
modified to explicitly represent particulate and
dissolved organic carbon dynamics, and to be
compatible with the sediment and contaminant
transport and fate models in the LMMBP.
A limitation of the PSEM is that it does not
represent lower food web components important
to the Lake Michigan ecosystem such as
Bythotrephes, Mysis, and possibly, Pontoporeia
and zebra mussels. Interactions among
phytoplankton groups and these lower food chain
components are important processes that can
influence organic carbon sorbent dynamics and
contaminant transport, fate and bioavailability.
More broadly, contemporary questions posed by
resource managers require consideration of
ecosystem productivity, risk-based ecosystem
responses and effects, and ecosystem
sustainability. Conventional water
quality/eutrophication models do not provide
answers to these questions because they contain
only very simplified representations of lower
food webs; they do not represent upper trophic
levels, and they do not represent linkages
between lower food webs and upper trophic
levels.
To address these important questions a new
generation of models is evolving which contain
explicit representations of ecosystem structure
and function. For example, Limno-Tech, Inc.
(1995, 1997) has developed and applied a
coupled primary productivity-exotic species
model to investigate responses of multiple algal
groups in Saginaw Bay to changes in external
phosphorus inputs and zebra mussel densities.
The original Chesapeake Bay Water Quality
Model (Cerco and Cole, 1994) has been
enhanced to include micro- and meso-
zooplankton, three functional groups of
submerged aquatic vegetation, epiphytes and two
types of benthic organisms, a filter-feeder and a
deposit-feeder.
The Lake Michigan Ecosystem Model (LMEM)
will be an enhanced version of the PSEM and
will constitute the first step towards a
54
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comprehensive Great Lakes Ecosystem
Productivity Model that could be used to
investigate ecosystem-level responses and
effects. The LMEM will include enhancements
to the phytoplankton-zooplankton kinetics, with
specific representation of lower food web
components important to the Lake Michigan
ecosystem.
2. Model Equations, Systems and Parameters - The
LMEM will be constructed using mass balance
principles and it will be compatible with other
models used for the LMMBP. The LMEM will
build upon the original eutrophication models
developed for Saginaw Bay (Bierman and Dolan,
1981, 1986a, 1986b; Bierman and Mcllroy,
1986; Bierman et al., 1980) and will contain
multiple nutrients, multiple algal groups, and
herbivorous and carnivorous zooplankton.
Additional enhancements will be included to
represent lower food chain components
important to the Lake Michigan ecosystem such
as Bythotrephes, Mysis, and possibly,
Pontoporeia and zebra mussels.
The final equations, systems and parameters in
the LMEM will be based on a literature review
of the Lake Michigan ecosystem, with emphasis
on the lower food web. Principal emphasis will
be placed on primary productivity and
interactions of primary producers with higher
trophic levels. A preliminary bibliography of the
Lake Michigan ecosystem has been assembled
and is appended to this QA/QC plan. A literature
review on contemporary water quality and
aquatic ecosystem models will also be
conducted.
3. Data Quality - The primary source for historical
data will be the USEPA STORET database.
STORET contains all of the field data collected
for Lake Michigan by the USEPA GLNPO since
1961. Emphasis will be placed on intensive
studies conducted in 1976-77 and 1982-83.
Because the quality of this historical information
is uncertain, all of the STORET data will be
screened for reasonableness by USEPA or its
contractors before they are used. Attempts will
be made to contact the originating laboratory in
the event that questions arise. Final model
development and application will be conducted
using the 1994-95 project data. These data will
be subject to a comprehensive QA/QC protocol
before they are used for the modeling effort.
An obstacle to development, calibration and
verification of the LMEM is that routine field
monitoring studies were not designed to measure
state variables or internal model coefficients in
"state-of-the-art" eutrophication models or in the
new generation of evolving ecosystem models.
Consequently, it will necessary to use data sets
of opportunity acquired during the many
specialized studies of Lake Michigan and other
Great Lakes. It will also be necessary to depend
on the published scientific literature for model
conceptual development and for many of the
internal model coefficients.
Appropriate care will be taken to use datasets
from reliable sources and to depend on personal
communications with investigators who have a
long history of experience in conducting studies
on Lake Michigan and other Great Lakes. All
data sources will be documented in detail and
periodic review will be made to the QA/QC plan
for the LMEM.
B. Model Development
1. Code Development and Maintenance Code
development and maintenance for the LMEM
will be a collaborative effort between Limno-
Tech, Inc. and USEPA LLRS. A detailed plan
for model coding can not be developed until the
conceptual framework for the LMEM is
finalized. Code development and maintenance
for the LMEM is expected to parallel these same
tasks for the PSEM.
At the present time there are two possible coding
frameworks for both the PSEM and the LMEM:
first, the WASP/IPX framework originally
developed for modeling toxic chemicals in the
Fox River (Velleux etal., 1994); and second, the
CE-QUAL-ICM framework developed for
modeling eutrophication in Chesapeake Bay
(Cerco and Cole, 1995). There are also two
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spatial segmentation grids for the LMMBP: first,
a 41 water column segment grid; and second, an
ultimate higher order grid that will represent
Lake Michigan at either a 5 km or 10 km spatial
scale. The coding framework(s) for these two
models will depend, in part, on the spatial
segmentation grid(s) to which each model will be
applied.
Code development will be conducted using the
RCS code management tool and all changes to
the computer code will be documented to the
fullest extent possible within the code itself, as
well as in a subsequent technical report. As
appropriate, periodic revisions will be made to
the QA/QC plan for the LMEM.
2. Model Documentation The development and
application of the LMEM will be documented in
the form of a technical report and/or scientific
paper for the peer-reviewed literature.
Documentation will include a description of the
model conceptual framework, model
assumptions, model state variables and process
mechanisms, governing equations and tables of
all model inputs and internal model coefficients.
Numerical values will be presented for all model
inputs and coefficients, along with sources from
which these values were obtained.
3. Model Validation and Uncertainty Analysis - A
detailed plan for model validation and
uncertainty analyses can not be developed until
the conceptual framework for the LMEM is
finalized. In general, it is expected that
validation and uncertainty analyses for the
LMEM will parallel these same tasks for the
PSEM. The QA/QC plan for the LMEM will be
revised in the future to include specific plans for
these tasks.
C. References
Bierman, V.J., Jr., D.M. Dolan, E.F. Stoermer, I.E.
Gannon, and V.E. Smith. 1980. The Development
and Calibration of a Spatially-Simplified, Multi-Class
Phytoplankton Model for Saginaw Bay, Lake Huron.
Great Lakes Environmental Planning Study, Great
Lakes Basin Commission, Ann Arbor, Michigan.
Contribution No. 33, 126 pp.
Bierman, V.J., Jr. and D.M. Dolan. 1981. Modeling
of Phytoplankton-Nutrient Dynamics in Saginaw
Bay, Lake Huron. J. Great Lakes Res., 7(4):409-439.
Bierman, V.J., Jr. and L.M. McHroy. 1986. User
Manual for Two-Dimensional Multi-Class
Phytoplankton Model with Internal Nutrient Pool
Kinetics. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth,
Large Lakes Research Station, Grosse He, Michigan.
EPA-600-3-86-061, 149pp.
Bierman, V.J., Jr. and D.M. Dolan. 1986a.
Modeling of Phytoplankton in Saginaw Bay: I:
Calibration Phase. J. Environ. Engin., 112(2):400-
414.
Bierman, V.J., Jr. and D.M. Dolan. 1986b.
Modeling of Phytoplankton in Saginaw Bay. JJ: Post-
Audit Phase. J. Environ. Engin., 112(2):415-429.
Bierman, V.J., Jr., J.V. DePinto, T.C. Young, P.W.
Rodgers, S.C. Martin, R. Raghunathan, and S.C.
Hinz. 1992. Development and Validation of an
Integrated Exposure Model for Toxic Chemicals in
Green Bay, Lake Michigan. U.S. Environmental
Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research
Station, Grosse He, Michigan. 2665 pp. plus
Appendices.
Cerco, C.F. and T.M. Cole. 1994. Three-
Dimensional Eutrophication Model of Chesapeake
Bay. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Technical Report EL-94-4.
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Cerco, C.F. and T. Cole. 1995. User's Guide to the
CE-QUAL-ICM Three-Dimensional Eutrophication
Model. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
DePinto, J.V., R. Raghunathan, and V.J. Bierman, Jr.
1993. Analysis of Organic Carbon Sediment-Water
Exchange in Green Bay, Lake Michigan. Water Sci.
Technol., 28(8-9): 149.
Limno-Tech, Inc. 1995. A Preliminary Ecosystem
Modeling Study of Zebra Mussels (Dreissena
polymorpha) in Saginaw Bay, Lake Huron. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 120
pp.
Limno-Tech, Inc. 1997. Application of a Coupled
Primary Productivity-Exotic Species Model for
Saginaw Bay, Lake Huron. U.S. Environmental
Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research
Station, Grosse He, Michigan. 26 pp. plus Appendix.
Velleux, M., J. Gailani, and D. Endicott. 1994. A
User's Manual to IPX, The In-Place Pollutant Export
Water Quality Modeling Framework. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 194
pp.
Preliminary Bibliography on Lake Michigan
Ecosystem
Baker, E.A., S.A. Tolentino, and T.S. McComish.
1992. Evidence for Yellow Perch Predation on
Bythotrephes cederstroemii in Southern Lake
Michigan. J. Great Lakes Res., 18(1):190.
Bowers, J.A., W.E. Cooper, and DJ. Hall. 1990.
Midwater and Epibenthic Behaviors of My sis relicta
Loven: Observations from the Johnson-Sea Link. JJ:
Submersible in Lake Superior and from a Remotely
Operated Vehicle in Northern Lake Michigan. J.
Plank. Res., 12(6): 1279.
Branstrator, D.K. 1995. Ecological Interactions
Between Bythotrephes cederstroemii and Leptodora
kindtii and the Implications for Species Replacement
in Lake Michigan. J. Great Lakes Res., 21(4):670.
Burkhardt, S. Seasonal Size Variation in the
Predatory Cladoceran Bythotrephes cederstroemii in
Lake Michigan. Freshwater Biol., 31(1):97.
Carrick, H.J., G.L. Fahnenstiel, and E.F. Stoermer.
1991. The Importance of Zooplankton-Protozoan
Trophic Couplings in Lake Michigan. Limnol.
Oceanogr., 36(7): 1335.
Carrick, H.J., G.L. Fahnenstiel, and W.D. Taylor.
1992. Growth and Production of Planktonic Protozoa
in Lake Michigan: In Situ Versus In Vitro
Comparisons and Importance to Food Web
Dynamics. Limnol. Oceanogr., 37(6): 1221.
Evans, M.S., M.A. Quigley, and J.A. Wojcik. 1990.
Comparative Ecology of Pontoporeia hoyi
Population in Southern Lake Michigan: The
Profundal Region Versus the Slope and Shelf
Regions. J. Great Lakes Res., 16(1):27.
Evans, M.S., G.E. Noguchi, and C.P. Rice. 1991.
The Biomagnification of Polychlorinated Biphenyls,
Toxaphene and DDT Compounds in a Lake Michigan
Offshore Food Web. Arch. Environ. Contamin.
Toxicol., 20(1):87.
Evans, M.S. 1992. Historic Changes in Lake
Michigan Zooplankton Community Structure: The
1960s Revisited with Implications for Top-Down
Control. Canadian. J. Fisher. Aquat. Sci.,
49(8): 1734.
Fitzgerald, S.A. and W.S.Gardner. 1993. An Algal
Carbon Budget for Pelagic-Benthic Coupling in Lake
Michigan. Limnol. Oceanogr., 38(3):547.
Gardner, W.S., P.P. Landrum, and J.F. Cavaletto.
1990. Lipid-Partitioning and Disposition of
Benzo[a]pyrene and Hexachlorobiphenyl in Lake
Michigan Pontoporeia hoyi and My sis relicta.
Environ. Toxicol. Chem., 9(10): 1269.
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Gauvin, J.M. 1989. Effects of Food Removal on
Nutrient Release Rates and Lipid Content of Lake
Michigan Pontoporeia hoyi. Canadian J. Fisher.
Aquat. Sci., 46(7): 1125.
Keilty, T.J. 1990. Evidence for Alewife ( Alosa
pseudoharengus) Predation on the European
Cladoceran Bythotrephes cederstroemii in Northern
Lake Michigan. J. Great Lakes Res, 16(2):330.
Klump, J.V., D.N. Edgington, and D.M. Robertson.
1997. Sedimentary Phosphorus Cycling and a
Phosphorus Mass Balance for the Green Bay (Lake
Michigan) Ecosystem. Canadian J. Fisher. Aquat.
Sci.,
Lehman, J.T., J.A. Bowers, and R.W. Gensemer.
1990. My sis relicta in Lake Michigan: Abundances
and Relationships with Their Potential Prey,
Daphnia. Canadian J. Fisher. Aquat. Sci., 47(5):977.
Lehman, J.T. 1991. Causes and Consequences of
Cladoceran Dynamics in Lake Michigan:
Implications of Species Invasion by Bythotrephes. J.
Great Lakes Res., 17(4):437.
Lehman, J.T. and C.E. Caceres. 1993. Food-Web
Responses to Species Invasion by a Predatory
Invertebrate: Bythotrephes in Lake Michigan.
Limnol. Oceanogr., 38(4):879.
Makarewicz, J.C., P. Bertram, and E.H. Brown, Jr.
1995. A Decade of Predatory Control of
Zooplankton Species Composition of Lake Michigan.
J. Great Lakes Res., 21(4):620.
Rudstam, L.G., P.P. Binkowski, and M.A. Miller.
1994. A Bioenergetics Model for Analysis of Food
Consumption Patterns of Bloater in Lake Michigan.
Trans. American Fisher. Soc., 123(3):344.
Sager, P.E. 1991. Functional Interaction of
Phytoplankton and Zooplankton Along the Trophic
Gradient in Green Bay, Lake Michigan. Canadian J.
Fisher. Aquat. Sci., 48(1): 116.
Schelske, C.L. and L. Sicko-Goad. 1990. Effects of
Chelated Trace Metals on Phosphorus Uptake and
Storage in Natural Assemblages of Lake Michigan
Phytoplankton. J. Great Lakes Res., 16(1):82.
Schneeberger, P.J. 1991. Seasonal Incidence of
Bythotrephes cederstroemii in the Diet of Yellow
Perch (Ages 0-4) in Little Bay de Noc, Lake
Michigan, 1988. J. Great Lakes Res., 17(2):281.
Sprules, W.G., S.B. Brandt, and D.J. Stewart. 1991.
Biomass Size Spectrum of the Lake Michigan Pelagic
Food Web. Canadian J. Fisher. Aquat. Sci.,
48(1):105.
Tarapchak, S.J. and R.A. Moll. 1990. Phosphorus
Sources for Phytoplankton and Bacteria in Lake
Michigan. J. Plankton Res., 12(4):743.
Vanderploeg, H.A., S.J. Bolsenga, and G.L.
Fahnenstiel. 1992. Plankton Ecology in an Ice-
Covered Bay of Lake Michigan: Utilization of a
Winter Phytoplankton Bloom by Reproducing
Copepods. Hydrobiologia, 243/244:175.
Yurista, P.M. and K.L. Schulz. 1995. Bioenergetic
Analysis of Prey Consumption by Bythotrephes
cederstroemii in Lake Michigan. Canadian J. Fisher.
Aquat. Sci., 52(1): 141.
Load Computations Models and Estimation
Methodologies
Terrestrial Emissions and Atmospheric Fate
and Transport Estimates for Atrazine and
Mercury
Principal Investigator: Ellen Cooter and Russell Bullock,
NOAA
This modeling component addresses the emissions of
agricultural use of atrazine from the soil, the emission of
mercury via an emissions inventory, development of a
database of driving meteorological conditions and the
estimation of fate and transport of atrazine and mercury
from the eastern two-thirds of the United States and
Canada to the surface of Lake Michigan. Three models
and an emissions inventory are required for this task.
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Emissions of Agricultural Use ofAtrazinefrom
Soil (ORTECH Soil Emissions Model)
Principal Investigator: M. Tevor Scholtz, ORTECH
Corporation
A. Model Development
1. Background Information
Development History The occurrence of
pesticides and other persistent organic pollutants
in areas of the globe which are being used, is of
growing concern. The effects of several
pesticides on animals and birds have been
observed and documented. The effects of
pesticides in the environment on humans are less
clear but, nonetheless, there is a growing
consensus that releases to the environment must
be minimized or even eliminated for some
pesticides.
In order to address these concerns regarding
persistent organic pollutants (POPs), the United
Nations Economic Commission for Europe
(UNECE) has initiated the development of a
protocol on POPs with Canada as the lead
country. A task force has been formed which
will assess the possible effects of POPs in the
environment and will investigate strategies to
eliminate POPs which are shown to have the
potential to induce adverse responses in humans
and in the environment. Participating countries
have been requested to submit production and
consumption as well as emissions inventory data
for a selection of priority POPs. Included in the
list are a number of pesticides, some of which are
in current use in Canada while others have been
banned or severely restricted.
The presence of pesticides in the Arctic and
upper Great Lakes indicates that the atmospheric
route is important and, in some cases, the
dominant pathway for the translocation of
pesticides following their application to
agricultural lands. Deposition to the Great Lakes
is thought to have significant contributions from
local sources as well as long-range transport over
regional and even global scales. The persistence
of significant air concentrations of certain
pesticides, the use of which has been
discontinued in North America for some years,
suggest that global transport is occurring from
other parts of the world where such pesticides are
still in use. Source/receptor relationships are
extremely complex where such a wide range of
distance scales is involved. Regional and global
models are, therefore, being used to investigate
such inter-relationships and to aid in the
interpretation of the sparse measurements which
are available. Emissions of pesticides to the
atmosphere are critical inputs required by these
models. Presently, there is no reliable way to
estimate the emission of pesticides to air which
result from their agricultural use. This model is
the culmination of some six years of research
which has involved developing, testing, and
implementing a modeling capability for
estimating the emissions of pesticides from
vegetation and soils.
Application History Development of a North
American Pesticide Emission Inventory - At the
current stage of the work, the potential for
emission of twenty pesticides has been
estimated; fourteen of these pesticides are on a
combined Canadian and European nations
priority list while the remaining six are heavily
used in Canada. Pesticides are applied to
agricultural crops and soils to control insects,
weeds, and fungi which would otherwise reduce
the productivity of cultivated land. Application
may be as a spray or a dust, or pesticide may be
incorporated into the soil at the time of planting
of seed or tilling of the soil. Depending on the
mode of application, some fraction of the applied
pesticide is eventually emitted to the atmosphere
from the soil surface and vegetation. Once in the
atmosphere, transport, chemical transformation,
and deposition of the pesticide to land and water
surfaces will occur. Persistent organics which
are deposited may also be re-emitted and in this
way transported over global scales. The
emissions model developed in this study is
suitable for estimating emissions on time scales
ranging from hourly to monthly, seasonal, or
annual periods. The model comprises a one-
dimensional numerical solution of the advection
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and diffusion of heat, moisture and pesticide in
agricultural soils with or without a crop cover. A
simple canopy model has been developed to
simulate the volatilization of pesticide from the
vegetation. The model is driven by hourly
meteorological observations which are available
from North American climate stations. A series
of experiments and sensitivity tests have been
made with the model in order to gain insight into
the post-application movement and emission of
pesticides. These tests show that episodic
emissions of pesticide due to tilling of the soil
should not be overlooked. Tilling is an
especially important emission source for
persistent pesticides used on treated seed; tilling
of the soil in the year subsequent to the planting
of treated seed exposes pesticide residues to the
atmosphere resulting in episodes of high
emission during the tilling season. The
emissions model is designed to run multi-year
simulations of pesticide emissions with annual
tilling so that pesticide residues are represented
in the emission factors. Details of the theoretical
development and testing of the pesticide
emissions model and its application to the
preparation of a North American pesticide
emissions inventory are provided in the report.
The pesticide emissions model has been used to
generate gridded emission factors for twenty
selected pesticides including those on the
international priority lists. The grid used covers
the whole of North America but emissions from
Mexico are not included in the inventory. The
grid projection is polarstereographic with a grid
size of 127 x 127 km; this grid is used by several
of the Canadian regional transport and deposition
models. The emissions over a two-year period
have been simulated using 1989 meteorology
obtained from approximately 80 climatic
stations. Other inputs required by the model are
gridded soil texture and properties, and the
methods by which the pesticides are applied.
Modeled seasonal and annual emission factors
for the twenty pesticides studied are provided in
the report on the 127 km grid. For the nine
pesticides which were still being used in North
America in 1989-1990, gridded seasonal and
annual emissions are reported. Environment
Canada (Yi-Fan Li, personal communication,
1995) provided the pesticide usage data required
to compute emissions using modeled emission
factors. The computed emissions include those
due to pesticide residues remaining from the
previous year's application.
As part of the Canada's involvement in the
United Nations International Global Atmospheric
Chemistry Program (IGAC), the CGEIC is
presently completing a global pesticide emission
inventory under the cooperative Global
Emissions Inventory Activities working group
(GEIA) which is a sub-program of IGAC. The
methodology being used for this global study is
similar to that described in the present report.
Planned Refinements The model will be
modified by Trevor Scholtz and associates at the
CGEIC to incorporate grid-specific information
and to enable it to make episodic emission
estimates on a gridded basis. A report
documenting the model changes will be
produced.
2. Model Parameters
Soil parameters:
Class
Texture
Field capacity
Saturation capacity
Permanent wilt point
Saturation hydraulic conductivity
Soil constant saturation matric potential
Geophysical, Climatological, and Crop
Parameters:
Last frost date
Canopy shading factor
Surface roughness
Root development
Canopy Parameters
Cuticle scale
Droplet diameter
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A.I. concentration
Pesticide application rate
Diffusivity in water
Diffusivity in cuticle
Air-side coefficient
Contact angle on cuticle
Friction velocity
3. Data Quality
Soil Texture The original model used the
UNEP/GRID (1992) two-minute
latitude/longitude global grid of soil classes
which was used to generate a FAO Soil Map of
the World. The values were regridded to the
CGEIC 127 x 127 km grid. For the LMMBP, the
U.S. Department of Agricultural (USD A) Natural
Resource Conservation Service State Soil
Geographic Database (STATSGO) are used.
Information regarding this database is found at:
http:// www.agnic.nal.usda.gov/agdb/
statsgo.html. This original database has been
modified to a 1 km spatial scale by scientists at
PSU. The 1 km soil texture will be aggregated to
the 36 km scale through area weighting. These
values will be used to drive both the mesoscale
meteorological model as well as the soil emission
model.
Meteorology Output from the modified
PSU/NCAR mesoscale meteorological model
version 5 (MM5-PX) will be supplied for each
36 km grid for each model hour over the study
domain. Meteorological inputs to the soil
emission model are as follows:
u wind velocity component
v wind velocity component
mixing ratio
pressure
precipitation
net radiation
deep soil temperature
Monin-Obukhov length
emissivity
Physical-Chemical Properties of Atrazine:
diffusivity in air (Sherwood etal., 1975)
diffusivity in water (Sherwood et al., 1975)
soil sorption (Wauchope et al., 1992)
solubility (Sunito et al., 1988)
Henry's Law constant (Sunito et al., 1988)
half-life in soil (Wauchope et al., 1992;
Howard, 1991)
Pesticide Application Rate:
Estimated annual total atrazine applied per
county acre representing 1995 is obtained from
the USCG National Water Quality Assessment
Pesticide National Synthesis Project.
Documentation for this database, including
sources and limitations is found at
http://water.wr.usgs.gov/pnsp/use92/
mapex.htlml.
These data are regridded to the 36 km mesoscale
modeling grid and reported as a total application
(kg per year) for each grid cell.
Mode of Application and Number (Timing) of
Application:
Assumptions reported in Scholtz et al. (1997)
will be used unless additional information is
obtained indicating other values are more
appropriate.
Application Timing:
Atrazine is most often applied either as a pre-
emergent or post-emergent spray. Emergence is
assumed to take place seven days after planting.
State level crop progress information is available
from the USDA National Agricultural Statistical
Service. Data and documentation may be found
at http://www.mannlib.cornell.edu/reports/nassr/
field/per-bb. Post-emergent application is
assumed to take place 28 days after planting.
Label instructions should be made after the plant
reached 38 cm and so only one pre-emergent and
one post-emergent application is assumed in the
United States. Scientists with the CGEIC will
61
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decide on the most appropriate application mode
for Canada.
B. Model Development
1. Code Development and Documentation
Solid Model Development - In this phase of the
work, the soil model was developed to simulate
the volatilization of pesticide from bare soil to
which pesticides had been applied for
agricultural purposes. In addition to
meteorological data, the model required
geophysical data and the soil and pesticide
properties to be specified. Once the model had
been coded, it was subjected to a series of
sensitivity tests to gain an understanding of those
model parameters which had the greatest
influence on the model results. During this
phase, the numerical model was also tested
against an analytical solution of the equations to
ensure that the code was giving realistic results
under the restricted conditions for which an
analytical solution is available.
Development of a Model of Volatilization from
a Vegetation Canopy - The canopy model which
has been developed is a simple mass transfer
resistance model similar to the big leaf dry
deposition models. The detailed mechanisms
whereby pesticides are partitioned between plant
material and the air, and subsequently
transported to the atmosphere, or washed off into
the soil, are not well understood. While some
data are available on the rate of loss of pesticide
applied to vegetation, these data are generally
single measurements without the accompanying
meteorological data needed to develop and test a
model. In formulating the canopy volatilization
model, every effort has been made to include the
expected physical processes while keeping the
model relatively simple to be consistent with
current understanding of canopy volatilization
processes. In addition to the partitioning
properties of the pesticide, parameters such as
the spray droplet size, wetting properties of the
carrier liquid and growth state of the canopy are
important and these parameters have been
included in the model. The modeling of more
complex processes which undoubtedly play a
role within the plant tissue was not attempted.
Integration of the Soil and Canopy Models for
Emissions For the preparation of a pesticide
emission inventory, it is necessary to integrate
the numerical soil and canopy models over an
extended period of simulations and to
accumulate the total emission to the atmosphere.
Given the amount of pesticide applied per unit
area and the accumulated loss per unit area, an
emission factor can be calculated. For pesticides
which are highly mobile in the soil and which
volatilize readily from the surface, the period of
model integration required to capture the total
loss from the soil may be quite short. For these
pesticides, the soil concentration falls to an
insignificant level at the end of the integration
period due to loss by volatilization and/or
leaching into the water table. Persistent
pesticides, on the other hand, generally have low
mobility in the soil and as a result, while
emission rates may be relatively low, emission
continues for an extended period which may
cover several years for highly persistent
insecticides such as DDT or lindane. Integrating
the pesticide emissions model for an entire year
requires considerable computer time. At present,
some of the parameters needed as input to the
model, such as pesticide degradation rate and
modes of application, are poorly known. As new
data become available for some of these
parameters, it will be necessary to re-run the
entire model. Since the objective of this part of
the study was to estimate emission factors for as
many as 100 pesticides, this would be an onerous
task. To avoid the need to re-run the model in
order to change certain parameters, an alternative
methodology was developed, based on the linear
properties of the model equations. This
methodology permits emission factors to be pre-
computed using standard model solutions and
subsequently combined according to application
scenarios and decay rate. Using this
methodology, changes in model parameters such
as mode of application and degradation rate can
be factored into the solutions without the need to
re-run the model for every scenario.
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2.
Full model code and code documentation reside
at the CGEIC. For information, contact:
Dr. M. Trevor Scholtz
Manager, Environmental Computing and
Modeling and Director, Canadian Global
Emissions Interpretation Centre (CGEIC)
ORTECH Corporation
2395 Speakman Drive
Mississauga, Ontario, Canada L5K IBS
Telephone: (905) 822-4111, Ext. 524
Fax: (905) 823-1446
E-mail: tscholtz@ortech.on.ca
Model Documentation - The principle source of
model documentation is in the report by Scholtz
etal. (1997).
3. Model Verification Comparison of model
results to results obtained from an analytical
solution verified the computer code and showed
that the numerical scheme, under the condition
where a comparison with the analytical solution
was possible, accurately simulated the solute
advection and diffusion in the soil.
An assessment of the major influences on the
diurnal pattern of pesticide emission concluded
that the major factor influencing short term
variations in volatilization rate was the water
flux. Variations in the aerodynamic resistance,
which are diurnally variable, were shown to be of
secondary importance.
Pesticide residues in the soil which persist
beyond the year of application can contribute
significantly to emission in subsequent years. In
the computation of total emissions in a particular
year, it will be necessary to consider more than
one year's application for persistent pesticides.
Comparison of pesticides volatilization model
results with field measurements from bare soil
has shown that the proposed air-surface
exchange model is able to predict hourly
volatilization rates of spray-applied triallate and
trifluralin, which are in reasonable agreement
with field measurements.
Comparisons between heat and moisture flux
measurements and the results obtained from the
heat and moisture transport modules of the model
show that these are also in reasonable agreement,
given that no soil temperature or moisture profile
data were available with which to initialize the
model.
Further model runs are needed to examine the
negative volatilization fluxes that have been
observed.
Concerning model sensitivity analysis, it is
difficult to rank the sensitivity of the model to
the various parameters tested since the sensitivity
is, to some extent, dependent on the specific
pesticide. Parameters to which the model seems
to be insensitive are the application rate, the
water film resistance and the diffusivity in the
cuticle (or cuticle resistance). The Effects of
precipitation are large, as would be expected.
Many of the modeled half-lives are in excess of
30-days while the observed data show relatively
short half-lives in some cases. The model does
not include pesticide dislodgement or
degradation and these processes could contribute
significantly to pesticide loss, leading to the
relatively short half-lives observed in the field in
some cases. Atrazine has a reported soil half-life
of 60 to 90 days and so this model characteristic
should not impact our LMMBP application
significantly. The controlling resistance for
transport from the leaf surface to the atmosphere
appears from the model to be the air-side
resistance.
C. References
Howard, P.H. (Ed.). 1991. Handbook of
Environmental Fate and Exposure Data for Organic
Chemicals. Volume HI: Pesticides. Lewis
Publishers, Chelsea, Michigan.
Li, Y.-F. 1995. Personal Communication.
Atmospheric Environment Service, Environment
Canada, Downsview, Ontario, Canada.
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Scholtz, M.T., A.C. McMillan, C. Slama, Y.-F. Li, N.
Ting, and K. Davidson. 1997. Pesticide Emissions
Modeling: Development of a North American
Pesticide Emissions Inventory. Final Report.
Canadian Global Emissions Interpretation Centre,
Mississauga, Ontario, Canada. 242 pp.
Sherwood, T.K., R.L. Pigford, and C.R. Wilke.
1975. Mass-Transfer. McGraw-Hill Publishers,
New York, New York.
Sunito, L.R., W.Y. Shiu, D. Mackay, J.N. Seiber, and
D. Gotfelty. 1988. Critical Review of Henry's Law
Constants for Pesticides. Rev. Environ. Contamin.
Toxicol, 103:1-59.
UNEP/GRID. 1992. Global Gridded FAO/UNESCO
Soil Units. Digital Raster Data on a 2-Minute
Geographic (Lat/Long) 10800 x 5400 Grid. In
Global Ecosystems Database, Version 1.0: Disc A.,
National Oceanic and Atmospheric Administration,
National Geophysical Data Center, Boulder,
Colorado. 1 signal-attribute spatial layer on CD-
ROM, 58.3 MB. First published in 1984.
Wauchope, R.D., T.M. Butler, A.G. Hornsby,
P.W.M. Angustijn-Bechers, and J.P. Burt. 1992.
The SCS/ARS/CES Pesticide Properties Database for
Environmental Decision-Making. Rev. Environ.
Contamin. Toxicol., 123.
Mercury Emissions Inventory
All inventory of anthropogenic sources of atmospheric
mercury has been developed and described in USEPA's
Mercury Study Report to Congress as mandated in
Section 112(n)(l)(B) of the Clean Air Act, as amended in
1990. This inventory accounts for a variety of industrial,
commercial and residential source types within all 50
states of the United States. It has been subjected to
rigorous peer review both inside and outside of USEPA
and has been judged to accurately describe the total mass
and spatial distribution of mercury emitted to the
atmosphere from anthroppgenic sources in the United
States. This emission inventory has been used to support
regional-scale atmospheric mercury deposition modeling,
the results of which are also described in USEPA's
Mercury Study Report to Congress. This regional scale
modeling showed that, in addition to total mass, the
chemical and physical forms of mercury emissions are
important in determining the patterns and intensity of
mercury deposition to the surface. Studies of the
chemical and physical forms of mercury emissions from
various source types are currently ongoing.
Atmospheric mercury emissions from natural sources and
from anthropogenically contaminated soils and water
bodies are not as well understood as are the current direct
anthropogenic emissions to air. It can be reasonably
assumed that these natural and recycled emissions are
mostly in the form of elemental mercury gas due to the
relatively high vapor pressure of elemental mercury
versus its oxidized compounds. However, the total mass
of natural and recycled mercury emissions and the spatial
distribution of those emissions are not confidently known
at this time. It may be possible to model natural and
recycled mercury in the form of a global-scale
background concentration if it can be determined that no
such emissions are significantly concentrated near Lake
Michigan.
Anthropogenic emissions of mercury from sources in
Canada are currently being surveyed by Canadian federal
and provincial governments and preliminary inventories
from this effort are now available. An accurate emission
inventory for Canada including chemical and physical
form definitions will be required for an accurate
modeling assessment of total mercury deposition to Lake
Michigan.
Emissions of mercury from anthropogenic sources in
Mexico and more distant countries might be adequately
accounted for by the global-scale background
concentration also used to account for natural and
recycled emissions. It is generally thought that oxidized
mercury emissions will mostly deposit to the surface or
convert to the elemental form within the transport
distance from Mexico to Lake Michigan. Atmospheric
mixing of the remaining mercury from these distant
anthropogenic sources could make their mercury plumes
indistinguishable from global-scale emissions. We
currently do not have a complete understanding of the
global-scale transport of atmospheric mercury. Thus, the
concept of a nearly constant global background
concentration of elemental mercury gas may be invalid.
However, in the absence of comprehensive emission
inventories for all industrial nations and global-scale
atmospheric models to use them, we are forced to employ
64
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some form of background concentration or constant
boundary influx concentration in our modeling of
atmospheric mercury deposition to Lake Michigan.
Meteorological
Generation of Driving
Conditions (MM5-PX)
A. Model Description
Background Information - Development history
(http://laurel.mmm.ucar.edu/mm5/overview.ht
ml). The PSU/NCAR mesoscale model is a
limited-area, hydrostatic or nonhydrostatic,
sigma-coordinate model designed to simulate or
predict mesoscale and regional scale atmospheric
circulation. It has been developed at PSU and
NCAR as a community mesoscale model and is
continuously being improved by contributions
from users at several universities and government
laboratories.
The Fifth-Generation NCAR/PSU Mesoscale
Model (MM5) is the latest in a series that was
developed from a mesoscale model used by
Anthes at PSU in the early '70's that was later
documented by Anthes and Warner (1978).
Since that time, it has undergone many changes
designed to broaden its usage. These include (i)
a multiple-nest capability, (ii) nonhydrostatic
dynamics, and (iii) a four-dimensional data-
assimilation capability as well as more physics
options.
The model (known as MM5) is supported by
several auxiliary programs, which are referred to
collectively as the MM5 modeling system. A
schematic diagram (Figure 7) is provided to
facilitate discussion of the complete modeling
system. It is intended to show the order of the
programs and the flow of the data and to briefly
describe their primary functions.
Terrestrial and isobaric meteorological data are
horizontally interpolated (programs TERRAIN
and DATAGRID) from a latitude-longitude mesh
to a variable high-resolution domain on either a
Mercator, Lamber conformal, or polar
stereographic projection. Since the interpolation
does not provide mesoscale detail, the
Additional Main Data
Capability Programs Sets
TERRESTRIAL
1 I
Landuse Terrain
GLOBALANALYSIS
I I I I
NMC TOGA ECMWF UNIDATA
OSERVAT1ONS
I I
Surface Rawinsonde
Figure 7. MM5 Modeling System.
interpolated data may be enhanced (program
RAWINS) with observations from the standard
network of surface and rawinsonde stations using
a successive-scan Cressman technique. Program
INTERP performs the vertical interpolation from
pressure levels to the sigma coordinate system of
MM5. Sigma surfaces near the ground closely
follow the terrain, and the higher-level sigma
surfaces tend to approximate isobaric surfaces.
Since the vertical and horizontal resolution and
domain size are variable, the modeling package
programs employ parameterized dimensions
requiring a variable amount of cote memory.
Some peripheral storage devices are also used.
MM5 model applications (http://laurel.mmm.
ujcar.edu/mm5/application.html). MM5 has
been used for a broad spectrum of theoretical and
real-time studies, including applications of both
predictive simulation and four-dimensional data
assimilation to monsoons, hurricanes, and
cyclones. On the smaller meso-beta and meso-
65
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gamma scales (2-200 km), MM5 has been used
for studies involving mesoscale convective
systems, fronts, land-sea breezes, mountain-
valley circulations, and urban heat islands. The
model allows for multiple levels of nesting for
cases involving scale interaction. A list of
selected refereed journal publications related to
PSU/NCAR mesoscale model version 5 is
provided below.
Albright, M.D., RJ. Reed, and D.W. Ovens.
1995. Origin and Structure of a Numerically
Simulated Polar low Over Hudson Bay. Tellus,
accepted for publication.
Alpert, P. and M. Tsidulko. 1994. Project
WIND Numerical Simulations with Tel Aviv
Model PSU-NCAR Model Run at Tel Aviv
University. In R.A. Pielke and R.P. Pearce
(Eds.), Mesoscale Modeling of the Atmosphere,
American Meteorology Society, Boston,
Massachusetts, Meteorological Monographs,
25(47):81-95.
Alpert, P., M. Tsidulko, and U. Stein. 1995.
Can Sensitivity Studies Yield Absolute
Comparisons for the Effects of Several
Processes? J. Atmos. Sci., 52:597-601.
Alpert, P., U. Stein, and M. Tsidulko. 1995.
Role of Sea Fluxes and Topography in Eastern
Mediterranean Cyclogenesis. Global-
Atmospheric-Ocean Syst., 3:55-79.
Alpert, P., S.O. Krichak, T.N. Krishnamurti, U.
Stein, and M. Tsidulko. July 1996. The Relative
Roles of Lateral Boundaries, Initial Conditions
and Topography in Mesoscale Simulation of Ice
Cyclogenesis. J. Appl. Meteorol., in press.
Bates, G.T., F. Giorgi, and S.W. Hosteller. 1993.
Towards the Simulation of the Effects of the
Great Lakes on Regional Climate. Mon. Wea.
Rev., 121:1373-1387.
Bates, G.T., S.W. Hosteller, andF. Giorgi. 1995.
Two-Year Simulation of the Great Lakes Region
with a Coupled Modeling System. Mon. Wea.
Rev., 1505-1522.
Braun, S.A. and R.A. Houze Jr. 1997. The
Evolution of the 10-11 June 1985 PRE-STORM
Squall Line: Initiation, Development of Rear
Inflow, and Dissipation. Mon. Wea. Rev.,
125:478-504.
Chatfield, R.B., J.A. Vastano, H.B. Singh, andG.
Sachse. 1996. A General Model of How Fire
Emissions and Chemistry Produce African /
Oceanic Plumes (O3, CO, PAN, Smoke) Seen in
TRACE-A. Revised for JGR (Atmospheres).
Chen, C, W.-K. Tao, P.-L. Lin, G.S. Lai, S.-F.
Tseng, and T.-C.C. Wang. 1997. The
Inlensificalion of ihe Low-Level Jet During the
Development of Mesoscale Convective Systems
on a Mei-Yu Front. Mon. Wea. Rev., in press.
Chen, S.S. and W.M. Frank. 1993. A Numerical
Study of the Genesis of Extratropical
Mesovortices. Part I: Evolution and Dynamics.
J. Atmos. Sci., 50:2401-2426.
Colle.B.A. and C.F. Mass. 1994. The Structure
and Evolution of Cold Surges East of the Rocky
Mountains. Mon. Wea. Rev., 123:2577-2610.
Colle, B.A. and C.F. Mass. 1996. An
Observational and Modeling Study of the
Interaction of Low-Level Southwesterly Flow
with the Olympic Mountains During COAST
IOP 4. Mon. Wea. Rev., 124:2152-2175.
Cortinas, J.V., Jr. and D. J. Stensrud. 1995. The
Importance of Understanding Mesoscale Model
Parameterization Schemes for Weather
Forecasting. Wea. Forecasting, 10:716-740.
Davis, C.A., M.T. Stoelinga, and Y.-H. Kuo.
1993. The Integrated Effect of Condensation in
Numerical Simulations of Extratropical
Cyclogenesis. Mon. Wea. Rev., 121:2309-2330.
Davis, C.A. 1995. Observations and Modeling
of a Mesoscale Cold Surge During WISPIT.
Mon. Wea. Rev., in press.
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Dudhia, J. 1993. A Nonhydrostatic Version of
the Penn State/NCAR Mesoscale Model:
Validation Tests and Simulation of an Atlantic
Cyclone and Cold Front. Mon. Wea. Rev.,
121:1493-1513.
Dudhia, J. 1995. Reply to Comment on "A
Nonhydrostatic Version of the Penn State /
NCAR Mesoscale Model. Validation Tests and
Simulations of an Atlantic Cyclone and Cold
Front" by J. Steppeler. Mon Wea. Rev.,
123:2573-2575.
Fritsch, J.M. and J.S. Kain. 1993. Convective
Parameterization for Mesoscale Models: The
Fritsch-Chappell Scheme. In K.A. Emanuel
and D.J. Raymond (Eds.), The Representation of
Cumulus in Numerical Models, Meteorological
Monographs, American Meteorological Society,
pp. 159-164.
Gao, K. and D.-L. Zhang. 1994. The Effects of
Diabatic Model Physics in the Meso-b-scale
Simulation of a Midlatitude Squall Line. Acta
Meteorol. Sinica (in Chinese), 52:321-331.
Giorgi, F., G.T. Bates, and S.J. Nieman. 1993.
The Multi-Year Surface Climatology of a
Regional Atmospheric Model Over the Western
United States. J. Climate, 6:75-95.
Giorgi, F., M.R. Marinucci, and G.T. Bates.
1993. Development of a Second Generation
Regional Climate Model (RegCM2) I: Boundary
Layer and Radiative Transfer Processes. Mon.
Wea. Rev., 121:2794-2813.
Giorgi, F., M.R. Marinucci, G.T. Bates, and G.
DeCanio. 1993. Development of a Second
Generation Regional Climate Model (RegCM2)
It: Convective Processes and Assimilation of
Lateral Boundary Conditions. Mon. Wea. Rev.,
121:2814-2832.
Giorgi, F., C.S. Brodeur, and G.T. Bates. 1994.
Regional Climate Change Scenarios Over the
United States Produced with a Nested Regional
Climate Model: Spatial and Seasonal
Characteristics. J. Climate, 7:375-399.
Gyakum, J.R., Y.-H. Kuo, Z. Guo, and Y.-R.
Guo. 1994. A Case of Rapid Continental
Mesoscale Cyclogeneis, Part II: Model and
Observational Diagnosis. Mon Wea. Rev.,
123:998-1024.
Hines, K.H., D.H. Bromwich, and T.R. Parish.
1995. A Mesoscale Modeling Study of the
Atmosphere Circulation of High Southern
Latitudes. Mon. Wea. Rev., 123:1146-1165.
Horel, Pechmann, Hahmann, and Geisler. 1994.
Simulations of the Amazon Basin Circulation
with a Regional Model. J. Climate, 7:56-71.
Hosteller, S.W., G.T. Bates, andF. Giorgi. 1993.
Interactive Nesting of a Lake Thermal Model
within a Regional Climate Model for Climate
Change Studies. J. Geophy. Res., 98:5045-5057.
Hosteller, S.W., F. Giorgi, and G.T. Bates. 1994.
Role of Lake-Atmosphere Feedbacks in
Sustaining Lakes Bonneville and Labontan
18,000 Years Ago. Science, 263:265-268.
Jokobs, H.J., H. Feldmann, H. Mass, and M.
Memmesheimer. 1995. The Use of Nested
Models for Air Pollution Studies: An
Application of the EURAD Model to a SANA
Episode. J. Appl. Meteorol., 34:13-1-1319.
Kain, J.S. and J.M. Fritsch. 1993. Convective
Parameterization for Mesoscale Models. The
Kain-Fritsch Scheme. In K.A. Emanuel and
D.J. Raymond (Eds.), The Representation of
Cumulus in Numerical Models, Meteorological
Monographs, American Meteorological Society,
pp. 165-170.
Kain, J.S. and J.M. Fritsch. 1995. Interactions
Between Parameterized and Explicitly-Resolved
Precipitation Regimes. Part I: Analysis of a
Control Simulation. Mon. Wea. Rev., to be
submitted.
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Kain, J.S. and J.M. Fritsch. 1995. Interactions
Between Parameterized and Explicitly-Resolved
Precipitation Regimes. Part II: Model Response
to the Details of Convective Parameterization
Scheme. Mon. Wea. Rev., to be submitted.
Kuo, Y.-H., J.R. Gyakum, and Z. Guo. 1995. A
Case of Rapid Continental Mesoscale
Cyclogenesis. Part I. Model Sensitivity
Experiments. Mon. Wea. Rev., 123:970-997.
Kuo, Y.-H., X. Zou, and Y.-R. Guo. 1996.
Variational Assimilation of Precipitable Water
Using a Nonhydrostatic Mesoscale Adjoint
Model. Part I: Moisture Retrieval and Sensitivity
Experiments. Mon. Wea. Rev., 124:122-147.
Kuo, Y.-H., R.J. Reed, and Y.-B. Liu. 1996.
The ERICA IOP5 Storm. Part IE: Mesoscale
Cyclogenesis and Precipitation Parameterization.
Mon Wea. Rev., 124:1409-1434.
Kuo, Y.-H., X. Zou, and W. Huang. 1996. The
Impact of GPS Data on the Prediction of an
Extratropical Cyclone: An Observing System
Simulation Experiment. J. Dyn. Atmos.
Oceanogr., accepted for publication.
Lakhtakia, M.N. and T.T. Warner. 1994. A
Comparison of Simple and Complex Treatment
of Surface Hydrology and Thermodynamics
Suitable for Mesoscale Atmospheric Models.
Mon. Wea. Rev., 122:880-896.
Leung, L.R. and S.J. Ghan. 1995. A Subgrid
Parameterization of Orographic Precipitation.
Theor. Appl. Climat., 52:95-118.
Leung, L.R., M.S. Wigmosta, S.J. Ghan, D.J.
Epstein, and L.W. Vail. 1996. Application of a
Subgrid Orographic Precipitation/Surface
Hydrology Scheme to a Mountain Watershed. J.
Geophys. Res., in press.
Leutbecher, M. and H. Volkert. 1996.
Stratospheric Temperature Anomalies and
Mountain Waves: A Three-Dimensional
Simulation Using a Multi-Scale Weather
Prediction Model. Geophy. Res. Letters,
23(23):3329-3332.
Lipton, A.E., G.D. Modica, S.T. Heckman, and
A. Jackson. 1995. Satellite-Model Coupled
Analysis of Convective Potential in Florida with
VAS Water Vapor and Surface Temperature
Data. Mon Wea. Rev., 123:3292-3304.
McHenry, J.N. and R.L. Dennis. 1994. The
Relative Importance of Oxidation Pathways and
Clouds to Atmospheric Ambient Sulfate
Production as Predicted by the Regional Acid
Deposition Model (RADM). J. Appl. Meteorol.,
33(7):890-905.
Modica, G.D. and S.T. Heckman. 1994. An
Application of an Explicit Microphysics
Mesoscale Model to a Regional Icing Event. J.
Appl. Meteorol., 33:53-64.
Molders, N., H. Hass, H.J. Jakobs, M. Laube,
and A. Ebel. 1994. Some Effects of Different
Cloud Parameterizations in a Mesoscale Model
and a Chemistry Transport Model. J. Appl.
Meteorol., 33:527-545.
Molders, N., M. Laube, and G. Kramm. 1995.
On the Parameterization of Ice Microphysics in
a Mesoscale Alpha Weather Forecast Model.
Atmos. Res., 38:207-235.
Oncley, S.P. and J. Dudhia. 1995. Evaluation of
Surface Fluxes form MM5 Using Observations.
Mon Wea. Rev., 123:3344-3357.
Pan, Z., M. Segal, R. Turner, and E. Takle.
1995. Model Simulation of Impacts of Transient
Surface Wetness on Summer Rainfall in the
United States Midwest During Drought and
Flood Years. Mon. Wea. Rev., 123:1575-1581.
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Persson, P., G.Ola, and T.T.Warner. 1995. The
Nonlinear Evolution of Idealized, Unforced,
Conditional Symmetric Instability: A Numerical
Study. J. Atmos. Sci., 52, accepted for
publication.
Reed, R.J., Y.-H. Kuo, and S. Low-Nam. 1994.
An Adiabatic Simulation of the ERICA IOP4
Storm: An Example of Quasi-Ideal Frontal
Cyclone Development. Mon. Wea. Rev.,
122:2688-2708.
Reisner, J., R.M. Rasmussen, and R.T. Bruintjes.
1997. Explicit Forecasting of Supercooled
Liquid Water in Winter Storms Using a
MesoscaleModel. Quart. J. Roy. Meteorol. Soc.,
accepted for publication.
Rotunno, R. And J.-W. Bao. 1996. A Case
Study of Cyclogenesis Using a Model Hierarch.
Mon Wea. Rev., 124:1051-1066.
Seaman, N.L., D.R. Stauffer, and A.M. Gibbs.
1995. A Multi-Scale Four-Dimensional Data
Assimilation System Applied in the San Joaquin
Valley During SARMAP: Part I: Modeling
Design and Basic Performance Characteristics.
J. Appl. Meteorol., in press.
Smith, Lakhtakia, Capehart, and Carlson. 1994.
Initialization of Soil-Water Content in Regional-
Scale Atmospheric Prediction Models. Bull.
AMS, 75:585-593.
Sousounis, P.J. and J.M. Fritsch. 1994. Lake
Aggregate Mesoscale Disturbance. Part II: A
Case Study of the Effects on Regional and
Synoptic-Scale Weather Systems. Bull. Amer.
Meteorol. Soc., 75:1793-1811.
Stauffer, D.R. and N.L. Seaman. 1994.
Multiscale Four-Dimensional Data Assimilation.
J. Appl. Meteorol., 33:416-434.
Stauffer, D.R., N.L. Seaman, and A.M. Lario-
Gibbs. 1994. A Multi-Scale Four-Dimensional
Data Assimilation System Applied in the San
Joaquin Valley During SARMAP. Part I:
Modeling Design and Basic Performance
Characteristics. J. Appl. Meteorol, 33, 43 pp.,
accepted for publication.
Steenburgh, W.J. and C.F. Mass. 1994. The
Structure and Evolution of a Simulated Rocky
Mountain Lee Trough. Mon. Wea. Rev.,
122:2740-2761.
Steenburgh, W.J. and C.F. Mass. 1996.
Interaction of an Intense Extratropical Cyclone
with Coastal Orography. Mon. Wea. Rev.,
scheduled to appear in the June issue.
Steenburgh, W.J. and C.F. Mass. 1996. The
Influence of Terrain-Induced Circulations on
Wintertime Temperature and Snow Level in the
Washington Cascades. Wea. Forecasting,
submitted.
Stensrud, D.J. and J.M. Fritsch. 1994a.
Mesoscale Convective Systems in Weakly
Forced Large-Scale Environments. Part II:
Generation of a Mesoscale Initial Condition.
Mon. Wea. Rev., 122:2068-2083.
Stensrud, D.J. and J.M. Fritsch. 1994b.
Mesoscale Convective Systems in Weakly
Forced large-Scale Environments. Part III:
Numerical Simulations and Implications for
Operational Forecasting. Mon Wea. Rev.,
122:2084-2104.
Stensrud, D.J., R.L. Gall, S.L. Mullen, and K.W
Howard. 1995. Model Climatology of the
Mexican Monsoon. J. Climate, 8:1775-1794.
Stensrud, D.J., R.L. Gall, and M.K. Nordquist.
1997. Surges Over the Gulf of California During
the Mexican Monsoon. Mon Wea. Rev.,
125:417-437.
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Stoelinga, M.T. 1996. A Potential Vorticity-
Based Study of the Role of Diabatic Heating and
Friction in a Numerically Simulated Baroclinic
Cyclone. Mon. Wea. Rev., 124:849-874.
Wang, W. and N.L. Seaman. 1997. A
Comparison Study of Convective
Parameterization Schemes in a Mesoscale
Model. Mon. Wea. Rev., 125:252-278.
Wetzel, M.A. and G.T. Bates. 1994.
Comparison of Simulated Cloud Cover with
Satellite Observations Over the Western U.S. J.
Climate, in press.
Wetzel, M.A. and G.T. Bates. 1995.
Comparison of Simulated Cloud Cover with
Satellite Observations Over the Western United
States. J. Climate, 8:296-314.
Wetzel, M.A. and G.T. Bates. 1995.
Comparison of Simulated Cloud Cover with
Satellite Observations Over the Estern United
States. J. Climate, 8:296-314.
Zaitao,P. 1994. Model Simulation of Effects of
Transient Surface Wetness on Summer Rainfall
in the U.S. Midwest During Drought and Flood
Years. Mon. Wea. Rev., accepted for
publication.
Zhang, D.-L., J.S.Kain, J.M. Fritsch, and K. Gao.
1994. Comments on "Parameterization of
Convective Parameterization in Mesoscale
Numerical Models: A Critical Review" Mon
Wea. Rev., 122:2222-2231.
Zhang, D.-L. and N. Bao. 1994. Oceanic
Cyclogenesis as Induced by a Mesoscale
Convective System Over Land. Part I: A 90-h
Real-Data Simulation. Mon. Wea. Rev.,
submitted.
Zhang, D.-L. and R. Harvey. 1995.
Enhancement of Extratropical Cyclogenesis by a
Mesoscale Convective System. J. Atmos. Sci.,
52:1107-1127.
Zheng, Y., Q. Xu, and D.J. Stensrud. 1994. A
Numerical Study of the 7 May 1985 Mesoscale
Convective System. Mon Wea. Rev., 123:1781-
1799.
Zou, X., Y.-H. Kuo, and Y.-R. Guo. 1995.
Assimilation of Atmospheric Radio Refractivity
Using a Nonhydrostatic Adjoint Model. Mon.
Wea. Rev., 123:2229-2249.
Zuo, X. 1996. Tangent Linear and Adjoint of
"On-Off" Processes and Their Feasibility for Use
in Four-Dimensional Variational Data
Assimilation. Tellus, accepted for publication.
Zou, X. and Y.-H. Kuo. 1996. Rainfall
Assimilation Through an Optimal Control of
Initial and Boundary Conditions in a Limited-
Area Mesoscale Mode. Mon. Wea. Rev., 2359-
2882.
Planned Changes/Refinements A number of
shortcomings have been noted in the original
MM5 approach to modeling the planetary
boundary layer. Most particularly, the treatment
of soil/vegetation interactions and convection
phenomenon that are critical to the long-range
fate and transport of atmospheric pollutants such
as ozone. In response, a modified planetary
boundary layer scheme was developed and is
described in Pleim and Chang (1992) and Pleim
and Xiu( 1995).
A critical consideration when attempting to link
models, with an eventual goal of coupling, is
comparability of the underlying model physics.
The original MM5 planetary boundary layer
treatment is incompatible with the ORTECH soil
emissions model physics. The MM5-PX physics
are sufficiently similar that they can be
considered comparable and the proposed model
linkage for atrazine should yield results very
close to that of a fully coupled.
2. Model parameters and how they will be specified
(http://laurel.mmm.ucar.edu/tutorial-v2-notes.
html) Although the MM5 contains many
fundamental physical relationships,
parameterizations must still be used. Often there
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are several parameterization choices for each
process. The most critical parameterized
processes and options are described below.
Cumulus parameterizations none uses no
cumulus parameterization at grid sizes < 5-10
km.
Anthes-Kuo based on moisture convergence,
mostly applicable to larger grid sizes > 30 km.
Tends to produce much convective rainfall, less
resolved-scale precipitation, specified heating
profile, moistening dependent upon relative
humidity.
Grell - based on rate of destabilization or quasi-
equilibrium, simple single-cloud scheme with
updraft and downdraft fluxes and compensating
motion determining heating/moistening profile.
Useful for smaller grid sizes 10-30 km, tends to
allow more resolved scale rainfall than
convective rainfall.
Aradkawa-Schubert - multi-cloud scheme that is
otherwise like Grell scheme. Based on a cloud
population, allowing for entrainment into
updrafts and allows for downdrafts. Suitable for
larger scales, > 30 km grid sizes, possibly
expensive compared to other schemes.
Fritsch-Chappell based on relaxation to a
profile due to updraft, downdraft and subsidence
region properties. The convective mass flux
remains 50% of available buoyant energy in the
relaxation time. Fixed entrainment rate.
Suitable for 20-30 km scales due to single-cloud
assumption and local subsidence. See Fritsch
and Chappell (1980) and Kain and Fritsch (1993)
for details.
Kain-Fritsch - similar to Fritsch-Chappell, but
uses a sophisticated cloud-mixing scheme to
determine entrainment/detrainment, and
removing all available buoyant energy in the
relaxation time. See Kain and Fritsch (1993) for
details.
Betts-Miller - based on relaxation adjustment to
a reference post-convective thermodynamic
profile over a given period. This scheme is
suitable for > 30 km, but may not be suitable for
severe convection. See Betts (1986), Betts and
Miller (1986), and Betts and Miller (1993) for
details.
PEL Schemes none no surface layer,
unrealistic in real-data simulations.
Bulk PEL - suitable for coarse vertical resolution
in boundary layer, e.g., > 250 m vertical grid
sizes. Two stability regimes.
High-Resolution Blackadar PEL suitable for
high-resolution PEL, e.g., five layers in lowest
km, surface layer < 100 m thick. Four stability
regimes, including free convective mixed layer.
Burk-Thompson PEL suitable for coarse and
high-resolution PEL. Predicts turbulent kinetic
energy for use in vertical mixing, based on
Mellor-Yamada formulas.
Explicit Moisture Scheme dry, no moisture
prediction. Zero water vapor.
Stable Precip nonconvective precipitation.
Large scale saturation removed and rained out
immediately. No rain evaporation or explicit
cloud prediction.
Warm Rain cloud and rain water fields
predicted explicitly with microphysical
processes. No ice phase processes.
Simple Ice (Dudhia) - adds ice phase processes to
above without adding memory. No supercooled
water and immediate melting of snow below
freezing level.
Mixed-Phase (Reisner) - adds supercooled water
to above and allows for slow melting of snow.
Memory added for cloud ice and snow. No
graupel or riming processes.
Goddard Microphysics includes additional
equations for prediction of ice number
concentration and graupel. Suitable for cloud-
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resolving models. See Tao et al. (1989, 1993)
for details.
Reisner Graupel - based on mixed-phase scheme
but adding graupel and ice number concentration
prediction equations. Also suitable for cloud-
resolving models.
Radiation Schemes none no mean tendency
applied to atmospheric temperature, unrealistic in
long-term simulations.
Simple Cooling atmospheric cooling rate
depends just on temperature. No cloud
interaction or diurnal cycle.
Surface Radiation this is used with the above
two options. It provides diurnally varying
shortwave and longwave flux at the surface for
use in the ground energy budget. These fluxes
are calculated based on atmospheric column-
integrated water vapor and low/middle/high
cloud fraction estimated from relative humidity.
Cloud-Radiation Scheme - sophisticated enough
to account for longwave and shortwave
interactions with explicit cloud and clear-air. As
well as atmospheric temperature tendencies, this
provides surface radiation fluxes. May be
expensive but little memory requirement.
CCM2 Radiation Scheme multiple spectral
bands in shortwave and longwave, but cloud
treated simply based on RH. Suitable for larger
grid scales, and probably more accurate for long
time integrations. Also provides -0 radiative
fluxes at surface.
Ground Temperature Schemes - none - no ground
temperature prediction. Fixed surface
temperature, not realistic.
Force/Restore (Blackadar) Scheme - single slab
and fixed-temperature substrate. Slab
temperature based on energy budget and depth
assumed to represent depth of diurnal
temperature variation (-10-20 cm).
5-Layer Soil Model temperature predicted in
1,2,4,8,16 cm layers (approximately) with fixed
substrate below using vertical diffusion equation.
Thermal inertia same as force/restore scheme,
but vertically resolves diurnal temperature
variation allowing for more rapid response of
surface temperature.
Data Quality - Data obtained outside of LMMBP.
(http://laurel.mmm.ucar.edu/mm5/tutorial-v2-
notes .html). A great deal of input information is
needed to set up a prognostic simulation. Three
program modules focus on the input and
modification of these data are described below.
TERRAIN The program that begins any
complete forecast simulation is TERRAIN. This
program horizontally interpolates (or analyzes)
the latitude-longitude interval terrain elevation
and land use categories onto the chosen
mesoscale domains. The model domain settings
(except for moving nests) are constructed in
TERRAIN program. Users may use this program
to check the correctness of the domain settings
first without generating terrain height and land-
use files. Once the domains are correctly set,
users can then go on to run the program
TERRAIN again to produce the terrain height
and land-use files, which will be used by
DATAGRID later.
DATAGRID - The purpose of DATAGRID is to
access archived low-resolution meteorological
analyses:
Latitude-longitude grids
- NMC: Global Analyses (1.5ox 2.5o).
- ECMWF: Global Grids (2.5ox 2.5o; 1980-
1989 only).
TOGA: Basic Level ffl data sets (2.5ox
2.5o).
- Unidata: NMCMRF forecasts (2.5ox 5 .Oo).
Horizontally interpolate these analyses to the
model grid. Write the interpolated analyses for
input to program RAWINS. The fields created
by DATAGRID are used:
72
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To create model input fields directly
from the DATAGRID output.
As first-guess fields for subsequent
objective analysis (program RAWESTS).
Nonhydrostatic model input (or output) fields
are:
3-D Field
Name
U wind
V wind
Vertical wind
Pressure
perturbations
Mixing ratio
Coriolis
parameter
Map-scale
factor
Map-scale
factor
latitude
longitude
latitude
longitude
Ground
temperature
Terrain
elevation
Land use
Snow cover
Field ID (8
Characters)
U
V
w
pp
Q
COROLIS
MAPFACCR
MAPFACDT
LATITCRS
LONGICRS
LATITDOT
LONGIDOT
GROUNDT
TERRAIN
LAND USE
SNOWCOVER
Unit
kPa m/s
kPam/s
kPam/s
kPapa
kPa kg/kg
1/s
dimensionless
dimensionless
degree
degree
degree
degree
K
m
categories
dimensionless
RAWESfS - The purpose of RAWINS is to
improve meteorological analyses (the first guess)
on the mesoscale grid by objective analysis of
surface and upper-air observations. The analyses
input to RAWINS as the first-guess are generally
the low-resolution analyses output from program
DATAGRID. RAWINS may also use a MM5
forecast as the first guess.
RAWINS capabilities include:
Choice of Cressman-style or Multiquadric
objective analysis.
Various tests to screen the data for
suspect observations.
Procedures to input bogus data.
Expanded Grid - if you used an expanded
grid in TERRAIN and DATAGRID,
RAWTNS can incorporate data from
outside your grid to improve analyses
near the boundaries. RAWINS cuts down
the expanded grid to the unexpanded
dimensions on output.
Additional levels: RAWINS can
interpolate from mandatory pressure
levels to additional levels you specify for
an analysis with higher vertical
resolution.
RAWINS output is used to:
Provide fields for initial and boundary
conditions.
Provide three-dimensional fields for
analysis-nudging and four-dimensional
data assimilation.
Provide surface fields for surface-analysis
nudging and four-dimensional data
assimilation.
Source of Observations - NMC operation global
surface and upper-air observations subsets as
archived by the data support section at NCAR.
upper-air data: ROBS, in MNC ON29
format.
surface data: M.C. surface ADP data, in
M.C. ON29 format.
real-time (or recent) surface and upper-air
observations from Unidata, in NetCDF
format.
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B. Model Development
1. Code Development/Maintenance and
Documentation - The PSU/NCAR MM5 Source
Code Documentation NCAR/TN-392+STR, by
Philip Haagenson, Jimmy Dudhia, David
Stauffer, and Georg Grell.
2. Model Documentation - (http://laurel.mmm.ncar.
edu/mm5/doc.html). The MM5 is a community
research model. It is open for access and
modification by any research scientist.
Documentation for the downloadable version is
available as listed below. The full set has been
broken into files whose sizes are listed in
parentheses. All told, the full document is on the
order of 100 pages. It should be noted that since
the model is designed primarily for research
applications, modification and development will
continue. These files may be found at WWW
site:
A description of the
PSU/NCAR MM5 includes:
Fifth-Generation
Cover page (22928 bytes)
Table of Contents, Preface, and
Acknowledgments (45613 bytes)
Chapter 1: Introduction and Chapter 2:
Governing Equations and Numerical
Algorithms (176803 bytes)
Chapter 3: The Mesh-Refinement Scheme
(72278 bytes)
Chapter 4: Four-Dimensional Data
Assimilation (200171 bytes)
Chapter 5: Physical Parameterizations
(463327 bytes)
Appendices (173606 bytes)
References (51604 bytes)
Due to the ongoing nature of the research and
development, documentation may not be up-to-
date or complete. Following is a list of available
MM5 modeling system documentation as of July
1997. Among them, the PSU/NCAR Mesoscale
Modeling System Tutorial Class Notes' is
updated most frequently. These on-line MM5
documents are broken up into a number of
smaller postscript files (the sizes of the files
appear as part of the title). If you use the gopher
or Mosaic browser to download the files, they
may have a different file size. Users can
download and print the documents at their site.
Documentation is also available from NCAR's
anonymous ftp site: ftp://ftp.ucar.edu/
mesouser//Documents. When downloading from
the anonymous ftp site, just get the *.tar.Z file.
To order the hardcopy MM5 documents, send e-
mail to Milli Butterworth (butterwo@ncar.ucar.
edu) of UCAR Information Support Services.
The fee for the documentation is $10.00 per
document (includes shipping and handling).
Terrain and Land Use for the Fifth-
Generation PSU/NCAR MM5: Program
TERRAIN NCAR/TN-397+IA, by Yong-
Run Guo and Sue Chen.
Data Ingest and Objective Analysis for
the PSU/NCAR Modeling System:
Programs DATAGRJD and RAWINS
NCAR/TN-376+IA by Kevin Manning
and Philip Haagenson.
A Description of the Fifth-Generation
PSU/NCAR MM5 NCAR/TN-398+STR,
by Georg Grell, Jimmy Dudhia, and
David Stauffer.
The PSU/NCAR MM5 Source Code
Documentation NCAR/TN-392+STR, by
Philip Haagenson, Jimmy Dudhia, David
Stauffer, and Georg Grell.
PSU/NCAR Mesoscale Modeling System
Tutorial Class Notes by Sue Chen, Jimmy
Dudhia, Dave Gill, Yong-Run Guo,
Kevin Manning, Dave Stauffer, and Wei
Wang.
PSU/NCAR Mesoscale Modeling System
Tutorial Class Notes (MM5 Modeling
System Version 2) by Jimmy Dudhia,
Dave Gill, Yong-Run Guo, Dan Hansen,
Kevin Manning, and Wei Wang, February
1997.
74
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PSU/NCAR Mesoscale Modeling System
Tutorial Class Notes (MM5 Modeling
System Person 2) by Jimmy Dudhia,
Dave Gill, Yong-Run Guo, Dan Hansen,
Kevin Manning, and Wei Wang, July
1997.
Model Validation and Uncertainty Analysis
(http: //www.mcs.anl.gov/Projects/autodiff/
weather/mmS.html). Sensitivity Analysis of a
Mesoscale Weather Model, Christian Bischof,
Gordon D. Pusch, and Ralf Knoesel.
MM5, which was developed jointly by PSU
Meteorology Department and NCAR, is a 3-D
primitive-equation mesoscale weather model.
Sensitivity analysis techniques are employed in
atmospheric modeling, e.g., to develop a measure
of reliability of a forecast or to assess to what
extent a linearization of the model predicts the
overall model behavior.
To validate the sensitivity-enhanced MM5 code
generated by ADIFOR, we can use automatic
differentiation (AD) to produce a Tangent Linear
Model (TLM) fromMMS by applying first-order
perturbation theory. We then compare the
sensitivities predicted by the TLM to divided-
difference estimates obtained by running MM5
with small but finite perturbations about the base
state.
Work continues on a sensitivity-enhanced
version of the Massively Parallel Mesoscale
Model MPMM, a code developed here at
Argonne under an internal grant, with additional
support from the U.S. Air Force and the USEPA.
The sensitivity-enhanced MPMM will make
studies of much more complex problems
practical, including treatment of nested
subdomains. For more information, contact
Gordon Pusch at or Chris
Bischof . Argonne
National Laboratory/Mathematics and Computer
Science Division/autodiff@mcs.anl.gov.
C. References
Anthes, R.A. and T.T. Warner. 1978. Development
of Hydrodynamic Models Suitable for Air Pollution
and Other Mesometeorological Studies. Mon Wea.
Rev., 106:1045-1078.
Betts, A.K. 1986. A New Convective Adjustment
Scheme. Part I: Observational and Theoretical Basis.
Quart. J. Roy. Meteorol. Soc., 112:677-692.
Betts, A.K. and M.J. Miller. 1986. A New
Convective Adjustment Scheme. Part II: Single
Column Tests Using GATE Wave, BOMEX, ATEX
and Arctic Air-Mass Data Sets. Quart. J. Roy.
Meteorol. Soc., 112:693-709.
Betts, A.K. and M.J. Miller. 1993. The Betts-Miller
Scheme. In K.A. Emanuel and D.J. Raymond
(Eds.), The Representation of Cumulus Convective in
Numerical Models. American Meteorological
Society, 246 pp.
Fritsch, J.M. and C.F. Chappell. 1980. Numerical
Prediction of Convectively Driven Mesoscale
Pressure Systems. Part I: Convective
Parameterization. J. Atmos. Sci., 37:1722-1733.
Kain, J.S. and J.M. Fritsch. 1993. Convective
Parameterization for Mesoscale Models: The Kain-
Fritsch Scheme. In K.A. Emanuel and D.J.
Raymond (Eds.), The Representation of Cumulus
Convection in Numerical Models. American
Meteorological Society, 246 pp.
Pleim, J.E. and J.S. Chang. 1992. A Non-Local
Closure Model for Vertical Mixing in the Convective
Boundary Layer. Atmos. Environ., 26A:965-981.
Pleim, J.E. and A. Xiu. 1995. Development of a
Surface Flux and Planetary Boundary Layer Model
for Application in Mesoscale Models. J. Appl.
Meteorol., 34:16-32.
Tao, W.-K., J. Simpson, and M. McCumber. 1989.
An Ice-Water Adjustment Scheme. Mon Wea. Rev.,
117:231-235.
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Tao, W.-K. and J. Simpson. 1993. Goddard
Cumulus Ensemble Model. Part I: Model
Description. Atmos. Oceanic. Sci., 4:35-72.
CMAQ
When concern over air quality developed in the United
States and Canada several decades ago, the problem
appeared to consist essentially of excessive local
concentrations of common pollutants such as sulfur
dioxide, particulates, carbon monoxide, and ozone. Air
quality is now recognized as a much more complex
problem or group of problems that span many pollutants
having media-specific behaviors over very large
geographic areas.
The role of atmospheric transport and deposition to the
Great Lakes basin has been addressed under several
modeling constructs, including mass balance models. In
principle, the complex movements of pollutants through
different parts of the environment can be described
through a mass balance model. In practice, however, the
data requirements needed to make reasonable estimates
of the many processes involved are large, and sufficient
data for these calculations usually are not available.
Uncertainties are substantial even with the best available
data on atmospheric and non-atmospheric inputs. The
LMMBP study will seek to reduce uncertainty in the
atmospheric component of the mass balance by
employing mathematical models of atmospheric transport
and deposition, to provide estimates for spatial and
temporal gaps in actual monitoring databases and to test
hypotheses about characterizations of atmospheric
transformations and removal.
Air Quality Simulation Models (AQSMs) are frequently
used to characterize the emission, transport, and
deposition of hazardous air pollutants over large
geographic areas. These models incorporate fairly
extensive source emission inventories and meteorological
databases (e.g., wind fields, temperature, mixing height),
and apply the collected data to simulated processes such
as dispersion, transformation, and deposition. The
models are run to generate estimates of pollutant
concentrations and deposition rates over a spatial and
temporal pattern.
The mathematical relationships between emissions and
concentration (or deposition) are typically nonlinear, due
to the influences of the atmospheric transport, chemical
and physical transformations, and deposition processes.
Therefore, one cannot extrapolate, based on
measurements alone, the quantitative relationship
between changes in emissions and changes in
atmospheric concentrations (or deposition). AQSMs
attempt to account for the nonlinear physical and
chemical processes influencing atmospheric
concentrations deposition.
Development of AQSMs started in the late seventies.
The Urban Airshed Model (UAM; Scheffe and Morris,
1993) followed by the Regional Oxidant Model (ROM;
Lamb, 1983) provided Eulerian-based models for ozone,
the former for urban and the latter for regional scale.
Strategies for State Implementation Plans (SIPS) used
ROM to provide boundary conditions for UAM
simulations. Attention to acid deposition issues was
addressed in the eighties with the development and
evaluation of regional acid deposition models such as the
Regional Acid Deposition Model (RADM; Chang etal,
1987), the Acid Deposition and Oxidant Model (ADOM;
Venkatram et al., 1988) and the Sulfur Transport and
Emissions Model (STEM; Carmichael et al, 1986).
Other major modeling systems included the Regional
Lagrangian Modeling of Air Pollution model (RELMAP;
Eder et al., 1986), a Lagrangian framework system, and
semi-empirical and statistical models. Models of this
period were designed to address specific air pollution
issues, such as ozone or acid deposition. Thus, flexibility
to deal with other issues such as particulate matter or
toxics was very limited. With the passage of the CAAA-
90, a wide range of additional issues was identified
including visibility, and fine and coarse particles, as well
as indirect exposure to toxic pollutants such as heavy
metals, semi-volatile organic species, and nutrient
deposition to water bodies.
In the nineties, the USEPA embarked upon the
development of an advanced modeling framework to
meet the challenge posed by the CAAA-90. The Models-
3 framework has been designed for holistic
environmental modeling utilizing state of science
representation of atmospheric processes in a high
performance computing environment. Descriptions of
Models-3 can be found in Novak et al. (1988) and Byun
et al. (1998). The science components in Models-3 are
called the Community Multi-scale Air Quality (CMAQ)
system and are described briefly in Ching et al. (1998).
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The Models-3/CMAQ system is designed as a multi-
pollutant, multi-scale Eulerian framework air quality and
atmospheric deposition modeling system. It contains
state-of-science parameterizations of atmospheric
processes affecting transport, transformations and
deposition of such pollutants as ozone, particulate matter,
airborne toxics, and acidic and nutrient pollutant species.
It is this new modeling system that will be further
enhanced and applied to address the specific areas of
concern for the LMMBP study.
References
Byun, D., J. Young, G. Gipson, J. Godowitch, F.
Binkowski, S. Roselle, B. Benjey, J. Plein, J. Ching, J.
Novak, C. Coats, T. Odman, A. Hanna, K. Alapaty, R.
Mathur, J. McHenry, U. Shankar, S. Fine, A. Xiu, and C.
Jang. 1998. Description of the Models-3 Community
Multi-scale Air Quality (CMAQ) Modeling System. In -
10th Joint AMS and AW&MA Conference on the
Applications of Air Pollution Meteorology, pp. 264-268,
Phoenix, Arizona. January 11-16, 1998.
Carmichael, G.R., L.K. Peters, and T. Kitada. 1986. A
Second Generation Model for Regional-Scale
Transport/Chemistry/Depostion. Atmos. Environ.,
20:173-188.
Chang, J.S., R.A. Brost, I.S.A. Isaksen, S. Madronich, P.
Middleton, W.R. Stockwell, and C.J. Walcek. 1987. A
Three-Dimensional Eulerian Acid Deposition Model.
Physical Concepts and Formulation. J. Geophys. Res.,
92:14681-14700.
Ching, J., D. Byun, I. Young, F.S. Binkowski, J. Pleim, S.
Roselle, J. Godowitch, W. Benjey, and G. Gipson. 1998.
Science Features in Models-3 Community Multiscale Air
Quality System. In 10th Joint AMS and AW&MA
Conference on the Applications of Air Pollution
Meteorology, pp. 269-273, Phoenix, Arizona. January
11-16,1998.
Eder, B.K., D.H. Conventry, T.L. Clark, and C.E.
Bollinger. 1986. RELMAP: A Regional Lagrangian
Model of Air Pollution - User's Guide. Final Project
Report. U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina. EPA-600/8-86-
013.
Lamb, R.C. 1983. A Regional Scale (1000 km) Model
of Photochemical Air Pollution, 1, Theoretical
Formulation. U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina. EPA-600/3-83-
035.
Novak, J., J. Young, D. Byun, C. Coats, B. Benjey, J.
Gipson, S. LeDuc, and G. Walter. 1998. Models-3: A
Unifying Framework for Environmental Modeling and
Assessments. In 10th Joint AMS and AW&MA
Conference on the Applications of Air Pollution
Meteorology, pp. 259-263, Phoenix, Arizona. January
11-16, 1998.
Scheffe, R.D. and R.E. Morris. 1993. A Review of the
Development and Application of the Urban Airshed
Model. Atmos. Environ., 27B:23-39.
Venkatram, A., P. Karamchandani, and P. Misra. 1988.
Testing a Comprehensive Acid Deposition Model.
Atmos. Environ., 22:737-747.
Tributary Loading
Principal Investigator: David Hall, USGS
Project Officer: Glenn Warren, USEPA, GLNPO
Because the preparation of this section was delayed, it is
being included as Appendix G. The entire document
provided by the USGS is entitled, "Quality Systems and
Implementation Plan (QSIP)" and can be obtained from
David Hall, USGS, Middleton, Wisconsin.
PCB Tributary Loading Models
Project Liaison: Dale Patterson, WDNR
Principal Modeler: Mark Velleux, WDNR
Support Modeler: Jim Ruppel, WDNR
A. Model Description(s)
1. Background Information Ongoing PCB
transport and fate model development for three
Wisconsin tributaries to Lake Michigan will
provide estimates of present and future PCB
export to Lake Michigan. The three tributaries to
be modeled are: 1) the lower Fox River; 2) the
Sheboygan River; and 3) the Milwaukee
River/Cedar Creek. All three tributary models
77
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will be based on the IPX framework development
as part of the GBMBS effort (Velleux et al.,
1996). Additional information on the
background of IPX model framework is provided
in the section on the PCB/TNC model for Lake
Michigan.
2. Model State Variables and Parameters PCBs
will be simulated as one state variable, total
PCBs; solids will also be simulated as one, two,
or three state variables, depending on the range
of physical characteristics observed for particles
in each tributary. Total PCBs will be computed
as congener and/or Aroclor sums. If data exist to
define initial conditions and external loads,
mercury and TNC may also be simulated for the
lower Fox River.
External loading of PCBs and solids from
tributary watersheds will be estimated using the
results of independent rainfall-runoff models and
field verification data collected as part of priority
watershed project and other non-point source
pollution characterization efforts. In the lower
Fox River, there are 19 point sources; total
suspended solids (TSS) loads from these
dischargers are monitored daily; PCB loads are
assumed to be as monitored during the 1989
GBMBS. There are no significant TSS or PCB
dischargers in the spatial domain of the
Sheboygan or Milwaukee River.
Initial PCB concentrations and other river bed
sediment characteristics will be estimated from
spatial analysis of sediment core samples and
results of sediment probing. Boundary
conditions will be estimated from mass balance
study data as well as from archival data sources
such as the USEPA STORET database.
Transport parameters specified include advective
and dispersive water column transport and
particle transport. Advective transport will be
based on flow measurements. Dispersive
transport will be estimated from theoretical
principles and confirmed through calibration of
a conservative tracer (chloride) where data exist.
In the lower Fox River, advective and dispersive
pore water transport is also included and is
estimated from the results of a regional
groundwater transport model. Particle transport
parameters include settling and resuspension.
Particle settling velocities will be estimated from
grain size data and calibration. Resuspension
velocities will be estimated from the results of
SEDZL-based sediment transport estimates and
calibration. Sediment core data will be used to
independently confirm predicted burial rates
which are computed in the model as the
difference between settling and resuspension
fluxes.
Particle and contaminant physicochemical
parameters specified include the ratio of organic
carbon to solids, water column and sediment
DOC, sediment bulk density, volatile exchange
between the surface water and atmosphere, and
partitioning between dissolved and particulate
carbon sorbent compartments. Equilibrium
partitioning is assumed. Chemical
transformations by biotic or abiotic reactions are
assumed to be negligible.
3. Data Quality - The data used will be extracted
from the project database, other data collection
efforts for each tributary, and archival sources
such as STORET. Other data collection efforts
include: 1) the GBMBS; 2) Sheboygan River
Remedial Investigation/Feasibility Study (RI/FS)
and food chain study; and 3) the Milwaukee
River Mass Balance Study. However, the
completeness and quality of data for each
tributary differs widely. These differences will
affect the accuracy of model results.
The lower Fox River has been studied
extensively. In addition to the LMMBP and
GBMBS, a series of extensive follow-up efforts
to characterize PCB distributions in the water,
sediments, and fish have been completed. These
studies provide extensive data sets specifically
tailored for model development. The
completeness and quality of these data will
permit development of a research quality model.
The Milwaukee River has also been well studied,
although less so than the lower Fox River. In
addition to the LMMBP, the Milwaukee River
78
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Mass Balance Study provides information to support
model development. However, PCB sources as
well as distributions in the river sediments are
less well known. These factors will limit the
accuracy of model development to the screening-
level.
The Sheboygan River is less well studied.
Although listed on the USEPA National Priority
List of Superfund Sites in May, 1986, only a
portion of the PCB-impacted areas of the river
have been studied; estimates of PCB
distributions in river sediments are highly
uncertain. With the exception of data collected
as part of the LMMBP, few water column PCB
data exist to support model development. These
factors will limit the accuracy of model
development to the screening-level.
All assumptions and simplifications needed to
develop each tributary model will be identified
and discussed in the final report.
B. Model Development
1. Code Development and Maintenance IPX is
coded in ANSI standard FORTRAN 77, with
subroutines and common variable blocks stored
in separate source and include files. A UNIX
Makefile is maintained for program compilation.
Model source code and all associated files will
be maintained in a limited access file space; as
model development proceeds, Digital UNIX RCS
will be used for code maintenance. Code
modifications will be done in-house at WDNR
with assistance from LLRS.
2. Model Documentation - Model documentation is
provided in a series of reports and publications
cited above. A User's Guide, based on Velleux
etal. (1994) is maintained by WDNR and LLRS.
As the model program is revised and modified,
updated documentation is added to the User's
Guide. Documentation efforts will be
coordinated with LLRS for consistency.
3. Code Verification - Code modifications will be
carefully implemented and tested to verify proper
model performance. Modifications to the code
will be checked with an appropriate number of
hand calculations and verified by testing against
results from the original version to ensure proper
function of the code. Code verification efforts
will be coordinated with LLRS for consistency.
4. Code Documentation The IPX code has been
internally documented. The history of revisions
ot the code is maintained as chronological entries
within the header comments of each file.
Documentation of future revisions will also be
included within RCS. Code verification efforts
will be coordinated with LLRS for consistency.
5. Model Calibration /Validation and Uncertainty -
Comparison to observed and predicted chemical
concentrations in water, suspended solids, and
sediment serves as the basis for model calibration
and validation. These comparisons define model
goodness-of-fit and include time series and point-
in-time analysis of predictions and residuals.
Calibrated model predictions of PCB and TSS
export to Lake Michigan will be compared to
export values estimated by the USGS using
alternative methods. Additional information on
the model calibration and verification is provided
in the section on the PCB/TNC model for Lake
Michigan.
Quantitative uncertain analysis of model results
for the lower Fox River model will be completed
using the uncertainty analysis methodology
developed for the Fox River/Green Bay models
as part of the 1989 GBMBS (Di Toro and
Parkerton, 1993). Uncertainty analysis of model
results for the Sheboygan River and Milwaukee
River screening-level models will be qualitative
but will draw from the more rigorous analysis for
the lower Fox River.
Model results will be qualified as all models are
simplifications of natural systems and contain
many explicit and implicit assumptions. It is also
expected that the LMMBP Science Review Panel
will provide caveats for the model results and
include recommendations for future research to
reduce model uncertainty. Managers will need to
decide whether or not to use model results and
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whether or not to conduct research to improve
these models.
C. References
Di Toro, D.M. and T.F. Parkerton. 1993. Final
Report: Uncertainty Analysis Methodology for Green
Bay Models. Report to the U.S. Environmental
Protection, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse
lie, Michigan.
Velleux, M., J. Gailani, and D. Endicott. 1994. A
User's Manual to IPX, The In-Place Pollutant Export
Water Quality Modeling Framework. U.S.
Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 194
pp.
Velleux, M., J. Gailani, and D. Endicott. 1996.
Screening-Level Approach for Estimating
Contaminant Export From Tributaries. J. Environ.
Engin., 122(6):503-514.
Atmospheric Loading for Mercury
Principal Investigator: Gerald J. Keeler, University of
Michigan
Project Officer: Angela Bandemehr, USEPA, GLNPO
A. Project Planning and Organization
1. Introduction This project to calculate total
atmospheric mercury deposition estimates to
Lake Michigan is in support of the larger
LMMBP. The USEPA has adapted the mass
balance approach to provide a consistent
framework for integrating load estimates,
ambient monitoring data, process research, and
modeling to develop a predictive tool to guide
future toxic load reduction efforts for Lake
Michigan. The USEPA will coordinate the
development of a suite of integrated mass
balance models to simulate the transport, fate,
and bioaccumulation of toxic chemicals in Lake
Michigan. The four main goals of the LMMBP
are to:
a.
b.
c.
d.
2.
Determine loading rates for critical
pollutants from major source categories
(tributaries, atmospheric deposition,
contaminated sediments) to establish a
baseline loading estimate to gauge future
progress, and to better target future load
reduction estimates.
Predict the environmental benefits (in terras
of reducing concentrations) of specific load
reduction alternatives for toxic substances,
including the time required to realize the
benefits.
Evaluate the environmental benefits of load
reductions for toxic substances expected
under existing statutes and regulations and,
thereby, determine if there is a need for
more stringent, future regulations to realize
further benefits.
Improve our understanding of how key
environmental processes govern the
transport, fate, and bioavailability of toxic
substances in the ecosystem.
The LMMBP model will initially use
observation-based interpolation of atmospheric
monitoring data, collected as part of the
Enhanced Monitoring Program, to estimate
atmospheric loading. The UMAQL collected
samples for particulate phase mercury, vapor
phase mercury, and mercury in precipitation
from five sampling sites around Lake Michigan
during the Lake Michigan Loading Study (July 1,
1994 through October 31, 1995). The UMAQL
will utilize this monitoring data in a multi-tiered
comprehensive approach to estimate both wet
and dry atmospheric deposition estimates and
associated uncertainties.
Background - Mercury is a toxic bioaccumulative
substance in aquatic ecosystems. In its
methylated form, mercury has been observed to
bio-concentrate more than a million fold in the
aquatic food chain. Consumption advisories are
presently in effect for fish caught in Lake
Michigan, Lake Superior, Michigan inland lakes,
and a number of Wisconsin inland lakes because
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of elevated mercury concentrations.
Atmospheric deposition is widely recognized as
an important link in the cycling of mercury in the
environment and has been identified as the
primary pathway for inputs of mercury to Lake
Michigan. Consequently, mercury has been
identified as a critical pollutant for study and has
specifically been targeted in the 1987 GLWQA
and Section 112(m) of the CAAA-90.
The GLWQA states that it is the goal of the
Governments of Canada and the United States to
restore and maintain the chemical, physical and
biological integrity of the waters of the Great
Lakes Basin Ecosystems. Further, these
Governments have agreed to make a maximum
effort to develop programs, practices and
technology necessary for a better understanding
of the Great Lakes Basin Ecosystem. As part of
this effort, Annex 2 of the GLWQA mandates the
development of LaMPs for each of the five Great
Lakes, in an effort to address these issues on a
lake-by-lake basis. A variety of activities,
mandated by the GLWQA and the CAAA-90,
including the LMMBP, are being performed in
an effort to provide the information necessary to
carry out the LaMP developed for Lake
Michigan.
3. Project Objectives - The overall objective of this
project is to obtain estimates of total mercury
loading to Lake Michigan due to atmospheric
deposition. These estimates will be based on
data collected as part of the Enhanced
Monitoring Program, including simultaneous
measurements of mercury in air and water during
lake-wide mass balance surveys, and during the
intensive work sponsored by the USEPA.
The specific objectives of this project are as
follows:
1. Determine that portion of the atmospheric
deposition loading of total mercury to Lake
Michigan due to "wet deposition".
Estimates of the uncertainties associated
with this calculation will also be addressed.
2. Determine that portion of the atmospheric
deposition loading of total mercury to Lake
Michigan due to "dry deposition". To
achieve this latter objective, two sub-
objectives will also need to be addressed.
Namely, this project will need to develop
methods for:
(a) the determination of dry deposition
velocities and mercury volatilization
rates, and
(b) the determination of the vapor-phase
concentrations for mercury in the air-
water interface.
Estimates of the uncertainties associated with
this calculation will also be addressed.
4. Project Description Meeting the project
objectives described in Section I will require the
utilization of newly developed wet- and dry-
deposition estimation techniques that incorporate
databases not previously used in atmospheric
deposition calculations. The uncertainties in
making over-water estimations with little
meteorological or chemical data available are
inherently large. Recent innovations in radar
technology and advances in computer hardware
have enabled scientists to begin developing new
numerical methods of open water meteorological
parameterization. The technical approaches and
techniques used to achieve each of these
objectives are discussed in detail below. A
summary table of the methods to be used in these
analyses is given at the end of Task 1.
Task 1: Determine that portion of the
atmospheric deposition loading of total mercury
to Lake Michigan due to "wet deposition"
Wet-deposition loading estimates will be
calculated using a 5 km grid scale resolution for
total mercury. A multi-level approach will be
used to calculate the loading to Lake Michigan
due to wet-deposition.
Level One estimates are straightforward and will
employ a modification of the method used by
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Voldner and Alvo (1993). In this method,
estimates of the total wet deposition to each grid
cell within the 5 km grid domain will be derived
from available monitoring site data using a
spatial interpolation technique called kriging.
First, monthly volume-weighted precipitation
concentrations will be calculated for each
LMMBP sampling site. These concentration
values will then be interpolated across Lake
Michigan using the aforementioned "kriging"
technique, to obtain an estimate of "average
concentration" for each grid cell. Similarly,
monthly precipitation totals obtained from each
of the NWS rain gauge network sites
(approximately 700) will be "kriged" to obtain
precipitation totals for each 5 km grid cell. The
final wet deposition estimate would result from
taking the product of the "kriged" concentration
and precipitation fields. Uncertainties in this
method will be based on uncertainties in the
measurement and analysis of the samples, plus
the uncertainties due to the interpolation
technique (which are dependent on the location
of the grid cell location with respect to the
locations of the measurement sites used in the
"kriging" analysis). This approach does not
address the problems associated with differences
in "over-water" vs. "land-based" precipitation
and meteorology.
Level Two estimates will attempt to decrease the
uncertainty in the wet deposition estimates by
using measured high-resolution precipitation
data, derived from NWS Weather Surveillance
Radar (WSR) observations. The interpolation of
land-based precipitation depth over Lake
Michigan ignores the surface forcings such as
heat transfer, evaporation, frictional drag, and
terrain induced flow modification the lake can
impart on the overlying atmosphere. These
surface forcings can change the overlying
atmospheric stability and strongly affect
precipitation processes. Changnon and Jones
(1972) found average annual precipitation is 6%
less over Lake Michigan than the surrounding
land area. As in the Level One estimation
technique, volume-weighted mercury
concentrations will interpolated onto the 5 km
over-water grid using the "kriging" technique.
However, the gridded precipitation field will not
be derived from a "kriging" of land-based
precipitation gauge data. Rather, rainfall
estimates for each grid cell will be determined
using high-resolution rainfall estimates derived
from WSR data by NASA's Marshall Space
Flight Center (MSFC). The final wet deposition
estimates would result from taking the product of
the "kriged" concentration field and the WSR
derived precipitation fields.
Level Three estimates of the wet deposition
loading to Lake Michigan will offer the best
degree of precision by utilizing "adjusted MSFC
radar data". Although radar reflectivity is a
direct measurement technique, it also has some
inherent uncertainties. The empirical
relationship between reflectivity factor (Z) and
rainfall rate (R) is based upon droplet size
distributions, which may be event specific.
Additional errors can result when vertical air
motions exceed the raindrop terminal velocity,
particularly in convective storms (Burgess and
Ray, 1986). Several methods of "rain gauge
calibration" of the MSFC radar data are currently
being evaluated. Adjusting the MSFC radar data
to reflect the land-based rain gauge
measurements will allow for a more accurate
representation of the localized deposition
patterns observed over the lake surface due to a
more accurate description of the distribution of
precipitation across the domain during the time
period of interest.
An assessment of the climatological
representativeness of the LMMBP data set will
also be completed. A preliminary investigation
revealed that, in general, precipitation in 1994
around Lake Michigan was significantly less
than the 30-year climatological average.
In fact, the Chicago area experienced the driest
year in more than 30 years, receiving only 29.6
inches of precipitation in 1994. Monthly
climatologically averaged mercury wet
deposition estimates will be estimated using
"kriged" monthly climatological-average
precipitation depths rather than the
measurements made during the LMMBP study.
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This will provide a meteorological representative
mercury contribution to Lake Michigan from wet
deposition and will characterize the average
impact of the urban areas in the southern portion
of the lake. This data will form the basis on
which future wet deposition measurements could
be compared.
Task 2. Determine that portion of the
atmospheric deposition loading of total mercury
to Lake Michigan due to "dry deposition". This
task includes: 1) the determination of dry
deposition velocities and volatilization rates, and
2) the determination of vapor-phase
concentrations in the air-water interface.
Level One - Vapor exchange across the air-water
interface and particle dry deposition constitute
the remaining portion of atmospheric deposition
not addressed in Task 1. Studies have shown
that a significant mass of atmospheric
contaminants, monitoring site data using an
interpolation scheme called kriging. First, the
ambient concentration values will be interpolated
across Lake Michigan using the aforementioned
kriging" technique, to obtain an estimate of
"average concentration" for each grid cell at 5
km resolution. The deposition velocity for each
point over the lake will be calculated using
meteorological data provided by NOAA-GLERL.
The final dry deposition estimate of each grid
cell would result from taking the product of the
"kriged" concentration field and calculated
deposition velocity. Uncertainties in this method
will be based on uncertainties in the
measurement and analysis of the samples, plus
the uncertainties due to the interpolation
technique.
Level Two In recent years the UMAQL has
done considerable work in the development of a
deposition model (Pirrone et al., 1995a,b) which
takes into consideration the important
mechanisms involved in the transfer of
atmospheric contaminants to a receptor water
surface. Recent work was successful in
calculating the atmospheric input of
contaminants to Lake Michigan during the Lake
Michigan Urban Air Toxics Study (LMUATS) in
the 1991 (Pirrone et al., 1995a,b), the
Atmospheric Exchange Over Lakes and Ocean
Surfaces Study (AEOLOS) in 1994 (Vette et al.,
1996), and to Lakes Huron, Erie and St. Clair
during two pilot studies carried out in the 1992
and 1994 (Pirrone et al., 1995c; Keeler and
Pirrone, 1996). The results obtained during these
studies have shown that due to large spatial and
temporal variability of parameters (i.e., particle
deposition velocity, Henry's law constant, gas-
particle partitioning coefficient, ambient
concentration, meteorological parameters)
governing the transfer mechanisms of
atmospheric contaminants, the deposition flux
and gas exchange rate may vary by several orders
of magnitude during the over-water transport.
Parameterizations from this model will be used
by the UMAQL modelers in conjunction with the
NOAA-GLERL 5 km over-water meteorological
data to improve our understanding of the effects
of different meteorological conditions on dry
depositon processes to Lake Michigan.
Michigan
Analysis Level
Level One
Level Two
Level Three
Method to Obtain Gridded
Concentration Field
Kriging of site data
Kriging of site data
Kriging of site data
Method to Obtain Gridded
Precipitation Fields
Kriging of NWS data
WSR radar
Adjusted WSR radar
Time Resolution of
Deposition Estimates
Monthly/annual
Monthly/annual
Monthly/annual
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The UMAQL team will work closely with the
LMMBP modelers to determine how best to
incorporate this deposition module into the mass
balance model for use in future load reduction
studies. Extensive experience in deterministic
and numerically modeling coupled with
extensive experience in gridding both
meteorological and emissions data will allow the
UMAQL Team to efficiently communicate with
the whole lake water modelers. Coupling the
atmospheric models together with the lake
hydrodynamic models will be a much more
efficient process if both atmospheric and water
modelers can communicate in an effective
manner.
As was the case for the wet deposition estimates
discussed under Task 1, a quantitative estimate
of the uncertainties in these estimates will be
included with the final results.
5. Personnel Descriptions
Gerald J. Keeler Ph.D. Dr. Keeler presently
holds an appointment as an Associate Professor
in the Department of Environmental and
Industrial Health as well as in the College of
Engineering in the Department of Atmospheric,
Oceanic, and Space Sciences, at the University of
Michigan in Ann Arbor. He is also the Director
of the UMAQL which he established in 1990.
Dr. Keeler has extensive experience in planning,
conducting, and managing large field studies
aimed at understanding air quality and
environmental problems. His focus has been on
the measurement and modeling of atmospheric
contaminants focusing on trace elements
including mercury. He has been involved in
research and monitoring programs in many parts
of the United States and Canada. In 1991 he was
the Principal Investigator for the Lake Michigan
Urban Air Toxics Study (LMUATS) jointly
performed by the UMAQL and USEPA-NERL.
The LMUATS was the first study to investigate
the importance of the Chicago/Gary
urban/industrial area on toxic deposition to Lake
Michigan (Keeler, 1994). Since 1991, Dr.
Keeler has been a leader in atmospheric mercury
research and methods development. His
extensive experience in atmospheric mercury led
to Dr. Keeler being invited to be on the Mercury
Atmospheric Processes Expert Panel which
included the top mercury scientists in the world.
The UMAQL has been performing direct
measurements of hazardous pollutant levels and
deposition on Lake Michigan each year since
1991. The UMAQL has been investigating the
transport and deposition of hazardous air
pollutants across Michigan looking at a variety of
semi-volatile organic carbons as well as trace
elements. He will serve as coordinator for this
interagency project and be responsible for the
mercury modeling and interpretation.
Thomas M. Holsen, Ph.D. Dr. Holsen is
currently an Associate Professor and the
Associate Chairman of Environmental
Engineering Division in the Chemical and
Environmental Engineering Department at
Illinois Institute of Technology. His research
interests include the environmental chemistry,
transport, transformations and fate of
hydrophobic organic chemicals in the Great
Lakes. Recent research has focused on the
development of instruments and techniques to
measure the dry deposition of toxic compounds
to natural surfaces. He is currently a co-principal
investigator on three USEPA funded projects
investigating the deposition of toxic chemicals in
the Great Lakes region. He has published
extensively on the absolute and relative
importance of atmospheric deposition of toxic
substances to and their cycling within the Great
Lakes. He was a critical reviewer of the
Identification of Sources section of the Great
Waters Report to Congress for 1993. He has over
40 publications and has successfully supervised
research projects sponsored by the USEPA,
OSWR, and HWRIC.
Frank J. Marsik, Ph. D. Dr. Marsik currently
holds an appointment as a Post-doctoral
Research Fellow in the Department of
Environmental and Industrial Health at the
University of Michigan in Ann Arbor. His
doctoral research focused on the
micrometeorological aspects of earth-atmosphere
turbulent exchange processes. He has extensive
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experience in planning and conducting
micrometeorological support for various air
quality field programs. Among the projects in
which Dr. Marsik has participated are the 1990
Lake Michigan Ozone Pilot Study, the 1991
LMUATS, as well as the 1992 and 1993 forest-
atmosphere exchange measurement campaigns
associated with the USEPA' s Southern Oxidants
Study. He has most recently been working with
scientists at NOAA's Atmospheric Turbulence
and Diffusion Division on methods development
related to surface mercury flux/deposition
measurements.
Matthew S. Landis, M.S. - Mr. Landis is a Ph.D.
student at the University of Michigan and
currently serves as a graduate research assistant
at the UMAQL. His MS research focused on
development and evaluation of inorganic wet
deposition collection and analysis methods. He
has extensive experience in conducting and
evaluating field air sampling projects. While
with the Pennsylvania Department of
Environmental Resources Bureau of Air Quality,
Mr. Landis worked in conjunction with an
USEPA-NEIC investigation on inorganic
emissions from a hazardous waste recycling
facility and with a mobile analytical laboratory
study of organic emissions from point sources.
He has participated in the 1994-95 AEOLOS
intensive studies on Lake Michigan, the 1995
South Florida Atmospheric Mercury Project, and
has coordinated the atmospheric mercury
component of the LMMBP study. In addition,
Mr. Landis conducted the trajectory analysis
portion of the 1992-93 Trace Element Transport
and Deposition Study sponsored by the
Adirondack Lake Sampling Survey in
collaboration with Dr. Ilhan Olmez at the MIT
Nuclear Reactor Laboratory. His Ph.D. research
is focusing on the long-range transport, in cloud
processing, and wet deposition of inorganic trace
elements to the Great Lakes.
B. Model Description
1. Researcher Responsibilities Dr. Gerald J.
Keeler, Principal Investigator for this project,
will be responsible for oversight of the modeling
phase of this project. Dr. Keeler will also be
responsible for all communications between the
UMAQL and USEPA QA/QC Officers. Dr.
Frank Marsik will be responsible for
meteorological data verification and consistency
analysis. Matthew S. Landis will be responsible
for the preparation of input data, performance of
deposition model runs and interpretation of
results.
2. Model parameters The wet deposition
estimation model will be written in SAS and will
utilize the variogram and krige2d procedures. A
detailed description of the SAS 6.12
implementation of the variogram and kriged
procedures and the main equations can be found
in the SAS Institute Inc., SAS/STAT® Technical
Report: Spatial Prediction Using the SAS
System, SAS Institute, Inc., Gary, North Carolina,
1996. 80pp.
The dry deposition estimation model will consist
of two separate linked models. The first model
will be written in SAS and will utilize the
variogram and krige2d procedures to estimate
particulate phase and vapor phase mercury
concentrations onto the NOAA-GLERL 5 km
over-water grid. The second model will be
written in FORTRAN 77 and will use the
mercury concentration estimates generated in the
first model and high resolution over-water
meteorological data supplied by NOAA-GLERL
to estimate particle dry deposition.
3. Computer Aspects A typical wet deposition
model run for one-year over Lake Michigan at 5
km/1 month resolution takes approximately 30
CPU minutes on an IBM compatible PC (with a
200 megahertz 32 bit processor and 64
megabytes of RAM).
A typical dry deposition model run for one year
over Lake Michigan at 5 km/1 hour resolution
takes approximately 6 CPU hours on a Sun Spare
20 Workstation (with a 100 megahertz 64 bit
processor and 48 megabytes of RAM).
4. Data Quality - The input data used for the
modeling studies associated with this project will
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be obtained from three major sources: (i)
National Climatic Data Center (NCDC) in
Asheville, North Carolina; (ii) NOAA-GLERL in
Ann Arbor, Michigan; and (iii) the UMAQL in
Ann Arbor, Michigan. The NCDC provided the
TD3220 digital database, which includes NWS
cooperative station precipitation depth. The
NOAA-GLERL provided high resolution over-
water Lake Michigan meteorological data. The
UMAQL provided particulate phase mercury,
vapor phase mercury and mercury in
precipitation data collected during the USEPA,
GLNPO sponsored Lake Michigan Loading
Study. Preliminary QA/QC was performed on
these data sets by the respective sources. The
UMAQL visually interrogates all of the input
chemical and meteorological data sets for
consistency and accuracy prior to use.
C. Model Development
1. Code Development and Maintenance - The S AS®
System is an integrated system of software
providing complete control over data access,
management, analysis, and presentation. SAS
Version 6.12 was developed and tested by the
SAS Institute, Inc. The Institute is a private
company devoted to the support and further
development of its software and related services.
The dry deposition models for this project are
presently being developed by the UMAQL.
During the code development process, the
UMAQL will keep complete records of model
development, modifications made to the code,
and code validation procedures. Model
development records will include: (i) model
assumptions; (ii) model parameter values and
sources; (iii) changes and verification of changes
made in the code; (iv) actual input used; (v)
output of model runs and interpretation; and (vi)
validation of the models.
2. Model Documentation Full documentation for
SAS 6.12 is available from the SAS Institute,
Inc., SAS Companion/or the Microsoft Windows
Environment, Version 6, First Edition.
Documentation for the Variogram and Krige2d
procedures and the main equations can be found
in the SAS Institute, Inc., SAS/STAT® Technical
Report: Spatial Prediction Using the SAS
System, SAS Institute, Inc., Gary, North Carolina,
1996. 80pp.
The UMAQL will provide complete
documentation for the dry deposition model
being developed as part of this project. The
documentation will include: (i) the equations on
which the model is based; (ii) the underlying
assumptions; (iii) the boundary conditions that
can be incorporated into the model; (iv) the
method used to solve the equations; and (v) the
limiting conditions. The UMAQL will also
include instructions for operating the code
including instructions for preparing data files,
programmer's instructions, and computer
operator instructions.
3. Code Verification The SAS Institute, Inc.
performed all code verification associated with
SAS Version 6.12. Verification for the dry
deposition model will be preformed by the
UMAQL. The objective of the code verification
process is to verify the precision and accuracy of
the computational algorithms used to solve the
governing equations and to assure that the
computer code is fully operational.
4. Code Documentation The SAS Institute Inc.
performed documentation of the SAS Version
6.12 code. Documentation of the dry deposition
model code will be preformed by the UMAQL.
Code documentation will include model
specifications; model descriptions, description of
routines; and description of databases. The
UMAQL will carefully inspect all model code
developed as part of this project to reveal
potential programming or logical errors.
Comprehensive internal code documentation will
also be incorporated into each of the models to
aid in code development and maintenance, model
documentation, and code verification.
D. Model Validation
Model validation is the comparison of model results
with numerical data independently derived from
environmental observations. Since the models
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currently under development will estimate over-water
atmospheric deposition to Lake Michigan, where few
observations were made, it will be very difficult to
validate these models. The UMAQL will make every
effort to use what data is available to evaluate the
mercury deposition models. Comparisons with other
literature estimates will be performed when possible.
E. Record Usage and Management
1. Data records - All data generated by the UMAQL
will be recorded in electronic format. All
databases are backed up either to floppy disks, 8-
mm tape, or removable hard-drive media, which
are stored in different locations.
2. Records Management System A master
directory, LMMBP, will be created to hold all
data. Separate subdirectories will be created for
FINAL results. A complete description of the
data directory structure will be included in a
'readme' file located in the master directory.
3. Records Validation Electronic records
produced during the course of the project will be
stored in separate directories reserved for each
individual participant. Computer files are
manually validated by visually checking
approximately 10% of the data records for
accuracy. Record dates will be automatically
available on all computer databases.
4. Record Identification, Indexing, and Retention -
All database files will be identified by filename
and subdirectory structure. Final data records
will be retained on the computer drive until
reports and publications are written and
accepted, or throughout the length of the project,
which ever is longer. After completion of the
project, all electronic data will be duplicated on
tape or removable hard-drive media and stored in
replicate for the life of the tapes. Printed data
shall be stored for a period five years after
conclusion of the project.
5. Records Distribution and Storage Only final
data records will be distributed outside the
UMAQL. These records will be prepared by the
data manager and Matthew S. Landis, and will be
carefully reviewed by Dr. Gerald J. Keeler before
distribution and reporting. Interim storage of
preliminary data records is described above.
F. References
Burgess, D. and P. Ray. 1986. Principals of Radar.
In Mesoscale Meteorology and Forecasting.
American Meteorological Society, Boston,
Massachusetts.
Changnon, S. and D. Jones. 1972. Review of the
Influences of the Great Lakes on Weather. Water.
Res. Res., 8(2):360-371.
Keeler, G.J. 1994. The Lake Michigan Urban Air
Toxics Study. Final Report to the U.S.
Environmental Protection Agency. Atmospheric
Research and Exposure Assessment Laboratory, U.S.
Environmental Protection Agency, Research Triangle
Park, North Carolina. 286 pp.
Keeler, G.J. and N. Pirrone. 1996. Atmospheric
Transport and Deposition of Trace Elements to Lake
Erie from Urban Areas. Water Sci. Technol., 33:159-
265.
Pirrone, N., G.J. Keeler, and T.M. Holsen. 1995a.
Dry Deposition of Trace Elements to Lake Michigan:
A Hybrid-Receptor Deposition Modeling Approach.
Environ. Sci. Technol., 29:2112-2122.
Pirrone, N., G.J. Keeler, and T.M. Holsen. 1995b.
Dry Deposition of Semi volatile Organic Compounds
to Lake Michigan. Environ. Sci. Technol., 29:2123-
2132.
Pirrone, N., G. Glinsom, and G.J. Keeler. 1995c.
Ambient Levels and Dry Deposition Fluxes of
Mercury to Lakes Huron, Erie and St. Clair. Water,
Air, Soil Pollut, 80:179-188.
Vette, A., G.J. Keeler, and N. Pirrone. 1996.
Atmospheric Inputs of Trace Elements to Lake
Michigan During the Atmospheric Exchange Over
Lakes and Ocean Surfaces Study (AEOLOS).
Atmos. Environ., to be submitted.
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Voldner, E.G. and M. Alvo. 1993. Estimation of
Wet Deposition of Sulfur, Nitrogen, Cadmium, and
Lead to the Great Lakes. Environ. Sci. Technol.,
27:292-298.
Atmospheric Loadings of PCBs, TNC, and
Atrazine
Principal Investigator: Keri C. Hornbuckle, SUNY-
Buffalo
Co-Principal Investigator: Joseph V. DePinto, SUNY-
Buffalo
Project Officer: Angela Bandemehr, USEPA, GLNPO
A. Project Planning and Organization
1. Introduction This project to calculate
atmospheric deposition estimates for PCB
congeners, TNC, atrazine, nitrogen and
phosphorus to Lake Michigan is in support of the
larger LMMBP. The LMMBP is a multi-
investigator, multi-agency project designed to
provide future guidance for toxic load reduction
efforts in Lake Michigan. Through oversight by
the GLNPO, the project includes monitoring
field work, chemical process research, data
integration, and modeling of the transport, fate
and bioaccumulation of a suite of potentially
harmful compounds in Lake Michigan. The four
main goals of the LMMBP include:
1. To identify relative loading rates of critical
pollutants from tributaries, atmospheric
deposition, and contaminated sediments in
order to better target future load reduction
efforts and to establish a baseline loading
estimate to gauge future progress.
2. To develop the predictive ability to
determine the environmental benefits of
specific load reduction scenarios for toxic
substances and the time required to realize
those benefits.
3. To evaluate the benefits of load reductions
from existing environmental statues and
regulations.
4. To improve our understanding of key
environmental processes which govern the
cycling and bio-availability of contaminants
within relatively closed ecosystems.
The Atmospheric Modeling team, consisting of
researchers at the SUNY at Buffalo, Rutgers
University, and the Chesapeake Biological
Laboratory, will accomplish these objectives by
providing atmospheric loading estimates for
PCBs, TNC, atrazine, nitrogen and phosphorus.
The SUNY team will utilize chemical and
meteorological data provided by the LMMBP
monitoring efforts, chemical data collected by
the SUNY team aboard the Lake Guardian in
July, 1997, and meteorological modeling results
from the NOAA-GLERL. This document will
detail the SUNY team's three-tiered approach to
the loading estimates and quality control efforts
used in collecting, managing, and interpreting
data.
2 Project Hypothesis We hypothesize that the
magnitude of atmospheric deposition of semi-
volatile organic compounds, nitrogen and
phosphorous to Lake Michigan is dependent on
proximity to major industrial centers (spatial
factors) and seasonal meteorological/
hydrometeorological trends (temporal factors).
3. Project Objectives
A. To summarize the current knowledge of
atmospheric depositional processes and
loadings of the target chemicals to Lake
Michigan. Atmospheric deposition
processes and loading includes wet
deposition; dry particle deposition and; gas
exchange (absorption and volatilization).
B. To summarize and present data quality
based on reported laboratory and field
quality control sample results; suitability
for loading estimates and; comparability
with other available data.
C. To estimate atmospheric deposition and
loadings of the target compounds to Lake
Michigan with respect to: spatial (e.g.
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north-south) variability and; temporal
(seasonal, monthly) variability.
D. To calculate the uncertainty associated with
the atmospheric deposition and loading
estimates for the target compounds.
E. To provide the loading estimates in a format
accessible and useful to the whole lake
mass balance modeling effort.
4. Project Description Semi-volatile organic
compounds, nitrogen and phosphorous enter
Lake Michigan via washout in precipitation, dry
deposition of contaminated particles, and vapor
exchange, as well as through tributary, industrial
effluents and other direct sources. The relative
importance of all these chemical inputs to the
lake, especially atmospheric inputs, are poorly
understood for nearly all chemicals. This poses
a critical problem because atmospheric inputs
exert a very strong influence in our ability to
predict chemical behavior. For example,
Endicott et al. (1992) described a whole lake
mass balance model, called MICHTOX, for
PCBs in Lake Michigan. Their intention was to
predict the accumulation of PCBs in fish under a
number of remediation scenarios. One of their
conclusions was that under a no-action scenario,
PCBs in trout are expected to decrease by half in
about five years. This prediction of rapid decline
is caused by the rapid removal of PCBs from the
lake by volatilization - an output that the model
estimated to exceed all other losses, even burial
to the sediments. Unfortunately, the
volatilization rates estimated have significant
uncertainty associated with them. Hornbuckle et
al. (1995), using a modeling approach supported
by a large air and water sampling program,
reported volatilization loss of 520 kg for the
northern three-quarters of the lake. Pearson etal.
(1996), extrapolated these results to the southern
quarter of the lake and reported total
volatilization losses of about 680 kg.
Furthermore, recent unpublished work by Zhang
(1996) has indicated that the southwestern region
of the lake, near the heavy industries of Gary and
Chicago, experiences very large gaseous and
particulate deposition of PCBs. This deposition
exceeds volatilization and the atmosphere no
longer represents a sink but a source of PCBs to
Lake Michigan. All this recent work indicates
that the ability of models like MICHTOX to
predict long-term chemical behavior depend on
high quality estimates for atmospheric exchange.
Meeting the project objectives described above
will require development of new interpolation
methods not previously used in atmospheric
deposition calculations. Interpolation of
chemical concentrations (gases, particle-bound,
and in rain), rainfall, particle deposition is the
most difficult and time-consuming problem that
this project addresses. Interpolation in time is
difficult because of the sampling limitations
facing the LMMBP monitoring efforts. The
necessity of composite gas-phase samples makes
temporal interpolation of gas-phase samples
especially difficult. Interpolation in space is
difficult because of the very large area of the lake
and the small number of sampling stations
(relative to the observed spatial concentration
variability). This project uses a four-tiered
approach to the space/time interpolation. Level
One represents the lowest level of difficulty and
is reflective of techniques currently used by
researchers in the field. Levels Two, Three, and
Four utilize new techniques that require
progressively more computational rigor.
Level One includes calculations of the
instantaneous deposition fluxes at each of the
LMMBP sites. Preliminary values for Level One
calculations were presented in the mid-term
report of July, 1997. The methods summarized
by Vlahos et al (1995) and by Hoff et al (1996)
are applied.. An assessment of the quality and
representativeness of individual samples and
sampling sites has also been undertaken. This
work is necessary for all flux calculations and
will continue until all the USEPA-approved data
has been received and reviewed.
Level Two includes an interpolation of the
concentration data to describe over-lake values
on a monthly time scale. Concentrations in rain
over the lake are interpolated using kriging or
inverse distance weighting. Concentrations are
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determined for each cell in the 5 km grid domain.
Chemical concentrations in rain are interpolated
in time on a monthly basis as required by the
monthly samples available for most of the target
chemicals. Wet deposition is determined by
multiplying these concentrations by the rainfall
volume falling in each cell, at monthly intervals.
Rainfall over the lake has been estimated using
NWS rain gauge data, radar data and kriging
techniques by Dr. Jerry Keeler's group (See
QAPP, Keeler and Landis, August, 1997). For
consistency, we will be using the same rainfall
data set. Dry particle deposition is estimated in
a similar fashion: chemical concentrations on
particles are interpolated in space using inverse
distance weighting or kriging; particle deposition
is estimated using a method developed by Keeler
and Landis.
Gas-deposition is the largest but least uncertain
atmospheric loading for PCBs and TNC. It is the
largest deposition flux for atrazine in all seasons
except springtime. For the Level Two
calculations, gas deposition will be estimated by
interpolating chemical fluxes and chemical
concentrations from the land sites across the lake
using Geographic Information Systems (GIS) and
a inverse distance or kriging method to
interpolate in space. Level Two calculations will
only be estimated on a monthly basis, reflective
of the sample composite periods.
Level Three estimates whole lake loadings on an
hourly basis for the gas-phase compounds. This
level involves an interpretation of sources (non-
point) of chemicals to the air over Lake
Michigan. Such a fine time scale is not possible
for wet and dry deposition of the PCBs, TNC,
and atrazine because of the large uncertainties in
the data and the manner of sample collection
(monthly rain collection rather than event based).
Interpolation of gas-phase concentrations is
possible because of their dependence on highly
resolved meteorological conditions.
Gas-phase concentrations are temporally
interpolated on an hourly basis as a function of
1) water temperature, 2) land surface
temperature, 3) wind direction, and 4) wind
speed. Water and land surface temperatures
affect the equilibrium distribution of chemicals
between air and surfaces. The equilibrium
distribution is expected to affect but not
necessarily control gas-phase concentrations.
Wind direction determines whether the water
surface or the land surface temperatures should
be used in the prediction. Wind direction may
also be used to predict the importance of local
sources (or land vs. water sources). Wind speed
may be an important predictor due to mixing
with background air or resuspension of
contaminated dust. Meteorological data for this
interpolation includes the results of the NOAA-
GLERL hydrodynamic model (Schwab and
Beletsky, 1998) and data collected at the eight
sampling sites on land around the lake. A
complete description of the temporal
interpolation of gas-phase chemical
concentrations is included in the appendix.
Spatial interpolation across the lake (at hourly
time scale) will proceed as in Level Two.
Samples collected on the Lake Guardian during
the LMMBP field season will be included as a
verification of the interpolation and/or as
additional sites for spatial interpolation. Because
a preliminary review of the Lake Guardian
samples indicted greater than expected
imprecision between the samples collected at a
location/time using different sampling
apparatuses, a field study of these different
sampling apparatuses was conducted in Lake
Ontario in July, 1997 (see Sections 3 and 4). The
Lake Ontario samples will be used to assist in the
interpretation of the Lake Michigan Lake
Guardian data. The Lake Ontario samples will
not be used in the interpolation work directly.
Level Four is the incorporation of the flux
calculations into a coupled deposition and
emission modeling framework. The atmospheric
modeling group will be designing a system for
coupling the gas-exchange model with the lake
toxics model. The Level Four work will include
a set of screening models to examine the
dynamic interactions between air and water with
respect to toxic chemical deposition. These
screening models are necessary because the
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spatial interpolation of gas-phase concentrations,
as described in Levels One, Two, and Three,
ignore interaction of the air with the water below
it. This is not realistic and may provide
inaccurate estimates of gas-phase concentrations.
5. Personnel Descriptions
Keri C. Hornbuckle, Ph.D. - Dr. Hornbuckle is an
assistant professor of environmental engineering
in the Department of Civil, Structural and
Environmental Engineering at the University at
Buffalo. Dr. Hornbuckle's research interests
concern the fate and transport of organic
pollutants in natural systems, with special focus
on atmospheric processes that control long-term
ecosystem exposure to potentially toxic and
persistent contaminants. Dr. Hornbuckle's
research activities include field work, analytical
chemistry, and fate and transport modeling. Dr.
Hornbuckle will oversee the transfer of
information and organize all communications
required by this project, including exchange
between investigators, consultants, the mercury
project team, the project director at GLNPO, and
the whole-lake mass balance modeling team. She
is responsible for the chemical modeling and
interpretation.
Joseph V. DePinto, Ph.D. Dr. DePinto is
Professor of Civil Engineering and Director of
the Great Lakes Program at the SUNY at
Buffalo. In the broad area of understanding and
quantifying the impacts of pollutants on natural
aquatic systems, Dr. DePinto has received over
$4 million in grants and contracts. These studies
have led to over 80 scientific publications in this
area and the direction of 34 Master's theses and
10 Ph.D. dissertations. Dr. DePinto has been a
part of the Great Lakes research community for
twenty years. During that time he has conducted
research throughout the Great Lakes basin on
such topics as nutrient-eutrophication, toxic
chemical exposure and bioaccumulation analysis,
contaminated sediment analysis and remediation,
biotic trophic structure and functioning, and
watershed, tributary, whole lake modeling. He
has also had considerable experience in exposure
analysis of contaminants through deterministic
modeling. For example, he was a member of the
modeling team that undertook the development
and application of the integrated exposure model
for PCBs in Green Bay, Lake Michigan.
Two principal consultants, Eisenreich and Baker,
are named on this proposal as an indication of
their commitment and involvement to the project.
Their role in the project is provide guidance to
the general project and to carry out specific
duties as described below.
Steven J. Eisenreich, Ph.D. Dr. Eisenreich is
Professor of Environmental Chemistry and
Chairman of the Department of Environmental
Sciences at Rutgers University. His research
interests include the environmental chemistry,
transport, transformations, and fate of
hydrophobic organic chemicals in the Great
Lakes. He has published extensively (-110
publications) on the absolute and relative
importance of atmospheric deposition of toxic
substances to and their cycling within the Great
Lakes. He was instrumental in establishing the
Integrated Atmospheric Deposition Network
(IADN) in the Great Lakes region, assisted in
development of the concepts for the Great
Waters Program of the CAAA-90, is co-author of
the Relative Loadings section of the Great
Waters Report to Congress for 1993, and has
contributed to the scientific background report
for the 1995 Report to Congress.
Joel E. Baker, Ph.D. - Dr. Baker is an Associate
Professor at the University of Maryland's
Chesapeake Biological Laboratory in Solomons,
Maryland. Dr. Baker's research interests center
about the transport of hydrophobic organic
contaminants in the atmosphere and in surface
waters. His studies in the Great Lakes have
documented the importance of volatilization and
sediment resuspension in the lake-wide mass
balances of organic contaminants. He is one of
the original collaborators of the Chesapeake Bay
Atmospheric Deposition Study and recently co-
authored the report Relative Loadings of Toxic
Contaminants and Nitrogen to the Great Waters
for the USEPA's Great Waters Program.
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Mark L. Green, M.S. Mr. Green is a Ph.D.
student at the University at Buffalo. He has
extensive experience in the use of stochastic and
probability methods in high performance
computing. He is fluent in several programming
languages, including Fortran and C and is
proficient in the use of Arc/Info and GIS. He has
worked on a wide variety of computer modeling-
related projects in the geotechnical and
environmental fields. He received a B.S. in
chemical engineering from the University at
Buffalo. His masters thesis, from the University
of Buffalo, Department of Civil Engineering, is
titled, "Transport of Trichloroethylene Vapors in
a Random Porous Medium." His Ph.D., "Cross-
media Coupling of Mass Balance Models"
focuses on the interfacing of large and
independent computer models that operate under
different spatial and temporal scales.
Sondra M. Miller Ms. Miller is a masters
student at the University at Buffalo Department
of Civil, Structural and Environmental
Engineering. She has a B.S. in civil engineering
from University of Buffalo and has participated
in research-related activities for over three years.
As an undergraduate, Ms. Miller was selected as
an National Science Foundation fellow and
conducted research on biofilms at the University
at Buffalo Industry/University Cooperative
Research Center for Biosurfaces.
6. Researcher Responsibilities Dr. Keri
Hornbuckle, Principal Investigator for this
project, is responsible for oversight of the
modeling and data collection aspects of the
project. Dr. Hornbuckle is also responsible for
all communications between the participants, the
USEPA project director and QC/QA officers, the
mercury project participants, and the project
consultants. Dr. Joseph DePinto is responsible
for communications with the whole-lake
modelers and oversees the GIS modeling
applications. Dr. Steve Eisenreich is responsible
for the use of AEOLOS data in this study and
will assist in the interpretation of nutrient
concentrations in rain. The results of the
AEOLOS study will be used primarily for
additional verification of the spatial interpolation
model. The AEOLOS data will not be processed
through the same quality control procedure as the
LMMBP database, so cannot be used directly in
the interpolation models. Dr. Baker will assist in
the interpretation of chemical concentrations in
rain. Mr. Mark Green is responsible for the
preparation of input data, development of the
concentration interpolation models, and
interpretation of modeling results. Ms. Sondra
Miller is responsible for instantaneous flux
calculations, field sampling and analysis, and
interpretation of the Lake Guardian data.
B. Model Description
1. Model Parameters Database manipulation,
temporal interpolation and regressions will be
written in FORTRAN and C computer languages.
Preliminary regression and interpolations of gas-
phase concentrations are performed in Excel
MSOffice 97. Results for wet and dry particle
deposition and gas concentrations are spatially
interpolated and displayed in Arc/Info 7.01
(Kreis, 1995).
Rainfall and particle deposition will be modeled
in S AS as described in Keeler and Landis (1997).
2. Computer Aspects - The site database assembly
for eight sites is expected to require 180 CPO
minutes on Spare 10 Workstation (64 RAM, 55
Megahertz Processor). The site temporal
interpolation for eight sites requires 10 CPU
minutes and the site spatial interpolation requires
5 CPU minutes on Spare 10 Workstation.
3. Data Quality The data used in this study is
obtained from eight major sources: 1) The
LMMBP QA Officer Louis Blume. For data that
has not yet passed QC, data from the generating
laboratories will be used. This preliminary data
is from the Illinois State Water Survey (ISWS),
Rutgers University, The Chesapeake Biological
Laboratory, and the Indiana University. 2)
Meteorological data is from NOAA-GLERL, the
UMAQL and the LMMBP. For meteorological
data from the LMMBP (site data) that has not yet
passed QC, preliminary data is gathered from
ISWS and Indiana University. 3) Some Lake
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Guardian data is from the University at Buffalo
Environmental Engineering Laboratories.
Quality control on this data was generally
provided by the originators. An overview
assessment of the quality of raw data has been
undertaken. QC efforts for the University at
Buffalo data is reported below. Final loading
estimates provided to the modelers will be based
on USEPA verified data.
4. Model Development The modeling approach
for the temporal interpolation of gas-phase
concentrations is the most computer and person-
time intensive aspect of this work. In brief, the
approach involves: 1) an interpolation of the
discrete and composite chemical data over time.
This interpolation involves fitting the data to a
regression model using surface water
temperature, air temperature, wind direction and
wind speed as input parameters. The monthly
data will then be described on a much finer
temporal resolution. To complete this
regression, it is necessary to separate the air
arriving at each sampling station with respect to
its over-water or over-land origins. Using wind
direction to fractionate the sampled air, the
temperature regime used for the regression is
either the surface water temperature or the air
temperature measured at the site. A more
detailed description of this approach is found in
the appendix.
5. Model Validation Model validation is the
comparison of model results with numerical data
independently derived from environmental
observations. Over 65 air samples were
collected by the LMMBP aboard the Lake
Guardian during the 1994-95 season.
Approximately 20 more samples were collected
in the southern portion of the lake in 1993-94 by
AEOLOS. The data from these samples will be
used to test the validity of the model. This
approach is most useful for gas-phase PCB and
TNC. This is fortunate because the interpolation
procedure to be applied is the most finely
resolved and most rigorous for these compounds.
Concentrations of atrazine were near or below
detection limits. No measurements of nitrogen or
phosphorus were measured on the vessel and few
rain events were captured.
6. Record Use And Management All data
generated by this project is stored electronically
in three separate hard drives and backed up on a
tape that is stored separately. Data that is
generated elsewhere but used on this project is
stored as above and also on floppy disks. A
master directory, containing only original files is
stored on a hard drive.
Computer calculations are manually and
randomly calculated at regular intervals. Record
dates are automatically available on all computer
databases.
After completion of the project, all electronic
data will be duplicated to a compact disk as a
permanent archive. Printed data will be stored
for at least five years after the completion of the
project.
7. Model Output/Products Loading and/or
concentration data will be provided to the whole
lake mass balance modelers in a manner that is
convenient for them. At this time, we intend to
provide concentration estimates for gas-phase
PCBs and TNC on a daily basis over a 5 km grid.
Gas-phase atrazine will be provided on a monthly
basis for the whole lake (average). Loadings of
wet and dry particle deposition will be provided
on a monthly basis for all chemicals. These
estimates, and appropriate determination of
uncertainty, will be provided in ASCII or cdf file
format electronically (ftp) as completed or at the
end of our funding period, which ever is earlier.
C. Replicate Air Sampling on the Lake Guardian
1. Project Description A survey on the Lake
Guardian is necessary to aid the atmospheric
modelers responsible for interpreting the
atmospheric data gathered by the LMMBP.
During the LMMBP field season, over 65 air
samples were collected aboard the Lake
Guardian. Duplicate samples were collected at
about a 5% rate. These duplicates exhibited an
unusually large relative mean difference for the
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organics concentrations. The large mean differences
seriously reduce the LMMBP's confidence in the
samples, and therefore, reduce the utility of those
expensive and valuable samples. The purpose of
this summer's survey is to repeat the duplicate
sampling in hopes of illuminating the problem
that occurred originally.
Vapors and particles will be collected using high
volume air samplers equipped with XAD and
glass fiber filters mounted on the bow of the
Lake Guardian. Three hivols will be operated
simultaneously. One will be on the deck of the
bow. Two will be mounted on the yardarm.
Each sample will run for 12 hours. This
duplicate experiment will be repeated at least
three times over four days. The number of
samples is limited to the number of XAD
cartridges available (10 at this time). Gas-phase
and particulate phase organics will be sampled
and analyzed in the manner described in the
QAPP-Atmospheric Monitoring for the LMMBP
and the Lakes Michigan and Superior Loading
Studies. The exceptions to the sampling protocol
are as follows: air samples will be collected in
triplicate; each air sampler will be calibrated at
the beginning of each sample; sampling flow
rates will not be adjusted but recorded for each
sample collected; samplers will not be turned on
and off with the change in wind direction but run
continuously over 12 hours. Meteorological and
location information, along with other relevant
metadata, will be recorded continuously.
Further details of the methods applied on this
project are available in the LMMBP Methods
Compendium (USEPA, 1997).
2. Sequence of Survey Tasks/Events - Air sampler
are loaded to the vessel and secured to the bow.
1. The two air samplers on the yard arm are
checked for parts and operation.
2. Clean lab space is established: aluminum
foil covering a five ft lab bench.
3. Freezer space established (16 ft2).
4. Air samplers cleaned and rinsed with
acetone and water.
5. XAD and GFF loaded, air samplers
calibrated (for each sample collected).
6. Wind speed, air temperature, surface water
temperature, precipitation will be monitored
hourly or as available.
XAD and GFF will be changed and sampler flow
rate measured at ~ 8 a.m. and 8 p.m. daily. XAD
samples are wrapped (XAD still in cartridge) in
combusted aluminum foil several times and
stored in individual plastic bags in the freezer.
Samples are transported from the ship to the lab
in a cooler.
Steps 5-8 will be repeated daily.
Upon return to Buffalo, XAD and filter samples
will be stored in a previously unused freezer until
analysis.
3. Measurement/Data Acquisition The
experimental design follows the Great Lakes
Water Quality Survey Study on Lakes Michigan,
Huron, Erie, Ontario, and Superior (Warren,
4.29.97 Draft Plan) with the following
exceptions:
Nine XAD-2 samples, five polyurethane foam
samples, and 10 glass fiber filter samples will be
collected on the Lake Guardian while in Lake
Ontario.
The XAD-2 samples (critical samples) will be
collected in triplicate using two samplers on a
yard-arm and one on the bow. PUF samples
(non-critical) will be collected in duplicate or as
single samples, depending on the Lake Guardian
schedule. Surface film samples (non-critical)
may be collected to screen for hydrocarbon films
on the water surface. Wipes of the ship deck
surface (non-critical) may be collected to screen
for PCBs adsorbed to the deck.
Although the data collected here will support the
data collected as part of the LMMBP work in
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1994-1995, this work will be conducted on Lake
Ontario. The change in lakes in inconsequential
as this study is designed to test the sampling
protocol used in the LMMBP.
The air samples will be analyzed for PCB
congeners, and TNC.
4. Sample Handling and Custody Requirements
Each sample will be labeled with date, start and
stop times, media type, and operator. All air
samples are stored in freezers on the ship and at
the University at Buffalo. Samples are
transported to and from the ship on ice in a
cooler by Hornbuckle and graduate students.
D. Sample Extraction and Analysis
1. Analytical Methods Extraction, cleanup, and
concentration of air samples collected on XAD-2
is described in detail in Harlin et al, 1995. In
brief, XAD air samples collected on the Lake
Guardian are transferred to glass, foil-lined jars
and sealed in plastic bags in a -10°C freezer until
analysis. Samples are extracted with 50:50
acetone-.hexane overnight. The resulting solvent
solution is reduced to about 1 mL using a rotary
evaporator. Interfering compounds are removed
and analytes separated into different fraction
with silica gel (3% deactivated). The first
fraction (hexane) contains all PCBs and the
pesticides HCB and DDE. The second fraction
(40% DCM, 60%hexane) contains all PAHs and
pesticides a and y HCH, dieldrin, DDD, DDT,
y-chlordane, cc-chlordane, and TNC. Fraction
three (methanol) contains atrazine and two
metabolites (deisopropylatrazine and
deethylatrazine). The samples are then
concentrated to the desired volume with a slow
stream of ultra-pure nitrogen. Final volumes
depend on sample matrix, site, and date. Each
sample is spiked with a known amount of
internal standard. Subsamples are then
transferred to autosampler microvials for
capillary GC-EC or GC-MSD analysis.
Exceptions to the method described by Harlin
and Surratt( 1995) include:
Samples will only be analyzed for PCBs,
although all fractions will be retained for future
analysis.
Gas Chromatography-Electron Impact Mass
Spectrometry will be used instead of Ion
Trap Mass Spectrometry.
PCB congeners 2,4,6-trichlorobiphenyl
(#30) and 2,2',3,4,4',5,6,6'-
octachlorobiphenyl (#204) are added as
internal standards
All samples are spiked prior to extraction
with PCB congeners 3,5-dichlorobiphenyl
(IUPAC #14); 2,3,5,6-tetrachlorobiphenyl
(#65); 2,3,4,4',5,6-hexachlorobiphenyl
(#166) as surrogates
2. Quality Control The data collected for this
project will be analyzed and reported in a manner
that assesses precision, accuracy,
representativeness, completeness, and
comparability with other projects.
Precision, defined as the relative uncertainty
about a given measurement, is assessed by
replicate analyses. Precision will be monitored
by the analysis of 10% of the extracts of the air
samples split into two equal fractions and each
analyzed as separate samples. All XAD/GFF air
samples are measured in triplicate, with two
collected on the yard-arm sampler and one on the
bow sampler. Air samples collected with
PUF/GFF are sampled individually.
Accuracy, defined as the absolute uncertainty
about the true value, will be assessed by
surrogate spike recoveries in every sample and
by spike experiments with performance
standards. The compounds serving as surrogates
will differ for each compound class. Surrogates
for air XAD and GFF samples are added to the
media prior to extraction. All compounds will be
reported on a compound-specific basis (e.g. PCB
congeners).
Field blanks will consist of 10% of the samples
collected. Air field blanks are XAD plugs and
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filters carried to the field and returned to the
laboratory unopened. Sample results will not be
corrected for blank values; analyte
concentrations in samples and blanks will be
reported.
Comparability expresses the confidence with
which one data set can be compared to another,
either between laboratories or within a laboratory
for different batches of samples. All data in this
study will have internal comparability due to the
use of self-consistent field and analytical
procedures, and can be monitored by surrogate
spike recovery performance. The manner in
which the samples were collected and analyzed
is designed to be highly comparable to the
LMMBP data set collected on Lake Michigan in
1994-1995. Comparability between these data
and other investigators' data will be dependent
on the similarity of the field and analytical
methods used between the studies. This can be
determined by comparing accuracy measures.
Data will be reported in units consistent with
other studies of toxics in air.
Completeness is defined as the percentage of
acceptable data needed to validate the study. It
is calculated as the number of samples with
concentrations above detection passing QA
criteria divided by the number of samples
analyzed having concentrations above detection
multiplied by 100. Completeness for this study
is set at 90%; reanalysis of a extract sample that
fails QA will be performed. Sample data not
meeting QA criteria will be flagged.
Samples will be collected in a manner that
reduces external contamination (all equipment is
solvent rinsed and dried prior to use,
precombusted aluminum foil is used to seal
samples) and prevents their misidentification.
Where possible, air, water, soil, and vegetation
samples will be collected simultaneously in order
to improve comparability between media types.
Upon collection, samples will be labeled by type,
date, time, and replicate. An example of an air
sample label: indicating an air sample collected
on quartz fiber filter on July 1, 1997 at l:45pm.
Once collected, samples are tightly wrapped in
precombusted foil, sealed in a plastic bag, and
frozen at -10°C or lower. After collection, all
samples will be protected from ultra-violet light.
The high volume air sampler will be calibrated in
the field, prior to each sample, using the portable
calibration unit bought from the airsampler
manufacturer (Graseby/GMW).
A final report will be issued to the USEPA
Project Officer upon completion of all sample
analyses and data interpretation at the end of the
project period. The final report will contain the
complete data set and QA/QC results.
E. References
Endicott, D.D., W.L. Richardson, and D.J. Kandt.
July 1992. MICHTOX: A Mass Balance and
Bioaccumulation Model for Toxic Chemicals in Lake
Michigan. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth,
Large Lakes Research Station, Grosse He, Michigan.
77 pp. + Appendix.
Harlin, K. and K. Surratt. March 1995. Analysis of
PCBs, Pesticides, and PAHs in Air and Precipitation
Samples: Sample Preparation Procedures. Illinois
State Water Survey, Standard Operating Procedure
CH-PR-001.3, Revision 3.0.
Harlin, K., K. Surratt, and C. Peters. November
1995. Standard Operating Procedure for the Analysis
of PCBs and Organochlorine Pesticides by GC-ECD.
Illinois State Water Survey, Standard Operating
Procedure CH-TN-002.3, Revision 3.0.
Hoff, R.M., W.M.J. Strachan, C.W. Sweet, C.H.
Chan, M. Shackleton, T.F. Bidleman, K.A. Brice,
D.A. Burniston, S. Cussion, D.F. Gatz, K. Harlin, and
W.H. Schroeder. 1996. Atmospheric Deposition of
Toxic Chemicals to the Great Lakes: A Review of
Data Through 1994. Atmos. Environ., 30:3505-3527.
Hornbuckle, K.D., C.W. Sweet, R.F. Pearson, D.L.
Swackhamer, and SJ. Eisenreich. 1995. Assessing
Annual Water-Air Fluxes of Polychlorinated
Biphenyls in Lake Michigan. Environ. Sci. Technol.,
29:869-877.
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Keeler, G.J. and M.S. Landis. August 1997. Quality
Systems and Implementation Plan (QSIP), Completed
for the EPA Project, Atmospheric Loading of Total
Mercury to Lake Michigan During the Lake
Michigan Mass Balance Study. Report to the U.S.
Environmental Protection Agency, Great Lakes
National Program Office, Chicago, Illinois.
Kreis, B.D. 1995. Arc/Info Quick Reference.
On Word Press, Santa Fe, California.
Mackay, D., S. Paterson, and W.H. Schroeder. 1986.
Model Describing the Rate of Transfer of Organic
Chemicals Between Atmosphere and Water.
Environ. Sci. Technol., 20:810-816.
Pearson, R.F., K.C. Hornbuckle, SJ. Eisenreich, and
D.L. Swackhamer. 1996. PCBs in Lake Michigan
Water Revisited. Environ. Sci. Technol., 30:1429-
1436.
F. Appendix - Proposed PCB Interpolation and Data
Interpretation
Problem Statement We have a collection of N
sample masses collected from N sample volumes
over a given period of sample time. In other words:
Total sample mass (Mtota,)
total
N
= S
Total sample volume (Vtotal)
vtotal - s vt
i=\
Schwab, D.J. and D. Beletsky. 1998. Lake Michigan
Mass Balance Study: Hydrodynamic Modeling
Project. Great Lakes Environmental Research
Laboratory, National Oceanic and Atmospheric
Administration, Ann Arbor, Michigan. ERL-
GLERL-108, 53 pp.
USEPA. 1997. Lake Michigan Mass Balance Study
(LMMB) Methods Compendium, Volume 3: Metals,
Conventionals, Radiochemistry, and Biomonitoring
Sample Analysis Techniques. U.S. Environmental
Protection Agency, Great Lakes National Program
Office, Chicago, Illinois. EPA-905/R-97-012c.
Vlahos, P., D. Mackay, S.J. Eisenreich, and K.C.
Hornbuckle. 1995. Exchange of Chemicals Between
the Atmosphere and Lakes. In: A. Lerman, D.W.
Imboden and J.R. Gat (Eds.), Physics and Chemistry
of Lakes, pp. 167-1784. Heidelberg, Springer-
Verlag.
Zhang, H. 1996. Enhanced Air/Water Exchange of
Polychlorinated Biphenyls in Southern Lake
Michigan in the Chicago Plume. M.S. Thesis,
University of Minnesota.
Total sample concentration (Ctotal)
N
S M.
total
! =
'total
total
N
X
1=1
Since there are discrete sample times (one-hour
intervals) at which meteorological parameters are
known, a determination of where individual sample
masses originated from can be utilized. Thus for the
N discrete samples:
N = NL + Nw
where NL denotes number of discrete samples from
land-based source and Nw denotes the number of
discrete samples from water-based sources. Thus,
expanding the series representation for the total
sample concentration
97
-------
'total
N
w
i=l TL
M
RN
NL Nw
S M. + S M.
'total
N
1=1
v
V total ™-WpCB
RN
NL p
y.
\
°L Nw
' + E
P°*
j,W
total
Finally, rearranging into a form that we can use for
least squares fit for the unknown quantities of
interest substituting
P° = exp
+ A
Now converting the sample into an expression obtained
from the ideal gas law we have
P,° V. MWPCB
M. = -J 1 ™*
substituting
NL P ° V MW
fi Yt MWpCB
' = 1 RT^
p° V MW
mry
PCB
RT
'
W
•'total
N
s r
We can now further simplify the total concentration
expression by assuming that all discrete sample
volumes are equal then
W
- ~Y-,
- exp
R
w
MWn
T.L
RN
also, wind speed can be incorporated to account for
enhanced transport at high wind speed. We assume
an exponential power law relationship that has been
used in the literature frequently:
N y
V = H V V = total
* 4~4~1 ^^ * 1 ' J
total
N
W
then
Note these functions are valid for any wind speed
value (zero wind speed results in an effective wind
speed enhancement of one). Now substituting we
get:
98
-------
to*"
RN
RN
Derivatives with respect to parameters
B'
MWL
PCB
•'total
+ A'
1AVG
-P -
1AVG
The above equation uses the available data
completely with no approximation. In order to solve
for the required parameters we must approximate this
equation with two average values
N1
s
1=1
1AVG
Tw
1AVG
Ws
N
N
N'
,w
N
w
S Ws,
w
N
w
ws
W i=l
'AVG
MW,
PCB
RNT.
AVG
Units and parameter definitions
N
w
LAVG
AVG
B
w
MW,
PCB
^total
•'total
w
MW,
PCB
•'total
MW,
RNT.
AVG
PCB
RNT,
AVG
B]
rrt W
1AVG
+ A
w\
LAVG
exp -
BL
TL
1AVG
A
B
w
+ AW +
1AVG
exp
B
w
+ A
AVG
MF ng R ™3 ' atm
PCB [mol\ " [mol • K
TL ,K] WSL
AVG
TAVG(K] W,*m
m
s.
m
s
0.0821
X
L
V
s
m
L
JH
PCB
''total
RNT.
AVG
exp -
+ A
1AVG
99
-------
c.
MW,
PCS
total
RNT.
w
AVG
w
exp -
PCS
'total
L
AVG
B
w
Tw
1AVG
+ A
w\
exp
+ A
±AVG
Flux = FPCB = DAW
(fw-fA]
w
-L z = J-
~
H
RT
= ~ + ~ D
AW
AW
These equations are identical to the partial pressure-based
model. As shown by Mackay et al, a total air-water
exchange mass balance can be assembled to give the net
water to air flux as:
C,
total
1AVG
A very simple two-resistance model for partial
pressure based volatilization/adsorption flux
calculation
if the net flux (N) is zero and steady-state is achieved
D
AW
Flux =
K
P°\
- V \ r - —
- ^W ^PCBa rr
_L + RT
w
w
where FPCB [mol/(m2s)] denotes the flux from water to
air, kA, kw, and Kw [m/s] denote the water, air, and
overall water mass transfer coefficient, H
[Pam3/(mol)] is Henry's law constant, R [8.314
Pam3/(mol°K)] denotes the ideal gas law constant,
CWPCB [mol/m3] denotes the dissolved chemical
concentration, and P° [Pa] denotes the chemical
gaseous partial pressure in the atmosphere. In
fugacity form
where DR denotes the wet deposition dissolution
transport parameter, DP denotes the dry deposition
transport parameter, DD denotes the wet deposition
particle scavenging transport parameter, and DAW
denotes the overall water transport parameter
[mol/(m3sPa)]. This equation shows that air and
water will tend to approach a steady-state but a non-
equilibrium condition occurs.
Similar equations can be derived for the land-based
source, which will give us the means to estimate land
and water surface flux values for the system. Once
the fugacity, (partial pressure), distribution is
estimated, a linear regression can be also used to
estimate the enthalpy change for each domain.
Important assumptions and approximations:
1. No consideration of analytic or sampling error is
accounted for.
100
-------
2 Ideal gas law is valid were implemented (very
dilute samples).
3 The molecular weight used is an approximation
(average of many congeners).
4. The air temperature at the site (local
meteorological data) is used to approximate the
local land surface temperature.
5. The closest cell surface water temperature
(Schwab data) approximates the water surface
temperature.
6. The hourly sample volumes are assumed constant
and steady for all sites.
7. The land- and water-based sample partial
pressures are approximated by using land- and
water-based average temperatures.
All quantities are known in the final equation except
the land- and water-based partial pressures. At first
glance, it seems that all site data can be combined to
estimate the land- and water-based partial pressure as
there should not be significant local effects
(essentially same site characteristics), obviously this
is not true for the Chicago area. I think it would also
be very interesting to investigate each individual site
to see if this preliminary assumption holds true. This
analysis incorporates not only wind direction, but
also land- and water-based temperature variability.
If the ambient air sampled at the sites exhibits local
equilibrium tendency and the sorption-desorption
reactions are fast enough, then the temperature
gradient should dominate the fate and transport of
PCBs.
- Vgas Vliquid « Vgas
RT
gas
Thus, substitution and transformation of variable, we
obtain:
RT2
separating variables and integrating yields:
InP ° = — + A
T
B =
12
R
which represents a linear relationship between the
partial pressure and temperature.
Changing notation for land- and water-based source,
we have:
•pL
-W
+ A
w
R
From the Clausius-Clapeyron equation we have:
dP1
dT T&V,
12
12
This brief report summarizes my thoughts on a
possible procedure that utilizes the available data in
such a way as to increase the accuracy of temporal
and spatial estimates of PCB concentrations over and
around Lake Michigan.
approximating the volume change and applying the
ideal gas law
101
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Appendices
Appendix A
Lake Michigan Mass Balance Project:
Modeling Work Plan
Issue Identification
The Laurentian Great Lakes have proven highly
susceptible to the effects of anthropogenic pollutants
including nutrients and toxic chemicals. Persistent toxic
chemicals, such as PCBs, remain a threat to human and
ecosystem health in the Great Lakes, despite decade-old
limitations on their production and use. Other toxics, such
as mercury and current-use pesticides, continue to
accumulate in the Great Lakes due to non-point sources.
In the Great Lakes basin, nearshore sediment
contamination by persistent toxics is widespread: all of the
42 Areas of Concern designated by the IJC suffer
impairments from contaminated sediments. In the lakes
themselves, the problem of contaminated sediments is
compounded by the deposition of persistent toxic
chemicals from near-field and regional-scale atmospheric
transport. Biomagnification of toxics through the aquatic
food web results in concentrations in top predator fish
which exceed consumption guidelines, and greatly exceed
more stringent, risk-based criteria. As a consequence,
reproductive failure and deformities of fish and fish-
consuming wildlife are reported, commercial and
recreational fisheries are closed or limited by consumption
advisories, and other impacts including developmental
retardation in children of sports fishermen have been
documented (Environment Canada, 1991). Although
actions taken to control bioaccumulative toxics were
initially effective in reducing contaminant concentrations
in the Great Lakes, such trends have generally not been
observed in recent years. Understanding the sources,
transport pathways, fate, and bioaccumulation of persistent
toxic chemicals is essential to allow development of
effective remedial action plans and load reduction efforts
to further reduce contaminant concentrations in the
ecosystem. While considerable progress has been made in
understanding the cycling of toxics in the Great Lakes
ecosystems, there is still a lack of quantitative information
from which to forecast the effectiveness of toxics
management alternatives.
In response to these issues, efforts to control toxic
chemicals on a lake-wide basis are being developed for
each of the Great Lakes. The USEPA, GLNPO has
proposed a mass balance approach to develop a LaMP to
address toxics in Lake Michigan (USEPA, 1995a). The
LMMBP will also study hazardous air pollutants for the
CAAA's Great Waters Program. The mass balance
approach, demonstrated in the GBMBS, provides a
consistent framework for integrating load estimates,
ambient monitoring data, process research efforts, and
modeling, leading to the development of scientifically
credible, predictive cause-effect tools. The primary goal
of the mass balance study is to develop a sound, scientific
base of information to guide future toxics load reduction
efforts for Lake Michigan at the state and federal levels.
From this goal, a number of specific objectives have been
identified. Several of the plan's objectives call for
identifying and quantifying the sources of toxics to Lake
Michigan, as well as establishing cause-effect
relationships and developing forecasting tools:
1. Determine loading rates for critical pollutants from
major source categories (tributaries, atmospheric
102
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deposition, contaminated sediments) to establish a
baseline loading estimate to gauge future progress, and
to better target future load reduction efforts.
2. Predict the environmental benefits (in terms of
reducing concentrations) of specific load reduction
alternatives for toxic substances, including the time
required to realize the benefits.
3. Evaluate the environmental benefits of load reductions
for toxic substances expected under existing statutes
and regulations and, thereby, determine if there is a
need for more stringent, future regulations to realize
further benefits.
4. Improve our understanding of how key environmental
processes govern the transport, fate, and
bioavailability of toxic substances in the ecosystem.
The mass balance project will be based upon the Enhanced
Monitoring Program (EMP), a comprehensive, two-year
synoptic survey for selected toxic chemicals in the Lake
Michigan ecosystem. The EMP will include tributary load
and atmospheric deposition monitoring; ambient water
column, biota, and sediment sampling; and additional
measurements to define and confirm transport and fate
processes. In support of the mass balance study, the
USEPA, ORD, NHEERL, MED-Duluth, CBSSS at the
LLRS, Grosse He, Michigan, in cooperation with the
ORD, Atmospheric Research and Exposure Assessment
Laboratory (AREAL), the NOAA-GLERL, and other
cooperators, will develop a suite of integrated mass
balance models to simulate the transport, fate and
bioaccumulation of toxic chemicals in Lake Michigan.
This work plan describes these models, the manner in
which they will be integrated, the relationship between
their development and the EMP data, and their intended
application.
This project directly supports the development of a LaMP
for Lake Michigan, mandated under Section 118 of the
1992 Clean Water Act (CWA) as well as Annex 2 of the
GLWQA, and a study for the Great Waters Program
mandated by Title HI, Section 112(m) of the CAAA-90.
USEPA also intends the LaMP to serve as the basis for
development and submission of State Water Quality
Management Plans developed in accordance with Sections
208 and 303(b) of the CWA, as implemented through 40
CFR 130.6.
Modeling Purpose and Objectives: Mass
Balance Approach
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 is proposed in this work plan, to
address the relationship between sources of toxic
chemicals and concentrations in air, water, sediment, and
biota. This approach integrates load estimation, ambient
monitoring and research efforts within a modeling
framework that is compatible with both scientific as well
as ecosystem management objectives. The mass balance
approach estimates 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 web. Based upon these estimates, the
mass balance can determine the rate of change in
concentrations and inventories of toxics as inputs such as
atmospheric and tributary loadings are changed, or other
aspects of the system are perturbed. Thus, the mass
balance can serve as a useful tool to estimate or predict the
outcome of alternatives under consideration for toxics
management.
More specifically, the modeling efforts associated with the
LMMBP will meet 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 EMP 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 EMP data, based upon models for
hydrodynamic and sediment transport,
eutrophication/organic carbon dynamics, toxics
transport and fate, and food web bioaccumulation.
103
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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 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 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 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 modeling will be to simulate the transport,
fate and bioaccumulation of four priority toxics in Lake
Michigan: PCB congeners, TNC, atrazine, and total
mercury. These toxics are collectively referred to as
"contaminants" in this work plan. Rationale for the
selection of these contaminants is presented in the Mass
Balance Project work plan, and briefly reviewed here:
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,
atmospheric and watershed/tributary transport
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 contaminants of greatest concern to human and
ecosystem health the Great Lakes (Ludwig et al,
1993; Gilbertson, 1988).
TNC is a bioaccumulative chlordane, representative of
cyclodiene insecticides used in the 1970s. Like PCBs,
TNC is bioaccumulative and concentrations in Lake
Michigan fish exceed consumption guidelines.
Atrazine is a current-use herbicide in wide use
throughout the Great Lakes basin. It is reactive,
undergoing several biotic and abiotic transformations
in soil; little is known about its fate in receiving
waters such as the Great Lakes. Atrazine is soluble
relative to the other mass balance contaminants,
therefore, partitioning and bioconcentration should be
relatively insignificant.
Mercury is a metal which, in its methylated form, is
bioaccumulative and toxic. Mercury concentrations
have reportedly increased in surface waters, including
the Everglades and inland lakes of the Midwest, but
apparently not in the Great Lakes. Mercury
concentrations in fish exceed consumption guidelines,
for some species and locations in the Great Lakes.
Concern that increasing atmospheric emissions, from
sources such as coal-fired power generation and waste
incineration, will lead to increased atmospheric
deposition to the Great Lakes also motivates inclusion
of mercury in this mass balance effort.
Background - Prior Modeling Efforts
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:
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 in Lakes Erie,
Huron, and Ontario. The model was calibrated and tested
using data from 1976 and 1977. The importance of
climatic factors on limnological (including eutrophication)
104
-------
processes in Lake Michigan was demonstrated, as the
severe winter and extensive ice cover of 1976-77
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.
Completely Mixed Lake 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.
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.
Food Web Bioaccumulation Model A food web
bioaccumulation model was developed by Connolly and
Thomann (1985) 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
PCB accumulation, in comparison to direct chemical
uptake from water. Substantial residual variance in lake
trout PCB concentrations (within age class CV ~ 1) was
not explained by this lake-wide, average-individual model.
MICHTOX An integrated mass balance and
bioaccumulation model for PCBs and 10 other toxic
chemicals was developed as a planning tool for the
LMMBP (Endicott et al., 1992). The MICHTOX mass
balance was calibrated to suspended solids and plutonium
data for the southern lake basin, while the bioaccumulation
model combined Connolly and Thomann's 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 remain. These are the most significant transport
fluxes for PCBs (as illustrated by predicted annual PCB
fluxes, Figure 1) and presumably other hydrophobic
contaminants. Major data gaps for other priority toxics
allowed only order-of-magnitude estimates of load-
concentration relationships. Available monitoring data for
toxic chemical concentrations in tributaries, air, lake
water, sediment, and biota are not adequate to define
loading trends in the last decade, 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, concentrations
and transport fluxes on a spatially-resolved basis, and
localized descriptions of food web structures.
Green Bay Mass Balance Study This study 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, provide 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.
105
-------
T,Jii»*«gl
Fox River
Loading
200
Net
Volatilization
1900
Atmospheric
Deposition
240 - '
Main Lake
Tributary Loading
160
Export to
Lake Huron
14
Sediment
Burial
1200
1990 Lake Michigan
PCS Inventory
Water Column = 2100 kg
Active Sediment = 26,000 kg
(0-3.3 cm interval)
Figure 1. 1990 MICHTOX estimates of PCB fluxes (PCB fluxes in kg).
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 phy toplankton
production and decay. The relative significance of
hydraulic and sediment transport, burial, volatilization,
and open lake boundary exchange processes upon the PCB
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 mass
balance study 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.
SEDZL The GBMBS also provided data to test a
predictive two-dimensional, hydrodynamic and sediment
transport model of the Fox River, SEDZL (USEPA,
1995b). SEDZL incorporates realistic descriptions of
cohesive sediment resuspension, flocculation and
deposition processes, and contaminant sorption, which are
106
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critical for accurate prediction of hydrophobic
contaminant transport. These process descriptions are
based on laboratory and field experiments with river, bay,
and lake sediments. A three-dimensional bed sub-model
is used to describe sediment bed properties which vary
with depth as well as location. The fine spatial resolution
of the model allows detailed simulation of in-place
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 has 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 has also been applied to large water
bodies such as Lake Erie, and marine coastal waters
including Santa Barbara Channel, and Atchafalaya Bay
where wave action as well as currents force sediment
resuspension. A three-dimensional version of SEDZL is
being tested currently on Green Bay.
Modeling Framework
The model design for the LMMBP is based upon the
linked sub-model approach used in the GBMBS, and
retains the same basic models: hydrodynamics, sediment
transport, sediment bed dynamics, eutrophication/ sorbent
dynamics, contaminant transport and fate, and food web
bioaccumulation. A schematic representation of the
overall mass balance design is shown in Figure 2. The
Lake Michigan submodels will be applied at several
different levels of resolution, and will incorporate
predictive hydrodynamic and sediment transport
simulations as the modeling "foundation". This approach
is consistent with other state-of-the-art ecosystem
modeling exercises, such as the Chesapeake Bay
Watershed Model (Linker et al., 1993), which emphasize
increasing computational effort, complexity, and
predictive resolution. As discussed below, linkages will
also be established with atmospheric transport and
watershed delivery models, to allow simulation of
multimedia toxics transport as well as loads and boundary
conditions to the lake. Ultimately, such linkages will be
essential to relate watershed and "airshed" management to
water quality. Descriptions of the lake process,
atmospheric and watershed delivery model frameworks
follow.
Lake Process Models
The mass balance for toxics in Lake Michigan will be
comprised of linked hydrodynamic, eutrophication/sorbent
dynamics, particle transport, contaminant transport and
transformation, and bioaccumulation simulations. Each of
these models represents significant processes affecting the
mass balance for toxic chemicals. The hydrodynamic
model predicts water movements necessary to describe the
three-dimensional transport of dissolved and particulate
constituents in the water column. The eutrophication
model describes the production, respiration, grazing and
decomposition of planktonic biomass within the lake. The
particle transport model describes the resuspension,
transport and deposition of particulate materials including
sorbent phases necessary to describe the movement of
particle-associated contaminants. The contaminant
transport and fate model describes contaminant
partitioning between dissolved and sorbed phases, transfer
between media (air, water, sediment), and biogeochemical
transformations. The bioaccumulation model simulates
contaminant accumulation from water and sediments to
predator fish via direct exposure and trophic transfer
through benthic and pelagic food webs. Together, these
submodels form an integrated description of toxic
chemical cycling in the aquatic ecosystem, with which to
predict the relationship between loadings and
concentrations for contaminants of interest.
A. Hydrodynamics
The Princeton Ocean Model (POM; Blumberg and
Mellor, 1987) will be used to compute three-
dimensional current fields in the lake. The POM will
simulate large- and medium(km)-scale circulation
patterns, vertical stratification and velocity
distribution, seiche, and surface waves. This model
will also be used to simulate a thermal balance for the
lake, and will generate turbulent shear stresses for the
sediment transport model. 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,
1987). This model will be used to develop year-long
simulations on a 5 km horizontal grid, with 15 sigma-
coordinate vertical levels, at one-hour intervals for
107
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watershed
load model
Load Reduction
Alternatives
watershed
sources
air
sources
\
V
lake
hydrodynamics
model
1
-------
Lake Michigan. Observed and simulated
meteorological data will be used to define model
forcing functions. Extensive measurements of
temperature, transmissivity, and current distributions
collected in Lake Michigan during 1982-1983 will
provide the necessary data for model confirmation;
measurements of daily surface temperature (from
satellite) and temperature, transmissivity, and current
distributions will also be used to confirm
hydrodynamic simulations for 1994-1995.
The hydrodynamic model is the appropriate transport
foundation for an accurate lake mass balance model,
for a number of reasons. A confirmed hydrodynamic
model offers a credible basis for extrapolating
transport, in terms of forecasting the response to
expected and extreme meteorological forcing
functions, that is desirable for a mass balance
simulation. The hydrodynamic model results are
scalable to provide transport predictions at the desired
spatial and temporal resolution. This is useful when
considering that the various processes incorporated in
the mass balance are not necessarily modeled at the
same scale or resolution, yet all depend upon a
consistent transport simulation. In particular, the
sediment and contaminant transport model described
below, requires high resolution simulations of current-
and wave-induced shear stress to predict sediment
transport. Hydrodynamic models are also
transportable, with little system-specific
parameterization in comparison to traditional water
quality models. A mass balance design based upon
hydrodynamic transport is advantageous, for instance,
when considering transporting the mass balance model
from Lake Michigan to the other Great Lakes.
B. Sediment and Contaminant Transport
A three-dimensional version of the sediment transport
model, SEDZL, will be used to simulate the movement
of sediment particles in both the water column and
sediment bed, including settling, resuspension,
flocculation, transport and deposition. SEDZL will
simulate the significant short- and long-term processes
which transport sediment particles and particle-
associated contaminants in the lake. SEDZL will be
linked to hydrodynamic output from the POM, and
will be based upon the same three-dimensional water
column grid. State variables will include three
particle classes (plankton/biotic solids, cohesive fine-
grained sediment/detritus, and coarse-grained solids)
and PCBs. SEDZL will simulate the 1982-1983 and
1994-1995 periods for which hydrodynamic forecasts
will be available, as well as intensive confirmation
data provided by sediment trap and radionuclide
monitoring. Further confirmation data for 1994-95
will be provided by remote sensing, transmissometer
arrays, and water intake monitoring. Sediment bed
properties, particle resuspension rate parameters,
flocculation parameters and settling properties
necessary for the model will be determined by field
measurements to be performed on Lake Michigan
sediments, and by results of experiments conducted
with other sediments from the Great Lakes.
Allochthonous sediment loadings will be estimated for
tributary export, shoreline erosion, and atmospheric
particle deposition. Autochthonous production will be
provided from the eutrophication/sorbent dynamics
model, and input as loadings to the sediment transport
model.
The sediment transport model is applied to predict the
transport of particles in the lake, which predominantly
carry hydrophobic contaminants for near-shore
locations such as tributary mouths, to deposition zones
usually in deep water. The transport of sediment and
associated contaminants is a complex interaction of
the properties of sediment particles and the sediment
bed, circulation, bathymetry, and turbulent shear
stresses applied by waves and current. Moving from
shore to deep water, regimes of sediment transport are
encountered, resulting in distinct distributions of grain
size, bed thickness, sedimentation rate, and
contaminant concentrations in the lake sediments.
Contaminants move along this gradient associated
primarily with the fine-grained sediments, yet their
transport is influenced by the entire particle
assemblage. In terms of resuspension and deposition,
most sediment transport is associated with the
sequence of short, infrequent events such as storms.
SEDZL simulates the interactions and dynamics of
sediment transport, and offers predictive capabilities
beyond that obtainable by a calibrated-transport
approach. Advantages include compatibility with the
hydrodynamic simulation, high spatial resolution
consistent with the spatial variability of the
resuspension process, and verified process
descriptions for the dynamics of sediment
109
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resuspension and deposition under event conditions
which are the most difficult to model. SEDZL
predictions have been confirmed mostly in tributary
systems; in large water bodies simulations have been
conducted for events, with only limited confirmation.
Thus, significant development is still required for
credible application of SEDZL in the Lake Michigan
mass balance model. Sediment and contaminant
transport model predictions will require extensive
confirmation against BMP data to ensure model
credibility.
The alternative approach to treating sediment transport
is descriptive, where direct calibration of total
suspended solids and associated particle tracers is
used to specify settling and resuspension fluxes. The
descriptive approach ensures a model calibration that
is consistent with available observations. However,
the spatial complexity and event-responsive nature of
sediment transport described above introduce too
many degrees of freedom to allow model calibration
to the data being generated by the BMP. This
approach relies entirely upon fitting suspended
constituent data, which will be too sparse (both in
space and time) to allow accurate description of
sediment transport fluxes. The second major
disadvantage of descriptive transport, is that the
resulting model has no forecasting basis other than
replaying the calibration. Attempts to go beyond the
calibration are, in general, weak emulations of
predictive transport approaches.
C. Eutrophication/Sorbent Dynamics
The eutrophication/sorbent dynamics (BSD) model
predicts 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 et al., 1993). The
ESD model simulates the non-conservative,
seasonally-variable dynamics of the biotic organic
carbon pool, which has a significant influence upon
partitioning of HOCs (Dean et al., 1993). Such a
model was applied to simulate the dynamics of
organic carbon states in Green Bay as part of the
GBMBS (DePinto et al., 1993). However, a more
resolute, multi-class eutrophication model (Bierman
and Mcllroy, 1986) will be applied to Lake Michigan,
and the linkage between plankton and organic carbon
states will be refined. Model outputs include
autochthonous solids loads, and transformation and
decay rates, that will be used as inputs for the
sediment transport and the contaminant transport and
fate models. The biomass growth rates may also be
linked to the plankton bioconcentration submodel of
the food web bioaccumulation model.
The ESD model is an important component of the
mass balance model for hydrophobic contaminants,
because it simulates the dynamics of a significant
sorbent particle class (phytoplankton) in the water
column. The dynamics of phytoplankton production
and loss cannot be adequately described by seasonal
EMP limnological monitoring, which will occur too
infrequently to observe major events such as blooms,
assemblage shifts, and die-off s. Furthermore, the ESD
model component will allow forecasting for integrated
toxics and nutrient management options, because mass
balances for toxics and nutrients are coupled via
eutrophication/sorbent dynamics processes. Finally,
the ESD model is the appropriate framework for
inclusion of zebra mussels in the mass balance model.
Zebra mussels, which at high density can impact the
lower food web and alter sediment and contaminant
transport, are currently (1994) infesting Lake
Michigan and are reaching high densities in suitable
locations such as Green Bay.
D. Contaminant Transport and Fate
The mass balance for toxic chemicals in the lake will
be computed in a contaminant transport and fate
(CTF) model which describes contaminant transport,
intermedia exchange, phase distribution, and
biogeochemical transformations, in both the water
column and sediments. The CTF model will be
calibrated and confirmed for each of the priority
toxics: atrazine, mercury, selected individual and sum
of PCB congeners, and TNC. Mass balance analyses
will be performed for each contaminant, to evaluate
the significant source, transport, and loss pathways.
Effectiveness of alternative load reduction scenarios
upon reducing toxic chemical concentrations, will also
110
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be forecast. Although calibration and confirmation
will be limited to the period of available BMP data,
the CTF model will be required to forecast
contaminant concentrations for substantially longer
periods: on the order of 20-50 years. Long
simulations are necessary because of the substantial
lag time associated with the chemical concentration
response in the lake to changing loads. The lag time
is associated with the residence time of contaminants
in the surficial sediments, which is constrained by
confirmation of CTF model hindcasts for cesium-137
and/or plutonium-239/240. These particle-associated
radionuclides have been demonstrated as important
tracers for the long-term transport of sediments and
contaminants in Lake Michigan and the Great Lakes.
Because their loading histories are known with
relative certainty, available water and sediment data
for these contaminants are directly useful for model
confirmation. Such data are critical to develop of a
model intended to make long-term forecasts,
especially since BMP monitoring will be only two
years in duration. Intensive sediment trap data
collected in 1982-1983 (Robbins and Eadie, 1991) and
water column measurements from the same period,
will provide further measurements for confirmation of
particle transport fluxes.
A schematic diagram of the CTF model as applied for
PCBs in Lake Michigan is presented in Figure 3.
Chemical fluxes between model compartments are
computed from advective and dispersive transport of
aqueous and particulate contaminant fractions. The
model will describe chemical partitioning between
dissolved and particulate sorbent compartments,
including multiple particle types, using an organic
carbon-based equilibrium assumption. Both local
equilibrium and first-order kinetic partitioningprocess
descriptions will be tested. Chemical transformations
such as hydrolysis and biodegradation are modeled as
first-order or pseudo first-order reactions, with
daughter chemicals retained in the mass balance as
additional state variables (for atrazine, these include
diethylatrazine and deisopropylatrazine). For
mercury, a two-state (organic and inorganic) multiple-
sorbent class framework proposed by Thomann (1993)
will be applied.
The CTF model incorporates simulations of other
submodels (Figure 2) by the following linkages:
Submodel
Data Linkage
POM/SEDZL
Eutrophication/
sorbentdynamics
Meteorological model
Atmospheric model
Watershed delivery
model
Hydrodynamic and sediment
transport; water temperature
Autochthonous load;
transformation and decay
rates
Wind and air temperature
Boundary conditions and
fluxes
Tributary loads
The CTF model will be linked to hydrodynamic and
sediment transport simulations, by appropriate
filtering and averaging of transport fields (Hamrick,
1993; Dortch etal., 1992). Total suspended solids and
]T,PCB (sum of congeners) simulations will be
reproduced in both SEDZL and CTF models,
providing computational "tracers" to validate the
transport linkages.
The CTF model will be applied at an intermediate
(Level 2) scale. In the water column, segment
resolution is defined at a scale compatible with the
definition of food web zones (approximately 20 x 40
km), with 2-5 vertical layers. In sediments,
segmentation will be based upon discretization of
deposition regime and contaminant distribution, with
1 cm vertical resolution. Fine-scale simulations are
necessary for accurate predictions of hydrodynamic
and cohesive particle transport as well as accurate
simulation of short-duration event processes.
However, the computational cost of fine-scale models
is high and makes long-term (20 to 30 year)
simulations infeasible, especially with the significant
number of state variables required for multiple
contaminants, sorbent phases, etc. Resolution at the
scale of POM and SEDZL is also not appropriate for
the mass balance objectives of this project.
Intermediate scale models have substantially lower
computational cost and have been demonstrated for
contaminant transport and transformation over
temporal and spatial scales appropriate for toxics
111
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Watershed
Tributary
Loading
Epilimnion
Hypolimnion
Surficial
Sediment
Transport
and
Exchange
sorbed chemical
PDC
dissolved
chemical
EUJaiv >*ir i i yfUsmnn
iment [~Q~
nixing \t 1
Sediment
mixing
Burial
Subsurficial
Sediment
Layers
Deep
Sediment
(all sediment layers share common
description of contaminant partitioning)
Figure 3. Contaminant transport and fate model schematic.
112
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exposure prediction and linkage to bioaccumulation
models (DePinto et al, 1993; Connolly et al., 1992).
Although CTF model descriptions are generally well-
defined, no single framework presently available has
the capacity to accurately predict all components of
CTF while retaining the aggregate behavior of
hydrodynamic and sediment transport simulations. To
develop an appropriate framework for the LMMBP
and future lake-wide analysis and management
projects, existing and developmental mass balance
water quality modeling frameworks such as those used
for Chesapeake Bay (Cerco and Cole, 1993), Green
Bay (Bierman et al., 1992; Velleux et al., 1994), and
other projects (Richards, 1990; Katopodes, 1994) will
be reviewed. Appropriate features of these
models will be synthesized into a single framework
and extended to meet the requirements of the
LMMBP.
D. Food Web Bioaccumulation
A bioaccumulation model simulates 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 is well defined, and equates
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 include 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
modeling objective here), the bioaccumulation mass
balance is 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 GBMBS (Connolly
et al., 1992). The model developed for that project,
FDCHN, will be adapted for use in Lake Michigan.
FDCHN is a time-variable, population-based age class
model, incorporating realistic descriptions of
bioenergetic, trophodynamic, and toxicokinetic
processes. The general features of FDCHN are well-
suited to a modeling application such as the LMMBP.
For Lake Michigan, bioaccumulation of PCB
congeners and TNC will be modeled for lake trout and
coho salmon food webs. Food web bioaccumulation
will be simulated for sub-populations of lake trout in
three distinct biotic zones. The general structure of
the lake trout food web in Lake Michigan is shown in
Figure 4. In each zone, different food webs support
lake trout, including benthic and pelagic food web
linkages. Biotic zones are defined by the
approximately 50-mile range of movement of lake
trout. The coho salmon, in comparison, is strictly
pelagic. Although the coho food web is simpler, the
bioaccumulation simulation must account for
significant migration over the two year lifetime of this
stocked salmonid in Lake Michigan.
It should be recognized that FDCHN, and in fact, all
current food web bioaccumulation models, is not
predictive in terms of the dynamics of the food web
itself. In other words, the food web structure is
described as model input. FDCHN does not predict
changing forage composition, trophic status in
response to nutrients, exotic species invasion, or
fisheries management. Yet such factors have been
demonstrated to alter food web structures in the Great
Lakes, and these changes have been suggested to
affect bioaccumulation in top predators including
salmonids.
To address the sensitivity of bioaccumulation
predictions to food web dynamics, the SIMPLE model
(Jones et al., 1993), a bioenergetic model for fish
population dynamics in the Great Lakes, will be used
to construct scenarios for food web change that will
then be tested in FDCHN. While less satisfactory than
an integrated population dynamics simulation, such
testing will demonstrate the sensitivity of
bioaccumulation predictions to food web dynamics in
comparison to changes in contaminant concentrations
in fish due to reducing exposure concentrations.
Atrazine bioaccumulation will not be modeled,
because it is not expected to accumulate in biota due
to its low hydrophobicity. It is not presently feasible
to model bioaccumulation of mercury because a mass
balance for the bioaccumulative fraction (the methyl
113
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Lake Trout
Sculpins
Alewife
Diporeia/
Benthos
Rainbow
Smelt
Bythotrephes
Herbivorous
Zooplankton
Mysis
Detritus
Phytoplankton
Figure 4. Lake trout food web in Lake Michigan.
species) is beyond present analytical and modeling
capabilities. As identified in Mercury in the Great
Lakes: Management and Strategy (Rossmann et al.,
1993), the development of such capabilities must
initially take place on small, constrained ecosystems
as opposed to the Great Lakes. This is consistent with
the research approach of Porcella (1992) in developing
the Electric Power Research Institute (EPRI) Mercury
Cycling Model, which was based upon data gathered
from Little Rock Lake and other bog seepage lakes in
Wisconsin.
A number of FDCHN enhancements will be
considered in the Lake Michigan application. These
include incorporating specialized sub-models for
phytoplankton (Swackhamerand Skoglund, 1993) and
Diporeia (Landrum etal., 1992), the organisms at the
base of the pelagic and benthic food webs. The
bioaccumulation process formulations of Gobas
(1993), Barber et al. (1991), and Sijm et al. (1992)
will be reviewed for possible updating of FDCHN
toxicokinetic descriptions. The detailed bioenergetics
model of Hewett and Johnson (1991), which is
currently employed in simplified form in FDCHN,
may also be more fully incorporated in the model.
Finally, a individual-based modeling (IBM) approach
may be tested, if individual fish are sampled during
the BMP.
Atmospheric Transport and Deposition
Current estimates suggest that atmospheric deposition is
the major source of several contaminants to Lake
Michigan, including PCBs (Pearson, 1994), and mercury
(Rossmann et a/., 1993). In addition, net volatilization to
the atmosphere may be the predominant loss mechanism
for semi-volatile contaminants such as PCBs from Lake
Michigan (Endicott et al., 1992) as well as Lake Superior
(Jeremiason et al., 1994). Due to the importance of the
deposition and exchange of toxics between Lake Michigan
and the atmosphere, air-water fluxes of contaminants must
be accurately predicted. This will be accomplished
initially by observation-based interpolation/extrapolation
of atmospheric monitoring data. A longer-term objective
114
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will be to model the deposition and exchange of
contaminants by linkage and coupling between the CTF
model and a compatible atmospheric transport model. The
Regional Paniculate Model (RPM) is being developed by
the USEPA NERL as the atmospheric model for this
application. Based on discussions at an air/water
modeling workshop held on 14-15 June 1995 in Detroit,
there appears to be sufficient air emissions information for
atmospheric simulations of atrazine and mercury only.
There does not currently appear to be enough information
about air emissions of PCB congeners and TNC to allow
a scientifically credible simulation of the atmospheric
transport and deposition of these substances.
A. Observation-Based
Observation-based interpolation/extrapolation of
atmospheric monitoring data will be used to estimate
over-lake wet deposition, dry deposition, and vapor
phase contaminant concentration distributions. These
estimates will be based upon: (1) routine monitoring
at nine land-based sites, (2) ship-board sampling in
conjunction with open water monitoring, and (3) three
intensive studies focusing on Chicago as an urban
source of air toxics.
Measurements from the Integrated Atmospheric
Deposition Network (IADN) and BMP will be used to
drive the CTF model. An overview of the procedures
to be used for deriving atmospheric loadings from
monitoring data is provided in the Atmospheric
Monitoring Overview and Appendix 3 of the Mass
Balance Project Work Plan. The Lake Michigan
Atmospheric Technical Workgroup will be
responsible for calculating atmospheric loadings. This
effort must be coordinated with the Modeling
Workgroup to ensure compatibility with regard to
contaminants of interest, simulation time periods, and
spatial scales.
The primary use of observed atmospheric loadings
will be to calibrate the CTF model using the best
available information to characterize present
conditions. Ambient gas phase observations above the
water surface will be used in the air/water surface
exchange calculations performed by the CTF model.
B. Atmospheric Transport and Deposition Model
An "engineering" version of the RPM adapted for
atrazine will simulate transport above the watershed
and lake, the gas/particle partitioning and
transformations of atrazine in the atmosphere, and the
significant deposition and exchange processes with the
watershed and lake. This engineering version of the
RPM will use the results of previous simulations of
the RADM to determine the total particulate mass
loadings and particle size distributions which affect
the behavior of particulate atrazine. Atmospheric
transport and deposition in both the RADM and RPM
is driven by a meteorological model, the Penn
State/NCARMesoscaleModel - Generation 5 (MM5).
The MM5 generates diagnostic simulations of wind,
temperature, humidity, cloud cover and other
meteorological variables using a four-dimensional
data assimilation (FDDA) technique to continually
correct certain model variables toward observed
values during the simulation to control errors.
Emission inventory data are used to define
contaminant source inputs, although specified
boundary condition data may be used to augment
emission inventories.
Atrazine will be considered a minor constituent of the
total mass loading in the particulate matter and its
transport and deposition will be estimated based on
the RPM results for sulfate and nitrate particulate
matter. Simulations of mercury transport and
deposition to Lake Michigan may eventually be
obtained from the RPM. However, at this time there
is significant uncertainty about the importance of
particulate mercury in atmospheric loading and
deposition. Since there are already sufficient
measurements available to make a credible estimate of
the air component of mercury loading to Lake
Michigan, the initial focus of this air modeling effort
will be on atrazine.
The volatile flux of atrazine may be a significant mass
balance component for both the lake and regional
atmosphere. Because volatile flux is driven by the
temperature and concentration (fugacity) gradients
between water and air, contaminant transport and fate
models for lake and atmosphere must each use
consistent models of the air/water interface to estimate
this volatile flux. The RPM, RADM and MM5 all
115
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currently use the same terrain-following vertical
model structure with a bottom layer thickness of
approximately 80 meters. This definition for the
bottom layer of these three air models may require
modification to assure consistency with the CTF
model.
The diagnostic and analytic capabilities provided
through atmospheric modeling can complement
observation based loading calculations by providing
enhanced temporal and spatial resolution of deposition
during time periods consistent with observations.
Although this potential for enhancing resolution of the
observed input field is important, atmospheric
modeling provides an objective method of linking
atmospheric sources directly to watershed/water body
impacts. Consequently, the atmospheric model should
be a valuable tool in the regulatory decision-making
process for assessing the aquatic impacts due to
modifying emission releases in future or past
scenarios. The role of atmospheric modeling and
plans for model deployment are discussed further in
the Atmospheric Modeling Plan below.
C. Air/Water Linkage
Based on discussion at the air/water modeling
workshop in Detroit (June 1995), it was determined
that complete computational coupling of the air and
water models would not be feasible, at least in the
near term, due to the differences in time scales for the
important physical processes in the air and water
media. The redistribution and reaction of
contaminants in the water media occur on time scale
that are much longer than those for the air media.
Water quality models are typically used to simulate
multi-year periods, whereas regional-scale air quality
models like the RPM and RADM are rarely applied
for periods of longer than a few days due to the
amount of computing required. Paniculate fluxes of
contaminants from the air to the lake are not affected
by concentrations in the lake, and downward volatile
fluxes can be adequately estimated using observed and
modeled water concentration data. Therefore, the
RPM will be used to simulate important depositional
periods spanning a few weeks or months, and
climatology and statistical methods will be used to
estimate atmospheric inputs to the Lake Michigan
mass balance on time scales of seasons to years.
The linkage outputs are wet and dry deposition
contaminant fluxes and near surface atmospheric
concentrations. The output fluxes and concentrations
will be used to define input atmospheric loads and the
gradient for gas exchange for the CTF model.
Linkage can also occur in the other direction, where
volatilization is treated as a source of contaminants to
the RPM. However, this reversed flux from water to
air does not appear to be significant for atrazine due to
its water solubility. In the future, if the simulation of
atmospheric PCB transport and deposition is
attempted, a coupling may be necessary between the
RPM and the CTF models where the models run
simultaneously to simulate the bi-directional transfer
and feedback of contaminant mass balances for air and
water. In this case, volatile exchange (volatilization or
absorption) would be computed based on simultaneous
conditions in both the atmosphere and water column.
Watershed Delivery
Transport and fate frameworks may be applied to predict
the multimedia delivery of toxics from the watershed to
the lake. While contaminant loadings from major
tributaries are being monitored as part of the LMMBP,
these data alone may not be sufficient to accurately define
contaminant inputs from the watersheds, tributaries, and
harbors that adjoin the lake. Furthermore, quantifying
tributary loads based upon monitoring at the river mouth
does not identify sources of toxic chemicals. For instance,
atmospheric deposition to the watershed will indirectly
contribute to tributary loading. Depending upon the actual
source, toxics loading from the watershed may or may not
decline over time without action, respond to meteorology,
hydrology, or land use change. Modeling these significant
loads would produce more complete and accurate load
estimates and allow more realistic long-term forecasting
ability.
While such modeling capability is important for
forecasting purposes, this development should be
addressed separately due to the difficulty of managing
such efforts within a project of this scope and duration.
Development of watershed delivery models is distinct
from the lake mass balance model development, because
these models simulate toxics transport and fate at
fundamentally different scales and have unique data
requirements. Furthermore, it is not clear that watershed
simulation, on this scale is feasible, at this time. Results
116
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of the LMMBP will be useful for identifying specific
toxics and watersheds to prioritize for watershed delivery
modeling, based upon the magnitude of tributary loading
estimates.
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 5. Level 1 is
resolved at the scale of lake basins (characteristic length,
L = 150 km), with an associated seasonal temporal
resolution. This is a screening-level model resolution used
in MICHTOX. Level 2 is resolved at a regional scale
defined by food webs (L = 40 km) including gross
resolution of the nearshore and offshore regions; temporal
resolution is weekly-to-monthly. This resolution is
roughly comparable to that achieved by models developed
in the GBMBS. Level 3 is a hydrodynamic scale
resolution (L = 5 km), with associated daily temporal
resolution. Level 3 is scaled to resolve and predict particle
transport processes as well as hydrodynamic transport.
Although LaMP and Great Lakes Waters Program
objectives are "lake-wide", these emphasize biotic
impairments occurring primarily in localized, nearshore
regions. LaMP objectives also require that the transport of
contaminants from tributaries and other near-shore sources
to the open lake be resolved. Therefore, the Level 1 model
is not adequate for the study objectives. Level 2 resolution
is adequate for most modeling objectives, but not for
resolution of significant hydrodynamic and sediment
mass
balance
stations
LEVEL 1
(7 surface water segments)
LEVEL2
(20 X 40 km surface water grid)
LEVEL 3
(5 km surface water grid)
note: surface sediment segmentation is similar but not identical to surface water segmentation. Biotic zones
in bioaccumulation model are superimposed on mass balance model segments.
Figure 5. Surface water segmentation for alternative Lake Michigan mass balance model levels.
117
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transport events. Level 3 resolution is required for
accurate hydrodynamic and sediment transport modeling
and is 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 would also be valuable in relating
toxics loading from the 10 Areas of Concern (AOCs)
adjoining Lake Michigan, which must be addressed by the
RAP process, to the LaMP via the LMMBP.
The modeling design for the LMMBP will be based upon
the development of several submodels, at two levels of
resolution. The CTF model will be resolved at a level
comparable to Level 2; the eutrophication model will be
resolved at the same level. Because the CTF will be
linked to atmospheric fate and transport model predictions,
the two will share the Level 2 resolution at the Lake
Michigan surface. The POM and SEDZL models will be
Level 3 resolution. Results of these transport models will
be spatially and temporally averaged prior to coupling to
the CTF model. The rationale for specifying different
resolutions is that hydrodynamic and predictive sediment
transport models demand a Level 3 resolution, and these
models offer the best capability for transport simulation
and forecasting. A lower resolution is specified for CTF
and BSD because these models have been demonstrated at
this resolution, and the need for Level 3 toxics resolution
is not clear.
Model Quality Assurance
QAPPs will be prepared and implemented for each sub-
modeling effort, consistent with MED-Duluth Quality
Assurance Guidelines for Modeling Development and
Application Projects. The QAPPs will specify procedures
for code development, testing, modification, and
documentation, as well as methods and measures to be
applied in model calibration, confirmation, and uncertainty
analysis.
Validation
Validation of submodels will include testing for local and
global conservation of mass (and continuity), momentum,
and energy. Numerical solutions will be tested for
properties including stability, convergence, and numerical
dispersion, against analytical solutions and output of
demonstrated models. These tests will be repeated
following model code modifications. Input data, including
forcing functions and initial conditions, will be checked by
graphical inspection. Averaging and filtering methods
used to link models of different resolution, will be tested
by repeating tracer simulations in each model and
comparing consistently-averaged results.
Calibration
Each of the lake process submodels will require some
degree of parameter calibration. However, the overall
modeling design is intended to minimize the reliance upon
calibration, to better constrain the results. By simulating
hydrodynamics and sediment transport, it will not be
necessary to calibrate transport in the ESD and CTF
models. The toxics transport and fate model will
incorporate phy sicochemical process-based descriptions of
partitioning, volatilization, diffusive sediment exchange,
and transformation, in order to reduce degrees of
parameter freedom. Likewise, the bioaccumulation model
will base contaminant uptake and excretion parameters
upon process descriptions that separate chemical-specific
(Kow) and organism-specific (lipid content) factors. The
objective of model design is to construct a framework
capable of simulating a wide range of contaminants in a
simple, consistent, scientifically defensible manner.
Within each submodel, calibration parameters will be
identified. Best estimates for initial values and allowable
ranges will be based upon the literature and proceeding
model applications. Logs of parameter values tested
during calibration will be maintained as documentation of
this procedure, along with the appropriate residual
statistics. Spatial or temporal variation of parameter values
will be allowed only if justified by consideration of the
process(es) involved. Although the objective of
calibration is to identify optimum parameter values which
minimize residual errors, it is necessary to balance the
goodness-of-fit with other criteria: for example, the
realistic range of the parameters, independent
measurements or estimates (and the degree to which these
estimates are judged to be reliable), as well as the
importance and sensitivity of the various parameters in the
model.
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Confirmation
Short-term, annual, and long-term model results will be
confirmed, to assure that the models and submodels will
yield reliable and informative predictions (Chapra and
Reckhow, 1983). Confirmation will include model
performance evaluation: inspection and quantification of
residual errors for state variables, on both local and
regional bases. Data uncertainty will be quantified by
ANOVA methods. Independent observations, including
sediment trap fluxes, water intake, total suspended solids
monitoring, vertical current meter and transmissometer
arrays, contaminant partitioning distribution data, and
predator-prey contaminant ratios, will be used to confirm
process submodels. Long-term confirmation will be
provided by the radionuclide simulations, and to a lesser
extent by performing hindcasts for PCB and mercury.
Goals for Accuracy
The stated goal for model accuracy is prediction of
lakewide average concentrations of toxics in water
(volume-weighted average), surficial sediment (spatial
average), and top predator fish (average fish in each biota
zone) within a factor of two of the average concentrations
based upon monitoring data. To achieve this model
accuracy, loadings and contaminant mass in each
compartment must be determined to within 25% of the
actual lakewide, annual average value. Approximately
20% of the samples for toxics analyses should be
replicates, as a basis for estimating measurement
variability. (In this context, replication refers to multiple
observations per model segment and sampling interval.)
In addition, 75% of loading and ambient samples in all
compartments must be quantified for each contaminant
(completen'ess). These data quality objectives are based
upon expert opinion, and experience gained in the
GBMBS. Failure of the EMP to achieve these goals will
degrade the accuracy of the mass balance and model
predictions.
Analysis of Uncertainty
It should be recognized that model accuracy refers to a
comparison of model predictions to data collected during
the EMP. In a forecasting application, the accuracy of
model predictions will degrade over time. In either case,
parameterization error is a significant source of model
prediction uncertainty. To evaluate and quantify the
effects of parameterization error, uncertainty analysis will
be performed for selected model simulations. The
parameter variance-co variance estimation procedure of Di
Toro and Parkerton (1993) will be applied to estimate
data, parameter, and model error components. With these
estimates, confidence intervals for model predictions will
be generated using Monte Carlo/Latin Hypercube
simulation. Uncertainty analysis will also provide a check
on the quality of model parameterization and calibration,
via the estimation of parameter errors, which will be
applied periodically during model development.
Model Application and Computational Aspects
Annual Simulations
Annual simulations will be run with the integrated
submodels for the EMP period of 1994-1995. Results will
be analyzed in terms of regional and lake-wide
contaminant loads, fluxes and inventories, and spatial and
temporal gradients of contaminant concentrations.
Bioaccumulation simulations will be analyzed in terms of
relative accumulation pathways, spatial and temporal
variability of contaminant concentration ratios (BCF,
BAF, BSAF, predator/prey), and influence of diet, age,
and migration factors. As indicated above, annual
simulations for hydrodynamics and sediment transport will
also be developed for the period 1982-1983. This will
provide four years of transport data, which will be
"sampled" to construct synthetic transport fields for long-
term CTF simulations. Deviation of climatic,
meteorological and limnological conditions during the
EMP, from expected conditions based upon the long-term
record will be investigated.
Long Term Simulations
Long term simulations will include both hindcast and
forecast applications. CTF forecasts will be performed to
determine time to steady state, for both continuing and
discontinued loads. Forecasts will also be run to evaluate
reductions in exposure concentrations resulting from
elimination of tributary and/or atmospheric loading. These
forecasts will be propagated through the food web
bioaccumulation model for PCBs and TNC, to estimate
time for sport fish contaminant concentrations to decline
below criteria limits. As described above, SIMPLE model
scenarios will be used to test the sensitivity of long-term
bioaccumulation predictions to food web dynamics. Based
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upon the results of long term simulations, graphs will be
developed to illustrate the fundamental loading-
concentration relationships, for both transient and steady
state conditions.
Computational Requirements
The POM is implemented on Lake Michigan using a 5 km
horizontal grid. At this resolution, the Lake Michigan
model requires approximately one CPU min/day
simulated, or six CPU hours for an annual simulation
(based on Cray Y-MP performance). The SEDZL three-
dimensional Lake Michigan model will require about 50
CPU hours for an annual simulation. Eutrophication,
CTF, and bioaccumulation model CPU requirements are
comparatively negligible. Short-term runs will be
conducted on high-performance workstations (DEC 2100,
3600, and 4700 AXP), although annual simulations and
storage of transport fields (0.5 GB per annual simulation)
are only feasible on a supercomputer. The NESC
supercomputer will be used for coupling the hydrodynamic
model with the sediment and contaminant transport model.
Approximately 500-2000 CPU hours (Cray Y-MP) will be
required annually to support model development and
application.
CPU requirements for the MM4 meteorological model, run
using an 80 km grid size and a nested 18 km grid over the
Great Lakes would require approximately 1000 Cray Y-
MP CPU hours for a one year simulation, and would
generate 100 GB of output data.
Model results will be visualized using Advanced
Visualization Systems (AVS) software running on the
NESC supercomputer, AXP and Sun Spare workstations.
Volumetric modeling of lake model predictions will be
used both to assist model development (performance
evaluation and comparison to data) and for presentation of
results. Simulation of events of specific interest may be
animated in AVS, with technical support from NESC and
RTP visualization labs.
Modeling Data Requirements
This section defines field data requirements for the Lake
Michigan mass balance modeling effort, in terms of how
data will be used for model development, confirmation and
application. Substantially greater detail of the BMP design
may be found in the LMMBP Work Plan. Through work
group involvement, the modeling committee has offered
input to the BMP design to maximize the utility of the
sampling and analytic effort, within the overall project
constraints defined by GLNPO. It should also be noted
that data management and database development are the
responsibility of GLNPO.
Data may be categorized in three groups, according to
their usage in the modeling process:
Loadings, boundary and initial conditions, and forcing
functions Data that is specified externally (based
upon observations or other models), and input to the
model. Loadings are external sources of mass for
constituent state variable, including contaminants,
sediments, sorbents, and nutrients. Boundary
conditions are state variable concentrations in media
adjacent but external to the model (i.e., the
atmosphere and Lake Huron water across the Straits of
Mackinaw). Initial conditions are the concentrations
of state variables at the beginning of the model
simulations. Forcing functions include other data to
which the model responds, such as meteorology.
Constituent observations in water, sediment, and biota
- Data that are compared to model predictions of state
variable concentrations; they may be either
observations of the state variables themselves, or of
other constituents used as surrogates for state
variables. Model performance is principally evaluated
in terms of the residuals (differences) between
observations and predictions for state variables.
Appropriate spatial and temporal allocation of the
point observations is necessary for comparability with
model predictions, which are spatially and temporally
continuous.
Process data - Data that are used to confirm particular
aspects of the model formulation and
parameterization. Process data are usually specific in
terms of constituents and media, and are based upon
field and/or laboratory experiments. Process data is
particularly useful in confirming aspects of the model
parameterization which is unconstrained by other
observations.
Loadings, Boundary Conditions, and Forcing
Functions
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Loadings and boundary conditions necessary for the
toxics, solids, and nutrient mass balances will be based
upon monitoring data for the atmosphere, tributaries, and
Lake Huron. Continuous estimates of loads, for the 1994-
1995 BMP period, will be required for the parameters
listed in Table 1. Atmospheric loads from dry and wet
deposition will be resolved as weekly averages on the
Level 2 model grid. Tributary loads will be computed as
daily (for events) or weekly (non-event) averages, for each
river. The computation of load estimates is considered the
responsibility of Atmospheric and Tributary Workgroups.
Boundary conditions of concern to the mass balance
include vapor-phase air concentrations, and concentrations
of state variables in Lake Huron water. Over-water air
concentrations will be estimated, based upon the routine
(shore-based) and Air Intensive monitoring data. Water
quality data from Station 54M, located in northern Lake
Huron, will be used to describe the lake boundary
condition.
Meteorological data including wind speed and direction,
temperature, and solar radiation will be collected from
land and ship-based atmospheric monitoring, NWS surface
observing stations, and NOAA mid-lake weather buoys.
These data will be used to synthesize over-water
momentum and heat flux fields, forcing functions for the
hydrodynamic model. Ice cover data will also be used as
a model forcing function.
Water Column
Water column monitoring will be conducted to determine
the spatial distribution and inventory of mass balance state
variables in the lake, on a seasonal sampling basis. State
variables to be measured in the water column are listed in
Table 2. The basic monitoring program consists of
sampling on eight cruises' conducted aboard the Lake
Guardian. Five cruises (April, August, and October 1994;
April and September 1995) will sample the 41 BMP
stations; three other cruises (June 1994; January and
August 1995) will sample a station subset. On all cruises,
enhanced vertical sampling resolution will be obtained at
nine open-water master stations. In addition to discrete
samples for the parameters in Table 2, continuous vertical
profiles of conductivity, temperature and transmissivity
will be recorded at all stations. Supplemental water
column monitoring data will be provided by NOAA-
GLERL (weekly-monthly sampling at several southern
basin stations), air intensive studies, biota sampling, and
municipal water intake components. The parameters of
interest from these data sources are identified in Table 3.
PCB concentrations (in all media) are to be reported using
a standard congener list according to GLNPO Data
Reporting Standards. Surrogate recovery data as well as
below-detection limit and below-quantification limit
results are required for modeling data reduction. Mercury
data will be reported for total mercury and methylmercury
(if available).
Sediment
Sediment sampling will be conducted to estimate the
distribution of sediments, contaminants, nutrients, and
selected other parameters in surficial sediments throughout
the lake, as well as the fine-scale vertical distribution of
contaminants in selected sediment cores.
The primary use of this data is to define initial conditions,
as the sediments contain the largest inventory of
contaminants in the system. More than 100 box cores,
gravity cores and PONAR grab samples will be collected,
providing nearly uniform coverage of Lake Michigan
sediment locations and types. Parameters of interest in
sediment samples are listed in Table 4. The top centimeter
of cores will be sampled as the surficial sediment, as will
surface grab samples. Approximately 30 sediment cores
from deposition basins will be sampled at 1-cm intervals
and analyzed for lead-210, cesium-137, and ancillary
sediment parameters; 10 of these cores will also be
analyzed for contaminants. Trap material from four near-
bottom sediment traps will also be analyzed for parameters
in Table 4, to better define constituent concentrations for
resuspendable, sediments in non-depositional zones. This
data will be augment prior to sediment surveys conducted
by Cahill (1981), Robbins and Edgington (1975), and
Eisenreich et al. (1991).
Biota
Biota will be sampled in defined food webs and zones, on
a seasonal basis. The top predators of interest, lake trout
and coho salmon, will be sampled as discrete age classes.
Based upon the collection success in a particular season
and zone, individual as well as composite fish may be
analyzed for the parameters in Table 5.
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Table 1. Parameters Required for Atmospheric and Tributary Loads
Parameter
Atmosphere
Tributary
PCB congeners, TNC, atrazine
(+DEA and DIA), mercury
(+methyl if available)
Total suspended solids
Particulate organic carbon
Dissolved organic carbon
Total phosphorus
Soluble reactive P
Total dissolved P
Nitrate-nitrite
Total Kjeldahl N
Ammonia
Dissolved silica
Biogenic silica
Chlorophyll a
Chloride
Hardness
Conductivity
Alkalinity
Other data
Vapor concentration, wet and dry
deposition fluxes
Particle size and deposition
velocity, wet and dry deposition
fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Wet and dry deposition fluxes
Rainfall, snowfall, pH, T, relative
humidity, solar radiation, wind
speed ad direction, wave height
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Tributary load
Flow, velocity, stage, T,
transmissivity, pH, D.O.
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Table 2. Water Column State Variables
Parameter
Phases/Comment
PCB congeners
TNC
Atrazine (+ DBA and DIA)
Mercury (+ methyl)
Total suspended solids
Particulate organic carbon
Dissolved organic carbon
Total phosphorus
Soluble reactive P
Nitrate-nitrite
Total Kjeldahl N
Ammonia
Dissolved silica
Biogenic silica
Chlorophyll a
Chloride
Hardness
Alkalinity
pH
Secchi disk
Light extinction
C-14 primary production
Phytoplankton (abundance and biovolume)
Dissolved and particulate
Dissolved and particulate
Dissolved and particulate (master and biota stations)
Dissolved and particulate (master and biota stations)
Total and dissolved
Dissolved
Dissolved
Total
Dissolved
Dissolved
Particulate
Master stations
Master stations
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Table 3. Supplemental Water Column Monitoring Data
Study Component Parameter
NOAA-GLERL Monitoring Total phosphorus
Soluble reactive phosphorus
Nitrate, ammonia
Dissolved and paniculate silica
Chlorophyll a
Paniculate organic carbon
Dissolved organic carbon
Chloride
Temperature
Secchi disk
Bacteria, phyto- and zooplankton counts
Air intensive Wind and wave height
Volatile flux (PCB congeners, mercury)
Over-water deposition fluxes (PCB congener, TNC)
atrazine, and mercury
Plankton sampling (phyto-, zooplankton, and detritus) Dry weight/volume
particle fractions)
PCB congeners, TNC
Mercury
Remote sensing (NOAA) Surface temperature and reflectance
Municipal water intake Temperature and transmissivity (calibrated to TSS)
Table 4. Sediment Parameters of Interest
Parameter Surficial Sediment Sediment Cores Sediment Traps
PCB congeners
TNC
Atrazine*
Mercury
Total organic carbon
Cumulative dry weight
Gross particle downflux
% moisture
Porosity (derived)
Grain size
Pb-210andCs-137
Total phosphorus
Extractable/bioavailable
Total nitrogen
Ammonia
Total Kjeldahl N
Biogenic silica
All
All
Selected
All
All
All
All
All
All
All
All
All
All
All
All
Selected
Selected
Selected
Selected
Selected
All
All
All
Composite
Composite
Composite
All
All
All
All
All
All
All
All
* Selected sediment samples should be analyzed for the presence of atrazine, even though this contaminant is not believed
to associate with sediments.
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Table 5. Biota Parameters
Parameter
Top Predators Forage Fish Invertebrates Phyto-, zooplankton, and detritus fractions
Age
Weight
Length
Sex
% Moisture
% Lipid
POC
PCB congeners
TNC
Mercury
Atrazine
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X (+ methyl, if available)
Individual-based sampling provides better information as
to the source of contaminant variability. Forage fish will
be collected in conjunction with top predators, and
analyzed as composites according to size. Invertebrates
(Mysis and Diporeia) will also be sampled at the same
times and locations as fish; phytoplankton and
zooplankton will be sampled in conjunction with water
sampling cruises.
Transport
Additional data will be required to confirm transport
simulations. Remote sensing of lake surface temperature
and reflectance (a surrogate for suspended solids at the
lake surface), municipal water intake measurements of
temperature and transmissivity (correlated to suspended
solids), and vertical instruments arrays measuring
temperature, transparency, depth and current velocity will
temperature and transmissivity (correlated to suspended
solids), and vertical instruments arrays measuring
temperature, transparency, depth and current velocity will
provide information about water and particle transport
transients at a resolution not attainable by conventional
ship-based sampling. Wave height data from ship and
buoy observations will be used to confirm the wave
submodel used in the transport simulations.
Particle and Contaminant Fluxes
To obtain accurate mass balance results, large-magnitude
contaminant and particle fluxes between the atmosphere
and the lake, and the lake and the sediment, will be
monitored. These include atmospheric wet and dry
deposition, net volatilization flux, and net settling and
resuspension rates. Monitoring for wet and dry deposition
fluxes will be conducted during routine and intensive
atmospheric sampling; volatilization flux at the lake
surface will also be monitored during intensive ship-based
sampling. Sequencing sediment trap arrays will be
deployed at deep water locations, to measure settling and
resuspension fluxes for solids, POC and selected nutrients
(Table 4). Sedimentation fluxes will be determined from
Pb-210 profiles in sediment core samples, sediment mixing
depth from Cs-137 profiles, and sediment focusing factors
from Pb-210 and Cs-137 inventories.
Contaminant Partitioning
All water column contaminant samples will be separated
into dissolved and particulate fractions by filtration, and
will be accompanied by measurements of total suspended
solids, POC, and DOC. Although this data will provide
the basis for confirming the description of partitioning in
the CTF model, additional data will be required to define
the contaminant distribution between sorbent phases
within these fractions. These include the organic carbon
partition coefficient, K()C, the DOC partition coefficient,
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Kdoc, and the biotic and detrital fractions of POC. The
partition coefficients will be treated as contaminant-
specific parameters, and will be based upon the literature
as well as process data from laboratory experiments. POC
fractions will be based upon surrogate measures, including
chlorophyll, developed from phytoplankton sampling and
biovolume data. Contaminant partitioning in sediment pore
water will be described using data from the literature and
from field and laboratory experiments.
Contaminant Transformation
Transformation between state variables is of concern for
atrazine, mercury, and PCB mass balances. Because
atrazine is known to degrade in soil as well as water, the
concentration of diethylatrazine and deisopropylatrazine
will be measured with the parent compound in all tributary
and water samples. These data will confirm the location
and rates of atrazine transformation. Mercury methylation
and demethylation rates are not being measured for the
LMMBP, consistent with the total mercury mass balance
objective. Operationally, a sediment equilibrium constant
between organic and inorganic mercury states will be
defined for CTF modeling, based upon the literature. PCB
congener dehalogenation rates will be estimated from data
in the literature. Prior modeling efforts, including the PCB
mass balance models for Green Bay and the Fox River, as
well as process research (Rhee et al., 1993) have
suggested that dehalogenation is probably negligible for
the range of PCB sediment concentrations observed in
Lake Michigan.
Resuspension
The relationship between shear stress and resuspension
rate is critical for sediment transport modeling, and must
be measured for representative sediments throughout the
lake. Although a number of flume devices have been used
in the laboratory for this purpose, the bottom-resting
seaflume (Hawley, 1991) has been deployed previously in
the Great Lakes. For this project, the seaflume will be
modified to improve quantitative results, and deployed to
test sediment resuspension properties at master stations,
sediment trap and vertical instrument array locations, and
other locations to obtain data for a variety of sediment
substrates. This information will be used to estimate
resuspension properties throughout the lake, based upon
the spatial distribution of sediment physical properties.
Eutrophication
Specialized process measurements required for the
eutrophication model include C-14 primary production,
phytoplankton and zooplankton abundance and biovolume,
light extinction, and incident solar radiation.
Bioconcentration and Bioac cumulation
Species- and contaminant-specific toxicokinetic
parameters required for the bioaccumulation model, will
be based upon the literature and prior modeling studies.
This parameterization will be refined by calibration to
biota contaminant data.
Data for movement and migration patterns, feeding habits,
and seasonal growth rates of fish are also required for the
bioaccumulation model. Fish are not perfect integrators
of lake-wide toxics exposure; rather, their contaminant
burden reflects their exposure (particularly through diet)
along a chemical gradient defined by their movements
over seasons and years. National Biological Survey (NBS)
personnel interviews, reports and file data will be used to
construct fish migration patterns. Feeding habits will be
based upon gut contents analysis for top predator and
forage fish. Age-weight relationships will be developed
for the collected fish, to define their rate of growth at each
collection location.
Supporting Studies List
A draft LMMBP work plan was distributed for public
comment by GLNPO in October, 1993. A substantial
number of comments were received, including suggestions
for research and additional monitoring to support the mass
balance objectives. These suggestions were organized,
and the following list of candidate "supporting studies"
was developed:
Candidate Supporting Studies for LMMBP:
Measure contaminant concentrations in plankton;
confirm separation of phytoplankton,
zooplankton, detritus.
Monitor movement/migration of food web fish
species.
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Gut contents analysis (diet composition by
weight; gut fullness) to define food web structure
and seasonal variance.
Measure methyl mercury in water, sediment, and
biota for understanding mercury cycling and
bioaccumulation.
Measure rates of contaminant uptake by
phytoplankton, including relationship between
uptake and growth.
Measure seasonal changes to invertebrate growth
and lipid.
Routes of contaminant transfer to benthic
organisms; linkages between food web structure
and contaminant concentrations in invertebrates;
dietary composition and feeding behavior of
Diporeia and Mysis.
Measure rates of uptake (diet/dermal/respiration)
and elimination (respiration/excretion/
metabolism) for PCB congeners and TNC in lake
trout, alewife, and smelt.
Study role of lipid transfer and synthesis upon
hydrophobic contaminant accumulation by
invertebrates.
Research of sediment bioturbations by sculpins,
Mysis, Diporeia, etc.
Improve biotic carrier (birds, insects, fish) flux
estimates for contaminants.
Measure transformation rates of atrazine in Lake
Michigan.
Measure air-water exchange fluxes for
contaminants.
Determine effect of chemical hydrophobicity/
lipophilicity (Kow) and XAD-2 resin separation
efficiency for dissolved and DOC-bound phases.
Research the effects of sampling equipment upon
dissolved HOC measurements and blanks.
Study fate and bioavailability of atmospheric
particulate matter in the water column.
Analyze PCDD, PCDF and coplanar PCB
congeners in sediment and fish.
Process research on mercury species
transformation, sorption, and bioaccumulation.
Measure sediment nutrient fluxes.
Study organic carbon sorbent kinetics (especially
particle degradation/mineralization rates): vertical
resolution in water column/BNL/sediments.
Improve measurements or estimates of flow across
Straits of Mackinaw.
Acquire/interpret remote sensing data for surface
temperature, total suspended solids and
chlorophyll.
Water intake monitoring for temperature and
transmissivity.
LMMBP integration with University of
Michigan/NOAA thermal fronts study.
Measure tributary contaminant loading during
high-flow events.
Estimate solids load from shoreline and bluff
erosion.
Monitor other significant point source loads for
evaluating effectiveness of load reduction efforts.
Research and estimation of contaminant loading
from storm sewers/urban runoff.
Model coupling of atmospheric and lake mass
balances for hazardous air pollutants.
Watershed deliver modeling to estimate present
and future tributary loading of nutrients, solids
and contaminants.
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Develop methods of distinguishing and separating
particles into biotic and abiotic, as well as
functional organic carbon sorbent classes.
Fine-scale mapping of density, porosity, particle
size and organic carbon content of surficial
sediments based upon acoustical profiling or
sediment surveys.
Measure sediment mixed layer depth, particle
residence time, and sedimentation velocity
throughout depositional zones by coring and Cs
and Pb-210 radiodating.
Measure particle and contaminant exchange
between sediment and water column: sediment
trap measurement of vertical fluxes of solids,
POC, Cs and Be, chlorophyll, nutrients, and
selected contaminants.
Measure sediment resuspension properties as
function of shear stress.
Measure rates of contaminant desorption from
resuspended sediment particles.
Sampling and analysis of sediment pore water
chemistry.
Measure in-lake temperature, current and
suspended solids profiles.
Measure particle settling velocity (including
effects of flocculation).
Research and measurement of dissolved and
DOC-bound contaminant exchange between
sediment and water.
The final selection of supporting studies necessary to
support the modeling effort for the LMMBP, was based
upon prioritization of modeling data requirements, utility
in relationship to the model paradigms, and availability of
demonstrated methods. Several supporting studies have
been funded, as described in Extramural Plan below.
However, at this time a number of high-priority efforts
have not been initiated, due to lack of adequate time for
planning, funding and personnel shortfalls, and constraints
upon extramural modeling vehicles. These efforts are
described below:
Eutrophication/Sorbent Dynamics (Research
and Submodel)
The BSD model will require development or modification
of existing models, to refine the relationships between
biotic and organic carbon state variables, and to
incorporate linkages to hydrodynamic and sediment
transport submodels. In addition, research of specific
processes related to understanding and modeling the
dynamics and transformations of organic carbon states in
Lake Michigan will be important to develop and accurate,
scientifically-defensible toxics mass balance model. In
Lake Michigan, the loss and transformation of particulate
organic carbon states appears to be particularly significant
(Eadie et a/., 1983; Eadie, 1987). Accurate simulation of
the sorbent dynamics is critical, because the major
transport, fate and bioaccumulation processes for toxics
are all mediated by partitioning.
Sediment Transport Process Measurements
Measurement of sediment resuspension properties is
essential for accurate sediment transport simulation. The
measurements should establish the relationship between
resuspension rate and applied shear stress, for an
appropriate range of shear stresses both above and below
the critical shear stress, including consideration of the
effects of sediment ageing, compaction, and armoring.
Methods for extrapolation of results to the whole lake,
such as acoustical impedance, should be tested in
conjunction with sediment coring. This research should
evaluate the variation in sediment resuspension properties
both vertically and areally (at different spatial scales), as
well as the relationship between resuspension properties
and sediment contaminant concentrations. Although
aspects of this process may be addressed by deployment of
the seaflume, continued development will be necessary to
ensure compatibility with modeling requirements.
Estimates of Shoreline Erosion Load: Dynamics
and Variability
According to both contemporary (Colman and Foster,
1994) and historical sources, bluff and shoreline erosion
is the major component of sediment loading to southern
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Lake Michigan. Although the majority of the erosional
load is sand, as much as 25% is fine-grained material.
Both components are probably significant influences upon
sediment and contaminant transport. To be useful for
modeling, the estimates of coarse- and fine-grained
erosional loading must be resolved in terms of both
temporal and spatial distribution. Estimates based upon
relationships to factors such as wind and wave intensity,
and water level, could be incorporated in the sediment and
contaminant transport model. Survey of the literature
reveals no such estimation methods, however.
Vertical Contaminant Concentration Profiles in
Sediment
Analysis of the top 1 cm of sediment cores, was
recommended by the Sediment Workgroup as the optimum
method to sample the distribution of toxics in the surficial
mixed layer of lake sediments. From a mass balance
perspective, this data will provide an adequate measure of
the resuspendable toxic chemical associated with the
sediment. Additional sampling of deeper sediment layers
will be necessary to measure sediment-associated toxics at
locations in the lake where greater than 1 cm of sediment
resuspension is predicted, as well as to define vertical
contaminant gradients which will increase contaminant
fluxes via sediment mixing, bioturbation, and benthic
irrigation processes. Analysis of sediment cores collected
in 1991-1992 may satisfy this latter need, at least for
PCBs. However, sediments subject to greater than 1 cm of
resuspension will be located in shallower lake regions,
areas where coring and vertical profile analyses have not
been performed. Because sediment core samples will be
archived, it may be possible to defer analysis until
estimates of maximum resuspendable depth can be
obtained from the sediment transport model.
Volatilization Mass Transfer Rate
The volatile exchange of semivolatile toxics is driven by
the local concentration gradient between the water and air,
at a rate specified by a volatilization mass transfer
coefficient (kv). kv is generally estimated using semi-
empirical relationships based upon two-film, surface
renewal, and penetration mass transfer descriptions.
Depending upon the relationship chosen, kv estimates can
vary by as much as a factor of 5-10, directly influencing
the computation of volatile flux. Furthermore, the
different relationships vary in terms of kv sensitivity to
environmental variables including wind speed, wave
height, fetch. For semi-volatile contaminants in Lake
Michigan, this variability introduces considerable
uncertainty into the mass balance. Although
measurements of volatile flux have been performed for
toxic chemicals in the laboratory, and for tracers (O2, CO2,
H2O, Rn) in streams, lakes, and oceans, direct
environmental measurements are necessary in Lake
Michigan to measure volatile exchange of hazardous air
pollutants, especially PCBs and mercury.
Tributary Sampling During Sediment
Resuspension/Transport Events
Highly-resolved monitoring and detailed modeling of
sediment and contaminant transport in Great Lakes
tributaries, has demonstrated that tributary loading is
strongly related to extreme high flow events for
contaminants originating from tributary sediments (Gailani
etal., 1994;VelleuxandEndicott, 1994). Unless the BMP
monitoring program samples such events in tributaries
with significant in-place pollutants, it is likely that
tributary loading will be significantly underestimated. It
is unclear whether the BMP tributary sampling effort can
adequately address this requirement, in particular the "first
flush" of contaminants which occurs on the rising limb of
the hydrograph.
Watershed Contaminant Delivery Model
The need for a watershed component to the LMMBP was
described above. Depending upon the specific toxic
chemical, watershed delivery encompasses a number of
source and transport pathways. For atrazine, the source is
spring agricultural application; runoff and groundwater
transport from cultivated land are principal transport
mechanisms. For PCBs and mercury, some combination
of atmospheric deposition, nonpoint sources, and
contaminated sediments appear to serve as watershed
sources. Unless mass balance analysis is applied on the
watershed, as it will for the atmosphere and lake,
relationships between sources and tributary loading
necessary for load reduction efforts will not be
established. The severity of such a limitation upon the
utility of the modeling results for each contaminant, will
depend upon the magnitude of the watershed load relative
to both air/water and sediment/water mass fluxes.
Relative magnitudes of contaminant loads and mass fluxes
will be determined as part of the mass balance project,
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suggesting that a watershed contaminant monitoring and
modeling effort be designed and conducted subsequent to
this project. Tributary monitoring and load estimates will
also serve to identify specific watersheds for contaminant
delivery modeling efforts.
Development of User Interface and Model
Integration System
The drawback of the linked submodel framework, is that
model execution and data transfer become a complex,
repetitive series of computer operations. Thus, use of the
models is beyond the general capabilities of scientists and
decision-makers, thereby limiting interaction with the
models for both scientific and managerial interests. This
situation would be greatly improved if the processes of
model development and application was systematized and
automated. To this end, a computer-based model
integration system should be developed for the LMMBP
models, with graphic user interfaces constructed for data
analysis, model visualization, scenario management, etc.
Such development would greatly facilitate the accessibility
and utility of the models.
In-House Plan (MED-Duluth/LLRS)
The MED-Duluth/LLRS inhouse modeling team will lead
the lake mass balance modeling effort. They will be
responsible for the following tasks:
Screening-Level (MICHTOX) Analysis
The screening-level mass balance analysis performed for
PCBs will be extended to the other toxics of concern:
atrazine, mercury, and TNC. This will provide an
operational model for evaluating transport and fate
pathways for the different contaminants, testing air model
linkages, and rapid incorporation of toxics loading and
ambient monitoring data into the mass balance. The
screening model will continue to serve its present function
as a means of communicating and demonstrating the mass
balance paradigm.
Submodel Development and Linkage
The inhouse team will lead development of the sediment
and contaminant transport, CTF, and food web
bioaccumulation models and model linkages.
Green Bay Prototype Application
The integrated submodel framework will be prototyped on
Green Bay, using the GBMBS data for testing and
confirmation. Sediment and contaminant transport, CTF,
and food web bioaccumulation submodels will be linked
to simulate the 1989-1990 mass balance for PCBs and lead
in the Fox River/Green Bay ecosystem. The extensive data
for suspended solids, PCBs, and lead will allow for
comprehensive testing of the Lake Michigan submodels,
except that Green Bay Organic Carbon Based Sorbent
Dynamics Model (GBOCS) (DePinto etal., 1993) will be
substituted for the BSD model. Such a test application is
necessary for productive model development in advance of
the BMP data.
Model Development for Lake Michigan
The inhouse team will perform data reduction,
construction of input data sets, calibration and
confirmation of the sediment and contaminant transport,
CTF, and food web bioaccumulation models. Linkages
with the eutrophication/sorbent dynamics and atmospheric
transport models will be established.
Lake Michigan Model Application
The integrated submodel framework will be applied to
Lake Michigan, including both short- and long-term
simulations for both scientific and managerial objectives.
Extramural Plan
The expertise of a large number of extramural researchers
will be required for a successful LMMBP modeling effort.
Academic, consultant, and government collaborators will
be funded to provide specialized expertise including:
submodel process formulation, experimental design and
conduct, data analysis, model development, and scientific
peer review. Several cooperative agreements are in
progress to develop and parameterize transport, fate and
bioaccumulation process descriptions, funded by an MED-
Duluth/LLRS initiative for reducing uncertainty in toxic
chemical models for the Great Lakes. These include:
Colloid Mediated Transport of Hvdrophobic Organic
Contaminants Across the Sediment-Water Interface in
the Great Lakes Ecosystem (Yu-Ping Chin, Ohio
State University) Development and application of
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methods to characterize and quantify organic colloidal
matter residing in the pore water of Great Lakes
sediments, study the effect of pore water colloids upon
HOC distribution, and estimate on the basis of
experimental measurements the ability of porewater
colloids to facilitate the exchange of HOCs between the
sediment bed and the overlying water column.
Reducing the Uncertainty in Modeling Dietary
Transfer of Hvdrophobic Contaminants (Robert
Thomann, Manhattan College) Investigation of the
dietary accumulation process of HOCs from detrital
organic carbon to a benthic invertebrate species,
leading to an improved submodel for macrobenthos
bioaccumulation.
An interagency agreement between MED-Duluth/LLRS
and the NOAA-GLERL has been established to fund the
following research:
Accumulation and Mixing of Recent Sediments in
Lake Michigan Collection and dating of sediment
cores taken at various locations in the lake, to generate
lakewide distributions of sedimentation rate, mixed
layer thickness, and Cs-137 and excess Pb-210
inventories.
Bioaccumulation of Organic Contaminants by
Diporeia spp.: Kinetics and Factors Affecting
Bioavailabilitv Investigation and modeling of
bioaccumulation rates of PCB congeners, including
factors such as temperature, sediment composition,
and availability of fresh detritus. Rates of porewater
irrigation by Diporeia will also be measured.
Hydrodvnamic Model of Lake Michigan
Development and confirmation of a three-dimensional
hydrodynamic model, as described above.
Sediment Resuspension and Transport in Lake
Michigan Instrument platforms will be deployed to
measure vertical water column distributions of
temperature, transparency, and current at selected
locations in the lake. Seaflume device will be
deployed to measure sediment resuspension
properties.
Sorption. Flux and Transport of Hvdrophobic Organic
Chemical (Wilbert Lick, University of California)
Study of sorption process for HOCs on fine-grained
sediment particles and incorporation of this
information into CTF models. Experiments will be
performed to measure equilibrium partition
coefficients and chemical sorption rates to and from
sediments, under well-controlled conditions, in both
suspended solids and deposited bottom sediments.
Uptake and Loss of PCBs by Phvtoplankton:
Importance to Mass Balance Models (Deborah
Swackhamer, University of Minnesota) Investigation
of the relationship between phytoplankton growth and
HOC uptake kinetics, and HOC loss from
phytoplankton by desorption and exudation. A
submodel describing the dynamics of HOC
accumulation in phytoplankton will be developed to
incorporate this experimental data.
Use of Sediment Traps for the Measurement of
Particle and Associated Contaminant Flux in Lake
Michigan Deployment of sequential-sampling
sediment traps, to measure gross downward fluxes of
particulate matter and organic carbon, and to collect
and analyze samples of the resuspendable sediment
pool from selected depositional and non-depositional
regions of the lake.
Additionally, several aspects of the BMP sediment
sampling program (sediment core collection, radiometric
dating, analysis for contaminants) have been coordinated
with other programmatic missions and funding sources,
including the MED-Duluth/LLRS Mercury Fate and
Accumulation Project and the ERL-Duluth Great Lakes
EMAP Project.
A number of vehicles may be used to address the needs for
additional supporting studies identified above. These
include solicitation and competitive selection of
cooperative agreements, funding work assignments
through existing Agency contracts, and interagency
agreements.
Schedule
The schedule for LMMBP model development is
complicated, for it must accommodate a number of
incongruous objectives and factors: substantial model
development lead time, uncertainty as to the schedule of
data delivery, potential disruption of extramural vehicles,
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lack of funding to initiate necessary modeling tasks, and
institutional requirements to rapidly develop interim and
final results. In particular, timely project completion will
be contingent upon stable funding, staffing, and
extramural vehicles. Interagency agreement, cooperative
agreement, and inhouse model development efforts have
already begun, with additional model development efforts
initiated in FY95. It is expected that a reasonably
complete BMP data set will not be available until 1997,
allowing two years for model development and testing,
Green Bay prototype application, and conduct of
supporting research. Initial simulations from the
hydrodynamic and sediment transport models will provide
transport linkages to BSD and CTF models in late 1995
and 1996. By 1997, the linked submodels will be
operational, although confirmation and refinement of
simulations for the BMP period (1994-1995) will require
another year. Long-term model simulations will be
conducted in 1998. Project completion, including
preparation of final reports and transfer of the modeling
system to GLNPO, is expected in 1999.
Atmospheric Modeling Plan
Introduction
Atmospheric modeling provides a direct link between air
toxics emissions and the greater Lake Michigan watershed.
The Atmospheric model should be viewed as a
comprehensive system, including not only the air quality
simulation model (AQSM) which provides concentration
and deposition fields, but also the meteorological and
emissions models required to drive the AQSM. The
atmospheric modeling system provides the following
information useful to the aquatic mass balance model:
1. direct wet and dry deposition loadings,
2. near-water, ambient gas phase concentrations used
in mass balance surface exchange calculations,
3. meteorological fields of wind speed and direction,
air temperature, heat flux, and radiation to drive
hydrodynamic processes influencing
sediment/water exchange, air/water exchange, and
water column advection and dispersion.
As stated previously, sufficient air emissions data do not
currently exist to allow a credible simulation of the
transport and deposition of PCBs and TNC. The focus of
the atmospheric modeling effort will be on atrazine, with
a possible treatment of mercury if project resources allow.
The interaction between the air/water interface may be
bidirectional for certain toxic substances. During certain
time periods, volatization of PCBs from the lake surface
will increase ambient air concentrations over water, and
may act as a major source in itself for downwind
receptors. In order for PCBs to be adequately modeled for
the purposes of determining the overall mass balance for
Lake Michigan, new and advanced model coupling
techniques will likely need to be developed which are not
included in this modeling plan. Since the focus of this
effort will be on atrazine, and atrazine is not known to be
significantly volatilized from the lake surface, a one-way
flux from air to water will be modeled. Atmospheric
modeling will assist near-term program specific tasks and
process oriented research by:
1. providing concentration and deposition fields for
aquatic mass balance inputs,
2. supporting regulatory analyses addressing impacts
resulting from various emission control strategies,
3. serving as an integrator of available information
(e.g., emissions, meteorology, ambient air
chemistry) to enhance our understanding of
transformation and deposition processes and
provide direction for continued research.
The following plan describes the near-term (1995-1996)
and long-term approaches for regional scale atmospheric
modeling within the Mass Balance Project.
Air Quality Simulation Model
A. Model Description
A dual track model development effort will address
near-term program needs and research interests for the
LMMBP. Modeling will be based on variations of the
RADM and the RPM, which utilize a gridded Eulerian
framework to treat the relevant transport,
transformation and deposition processes. The dual
track reflects an immediate model development
objective to be program responsive and the ongoing
interest in enhancing the scientific credibility of the
modeling efforts toward reducing uncertainty and
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improving process level understandings. The operational
and research grade models will be based on similar
geometric frameworks, thus minimizing the interfacing
with meteorological, emissions and aquatic mass balance
models. Generally speaking, the operational model will
incorporate highly parameterized and available chemical
transformation, particle description, and deposition
schemes. Research grade modeling will build upon
operational-grade models by incorporating improved
process characterizations utilizing process-related
observed data and more sophisticated, mechanistic
treatment.
Spatial scales. The modeling domain will extend
throughout the continental U.S. (perhaps extending
westward only to the Rocky Mountain region) and
consist of a double-nested horizontal grid arrangement
of 54 km and 18 km grids (this may change to a 60/20
configuration). The 18 km grid would overlay the
Great Lakes basin. Generally 15 vertical layers will
be used to represent the atmosphere through 100 mb
(roughly 15 km). Some preliminary modeling may be
conducted with 80 km grid cells and 6 vertical levels
to test newly coded parameterization schemes.
Certain research grade models may be based on 25
vertical levels for improved characterization of
meteorological processes affecting vertical mixing and
transport.
B. Operational Model
The operational model will be based on simplified
treatments of particle characterizations, chemical
transformations and deposition. Gas phase chemistry
of oxidants and relevant radical initiation/destruction
processes will be simulated by a preliminary RADM
application, rather than calculated explicitly with
complex chemical and physical mechanisms for
paniculate matter in the RPM. For example, particle
concentrations and size distributions will be estimated
in the RPM from the pollutant concentration data
obtained from the RADM simulation. Phase
distribution between particles and gas-phase will be
based on best available thermodynamic data.
Similarly, deposition processes will utilize existing
algorithms and available data. Basically, "off-the-
shelf", highly parameterized components will be used
to economize model development and CPU times,
respectively. For discussion purposes, the operational
model will be referred to as the "engineering" version
of the RPM. A working version of the operational
model is now being developed and should be complete
in early 1996.
C. Research-Grade Modeling
Using the same general model structure as the
operational model, the research-grade model would be
enhanced through continual updating of
parameterization schemes and the incorporation of
mechanistic chemistry and particle characterization
algorithms. The research grade model will be referred
to simply as the RPM, a derivative of RADM
including treatment of sulfur, nitrogen and organic-
based aerosols relying on more deterministic
treatments of gas and aqueous-phase chemistry and
phase distribution processes. Application of the RPM
would not require a previous application of the
RADM.
Meteorological Modeling
A. Model Description
Meteorological information for the toxics transport
and deposition modeling will be obtained from the
Penn State/NCAR Mesoscale Modeling System
Generation 4 (MM4) and Generation 5 (MM5). The
MM4 and MM5 are Eulerian-grid, primitive-equation
meteorological models which can employ four-
dimensional data assimilation (FDDA) for diagnostic
applications to constrain their simulations to the
observed conditions. They can also be used for
prognostic applications, but typical model error
growth limits these forecast periods to about 48 hours.
The MM5 has been developed as an extension of the
MM4 to allow non-hydrostatic modeling of
atmospheric physics. This Eulerian model, when
using the non-hydrostatic physics, can resolve
horizontal scales down to 4 km. It has improved
computational grid nesting capabilities to allow up to
nine simultaneous grids with the capability of moving
nests to follow small-scale phenomena of interest
(squall lines, mesoscale convective complexes, etc.).
Initial applications will use existing model output
from the MM4 at an 80 km horizontal grid scale and
15 vertical levels. Meteorological information on a
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smaller horizontal scale will be produced using
objective spatial analysis schemes and interpolation.
MM5 applications should be possible beginning in
late 1995.
Inputs required by the MM4 and MM5 models
include: hemispheric-scale meteorological model
analyses from the U.S. National Meteorological
Center (NMC) and/or from the European Center for
Medium-Range Weather Forecasting (ECMWF),
terrain height and surface type information at the
horizontal scale of the modeling grid, observed
meteorological data at the Earth's surface (at three-
hour intervals for FDDA applications), and observed
meteorological data at various vertical levels in the
atmosphere (at 12-hour intervals for FDDA
applications). Normal model outputs include:
horizontal wind vectors, temperature, water vapor
mixing ratio, atmospheric pressure, convective (sub-
grid-scale) precipitation and non-convective
(resolvable grid-scale) precipitation. Special model
outputs obtainable without code modification include
cloud water and cloud ice density. Modifications can
be made to extract the heat and momentum flux
variables that are currently internal to the model code.
The RADM and RPM currently use a meteorological
data pre-processor to read MM4 output data and
format them for air-quality model input. The MM4
has normally been operated with the same horizontal
and vertical grid definition as the air-quality model to
which data is provided. Thus, the meteorological data
pre-processor is used to simply modify the
computational data format. At this point there are no
plans to allow feedback of chemical and aerosol
results from the air-quality model to the
meteorological model. It has been shown that aerosol
loading of the atmosphere does affect radiative energy
transfers, and these feedback mechanisms could be
significant to purely prognostic simulations.
However, the MM4 and MM5 will be applied in a
diagnostic mode using four-dimensional data
assimilation of observed meteorological variables to
reduce model errors, and a treatment of radiative
energy feedback is not necessary.
We envision that the meteorological model could
supply both the air-chemistry model and the
hydrodynamic model with meteorological inputs, but
both links would be forward only (one-way). We
realize that water surface temperature and roughness
(wave height) information from the hydrodynamic
model could be used to provide feedback forcing to
the meteorological model, but such two-way linking
would require the same level of effort as two-way
linking to the air-chemistry model, which has thus far
been beyond the scope of our research and
development projects.
B. Meteorological Scenarios
Time periods for modeling will be determined by
considering availability of processed MM4
simulations and relevance to the LMMBP. Currently,
MM4 has been exercised for 1990 as part of the
Interagency Workgroup on Air Quality Modeling
(IWAQM) and initial modeling will therefore be
restricted to that year. Issues to be resolved include
the identification of meteorological periods and the
method of producing annual estimates. Limitations on
CPU time and storage media may restrict full, 365-day
simulations. Consideration will be given to
aggregating meteorological episodes to represent
reasonable distribution of events in order to reduce
total execution time. These computational savings
become more important as we progress from
operational to research-grade models.
Emissions Data and Modeling
Emissions data at the county level by season are available
for mercury and atrazine. These data will be gridded into
RADM compatible formats using standard GIS
procedures. Eventually, these emission files should be
updated as information becomes available from the
Regional Air Pollutant Inventory Database System
(RAPIDS). The availability of that inventory ultimately
will influence the selection (if any) of additional
substances to be modeled beyond these two toxics.
Emissions data are not available for restricted/banned
chemicals such as PCBs and TNC. The types of modeling
analyses for these toxics will be restricted to determining
transport patterns from lake surfaces.
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Proposed Model Simulations
Atrazine - Atrazine modeling would be performed using
the MM5, RADM and the engineering version of RPM for
the 1994-1995 study period. One- to two-week
simulations would be performed for important depositional
episodes and a statistical aggregation technique would be
used to estimate concentrations and deposition rates
throughout the study period. The RPM would consider
particle-gas phase interactions for atrazine.
Mercury - Mercury modeling has been conducted with the
RELMAP for the continental U.S. (Bullock, 1997). This
modeling effort provided mercury air concentration and
deposition estimates on a 40 km horizontal scale.
Modification of the RPM to provide higher-resolution
mercury concentration and deposition estimates is
possible. Transformation and deposition processes would
be based on the RELMAP effort with the addition of new
gas-phase chemical mechanisms to reflect recent scientific
advances.
PCBs and TNC - Modeling is not planned for PCBs or
TNC. The LMMBP may want to consider supporting
emission inventory work for banned substances such as
TNC and PCBs. The value of atmospheric modeling of
banned substances for regulatory purposes requires clear
definition and understanding before committing large
resources.
Interfacing/Linking Issues
A. Unidirectional Linking
The initial modeling efforts will provide unidirectional
inputs from the atmosphere to the Lake. The model
output will consist of hourly wet and dry deposition
and ambient gas phase concentration estimates above
the lake surface on an 18 km (or other) basis. An
interfacing system needs to be developed to
interpolate the atmospheric estimates over comparable
lake area domains. Note that the output will include
concentration data above the lake surface required for
air/water exchange calculations in the mass balance
models. An interface should also be developed
between the MM4 output files and the hydrodynamic
model used in mass balance modeling. Analogous
interpolation and extrapolation needs to be performed
on monitoring data that are used to provide
atmospheric loadings to the aquatic mass balance
models. However, the large output files and
consistent framework associated with the atmospheric
models suggests that a specific, perhaps user friendly,
software be developed for this interfacing, particularly
if future technology transfer efforts are to be
conducted with State agencies.
B. Bidirectional Linking
A longer term objective is the more complete
interactive operation of the aquatic and atmospheric
models in which the interfacing is imbedded in the
modeling construct and the lower atmosphere is
impacted by air/water exchange of gaseous species.
This linkage is being addressed through USEPA's
High Performance Computing (HPCC) program. The
end product will be the capability to perform direct
source to aquatic effect simulations incorporating
more realistic physical treatment of exchange
processes, without intermediate interface processing
steps.
Atmospheric Modeling Schedule
Time
Frame
Products
1/96 Operational engineering version of
RPM
4/96 MM5, RADM and RPM modified to fit
CTF model grid
1/96-7/96 Engineering RPM adapted for atrazine,
results obtained for selected time
periods in 1994 and 1995
9/96 Operational RPM with integrated gas
and particle mechanisms for sulfates,
nitrates, and some organics
9/96-1/97 Long-term atrazine deposition results
obtained from engineering RPM using
a statistical aggregate technique
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Time
Frame
Products
1/96-1/97 Construction of model deposition and
phase distribution algorithms based on
field data and related University
cooperative research
Episodic runs for 1994 intensive period
to evaluate full-scale RPM model
performance for atrazine
Refinement of operational engineering
RPM
1/97 Begin seasonal aggregation runs with
full RPM
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Michigan. 132 pp.
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Uncertainty Analysis Methodology for Green Bay Models.
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Research Station, Grosse He, Michigan.
Dortch, M.S., R.S. Chapman, and S.R. Abt. 1992.
Application of Three-Dimensional, Lagrangian Residual
Transport. J. Hydr. Engin., 118(6):831-848.
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Simmons, M.J. McCormick, S.J. Eisenreich, G.L. Bell,
R.L. Pickett, K. Johansen, R. Rossmann, N. Hawley, and
T. Voice. 1983. The Cycling of Toxic Organics in the
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Eadie, B.J. 1987. Kinetics of Sediment-Water Exchange
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1991. Volatilization of Polychlorinated Biphenyls From
Green Bay, Lake Michigan. Department of Civil and
Mineral Engineering, University of Minnesota, Navarre,
Minnesota. 147 pp.
Endicott, D.D., W.L. Richardson, and D.J. Kandt. 1992.
MICHTOX: A Mass Balance and Bioaccumulation Model
for Toxic Chemicals in Lake Michigan. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. 183 pp.
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Lakes and Associated Effects, Volume 1 - Contaminant
Levels and Trends. Department of Fisheries and Oceans,
Environment Canada, Toronto, Ontario, Canada. 488 pp.
Gailani, J.Z., W. Lick, M.K. Pickens, C.K. Ziegler, and
D.D. Endicott. March 1994. Sediment and Contaminant
Transport in the Buffalo River. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 54 pp.
Gilbertson, M. 1988. Epidemics in Birds and Mammals
Caused by Chemicals in the Great Lakes. In: M.S. Evans
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1994. PCBs in Lake Superior, 1978-1992: Decreases in
Water Concentrations Reflect Loss by Volatilization.
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in Lake Ontario: The Conflict Between Predator Demand
and Prey Supply. Transact. Amer. Fisher. Soc., 122:1002-
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Variation in the Bioavailability of Polycyclic Aromatic
Hydrocarbons to the Amphipod Diporeia (spp.) With
Sediment Aging. Environ. Toxicol. Chem., 11(8): 1197-
1208.
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of the Clean Air Act on Chesapeake Bay Water Quality.
EMECS '93, Baltimore, Maryland.
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Aulerich, S. Bursian, H.J. Auman, P.D. Jones, L.L.
Williams, D.E. Tillitt, and M. Gilbertson. 1993. A
Comparison of Water Quality Criteria for the Great Lakes
Based on Human and Wildlife Health. J. Great Lakes
Res., 19(4):789-807.
Pearson, R.F. 1994. PCBs in Lake Michigan Water:
Comparison to 1980 and A Mass Budget for 1991.
Masters' Thesis, University of Minnesota, Minneapolis,
Minnesota. 88 pp.
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Programs. In: K.G. Harrison (Ed.), Michigan
Environmental Science Board Mercury Meeting Minutes,
pp. 2-8. Michigan Environmental Science Board, Lansing,
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1993. Long-Term Study of the Anaerobic Dechlorination
of Aroclor 1254 With and Without Biphenyl Enrichment.
Environ. Sci. Technol., 27(6): 1190-1192.
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Classification of Great Lakes Tributaries. Water Quality
Laboratory, Heidelberg College, Tiffin, Ohio.
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Dynamic Mass Balance of PCB and Suspended Solids in
Saginaw Bay-A Case Study. In D. Mackay, S.
Patterson, and S.J. Eisenreich (Eds.), Physical Behavior of
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Estimating the Long-Term Response of the Great Lakes to
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Laboratory, Ann Arbor, Michigan. NOAA Technical
Memorandum ERL GLERL-57.
Robbins, J.A. and B.J. Eadie. September 1991. Seasonal
Cycling of Trace Elements 137Cs, 7Be, and 239+240pu in Lake
Michigan. J. Geophys. Res., 96(C9): 17,081-17,104.
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Modeling of Lake Michigan and Consideration of the
Anomalous Ice Cover of 1976-1977. J. Great Lakes Res.,
7(4):467-480.
Rossmann, R., D. Endicott, and J.W. Nichols. 1993.
Mercury in the Great Lakes: Management and Strategy.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 28 pp.
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Life-Cycle Biomagnification Study of Fish. Environ. Sci.
Technol., 26(11):2162-2174.
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Monteith. May 1983. Chloride Pollution of the Great
Lakes. Water Pollut. Contr. Fed. J., 55(5):513-521.
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Swackhamer, D.L. and R.S. Skoglund. 1993.
Bioaccumulation of PCBs by Algae: Kinetic Versus
Equilibrium. Environ. Toxicol. Chem., 12(5):831-838.
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Dependent Model of PCB in a Lake Michigan Food Chain.
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Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. EPA-600/S3-84-
026, 3 pp.
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Chemical Model of Toxic Substances in the Great Lakes.
J. Great Lakes Res., 9(4):474-496.
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Modeling Work Plan (Draft). U.S. Environmental
Protection Agency, National Health and Environmental
Effects Research Laboratory, Mid-Continent Ecology
Division-Duluth, Large Lakes Research Station, Grosse
He, Michigan.
USEPA. 1995b. User Manual for SEDZL: A Two-
Dimensional Hydrodynamic, Sediment Transport and
Contaminant Transport Model. U.S. Environmental
Protection Agency, Office of Research and Development,
Mid-Continent Ecology Division-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 126 pp.
Velleux, M.L. 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., J. Gailani, and D. Endicott. 1994. A User's
Manual to IPX, The In-Place Pollutant Export Water
Quality Modeling Framework. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 194 pp.
139
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Appendix B
Lake Michigan Mass Balance Project
Modelers' Curriculum Vitae
Contents
U.S. Environmental Protection Agency, Office of
Research and Development, Mid-Continent Ecology
Division-Duluth, Community-Based Scientific Support
Staff, Large Lakes Research Station, Grosse He, Michigan
Douglas D. Endicott, Environmental Engineer
Russell G. Kreis, Jr., Biologist
William L. Richardson, Environmental Engineer
Kenneth R. Rygwelski, Environmental Scientist
SoBran, Incorporated, U.S. Environmental Protection
Agency, Large Lakes Research Station, Grosse He,
Michigan
James Pauer, Water Quality Modeler
Xiaomi Zhang, Water Quality Modeler
Xin Zhang, Mathematical Modeler
Limno-Tech, Incorporated, Ann Arbor, Michigan
Victor J. Bierman, Jr., Associate Vice-President
Tim Feist, Environmental Scientist
Scott Mines, Senior Environmental Engineer
National Oceanic and Atmospheric Administration,
Atmospheric Modeling Division, Office of Research and
Development, National Exposure Research Laboratory,
Research Triangle Park, North Carolina
Russell Bullock, Meteorologist
Ellen Cooler, Meteorologist
ORTECH Corporation, Canadian Global Emissions
Interpretation Centre, Mississauga, Ontario, Canada
M. Trevor Schlotz, Director
National Oceanic and Atmospheric Administration, Great
Lakes Environmental Research Laboratory, Ann Arbor,
Michigan
David Schwab, Oceanographer
Cooperative Institute for Limnology and Ecosystem
Research, University of Michigan, Great Lakes
Environmental Research Laboratory, Ann Arbor,
Michigan
Dmitry Beletsky, Research Fellow
Wisconsin Department of Natural Resources, Madison,
Wisconsin
Dale Patterson, Chief, Water Quality Section
Mark Velleux, Water Resource Engineer
U.S. Army Corps of Engineers, Waterways Experiment
Station, Environmental Laboratory, Environmental
Processes and Effects Division, Vicksburg, Mississippi
Thomas Cole, Research Hydrologist
Mark Dortch, Supervisory Research Civil
Engineer
140
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Douglas D. Endicott
Environmental Research Engineer
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311GrohRoad
Grosse He, Michigan 48138
(734) 692-7613
Fax: (734) 692-7603
endicott.douglas@epamail.epa.gov
Role in the Lake Michigan Mass Balance
Project
Responsible for in-house model development and
applications.
Education
B.S.E. (cum laude), Environmental Science Engineering,
University of Michigan, 1983
M.S.E., Environmental Engineering, University of
Michigan, 1984
Professional Experience
Research Environmental Engineer (Modeling Team
Leader), USEPA, LLRS, 1988-Present.
Environmental Engineer (Adsorption Treatment
Research), Technical Support Division, USEPA, Office of
Drinking Water, Cincinnati, Ohio, 1985-1988.
Environmental Engineer (Hazardous Waste RI/FS),
Engineering Science, Atlanta, Georgia, 1984-1985.
Publications
Gailani, J., W. Lick, K. Ziegler, and D. Endicott. 1996.
Development and Validation of a Fine-Grained Sediment
Transport Model for the Buffalo River. J. Great Lakes
Res., 22(3):765-778.
Velleux, M. and D. Endicott. 1996. Long-Term
Simulation of PCB Export from the Fox River to Green
Bay. J. Great Lakes Res., 21(3):359-372.
Endicott, D.D. and P.M. Cook. 1994. Modeling the
Partitioning and Bioaccumulation of TCDD and Other
Hydrophobic Organic Chemicals in Lake Ontario.
Chemosphere, 28(l):75-87.
Velleux, M. 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.
Velleux, M., J. Gailani, and D. Endicott. 1994.
Screening-Level Approach for Estimating Contaminant
Export from Tributaries. J. Environ. Engin., 122 (6):503-
514.
Endicott, D.D. and W.J. Weber, Jr. 1985. Lumped
Parameter Modeling of Multicomponent Adsorption in the
Treatment of Coal-Conversion Wastewater by GAC.
Environ. Progress., 4:2.
Grasso, D., D.D. Endicott, S. Liang, and W.J. Weber, Jr.
1985. Simulation of DOC Removal in Activated Carbon
Beds - Discussion. J. Environ. Engin., 237.
Reports
Velleux, M.,J. Gailani, and D. Endicott. 1995. A User's
Guide to IPX, The In-Place Pollutant Export Water
Quality Modeling Framework. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 194pp.
Endicott, D.D. and DJ. Kandt. 1994. (I) Far Field
Models for Buffalo and Saginaw Rivers and (II) Food
Chain Bioaccumulation Model for Saginaw River/Bay.
Assessment and Remediation of Contaminated Sediments,
Remedial Action Modeling (ARCS/RAM) Work Group.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 120 pp.
Gailani, J., W. Lick., M.K. Pickens, C.K. Ziegler, and
D.D. Endicott. 1994. Sediment and Contaminant
Transport in the Buffalo River. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 70 pp.
141
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Rossmann, R., D. Endicott, and J.W. Nichols. 1993.
Mercury in the Great Lakes: Management and Strategy.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 28 pp.
Endicott, D.D., W.L. Richardson, and D.J. Kandt. 1992.
MICHTOX: A Mass Balance and Bioaccumulation Model
for Toxic Chemicals in Lake Michigan. Draft Report.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 183 pp.
Endicott, D.D., W.L. Richardson, T.F. Parkerton, and
D.M. DiToro. 1991. A Steady State Mass Balance and
Bioaccumulation Model for Toxic Chemicals in Lake
Ontario. Report to the Lake Ontario Fate of Toxics
Committee. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 121 pp.
Endicott, D.D. 1989. Activated Carbon Adsorption of
Dibromochloro-propane: Modeling of Adsorber
Performance Under Conditions of Water Treatment.
Internal Report. U.S. Environmental Protection Agency,
Technical Support Division, Cincinnati, Ohio. National
Technical Information Service PB 90 151-341 AS.
Endicott, D.D. and W.L. Richardson. 1989. A Model of
Steady State Exposure and Bioaccumulation for Toxic
Chemicals in Lake Ontario. Report to the Lake Ontario
Fate of Toxics Committee. U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse lie,
Michigan. 71 pp.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1988. Confined Disposal Facility Far-Field Modeling
Project Report: An Application to Saginaw Bay. Internal
Report. U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 11 pp.
Books
Endicott, D.D. and J.P. Connolly. 1993. Process
Parameterization Uncertainty in Models of Toxics in the
Great Lakes, Part 1: Process Parameterization in Chemical
Mass Balance Models. In - Reducing Uncertainty in Mass
Balance Models of Toxics in the Great Lakes Lake
Ontario Case Study, pp. 166-210. Donald W. Rennie
Memorial Monograph Series, Great Lakes Monograph
Number 4, State University of New York, Buffalo, New
York.
Endicott, D.D. and J.P. Connolly. 1993. Process
Parameterization Uncertainty in Models of Toxics in the
Great Lakes, Part 2: Process Parameterization in Models
of Chemical Accumulation in Aquatic Animals. In
Reducing Uncertainty in Mass Balance Models of Toxics
in the Great Lakes Lake Ontario Case Study, pp. 211-
234. Donald W. Rennie Memorial Monograph Series,
Great Lakes Monograph Number 4, State University of
New York, Buffalo, New York.
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1990. Lake Ontario TCDD Modeling Report. In-U.S.
Environmental Protection Agency, New York State
Department of Environmental Conservation, New York
State Department of Health, and Occidental Chemical
Corporation (Eds.), Lake Ontario Bioaccumulation Study,
Final Report, Chapter 8, 65 pp.
Presentations
Endicott, D.D., W.L. Richardson, K.R. Rygwelski, X.
Zhang, J.J. Pauer, and X. Zhang. 1997. Conceptual and
Mathematical Models for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Richardson, W.L., D.D. Endicott, and K.R. Rygwelski.
1997. Quality Assurance for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
142
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Rygwelski, K.R., W.L. Richardson, and D.D. Endicott.
1997. A Screening-Level Model Evaluation of Atrazine in
the Lake Michigan Basin. 40th Conference on Great
Lakes Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Endicott, D.D. and W.L. Richardson. 1996. Modeling and
Managing Toxic Chemicals: The Lake Michigan Mass
Balance Study. Seventeenth Annual Meeting of the
Society of Environmental Toxicology and Chemistry,
Washington, D.C.
Endicott,D.D. 1995. PCB Partitioning,Bioaccumulation,
and Sediment-Water Interactions in Green Bay, Lake
Michigan. U.S. Environmental Protection Agency
Contaminant Sediment Effects Research Workshop,
Duluth, Minnesota. January 31, 1995.
Endicott, D.D. 1994. Contaminant Bioaccumulation and
Food Web Models. U.S. Environmental Protection
Agency Mass Balance Modeling and Risk Assessment
Workshop, Ann Arbor, Michigan. November 14, 1994.
Endicott, D.D. 1994. Green Bay/Fox River Mass Balance
Case Study. U.S. Environmental Protection Agency Mass
Balance Modeling and Risk Assessment Workshop, Ann
Arbor, Michigan. November 14, 1994.
Endicott, D.D. 1994. Modeling Frameworks, Data, and
Uncertainty. U.S. Environmental Protection Agency Mass
Balance Modeling and Risk Assessment Workshop, Ann
Arbor, Michigan. November 14, 1994.
Endicott, D.D. 1994. Utility of Toxics Modeling in the
Great Lakes: Lake Michigan Mass Balance Project. U.S.
Environmental Protection Agency Watershed, Estuarine,
and Large Lakes Modeling (WELLM) Workshop, Bay
City, Michigan. April 18, 1994.
Endicott, D.D., J.Z. Gailani, and M. Velleux. 1994.
Simulating the Transport, Fate, and Bioaccumulation of
Persistent Toxic Chemicals in the Great Lakes: The Green
Bay Mass Balance Study. Conference on Environmental
Impact Prediction: Simulation for Environmental
Decision-Making. Research Triangle Park, North Carolina.
Endicott, D.D., D. Griesmer, and L. Mackelburg. 1994.
PCB Partitioning and Bioaccumulation in Green Bay, Lake
Michigan. Poster Presentation. Fifteenth Annual Meeting
of the Society of Environmental Toxicology and
Chemistry, Denver, Colorado. October 30-November 3,
1994.
Dolan, D.M., D. Endicott, A.H. El-Shaarawi, and K.
Freeman. 1993. Estimation of Replacement Values for
Censored Data in Green Bay Point Sources. 36th
Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin. June 4-10, 1993.
Endicott, D.D., W.L. Richardson, and DJ. Kandt. 1993.
MICHTOX, A Mass Balance and Bioaccumulation Model
for Toxic Chemicals in Lake Michigan. 36th Conference
on Great Lakes Research, International Association for
Great Lakes Research, St. Norbert College, DePere,
Wisconsin. June 4-10, 1993.
Endicott, D. and M. Velleux. 1993. A Mass Balance
Model for Predicting the Transport of Contaminants in the
Lower Fox River and their Export to Green Bay, Lake
Michigan. Sixth International Symposium on the
Interactions Between Sediments and Water, Santa Barbara,
California. December 5-8, 1993.
Gailani, J., W. Lick, K. Pickens, C.K. Ziegler, and D.
Endicott. 1993. Sediment and Contaminant Transport in
the Fox River. Sixth International Symposium on the
Interactions Between Sediments and Water, SantaBarbara,
California. December 5-8, 1993.
Gailani, J., K. Pickens, W. Lick, C.K. Ziegler, and D.
Endicott. 1993. Sediment and Contaminant Transport in
the Buffalo River. 36th Conference on Great Lakes
Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin. June
4-10, 1993.
Kandt, D.J., D.D. Endicott, and R.G. Kreis, Jr. 1993.
Incorporating Zebra Mussel Into Food Chain
Bioaccumulation Models for the Great Lakes. 36th
Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin. June 4-10, 1993.
143
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Richardson, W.L., D.D. Endicott, R. Jourdan, and J.
Gailani. 1993. Visualization for Great Lakes Research
and Water Quality Management. Great Lakes
Visualization Workshop, Cleveland, Ohio. July 15-16,
1993.
Velleux, M.L., D. Endicott, and K. Freeman. 1993. A
Mass Balance Model for Estimating Contaminant Export
From the Lower Fox River to Green Bay. 36th
Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin. June 4-10, 1993.
Endicott, D.D. 1992. Quantifying Uncertainty in a Lake
Ontario Level 1 Model. Workshop on Reducing
Uncertainty in Mass Balance Models of Toxics in the
Great Lakes, Buffalo, New York. February 3-5, 1992.
Endicott, D.D. and J.P. Connolly. 1992. Process
Parameterization Uncertainty in Mass Balance Models of
Toxics in the Great Lakes. Part 1: Process
Parameterization in Chemical Mass Balance Models.
Workshop on Reducing Uncertainty in Mass Balance
Models of Toxics in the Great Lakes, Buffalo, New York.
February 3-5, 1992. 34pp.
Endicott, D.D. 1991. Bioaccumulation Models for
Benthic Organisms: Current Status and Data
Requirements. U.S. Environmental Protection Agency
Seminar, U.S. Environmental Protection Agency, Office of
Research and Development, Environmental Research
Laboratory, Duluth, Minnesota.
Endicott, D.D. 1991. Far-Field Model Development:
Mass Balance and Bioaccumulation of Toxic Chemicals.
Assessment and Remediation of Contaminated Sediments
Workshop, Chicago, Illinois.
Endicott, D.D., D.J. Kandt, and W.L. Richardson. 1991.
Looking Back to Saginaw Bay: Post-Audit Verification of
a PCB Mass Balance Model. 34th Conference on Great
Lakes Research, International Association for Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1991. Modeling the Partitioning and Bioaccumulation of
TCDD and Other Hydrophobic Organic Chemicals in Lake
Ontario. Eleventh International Symposium on
Chlorinated Dioxins and Related Compounds, Research
Triangle Park, North Carolina. September 25, 1991.
Richardson, W.L. and D.D. Endicott. 1991. Utility of
Transport, Fate and Bioaccumulation Models in
Regulating Toxic Compounds in the Great Lakes. Twelfth
Annual Meeting of the Society for Environmental
Toxicology and Chemistry (SETAC), Seattle, Washington.
November3-7, 1991.
Velleux, M., D. Endicott, and J. DePinto. 1991. A Mass
Balance Analysis of Contaminant Transport and Fate in
the Lower Fox River. 34th Conference on Great Lakes
Research, International Association for Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1989. A Model of TCDD in Lake Ontario. 32nd
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin. May 30-June 2, 1989.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1989. Predicted Water Quality Impacts of CDF Leakage
on Saginaw Bay. 32nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Madison, Wisconsin.
May 30-June 2, 1989.
Endicott, D.D. 1988. Modeling TCDD in Lake Ontario.
Lake Ontario TCDD Bioaccumulation Study Review,
Niagara Falls, New York.
Endicott, D.D. 1988. Development and Parameterization
of a Lake Ontario TCDD Model. Presented at the
Modeling Expert Panel Meeting, U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan.
144
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Endicott, D.D. 1986. Scale-up Methodology for Small-
Scale Adsorber Studies. Presented at the U.S.
Environmental Protection Agency Program Peer Review
"Granular Activated Carbon Research vs. Regulatory
Agenda Needs for Phase n Organic Compounds",
Cincinnati, Ohio.
145
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Russell G. Kreis, Jr.
Other Appointments
Chief, CBSSS
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311 GrohRoad
Grosse lie, Michigan 48138
(734) 692-7615
Fax: (734) 692-7603
rgk @ lloyd.grl .epa.gov
Role in the Lake Michigan Mass Balance
Project
Liaison between modeling workgroup and biota
workgroup. Insure that biological aspects of modeling
projects are credible.
Education
B.S., Biology, Eastern Michigan University, Ypsilanti,
Michigan, 1972
M.S., Biology, Eastern Michigan University, Ypsilanti,
Michigan, 1974
Ph.D., Resource Ecology, University of Michigan, Ann
Arbor, Michigan, 1984
Previous Positions
Research Aquatic Biologist, USEPA, LLRS, Grosse He,
Michigan, 1986-1996
Research Associate, Department of Geology. University of
Minnesota, Duluth, Minnesota, 1984-1986
Research Assistant, Great Lakes Research Division,
University of Michigan, Ann Arbor, Michigan, 1974-1984
Research Interests and Skills
Great Lakes Ecology and Biology
Algal/Diatom Ecology, Systematics, and Morphology
Professional Societies
International Association for Great Lakes Research
(Secretary 1986-1988; Technical Advisory Committee)
Michigan Botanical Society
Acting Station Chief and Station Group Leader, MED-
Duluth, LLRS, Grosse He, Michigan, 1995.
Co-Chairman, Green Bay Mass Balance Biota Committee.
Chairman, Green Bay Mass Balance Food Chain Modeling
Subcommittee.
Member, ARCS Risk Assessment and Modeling
Workgroup (GLNPO).
Member, Great Lakes EMAP Planning Committee.
Member, National Sea Grant Zebra Mussel Review Panel.
Member, Detroit River Remedial Action Sediment
Subcommittee.
Member, Detroit River Remedial Action Technical
Advisory Committee.
Member, Lake Michigan Mass Balance Biota Workgroup.
Publications
Peer-Reviewed Journals
Velleux, M.L., I.E. Rathbun, R.G. Kreis, Jr., J.L. Martin,
MJ. Mac, and M.L. Tuchman. 1993. Investigation of
Contaminant Transport from the Saginaw Confined
Disposal Facility. J. Great Lakes Res., 19(1):158-174.
Hoke, R.A., J.P. Giesy, and R.G. Kreis, Jr. 1992.
Sediment Pore Water Toxicity Identification in the Lower
Fox River and Green Bay, Wisconsin, Using the Microtox
Assay. Ecotoxicol. Environ. Safety, 23:343-354.
Ankley, G.T., K. Lodge, D.J. Call, M.D. Baker, L.T.
Brooke, P.M. Cook, R.G. Kreis, Jr., A.R. Carlson, R.D.
Johnson, G.J. Niemi, R.A. Hoke, C.W. West, J.P. Giesy,
P.O. Jones, and Z.C. Fuying. 1992. Integrated
Assessment of Contaminated Sediments in the Lower Fox
River and Green Bay, Wisconsin. Ecotoxicol. Environ.
Safety, 23:46-64.
Cook, R.B., R.G. Kreis, Jr., J.C. Kingston, K.E. Camburn,
S.A. Norton, M.J. Mitchell, B. Fry, and L.C.K. Shane.
1990. Paleolimnology of McNearney Lake: An Acidic
Lake in Northern Michigan. J. Paleolimnol., 3:13-34.
146
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Kingston, J.C., R.B. Cook, R.G. Kreis, Jr., K.E. Camburn,
S.A. Norton, P.R. Sweets, M.W. Binford, M.J. Mitchell,
S.C. Schindler, L.C.K. Shane, and G.A. King. 1990.
Paleoecological Investigations of Recent Lake
Acidification in the Northern Great Lakes States. J.
Paleolimnol., 4:153-201.
Rosiu,C.J.,J.P. Giesy, and R.G. Kreis, Jr. 1989. Toxicity
of Vertical Sediments in the Trenton Channel, Detroit
River, Michigan to Chironomus tentans (Insecta:
Chironomidae). J. Great Lakes Res., 15(4):570-580.
Giesy, J.P., C.J. Rosiu, R.L. Graney, J.L. Newsted, A.
Benda, R.G. Kreis, Jr., and F.J. Horvath. 1988. Toxicity
of Detroit River Sediment Interstitial Water to the
Bacterium Photobacteriumphosphoreum. J. Great Lakes
Res., 14(4):502-513.
Giesy, J.P., R.L. Graney, J.L. Newsted, C.J. Rosiu, A.
Benda, R.G. Kreis, Jr., and F.J. Horvath. 1988.
Comparison of Three Sediment Bioassay Methods Using
Detroit River Sediments. Environ. Toxicol. Chem.,
7(6):483-498.
Cook, R.B., C.A. Kelley, J.C. Kingston, and R.G. Kreis,
Jr. 1987. Chemical Limnology of Soft Water Lakes in the
Upper Midwest. Biogeochem., 4:97-117.
Charles, D.F., D.R. Whitehead, D. Anderson, R. Bienert,
K.E. Camburn, T. Crissman, R.B. Davis, B. Fry, R.A.
Kites, J.S. Kahl, J.C. Kingston, R.G. Kreis, Jr., M.J.
Mitchell, S.A. Norton, L. Roll, J.P. Smol, P.R. Sweets, A.
Uutala, J. White, M. Whiting, and R. Wise. 1986. The
PIRLA Project (Paleoecological Investigation of Recent
Lake Acidification): Preliminary Results for the
Adirondacks, New England, N. Great Lakes States, and N.
Florida. Water, Air, and Soil Pollut., 30:355-365.
Scavia, D., G.L. Fahnenstiel, J.A. Davis, and R.G. Kreis,
Jr. 1984. Small-Scale Nutrient Patchiness: Some
Consequences and a New Encounter Mechanism. Limnol.
Oceangr., 29(4):785-793.
Kreis, R.G., Jr., T.B. Ladewski, and E.F. Stoermer. 1983.
Influence of the St. Marys River Plume on Northern Lake
Huron Phytoplankton Assemblages. J. Great Lakes Res.,
9(1):40-51.
Stoermer, E.F., R.G. Kreis, Jr., and L. Sicko-Goad. 1981.
A Systematic, Quantitative and Ecological Comparison of
Melosira islandica O. Mull, with M. granulata (Ehr.)
Ralfs from the Laurentian Great Lakes. J. Great Lakes
Res., 7(4):345-356.
Kreis, R.G., Jr. and E.F. Stoermer. 1979. Diatoms of the
Laurentian Great Lakes JJI. Rare and Poorly Known
Species of Achnanthes Bory and Cocconeis Ehr.
(Bacillariophyta). J. Great Lakes Res., 5(3-4):276-291.
Stoermer, E.F. and R.G. Kreis, Jr. 1978. Preliminary
Checklist of Diatoms (Bacillariophyta) from the
Laurentian Great Lakes. J. Great Lakes Res., 4(2): 149-
169.
Reports
Kreis, R.G., Jr. 1995. Data and Summary Report for
Zebra Mussel Analyses from the Smithland Lock and
Dam, Ohio River, and the Black Rock Lock, Niagara
River. Report to the U.S. Army Corps of Engineers,
Waterways Experiment Station, Vicksburg, Mississippi.
46 pp.
Hedtke, S., A. Pilli, D. Dolan, G. McRae, B. Goodno, R.
Kreis, G. Warren, D. Swackhamer, and M. Henry. 1992.
Environmental Monitoring and Assessment Program.
EMAP - Great Lakes Monitoring and Research Strategy.
U.S. Environmental Protection Agency, Office of
Research and Development, Environmental Research
Laboratory, Duluth, Minnesota. EPA-620/R-92/001, 204
pp.
Kreis, R.G., Jr., K.R. Rygwelski, and V.E. Smith (Eds.).
1990. Procedures for the Assessment of Contaminated
Sediments in the Laurentian Great Lakes as Developed in
the Detroit River Trenton Channel In-Place Pollutants
Study, 1985-1988. Report to the Michigan Department of
Natural Resources, Lansing, Michigan. 540 pp.
Kingston, J.C., K.E. Camburn, R.G. Kreis, Jr., and R.B.
Cook. 1989. Diatom Distribution and Association in Soft-
Water Lakes of Minnesota, Wisconsin, and Michigan. In-
D.F. Charles and D.R. Whitehead (Eds.), Paleoecological
Investigation of Recent Lake Acidification: 1983-1985,
Section 9, pp. 1-31. Electric Power Research Institute,
Palo Alto, California. EPRIEN-6526.
147
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Kreis, R.G., Jr. 1989. Variability Study-Interim Results.
In D.F. Charles and D.R. Whitehead (Eds.),
Paleoecological Investigation of Recent Lake
Acidification: 1983-1985, Section 4, pp. 1-48. Electric
Power Research Institute, Palo Alto, California. EPRIEN-
6526.
Kreis, R.G., Jr., J.C. Kingston, K.E. Camburn, and R.B.
Cook. 1989. Diatom-pH Relationships in the Northern
Great Lakes Region for Predicting Past Lake Acidity. In -
D.F. Charles and D.R. Whitehead (Eds.), Paleoecological
Investigation of Recent Lake Acidification (PIRLA):
1983-1985, Section 10, pp. 1-35. Electric Power Research
Institute, Palo Alto, California. EPRI EN-6526.
Kreis, R.G., Jr., J.E. Rathbun, A.E. Maccubbin, R.A.
Hites, V.E. Smith, M.J. Mac, J.C. Filkins, S.A. Rudolph,
M.D. Mullin, K.A. Vargo, and K.P. McGunagle. 1989.
An Investigation of Neoplasia in Detroit River Fish and Its
Relationship to Sediment Contamination. Report to the
Michigan Department of Natural Resources, Lansing,
Michigan. 91 pp.
Sweets, P.R., R.A. Garren, and R.G. Kreis, Jr. 1989. The
Relationship Between Surface Sediment Diatoms and pH
in Northern Florida. In - D.F. Charles and D.R. Whitehead
(Eds.), Paleoecological Investigation of Recent Lake
Acidification (PIRLA): 1983-1985, Section 13, pp. 1-17.
Electric Power Research Institute, Palo Alta, California.
EPRI EN-6526.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1989. Contaminated Sediment Studies
of the Trenton Channel, Detroit River Data Report.
Report to the Michigan Department of Natural Resources,
Lansing, Michigan. 79 pp. and 10 diskettes.
Kreis,R.G.,Jr.(Ed.). 1988. Integrated Study of Exposure
and Biological Effects of In-Place Pollutants in the Detroit
River, Michigan: An Upper Great Lakes Connecting
Channel. Final Report to the U.S. Environmental
Protection Agency, Great Lakes National Program Office,
Chicago, Illinois. 153 pp.
Kreis,R.G.,Jr.(Ed.). 1988. Integrated Study of Exposure
and Biological Effects of In-Place Pollutants in the Upper
Great Lakes Connecting Channels: Interim Results. Final
Report to the Upper Great Lakes Connecting Channels
Study Activities Workgroups for Tasks in Activities C, G,
andH. 1200pp.
Rathbun, J.E., R.G. Kreis, Jr., E.L. Lancaster, M.J. Mac,
and M.J. Zabik. 1988. Pilot Confined Disposal Facility
Biomonitoring Study: Channel/Shelter Island Diked
Facility, Saginaw Bay, Bay City, Michigan, 1987. Report
to the U.S. Environmental Protection Agency, Region V,
Water Division, Chicago, Illinois. 129 pp.
Richardson, W.L. and R.G. Kreis, Jr. 1988. Historical
Perspectives of Water Quality in Saginaw Bay. In
Proceedings: A New Way for the Bay, A Workshop for
the Future of Saginaw Bay, Section 5, pp. 138-180.
Sponsored by the East Central Michigan Planning and
Development Region, Greater Saginaw Bay Fishing
Consortium, Michigan Department of Natural Resources,
and Michigan Sea Grant College Program. Delta College,
University Center, Michigan.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1988. Project Planning for the Green
Bay Physical-Chemical Mass Balance and Food Chain
Models. Report to the U.S. Environmental Protection
Agency, Great Lakes National Program Office, Chicago,
Illinois. 339 pp.
Kreis, R.G., Jr. (Ed.). 1987. Integrated Study of Exposure
and Biological Effects of In-Place Pollutants in the Upper
Connecting Channels: Interim Results. Interim Report to
the Upper Great Lakes Connecting Channels Study
Activities Workgroups for Tasks in Activities C, G, andH.
700 pp.
Kreis, R.G., Jr. and J.E. Rathbun. 1987. Biological
Studies in Monroe Harbor (River Raisin), Michigan. In -
K.R. Rygwelski and V.E. Smith (Eds.), Summary Report:
An Integrated Approach to a Study of Contaminants and
Toxicity in Monroe Harbor (River Raisin), Michigan, A
Great Lakes Area of Concern, Section 7.2, pp. 78-128.
U.S. Department of Commerce, Springfield, Virginia.
National Technical Information Service PB 88-126 008.
148
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Kingston, J.D., K.E. Camburn, and R.G. Kreis, Jr. 1986.
Diatom Analysis Methods Used for Northern Great Lakes.
In D.F. Charles and D.R. Whitehead (Eds.),
Paleoecological Investigation of Recent Lake Acidification
(PIRLA): Methods and Project Description, Section 6.5,
pp. 29-30. Electric Power Research Institute, Palo Alto,
California. EPRIEA-4906.
Kreis, R.G., Jr. 1986. Variability Study. In- D.F. Charles
and D.R. Whitehead (Eds.), Paleoecological Investigation
of Recent Lake Acidification (PIRLA): Methods and
Project Description, Section 17.1, pp. 1-19. Electric
Power Research Institute, Palo Alto, California. EPRIEA-
4906.
Sweets, P.R., R.G. Kreis, Jr., J.C. Kingston, and K.E.
Camburn. 1986. Northern Great Lakes States Sediment
Coring Field Notes (PIRLA). Paleoecological
Investigation of Recent Lake Acidification (PIRLA).
Unpublished Report Series, Report Number 1. Electric
Power Research Institute, Palo Alto, California.
Kreis, R.G., Jr. and C.P. Rice. 1985. Status of Organic
Contaminants in Lake Huron: Atmosphere, Water, Algae,
Fish, Herring Gull Eggs, and Sediment. The University of
Michigan, Great Lakes Research Division, Ann Arbor,
Michigan. Special Publication 114, 169pp.
Kreis, R.G., Jr., E.F. Stoermer, and T.B. Ladewski. 1985.
Phytoplankton Species Composition, Abundance, and
Distribution in Southern Lake Huron, 1980; Including a
Comparative Analysis With Conditions in 1974 Prior to
Nutrient Loading Reductions. The University of
Michigan, Great Lakes Research Division, Ann Arbor,
Michigan. Special Report 107, 377 pp.
Kreis, R.G., Jr. 1984. Comparative Analysis of 1980
Southern Lake Huron Phytoplankton Assemblages with
Conditions Prior to Nutrient Loading Reductions.
Doctoral Dissertation, The University of Michigan,
University of Microfilms, Ann Arbor, Michigan. 503 pp.
Stoermer, E.F., R.G. Kreis, Jr., and T.B. Ladewski. 1983.
Phytoplankton Species Composition, Abundance, and
Distribution in Southern Lake Huron, 1980; Including a
Comparative Analysis with Conditions in 1974 Prior to
Nutrient Loading Reductions. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse lie,
Michigan. EPA-600/3-83-089, 656 pp.
Stoermer, E.F., R.G. Kreis, Jr., E.G. Theriot, and T.B.
Ladewski. 1983. Phytoplankton Abundance, Species
Distribution, and Community Structure in Saginaw Bay
and Southern Lake Huron in 1980. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. EPA-600/S3-83-091, 3 pp.
Ladewski, E.G., R.G. Kreis, Jr., and E.F. Stoermer. 1982.
A Comparative Analysis of Lake Huron Phytoplankton
Assemblages After Entrainment at Selected Water Intake
Facilities. The University of Michigan, Great Lakes
Research Division, Ann Arbor, Michigan. Special Report
92, 120pp.
Davis, C.O., C.L. Schelske, and R.G. Kreis, Jr. 1980.
Influences of Spring Nearshore Thermal Bar. In C.L.
Schelske, R.A. Moll, and M.S. Simmons (Eds.),
Limnological Conditions in Southern Lake Huron, 1974
and 1975, Section 7, pp. 140-164. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse lie,
Michigan. EPA-600/3-80-074.
Stoermer, E.F. and R.G. Kreis, Jr. 1980. Phytoplankton
Composition and Abundance in Southern Lake Huron.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. EPA-600/3-80-
061,383pp.
Stoermer, E.F., R.G. Kreis, Jr., and T.B. Ladewski. 1976.
Distribution and Abundance of Phytoplankton. In - C.L.
Schelske, E.F. Stoermer, J.E. Gannon, and M.S. Simmons
(Eds.), Biological, Chemical, and Physical Relationships
in the Straits of Mackinac, Section VI, pp. 72-132. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. EPA-600/3-76-095.
149
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Manuscripts in Press, Review, and Preparation
Kreis, R.G., Jr. and D.C. Charles. Sources and Estimates
of Variability Associated with Paleolimnology Analyses
of PIRLA Sediment Cores. J. Paleolimnol., in preparation.
Kreis, R.G., Jr., J.E. Rathbun, K.A. Freeman, L.L.
Huellmantel, K.A. Ahlgren,E.L. Lancaster, M.J. Mac, J.C.
Filkins, M.D. Mullin, and V.E. Smith. Confined Disposal
Facility Biomonitoring Study: Channel Shelter Island
Diked Facility, Saginaw Bay, Bay City, Michigan. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan, in preparation.
Kreis, R.G., Jr., J.E. Rathbun, M.D. Mullin, R. Rossmann,
and L.L. Wallace. Investigation of Contaminant
Concentrations in Zebra Mussels Collected From the
Detroit Edison Power Plant, Monroe, Michigan. Report
for the Official Record. U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse lie,
Michigan, in preparation.
Kreis, R.G., Jr. Relationship of the Detroit River and Lake
Erie. Proceedings of the Lake Erie Citizens' Forum: Its'
Ecology and Economy. Citizens Environment Alliance,
Windsor, Ontario, in press.
Presentations
Mackelburg, L. and R.G. Kreis, Jr. 1997. Length:Weight
Relationships in Zebra Mussel (Dreissena polymorphd)
Populations. Seventh International Zebra Mussel and
Aquatic Nuisance Species Conference, New Orleans,
Louisiana. January 28-31, 1997.
Bierman, V.J., D. Dilks, T.J. Feist, J.V. De Pinto, and R.G.
Kreis, Jr. 1997. Mass Balance Modeling of Zebra
Mussel, Blue-Green Phytoplankton and Phosphorus
Dynamics in Saginaw Bay, Lake Huron. Seventh
International Zebra Mussel and Aquatic Nuisance Species
Conference, New Orleans, Louisiana. January 28-31,
1997.
Kreis, R.G., Jr., R. Rossmann, M.D. Mullin, W.C. Hall, A.
Sanchez, and M. Rathbun. 1996. Heavy Metal and
Organic Contaminant Concentrations in Zebra Mussels
from Saginaw Bay, Lake Huron. Presented by L.
Mackelburg. 39th Conference on Great Lakes Research,
International Association for Great Lakes Research,
Erindale College, University of Toronto, Mississauga,
Ontario, Canada. May 26-30, 1996.
Kreis, R.G., Jr., R.R. Rossmann, M.D. Mullin, W.C. Hall,
A. Sanchez, and M. Rathbun. 1996. Contaminant
Concentrations in Zebra Mussels Along a Gradient in
Saginaw Bay, Lake Huron. Sixth International Zebra
Mussel and Other Aquatic Nuisance Species Conference,
Dearborn, Michigan. March 5-7, 1996.
Kreis, R.G., Jr. 1995. Review of Contaminant
Accumulation by Zebra Mussels. Fourth U.S. Army Corps
of Engineers Zebra Mussel Workshop, New Orleans,
Louisiana. November 27-30, 1995.
Kreis, R.G., Jr. 1995. Overview of Contaminant
Accumulation by Zebra Mussels. Fifth International Zebra
Mussel Conference, Toronto, Ontario, Canada. February
21-24, 1995.
Endicott, D., D. Griesmer, R. Kreis, and L. Mackelburg.
1994. Polychlorinated Biphenyl Partitioning and
Bioaccumulation in Green Bay, Lake Michigan (Poster).
Fifteenth Annual Meeting, Society of Environmental
Toxicology and Chemistry, Denver, Colorado. October
30-November 3, 1994.
Kreis, R.G., Jr. and L.L. Wallace. 1994. Species
Composition and Size Spectrum of Diatoms Ingested by
Zebra Mussels, Western Lake Erie. 37th Conference on
Great Lakes Research, International Association for Great
Lakes Research, Windsor, Ontario, Canada. June 5-9,
1994.
Besser, J.M., J.A. Kubitz, J.P. Giesy, S. Benzie, A.
Ostaszewski, and R.G. Kreis, Jr. 1994. Effects of
Dredging on the Sediment Quality of Elizabeth Park
Marina and Trenton Channel, Detroit River. 37th
Conference on Great Lakes Research, International
Association for Great Lakes Research, Windsor, Ontario,
Canada. June 5-9, 1994.
150
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Kreis, R.G., Jr. 1994. Relationship of the Detroit River
and Lake Erie. Session IV: Toward Ecosystem
Restoration and Protection/Research. Citizens' Forum on
Lake Erie: Its Ecology and Economy, Windsor, Ontario,
Canada. June 4-5, 1994.
Kreis, R.G., Jr., M.D. Mullin, R. Rossmann, and L.L.
Wallace. 1994. Contaminants in Zebra Mussel Size
Classes and a Comparison of Whole Mussel, Tissue, and
Shell Concentrations. Fourth International Zebra Mussel
Conference, Madison, Wisconsin. March 7-10, 1994.
Kreis, R.G., Jr. and L.L. Wallace. 1993. Associations of
Diatoms and Zebra Mussels: Epizoic and Ingested
Diatoms. Twelfth North American Diatom Symposium,
Winnipeg, Manitoba, Canada. September 23-25, 1993.
Connolly, J.P., T.F. Parkerton, and R. Kreis. 1993. A
Model-Based Evaluation of PCB Bioaccumulation in
Green Bay Walleye and Brown Trout. 36th Conference on
Great Lakes Research, International Association for Great
Lakes Research, St. Norbert College, DePere, Wisconsin.
June 4-10, 1993.
Kandt, D.J., D.D. Endicott, and R.G. Kreis, Jr. 1993.
Incorporating Zebra Mussel Into Food Chain
Bioaccumulation Models for the Great Lakes. 36th
Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin. June 4-10, 1993.
Kreis, R.G., Jr., E.F. Stoermer, and R.J. Stevenson. 1993.
Diatom Assemblages as Biotic Condition Indicators
Within EMAP-Great Lakes. 36th Conference on Great
Lakes Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin. June
4-10, 1993.
Kreis, R.G., Jr., M.D. Mullin, R. Rossmann, and L.L.
Wallace. 1993. Heavy Metal and Organic Contaminants
in Four Size Classes of Zebra Mussels. 36th Conference
on Great Lakes Research, International Association for
Great Lakes Research, St. Norbert College, DePere,
Wisconsin. June 4-10, 1993.
Kreis, R.G., Jr., M.D. Mullin, G.J. Warren, and D.S.
Devault. 1993. Spatial and Seasonal Distribution of PCBs
and Dieldrin in Green Bay Phytoplankton and
Zooplankton. 36th Conference on Great Lakes Research,
International Association for Great Lakes Research, St.
Norbert College, DePere, Wisconsin. June 4-10, 1993.
Rathbun, J.E., R.G. Kreis, Jr., L.B. Liebenstein, M.D.
Mullin, D.S. Devault, and G. Boronow. 1993. Spatial and
Seasonal Distribution of PCBs and Dieldrin in Green Bay
Forage and Predator Fish. 36th Conference on Great
Lakes Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin. June
4-10, 1993.
Kreis, R.G., Jr., M.D. Mullin, R. Rossmann, and L.L.
Wallace. 1991. Organic Contaminant and Heavy Metal
Concentrations in Zebra Mussel Tissue From Western
Lake Erie. Second International Zebra Mussel Research
Conference, Rochester, New York. November 19-22,
1991.
Connolly, J.P., T.F. Parkerton, S. Taylor, and R.G. Kreis,
Jr. 1991. PCBs in Green Bay Fish: The Importance of
Diet and Migration to Observed Concentration. 34th
Conference on Great Lakes Research, International
Association for Great Lakes Research, State University of
New York at Buffalo, Buffalo, New York. June 3-6,1991.
Kreis, R.G., Jr., M.D. Mullin, R. Rossmann, J.L. Utz, J.E.
Reidy, K.A. Vargo, and K.T. Smith. 1991. Contaminant
Concentrations in Zebra Mussel Tissue from Western
Lake Erie, Monroe, Michigan. 34th Conference on Great
Lakes Research, International Association for Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Kreis, R.G., Jr., D. Woodring, and A.G. Kizlauskas. 1989.
A Ranking System for Hazardous Sediments in the
Laurentian Great Lakes. Tenth Annual Meeting of the
Society for Environmental Toxicology and Chemistry,
Toronto, Ontario, Canada. October 28-November 2,1989.
Velleux, M., J. Martin, J. Rathbun, and R. Kreis, Jr. 1989.
Predicted and Observed Impacts of Contaminant Transport
From the Saginaw Bay Diked Facility. Tenth Annual
Meeting of the Society for Environmental Toxicology and
Chemistry, Toronto, Ontario, Canada. October 28-
November 2, 1989.
151
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Kreis, R.G., Jr., J.P. Connolly, and G. Boronow. 1989.
Food Chain Modeling in the Green Bay Mass Balance
Study. 32nd Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Wisconsin, Madison, Wisconsin. May 30-
June 2, 1989.
Rathbun, J., R. Kreis, Jr., E. Lancaster, M. Mac, and M.
Zabik. 1989. Pilot Biomonitoring Study at the Saginaw
Confined Disposal Facility, 1987. 32nd Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Wisconsin, Madison,
Wisconsin. May 30-June 2, 1989.
Woodring, D. and R.G. Kreis, Jr. 1989. Development of
a Sediment Action Index: Extensions for Risk Assessment
and Resolution of Alternative Management Objectives.
32nd Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin. May 30-June 2, 1989.
Rathbun, J.E., V.E. Smith, and R.G. Kreis, Jr. 1989. The
Use of Bivalve Mollusks in PCB Biomonitoring. 37th
Annual Meeting of the North American Benthological
Society, Guelph, Ontario, Canada. May 16-19, 1989.
Kreis, R.G., Jr., J.D. Kingston, and K.E. Camburn. 1989.
Between-Core Variability of McNearney Lake Diatom
Populations and Implications for Past Lakewater-pH
Inference. Tenth North American Diatom Symposium,
University of Minnesota Forestry and Biological Station,
Lake Itasca, Minnesota. October 1989.
Kreis, R.G., Jr., J.E. Rathbun, M.D. Mullin, E.L.
Lancaster, and M .L. Tuchman. 1988. Pilot B iomonitoring
Study at the Saginaw Confined Disposal Facility, 1987.
Statistical Methods Workshop for the Assessment of Point
Source Pollution, Canada Centre for Inland Waters,
Burlington, Ontario, Canada. September 1988.
Richardson, W.L., R.G. Kreis, Jr., J.L. Martin, M.D.
Mullin, and J.C. Filkins. 1988. Results of the Binational
Study of the Great Lakes Upper Connecting Channels-
The Detroit River. Third Chemical Congress of North
America and 195th American Chemical Society National
Meeting, Toronto, Ontario, Canada. June 5-10, 1988.
Kreis, R.G., Jr. and D. Woodring. 1988. Development of
a Sediment Action Index for the Great Lakes: The Lower
Detroit River as a Pilot Application. 31st Conference on
Great Lakes Research, International Association for Great
Lakes Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
Richardson, W.L., R.G. Kreis, Jr., and J.L. Martin. 1988.
A Modeling Framework for Planning a Mass Balance
Project for Green Bay, Lake Michigan. 31st Conference
on Great Lakes Research, International Association for
Great Lakes Research, McMaster University, Hamilton,
Ontario, Canada. May 16-20, 1988.
Rosiu, C.J., J.P. Giesy, and R.G. Kreis, Jr. 1988. Detroit
River Sediment Quality Assessment Using the
Chironomus tentans Bioassay. 31st Conference on Great
Lakes Research, International Association for Great Lakes
Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
Giesy, J.P., R.L. Graney, J.L. Newsted, C.J. Rosiu, A.
Benda, F.J. Horvath, and R.G. Kreis, Jr. 1987. Toxicity
of Sediments of the Lower Detroit River. 30th Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Michigan, Ann
Arbor, Michigan. May 11-14, 1987.
Kreis, R.G., Jr., C.P. Rice, and R. Rossmann. 1986.
Organic Contaminants in Lake Huron Fish: Assessment of
Monitoring and Historical Trends. 29th Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Toronto, Scarborough,
Ontario, Canada. May 26-29, 1986.
Kreis, R.G., Jr., J.C. Kingston, K.E. Camburn, and R.B.
Cook. 1985. Diatom Stratigraphy of an Acidic Lake in
Northern Michigan. Eighth North American Diatom
Symposium, Hancock Biological Station, Murray State
University, Murray, Kentucky. October 1985.
Kreis, R.G., Jr., J.C. Kingston, K.E. Camburn, and R.B.
Cook. 1985. Quantification of Diatom-pH Relationships
for Predicting Past pH Conditions in Lakes from the
Northern Great Lakes Region. 48th Annual Meeting of
American Society of Limnology and Oceanography with
the Ecological Society of America, University of
Minnesota, Minneapolis, Minnesota. June 1985.
152
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Kreis, R.G., Jr., J.C. Kingston, K.E. Camburn, and R.B.
Cook. 1984. Relationship Between Surface Sediment
Diatom Assemblages and Lakewater Characteristics in
Northern Wisconsin Lakes. 35th Annual Meeting of the
American Institute of Biological Sciences with the
Phycological Society of America, Colorado State
University, Fort Collins, Colorado. August 1984.
Kreis, R.G., Jr. 1984. Comparative Analysis of 1980
Southern Lake Huron Phytoplankton Assemblages with
Conditions Prior to Nutrient Loading Reductions. 27th
Conference on Great Lakes Research, International
Association for Great Lakes Research, Brock University,
St. Catharines, Ontario, Canada. April 30-May 3, 1984.
Kreis, R.G., Jr., E.F. Stoermer, and R. Rossmann. 1983.
Historical Perspectives of Lake Huron Algal Studies
Including the Current Lake Status as Determined from
1980 Sampling. Seventh North American Diatom
Symposium, Ohio State University, Barneby Center,
Hocking Hills, Ohio. September 28-October 1, 1983.
Kreis, R.G., Jr., E.F. Stoermer, and R. Rossmann. 1983.
Historical Perspectives of Lake Huron Algal Studies
Including the Current Lake Status as Determined from
1980 Sampling. 26th Conference on Great Lakes
Research, International Association for Great Lakes
Research, State University of New York, Oswego, New
York. May 23-27, 1983.
Kreis, R.G. Jr. 1981. Tracing the Lake Superior Water
Mass in Northern Lake Huron Using Periphyton from the
St. Marys River. Sixth North American Diatom
Symposium, Central Michigan University Biological
Station, St. James, Michigan. September 9-12, 1981.
Kreis, R.G., Jr. 1981. Tracing the Lake Superior Water
Mass in Northern Lake Huron Using Periphyton from the
St. Marys River. 32nd Annual Meeting of the American
Institute of Biological Sciences with the Phycological
Society of American, Indiana University, Bloomington,
Indiana. August 16-21, 1981.
Kreis, R.G., Jr. and E.F. Stoermer. 1981. Tracing the
Lake Superior Water Mass in Northern Lake Huron Using
Periphyton from the St. Marys River. 24th Conference on
Great Lakes Research, International Association for Great
Lakes Research, Ohio State University, Columbus, Ohio.
April 28-30, 1981.
Kreis, R.G., Jr. 1981. Tracing the Lake Superior Water
Mass in Northern Lake Huron Using Periphyton Diatoms
from the St. Marys River. 85th Annual Meeting of the
Michigan Academy of Science, Arts, and Letters, The
University of Michigan, Ann Arbor, Michigan. March 20-
21, 1981.
Kreis, R.G., Jr. and E.F. Stoermer. 1979. Auxospore
Populations from the St. Lawrence Great Lakes. Poster
Presentation. Fifth North American Diatom Symposium,
The University of Michigan Biological Station, Pellston,
Michigan. September 20-23, 1979.
Kreis, R.G., Jr. and E.F. Stoermer. 1979. Diatoms of the
Laurentian Great Lakes IH. Rare and Poorly Known
Species of Achnanthes Bory and Cocconeis Ehr.
(Bacillariophyta). 22nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Rochester, Rochester. New York,
April 30-May 3, 1979.
Kreis, R.G., Jr. 1978. Poorly-Known Species of
Achnanthes and Cocconeis from the Upper Laurentian
Great Lakes. Fourth North American Diatom Symposium,
Iowa Lakeside Laboratory, Milford, Iowa. September 28-
October 1, 1978.
Kreis, R.G., Jr. 1978. An Investigation of Diatom
(Bacillariophyta) Habitats in Mullet Lake, Cheboygan
County, Michigan. 82nd Annual Meeting of the Michigan
Academy of Science, Arts, and Letters, Eastern Michigan
University, Ypsilanti, Michigan.
Kreis, R.G., Jr. 1977. The Aquatic Flora of Lake St.
Helen, Roscommon County, Michigan. 81st Annual
Meeting of the Michigan Academy of Science, Arts, and
Letters, Central Michigan University, Mount Pleasant,
Michigan. March 1977.
153
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Kreis, R.G., Jr. 1976. Planktonic Diatoms from Lake
Huron and the Straits of Mackinac. Third North American
Diatom Symposium, Academy of Natural Sciences of
Philadelphia, Philadelphia, Pennsylvania. September
1976.
154
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William L. Richardson
Environmental Engineer
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311 GrohRoad
Grosse He, Michigan 48138
(734)692-7611
Fax: (734) 692-7603
wlr@lloyd.grl.epa.gov
Role in the Lake Michigan Mass Balance
Project
Chairperson, Modeling Workgroup. Facilitates overall
development of Lake Michigan models by coordinating
efforts between participating organizations and between
project workgroups. Directing data management aspects
of model development and participating in model
development and application for atrazine and trans-
nonachlor.
Education
B.S.E., Civil Engineering, University of Michigan, Ann
Arbor, Michigan
Graduate Studies in Water Resource Engineering,
University of Pennsylvania and University of Michigan.
Attended three Manhattan College Summer Institutes for
Mathematical Modeling
Licensed Professional Engineer, State of Michigan
Professional Experience
Thirty-three years experience
predecessor agencies.
with USEPA and
Staff engineer for Delaware Estuary Comprehensive Study
which was one of the first projects to use computers and
systems analysis to solve water quality problems.
Research Physical Scientist for USEPA/ORD Great Lakes
Research Program at Grosse He, Michigan.
Developed first calibrated PCB model for Saginaw Bay.
Applied eutrophication model for Saginaw Bay.
Developed five lake-in-series model for Great Lakes to
predict future concentrations of chloride.
Station Chief responsible for Great Lakes Research
Program from 1983 to 1994.
Chairman Green Bay Mass Balance Project.
Led efforts for modeling the Detroit River, Monroe
Harbor, and Flint River.
Developed MICHTOX for modeling toxic substances in
rivers and streams.
Developed and prepared the report "Guidance for Waste
Load Allocation of Toxic Chemicals in Rivers and
Streams" as used by the Office of Water and States.
Project Officer for modeling cooperative agreements
which led to eutrophication, toxic chemical, and food
chain models for the Great Lakes.
Publications
Peer-Reviewed Journals
Martin, J.L., W.L. Richardson, and S.C. McCutcheon.
1991. Modeling Studies for Planning: The Green Bay
Project. Water Res. Bull., 27(3):429-436.
Sonzogni, W.C., R.P Canale, D.C.L. Lam, W. Lick, D.
Mackay, C.K. Minns, W.L. Richardson, D. Scavia, V.
Smith, and W.J.J. Strachan. 1987. Large Lake Models
Uses, Abuses, and Future. J. Great Lakes Res.,
13(3):387-396.
Gorstko, A.B., Y.A. Dombrovsky, A.A. Matveyev, J.F.
Paul, W.L. Richardson, and A.F. Surkov. 1984.
Simulation Modeling as a Means of Studying Large
Aquatic Ecosystems. J. Great Lakes Res., 10(3):240-244.
Sonzogni, W.C., W.L. Richardson, P. Rodgers, and T.J.
Monteith. 1983. Chloride Pollution of the Great Lakes.
J. Water Pollut. Control Fed., 55(5):513-521.
Di Toro, D.M., A.M. Horzempa, M.M. Casey, and W.L.
Richardson. 1982. Reversible and Resistant Components
of PCB Adsorption-Desorption: Adsorbent Concentration
Effects. J. Great Lakes Res., 8(2):336-349.
155
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Reports
Richardson, W.L. 1993. Determining Appropriate Levels
of Complexity, Accuracy, and Cost to Fit the Management
Application. In - Reducing Uncertainty in Mass Balance
Models of Toxics in the Great Lakes, pp. 44-47. Donald
W. Rennie Memorial Monograph Series, Great Lakes
Monograph Number 4, State University of New York,
Buffalo, New York.
Endicott, D.D., W.L. Richardson, and D.J. Kandt. 1992.
MICHTOX: A Mass Balance and Bioaccumulation Model
for Toxic Chemicals in Lake Michigan. Draft Report.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 183 pp.
Endicott, D.D., W.L. Richardson, T.F. Robertson, and
D.M. DiToro. 1991. A Steady State Mass Balance and
Bioaccumulation Model to Toxic Chemicals in Lake
Ontario. Report to the Lake Ontario Fate of Toxics
Committee. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 121 pp.
Endicott, D.D. and W.L. Richardson. 1989. A Model of
Steady State Exposure and Bioaccumulation for Toxic
Chemicals in Lake Ontario. Report to the Lake Ontario
Fate of Toxics Committee. U.S. Environmental Protection
Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 71 pp.
i
Richardson, W.L. and R.G. Kreis, Jr. 1988. Historical
Perspectives of Water Quality in Saginaw Bay. In
Proceedings: A New Way for the Bay, A Workshop for
the Future of Saginaw Bay, Section 5, pp. 138-180.
Sponsored by the East Central Michigan Planning and
Development Region, Greater Saginaw Bay Fishing
Consortium, Michigan Department of Natural Resources,
and Michigan Sea Grant College Program, Delta College,
University Center, Michigan.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1988. Confined Disposal Facility Far-Field Modeling
Project Report: An Application to Saginaw Bay. Internal
Report. U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 11 pp.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1988. Project Planning for the Green
Bay Physical-Chemical Mass Balance and Food Chain
Models. Internal Report. U.S. Environmental Protection
Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse lie,
Michigan. 339pp.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1988. Upper Great Lakes Connecting
Channels Study; Detroit River System Mass Budget
(UGLCCS Activities C.I and F.4). Internal Report. U.S.
Department of Commerce, Springfield, Virginia. National
Technical Information Service Publication PB 88-158 068.
235 pp.
Richardson, W.L. and R.G. Kreis, Jr. 1987. Historical
Perspectives of Water Quality in Saginaw Bay. Internal
Report. U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 42 pp.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1987. Summary Report: An Integrated
Approach to a Study of Contaminants and Toxicity in
Monroe Harbor (River Raisin), Michigan, A Great Lakes
Area of Concern Draft Report. Internal Report. U.S.
Department of Commerce, Springfield, Virginia. National
Technical Information Service Publication PB 88-126 008,
182pp.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1987. Input-Output Mass Loading
Studies of Toxic and Conventional Pollutants in Trenton
Channel, Detroit River: Activities C.I and F.5 in the
Upper Great Lakes Connecting Channels Study
(UGLCCS). Internal Report. U.S. Department of
Commerce, Springfield, Virginia. National Technical
Information Service Publication PB 88-158 514, 310 pp.
U.S. Environmental Protection Agency, Large Lakes
Research Station. 1987. Users' Manual for the Transport
and Fate Model MICHRIV. Internal Report. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. 51 pp.
156
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Filkins, J.C., M.D. Mullin, W.L. Richardson, V.E. Smith,
J. Rathbun, S.G. Rood, K.R. Rygwelski, and T. Kipp.
1985. Report on the Distribution of Polychlorinated
Biphenyls in Sediments of Lower River Raisin, Monroe
Harbor, Michigan, 1983 and 1984. Internal Report. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. 58 pp.
Richardson, W.L. 1985. Learning the Great Lakes "Lab"
EPA Journal, 11(2): 11-12.
Richardson, W., B. Eadie, and W. Willford. 1985. Action
Plan for Federal Research and Monitoring on the Great
Lakes Toxic Substances. Internal Report. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse lie, Michigan. 37 pp.
Smith, V.E., I.E. Rathbun, S.G. Rood, K.R. Rygwelski,
W.L. Richardson, and D.M. Dolan. 1985. Distribution of
Contaminants in Waters of Monroe Harbor (River Raisin),
Michigan and Adjacent Lake Erie. Internal Report. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse fle, Michigan. 153pp.
Rathbun, J.E., M.L. Gessner, V.E. Smith, D.M. Lemon,
D.J. Brokaw, M.A. Hoeft, W.L. Richardson, and K.R.
Rygwelski. 1984. Bioaccumulation to Total PCBs and
PCB Homologs in Caged Clams, Channel Catfish, and
Fathead Minnows in the Monroe Harbor - River Raisin,
Michigan (1984). Internal Report. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 74 pp.
Richardson, W.L. 1983. Transport and Fate of Toxicants
in the Great Lakes. In - J.H. Baldwin (Ed.), Proceedings
of the 12th Annual Conference of the National Association
for Environmental Education, Crossroads and Technology
Society, pp. 29-30. NAEE Publishers, Troy, Ohio.
Sonzogni, W.C., W.L. Richardson, P. Rodgers, and T.J.
Monteith. 1981. Chloride Budget for the Great Lakes: A
Current Assessment. Great Lakes Basin Commission, Ann
Arbor, Michigan. Great Lakes Environmental Planning
No. 39, 44 pp.
Richardson, W.L. 1980. Toxic Substance Modeling
Research at the Large Lakes Research Station. In - R.V.
Thomann and T.O. Barnwell (Eds.), Proceedings of a
Workshop on Verification of Water Quality Models, pp.
202-213. U.S. Environmental Protection Agency, Office
of Research and Development, ERL-Duluth, Minnesota.
EPA-600/9-80-016.
Richardson, W.L. 1980. Data Management Requirements
for Great Lakes Water Quality Modeling. In - W.R. Swain
and V.R. Shannon (Eds.), Proceedings of the Second
American-Soviet Symposium on the Use of Mathematical
Models to Optimize Water Quality Management, Section
3, pp. 37-57. U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan.
EPA-600/9-80-033.
Paul, J.F., W.L. Richardson, A.B. Gortsko, and A.A.
Matveyev. 1979. Results of a Joint USA/USSR
Hydrodynamic and Transport Modeling Project,
Appendices B, C, and D. U.S. Environmental Protection
Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse lie,
Michigan. EPA-600/3-79-101.
Paul, J.F., W.L. Richardson, A.B. Gortsko, and A.A.
Matveyev. 1979. Results of a Joint USA/USSR
Hydrodynamic and Transport Modeling Project. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. EPA-600/3-79-015, 90 pp.
Bierman, V.J., Jr., W. Richardson, and T.T. Davies. 1978.
Mathematical Modeling Strategies Applied to Saginaw
Bay, Lake Huron. In T.T. Davies and V.R. Lozanskiy
(Eds.), American-Soviet Symposium on the Use of
Mathematical Models to Optimize Water Quality
Management, pp. 397-430. U.S. Environmental Protection
Agency, Office of Research and Development, Gulf
Breeze, Florida. EPA-600/9-78-024.
Richardson, W.L. 1978. Preliminary Analysis of PCB in
Saginaw Bay-1977. Internal Report. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 19 pp.
157
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Richardson, W.L., J.C. Filkins, and R.V. Thomann. 1978.
Preliminary Analysis of the Distribution and Mass Balance
of PCBs in Saginaw Bay 1977. Internal Report. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse lie, Michigan. 56 pp.
Richardson, W.L. 1977. STORET and the Great Lakes
DataBase. In - Proceedings of the Third Annual STORET
User's Meeting. U.S. Environmental Protection Agency.
November 2, 1977.
Bierman, V.J., Jr. and W.L. Richardson. 1976.
Mathematical Model of Phytoplankton Growth and Class
Succession in Saginaw Bay, Lake Huron. In Water
Quality Criteria Research of the U.S. Environmental
Protection Agency, pp. 159-173. U.S. Environmental
Protection Agency, Office of Research and Development,
Corvallis, Oregon. EPA-600/3-76-079.
Richardson, W.L. 1976. A Mathematical Model of
Pollutant Cause and Effect in Saginaw Bay, Lake Huron.
In Water Quality Criteria Research of the U.S.
Environmental Protection Agency, pp. 138-158. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Corvallis, Corvallis, Oregon.
EPA-600/3-76-079.
Richardson, W.L. and N.A. Thomas. 1976. A Review of
EPA's Great Lakes Modeling Program. In W.R. Ott
(Ed.), Proceedings of the Conference on Environmental
Modeling and Simulation, pp. 20-25. U.S. Environmental
Protection Agency, Office of Research and Development
and Office of Planning and Management, Cincinnati,
Ohio. April 19-22, 1976. EPA-600/9-76-016.
Bierman, V.J., Jr., W.L. Richardson, and D.M. Dolan.
1975. Responses of Phytoplankton Biomass in Saginaw
Bay to Changes in Nutrient Loadings. International
Reference Group on Upper Great Lakes Pollution,
International Joint Commission, Windsor, Ontario,
Canada. 36 pp.
Richardson, W.L. 1974. Modeling Chloride Distribution
in Saginaw Bay. Jji - N.A. Rukavina, J.S. Seddon, and P.
Casey (Eds.), Proceedings of the 17th Conference on Great
Lakes Research, International Association for Great Lakes
Research, pp. 462-470. Braun-Brumfield Publishers, Ann
Arbor, Michigan.
International Joint Commission, Water Quality Board
Reports. 1974,1975, and 1976, Appendix B. Prepared all
sections relating to Lake Erie and Saginaw Bay. For 1975
report, wrote sections on Great Lakes Surveillance and
Mathematical Modeling of Lake Ontario.
Federal Water Pollution Control Administration. 1969.
Immediate Water Pollution Control Needs, Interstate
Streams, Delaware River Basin and the State of Delaware.
U.S. Department of the Interior. Numerous in-house
monitoring and evaluation reports of water quality in the
Great Lakes and connecting channels.
Federal Water Pollution Control Administration. 1969.
Artificial Aeration in the Delaware Estuary. Feasibility
Report No. 1.
Federal Water Pollution Control Administration. 1966.
Statistical Analysis of Dissolved Oxygen in the Delaware
Estuary. Technical Memorandum.
Federal Water Pollution Control Administration. 1966.
Delaware Estuary Comprehensive Study, Preliminary
Report and Findings. Philadelphia, Pennsylvania.
Books or Book Chapters
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1990. Lake Ontario TCDD Modeling Report. In - U.S.
Environmental Protection Agency, New York State
Department of Environmental Conservation, New York
State Department of Health, and Occidental Chemical
Corporation (Eds.), Lake Ontario Bioaccumulation Study,
Final Report, Chapter 8. 65 pp.
Richardson, W.L., V.E. Smith, and R. Wethington. 1983.
Dynamic Mass Balance of PCB and Suspended Solids in
Saginaw Bay-A Case Study. In -D. Mackay, S. Patterson,
and S.J. Eisenreich (Eds.), Physical Behavior of PCBs in
the Great Lakes, pp. 329-366. Ann Arbor Science
Publishers, Ann Arbor, Michigan.
Richardson, W.L. 1976. An Evaluation of the Transport
Characteristics of Saginaw Bay Using a Mathematical
Model of Chloride. In R.P. Canale (Ed.), Modeling
Biochemical Processes in Aquatic Ecosystems, pp.
113-139. Ann Arbor Science Publishers, Ann Arbor,
Michigan.
158
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Presentations
Richardson, W.L., D.D. Endicott, and R.J Kreis. 1997.
The Value of Mathematical Modeling in Managing the
Great Lakes. Plenary Presentation. 40th Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Buffalo, Buffalo, New
York. June 2, 1997.
Richardson, W.L., D.D. Endicott, and K.R. Rygwelski.
1997. Quality Assurance for the Lake Michigan Mass
Balance Modeling Project. 40th Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Buffalo, Buffalo, New York. June
2,1995.
Rygwelski, K.R., D.D. Endicott, and W.L. Richardson
1997. A Screening Model for Atrazine in Lake Michigan.
40th Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Buffalo, Buffalo, New York. June 2, 1995.
Richardson, W.L. November 1994. Overview of the
Modeling Process for Assessment of Contaminated
Sediment. U.S. Environmental Protection Agency, Great
Lakes National Program Office ARCS/RAM Workshop,
Chicago, Illinois. November 1994.
Richardson, W.L. and D.D. Endicott. November 1994. A
Screening Model for Establishing Load-Response
Relationships in Lake Michigan. Fifteenth Annual
Meeting of the Society of Environmental Toxicology and
Chemistry, Denver, Colorado. November 1994.
Endicott, D.D., W.L. Richardson, and D.J. Kandt. 1993.
MICHTOX, A Mass Balance and Bioaccumulation Model
for Toxic Chemicals in Lake Michigan. 36th Conference
on Great Lakes Research, International Association for
Great Lakes Research, St. Norbert College, DePere,
Wisconsin. June 4-10, 1993.
Richardson, W.L., D.D. Endicott, R. Jourdan, and J.
Gailani. 1993. Visualization for Great Lakes Research
and Water Quality Management. Great Lakes
Visualization Workshop, Cleveland, Ohio. July 15-16,
1993.
Endicott, D.D., D.J. Kandt, and W.L. Richardson. 1991.
Looking Back to Saginaw Bay: Post-Audit Verification of
a PCB Mass Balance Model. 34th Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of New York at Buffalo, Buffalo,
New York. June 3-6, 1991.
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1991. Modeling the Partitioning and Bioaccumulation of
TCDD and Other Hydrophobic Organic Chemicals in Lake
Ontario, llth International Symposium on Chlorinated
Dioxins and Related Compounds, Research Triangle Park,
North Carolina. September 25, 1991.
Richardson, W.L. and D.D. Endicott. 1991. Utility of
Transport, Fate, and Bio-Accumulation Models in
Regulating Toxic Compounds in the Great Lakes. 12th
Annual Meeting of the Society of Environmental
Toxicology and Chemistry, Seattle, Washington.
November 3-7, 1991.
Endicott, D.D., W.L. Richardson, and D.M. Di Toro.
1989. A Model of TCDD in Lake Ontario. 32nd
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin. May 30-June 2, 1989.
Richardson, W. and W. Willford. 1989. Management
Perspectives in Application of the Mass Balance
Approach, for Managing Toxic Substances: The Green
Bay Mass Balance Project. 32nd Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Wisconsin, Madison, Wisconsin.
May 30-June 2, 1989.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1989. Predicted Water Quality Impacts of CDF Leakage
on Saginaw Bay. 32nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Madison, Wisconsin.
May 30-June 2, 1989.
El-Shaarawi,A.H.,K.Kuntz, and W.L. Richardson. 1988.
Maximum Likelihood Estimation of Water Quality
Concentrations From Censored Data. 31 st Conference on
Great Lakes Research, International Association for Great
Lakes Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
159
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Richardson, W.L., R.G. Kreis, Jr., and J.L. Martin. 1988.
A Modeling Framework for Planning a Mass Balance
Project for Green Bay, Lake Michigan. 31st Conference
on Great Lakes Research, International Association for
Great Lakes Research, McMaster University, Hamilton,
Ontario, Canada. May 16-20, 1988.
Richardson, W.L., R.G. Kreis, Jr., J.L. Martin, M.D.
Mullin, and J.C. Filkins. 1988. Results of the Binational
Study of the Great Lakes Upper Connecting Channels-trie
Detroit River. Third Chemical Congress of North America
and 195th American Chemical Society National Meeting,
Toronto, Ontario, Canada. June 5-10, 1988.
Richardson, W.L., J.L. Martin, S. McCutcheon, and J.
Paul. 1988. Influence of Modeling in Planning Large
Scale Integrated Water Quality Studies: Green Bay Case
Study. American Water Resources Conference,
Milwaukee, Wisconsin. November 7-10, 1988.
Rygwelski, K.R., J.L. Martin, W.L. Richardson, and S.L.
Kleiber. 1988. Mass Budget of Toxic and Conventional
Pollutants in the Trenton Channel. 31st Conference on
Great Lakes Research, International Association for Great
Lakes Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
Smith, V.E., S.G. Rood, W.L. Richardson, and T.D.
Fontaine. 1988. Mass Budgets of Conventional and Toxic
Pollutants in the Detroit River, 1986. 31st Conference on
Great Lakes Research, International Association for Great
Lakes Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
Richardson, W.L., K.R. Rygwelski, and R.P. Winfield.
1985. Mass Balances of Toxic Substances in an IJC Class
A Area of Concern. 28th Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Milwaukee,
Wisconsin. June 3-6, 1985.
Richardson, W.L. and V.E. Smith. 1984. Hazard
Assessment in Monroe Harbor, Michigan (Lake Erie), A
Great Lakes Area of Concern. 27th Conference on Great
Lakes Research, International Association for Great Lakes
Research, Brock University, St. Catharines, Ontario,
Canada. April 30-May 3, 1984.
Richardson, W.L. 1983. Air Impacts on the Great Lakes.
Fall Meeting of the Michigan Chapter of Air Pollution
Control Association. October 25, 1983.
Winfield, R.P., W.L. Richardson, M. Labiak, K.
Rygwelski, D.M. Di Toro, and R. Andrews. 1983.
Mathematical Models of the Fate of Pentachlorophenol in
an Experimental Stream. Fourth Annual Meeting of the
Society of Environmental Toxicology and Chemistry,
Arlington, Virginia. November 6-9, 1983.
Richardson, W.L., V.E. Smith, and R. Wethington. 1982.
Model of PCB Mixtures in Saginaw Bay. 25th Conference
on Great Lakes Research, International Association for
Great Lakes Research, Sea Lamprey Control Centre, Sault
Ste. Marie, Ontario, Canada. May 4-6, 1982.
Gorstko, A.B., Y.A. Dombrovskiy, A.A. Matveyev, J.F.
Paul, W.L. Richardson, and A.F. Surkov. 1981. Imitative
Modeling: An Instrument for Researching and Projecting
the State of the Ecosystem of Large Water Bodies. 24th
Conference on Great Lakes Research, International
Association for Great Lakes Research, Ohio State
University, Columbus, Ohio. April 28-30, 1981.
Richardson, W.L. 1981. Mass Balance of PCB and
Suspended Solids in Saginaw Bay. International
Workshop on PCBs in the Great Lakes, Sponsored by the
University of Toronto, University of Minnesota,
University of Michigan, Ontario Ministry of the
Environment, Canada Centre for Inland Waters, Great
Lakes Environmental Research Laboratory (NOAA), and
the Michigan Sea Grant Program, Toronto, Ontario,
Canada. December 1981.
Richardson, W.L., J.C. Filkins, R.V. Thomann, and J.A.
Mueller. 1981. Dynamic Mass Balance of PCB in
Saginaw Bay. 24th Conference on Great Lakes Research,
International Association for Great Lakes Research, Ohio
State University, Columbus, Ohio. April 28-30,1981.
Richardson, W.L., E. Smith, and J. Filkins. 1980.
Distribution of Aroclor 1254 in Saginaw Bay During 1977.
23rd Conference on Great Lakes Research, International
Association for Great Lakes Research, Queen's University,
Kingston, Ontario, Canada. May 19-22, 1980.
160
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Richardson, W.L. 1979. Toxic Substances Modeling
Research at the Large Lakes Research Station. National
Workshop on the Verification of Water Quality Models,
West Point, New York. March 1979.
Paul, J.F., W.L. Richardson, A.B. Gorstko, and A.
Matveyev. 1978. A Mutual Exchange of Data for the
Great Lakes, Lake Baikal and Azov Sea Under the
U.S.-U.S.S.R. Environmental Agreement. 21st
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Windsor, Windsor, Ontario, Canada. May 9-11, 1978.
Richardson, W.L., J. Paul, A.B. Gorstko, and A.A.
Matveyev. 1978. Comparison of Hydrodynamic Models
for Lake Baikal and the Sea of Azov with Field
Observations. 21st Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Windsor, Windsor, Ontario, Canada. May
9-11, 1978.
Richardson, W.L. 1977. Seminar on USA/USSR Joint
Modeling Project. Manhattan College, New York, New
York. December 1977.
Richardson, W.L. 1977. The International Surveillance
Plan for the Great Lakes. USA/USSR Environmental
Agreement on Protection and Management of Water
Quality in Lakes and Estuaries, Institute for Mechanics
and Applied Mathematics, Rostov-on-Don, USSR. June
1977.
Richardson, W.L. 1977. Summary of Modeling
Approaches for the Great Lakes. USA/USSR
Environmental Agreement on Protection and Management
of Water Quality in Lakes and Estuaries, Institute for
Mechanics and Applied Mathematics, Rostov-on-Don,
USSR. June 1977.
Richardson, W.L. 1977. Utility of Eutrophication Models
for Great Lakes Water Quality Management. 20th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Michigan, Ann Arbor, Michigan. May 10-12, 1977.
Bierman, V.J., Jr., W.L. Richardson, and D.M. Dolan.
1976. A Multi-Class Model of Phytoplankton Production
in Saginaw Bay, Lake Huron. 19th Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Guelph, Guelph, Ontario, Canada.
May 4-6, 1976.
Richardson, W.L. 1976. An International Surveillance
Plan for the Great Lakes. Annual Meeting of the
International Joint Commission, Windsor, Ontario,
Canada. July 1976.
Richardson, W.L. 1976. Great Lakes Water Quality
Assessment for 1975. Annual Meeting of the International
Joint Commission, Windsor, Ontario, Canada. July 1976.
Richardson, W.L. and V.J. Bierman, Jr. 1975. A Time
Variable Model of Chloride Distribution in Saginaw Bay,
Lake Huron. 18th Conference on Great Lakes Research,
International Association for Great Lakes Research, State
University of New York, Albany, New York. May 20-23,
1975.
161
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Kenneth R. Rygwelski
Environmental Scientist
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311GrohRoad
Grosse He, Michigan 48138
(734) 692-7641
Fax: (734) 692-7603
krr@lloyd.grl.epa.gov
Role in the Lake Michigan Mass Balance
Project
Member of Modeling Worgroup. Developing and
applying models for atrazine and mercury.
Education
Graduate Certificate, Hazardous Waste Control, Wayne
State University, Detroit, Michigan, 1995
M.S., Chemical Engineering, Wayne State University,
Detroit, Michigan, 1983
B.S., Chemistry, Michigan Technological University,
Houghton, Michigan, 1972
Professional Experience
From 1995 to present - Work involves research projects
including development and application of mathematical
mass balance models for mercury and atrazine in Lake
Michigan. Maintains an atrazine screening-level model,
MICHTOX, which is a WASP-based model. This model
is a precursor to a more finely segmented WASP model.
Mercury modeling will likely utilize a WASP model that
will take into consideration the various mercury species
likely to exist. Currently, MINTEQA2, a metal speciation
model is running that describes the likely composition of
mercury species in Lake Michigan. MINTEQA2 will be
utilized either independently of the mercury transport and
fate model or incorporated within the transport and fate
model. I expect to be involved in various aspects of both
the atrazine and mercury modeling activities, including,
model development, loading estimation, data review,
model computations, and report writing. Previous
experience includes managing on-site contractor staff for
ADP support at the LLRS with database management
responsibilities for several large USEPA projects. Before
that was staff inorganic chemist responsible for analytical
chemistry for heavy metals.
Publications
Book Chapters
Rygwelski, K.R. 1984. Partitioning of Toxic Trace
Metals Between Solid and Liquid Phases in the Great
Lakes. In: J.O. Nriagu and M.S. Simmons (Eds.), Toxic
Contaminants in the Great Lakes, pp. 321-333. John Wiley
and Sons, New York, New York.
EPA Ecological Research Series
Rygwelski, K.R. (Ed.). 1987. Input-Output Mass Loading
Studies of Toxic and Conventional Pollutants in Trenton
Channel, Detroit River: Activities C.I and F.5 in the
Upper Great Lakes Connecting Channels Study
(UGLCCS). U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. EPA-
600/3-88-033.
Rygwelski, K.R. and V.E. Smith (Eds.). 1987. Summary
Report: An integrated Approach to a Study of
Contaminants and Toxicity in Monroe Harbor (River
Raisin), Michigan, A Great Lakes Area of Concern. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. EPA-600/3-87-044, 182 pp.
Rygwelski, K.R. 1984. Field and Laboratory Methods for
Flint River Surveys. In - Technical Guidance Manual for
Performing Waste Load Allocations, Book n Streams
and Rivers, Chapter 3 - Toxic Substances, Appendix C, pp.
C1-C13. U.S. Environmental Protection Agency, Office
of Water Regulations and Standards, Monitoring and Data
Support Division, Washington, D.C. EPA-440/4-84-002.
Rygwelski, K.R. 1984. Volatilization. In Technical
Guidance Manual for Performing Waste Load Allocations,
Book II Streams and Rivers, Chapter 3 - Toxic
Substances, Chapter 3.3.4, pp. 8-84. U.S. Environmental
Protection Agency, Office of Water Regulations and
Standards, Monitoring and Data Support Division,
Washington, D.C. EPA-440/4-84-002.
162
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Rygwelski, K.R., J.M. Townsend, and V.E. Smith. 1984.
Partitioning of Cadmium, Copper, Lead, and Zinc Among
Paniculate Fractions and Water in Saginaw Bay (Lake
Huron). U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. EPA-600/S3-84-
069,4 pp.
Smith, V.E., K.W. Lee, J.C. Filkins, K.W. Hartwell, K.R.
Rygwelski, and J.M. Townsend. 1977. Survey of
Chemical Factors in Saginaw Bay (Lake Huron). U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. EPA-600/3-77-125, 143pp.
National Technical Information Service Reports
Rygwelski, K.R. and V.E. Smith (Eds.). 1987. Summary
Report: An Integrated Approach to a Study of
Contaminants and Toxicity in Monroe Harbor (River
Raisin), Michigan, A Great Lakes Area of Concern. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. National Technical Information
Service Publication PB 88-126 008, 182 pp.
Smith, V.E., S.P. Hendricks, I.E. Rathbun, S.G. Rood, and
K.R. Rygwelski. 1987. Metals, Organics, and General
Water Chemistry. In K.R. Rygwelski and V.E. Smith
(Eds.), Summary Report: An Integrated Approach to a
Study of Contaminants and Toxicity in Monroe Harbor
(River Raisin), Michigan, A Great Lakes Area of Concern,
Section 7.1, pp. 61-78. U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse He,
Michigan. National Technical Information Service
Publication PB 88-126 008, 182 pp.
Smith, V.E., S.P. Hendricks, J.E. Rathbun, S.G. Rood, and
K.R. Rygwelski. 1987. Zooplankton and
BioaccumulationBioassays. In- K.R. Rygwelski and V.E.
Smith (Eds.), Summary Report: An Integrated Approach
to a Study of Contaminants and Toxicity in Monroe
Harbor (River Raisin), Michigan, A Great Lakes Area of
Concern, Section 7.2, pp. 78-128. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. National Technical Information Service
Publication PB 88-126 008, 182 pp.
Rygwelski, K.R., J.M. Townsend, R.J. Cleghorn, V.E.
Smith, and J.M. Spurr. 1984. Partitioning of Cadmium,
Copper, Lead, and Zinc Among Particulate Fractions and
Water in Saginaw Bay (Lake Huron). U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. National Technical Information Service
Publication PB 84-209 899, 139 pp.
Internal Reports
Kreis, R.G., Jr., K.R. Rygwelski, and V.E. Smith (Eds.).
1990. Procedures for the Assessment of Contaminated
Sediments in the Laurentian Great Lakes as Developed in
the Detroit River-Trenton Channel In-Place Pollutants
Study, 1985-1988. Report to the Michigan Department of
Natural Resources, Lansing, Michigan. 540 pp.
U.S. Environmental Protection Agency. 1988. Project
Planning for the Green Bay Physical-Chemical Mass
Balance and Food Chain Models. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 339 pp.
Woodring, D., A.R. Houssari, K.R. Rygwelski, and J.L.
Martin. 1987. Users' Manual for the Transport and Fate
Model MICHRIV. U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse He,
Michigan. 51 pp.
Dolan, D.M., M.L. Gessner, S. Hendricks, D.A. Griesmer,
and K.R. Rygwelski. 1985. Correlations of Bioassay
Results and Toxicant Concentrations at Monroe Harbor,
Michigan, 1983-1984. U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse He,
Michigan. 135 pp.
Filkins, J.C., M.L. Gessner, J. Rathbun, and K. Rygwelski.
1985. Monroe Harbor Study Field Methodology Report.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 78 pp.
163
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Filkins, J.C., M.D. Mullin, W.L. Richardson, V.E. Smith,
J. Rathbun, S.G. Rood, K.R. Rygwelski, and T. Kipp.
1985. Report on the Distribution of Polychlorinated
Biphenyls in Sediments of Lower River Raisin, Monroe
Harbor, Michigan 1983 and 1984. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 58 pp.
Smith, V.E., I.E. Rathbun, S.G. Rood, K.R. Rygwelski,
W.L. Richardson, and D.M. Dolan. 1985. Distribution of
Contaminants in Waters of Monroe Harbor (River Raisin),
Michigan and Adjacent Lake Erie. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 153 pp.
Rathbun, I.E., M.L. Gessner, V.E. Smith, D.M. Lemon,
D.J. Brokaw, M.A. Hoeft, W.L. Richardson, and K.R.
Rygwelski. 1984. Bioaccumulation to Total PCBs and
PCB Homologs in Caged Clams, Channel Catfish, and
Fathead Minnows in the Monroe Harbor - River Raisin,
Michigan (1984). U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse He, Michigan. 74 pp.
Winfield, R.P., W.L. Richardson, M. Labiak, K.
Rygwelski, D.M. Di Toro, and R. Andrews. 1983.
Mathematical Models of the Fate of Pentachlorobiphenyls
in an Experimental Stream. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. 31 pp.
Presentations
Endicott, D.D., W.L. Richardson, K.R. Rygwelski, X.
Zhang, J.J. Pauer, and X. Zhang. 1997. Conceptual and
Mathematical Models for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Richardson, W.L., D.D. Endicott, and K.R. Rygwelski.
1997. Quality Assurance for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Rygwelski, K.R., W.L. Richardson, and D.D. Endicott.
1997. A Screening-Level Model Evaluation of Atrazinein
the Lake Michigan Basin. 40th Conference on Great
Lakes Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Martin, J.L., M. Velleux, and K. Rygwelski. 1989.
Screening-Level PCB of Model of Green Bay, Lake
Michigan. 32nd Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Wisconsin, Madison, Wisconsin. May 30-
June 2, 1989.
Dolan, D.M., S.A. Megens, and K. Rygwelski. 1988.
Total Phosphorus Loadings from the Detroit River to Lake
Erie in 1986. 31st Conference on Great Lakes Research,
International Association for Great Lakes Research,
McMaster University, Hamilton, Ontario, Canada. May
16-20, 1988.
Rygwelski, K.R., J.L. Martin, W.L. Richardson, and S.L.
Kleiber. 1988. Mass Budget of Toxic and Conventional
Pollutants in the Trenton Channel. 31st Conference on
Great Lakes Research, International Association for Great
Lakes Research, McMaster University, Hamilton, Ontario,
Canada. May 16-20, 1988.
Bridgham, S.D., D. McNaught, C. Meadows, K.
Rygwelski, D. Dolan, and M. Gessner. 1985. Factors
Responsible for the Inhibition or Stimulation of Two Great
Lakes Ecosystems. 28th Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Milwaukee,
Wisconsin. June 3-5, 1985.
164
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Mathews, S.H., D.M. Dolan, K.R. Rygwelski, and D.
Griesmer. 1985. Correlations of Biological Effects and
Metal Contaminants at Monroe Harbor, Michigan. 28th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Milwaukee, Wisconsin. June 3-5, 1985.
Richardson, W.L., K.R. Rygwelski, and R.P. Winfield.
1985. Mass Balances of Toxic Substances in an IJC Class
A Area of Concern. 28th Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Milwaukee,
Wisconsin. June 3-5, 1985.
Winfield, R.P., W.L. Richardson, M. Labiak, K.
Rygwelski, D.M. Di Toro, and R. Andrews. 1983.
Mathematical Models of the Fate of Pentachlorobiphenyl
in an Experimental Stream. Fourth Annual Meeting of the
Society of Environmental Toxicology and Chemistry,
Arlington, Virginia. November 6-9, 1983.
Rygwelski, K.R. and J.M. Townsend. 1981. Partitioning
of Cadmium, Copper, Lead, and Zinc Among Water and
Particulate Fractions in Saginaw Bay, Lake Huron. 24th
Conference on Great Lakes Research, International
Association for Great Lakes Research, The Ohio State
University, Columbus, Ohio. April 28-30, 1981.
Rygwelski, K.R., J.M. Spurr, and J.M. Townsend. 1978.
Necessary Quality Control for the Analysis of Trace
Metals in Lake Water. 21st Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Windsor, Windsor, Ontario,
Canada. May 9-11, 1978.
165
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James J. Pauer
Water Quality Modeler
SoBran, Incorporated
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311GrohRoad
Grosse He, Michigan 48138
(734) 692-7635
Fax: (734) 692-7603
jjp@lloyd.grl.epa.gov
Role in the Lake Michigan Mass Balance
Project
Eutrophication (phytoplankton, solids) modeling.
Education
Ph.D., Environmental Engineering, Michigan
Technological University, Houghton, Michigan
Training
Advanced Water Quality Modeling Short Course by Steve
Chapra, 1995
QUAL2E Modeling Course by Brown and Barnwell,
Athens, Georgia
Waste Load Allocation Course by Ray Whittemore, Tufts
University
Experience as Related to Modeling
Three years experience in water quality modeling and
impact assessment studies (CSIR, South Africa).
Publications
Pauer, JJ. 1996. Nitrification in Lake and River Systems
Doctoral Thesis, Michigan Technological University,
Houghton, Michigan.
Presentations
Endicott, D.D., W.L. Richardson, K.R. Rygwelski, X.
Zhang, J.J. Pauer, and X. Zhang. 1997. Conceptual and
Mathematical Models for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Great Lakes Center for Environmental Research
and Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Pauer, J.J. 1995. River Nitrification: Are Large Ranges in
Reported Rate Coefficients Trying to Tell Us Something?
WEFTEC '95 Conference.
Pauer, J.J. The Impact of the SAPPI Tugela Mill Effluent
on Dissolved Oxygen in the Tugela River. South African
Pulp and Paper Technical Conference, South Africa.
166
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Xiaomi Zhang
Water Quality Modeler
SoBran, Incorporated
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311GrohRoad
Grosse lie, Michigan 48138
(734) 692-7624
Fax: (734) 692-7603
zxm@lloyd.grl.epa.gov
Role in the Lake Michigan Mass Balance
Project
Responsible for general water quality model development
and application. Calibrate the transport submodel by using
hydrodynamic model output and adjusting WASP input so
that measured temperature regimes are simulated.
Responsible for implementing transport and fate
submodels at various time and spatial scales and apply to
PCBs, mercury, rrans-nonachlor, and atrazine.
Education
M.S., Civil Engineering (Environmental), State University
of New York at Buffalo, Buffalo, New York, 1995.
M.A., Geology (Geophysics), State University of New
York at Buffalo, Buffalo, New York, 1992.
B.S., Geophysics, ChangChun GeoScience and
Technology University, ChangChun, China, 1984.
Training
Oracle Training Certified, January 1997.
Experience
Environmental Engineer/Water Quality Modeler, SoBran,
Incorporated, May 1995-Present
Develop, calibrate, diagnose water quality models
describing toxic contaminant transport and fate in the
aquatic environment. Experience with the modeling
frameworks including WASP4 type models such as
GBTOX, IPX etc. Work assignments and
accomplishments include: writing GBTOX user's guide
and Green Bay Mass Budget diagram generation guide;
analysis for Lake Michigan PCB volatilization flux; Lake
Michigan Level II Segmentation scheme design; IPX,
GBTOX model codes modification for LMMBP; and
vertical dispersion coefficients calibration for LMMBP by
using those models.
Research Assistant, Great Lake Program, State University
of New York at Buffalo, Buffalo, New York, 1993-May
1995
Recalibration of GBTOX model for GBMBS including
both organic carbons model and toxic chemical model
calibrations. Masters' thesis research focused on the
effect of spatial resolution (i.e. segmentation scheme) on
the biochemical transformation parameters and toxic
chemical partition coefficients and long-term management
diagnosis (using GBMBS generated data)
Publications
Zhang, X. and W Richardson. 1995. GBTOX User's
Guide and Green Bay Mass Budget Diagram Generation
Guide. U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse lie, Michigan. 57 pp.
Raghunathan, R., J. DePinto, S. Martin, V. Bierman, Jr., P.
Rodgers, T. Young, and X.Zhang. 1994. Development of
a Toxic Chemical Dynamics Model (GBTOX) for the
Green Bay Mass Balance Study. Part 1: Model
Framework and Calibration; Part 2: Model Diagnosis and
Interpretation. J. Great Lakes Res., in preparation.
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. 132 pp.
167
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Presentations Zhang, X. 1996. Relationship Between the Models of the
Lake Michigan Modeling Framework and Inputs Needed
Endicott, D.D., W.L. Richardson, K.R. Rygwelski, X. for the Contaminant Mass Balance Model (the Modified
Zhang, J.J. Pauer, and X. Zhang. 1997. Conceptual and IPX). Third Annual Meeting of Lake Michigan Mass
Mathematical Models for the Lake Michigan Mass Balance Project, Chicago, Illinois. December 10-12,1996.
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Center for Environmental Research and
Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
168
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Xin Zhang
Ph.D., Mathematical Modeler
PAI/SoBran, Incorporated
U.S. Environmental Protection Agency
ORD, NHEERL, MED-Duluth, CBSSS, LLRS
9311GrohRoad
Grosse He, Michigan 48138
(734)692-7631
Fax: (734) 692-7603
xin@lloyd.grl.epa.gov
Role in the Lake Michigan Mass Balance
Project
Responsible for modeling chemical bioaccumulation in
Lake Michigan food webs.
Education
Ph.D., Chemical Thermodynamics
Training
Postdoctoral training in uptake and bioaccumulation of
chemical substances in fish, plants, and other organisms,
modeling the dynamics of chemical distribution in aquatic
ecosystem and food chains, relationships between
chemical structure and environmental fate of organic
compounds.
Experience
Mathematical Modeler, PAI/SoBran, Incorporated, June
1996-present
Modeling food web bioaccumulation of PCBs as a part of
Lake Michigan Modeling Project.
Research Associate, Environmental Contaminants
Laboratory, School of Resource and Environmental
Management, Simon Eraser University, Canada,
November 1991-June 1996
Several projects on modeling studies of the environmental
fate and bioaccumulation of chemical contaminants in
Lake Ontario, Eraser-Thompson River, and Vancouver
Harbor.
Development of "Chemical Ranker" computer program
for the British Columbia government to rank organic
chemicals based on exposure and toxic effects to
organisms.
Development of a computer program "Food-Web
Bioaccumulation Model" to estimate the water and
sediment concentrations associated with acceptable
contaminant levels in fish. This program has been
formally and favorably reviewed by the USEPA for use in
its Great Lakes Water Quality Initiative (EPA-822-R-94-
002).
Development of quantitative molecular structure-property
relationships (QSPR) to predict physical chemical
properties of a large group of organic contaminants for
environmental hazard assessment.
Laboratory studies on the mechanism of bioaccumulation
of organic compounds in fish (guppy, goldfish, and
rainbow trout).
Publications
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.
Modeling the Environmental Fate and Food-Chain
Bioaccumulation of Pulp Mill Effluent Contaminants in
the Fraser-Thompson River System. 1995. Technical
Report. British Columbia Ministry of the Environment,
British Columbia, Canada.
Zhang, X. and F.A.P.C. Gobas. 1995. A Thermodynamic
Analysis of the Relationship Between Molecular Size,
Hydrophobicity, Aqueous Solubility and Octanol-Water
Partitioning of Organic Chemicals. Chemosphere,
31(6):3501-3521.
Chemical Property Characterization and Chemical
Exposure and Hazard Ranking of Chemicals in Pulp and
Paper Mill Effluents. 1994. Technical Report. British
Columbia Ministry of the Environment, British Columbia,
Canada.
169
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Gobas, F. and X. Zhang. 1994. Interactions of Organic
Chemicals with Organic Matter in the Aquatic
Environment, Jji - Jerry J. Hamelik (Ed.), Unavailability,
Physical, Chemical, and Biological Interactions. CRC
Press, Inc., New York, New York.
Gobas, F.A.P.C., X. Zhang, and R. Wells. 1993.
Gastrointestinal Magnification: The Mechanism of
Biomagnification and Food Chain Accumulation of
Chemicals. Environ. Sci. Technol., 27(12):2855-2863.
Gobas, F.A.P.C. and X. Zhang. 1992. Measuring
Bioconcentration Factors and Rate Constants of Chemicals
in Aquatic Organisms under Conditions of Variable Water
Concentrations and Short Exposure Time. Chemosphere,
25(12):1961-1972.
Zhang, X. and L.G. Heplor. 1991. Application of
Calorimetry to Investigations of Kinetics and Energetics of
Oxidation of Fuels: Experimental and Calculational
Methods for Initial Rates. Thermochim. Acta, 191:155-
159.
Presentations
Endicott, D.D., W.L. Richardson, K.R. Rygwelsi, X.
Zhang, J.J. Pauer, and X. Zhang. 1997. Conceptual and
Mathematical Models for the Lake Michigan Mass
Balance Project. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Center for Environmental Research and
Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Zhang, X. and F.A.P.C. Gobas. 1997. A Model for the
Bioaccumulation of Mercury Species in the Lake Ontario
Food Web. 40th Conference on Great Lakes Research,
International Association for Great Lakes Research,
Center for Environmental Research and Education,
Buffalo State College, Buffalo, New York. June 1-5,
1997.
Zhang, X. and F.A.P.C. Gobas. 1997. A Mass Balance
and Historical Contamination Profile of Mirex in Lake
Ontario Ecosystem. 40th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Center for Environmental Research and
Education, Buffalo State College, Buffalo, New York.
June 1-5, 1997.
Zhang, X. and FA.P.C. Gobas. 1995. ECOFATE: A User-
Friendly Environmental Fate, Bioaccumulation and
Ecological Risk Assessment Model for Contaminants in
Marine and Freshwater Aquatic Ecosystems: Application
and Validation. Second Society of Environmental
Toxicology and Chemistry World Congress, Vancouver,
British Columbia, Canada. November 5-9, 1995.
Wilcockson, J., F. Gobas, and X. Zhang. 1995.
Biomagnification and Bioavailability of
Hexachlorobiphenyl in Rainbow Trout. Second Society of
Environmental Toxicology and Chemistry World
Congress, Vancouver, British Columbia, Canada.
November 5-9, 1995.
Gobas, F. and X. Zhang. 1994. Mechanisms and
Simulation Models of Contaminant Bioconcentration and
Biomagnification in Aquatic Food-Webs. Fifteenth
Annual Meeting of the Society of Environmental
Toxicology and Chemistry, Denver, Colorado. October
30-November 3, 1994.
170
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Victor J. Bierman, Jr., Ph.D.
Environmental Engineering
Associate Vice-President
Limno-Tech, Incorporated
501 Avis Drive
Ann Arbor, Michigan 48108
(734) 973-8300
Fax: (734) 973-1069
Role in the Lake Michigan Mass Balance
Project
Direct the conceptualization and development of the
ecosystem model. Provide expert advice regarding model
construct, principles, testing, and parameter refinements.
Education
Ph.D., Environmental Engineering, University of Notre
Dame, Notre Dame, Indiana, 1974
M.S., Physics, University of Notre Dame, Notre Dame,
Indiana, 1971
A.B., Science, Villanova University, Vallanova,
Pennsylvania, 1966
Specialized Training and Coursework
Institute on Mathematical Modeling of Water Quality,
Manhattan College, Bronx, New York, 1985
Professional Experience
Associate Vice-President, Limno-Tech, Inc., Ann Arbor,
Michigan, 1997.
Senior Scientist, Limno-Tech, Inc., South Bend, Indiana,
1992-1997.
Senior Project Manager, Limno-Tech, Inc., South Bend,
Indiana, 1990-1992.
Adjunct Associate Professor, Department of Civil
Engineering and Geological Sciences, University of Notre
Dame, Notre Dame, Indiana, 1990-1992.
Associate Professor, Department of Civil Engineering,
University of Notre Dame, Notre Dame, Indiana, 1990-
1992.
Environmental Scientist, USEPA National Expert in
Environmental Exposure Assessment, Environmental
Research Laboratory, USEPA, Narragansett, Rhode Island,
1981-1986.
Adjunct Associate Professor, Department of Civil and
Environmental Engineering, University of Rhode Island,
Kingston, Rhode Island, 1985-1986.
Environmental Scientist, USEPA, LLRS, Grosse He,
Michigan, 1974-1981.
Systems Ecologist, Cranbrook Institute of Science,
Bloomfield Hills, Michigan, 1974.
Publications
Journal Articles
DePinto, J.V., R. Raghunathan, V.J. Bierman, Jr., P.W.
Rodgers, S.C. Hinz, andT.C. Young. 1995. Development
and Calibration of an Organic Carbon Based Sorbent
Dynamics Model (GBOCS) for the Green Bay Mass
Balance Study. Submitted for publication in the Journal
of Great Lakes Research.
DePinto, J.V., P. Sierzenga, R. Raghunathan, V.J.
Bierman, Jr., P.W. Rodgers, S.C. Hinz, and T.C. Young.
1995. Vertical Dynamics of Particulate Matter in Green
Bay: A Long-Term Radionuclide (137Cs) Mass Balance
Model. Submitted for publication in the Journal of Great
Lakes Research.
Havens, K.E., V.J. Bierman, Jr., E.G. Flaig, C. Hanlon,
R.T.James,B.L.Jones,andV.H.Smith. 1995. Historical
Trends in the Lake Okeechobee Ecosystem, VI, Synthesis.
Archiv. Hydrobiol., Supplement, 107:101-111.
James, R.T. and V.J. Bierman, Jr. 1995. A Preliminary
Modeling Analysis of Water Quality in Lake Okeechobee,
Florida: Calibration Results. Water Res., 29(12):2767-
2775.
171
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Martin, S.C., S.C. Hinz, P.W. Rodgers, V.J. Bierman, Jr.,
J.V. DePinto, and T.C. Young. 1995. Calibration of a
Hydraulic Transport Model for Green Bay, Lake
Michigan. J. Great Lakes Res., 21(4):599-609.
Raghunathan, R., J.V. DePinto, S.C. Martin, V.J. Bierman,
Jr., P.W. Rodgers, T.C. Young, and X. Zhang. 1995.
Development of a Toxic Chemical Dynamics Model
(GBTOX) for the Green Bay Mass Balance Study: Part
One - Model Framework and Calibration. Submitted for
publication in the Journal of Great Lakes Research.
Raghunathan, R., J.V. DePinto, S.C. Martin, V.J. Bierman,
Jr., P.W. Rodgers, T.C. Young, and X. Zhang. 1995.
Development of a Toxic Chemical Dynamics Model
(GBTOX) for the Green Bay Mass Balance Study: Part
Two Model Diagnostic Application. Submitted for
publication in the Journal of Great Lakes Research.
Smith, V.H., V,J. Bierman, Jr., B.L. Jones, and K.E.
Havens. 1995. Historical Trends in the Lake Okeechobee
Ecosystem, IV. Nitrogen:Phosphorus Ratios,
Cyanobacterial Dominance, and Nitrogen Fixation
Potential. Arch. Hydrobiol., Supplement, 107:71-88.
Young, T.C., V.J. Bierman, Jr., J.V. DePinto, and P.W.
Rodgers. 1995. Uncertainty of Fluvial Load Estimates
From the Upper Fox River During the Green Bay Mass
Balance Study. Submitted for publication in the Journal
of Great Lakes Research.
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1994. A Preliminary
Mass Balance Model of Primary Productivity and
Dissolved Oxygen in the Mississippi River Plume/Inner
Gulf Shelf Region. Estuaries, 17(4):886-899.
DePinto, J.V., R. Raghunathan, V.J. Bierman, Jr., P.W.
Rodgers, T.C. Young, and S.C. Martin. 1993. Analysis of
Organic Carbon Sediment-Water Exchange in Green Bay,
Lake Michigan. Water Sci. Technol., 28(8-9): 149-159.
Dilks, D.W., J.S. Helfand, V.J. Bierman, Jr., and L.
Burkhard. 1993. Field Application of a Steady-State
Mass Balance Model for Hydrophobic Organic Chemicals
in an Estuarine System. Water Sci. Technol., 28(8-9):263-
271.
Bonner, J.S., C.D. Hunt, J.F. Paul, and V.J. Bierman, Jr.
1992. Transport of Low-Level Radioactive Soil at Deep-
Ocean Disposal Sites. J. Environ. Engin., 118(1): 101-119.
Preston, S.D., V.J. Bierman, Jr., and S.E. Silliman. 1992.
Impact of Flow Variability on Error in the Estimation of
Tributary Mass Loads. J. Environ. Engin., 118(3):402-
419.
Bierman, V.J., Jr. 1990. Equilibrium Partitioning and
Biomagnification of Organic Chemicals in Benthic
Animals. Environ. Sci. Technol., 24(9): 1407-1412.
Preston, S.D., V.J. Bierman, Jr., and S.E. Silliman. 1989.
An Evaluation of Methods for the Estimation of Tributary
Mass Loads. Water Resources Res., 25(6): 1379-1389.
Walker, H.A., J.F. Paul, and V.J. Bierman, Jr. 1987.
Methods for Waste Load Allocation of Municipal Sewage
Sludge at the 106-Mile Ocean Disposal Site. Environ.
Toxicol. Chem., 6(6):475-489.
Bierman, V.J., Jr. and D.M. Dolan. 1986. Modeling of
Phytoplankton in Saginaw Bay: I. Calibration Phase. J.
Environ. Engin., 112(2):400-414.
Bierman, V.J., Jr. and D.M. Dolan. 1986. Modeling of
Phytoplankton in Saginaw Bay: JJ. Post-Audit Phase. J.
Environ. Engin., 112(2):415-429.
O'Connor, T.P., H.A. Walker, J.F. Paul, and V.J. Bierman,
Jr. 1985. A Strategy for Monitoring of Contaminant
Distributions Resulting From Proposed Sewage Sludge
Disposal at the 106-Mile Ocean Disposal Site. Marine
Environ. Res., 16:127-150.
Bierman, V.J., Jr., D.M. Dolan, R. Kasprzyk, and J.L.
Clark. 1984. Retrospective Analysis of the Response of
Saginaw Bay, Lake Huron, to Reductions in Phosphorus
Loadings. Environ. Sci. Technol., 18(1):23-31.
Bierman, V.J., Jr. and W.R. Swain. 1982. Mass Balance
Modeling of DDT Dynamics in Lakes Michigan and
Superior. Environ. Sci. Technol., 16(9):572-579.
Dolan, D.M. and V.J. Bierman, Jr. 1982. Mass Balance
Modeling of Heavy Metals in Saginaw Bay, Lake Huron.
J. Great Lakes Res., 8(4):676-694.
172
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Bierman, V.J., Jr. and D.M. Dolan. 1981. Modeling of
Phytoplankton-Nutrient Dynamics in Saginaw Bay, Lake
Huron. J. Great Lakes Res., 7(4):409-439.
Dolan, D.M., V.J. Bierman, Jr., M.H. Dipert, and R.D.
Geist. 1978. Statistical Analysis of the Spatial and
Temporal Variability of the Ratio Chlorophyll a to
Phytoplankton Cell Volume in Saginaw Bay, Lake Huron.
J. Great Lakes Res., 4(l):75-83.
Refereed Book Chapters
Bierman, V.J., Jr. 1993. Partitioning of Organic
Chemicals in Sediments: Estimation of Interstitial
Concentrations Using Organism Body Burdens. In - J.V.
DePinto, W. Lick, and J.F. Paul (Eds.), Transport and
Transformation of Contaminants Near the Sediment-Water
Interface, pp. 149-170. Lewis Publishers, Chelsea,
Michigan.
Walker, H.A., J.F. Paul, and V.J. Bierman, Jr. 1990. A
Convective-Dispersive Transport Model for Wastes
Disposed at the 106-Mile Ocean Disposal Site. In D.J.
Baumgartner and L.W. Duedall (Eds.), Oceanic Processes
in Marine Pollution, Volume 6 - Physical and Chemical
Processes: Transport and Transformation, pp. 53-61.
Krieger, Malabar, Florida.
Gentile, J.H., V.J. Bierman, Jr., J.F. Paul, H.A. Walker,
and B.C. Miller. 1989. A Hazard Assessment Research
Strategy for Ocean Disposal. In - M.A. Champ and P.K.
Park (Eds.), Oceanic Processes in Marine Pollution,
Volume 3 Marine Waste Management: Science and
Policy, pp. 200-212. Krieger, Malabar, Florida.
Paul, J.F., V.J. Bierman, Jr., H.A. Walker, and J.H.
Gentile. 1989. Application of a Hazard Assessment
Research Strategy for Waste Disposal at the 106-Mile
Ocean Disposal Site. In - D.W. Hood, A. Schoener, and
P.K. Park (Eds.), Oceanic Processes in Marine Pollution,
Volume 4 Scientific Monitoring Strategies for Ocean
Waste Disposal, pp. 149-160. Krieger, Malabar, Florida.
Reed, M. and V.J. Bierman, Jr. 1989. A Protocol for
Designation of Ocean Disposal Sites. In - M.A. Champ
and P.K. Park (Eds.), Oceanic Processes in Marine
Pollution, Volume 3 Marine Waste Management:
Science and Policy, pp. 155-166. Krieger, Malabar,
Florida.
Paul, J.F., V.J. Bierman, Jr., W.R. Davis, G.L. Hoffman,
W.R. Munns, C.E. Pesch, P.P. Rogerson, and S.C.
Schimmel. 1988. The Application of a Hazard
Assessment Research Strategy to the Ocean Disposal of a
Dredged Material: Exposure Assessment Component. In-
D.A. Wolfe and T.P. O'Connor (Eds.), Oceanic Processes
in Marine Pollution, Volume 5 - Urban Wastes in Coastal
Marine Environments, pp. 123-135. Krieger, Malabar,
Florida.
Bierman, V.J., Jr., J.H. Gentile, J.F. Paul, D.C. Miller, and
W.A. Brungs. 1986. Research Strategy for Ocean
Disposal: Conceptual Framework and Case Study. In
H.L. Bergman, R.A. Kimerle, and A.W. Maki (Eds.),
Environmental Hazard Assessment of Effluents, pp. 313-
329. Pergamon Press, New York, New York.
Bierman, V.J., Jr. 1976. Mathematical Model of the
Selective Enhancement of Blue-Green Algae by Nutrient
Enrichment. In R.P. Canale (Ed.), Modeling
Biochemical Processes in Aquatic Ecosystems, pp. 1-31.
Ann Arbor Science Publishers, Ann Arbor, Michigan.
DePinto, J.V., V.J. Bierman, Jr., and F.H. Verhoff. 1976.
Seasonal Phytoplankton Succession as a Function of
Phosphorus and Nitrogen Levels. In - R.P. Canale (Ed.),
Modeling Biochemical Processes in Aquatic Ecosystems,
pp. 141-169. Ann Arbor Science Press, Ann Arbor,
Michigan.
Published Reports
Preston, S.D., V.J. Bierman, Jr., and S.E. Silliman. 1989.
Evaluation of Methods for the Estimation of Tributary
Mass Loading Rates. U.S. Geological Survey, Water
Resources Research Center, Purdue University, West
Lafayette, Indiana. Technical Report No. 187, 50 pp.
Bierman, V.J., Jr., S.D. Preston, and S.E. Silliman. 1988.
Development of Estimation Methods for Tributary
Loading Rates of Toxic Chemicals. U.S. Geological
Survey, Water Resources Research Center, Purdue
University, West Lafayette, Indiana. Technical Report No.
183,58pp.
173
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Bierman, V.J., Jr. and L.M. Mcllroy. 1986. User Manual
for Two-Dimensional Multi-Class Phytoplankton Model
With Internal Nutrient Pool Kinetics. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. EPA-600/3-86-061, 149pp.
Prager, J.C., V.J. Bierman, Jr., D.C. Miller, and J.H.
Gentile. 1984. Sampling the Oceans for Pollution: EPA
Research Strategy for Marine Waste Disposal. Dangerous
Properties of Industrial Materials Report, 4(5):2-8.
Paul, J.F., H.A. Walker, and V.J. Bierman, Jr. 1983.
Probabilistic Approach for the Determination of the
Potential Area of Influence for Waste Disposal at the 106-
Mile Ocean Disposal Site. In J.B. Pearce and D.C.
Miller (Eds.), 106-Mile Waste Disposal Site
Characterization Update Report. National Marine
Fisheries Services, Northeast Fisheries Center, Woods
Hole, Massachusetts. NOAA Technical Memorandum
NMFS-F/NEC-26.
Bierman, V.J., Jr., D.M. Dolan, E.F. Stoermer, J.E.
Gannon, and V.E. Smith. 1980. The Development and
Calibration of a Spatially Simplified, Multi-Class
Phytoplankton Model for Saginaw Bay, Lake Huron.
Great Lakes Basin Commission, Ann Arbor, Michigan.
Great Lakes Environmental Planning Study Contribution
No. 33, 126 pp.
Bierman, V.J., Jr. and D.M. Dolan. 1980. Responses of
Saginaw Bay, Lake Huron, To Reductions in Phosphorus
Loadings From the Saginaw River. U.S. Environmental
Protection Agency, Office of Research and Development,
ERL-Duluth, Large Lakes Research Station, Grosse He,
Michigan. EPA-600/3-80-099, 79 pp.
Proceedings
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1994. Mass Balance
Modeling of the Impacts of Nutrient Load Reductions in
the Mississippi River on Water Quality in the Northern
Gulf of Mexico. In Proceedings of the Surface Water
and Ecology Symposium, pp. 413-424. Water
Environment Federation, 67th Annual Conference and
Exposition, Chicago, Illinois.
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1994. Primary
Production and Dissolved Oxygen in the Mississippi River
Plume/Inner Gulf Shelf Region: Components Analysis and
Sensitivity to Changes in Physical Transport. In
Proceedings of Synthesis Workshop, Nutrient Enhanced
Coastal Ocean Productivity, Baton Rouge, Louisiana.
April 26-27, 1994.
Dilks, D.W., V.J. Bierman, Jr., and J.S. Helfand. 1994.
Sediment Quality Modeling in Response to Proposed
Sediment Quality Criteria. In - Proceedings of the Surface
Water and Ecology Symposium, pp. 707-713. Water
Environment Federation, 67th Annual Conference and
Exposition, Chicago, Illinois.
Raghunathan, R.K., J.V. DePinto, V.J. Bierman, Jr., and
P.W. Rodgers. 1994. Modeling of PCBs in Green Bay,
Lake Michigan: Sources, Mass Fluxes and Potential
Management Scenarios. In Proceedings of the Surface
Water and Ecology Symposium, pp. 103-112. Water
Environment Federation, 67th Annual Conference and
Exposition, Chicago, Illinois.
Rodgers, P.W., T.M. Heidtke, K.M. Feist, V.J. Bierman,
Jr., D.W. Dilks, and P.L. Freedman. 1994. Great Lakes
Environmental Assessment. In Proceedings of the
Surface Water and Ecology Symposium, pp. 293-304.
Water Environment Federation, 67th Annual Conference
and Exposition, Chicago, Illinois.
Mackay, D. and V.J. Bierman, Jr. 1993. Model
Paradigms: A Discussion of Simple and Complex Models.
In - Proceedings of a Conference on Reducing Uncertainty
in Mass Balance Models of Toxics in the Great Lakes -
Lake Ontario Case Study, pp. 142-165. Donald W. Rennie
Memorial Monograph Series, Great Lakes Monograph
Number 4, State University of New York, Buffalo, New
York.
Bierman, V.J., Jr. 1992. System Integration and Data
Management. In - Proceedings of the Great Lakes-Coastal
Ocean Program Workshop, Ypsilanti, Michigan.
174
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Bierman, V.J., Jr., S.C. Hinz, WJ. Wiseman, Jr., N.N.
Rabalais, and R.E. Turner. 1992. Mass Balance Modeling
of Water Quality Constituents in the Mississippi River
Plume/Inner Gulf Shelf Region. In Proceedings of the
Nutrient Enhanced Coastal Ocean Productivity (NECOP)
Synthesis Workshop, Publicaton TAMU-SG-92, 109, pp.
27-36, Texas A&M Sea Grant Program, Chauvin,
Louisiana.
Bierman, V.J., Jr., S.C. Hinz, W.J. Wiseman, Jr., N.N.
Rabalais, and R.E. Turner. 1992. Mass Balance Modeling
of Hypoxia and Associated Water Quality Parameters in
the Mississippi River Plume/Inner Gulf Shelf Region. In -
Proceedings of the Surface Water Quality and Ecology
Symposium, pp. 237-248. Water Environmental
Federation, 65th Annual Conference and Exposition, New
Orleans, Louisiana.
Bierman, V.J., Jr. 1988. Bioaccumulation of Organic
Chemicals in Great Lakes Benthic Food Chains. Jji - Y.
Hamdy and G. Johnson (Eds.), Proceedings of Workshop
on Aquatic Food Chain Modeling, pp. 82-119. Ontario
Ministry of the Environment, Toronto, Canada.
Reed, M. And V.J. Bierman, Jr. (Eds.). 1983.
Proceedings of a Workshop for the Development of a
Scientific Protocol for Ocean Dumpsite Designation. W.
Alton Jones Campus, University of Rhode Island, Rhode
Island. 122pp.
Bierman, V.J., Jr. 1980. A Comparison of Models
Developed for Phosphorus Management in the Great
Lakes. In - R.C. Loehr, C.S. Martin, and W. Rast (Eds.),
Proceedings of the llth Annual Cornell University
Conference on Phosphorus Management for the Great
Lakes, pp. 235-255. Ann Arbor Science Publishers, Ann
Arbor, Michigan.
Bierman, V.J., Jr. and D.M. Dolan. 1980. A Spatially-
Segmented Multi-Class Phytoplankton Model for Saginaw
Bay, Lake Huron. In W.R. Swain and V.R. Shannon
(Eds.), Proceedings of the Second American-Soviet
Symposium on the Use of Mathematical Models to
Optimize Water Quality Management, pp. 343-365. U.S.
Environmental Protection Agency, Office of Research and
Development, ERL-Duluth, Large Lakes Research Station,
Grosse He, Michigan. EPA-600/9-80-033.
Bierman, V.J., Jr. 1979. A Review of Phytoplankton-
Nutrient Kinetics Mechanisms in Mathematical Simulation
Models, With Special Attention to Reservoirs and
Impoundments. In Proceedings of Workshop on
Phytoplankton-Environmental Interactions in Reservoirs.
U.S. Army Corps of Engineers, Monterey, California.
Bierman, V.J., Jr., W.L. Richardson, and T.T. Davies.
1978. Mathematical Modeling Strategies Applied to
Saginaw Bay, Lake Huron. In American-Soviet
Symposium on Use of Mathematical Models to Optimize
Water Quality Management, Proceedings of a Symposium,
pp. 397-432. Kharkov and Rostov-on-Don, U.S.S.R. U.S.
Environmental Protection Agency, Office of Research and
Development, Gulf Breeze, Florida. EPA-600/9-78-024.
Bierman, V.J., Jr. and D.M. Dolan. 1976. Mathematical
Modeling of Phytoplankton Dynamics in Saginaw Bay,
Lake Huron. In Environmental Modeling and
Simulation, Proceedings of a Conference, pp. 773-779.
U.S. Environmental Protection Agency, Office of
Research and Development and Office of Planning and
Management, Cincinnati, Ohio. EPA-600/9-76-016.
Bierman, V.J., Jr. and W.L. Richardson. 1976.
Mathematical Model of Phytoplankton Growth in Saginaw
Bay, Lake Huron. In - Water Quality Criteria Research of
the U.S. Environmental Protection Agency, pp. 159-173.
U.S. Environmental Protection Agency, Office of
Research and Development, Corvallis, Oregon. EPA-
600/3-76-079.
Richardson, W.L. and V.J. Bierman, Jr. 1976. A
Mathematical Model of Pollutant Cause and Effect in
Saginaw Bay, Lake Huron. In Water Quality Criteria
Research of the U.S. Environmental Protection Agency,
pp. 138-158. U.S. Environmental Protection Agency,
Office of Research and Development, Corvallis, Oregon.
EPA-600/3-76-079.
Bierman, V.J., Jr., F.H. Verhoff, T.L. Poulson, and M.W.
Tenney. 1973. Multi-Nutrient Dynamic Models of Algal
Growth and Species Competition in Eutrophic Lakes. In -
Proceedings of a Symposium, pp. 89-109. Utah State
University, Logan, Utah.
175
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Verhoff, F.H., J.B. Carberry, V.J. Bierman, Jr., and M.W.
Tenney. 1973. Mass Transport of Metabolites, Especially
Phosphate in Cells. American Institute of Chemical
Engineers Symposium Series 129, 69:227-240.
Technical Reports
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1993. A Preliminary
Sensitivity Analysis of a Mass Balance Model for Primary
Productivity and Dissolved Oxygen in the Mississippi
River Plume/Inner Gulf Shelf Region. NECOP Progress
Report for Coastal Ocean Program Office, National
Oceanic and Atmospheric Administration, Silver Spring,
Maryland.
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.
U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 265 pp. plus
Appendices.
Bonner, J.S., C.D. Hunt, J.F. Paul, and V.J. Bierman, Jr.
1985. Prediction of Vertical Transport of Low-Level
Radioactive Wastes in Middlesex Soil at a Deep-Ocean
Disposal Site. Prepared for the U.S. Environmental
Protection Agency, Office of Radiation Programs,
Washington, D.C. 60pp.
Walker, H.A., J.A. Nocito, J.F. Paul, and V.J. Bierman, Jr.
1985. Methods for Waste Load Allocation of Municipal
Sewage Sludge at the 106-Mile Ocean Disposal Site. U.S.
Environmental Protection Agency, Environmental
Research Laboratory, Narragansett, Rhode Island.
Contribution No. 764, 113 pp.
Bishop, D.F., R. Swank, N.A. Thomas, and V.J. Bierman,
Jr. 1984. Summary of the ORD Workshop on State-of-
the-Art and Research Needs to Support NPDES Toxics
Management for Water Pollution Control. U.S.
Environmental Protection Agency, Central Regional
Laboratory, Annapolis, Maryland. 36 pp.
Prager, J.C., V.J. Bierman, Jr., J.F. Paul, and J.S. Bonner.
1984. Hazard Assessment of Low Level Radioactive
Wastes: A Proposed Approach to Ocean Permit Request
Analyses. Prepared for the U.S. Environmental Protection
Agency, Office of Radiation Programs, Washington, D.C.
76pp.
Bierman, V.J., Jr. 1978. Report on Development of a
Mathematical Model for Eutrophication in the Billings
Reservoir, Sao Paulo, Brazil. Prepared for the Pan
American Health Organization, Regional Office of the
World Health Organization, Washington, D.C. 22 pp.
Bierman, V.J., Jr. 1977. Evaluation of the Hydroscience
Lake Ontario Report to the Surveillance Subcommittee,
International Joint Commission. Prepared for the Expert
Committee on Ecosystems Aspects, International Joint
Commission, Windsor, Ontario, Canada. 20 pp.
Bierman, V.J., Jr., W.L. Richardson, and D.M. Dolan.
1975. Responses of Phytoplankton Biomass in Saginaw
Bay to Changes in Nutrient Loadings. Report to the
International Reference Group on Upper Lakes Pollution,
International Joint Commission, Windsor, Ontario,
Canada. 36 pp.
Invited Presentations
Coupled Phytoplankton-Zebra Mussel Model for Saginaw
Bay, Lake Huron. Workshop on Aquatic Ecosystem
Modeling, U.S. Army Corps of Engineers, Little Rock,
Arkansas. 1997.
A Water Quality Model for the Gulf of Mexico Program.
USEPA Gulf of Mexico Modeling Workshop, Metairie,
Louisiana. 1997.
A Mass Balance Analysis of Zebra Mussels,
Phytoplankton and Phosphorus for Saginaw Bay. Saginaw
Bay Watershed Conference, Saginaw Valley State
University, Saginaw, Michigan. 1996.
The Saginaw Bay: How Are We Doing? The State of the
Bay: A Report to the Community, Bay County Waterfront
Task Force, Bay City, Michigan. 1996.
176
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Estimated Responses of Water Quality on the Louisiana
Inner Shelf to Nutrient Load Reductions in the Mississippi
and Atchafalaya Rivers. First Gulf of Mexico Hypoxia
Management Conference, Kenner, Louisiana. 1995.
Mass Balance Modeling of Primary Productivity and
Dissolved Oxygen on the Louisiana Inner Shelf Portion of
the Northern Gulf of Mexico. The Environmental
Technology Forum, United Nations Environmental
Program: Protection of the Marine Environment from
Land-Based Activities, Washington, D.C. 1995.
Estimated Responses of Water Quality on the Louisiana
Inner Shelf to Nutrient Load Reductions in the Mississippi
and Atchafalaya Rivers. The Third Gulf of Mexico
Symposium, Corpus Christi, Texas. 1995.
Mass Balance Modeling of Primary Productivity and
Dissolved Oxygen on the Louisiana Inner Shelf Portion of
the Northern Gulf of Mexico. Fourth Scientific Meeting
of the Oceanographic Society, Newport, Rhode Island.
1995.
Mass Balance Modeling in the NECOP Program
Diagnostic Analysis of Primary Productivity and
Dissolved Oxygen in the Mississippi River Plume/Inner
Gulf Shelf Region. National Sea Grant Program Office,
National Oceanic and Atmospheric Administration, Silver
Spring, Maryland. 1993.
Mass Balance Modeling in the NECOP Program. 1993
NOAA Colloquium on Operational Environmental
Prediction, Office of Oceanic and Atmospheric Research,
National Oceanic and Atmospheric Administration, Silver
Spring, Maryland. 1993.
A Diagnostic Analysis of Primary Productivity and
Dissolved Oxygen in the Mississippi River Plume/Inner
Gulf Shelf Region Using a Mass Balance Modeling
Approach. 12th Biennial International Estuarine Research
Conference, Hilton Head Island, South Carolina. 1993.
Mass Balance Modeling of Water Quality in the
Mississippi River Plume/Inner Gulf Shelf Region. Center
for Marine Sciences, University of Southern Mississippi,
Stennis Space Center, Mississippi. 1992.
A Tier I Screening Model for NPDES Permit Limits to
Protect Sediment Quality. U.S. Environmental Protection
Agency, Environmental Research Laboratory, Duluth,
Minnesota. 1992.
Mass Balance Modeling of Primary Production and
Dissolved Oxygen Dynamics in the Mississippi River
Plume/Inner Gulf Shelf Region. National Sea Grant
Program Office, National Oceanic and Atmospheric
Administration, Silver Spring, Maryland. 1991.
Modeling Applications in the Green Bay Mass Balance
Study. Green Bay Research and Monitoring Workshop,
Neville Public Museum, Green Bay, Wisconsin. 1990.
Modeling the Fate of Organic Chemicals in Aquatic
Systems. 32nd Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Wisconsin, Madison, Wisconsin. May 30-
June 2, 1989.
Partitioning of Organic Chemicals in Sediments:
Estimation of Interstitial Concentrations Using Organism
Body Burdens. Workshop on Transport and
Transformation of Contaminants Near the Sediment-Water
Interface, U.S. Environmental Protection Agency,
Narragansett, Rhode Island. 1988.
Bioavailability and Fate of Organic Chemicals in Aquatic
Systems. U.S. Environmental Protection Agency,
Environmental Research Laboratory, Duluth, Minnesota.
1988.
A Modeling Perspective on Sources of Toxics in the Great
Lakes. 30th Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Michigan, Ann Arbor, Michigan. May 11-
14, 1987.
Bioaccumulation of Organic Chemicals in Great Lakes
Benthic Food Chains. Aquatic Food Chain Modeling
Workshop, Ontario Ministry of the Environment, Seneca
College, Ontario, Canada. 1987.
Multi-Class Phytoplankton Modeling. Workshop on
Water Quality Modeling of Chesapeake Bay, U.S. Army
Corps of Engineers, Annapolis, Maryland. 1987.
177
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Water Quality Modeling in Saginaw Bay, Lake Huron:
Results of a Long-Term Case Study. Department of Civil
and Environmental Engineering, University of Rhode
Island, Kingston, Rhode Island. 1985.
Hazard Assessment Approach to Potential Impacts of
Deep Ocean Waste Disposal. Department of Civil and
Environmental Engineering, University of Rhode Island,
Kingston, Rhode Island. 1983.
Mass Balance Modeling of DDT Dynamics in the Upper
Great Lakes. Department of Civil and Environmental
Engineering, Clarkson College, Potsdam, New York.
1982.
Mass Balance Modeling of Heavy Metals in Saginaw Bay,
Lake Huron. Department of Ocean Engineering,
University of Rhode Island, Kingston, Rhode Island.
1982.
Review of Major Developments in Modeling Chemical
Constituents. Great Lakes Environmental Chemistry
Major Developments of the Last Decade, Special
Symposium of the Central and Great Lakes American
Chemical Society Regions, University of Dayton, Dayton,
Ohio. 1981.
Controlling Phosphorus Inputs to the Great Lakes: What
is Enough? Great Lakes Fishery Laboratory, Ann Arbor,
Michigan. 1980.
Water Quality Modeling: Concepts, a Case History, and
Uses for Developing Management Strategies. Department
of Natural Resources, University of Michigan, Ann Arbor,
Michigan. 1980.
Multi-Class Phytoplankton Modeling in Aquatic Systems.
Department of Biology, University of Detroit, Detroit,
Michigan. 1980.
Modeling of Phytoplankton Dynamics with Internal
Nutrient Pool Kinetics. Department of Civil Engineering,
University of Michigan, Ann Arbor, Michigan. 1980.
Phytoplankton Simulation Modeling in Lakes, with
Applications to Management Decisions. International
Institute for Applied Systems Analysis, Laxenburg,
Austria. 1980.
Water Quality Models for the Great Lakes. Department of
Earth Sciences, Case Western Reserve University,
Cleveland, Ohio. 1979.
Seasonal Aspects of Vertical Phosphorus Dynamics in
Saginaw Bay, Lake Huron. Workshop on Shallow Lakes
and Reservoirs, International Institute for Applied Systems
Analysis, Laxenburg, Austria. 1978.
Seminar Presentation. Center for Great Lakes Research,
University of Wisconsin, Milwaukee. 1977.
Interactions Between Experimental Research and
Mathematical Modeling in the Great Lakes. Symposium
on Limnology in the Great Lakes, 40th Annual Meeting,
American Society of Limnology and Oceanography,
Michigan State University, East Lansing, Michigan. 1977.
Comments on Water Quality Modeling: Saginaw Bay,
Lake Huron, as an Example Study. Workshop on Water
Quality Modeling, International Institute for Applied
Systems Analysis, Laxenburg, Austria. 1977.
Seminar Presentation. Great Lakes Research Division,
University of Michigan, Ann Arbor, Michigan. 1976.
Lectures on Water Quality Modeling. Academy of
Sciences of the U.S.S.R., U.S.-U.S.S.R. Environmental
Agreement on Protection and Management of Water
Quality in Lakes and Estuaries. Siberian Branch, Institute
of Hydrodynamics, Novosibirsk, and the Limnological
Institute, Listvyanka, USSR. 1976.
Seminar Presentation. Department of Earth Sciences,
Case Western Reserve University, Cleveland, Ohio. 1976.
Seminar Presentation. University of Michigan Biological
Station, Pellston, Michigan. 1975.
Seminar Presentation. Department of Civil and
Environmental Engineering, Clarkson College of
Technology, Potsdam, New York. 1974.
Seminar Presentation. Department of Biology Seminar
Series, University of Notre Dame, Notre Dame, Indiana.
1973.
178
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Seminar Presentation. Center for Environmental Studies,
Argonne National Laboratory, Argonne, Illinois. 1973.
Contributed Papers
Bierman, V.J., Jr., D.W. Dilks, T.J. Feist, J.V. DePinto,
and R.G. Kreis, Jr. 1997. Mass Balance Modeling of
ZebraMussel, Blue-Green Phytoplankton and Phosphorus
Dynamics in Saginaw Bay, Lake Huron. Seventh
International ZebraMussel and Aquatic Nuisance Species
Conference, New Orleans, Louisiana.
Bierman, V.J., Jr., D.W. Dilks, T.J. Feist, and J.V.
DePinto. 1996. A Mass Balance Analysis of
Relationships Among Zebra Mussels, Blue-Green
Phytoplankton and Sediment Phosphorus Flux in Saginaw
Bay, Lake Huron. 39th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Erindale College, University of Toronto,
Mississauga, Ontario, Canada.
Chen, Z., T.D. Fontaine, VJ. Bierman, Jr., R.K.
Raghunathan, and T.A.D. Slawecki. 1996. Modeling
Phosphorus Transport in the Everglades Protection Area.
National Meeting of American Chemical Society, Orlando,
Florida.
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1994. Mass Balance
Modeling of the Impacts of Nutrient Load Reductions in
the Mississippi River on Water Quality in the Northern
Gulf of Mexico. WEFTEC '94, 67th Annual Conference
of the Water Environment Federation, Chicago, Illinois.
Bierman, V.J., Jr., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais, and R.E. Turner. 1994. A Diagnostic
Modeling Analysis of the Fate of Primary Production and
Dissolved Oxygen in the Mississippi River Plume/Inner
Gulf Shelf Region. American Society of Limnology and
Oceanography, Miami, Florida, and 37th Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Windsor, Windsor, Ontario,
Canada.
Bierman, V.J., Jr., V.H. Smith, H.W. Paerl, and M.P.
Sullivan. 1994. Risk of Nitrogen-Fixing Blue-Green
Algal Proliferation in the Freshwater Potomac Estuary.
American Society of Limnology and Oceanography,
Miami, Florida, and 37th Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Windsor, Windsor, Ontario,
Canada.
Dilks, D.W., V.J. Bierman, Jr., T.J. Feist, and D.E.
Mericas. 1994. Use of Models in Assessing Exotic
Species: A Zebra Mussel Example. 14th Annual
International Symposium, North American Lake
Management Society, Orlando, Florida.
Dilks, D.W., J.S. Helfand, and V.J. Bierman, Jr. 1994.
Sediment Quality Modeling in Response to Proposed
Sediment Quality Criteria. WEFTEC '94, 67th Annual
Conference of the Water Environment Federation,
Chicago, Illinois
Raghunathan, R.K., V.J. Bierman, Jr., P.W. Rodgers, and
J.V. DePinto. 1994. Modeling of PCBs in Green Bay,
Lake Michigan: Sources, Mass Fluxes and Potential
Management Scenarios. WEFTEC '94, 67th Annual
Conference of the Water Environment Federation,
Chicago, Illinois.
Rodgers, P.W., K.M. Feist, V.J. Bierman, Jr., D.W. Dilks,
and P.L. Freedman. 1994. Great Lakes Environmental
Assessment. WEFTEC '94, 67th Annual Conference of
the Water Environment Federation, Chicago, Illinois.
Bierman, V.J., Jr., J.V. DePinto, R. Raghunathan, S.C.
Martin, P.W. Rodgers, T.C. Young, and S.C. Hinz. 1993.
Diagnostic Application of the Green Bay Toxic Chemical
Dynamics Model (GBTOX). 36th Conference on Great
Lakes Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin.
DePinto, J.V., R. Raghunathan, V.J. Bierman, Jr., P.W.
Rodgers, S.C. Hinz, and T.C. Young. 1993. Development
and Calibration of an Organic Carbon Based Sorbent
Dynamics Model (GBOCS) for the Green Bay Mass
Balance Study. 36th Conference on Great Lakes
Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin.
179
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Dilks, D.W., J.S. Helfand, V.J. Bierman, Jr., and L.
Burkhard. 1993. Field Application of a Steady-State
Mass Balance Model for Hydrophobic Organic Chemicals
in an Estuarine System. Symposium on Contaminated
Aquatic Sediments: Historical Records, Environmental
Impact and Remediation, International Association on
Water Quality, Milwaukee, Wisconsin.
Feist, T.J., V.J. Bierman, Jr. and L. Beasley. 1993. Trend
Analysis of Saginaw River Phosphorus Loads, 1981 -1990.
36th Conference on Great Lakes Research, International
Association for Great Lakes Research, St. Norbert
College, DePere, Wisconsin.
James, R.T. and V.J. Bierman, Jr. 1993. A Water Quality
Model of Lake Okeechobee. Annual Meeting of
American Society of Limnology and Oceanography, and
Society of Wetland Scientists, Edmonton, Alberta,
Canada.
Martin, S.C., P.W. Rodgers, V.J. Bierman, Jr., S.C. Hinz,
J.V. DePinto, and T.C. Young. 1993. Calibration of a
Hydraulic Transport Model for Green Bay, Lake
Michigan. 36th Conference on Great Lakes Research,
International Association for Great Lakes Research, St.
Norbert College, DePere, Wisconsin.
Martin, S.C., P.W. Rodgers, V.J. Bierman, Jr., T.A.D.
Slawecki, J.V. DePinto, R.K. Raghunathan, and T.C.
Young. 1993. Partitioning Behavior of PCBs on
Dissolved and Particulate Organic Matter in Green Bay,
Lake Michigan. 36th Conference on Great Lakes
Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin.
Raghunathan, R., J.V. DePinto, S.C. Martin, J.V. Bierman,
Jr., P.W. Rodgers, T.C. Young, and S.C. Hinz. 1993.
Development and Calibration of a Toxic Chemical
Dynamics Model (GBTOX) for the Green Bay Mass
Balance Study. 36th Conference on Great Lakes
Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin.
Young, T.C., V.J. Bierman, Jr., J.V. DePinto, and P.W.
Rodgers. 1993. Assessing the Accuracy of Fox River
Load Estimates at the DePere Dam During the Green Bay
Mass Balance Study. 36th Conference on Great Lakes
Research, International Association for Great Lakes
Research, St. Norbert College, DePere, Wisconsin.
Bierman, V.J., Jr., S.C. Hinz, W.J. Wiseman, Jr., N.N.
Rabalais, and R.E. Turner. 1992. Mass Balance Modeling
of Primary Production and Dissolved Oxygen Dynamics in
the Mississippi River Plume/Inner Gulf Shelf Region.
Ocean Sciences Meeting, American Geophysical Union,
New Orleans, Louisiana.
Bierman, V.J., Jr., S.C. Hinz, W.J. Wiseman, Jr., N.A.
Rabalais, and R.E. Turner. 1992. Mass Balance Modeling
of Hypoxia and Associated Water Quality Parameters in
the Mississippi River Plume/Inner Gulf Shelf Region.
65th Annual Conference and Exposition, Water
Environment Federation, New Orleans, Louisiana.
Wiseman, W.J. Jr., N.N. Rabalais, R.E. Turner, and V.J.
Bierman, Jr. 1992. Mississippi River Effluent and
Hypoxia in the Louisiana. Poster presented at Estuarine
and Coastal Sciences Association/Estuarine Research
Federation Meeting, Plymouth, England.
DePinto, J.V., R.K. Raghunathan, V.J. Bierman, Jr., P.W.
Rodgers, S.C. Hinz, and T.C. Young. 1991. Development
and Calibration of an Organic Carbon-Based Sorbent
Model for Toxic Chemicals in Green Bay. 34th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of New
York at Buffalo, Buffalo, New York.
Weinle, M.E., V.J. Bierman, Jr., S.C. Hinz, and T.C.
Young. 1990. Mass Balance Modeling of Organic Carbon
Dynamics in Green Bay, Lake Michigan. 33rd Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Windsor, Windsor,
Ontario. Canada.
Bierman, V.J., Jr. 1989. Equilibrium Partitioning Theory
and Body Burdens of Organic Chemicals in Benthic
Invertebrates. 32nd Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Wisconsin, Madison, Wisconsin.
DePinto, J.V., R. Raghunathan, T.C. Young, V.J. Bierman,
Jr., and P.W. Rodgers. 1989. Sensitivity of PCB Fate in
Green Bay to Differentiation of Particle Properties. 32nd
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin.
180
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Preston, S.D., V.J. Bierman, Jr., and S.E. Silliman. 1989.
Evaluation of Error in the Estimation of Tributary
Contaminant Loading Rates. 32nd Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Wisconsin, Madison, Wisconsin.
Preston, S.D., V.J. Bierman, Jr., and S.E. Silliman. 1989.
Analysis of Error in the Estimation of Tributary Mass
Loads. Fall Meeting of the American Geophysical Union,
San Francisco, California.
Rodgers, P.W., S.C. Hinz, V.J. Bierman, Jr., J.V. DePinto,
and T.C. Young. 1989. WASP4 Transport Model
Development and Application to Green Bay, Wisconsin.
32nd Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin.
O'Connor, T.P., H.A. Walker, J.F. Paul, and V.J. Bierman,
Jr. 1985. Monitoring Sewage Sludge Dumped Over the
Deep Sea. American Geophysical Union, Baltimore,
Maryland. Abstract in EOS, 66(18):290.
Bonner, J.S., J.V. DePinto, V.J. Bierman, Jr., J.F. Paul,
and C.D. Hunt. 1984. Vertical Transport of Particles in
Aquatic Systems. Gordon Conference, Hampton, New
Hampshire.
Paul, J.F., H.A. Walker, and V.J. Bierman, Jr. 1984.
Evaluation of Methods for Using Current Meter Data to
Obtain Probabilistic Estimates for the Distribution of
Disposed Waste. Ocean Sciences Meeting, American
Geophysical Union, New Orleans, Louisiana.
Paul, J.F., H.A. Walker, and V.J. Bierman, Jr. 1984.
Probabilistic Estimates for the Dispersion of Ocean-
Disposed Wastes. 12th Annual Middle Atlantic Bight
Physical Oceanography, Newark, Delaware.
Dolan, D.M. and V.J. Bierman, Jr. 1983. The Effect of
Sediment-Water Interactions on Phosphorus
Concentrations in Saginaw Bay, 1974-1980. 26th
Conference on Great Lakes Research, International
Association for Great Lakes Research, State University of
New York, Oswego, New York.
Paul, J.F. and V.J. Bierman, Jr. 1983. A Case Study for
the Application of Exposure Assessment Methodologies
for Dredged Material Disposal in the Marine Environment.
Poster presented at Spring Meeting of the American
Geophysical Union, Baltimore, Maryland.
Paul, J.F., V.J. Bierman, Jr., and H.A. Walker. 1983. A
Case Study for the Application of Exposure Assessment
Methodologies for Dredged Material Disposal in Central
Long Island Sound. Poster Presentation - Fourth Annual
Meeting of the Society of Environmental Toxicology and
Chemistry, Arlington, Virginia.
Dolan, D.M., V.J. Bierman, Jr., and R. Kasprzyk. 1982.
Changes in the Water Supply Odor as Predicted by
Phytoplankton Abundance in Saginaw Bay, Lake Huron.
25th Conference on Great Lakes Research, International
Association for Great Lakes Research, Sea Lamprey
Control Centre, Sault Ste. Marie, Ontario, Canada.
Dolan, D.M., V.J. Bierman, Jr., and J.J. Fishwick. 1981.
Mass Balance Modeling of Heavy Metals in Saginaw Bay,
Lake Huron. 24th Conference on Great Lakes Research,
International Association for Great Lakes Research, Ohio
State University, Columbus, Ohio.
Dolan, D.M., V.J. Bierman, Jr., P. Gonzales, and B.
Paddy. 1980. Analysis of the Effect of Total Phosphorus
Load Reductions on Phosphorus Concentrations in
Saginaw Bay. 23rd Conference on Great Lakes Research,
International Association for Great Lakes Research,
Queen's University, Kingston, Ontario, Canada.
Kasprzyk, R., D.M. Dolan, and V.J. Bierman, Jr. 1980.
The Use of Non-Linear Least Squares in Evaluating
Phytoplankton Phosphorus Uptake Models. Annual
Meeting of the Biometric Society, American Statistical
Association, Houston, Texas.
Bierman, V.J., Jr. and D.M. Dolan. 1979. A Spatially-
Segmented, Multi-Class Phytoplankton Model for Saginaw
Bay, Lake Huron. 22nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Rochester, Rochester, New York.
181
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Bierman, V.J., Jr. and W.R. Swain. 1978. DDT Loss
Rates in Low Lipid Fishes of the Great Lakes. 21st
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Windsor, Windsor, Ontario, Canada.
Dolan, D.M., V.J. Bierman, Jr. and R.D. Geist. 1978.
Dynamic Mass Balance for Cadmium and Zinc in Saginaw
Bay, Lake Huron. 21st Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Windsor, Windsor, Ontario,
Canada.
Bierman, V.J., Jr. and D.M. Dolan. 1977. Mathematical
Modeling of Phytoplankton Growth Kinetics as a Function
of Multiple Nutrient Limitation. 20th Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Michigan, Ann Arbor,
Michigan.
Bierman, V.J., Jr., W.L. Richardson, and D.M. Dolan.
1976. A Multi-Class Model of Phytoplankton Production
in Saginaw Bay, Lake Huron. 19th Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Guelph, Guelph, Ontario, Canada.
Richardson, W.L. and V.J. Bierman, Jr. 1975. A Time-
Variable Model of Chloride Distribution in Saginaw Bay,
Lake Huron. 18th Conference on Great Lakes Research,
International Association for Great Lakes Research, State
University of New York, Albany, New York. May
20-23, 1975.
Bierman, V.J., Jr. 1974. Dynamic Mathematical Model of
Algal Growth and Species Competition for Phosphorus,
Nitrogen and Silica. 17th Conference on Great Lakes
Research, International Association for Great Lakes
Research, McMaster University, Hamilton, Ontario,
Canada. August 12-14, 1974.
Client Reports
Everglades Water Quality Model Calibration Report.
Prepared for South Florida Water Management District,
West Palm Beach, Florida. 1997.
Application of a Coupled Primary Productivity-Exotic
Species Model for Saginaw Bay, Lake Huron. Prepared
for the U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 1997.
Estimated Responses of Water Quality on the Louisiana
Inner Shelf to Nutrient Load Reductions in the Mississippi
and Atchafalaya Rivers. Prepared for the U.S.
Environmental Protection Agency, Gulf of Mexico
Program Office, Stennis Space Center, Mississippi. 1995.
A Preliminary Ecosystem Modeling Study of Zebra
Mussels (Dreissena polymorpha) in Saginaw Bay, Lake
Huron. Prepared for the U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse lie,
Michigan. 1995.
Preliminary Assessment of Nitrogen Impacts on the Lake
Okeechobee Ecosystem. Prepared for the South Florida
Water Management District, West Palm Beach, Florida.
1993.
Evaluation of Nitrogen Removal Eutrophication Risk for
the Freshwater Potomac Estuary. Prepared for
Metropolitan Washington Council of Governments,
Washington, D.C. 1993.
Phase n Screening Model Application to Dioxin (2,3,7,8-
TCDD) in the Columbia River. Prepared for the U.S.
Environmental Protection Agency, Region X, Seattle,
Washington. 1992.
Screening Level Analysis for Estimation of Sediment
Quality Criteria Impacts. Prepared for the Office of
Wastewater Enforcement and Compliance, U.S.
Environmental Protection Agency, Washington, D.C.
1992.
182
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Timothy J. Feist
Environmental Scientist
Limno-Tech, Incorporated
501 Avis Drive
Ann Arbor, Michigan 48108
(734) 973-8300
Fax: (734) 973-1069
Role in the Lake Michigan Mass Balance
Project
Assessment of ecosystem model constructs, selection of
the most appropriate model, and conceptual refinement
and development of additional trophic levels in the model.
Education
M.S., Fisheries and Wildlife (Limnology), Michigan State
University, East Lansing, Michigan, 1988
B.S., Fisheries and Wildlife, Michigan State University,
East Lansing, Michigan, 1985
Specialized Training and Coursework
Mathematical Modeling of Water Quality: Dissolved
Oxygen-Eutrophication, Manhattan College, Riverdale,
New York, June 1992.
Professional Experience
Environmental Scientist, LTI-Limno-Tech, Inc., Ann
Arbor, Michigan. 1990-Present.
Field Research Technician, Michigan State University,
East Lansing, Michigan. 1988-1990.
Student Assistant, Michigan Department of Natural
Resources, Lansing, Michigan, 1988.
Publications
Journal Articles
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.
Feist, T.J. and N.R. Kevern. 1989. Nutrient Study of a
New Reservoir, Sessions Lake, Michigan. Mich.
Academ., 21(4):339-358.
Presentations and Symposiums
Bierman, V.J., Jr., D.W. Dilks, T.J. Feist, J.V. DePinto,
and R.G. Kreis, Jr. 1997. Mass Balance Modeling of
Zebra Mussel, Blue-Green Phytoplankton and Phosphorus
Dynamics in Saginaw Bay, Lake Huron. Seventh
International Zebra Mussel and Aquatic Nuisance Species
Conference, New Orleans, Louisiana. January 28-31,
1997.
Bierman, V.J., Jr., D.W. Dilks, T.J. Feist, and J.V.
DePinto. 1996. A Mass Balance Analysis of
Relationships Among Zebra Mussels, Blue-Green
Phytoplankton and Sediment Phosphorus Flux in Saginaw
Bay, Lake Huron. 39th Conference on Great Lakes
Research, International Association for Great Lakes
Research, Erindale College, University of Toronto,
Mississauga, Ontario, Canada. May 26-30, 1996
Feist, T.J., C.E. Mericas, C.T. Cieciek, P. Adriaens, and A.
Barkovskii. November 1996. Evaluation of Aeration and
Bioaugmentation for Decreasing Sediment Thickness in
Austin Lake, Michigan. Sixteenth Annual International
Symposium on the North American Lake Management
Society.
Encouraging Science-Based Lake Management: What is
Needed in a Comprehensive Lake Management Plan.
Panel Presentation Lake Management in Michigan: A
Call to Action Workshop, Michigan Chapter North
American Lake Management Society. March 1996.
Dilks, D.W., V.J. Bierman, Jr., T.J. Feist, and D.E.
Maricas. November 1994. Use of Models in Assessing
Exotic Species: A Zebra Mussel Example. Fourteenth
International Conference of the North American Lake
Management Society, Orlando, Florida.
Feist, T.J., T.A.D. Slawecki, and D.e. Mericas. November
1994. Evaluating Watershed Impacts on Waterbodies
Using GIS. Fourteenth International Conference of the
North American Lake Management Society, Orlando,
Florida.
183
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DePinto, V.J., P.W. Rodgers, and T.J. Feist. June 1994.
When Do Sediment-Water Interactions Control the Water
Column Response of Large Lakes to Toxic Chemical Load
Reductions? 37th Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Windsor, Windsor, Ontario, Canada. June
5-9, 1994.
Feist, T.J., V.J. Bierman, Jr., and L. Beasley. June 1993.
Trend Analysis of Saginaw River Phosphorus Loads,
1981-1990. 36th Conference on Great Lakes Research,
International Association for Great Lakes Research, St.
Norbert College, DePere, Wisconsin. June 4-10, 1993.
Rodgers, P.W. and T.J. Feist. February 1993. Watershed
Management of Nutrients. Innovations in Water and
Wastewater Seminar in the 90's, University of Michigan,
Ann Arbor, Michigan.
Client Reports
Evaluation of Aeration and Bioaugmentation for
Decreasing Sediment Thickness in Austin Lake. Project
report for the City of Portage, Michigan, January 1996.
First and Second Sister Lakes Diagnostic/Feasibility
Study. Project report for the City of Ann Arbor
Department of Parks and Recreation, Ann Arbor,
Michigan, September 1995.
Interim Data Review Report. Project report for the South
Florida Water Management District, West Palm Beach,
Florida, March 1995.
A Preliminary Ecosystem Modeling Study of Zebra
Mussels (Dreissena polymorpha) in Saginaw Bay, Lake
Huron. Report to the U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse He,
Michigan. February 1995.
Higgins Lake Septic System and Lawn Fertilizer
Management Zones. Project report for the Higgins Lake
Foundation, Higgins Lake, Michigan, February 1994.
Higgins Lake Clean Lakes Study Pollution Control Plan.
Project report for Gerrish and Lyon Townships,
Roscommon County, Michigan, December 1992.
Impacts of the Greenaway Drain on Wolverine Lake,
Phase JJ Report. Project report for the Village of
Wolverine Lake, Michigan, September 1992.
Higgins Lake Diagnostic and Feasibility Study. Project
report for Gerrish and Lyon Townships, Roscommon
County, Michigan, May 1992.
Septic System Phosphorus Loadings to Higgins Lake,
Michigan. Proj ect report for the Higgins Lake Foundation,
Higgins Lake, Michigan, February 1992.
Dissolved Oxygen Monitoring of Higgins Lake - 1991.
Project report for the Higgins Lake Foundation, Higgins
Lake, Michigan, January 1992.
Evaluation of Potential Impacts on Juday Creek from
Proposed Detention Basins. Project report for the St.
Joseph County Drainage Board, South Bend, Indiana,
October 1991.
Impacts of Greenaway Drain on Wolverine Lake, Phase I
Report. Project report for the Village of Wolverine Lake,
Michigan, June 1990.
Effects of Artificial Destratification on Selected Water
Quality Parameters and Biota of Mud Lake, Oakland
County, Michigan. Batterson, T.R., R.S. Beebe and T.J.
Feist. Project report for Kobe Steel Ltd., Tokyo, Japan,
March 1989.
184
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Scott C. Hinz
Senior Environmental Engineer
Lirnno-Tech, Incorporated
501 Avis Drive
Ann Arbor, Michigan 48108
(734) 973-8300
Fax: (734) 973-1069
Role in the Lake Michigan Mass Balance
Project
Responsible for reviewing eutrophication models,
coordination of ecosystem model construct revisions,
expert evaluation of data input requirements, source code
for the ecosystem model, and model documentation.
Principal Expertise
Water Quality Modeling
Mathematical Model Development
Hydraulics/Hydrology
Computer Programming
Estuarine Assessment
Urban Nonpoint Source Pollution
Mixing Zone/NPDES Issues
Stormwater/Sewer Modeling
Education
M.S.E., Environmental Engineering, The University of
Michigan, Ann Arbor, Michigan, 1985.
B.S.E., Environmental Sciences Engineering, The
University of Michigan, Ann Arbor, Michigan, 1982.
Specialized Training and Coursework
Total Quality Improvement Training. Delta Systems and
LTI, Lirnno-Tech, Inc., Ann Arbor, Michigan, April-May
1993.
Total Quality Awareness Seminar. Ann Arbor Consulting
Association, Inc. and LTI, Lirnno-Tech, Inc., Ann Arbor,
Michigan, September 1992.
Technical Writing Seminar. The University of Michigan,
College of Engineering and LTI, Limno-Tech, Inc., Ann
Arbor, Michigan, February 1992.
Project Management Course. LTI, Limno-Tech, Inc., Ann
Arbor, Michigan, May-July 1990.
Experience Summary
Mr. Hinz has 16 years of experience in developing and
applying water quality, hydrologic, and hydrodynamic
models to systems throughout the United States. His
particular expertise and training is in the areas of water
quality and hydrologic assessments of natural systems.
Mr. Hinz is conversant in a wide range of programming
languages and is familiar with main-frame and
microcomputer systems. As a Senior Environmental
Engineer with Limno-Tech, Mr. Hinz has developed and
applied water quality models to evaluate toxic organic
chemicals, metals, eutrophication, and dissolved oxygen
problems. His major role at Limno-Tech is providing
advice, support, and technical review of complex water
quality modeling applications.
Mr. Hinz's work has included extensive enhancements to
USEPA's WASP4 and WASP5 toxics and eutrophication
models to simulate water quality on a wide range of water
bodies, including lakes, estuaries, and near-coastal zones.
He has also developed and applied finite element
hydrodynamic and water quality models for evaluating
toxic mixing zones in riverine and estuarine situations, as
well as standard USEPA-supported dilution models, such
as the CORMIX expert system software. In the area of
wet weather assessments, Mr. Hinz has developed
software for evaluating historical precipitation data and
applied runoff and sewer models (USEPA S WMM and the
Limno-Tech's own SOM models) to evaluate best.
management practices for controlling wet weather
discharges.
Professional Experience
Senior Environmental Engineer, Limno-Tech, Inc., Ann
Arbor, Michigan, 1988-Present.
Project Engineer, Limno-Tech, Inc., Ann Arbor, Michigan.
1982-1987.
Professional Affiliations
Water Environment Federation, 1993-Present
Michigan Water Environment Association, 1993-Present
185
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New England Water Environment Association,
1996-Present
International Association for Great Lakes Research,
1990-Present
Publications
Journal Articles
Martin, S.C., S.C. Hinz, P.W. Rodgers, V.J. Bierman, Jr.,
J. DePinto, and T.C. Young. 1995. Calibration of a
Hydraulic Model for Green Bay, Lake Michigan. J. Great
Lakes Res., 21(4):599-609.
DePinto, J.V., R.K. Raghunathan, V.J. Bierman, Jr., P.W.
Rodgers, S.C. Hinz, and T.C. Young. 1995. Development
and Calibration of an Organic Carbon Based Sorbent
Dynamics Model (GBOCS) for the Green Bay Mass
Balance Study. Submitted to the Journal of Great Lakes
Research.
Bierman, V.J., Jr., S.C. Hinz, D.W. Zhu, W.J. Wiseman,
Jr., N.N. Rabalais, and R.E. Turner. 1994. A Preliminary
Mass Balance Model of Primary Productivity and
Dissolved Oxygen in the Mississippi River Plume/Inner
Gulf Shelf Region. Estuaries, 17(4):886-899.
Presentations and Symposiums
Hinz, S.C., T.J. Fikslin, T.A.D. Slawecki, and D.W. Dilks.
1994. A Simplified Approach for Establishing Acute
Mixing Zones in Tidal. Water Environment Federation
67th Annual Conference and Exposition, Chicago, Illinois.
October 15-19, 1994.
Bierman, V.J., Jr., S.C. Hinz, W.J. Wiseman, Jr., N.N.
Rabalais, and R.E. Turner. 1992. Mass Balance Modeling
of Primary Production and Dissolved Oxygen Dynamics in
the Mississippi River Plume/Inner Gulf Shelf Region.
American Geophysical Union (AGU) Ocean Sciences
Meeting, New Orleans, Louisiana. January 27-31, 1992.
DePinto, J.V., R.K. Raghunathan, T. Young, V.J. Bierman,
Jr., and S.C. Hinz. 1991. Development and Calibration of
an Organic Carbon-based Sorbent Model for Toxic
Chemicals in Green Bay. 34th Conference on Great Lakes
Research, International Association of Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Weinle, M.E., V.J. Bierman, Jr., S.C. Hinz, and T.C.
Young. 1990. Mass Balance Modeling of Organic Carbon
Dynamics in Green Bay, Lake Michigan. 33rd Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Windsor, Windsor,
Ontario, Canada. May 13-17, 1990.
Hinz, S.C., P.L. Freedman, and M.P. Sullivan. 1989.
Modeling Total Residual Chlorine in the Upper Potomac
Estuary. Estuarine and Coastal Modeling Conference,
American Society of Civil Engineers, Newport, Rhode
Island. November 1989.
Rodgers, P.W., S. Hinz, V.J. Bierman, Jr., J.V. DePinto,
and T.C. Young. 1989. WASP4 Transport Development
and Application to Green Bay, Wisconsin. 32nd
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin. May 30-June 2, 1989.
Dilks, D.W. and S.C. Hinz. 1988. Dilution Modeling to
Define Toxic Impairment in 93 U.S. Estuaries. 61st
Annual Conference of the Water Pollution Control
Federation, Dallas, Texas. October 1988.
Published Proceedings
Hinz, S.C., T.J. Fikslin, T.A.D. Slawecki and D.W. Dilks.
1994. A Simplified Approach for Establishing Acute
Mixing Zones in Tidal Waters. In - Surface Water Quality
and Ecology, Volume 4: Proceedings of the Water
Environment Federation 67th Annual Conference and
Exposition, Chicago, Illinois. October 15-19, 1994.
Bierman, Jr., V.J., S.C. Hinz, D. Zhu, W.J. Wiseman, Jr.,
N.N. Rabalais and R.E. Turner. 1994. Mass Balance
Modeling of the Impacts of Nutrient Load Reductions in
the Mississippi River on Water Quality in the Northern
Gulf of Mexico. In - Surface Water Quality and Ecology,
Volume 4: Proceedings of the Water Environment
Federation 67th Annual Conference and Exposition,
Chicago, Illinois. October 15-19, 1994.
186
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Bierman, V.J., Jr., S.C. Hinz, W.J. Wiseman, Jr., N.N.
Rabalais and R.E. Turner. 1992. Mass Balance Modeling
of Water Quality Constituents in the Mississippi River
Plume/Inner Gulf Shelf Region. In - Nutrient Enhanced
Coastal Ocean Productivity: Proceedings of the NECOP
Synthesis Workshop, National Oceanic and Atmospheric
Administration, Chauvin, Louisiana. October 2-4, 1991.
Hinz, S.C., N. Katopodes, P. Freedman, M. Sullivan, and
S. Freudberg. 1990. Modeling Residual Chlorine in The
Potomac Estuary. In Estuarine and Coastal Modeling:
Proceedings of the 1989 American Society of Civil
Engineers, Estuarine and Coastal Circulation and Pollution
Transport Model Data Comparison Specialty Conference,
Newport, Rhode Island.
Client Reports
Phase 2 Preliminary Model Calibration Report Hudson
River PCB Reassessment RI/FS. September 1996. Final
report to U.S. Environmental Protection Agency, Region
H, ARCS. Prepared for TAMS Consultants, Inc., New
York, New York.
Preliminary Water Quality Assessment (of CSO-Related
Water Quality Effects in the Ohio River, Licking River,
and Banklick Creek). October 1996. Sanitation District
No. 1 of Campbell and Kenton Counties, Kentucky.
Mixing Zone Evaluation of Discharges to the Ohio River,
for Weirton Steel Corporation. 1995. Weirton, West
Virginia.
Phase 2 Preliminary Model Calibration Report - Hudson
River PCB Reassessment RI/FS. June 1995. Draft report
to U.S. Environmental Protection Agency, Region U,
ARCS. Prepared for TAMS Consultants, Inc., New York,
New York.
Tidal CORMIX Development and Application to Twenty
Candidate Discharge Sites in the Delaware Estuary. May
1995. Draft report for the Delaware River Basin
Commission, West Trenton, New Jersey.
Development and Validation of an Integrated Exposure
Model for Toxic Chemicals in Green Bay, Lake Michigan.
August 1992. Final report to U.S. Environmental
Protection Agency, Region V and the Great Lakes
National Program Office, Chicago, Illinois.
AARA Thermal Discharge Simulations to Meet NYDEC
Requirements. May 1992. Report to Foster Wheeler
Enviresponse, Inc.
Modeling Mixing Zone Impacts of Intermittent Blue Plains
Wastewater Chlorine Discharges. April 1992. Technical
report for Greeley & Hansen Engineering, Camp Springs,
Maryland and the Metropolitan Washington Council of
Governments, Washington, D.C.
Predicted Dilution of the South Coastal Outfall Plume: An
Application of the CORMIX2 Mixing Zone Model.
February 1992. Technical report for CABE Associates,
Inc., Dover, Delaware.
Model-Based Estimates of Washington, D.C. Combined
Sewer Overflows to the Anacostia River. September
1990. Technical report for the Metropolitan Washington
Council of Governments, Washington, D.C.
Analysis of Mixing Characteristics of Preliminary ARRA
Diffuser Design. July 1990. Technical report for O'Brien
and Gere Engineers, Syracuse, New York.
Development and Validation of an Integrated Exposure
Model for Toxic Chemicals in Green Bay, Lake Michigan.
March 1990. Two-year progress report to U.S.
Environmental Protection Agency, Region V and Great
Lakes National Program Office, Chicago, Illinois.
Development of a Water Quality Model for the Amelia
River. September 1988. Technical report for the U.S.
Environmental Protection, Region IV and Office of Water
Enforcement and Permits, Washington, D.C.
Estuarine Dilution Analyses to Estimate Toxic Substance
Impairment for 304(1) Identification. March 1988.
Technical report for the U.S. Environmental Protection
Agency, Office of Marine and Estuarine Protection and
Office of Water Regulation and Standards, Washington,
D.C.
Summary Report: Potomac River Residual Chlorine
Study. January 1988. Technical report for the D.C.
Department of Consumer and Regulatory Affairs and the
Metropolitan Washington Council of Governments,
Washington, D.C.
187
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Evaluation of Critical Conditions for Assessing the
Benefits of Increased Nitrification Treatment in Upper
Potomac Estuary. December 1987. Technical report for
the Metropolitan Washington Council of Government,
Washington, B.C.
Dissolved Oxygen Predictions for Alternative Wastewater
Treatment Scenarios in the Upper Potomac Estuary.
September 1987. Technical report for the Metropolitan
Washington Council of Government, Washington, D.C.
Validation of DEM to 1985 and 1986 Data. August 11,
1987. Technical report for the Metropolitan Washington
Council of Governments, Washington, D.C.
Review of the Waste Load Allocations for the Lower
Potomac and Little Hunting Creek Wastewater Treatment
Plants. July 1986. Technical report for Fairfax County,
Virginia.
Detroit River Plume Monitoring and Modeling Program.
March 1986. Technical report by Environmental Science
and Engineering, Inc., Gainesville, Florida; Limno-Tech,
Inc., Ann Arbor, Michigan; and Rama Rao and Alfred,
Inc., Detroit, Michigan (ReportNo. ESE 84-536-0542), for
the Detroit Water and Sewerage Department.
Water Quality Modeling and Analysis of Gunston Cove.
August 1985. Technical report for Fairfax County,
Virginia.
Projected Impacts of Lower Potomac Pollution Control
Plant on Gunston Cove Water Quality. December 1984.
Technical report Fairfax County, Virginia.
Methodology Recommendation for the Assessment of
Combined Sewer Overflow Impacts on Nearshore Lake
Water Quality in the Vicinity of Indiana Harbor. October
1984. Technical report for ESEI and U.S. Environmental
Protection Agency, Great Lakes National Program Office,
Chicago, Illinois.
A Waste Load Allocation for the Natchitoches and
Natchez Municipal Wastewater Treatment Facilities. 1984.
Technical report for the Louisiana Department of Natural
Resources, Baton Rouge, Louisiana.
Workshops/Short Courses
Green Bay Mass Balance Study Workshop. U.S.
Environmental Protection Agency, Great Lakes National
Program Office, Chicago, Illinois. Held in Green Bay,
Wisconsin, May 24-25, 1993.
Balancing The Bay Workshop: Implications of the Green
Bay/Fox River Mass Balance Study. U.S. Environmental
Protection Agency, Great Lakes National Program Office,
Chicago, Illinois. Held in Chicago, Illinois, May 24-25,
1993.
Estuarine Wasteload Allocation Workshop. U.S.
Environmental Protection Agency, Office of Research and
Development, Athens, Georgia and LTI, Limno-Tech, Inc.,
Ann Arbor, Michigan. Held in Danvers, Massachusetts,
November 7-9, 1989.
188
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Orren Russell Bullock, Jr.
Publications
Meteorologist
National Oceanic and Atmospheric Administration
ERL/ARL, Atmospheric Modeling Division
EPA Mail Drop 80
Research Triangle Park, North Carolina 27711
(919) 541-1349
Role in the Lake Michigan Mass Balance
Project
Primarily an advisory role on atmospheric modeling
aspects of the project. Help determine approaches and
solutions and review results.
Education and Training
B.S., Meteorology, North Carolina State University, 1980
M.S., Meteorology, North Carolina State University, 1984
Professional Experience
Meteorologist, NOAA, 1989-Present
Computer Programmer/Analyst, NOAA, 1987-1989
Senior Scientific Specialist, Program Resources, Inc. and
Computer Sciences Corporation, 1986-1987
Technical Specialist, Computer Sciences Corporation,
1984-1986
Senior Member of the Technical Staff, Computer Data
Systems, Inc., 1983-1984
Professional
American Meteorological Society (National and Local)
Secretary of Local Chapter, 1987-1988
Chairman of Local Chapter, 1991-1992
Phi Kappa Phi (Honorary Academic Society)
Bullock, O.K., Jr. 1997. Lagrangian Modeling of
Mercury Air Emission, Transport and Deposition: An
Analysis of Model Sensitivity to Emissions Uncertainty.
Sci. Total Environ., accepted for publication.
Bullock, O.R., Jr., W.G. Benjey, and M.H. Keating. 1997.
The Modeling of Regional-Scale Atmospheric Mercury
Transport and Deposition Using RELMAP. Environ.
Toxicol. Chem., in press.
Ching, J.K.S., E.S. Binkowski, and O.R. Bullock, Jr.
1997. Deposition of Semi-Volatile Air Toxic Pollutants to
the Great Lakes: A Regional Modeling Approach.
Environ. Toxicol. Chem., in press.
Bullock, O.R., Jr. 1994. A Computationally Efficient
Method for the Characterization of Sub-Grid-Scale
Precipitation Variability for Sulfur Wet Removal
Estimates. Atmos. Environ., 28:555-566.
Bullock, O.R., Jr., S.J. Roselle, and W.E. Heilman. 1989.
Development and Preliminary Testing of a First-
Generation Regional Aerosol Model. Internal Report.
U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.
Clark, T.L., O.R. Bullock, Jr., and S.J. Roselle. 1989.
Simulating Regional Visibility Using an Eulerian Aerosol
Model. Internal Report. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina.
Presentations
Bullock, O.R., Jr. 1996. Lagrangian Modeling of
Mercury Air Emission, Transport and Deposition with
Source-Type Discrimination. Fourth International
Conference on Mercury as a Global Pollutant, Hamburg,
Germany. August 4-8, 1996.
Bullock, O.R., Jr. 1993. Evaluation of MM4/FDDA
Simulations Using Independent Observations of Wind,
Temperature and Humidity. Third Penn State/NCAR
Mesoscale Model User's Workshop, Boulder, Colorado.
October 28, 1993.
189
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Bullock, O.R., Jr. 1993. A Workstation Concept for the Bullock, O.R., Jr. 1990. The Effects of Size-Dependent
Production of Dynamically-Constrained Meteorological Dry-Deposition Velocities in an Eulerian Regional-Scale
Characterizations for Use in Air-Quality Modeling. Ninth Paniculate Model. Eighteenth NATO/CCMS International
International Conference on Interactive Information and Technical Meeting on Air Pollution Modeling and Its
Processing Systems for Meteorology, Oceanography, and Application, Vancouver, British Columbia, Canada. May
Hydrology, Anaheim, California. January 17-22, 1993. 13-17, 1990.
Bullock, O.R., Jr. 1991. The Effect of Sub-Grid-Scale
Rainfall Analysis on Sulfate Wet Deposition Estimates in
the Regional Lagrangian Model of Air Pollution
(RELMAP). Seventh Joint Conference on Applications of
Air Pollution Meteorology with AWMA, New Orleans,
Louisiana. January 14-18, 1991.
190
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Ellen J. Cooter
(On assignment from the National Oceanic and
Atmospheric Administration, U.S. Department of
Commerce).
Meteorologist
Atmospheric Modeling Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina
(919)541-1334
Education
B.S., Meteorology, University of Oklahoma, 1976
M.S., Meteorology, University of Oklahoma, 1978
Ph.D., Meteorology, University of Oklahoma, 1985
Professional Experience
Meteorologist, NOAA Atmospheric Modeling Division,
1990-Present
Assistant State Climatologist, Oklahoma, 1981-1990
Graduate Research Assistant, University of Oklahoma,
1979-1981
North Dakota Weather Modification Board, Norman,
Oklahoma, 1978-1979
Graduate Teaching Assistant, University of Oklahoma,
1977-1978
Professional Appointments and Memberships
American Association of State Climatologists, Associate
Member, 1981-present
American Meteorological Society, Member, 1987-present
AMS Committee on Applied Climatology, 1991-present
Chair, AMS 9th Applied Climate Conference Program
Committee, 1994-1995
Chair, AMS Committee on Applied Climatology, 1995-
1997
Adjunct Assistant Professor of Agricultural Engineering,
Oklahoma State University, 1986-1990
Editorial Advisor, Climate Research, 1990-present
Sigma Xi, Member, 1991-present
National Research Council Research Advisor, 1994-
present
Adjunct Assistant Professor of Geography, North Carolina
State University, Chapel Hill, 1992-present
Publications
Dhakhwa, G.B., C.L. Campbell, E.J. Cooter, and S.K.
LeDuc. 1997. Use of Crop Models in Assessing the
Interactive Effects of Global Warming and CO2 Doubling
on Maize Production. Agricul. Forest Meteorol., in press.
Sampson, D.A., E.J. Cooter, P.M. Dougherty, and H. Lee
Allen. 1996. Comparison of the UKMO and GFDLGCM
Climate Projections in NPP Simulations for Southern
Loblolly Pine Stands. Climat. Res., 7(1): 55-69.
Cooter, E.J. and G.B. Dhakhwa. 1995. A Solar Radiation
Model for Use in Biological Applications in the South and
Southeastern USA. Agricul. Forest Meteorol., 78(1-2): 31-
51.
Cooter, E.J. and S.K. LeDuc. 1995. Recent Frost Date
Trends in the North-Eastern USA. Internal. J. Climat.,
15:65-75.
Cooter, E.J. and S.K. LeDuc. 1994. Recent Frost Date
Trends in the Northeastern United States. In - Nathaniel
Guttman (Ed.), NOAA National Environmental Watch
(CD-ROM) Prototype-1994, National Climate Data
Center, National Oceanic and Atmospheric
Administration, Asheville, North Carolina..
Cooter, E.J., M.B. Richman, and P.J. Lamb. 1994.
Documentation for the Southern Global Change Program
Climate Change Scenarios. Report to the U.S. Forest
Service, Southern Global Change Program Office,
Raleigh, North Carolina, Interagency Agreement Number
29-1163.
Cooter, E.J., B.K. Eder, S.K. LeDuc, andL. Truppi. 1993.
Climate Change Models and Forest Impact Research. J.
Forest., 91(9):38-43.
191
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Cooler, E.J., B.K. Eder, S.K. LeDuc, and L. Truppi. 1993.
General Circulation Model Output for Forest Climate
Change Research and Applications. U.S. Department of
Agriculture, Forest Service, Southeastern Forest
Experiment Station, Asheville, North Carolina. General
Technical Report SE-85, 38 pp.
Brooks, R.T., T.S. Frieswyk, D.M. Griffith, E. Cooler,
and L. Smith. 1992. New England's Forests: A Baseline
for the New England Forest Health Monitoring Program.
U.S. Department of Agriculture, Forest Service,
Northeastern Forest Experiment Station, Radnor,
Pennsylvania. Resource Bulletin NE-123, 89 pp.
Brooks, R.T., D.R. Dickson, W.G. Burkman, I. Millers, M.
Miller-Weeks, E. Cooler, and L. Smith. 1992. Forest
Heallh Monitoring in New England: 1990 Annual Report.
U.S. Departmenl of Agriculture, Foresl Service,
Norlheaslern Foresl Experimenl Slalion, Radnor,
Pennsylvania. Resource Bullelin NE-125, 59 pp.
Cooler, E.J., S.K. LeDuc, and L. Truppi. 1992. Climale
Research for Ecological Monitoring and Assessmenl: A
New England example. Climal. Res., 2:101-112.
Cooler, E., and W. Cooler. 1991. Impacls of Greenhouse
Warming on Waler Temperalure and Waler Quality in the
Southern United Slates. Climal. Res., 1(1): 1-12.
Cooler, E.J., S.K. LeDuc, L. Truppi and D.R. Block.
1991. The Role of Climale in Foresl Monitoring and
Assessmenl: A New England Example. U.S.
Environmenlal Protection Agency, Almospheric Research
and Exposure Assessmenl Laboratory, Research Triangle
Park, North Carolina. EPA-600/3-91-074, 109 pp.
Cooler, E. 1990. The Impacl of Climale Change on
Conlinuous Corn Produclion in Ihe Soulhern U.S.A.
Climal. Change, 16:53-82.
Cooler, E. 1990. A Heal Slress Climatology for
Oklahoma. Phys. Geogr., ll(l):17-35.
192
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Dr. M. Trevor Scholtz
Manager, Environmental Computing and Modelling and
Director, Canadian Global Emissions Interpretation Centre
ORTECH Corporation
2395 Speakman Drive
Mississauga, Ontario, Canada L5K 1B3
(905) 822-4111, Ext. 524
Fax: (905) 823-1446
tscholtz@ortech.on.ca
Role in the Lake Michigan Mass Balance
Project
Principal Investigator and project manager on a contract
with ORTECH to supply hourly atrazine emissions data
for the modelers in the LMMBS. The atrazine emissions
will be computed using an air-surface exchange model
driven by meteorological data supplied by the MM5
model.
Education
B.Sc., Chemical Engineering, University of Cape Town,
1958
M.A.Sc., Chemical Engineering, University of Toronto,
1961
Ph .D., Chemical Engineering, University of Toronto ,1965
Work Experience
Director of Research, TC Process Equipment,
Scarborough, Ontario, Canada, 1965-1970
Senior Lecturer, Department of Chemical Engineering,
University of Natal, South Africa, 1970-1978
Senior Consultant, Meteorological and Environmental
Planning Company, Markham, Ontario, Canada, 1978-
1984
Vice-President, Meteorological and Environmental
Planning Company, Markham, Ontario, Canada, 1984-
1989
Senior Scientist, ORTECH Corporation, Mississauga,
Ontario, Canada, 1989-1994
Manager, Environmental Assessment Technologies and
Director, Canadian Global Emissions Interpretation
Centre, ORTECH Corporation, Mississauga, Ontario,
Canada, 1994-1996
Manager, Environmental Computing and Modelling and
Director, Canadian Global Emissions Interpretation
Centre, Mississauga, Ontario, Canada, 1996-present
Experience
Preparation of regional and global emission inventories for
criteria pollutants, metals and persistent organic pollutants.
Development of an emission data pre-processing system
for preparing gridded emissions data for regional scale
atmospheric transport, transformation and deposition
models.
Processing of North American sulphur, nitrogen and
volatile organic carbon emissions for input to the Canadian
Regional Acid Deposition and Oxidants Model (ADOM).
Modeling of air movement and dispersion in a complex
valley, and the development and evaluation of a
supplementary control system.
Modeling and assessment of the dispersion from a gas
turbine generating complex.
Modeling of dispersion from refinery complexes and acid
plants.
Long-range transport modeling and assessment for major
industrial sources.
Development of a meteorologically based emissions model
for estimating emissions from open anthropogenic and
natural sources.
Modeling of the transport, diffusion and volatilization of
toxic organic substances from vegetated soils.
Modeling and assessment of dispersion with building wake
and complex structure effects.
Preparation of meteorological, and geophysical driver
fields for the Canadian Regional Acid Deposition and
Oxidants Model (ADOM).
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Development of a numerical planetary boundary layer
model for the Canadian regional Acid Deposition and
Oxidants Model (ADOM).
Modeling and assessment of moist plumes from cooling
tower operation and environmental impact.
Real-time modeling of iceberg motion for operational
applications.
Development of an operational ocean current model for
predicting surface currents and current profiles on the
Scotian Shelf.
Modeling of meteorologically forced ocean currents on the
Scotian Shelf during the Canadian Atlantic Storms Project
(CASP).
Publications
Scholtz, M.T., A.C. McMillan, C.F. Slama, Y-F. Li, N.
Ting, and K.A. Davidson. 1997. Pesticide Emissions
Modelling: Development of a North American Pesticide
Emissions Inventory. Canadian Global Emissions
Interpretation Centre Report CGEIC-1997-1.
Benkovitz, C.M., M.T. Scholtz, J. Pacyna, L. Tarrason, J.
Dignon, E.G. Voldner, P.A. Spiro, J.A. Logan, and T.E.
Graedel. 1996. Global Gridded Inventories of
Anthropogenic Emissions of SO2 and NOX. J. Geophy.
Res., 101(D22):39239-29253.
Li, Y-F., A.C. McMillan, and M.T. Scholtz. 1996. Global
HCH Usage with 1° x 1° Latitude/Longitude Resolution.
Environ. Sci. Technol., 30(12):3525-3533.
Scholtz, M.T., A.C. McMillan, C.F. Slama, Y-F. Li, N.
Ting, and K.A. Davidson. 1996. Gridded Seasonal
Atrazine Volatilization from Agricultural Lands in the
Great Lakes Basin. In Proceedings of the AWMA
Conference on Atmospheric Deposition to the Great
Waters. October 28-30, 1996.
Pacyna, J.M., M.T. Scholtz, and Y-F. Li. 1995. Global
Budget of Metal Sources. Environ. Res., 3:145-159.
Scholtz, M.T., E.G. Voldner, and E. Pattey. 1994.
Pesticide Volatilization Model Comparison with Field
Measurements. In Proceedings of the 87th AWMA
Annual Meeting, Paper 94-MP5.03, 87(3A):1-12,
Cincinnati, Ohio. June 19-24m, 1994.
Scholtz, M.T., C.F. Slama, and E.G. Voldner. 1993.
Pesticide Emission Factors from Agricultural Soils. In -
Proceedings of the 86th Annual AWMA Conference,
Paper93-MP-14.01,Denver,Colorado. June 13-18,1993.
Scholtz, M.T. and E.G. Voldner. 1993. Modelling Air-
Surface Exchange of Pesticides with Application to the
Estimation of Emission. In Proceedings of the First
Workshop on Emissions and Modelling of Atmospheric
Transport of Persistent Organic Pollutants and Heavy
Metals, Durham, North Carolina, May 6-7, 1993.
Sponsored by the U.S. Environmental Protection Agency
and the Cooperative Program for Monitoring and
Evaluation of the Long-Range Transmission of Air
Pollutants in Europe., October 1993. Report Number
EMEP/CCC 7/93-0-8917.
Scholtz, M.T. and E.G. Voldner. 1992. Estimation of
Pesticide Emissions to the Air Resulting from Agricultural
Applications. In - Proceedings of the 95th World Clean
Air Congress and Exhibition, Volume 2, Paper IU-17B-01,
Montreal, Quebec, Canada. August 30-September4,1992.
Scholtz, M.T., K.A. Davidson, and F. Vena. 1991.
Preparation of a Canadian Inventory of Biogenic Volatile
Organic Carbon Emissions from Vegetation. In
Proceedings of a Joint U.S. Environmental Protection
Agency/AWMA Conference on Emission Inventory Issues
in the 1990s, Durham, North Carolina. September 1991.
Scholtz, M.T., B. Weisman, L. Mahrt, and A.D. Christie.
1988. Generation of Meteorological Data Fields for the
ADOM Eulerian Regional Model. In - Han van Dop (Ed.),
Air Pollution Modelling and Its Application VI, Plenum
Publishing Company, New York, New York.
Scholtz, M.T., D.G. McGillivray, B. Weisman, and DA.
Greenberg. 1987. Modelling of Meteorologically Forced
Currents in the Scotian Shelf. In - Proceedings of Oceans
'87 Conference, IEEE, Halifax, Canada, September 1987.
194
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Scholtz, M.T., B. Weisman, A.D. Christie, and L. Mahrt.
1986. Generation of Meteorological Data Fields for the
ADOM Eulerian Regional Model. American
Meteorological Society, Proceedings of the Fifth Joint
Conference on Applications of Air Pollution Meteorology
with APCA, Chapel Hill, North Carolina. November 8-21,
1986.
Scholtz, M.T. and B. Weisman. 1985. A Multi-Layered,
Long-Range, Transport, Lagrangian Trajectory Model:
Comparison with Fully Mixed Single Layer Models. In -
C. De Wispelaere (Ed.), Air Pollution Modelling and Its
Application IV. Plenum Publishing Company, New York,
New York.
Scholtz, M.T. and C.J. Brouchaert. 1978. Modelling of
Stable Air Flows Over a Complex Region. J. Appl.
Meteorol., 17:1249-1257.
Scholtz, M.T. and O. Trass. 1970. Mass Transfer in a
Non-Uniform Impinging Jet, Part I: Stagnation Flow-
Velocity and Pressure Distribution. AJChE J., 16:90-96.
Scholtz, M.T. and O. Trass. 1970. Mass Transfer in a
Non-Uniform Impinging Jet, Part JJ: Stagnation Flow-
Velocity and Pressure Distribution. AJChE J., 16:97-104.
Scholtz, M.T. and O. Trass. 1964. Mass Transfer in the
Laminar Radial Wall Jet. AIChE J., 9:548.
Presentations
Scholtz, M.T. and B.C. Voldner. 1992. Air/Soil Exchange
of Volatile Toxics. CIRAC/AWMA-OS Joint
International Conference on Atmospheric Chemistry,
Toronto, Ontario, Canada. January 1992.
Scholtz, M.T. and E.G. Voldner. 1989. Development of
a Model for Predicting the Volatilization of Toxic
Materials from Vegetated Soils. Tenth Annual Meeting of
the Society of Environmental Toxicology and Chemistry,
Toronto, Ontario, Canada. October28-November2,1989.
Scholtz, M.T., K. Walsh, and L. Mahrt. 1986. A Study of
Drought Onset Due to Interactions Between Soil Moisture
and the Atmospheric Boundary Layer. Twentieth Annual
Congress, Canadian Meteorological and Oceanographic
Society, Regina, Canada. June 3-6, 1986.
Scholtz, M.T. and B. Weisman. 1980. A Model for
Predicting Air Movement and Dispersion in a Complex
Valley. 73rd Annual Meeting of the APCA, Montreal,
Quebec, Canada. June 22-27, 1980.
Weisman, B. and M.T. Scholtz. 1980. Dispersion Model
for Montreal East Development and Validation. 73rd
Annual Meeting of the APCA, Montreal, Quebec, Canada.
June 22-27, 1980.
195
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David J. Schwab
Oceanographer
U.S. Department of Commerce
National Oceanic and Atmospheric Administration
Great Lakes Environmental Research Laboratory
2205 Commonwealth Boulevard
Ann Arbor, Michigan 48105
(734)741-2120
Fax:(734)741-2055
schwab@glerl.noaa.gov
Specializations and Research Interests
Fields of Specialization and Research Interests:
Specialized in geophysical fluid dynamics problems in the
Great Lakes and other shallow enclosed seas including
theoretical, numerical, and observations investigations of
circulation, thermal structure, seiches, storm surges, wind
waves, and air-sea interaction. Current research interest -
numerical modeling of three dimensional lake-scale
circulation and thermal structure.
Education
Ph.D., Oceanic Science, University of Michigan, 1981
M.S., Physics, University of Wisconsin-Milwaukee, 1974
B.S. (Summa Cum Laude), Applied Mathematics and
Physics, University of Wisconsin-Milwaukee, 1972
Professional Experience
Oceanographer, Great Lakes Environmental Research
Laboratory, NOAA, 1980-present (GS-1360-13, 10/80;
GS-1360-14, 8/84)
Physical Scientist, Great Lakes Environmental Research
Laboratory, NOAA, 1975-1981 (GS-1301-11, 12/76; GS-
1301-12, 10/78)
Adjunct Assistant Professor in Department of Geography,
Atmospheric Sciences Program at the Ohio State
University, 1992-present
Visiting Scientist, VAW/ETH - Zurich, Switzerland, 1982
Adjunct Assistant Professor in Atmospheric and Oceanic
Science, Department of the University of Michigan, 1981-
1982
Research Specialist, Center for Great Lakes Studies,
University of Wisconsin-Milwaukee, 1975
Professional Honors and Awards
U.S. Department of Commerce, National Oceanic and
Atmospheric Administration Outstanding Performance
Award, 1979, 1980.
Selected outstanding graduate student in oceanography by
the College of Engineering at the University of Michigan,
1981.
National Oceanic and Atmospheric Administration/Great
Lakes Environmental Research Laboratory Distinguished
Authorship Award, 1984.
U.S. Department of Commerce, National Oceanic and
Atmospheric Administration Outstanding Performance
Award, 1987, 1988, 1989, 1995, 1996.
Professional Affiliations
American Geophysical Union, American Meteorological
Society, The Oceanography Society, International
Association for Great Lakes Research (Treasurer, 1986-
1989).
Review Panels Associate Editor, Journal of the Great
Lakes Research. Journal of Geophysical Research
Limnology and Oceanography, Journal of Physical
Oceanography, American Society of Civil Engineers,
Hydraulics Division, Canadian Journal of Water Pollution
Research, Atmospheric-Oceanic Annales, Geophysicae,
NOAA Sea Grant, International Joint Commission,
National Science Foundation.
International Activities Workshop on Physical Limnology
and Water Quality Modelling of Large Lake Systems,
Petrozavodsk, Russia, October 19-23, 1992.
Contracts and Grants Awarded
Great Lakes CoastWatch Program, NOAA Coastal Ocean
Program, 1990-92, $180K. Software Tools for
CoastWatch.
NOAA Coastal Ocean Program, 1992, $10K Great Lakes
Forecasting System (Co-Principal Investigator with K.W.
Bedford).
NOAA Coastal Ocean Program, 1991-96, $750K, Coastal
Hazards - Great Lakes Wind Forecasts.
196
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NOAA Coastal Ocean Program, 1992-94, $ 150K, Lake St.
Clair Macrophyte Study, Particle Trajectory Model for
Lake St. Clair.
U.S. Army Corps of Engineers, 1995, $6K, Lake Michigan
Mass Balance Study, Hydrodynamic Model of Lake
Michigan.
U.S. Environmental Protection Agency, 1995-97, $336K.
Publications
Journals
Beletsky, D., W.P. O'Connor, DJ. Schwab, and D.E.
Dietrich. 1997. Numerical Simulation of Internal Kelvin
Waves and Coastal Up welling Fronts. J. Phys. Oceanogr.,
in press.
Eadie, B.J., D.J. Schwab, G.A. Leshkevich, T.H.
Johengen, R.A. Assel, N. Hawley, R.E. Holland, M.B.
Lansing, P. Lavrentyev, G.S. Miller, N.R. Morehead, J.A.
Robbins, and P.L. Van Hoof. 1996. Recurrent Coastal
Plume in Southern Lake Michigan. EOS, Trans. Amer.
Geophys. Union.,77(35):337-338.
Schwab, D.J. and K.W. Bedford. 1996. Great Lakes
Forecasting, in Coastal Ocean Prediction. In - C. Moores
(Ed.), American Geophysical Union Coastal and Estuarine
Studies, in press.
Schwab, D.J. and D. Beletsky. 1996. Propagation of
Kelvin Waves Along Irregular Coastlines in Finite-
Difference Models. Submitted to Advances in Water
Resources.
Schwab, D.J., W.P. O'Connor, and G.L.Mellor. 1995. On
the Net Cyclonic Circulation in Large Stratified Lakes. J.
Phys. Oceanogr., 25(6):1516-1520.
Schwab, D.J. and K.W. Bedford. 1994. Initial
Implementation of the Great Lakes Forecasting System:
A Real-Time System for Predicting Lake Circulation and
Thermal Structure. Water Pollut. Res. J. Can.,
29(2/3):203-220.
Leshkevich, G.A., D.J. Schwab, and G.C. Muhr. 1993.
Satellite Environmental Monitoring of the Great Lakes: A
Review of NOAA's Great Lakes CoastWatch Program.
Photogram. Engin. Rem. Sens., 59(3):371-379.
Donelan, M.A., M. Skafel, H. Graber, P. Liu, D.J. Schwab,
and S. Venkatesh. 1992. On the Growth of Wind-
Generated Waves. Atmos.-Ocean., 30(3):457-478.
Schwab, DJ. 1992. A Review of Hydrodynamic
Modeling in the Great Lakes From 1950-1990 and
Prospects for the 1990's. In F. Gobas and A.
McQuorquodale (Eds.), Chemical Dynamics in Freshwater
Ecosystems, pp., 41-62, Lewis Publishers, Incorporated,
Chelsea, Michigan.
Schwab, D.J., G.A. Leshkevich, and G.C. Muhr. 1992.
Satellite Measurements of Surface Water Temperature in
the Great Lakes: Great Lakes CoastWatch. J. Great
Lakes Res., 18(2):247-258.
Schwab, D.J., A.H. Clites, C.R. Murthy, J.E. Sandall,
L.A. Meadows, and G.A. Meadows. 1989. The Effect of
Wind on Transport and Circulation in Lake St. Clair. J.
Geophys. Res., 94(C4):4947-4958.
Fahnenstiel, G.L., D. Scavia, G.A. Lang, J.H. Saylor, G.S.
Miller, and D.J. Schwab. 1988. Impact of Inertial Period
Waves on Fixed-Depth Primary Production Estimates. J.
Plankton Res., 10:77-87.
Liu, P.C. and D.J. Schwab. 1987. A Comparison of
Methods for Estimating U* from Given Uz and Air-Sea
Temperature Differences. J. Geophys. Res., 92(C6):6488-
6494.
Horn, W., C.H. Mortimer, and D.J. Schwab. 1986.
Wind-Induced Internal Seiches in the Lake of Zurich,
Observed and Modelled. Limnol. Oceanogr. 31 (6): 1232-
1254.
Schwab, D.J. and J.R. Bennett. 1986. A Lagrangian
Comparison of Objectively Analyzed and Dynamically
Modeled Circulation Patterns in Lake Erie. J. Great Lakes
Res., 13(4):515-529.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1985. A
Two-Dimensional Lake Wave Prediction System.
Environ. Software, l(l):4-9.
Liu, P.C., D.J. Schwab, and J.R. Bennett. 1984.
Comparison of a Two-Dimensional Wave Prediction
Model with Synoptic Measurements in Lake Michigan. J.
Phys. Oceanogr., 14:1514-1518.
197
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Schwab, D.J., J.R. Bennett, P.C. Liu, and M.A. Donelan.
1984. Application of a Simple Numerical Wave
Prediction Model to Lake Erie. J. Geophys. Res.,
89:3586-3592.
Schwab, D.J., G.A. Meadows, J.R. Bennett, H. Schultz,
P.C. Liu, I.E. Campbell, and H. Dannelongue. 1984. The
Response of the Coastal Boundary Layer to Wind and
Waves: Analysis of an Experiment in Lake Erie. J.
Geophys. Res., 89:8043-8053.
Schwab, D.J. and J.A. Morton. 1984. Estimation of
Overtake Wind Speed from Overland Wind Speed: A
Comparison of Three Methods. J. Great Lakes Res.,
10:68-72.
Hutter, K., G. Salvade, and D.J. Schwab. 1983. On
Internal Wave Dynamics in the Northern Basin of Lake of
Lugano. Geophys. Astrophys. Fluid Dyn., 27:299-336.
Schwab, D.J. 1983. Numerical Simulation of Low-
Frequency Current Fluctuations in Lake Michigan. J.
Phys. Oceanogr., 13:2213-2224.
Schwab, D.J. and D.B. Rao. 1983. Barotropic
Oscillations of the Mediterranean and Adriatic Seas.
Tellus, 35A:417-427.
Schwab, D.J. 1982. An Inverse Method for Determining
Wind Stress from Water Level Fluctuations. Dyn. Atmos.
Oceans, 6:251-278.
Bennett, J.R. and D.J. Schwab. 1981. Calculation of the
Rotational Normal Modes of Oceans and Lakes with
General Orthogonal Coordinates. J. Comp. Phys., 44:359-
376.
Rao, D.B. and D.J. Schwab. 1981. A Method of
Objective Analysis for Currents in a Lake. J. Phys.
Oceanogr., 11:739-750.
Schwab, D.J. 1981. Determination of Wind Stress from
Water Level Fluctuations. Doctoral Dissertation in
Oceanic Science at the University of Michigan, Ann
Arbor, Michigan. 108 pp.
Schwab, D.J., R.A. Shuchman, and P.C. Liu. 1981. Wind
Wave Directions Determined from Synthetic Aperture
Radar Imagery and from a Tower in Lake Michigan. J.
Geophys. Res., 86:2059-2064.
Schwab, D.J., P.C. Liu, H.K. Soo, R.D. Kistler, H.L.
Booker, and J.D. Boyd. 1980. Wind and Wave
Measurements Taken from a Tower in Lake Michigan. J.
Great Lakes Res., 6:76-82.
Schwab, D.J. 1978. Simulation and Forecasting of Lake
Erie Storm Surges. Mon. Wea. Rev., 106:1476-1487.
Schwab, D.J. 1977. Internal Free Oscillations in Lake
Ontario. Limnol. Oceanogr., 22:700-708.
Schwab, D.J. and D.B. Rao. 1977. Gravitational
Oscillations of Lake Huron, Saginaw Bay, Georgian Bay,
and the North Channel. J. Geophys. Res., 82:2105-2116.
Mortimer, C.H., D.B. Rao, and D.J. Schwab. 1976. A
Supplementary Note and Figure to "Free Surface
Oscillations and Tides of Lakes Michigan and Superior"
by C.H. Mortimer and E.J. Fee. Phil. Trans. Roy. Soc.
London, Ser. A, 281:58-60.
Rao, D.B., C.H. Mortimer, and D.J. Schwab. 1976.
Surface Normal Modes of Lake Michigan: Calculations
Compared with Spectra of Observed Water Level
Fluctuations. J. Phys. Oceanogr., 6:575-588.
Rao, D.B. and D.J. Schwab. 1976. Two-Dimensional
Normal Modes in Arbitrary Enclosed Basins on a Rotating
Earth: Application to Lakes Ontario and Superior. Phil.
Trans. Roy. Soc. London, Ser. A, 281:63-96.
Reports and Other Publications
Beletsky, D. and D.J. Schwab. 1996. Hydrodynamic
Modeling for the Lake Michigan Mass Balance Project.
Proceedings, U.S. Environmental Protection Agency
Workshop on Next Generation Environmental Models
Computational Methods, in press.
198
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Kelley, J.G.W., D.J. Welsh, D.J. Schwab, K.W. Bedford,
B. Hoch, and J.S. Hobgood. 1996. High-Resolution,
Short-Term Lake Forecasts for Lake Erie. In M.L.
Spaulding and R.T. Cheng (Eds.), Estuarine and Coastal
Modeling, Proceedings of the Fourth International
Conference, American Society of Civil Engineers, in
press.
Schwab, D.J. and K.W. Bedford. 1996. GLCFS A
Coastal Forecasting System for the Great Lakes. Preprint
AMS Conference on Coastal Oceanic and Atmospheric
Prediction, American Meteorological Society, pp. 9-14.
Schwab, D.J., D. Beletsky, W.P. O'Connor, and D.E.
Dietrich. 1996. Numerical Simulation of Internal Kelvin
Waves with Z-Level and Sigma Level Models. In - M.L.
Spaulding and R.T. Cheng (Eds.), Estuarine and Coastal
Modeling, pp. 298-312. Proceedings of the Fourth
International Conference, American Society of Civil
Engineers.
Kaun, C.-F., K.W. Bedford, and D.J. Schwab. 1995. A
Preliminary Analysis of the Lake Erie Portion of the Great
Lakes Forecasting System for Springtime Heating
Conditions. In D. Lynch and A. Davies (Eds.),
Quantitative Skill Assessments for Coastal Ocean Models,
Volume 48, pp. 397-424. Coastal and Estuarine Studies,
American Geophysical Union.
Schwab, D.J. and K.W. Bedford. 1995. Operational
Three-Dimensional Circulation Modeling in the Great
Lakes. Computer Modeling of Seas and Coastal Regions
n., pp. 387-396. Computational Mechanics Publication,
Boston, Massachusetts.
Schwab, D.J. and K.W. Bedford. 1995. Report of the
First Annual Great Lakes Forecasting System (GLFS)
User's Workshop. Ohio Sea Grant Program, Columbus,
Ohio. 11 pp.
Schwab, D.J., K.W. Bedford, and F.H. Quinn. 1995.
Overview of the Great Lakes Forecasting System. Preprint
of the Eleventh International Conference on UPS for
Meteorology, Oceanography, and Hydrology, pp. 132-133.
American Meteorological Society.
Bedford, K. And D. Schwab. 1994. The Great Lakes
Forecasting System, An Overview. Proceedings of the
1994 National Conference on Hydraulic Engineering, pp.
197-201.
Kelley, J., C.-C. Yen, J. Hobgood, D. Schwab, and K.
Bedford. 1994. Short-Term Forecasts for Lake Erie.
Proceedings of the 1994 National Conference on
Hydraulic Engineering, pp. 227-231.
Kelley, J., C.-C. Yen, K. Bedford, A.J. Hobgood, and D.
Schwab. 1993. Coupled Lake Erie Air-Sea Storm
Resolving Forecasts and Predictions, The Viento Project.
Proceedings of the Third International Conference of
Estuarine and Coastal Modeling, pp. 202-215. American
Society of Civil Engineers, New York, New York.
O'Connor, W.P. and D.J. Schwab. 1993. Sensitivity of
Great Lakes Forecasting System Nowcasts to
Meteorological Fields and Model Parameters.
Proceedings of the Third International Conference on
Estuarine and Coastal Modeling, pp. 149-157. American
Society of Civil Engineers, Waterway, Port, Coastal and
Ocean Division.
Bedford, K. and D. Schwab. 1991. The Great Lakes
Forecasting System Lake Erie Nowcasts/Forecasts.
Proceedings of the Marine Technology Society Annual
Conference (MTS '91), pp. 260-264. Marine Technology
Society, Washington, D.C.
Schwab, D.J., R.E. Jensen, and P.C. Liu. 1991.
Comparative Performance of Spectral and Parametric
Wave Prediction Models in Lake Michigan. Mechanics
Computing in 1990's and Beyond, Volume 1 - pp. 363-367.
Computational Mechanics, Fluid Mechanics, and
Biomechanics, American Society of Civil Engineers,
Columbus, Ohio.
Yen, C.-C., K. Bedford, and D. Schwab. 1991. Nowcast
Protocol for the Great Lakes Forecasting System. In
M.L. Spaulding, et al. (Eds.), Proceedings of the Second
International Conference of Estuarine and Coastal
Modeling, pp. 140-148, Tampa, Florida, November 12-15,
1991. American Society of Civil Engineers, New York,
New York.
199
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Bedford, K.W. and D.J. Schwab. 1990. Preparation of
Real-Time Great Lakes Forecasts. Cray Channels,
Summer 1990, pp. 14-17.
Bedford, K.W., C-C. Yen, J. Kempf, D.J. Schwab, R.
Marshall, and C. Kuan. 1990. A 3D-Stereo Graphics
Interface for Operational Great Lakes Forecasts. Estuarine
and Coastal Modeling Proceedings, pp. 248-257. WW
Division/American Society of Civil Engineers, Newport,
Rhode Island.
Bedford, K.W., C.J. Merry, and D.J. Schwab. 1989. Real-
Time Lake Erie Current and Temperature Field
Forecasting An Integrated Modeling and AVHRR
Methodology. ASPRS Technical Paper, Proceedings 1989
American Society of Photography and Remote Sensing,
pp. 77-78.
Schwab, D.J. 1989. The Use of Analyzed Wind Fields
from the Great Lakes Marine Observation Network in
Wave and Storm Surge Forecast Models. Preprint Volume
of the Second International Workshop on Wave
Hindcasting and Forecasting, Environment Canada, pp.
257-266. Atmospheric Environment Service, Downsview,
Ontario, Canada.
Schwab, D.J. 1988. A Numerical Wave Predictions
Model for Personal Computers. Proceedings of the 21st
International Conference on Coastal Engineering.
Schwab, D.J. 1987. Great Lakes Storm Surge and Seiche.
In - Great Lakes Forecasters Handbook, National Weather
Service Training Seminar for Great Lakes Operational
Marine Forecasting. 10 pp.
Schwab, D.J. 1987. Great Lakes Wave Prediction Model.
In - Great Lakes Forecasters Handbook, National Weather
Service Training Seminar for Great Lakes Operational
Marine Forecasting. 11 pp.
Schwab, D.J. and E.W. Lynn. 1987. Great Lakes Storm
Surge Planning Program (SSPP). National Oceanic and
Atmospheric Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan. NOAA
Technical Memorandum ERL-GLERL-65, 9 pp.
Richardson, W.S., D.J. Schwab, Y.Y. Chao, and D.M.
Wright. 1986. Lake Erie Wave Height Forecasts
Generated by Empirical and Dynamical Methods
Comparison and Verification. National Oceanic and
Atmospheric Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan. NOAA
Ocean Products Center Technical Note, 23 pp.
Bennett, J.R., D.J. Schwab, and E.W. Lynn. 1985.
"Pathfinder" An Interactive Model for Trajectory
Prediction in the Great Lakes. Proceedings of the First
Conference on Applications of Real-Time Oceanographic
Circulation Modeling, Sponsored by NOAA/NOS, Laurel,
Maryland.
Schwab, D.J. and P.C. Liu. 1985. Intercomparison of
Wave Measurements from a NOMAD Buoy and from a
Waverider Buoy in Lake Erie. Proceedings MTS-IEEEC
Conference Oceans '85. OceanEngin. Environ., pp. 1131-
1137.
Schwab, D.J., E.W. Lynn, and G.S. Spalding. 1985.
User's Manual for GLERL Data Access System (GDAS).
National Oceanic and Atmospheric Administration, Great
Lakes Environmental Research Laboratory, Ann Arbor,
Michigan. GLERL Open File Report, 45 pp.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1984. A
Two-Dimensional Lake Wave Prediction System.
National Oceanic and Atmospheric Administration, Great
Lakes Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Technical Memorandum ERL-GLERL-
51,70pp.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1984.
"Pathfinder"-A Trajectory Prediction System for the Great
Lakes. National Oceanic and Atmospheric
Administration, Great Lakes Environmental Research
Laboratory, Ann Arbor, Michigan. NOAA Technical
Memorandum ERL-GLERL-53, 37 pp.
Bennett, J.R., A.H. Clites, and D.J. Schwab. 1983. A
Two-Dimensional Lake Circulation Modelling System:
Programs to Compute Particle Trajectories and the Motion
of Dissolved Substances. National Oceanic and
Atmospheric Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan. NOAA
Technical Memorandum ERL-GLERL-46, 51 pp.
200
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Hutter, K. and D.J. Schwab. 1982. Baroclinic Channel
Models. Versuchsanstalt fur Wasserbau, Hydrologie und
Glaziologie, ETH-Zurich, Internal Report 162, 20 pp.
Schwab, D.J., J.R. Bennett, and A.T. Jessup. 1982. A
Two-Dimensional Lake Circulation Modeling System.
National Oceanic and Atmospheric Administration, Great
Lakes Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Technical Memorandum ERL-GLERL-
38,79 pp.
Schwab, D.J. and K. Hutter. 1982. Barotrophic and
Baroclinic Eigenmodes of Lake of Zurich and Lake of
Lugano. Versuchsanstalt fur Wasserbau, Hydrologie und
Glaziologie, ETH-Zurich, Internal Report 164, 118 pp.
Schwab, D.J., P.C. Liu, J.R. Bennett, G.A. Meadows, H.
Schultz, J.E. Campbell, and H. Dannelongue. 1982.
Coastal Boundary Layer Study Records Response of Lake
Erie to Storms. Coastal Oceanogr. Climat. News, 4:30-31.
Schwab, D.J., P.C. Liu, J.R. Bennett, and G.A. Meadows.
1981. Lake Erie Coastal Boundary Layer Study Measures
Flux of Energy During Storms. Coastal Oceanogr. Climat.
News, 4:10.
Schwab, D.J. and D.L. Sellers. 1980. Computerized
Bathymetry and Shorelines of the Great Lakes. National
Oceanic and Atmospheric Administration, Great Lakes
Environmental Research Laboratory, Ann Arbor,
Michigan. NOAA Data Report ERL-GLERL-32, 21 pp.
Richardson, W.S. and D.J. Schwab. 1979. Comparison
and Verification of Dynamical and Statistical Lake Erie
Storm Surge Forecasts. National Oceanic and
Atmospheric Administration, Great Lakes Environmental
Research Laboratory, Ann Arbor, Michigan. NOAA
Technical Memorandum NWS TDL-69, 19 pp.
Schwab, D.J. 1978. Storm Surge Studies on the Great
Lakes. American Society of Civil Engineers Convention,
Preprint 3353, 12 pp.
Schwab, D.J. 1978. Analytical and Empirical Response
Functions for Storm Surges on Lake Erie. Proceedings of
the International Symposium on Long Waves in the Ocean,
Canadian Marine Sciences Directorate, Manuscript Report
Series No. 53, pp. 140-144.
Schwab, D.J. 1977. An Objective Analysis Scheme for
Lake Currents. International Field Year for the Great
Lakes Bulletin No. 19, pp. 50-52.
Schwab, D.J. 1975. A Normal Mode Method for
Predicting Storm Surges on a Lake. University of
Wisconsin-Milwaukee, Center for Great Lakes Studies.
Special Report No. 20, 51 pp.
Presentations
Bedford, K.W. and D.J. Schwab. 1996. Lake Erie
Physics A Post Binational Study Survey. 39th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Toronto, Mississauga, Ontario, Canada. May 26-30,
1996.
Beletsky, D. and D.J. Schwab. 1996. Modeling of the
Annual Cycle of Thermal Structure and Circulation in
Lake Michigan., CGLAS/CILER Symposium, University
of Michigan, Ann Arbor, Michigan. January 1996.
Beletsky, D. and D.J. Schwab. 1996. Modeling of the
Annual Cycle of Thermal Structure and Circulation in
Lake Michigan. 1996 Ocean Sciences Meeting, American
Geophysical Union, San Diego, California. February 12-
16, 1996.
Schwab, D.J. and K.W. Bedford. 1996. GLCFS A
Coastal Forecasting System for the Great Lakes. AMS
Conference on Coastal Oceanic and Atmospheric
Prediction, American Meteorological Society, Atlanta,
Georgia. January 28-February 2, 1996.
Schwab, D.J. and K.W. Bedford. 1996. GLCFS A
Coastal Forecasting System for the Great Lakes. Twelfth
International Conference on Interactive Information and
Processing Systems for Meteorology, Oceanography, and
Hydrology, American Meteorological Society, Atlanta,
Georgia. January 28-February 2, 1996.
Schwab, D.J. and D. Beletsky. 1996. Application of POM
to the Great Lakes. Princeton Ocean Model Users Group
Meeting, Princeton University, Princeton, New Jersey.
June 10-12, 1996.
201
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Beletsky, D., W.P. O'Connor, and D.J. Schwab. 1995.
Numerical Simulation of Internal Kelvin Waves in Lakes.
38th Conference on Great Lakes Research, International
Association for Great Lakes Research, Michigan State
University, East Lansing, Michigan. May 1995.
Beletsky, D., W.P. O'Connor, and D.J. Schwab. 1995.
Hydrodynamic Modeling of Lake Michigan for the Lake
Michigan Mass Balance Project. U.S. Environmental
Protection Agency Workshop on Next Generation
Environmental Models Computational Methods
(NGEMCOM), Bay City, Michigan. August 1995.
Kelley, J.G.W., D.J. Welsh, D.J. Schwab, K.W. Bedford,
B. Hoch, and J.S. Hobgood. 1995. High-Resolution,
Short-Term Lake Forecasts for Lake Erie. Fourth
International Conference on Estuarine and Coastal
Modeling, San Diego, California. October 26-28, 1995.
O'Connor, W.P., D. Beletsky, and D.J. Schwab. 1995.
Numerical Simulation of Internal Kelvin Waves.
CGLAS/CILER Symposium, University of Michigan, Ann
Arbor, Michigan. January 1995.
Schwab, D.J. 1995. Progress Report on GLFS.
CGLAS/CILER Symposium, University of Michigan, Ann
Arbor, Michigan. January 1995.
Schwab, D.J. 1995. Marine Forecasts for the Great
Lakes. NOAA Colloquium on Operational Environmental
Prediction, Camp Springs, Maryland. July 1995.
Schwab, D.J. and K.W. Bedford. 1995. Operational
Three-Dimensional Circulation Modeling in the Great
Lakes. Computer Modeling of Seas and Coastal Regions
n, Cancun, Mexico. September 6-8, 1995.
Schwab, D.J. and K.W. Bedford. 1995. Operational
Three-Dimensional Circulation Modeling in the Great
Lakes. Computer Modeling of Seas and Coastal Regions
n, Cancun, Mexico. September 6-8, 1995.
Schwab, D.J., K.W. Bedford, and F.H. Quinn. 1995.
Overview of the Great Lakes Forecasting System.
American Meteorological Society, Eleventh International
Conference on UPS for Meteorology, Oceanography, and
Hydrology, Dallas, Texas. January 15-20, 1995.
Schwab, D.J., D. Beletsky, W.P. O'Connor, and D.E.
Dietrich. 1995. Numerical Simulation of Internal Kelvin
Waves with Z-Level and Sigma Level Models. Fourth
International Conference on Estuarine and Coastal
Modeling, San Diego, California. October 26-28, 1995.
Welsh, D.J., J.G.W. Kelley, D.J. Schwab, K.W. Bedford,
and B. Hoch. 1995. The Ongoing Development of the
Great Lakes Forecasting System. Fourth US/Canada
Workshop on Great Lakes Operational Meteorology,
Syracuse, New York. September 13-15, 1995.
Bedford, K. and D. Schwab. 1994. The Great Lakes
Forecasting System, An Overview. National Conference
on Hydraulic Engineering, Buffalo, New York. August 1-
5, 1994.
Kelley, J.G.W., C.-C. Yen, K. Bedford, J. Hobgood, and
D. Schwab. 1994. Short-Term Forecasts for Lake Erie.
National Conference on Hydraulic Engineering, Buffalo,
New York. August 1-5, 1994.
Schwab,D.J. 1994. Hydrodynamic Modeling in the Great
Lakes. Watershed, Estuarine and Large Lakes Modeling,
U.S. Environmental Protection Agency, National
Environmental Supercomputer Center, Bay City,
Michigan. April 18-20, 1994.
Schwab, D.J. 1994. Marine Forecasts for the Great
Lakes. NOAA Colloquium on Operational Environmental
Prediction, Camp Springs, Maryland. July 1994.
Schwab, D.J. 1994. Physical Oceanography. GreatLakes
Aquatic Ecosystem Research Consortium Colloquium.
Ann Arbor, Michigan. November 1994.
Schwab, D.J. and W.P. O'Connor. 1994. A Theory for the
Net Cyclonic Circulation in Large Stratified Lakes. 37th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Windsor, Windsor, Ontario, Canada. June 5-9, 1994.
Yen, J., J. Kelley, K.W. Bedford, J.S. Hobgood, and D.J.
Schwab. 1994. Distributed Computation of Air-Lake
Coupled Storm Resolving Forecasts Through a High
Bandwidth Satellite: The Viento Project. 37th Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Windsor, Windsor,
Ontario, Canada. June 5-9, 1994.
202
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Bedford, K. and D. Schwab. 1993. The Great Lakes
Forecasting System: Prospects for Regionwide
Implementation. U.S. Environmental Protection Agency
Supercomputer Workshop, Large Lakes Research Station,
Grosse lie, Michigan. August 3, 1993.
Bedford, K.W., J. Yen, J. Kelley, J. Hobgood, and D.J.
Schwab. 1993. Coupled Air-Sea, Storm Resolving
Forecasts and Predictions, The Viento Project. Third
International Conference on Estuarine and Coastal
Modeling, American Society of Civil Engineers,
Waterway, Port, Coastal and Ocean Division. Chicago,
Illinois. September 1993.
O'Connor, W.P. and D.J. Schwab. 1993. Sensitivity of
Great Lakes Forecasting System Nowcasts to
Meteorological Fields and Model Parameters. Third
International Conference on Estuarine and Coastal
Modeling. American Society of Civil Engineers,
Waterway, Port, Coastal and Ocean Division. Chicago,
fllinois. September 1993.
Schwab, D.J. 1993. The Great Lakes Forecasting System.
Commonwealth Center for Coastal Physical
Oceanography, Visiting Scientist Lecture Series, Norfolk,
Virginia. February 1993.
Schwab, D.J. 1993. The Great Lakes Forecasting System.
Workshop on Economic Assessment of Coastal
Forecasting in the United States, Woods Hole
Oceanographic Institution, Woods Hole, Massachusetts.
June 1993.
Schwab, D.J. 1993. Marine Forecasts for the Great
Lakes. NO A A Colloquium on Operational Environmental
Prediction, Silver Spring, Maryland. July 1993.
Bedford, K.W., O. Wai, J. Yen, L. Regenmorter, and D.J.
Schwab. 1992. Historical Reconstruction of a Thirty-Five
Year Data Base of Lake Erie Current and Temperature
Fields. 35th Conference on Great Lakes Research,
International Association for Great Lakes Research,
Waterloo, Ontario, Canada. June 1992.
Kelley, J.G.W., J.S. Hobgood, D.J. Schwab, and K.W.
Bedford. 1992. Feasibility of Using Short-Range
Meteorological Predictions in the Lake Erie Information
Forecasting System. 35th Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Waterloo, Waterloo, Ontario,
Canada. May 31-June 4, 1992.
Leshkevich, G.A., D.J. Schwab, and G.C. Muhr. 1992.
NOAA's CoastWatch: Satellite Environmental
Monitoring of the Great Lakes. First Thematic
Conference on Remote Sensing for Marine and Coastal
Environments, New Orleans, Louisiana. June 1992.
Merry, C., D. Welsh, Y.-F. Chu, K.W. Bedford, and D.J.
Schwab. 1992. Incorporating AVHRR Data into a
Surface Heat Flux Model for Lake Erie. 35th Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Waterloo, Waterloo,
Ontario, Canada. May 31-June4, 1992.
Schwab, D.J. 1992. Generation and Use of Gridded
Overwater Wind Fields in the Great Lakes. 35th
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Waterloo, Waterloo, Ontario, Canada. May 31-June 4,
1992.
Schwab, D.J. 1992. Marine Forecasts for the Great
Lakes. NOAA Colloquium on Operational Environmental
Prediction, Camp Springs, Maryland. June 1992.
Schwab, D.J. 1992. The Great Lakes Forecasting Project.
Workshop on Physical Limnology of Large Lake Systems
of Europe and North America. Petrozavadsk, Russia.
October 1992.
Schwab, D.J. and K.Bedford. 1992. Nowcasting Protocol
for the Great Lakes Forecasting System. AGU Ocean
Sciences Meeting, New Orleans, Louisiana. January 1992.
Weaks, M., A. Chester, D. Schwab, and W. Pichel. 1992.
NOAA's CoastWatch: A New Capability for Monitoring
the Coastal Ocean. AGU Ocean Sciences Meeting, New
Orleans, Louisiana. January 1992.
203
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Bedford, K., D.J. Schwab, and C.C.J. Yen. 1991.
Nowcast Protocol for the Great Lakes Forecasting System.
Second Conference on Estuarine and Coastal Modeling.
Tampa, Florida. November 1991.
Schwab,D.J. 1991. The Great Lakes Forecasting System.
Great Lakes Environmental Research Laboratory
Seminar, Ann Arbor, Michigan. March 1991.
Schwab, D.J., G.A. Leshkevich, and G.C. Muhr. 1991.
Great Lakes CoastWatch and the NOAA Ocean
Communications Network. 34th Conference on Great
Lakes Research, International Association for Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Bedford, K.W. and D.J. Schwab, D.J. 1990. The Great
Lakes Forecasting System - An Operational System for
Predicting the Physical Status of the Great Lakes. Fall
Meeting of the American Geophysical Union, San
Francisco, California. December 1990.
Bedford, K.W. and D.J. Schwab. 1990. The Great Lakes
Forecasting System An Overview of an Operational
System for Predicting the Physics and Related Variables
of the Great Lakes. 33rd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Windsor, Windsor, Ontario,
Canada. May 13-17, 1990.
Bedford, K.W., D.J. Schwab, and C. Merry, C. 1990.
Preparation of Real-Time Great Lakes Forecasts. Informal
Seminar, U.S. Army Corps of Engineers, Detroit District,
Detroit, Michigan. June 1990.
Bedford, K.W., C-C. Yen, J. Kempf, D.J. Schwab, R.
Marshall, and C. Kuan. 1990. A 3D-Stereo Graphics
Interface for Operational Great Lakes Forecasts. Estuarine
and Coastal Modeling Proceedings, WW Division
/American Society of Civil Engineers, Newport, Rhode
Island. November 1990.
Schwab, D.J. 1990. Great Lakes Circulation Patterns and
Models as Related to Underwater Recovery. Michigan
State Police Underwater Recovery Unit Training
Seminar, Grand Rapids, Michigan. January 1990.
Schwab, D.J. 1990. A Review of Hydrodynamic
Modeling in the Great Lakes from 1950-1990 and
Prospects for the 1990's. 33rd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Windsor, Windsor, Ontario,
Canada. May 13-17, 1990.
Schwab, D.J. and K.W. Bedford. 1990. The Great Lakes
Forecasting System. NOAA Workshop on Coastal
Circulation Models, Monterey, California. October 1990.
Schwab, D.J., G.S. Miller, C.R. Murthy, and K. Miners.
1990. Comparison of Modeled and Observed Drifter
Trajectories in Western Lake Erie. 24th Annual Congress
of the Canadian Meteorological and Oceanography
Society, Victoria, British Columbia, Canada. May 1990.
Bedford, K.W.,C.J. Merry, and D.J. Schwab. 1989. Real-
Time Lake Erie Current and Temperature Field
Forecasting An Integrated Modeling and AVHRR
Methodology. American Society of Photogrammetry and
Remote Sensing Fall Convention. September 1989.
Schwab, D.J. 1989. The Use of Analyzed Wind Fields
from the Great Lakes Marine Observation Network in
Wave and Storm Surge Forecast Models. Second
International Workshop on Wave Hindcasting and
Forecasting, Vancouver, British Columbia, Canada. April
1989.
Schwab, D.J. 1989. Estimation of Overtake Wind Fields
from the Great Lakes Marine Observation Network. 32nd
Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Wisconsin, Madison, Wisconsin. May 30-June 2, 1989.
Anaheim, C.A., M. Skafel, M. Donelan, H. Graber, P. Liu,
D.J. Schwab, and S. Venkatesh. 1988. Observations of
Spectral Changes of Waves in Shoaling Water. 22nd
Canadian Meteorological and Oceanographic Society
Annual Congress, Hamilton, Ontario, Canada. June 1988.
Schwab, D.J. 1988. A Numerical Wave Prediction Model
for Personal Computers. 21st International Conference on
Coastal Engineering, Malaga, Spain. June 1988.
Schwab, D.J. 1988. Storm Surges on the Great Lakes.
Technical Conference on Coastal Engineering for the
Great Lakes, Madison, Wisconsin. December 1988.
204
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Venkatesh, S., M. Donelan, H. Graber, P. Liu, D.J.
Schwab, and M. Skafel. 1988. Shallow Water Wind
Waves A Preliminary Analysis of Data from a Field
Study on Lake St. Clair. AMS Seventh Conference on
Ocean-Atmosphere Interaction. February 1988.
Schwab, D.J. 1987. Storm Surge Planning. Technical
Conference on Coastal Engineering for the Great Lakes,
Madison, Wisconsin. February 1-4, 1987.
Schwab, D.J. 1987. Great Lakes Wave Prediction Model.
National Weather Service Seminar for Great Lakes Marine
Forecasting, Ann Arbor, Michigan. September 1987.
Schwab, D.J. 1987. Great Lakes Storm Surge and Seiche.
National Weather Service Seminar for Great Lakes Marine
Forecasting, Ann Arbor, Michigan. September 1987.
Schwab, D.J., P.C. Liu, and M.A. Donelan. 1987.
WAVEDISS '85 (Wave Attenuation Variability and
Energy Dissipation in Shallow Seas) Analysis of Wave
Measurements in Lake St. Clair. 30th Conference on
Great Lakes Research, International Association for Great
Lakes Research, University of Michigan, Ann Arbor,
Michigan. May 11-14, 1987.
Schwab, D.J., and G.C. Muhr. 1987. Computer Models
for Waves and Water Levels in the Great Lakes. The
World Today...The World Tomorrow - Air Toxics and the
Great Lakes, Grand Rapids, Michigan. October 1987.
Schwab, D.J., R.W. Muzzi, L.A. Meadows, and G.A.
Meadows. 1987. Ship-Borne Acoustic Doppler Current
Profiler and Measurements in Shallow Waters. American
Geophysical Union Fall Meeting, San Francisco,
California. December 1987.
Liu, P.C. and D.J. Schwab. 1986. Estimating Sea Surface
Friction Velocity from Wind Speed and Air-Sea
Temperature Differences. Sixth Conference on Ocean-
Atmosphere Interaction of the American Meteorological
Society, Miami, Florida. January 1986.
Liu, P.C. and D.J. Schwab. 1986. On a Finite-Depth
Modification to the GLERL/Donelan Wave Prediction
Model. American Geophysical Union Fall Meeting, San
Francisco, California. December 8-12, 1986.
Schwab, D.J. 1986. Wave Research on the Great Lakes.
Ann Arbor Power Squadron, Ann Arbor, Michigan.
January 1986.
Schwab, D.J. and A.H. Clites. 1986. The Effect of Wind-
Induced Circulation on Retention Time in Lake St. Clair.
29th Conference on Great Lakes Research, International
Association for Great Lakes Research, University of
Toronto, Toronto, Ontario, Canada. May 26-29, 1986.
Schwab, D.J. and E.W. Lynn. 1986. Introduction to the
GLERL Data Access System (GDAS) for Computer
Storage of Time-Series Data Bases. Great Lakes
Environmental Research Seminar, Ann Arbor, Michigan.
January 1986.
Bennett, J.R., D.J. Schwab, and E.W. Lynn. 1985.
"Pathfinder"-An Interactive Model for Trajectory
Prediction in the Great Lakes. Applications of Real-Time
Oceanographic Circulation Modeling, Laurel, Maryland.
May 1985.
Schwab, D.J. 1985. A Numerical Wave Forecast Model
for the Great Lakes. NMC Seminar Series, National
Meteorological Center, Camp Spring, Maryland. February
1985.
Schwab, D.J. and J.R. Bennett. 1985. A Lagrangian
Comparison of Objectivity Analyzed and Dynamically
Modeled Circulation Patterns in Lake Erie.
IAMAP/IAPSO Joint Assembly, Honolulu, Hawaii.
August 1985.
Schwab, D.J., J.R. Bennett, and E.W. Lynn. 1985.
"Pathfinder"-An Interactive Model for Trajectory
Prediction in the Great Lakes. 28th Conference on Great
Lakes Research, International Association for Great Lakes
Research, University of Wisconsin, Milwaukee,
Wisconsin. June 3-5, 1985.
Schwab, D.J. and P.C. Liu. 1985. Intercomparison of
Wave Measurements Obtained from a NOMAD Buoy and
from a Waverider Buoy in Lake Erie. MTS-IEEE
Conference, Oceans '85, San Diego, California. November
1985.
205
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Bennett, J.R., D.J. Schwab, and P.C. Liu. 1984.
Predicting Wind Waves on the Great Lakes with a
Parametric Dynamical Model. Fifth Conference on
Ocean-Atmosphere Interaction, Miami, Florida. January
1984.
Liu, P.C. and D.J. Schwab. 1984. A Comparison of
Synoptic Wave Predictions in Lake Michigan with
Measurements. AGU/ASLO Ocean Sciences Meeting,
New Orleans, Louisiana. January 1984.
Schwab, D.J. 1984. Currents in the Great Lakes. Invited
Informational Seminar. USCG-Air Search and Rescue
Division, Mt. Clemens, Michigan. February 1984.
Schwab, D.J. and J.R. Bennett. 1984. Analysis of Lake
Erie Circulation Patterns. AGU Spring Meeting,
Cincinnati, Ohio. May 1984.
Schwab, D.J. 1983. Observed and Modelled Low-
Frequency Current Fluctuations in Lake Michigan
(Including 12 Minute Computer-Generated Mo vie of 1976
Circulation Patterns - "Return of the Gyres"). Great Lakes
Environmental Research Laboratory Seminar, Ann Arbor,
Michigan. April 1983.
Schwab, D.J. 1983. Numerical Models of Low-Frequency
Current Fluctuations in Lake Michigan. AGU Spring
Meeting, Baltimore, Maryland. June 1983.
Schwab, D.J. 1983. A Simple Numerical Wind Wave
Prediction Model for the Great Lakes. NOAA R/D
Science Seminar, Rockville, Maryland. November 1983.
Liu, P.C., D.J. Schwab, J.R. Bennett, G.A. Meadows, H.
Dannelongue, J.E. Campbell, and H. Schultz. 1982.
LEX-81: Measurement of Directional Wave Spectra and
Coastal Dynamics in Lake Erie. Ocean Sciences
AGU/ASLO Meeting, San Antonio, Texas. February
1982.
Meadows, G.A., H. Schultz, J.R. Bennett, D.J. Schwab,
and, P.C. Liu. 1982. The Response of the Coastal
Boundary Layer to Wind Waves. AGU/ASLO Joint
Meeting, San Francisco, California. December 1982.
Meadows, G.A., H. Schultz, J.R. Bennett, D.J. Schwab,
P.C. Liu, J.E. Campbell, and H. Dannelongue. 1982. The
Response of the Coastal Boundary Layer to Wind and
Waves: A Preliminary Analysis of an Experiment in Lake
Erie. Third Workshop on Great Lakes Coastal Erosion
and Sedimentation, Canada Centre for Inland Waters,
Burlington, Ontario, Canada. November 1982.
Schwab, D.J. 1982. An Inverse Method for Determining
Wind Stress from Water Level Fluctuations. Ocean
Sciences AGU/ASLO Joint Meeting, San Antonio, Texas.
February 1982.
Schwab, D.J. 1982. Storm Surge Research on Lake Erie.
Geophys. Colloquium Series-University of Hamburg,
Hamburg, West Germany. June 1982.
Schwab, D.J. 1982. Calculation of Seiches in Closed
Basins with the Lanczos Procedure. Internal Colloquim
Series, Research Institute for Hydraulics, Hydrology and
Glaziology, ETH-Zurich, Zurich, Switzerland. June 1982.
Schwab, D.J. and J.A. Morton. 1982. Calculation of
Overlake Winds from Overland Wind: A Comparison of
Methods. 25th Conference on Great Lakes Research,
International Association for Great Lakes Research, Sea
Lamprey Control Centre, Sault Ste. Marie, Ontario,
Canada. May 4-6, 1982.
Schwab,D.J. 1981. Determining Wind Stress from Water
Levels. Department of Atmospheric and Oceanic Sciences
Seminar, University of Michigan, Ann Arbor, Michigan.
February 1981.
Schwab, D.J. 1981. Determination of Wind Stress from
Water Level Fluctuations on Lake Erie. Great Lakes
Environmental Research Laboratory Seminar, Ann Arbor,
Michigan. March 1981.
Schwab, D.J. and D.B. Rao. 1981. Free Oscillations of
the Mediterranean and Adriatic Seas. Annual Meeting of
the American Society of Limnology and Oceanography,
Milwaukee, Wisconsin. June 1981.
Rao, D.B. and D.J. Schwab. 1980. Objective Analysis of
Currents in a Homogeneous Lake. Spring Meeting of the
American Geophysical Union, Toronto, Ontario, Canada.
May 1980.
206
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Schwab, DJ. 1980. Lake Michigan Waves-Synthetic
Aperture Radar (SAR) Images Compared to Wavestaff
Array Observations. Great Lakes Environmental
Research Laboratory Seminar, Ann Arbor, Michigan.
June 1980.
Schwab, D.J., P.C. Liu, and R.A. Shuchman. 1980.
Comparison of Wind Wave Spectra Determined from
Synthetic Aperture Radar Imagery and from a Tower in
Lake Michigan. Spring Meeting of the American
Geophysical Union, Toronto, Ontario, Canada. May 1980.
Schwab, D.J. 1979. Simulation and Forecasting of Great
Lakes Storm Surges. NOAA Techniques Development
Laboratory Seminar, Silver Spring, Maryland. July 1979.
Schwab, D.J. and W.S. Richardson. 1979. Verification
and Comparison of Statistical and Dynamical Lake Erie
Storm Surge Forecasts. 22nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Rochester, Rochester, New York.
April 30-May 3, 1979.
Schwab, DJ. 1978. Lake Erie Storm Surge Simulations.
Great Lakes Environmental Research Laboratory Seminar,
Ann Arbor, Michigan. May 1978.
Schwab, D.J. 1978. Analytical and Empirical Response
Functions for Storm Surges on Lake Erie. International
Symposium on Long Waves in the Ocean, Ottawa,
Ontario, Canada. June 1978.
Schwab, D.J. 1978. Storm Surge Studies on the Great
Lakes. American Society of Civil Engineers Annual
Convention, Chicago, Illinois. October 1978.
Schwab, D.J. 1977. Dynamical Simulation and
Forecasting of Wind Tides on Lake Erie. 20th Conference
on Great Lakes Research, International Association for
Great Lakes Research, University of Michigan, Ann
Arbor, Michigan. May 10-12, 1977.
Schwab, D.J. and D.B. Rao. 1976. External and Internal
Oscillations in Lakes. Second Annual Meeting of the
American Geophysical Union Midwestern Region, Ann
Arbor, Michigan. October 1976.
Rao, D.B. and D.J. Schwab. 1974. Two-Dimensional
Normal Modes in Arbitrary Enclosed Basins on a Rotating
Earth: Application to Lakes Ontario and Superior. 18th
Conference on Great Lakes Research, International
Association for Great Lakes Research, Hamilton, Ontario,
Canada. May 20-23, 1974.
Schwab, D.J. 1974. A Normal Mode Method for
Predicting Storm Surges on a Lake. Great Lakes
Environmental Research Laboratory Seminar, Ann Arbor,
Michigan. April 1974.
207
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Dmitry Beletsky
Research Fellow
Cooperative Institute for Limnology and Ecosystem
Research
The University of Michigan
National Oceanic and Atmospheric Administration
Great Lakes Environmental Research Laboratory
2205 Commonwealth Boulevard
Ann Arbor, Michigan 48105-2945
(734) 741-2360
Fax: (734) 741-2055
beletsky@glerl.noaa.gov
Role in the Lake Michigan Mass Balance
Project
Numerical hydrodynamic modeling for the Lake Michigan
Mass Balance Project with the Great Lakes version
(Schwab and Bedford, 1994) of the Princeton Ocean
Model of Blumber and Mellor (1987).
Education
Ph.D., Physical Limnology/Oceanography, Institute for
Lake Research, Russian Academy of Sciences, St.
Petersburg, Russia, 1992
M.S., Marine Engineering (Major in Oceanography),
Russian Hydrometeorological Institute, St. Petersburg,
Russia, 1982.
Professional Experience Related to Modeling
Consultant, Cooperative Institute for Limnology and
Ecosystem Research, University of Michigan, 1994-1995
Visiting Scientist, National Oceanic and Atmospheric
Administration, Great Lakes Environmental Research
Laboratory, 1994-1995
Research Scientist, Institute for Lake Research, Russian
Academy of Science, St. Petersburg, 1992-1994
Assistant Research Scientist, Institute for Lake Research,
Russian Academy of Science, St. Petersburg, 1989-1992
Research Assistant, Institute for Lake Research, Russian
Academy of Science, St. Petersburg, 1986-1989
Research and Teaching Assistant, Russian
Hydrometeorological Institute, St. Petersburg, 1985-1986
Projects Related to Modeling
Lake Circulation Model Studies, 1994-1995
Hydrodynamic Modeling of Lake Ladoga, 1992-1994
Hydrodynamic Modeling of Lake Onega, 1986-1992
Hydrodynamic Modeling of the White Sea, 1985-1986
Publications
Schwab, D.J. and D. Beletsky. 1997. Propagation of
Kelvin Waves Along Irregular Coastlines in Finite-
Difference Models. Submitted to Advances in Water
Resources.
Beletsky, D., W.P O'Connor, D.J. Schwab, and D.E.
Dietrich. 1997. Numerical Simulation of Internal Kelvin
Waves and Coastal Upwelling Fronts. J. Phys. Oceanogr.,
in press.
Beletsky, D., K.K. Lee, and D.J. Schwab. 1997. Recent
Advances in Hydrodynamic Modeling of the Great Lakes.
Proceedings of the XXVIJIAHR Congress, accepted.
Beletsky, D., W.P. O'Connor, and D.J. Schwab. 1997.
Hydrodynamic Modeling for the Lake Michigan Mass
Balance Project. In - G. Delic and M.F. Wheeler (Eds.),
Next Generation Environmental Models Computational
Methods, pp. 125-128. Proceedings of a U.S.
Environmental Protection Agency Sponsored Workshop at
the National Environmental Supercomputing Center,
August 7-9, 1995, Bay City, Michigan, SIAM,
Philadelphia, Pennsylvania.
Schwab, D.J., D. Beletsky, W.P. O'Connor, and D.E.
Dietrich. 1996. Numerical Simulation of Internal Kelvin
Waves with z-Level and Sigma Level Models. In - M.L.
Spaulding and R.T. Cheng (Eds.), Estuarine and Coastal
Modeling, pp. 298-312, Proceedings of the Fourth
International Conference, October 26-28, 1995, San
Diego, California, American Society of Civil Engineers,
New York, New York.
Beletsky, D. 1996. Numerical Modeling of Large Scale
Circulation in Lakes Onega and Ladoga. Hydrobiologia.
322:75-80.
208
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Naumenko, M.A., D. Beletsky, V.B. Rumyantsev, V.S.
Etkin, K.T., S. Litovchenko, and A.V. Smirnov. 1994.
Investigation of Hydrobiological Situation in Lake Onega
Using Joint Spaceborne Radar, Airborne and In Situ
Measurements. Internal. J. Rem. Sens. 15:2039-2049.
Beletsky, D., N.N. Filatov, and R.A. Ibraev. 1994.
Hydrodynamics of Lakes Ladoga and Onega. Water.
Pollut. Res. J. Canada, 29:365-384.
Beletsky, D., N.N. Filatov, and R.A. Ibraev. 1993.
Dynamics of Lakes Ladoga and Onega. In - N.N. Filatov
(Ed.), Problems of Physical Limnology, pp. 7-29, Northern
Water Problems Institute, Karelian Scientific Centre of
RAS, Petrozavodsk, Russia. (In Russian.)
Beletsky, D., Yu.L. Demin, and N.N. Filatov. 1991.
Comprehensive Investigation of Hydrophysical Fields in
Lake Onega as an Ocean Simulation Model. Izv., Atmos.
Ocean. Phys., 27:854-861.
Filatov, N.N., D. Beletsky, and L.V. Zaitsev. 1991.
Synthesis of Measurements and Numerical Modeling in
Lakes Hydrodynamics. Proceedings of the Conference on
Investigations of Stochastic Processes: Planning and Data
Analysis Petrozavodsk, pp. 114-115. (In Russian.)
Filatov, N.N., D. Beletsky, and L.V. Zaitsev. 1990.
Variability of Currents in Lake Onega During the Period
of Full Stratification Derived from In Situ Measurements
and Numerical Modeling. In Z. Kaufman (Ed.),
Ecological System of Lake Onega and the Tendencies of
Its Changing, pp. 85-94. Nauka Publ., Leningrad. (In
Russian.)
Filatov, N.N., D. Beletsky, and L.V. Zaitsev. 1990.
Variability of Hydrophysical Fields in Lake Onega.
"Onego" Experiment. Water Problems Department,
Karelian Scientific Center AS USSR, Petrozavodsk, 114
pp. (In Russian.)
Demin, Yu.L., I.O. Akhverdiev, D. Beletsky, and N.N.
Filatov. 1990. Hydrodynamical Diagnosis of Currents in
Large Lakes and Reservoirs. Department of
Computational Mathematics AS USSR, Preprint 267,
Moscow, 38 pp. (In Russian.)
Demin, Yu.L., D. Beletsky, and N.N. Filatov. 1989.
Diagnostic Calculation of the Summer Water Circulation
in Lake Onega. Izv., Atmos. Ocean. Phys., 25:410-411.
209
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Dale J. Patterson
Chief, Water Quality Modeling Section
Wisconsin Department of Natural Resources
WT/2, Box 7921
Madison, Wisconsin 53707
(608) 266-0155
Fax: (608) 267-2800
patted@dnr.state.wi.us
Role in the Lake Michigan Mass Balance
Project
I have been involved in the Mass Balance Modeling
project beginning with the Green Bay/Fox River Mass
Balance. In that project, I designed the sampling program
for all water column, sediment, point source and runoff
samples taken upstream of the DePere dam. I also
designed the sampling program for sediments and point
sources below the DePere dam. I supervised the
application of the WASP4 model to the river upstream of
the DePere dam and served on the Modeling Committee.
For the Lake Michigan Mass Balance, I have participated
on the Modeling Committee, but have not been directly
involved in data collection or modeling. I anticipate being
involved in modeling of tributary loads at key sites to
provide estimates of contaminant loadings to supplement
direct load calculations being done with collected tributary
data.
Education
B.S., Applied Mathematics and Physics, University of
Wisconsin, 1970
M.S., Civil and Environmental Engineering, University of
Wisconsin, 1981
Experience Related to Mathematical Modeling
I have worked with the Wisconsin Department of Natural
Resources since 1973 as a water quality modeler. I have
developed wasteload allocations for several segments of
significant rivers in the state. These segments had
multiple dischargers that were overloading the streams and
required reductions below the levels of categorical effluent
limits to correct dissolved oxygen problems due to
excessive organic loads. These model required
development of special techniques to determine the
oxygen demand of paper and pulp mill waste.
Conventional methods were not adequate for this task.
Prior to the development of this long term BOD method,
dissolved oxygen models were not successfully predictive.
Since 1987, I have been involved with development of
PCB and sediment transport models on the Fox and other
rivers in Wisconsin. This work included design and
collection of PCB and related data for 40 miles of the Fox
River where PCBs are known to be present in significant
quantities. Delineation of sediment deposits and
measurement of the concentration and mass of PCB
present were primary aims to provide data to drive
transport models. Water column collection provided data
to calibrate and verify the models predictive capability.
Publications
Polychlorinated Biphenyl (PCB) Contaminated Sediment
in the Lower Fox River: Modeling Analysis of Selective
Sediment Remediation. Wisconsin Department of Natural
Resources, Madison, Wisconsin. Publication WT-482-97,
1997.
Steuer, J., S. Jaeger, and D. Patterson. 1995. A
Deterministic PCB Transport Model for the Lower Fox
River Between Lake Winnebago and DePere, Wisconsin.
Wisconsin Department of Natural Resources, Madison,
Wisconsin. Publication WR 389-95, 283 pp.
Velleux, M., D. Endicott, J. Steuer, S. Jaeger, and D.
Patterson. 1995. Long-Term Simulation of PCB Export
from the Fox River to Green Bay. J. Great Lakes Res.,
21(3):359-372.
Patterson, D.L. 1986. Water Quality Modeling of the
Lower Fox River for Wasteload Allocation Development,
Cluster HI Water Quality Modeling. Wisconsin
Department of Natural Resources, Madison, Wisconsin.
Patterson, D.L. 1983. Water Quality Modeling of the
Upper Wisconsin River for Wasteload Allocation
Development, Segment D. Wisconsin Department of
Natural Resources, Madison, Wisconsin.
Patterson, D.L. 1980. Modified QUAL ffl Water Quality
Model Documentation Updates to 1989. Wisconsin
Department of Natural Resources, Madison, Wisconsin.
210
-------
Patterson, D.L. 1980. State of Wisconsin Wasteload
Allocation For the Lower Fox River, Data Base for River
Modeling of Water Quality. Wisconsin Department of
Natural Resources, Madison, Wisconsin.
Patterson, D.L. 1980. Water Quality Modeling of the
Lower Fox River for Wasteload Allocation Development,
Segment I and EL Wisconsin Department of Natural
Resources, Madison, Wisconsin.
Patterson, D.L. 1980. Water Quality Modeling of the
Lower Fox River for Wasteload Allocation Development,
Cluster HI Hydrodynamic Modeling. Wisconsin
Department of Natural Resources, Madison, Wisconsin.
Patterson, D.L., E. Epstein, and J. McEvoy. 1975. Water
Pollution Investigation - Lower Green Bay and Lower Fox
River. U.S. Environmental Protection Agency, Region V,
Chicago, Illinois. EPA-905/9-74-017, 371 pp.
Patterson, D.L. 1974. Lower Green Bay, An Evaluation
of Existing and Historical Conditions. U.S. Environmental
Protection Agency, Region V, Chicago, Illinois. EPA-
905/9-74-006.
Wisconsin Department of Natural Resources. 1973.
Water Quality Modeling of the Fox River. Wisconsin
Department of Natural Resources, Madison, Wisconsin.
211
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Mark Velleux, P.E.
Water Resources Engineer
Wisconsin Department of Natural Resources
P.O. Box 7921
Madison, Wisconsin 53707-7921
(608) 267-5262
Fax: (608) 267-2800
vellem@dnr.state.wi.us
Role in the Lake Michigan Mass Balance
Project
Develop contaminant transport models for major Lake
Michigan tributaries: Fox, Sheboygan, and Milwaukee
Rivers. Model results used to estimate tributary loads for
Level 2 to Lake Michigan mass balance model. Contribute
to development of the IPX framework for tributary and
lake models.
Education
M.S., Civil and Environmental Engineering, Clarkson
University, 1993
B.S., Civil and Environmental Engineering, University of
Michigan, 1987
Training
Modeling Fate and Transport of Toxic Substances in
Surface and Ground Waters, 33rd Seminar Institute in
Water Quality Control, Manhattan College, New York,
June 1988.
Storm Water Management Modeling Workshop (SWMM
4.2), U.S. Environmental Protection Agency, Detroit,
Michigan, September 1992.
Experience
Water Resource Engineer, Wisconsin Department of
Natural Resources, 1994-Present. Develop couple
sediment and contaminant transport models for Great
Lakes tributaries; estimate contaminant export from
tributaries to receiving waterbodies. Responsible for
development of PCBV transport model for the Fox River
downstream of DePere. Responsible for the continued
development of the IPX water quality modeling
framework.
Senior Mathematical Modeler, AScI Corporation, USEPA,
LLRS, Grosse He, Michigan, 1991-1994. Developed
coupled sediment and contaminant transport models for
Great Lakes tributaries. Contributed to the Green Bay
Mass Balance Study. Responsible for continued
development of contaminant transport models for the Fox
River and initial development of the IPX water quality
modeling framework.
Research Assistant, Clarkson University/University of
Buffalo, Potsdam/Buffalo, New York, 1990-1991.
Developed couple sediment and contaminant transport for
PCBs in the Fox River, Wisconsin and mirex in the
Oswego River, New York. Contributed to the GBMBS.
Mathematical Modeler, AScI Corporation, USEPA, LLRS,
Grosse lie, Michigan, 1988-1990. Developed a far-field
contaminant transport model for PCBs in Saginaw Bay,
Michigan, to examine the impact of contaminant migration
from confined disposal facilities. Contributed to the
GBMBS. Responsible for initial development of the PCB
transport and fate model for the Fox River downstream of
DePere.
Publications
Velleux, M., J. Gailani, and D. Endicott. 1996.
Screening-Level Approach for Estimating Contaminant
Export from Tributaries. J. Environ. Engin., 122(6):503-
514.
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.
Velleux, M.L., J. Gailani, F. Mitchell, and D. Endicott.
1993. In-Place Pollutants Export Model (IPX): User's
Guide and Description of Modifications Beyond
TOXI4LFR. Report to the U.S. Environmental Protection
Agency, Office of Research and Development, ERL-
Duluth, Large Lakes Research Station, Grosse lie,
Michigan. 3 pp.
Velleux, M.L., J.E. Rathbun, R.G. Kreis, Jr., J.L. Martin,
MJ. Mac, and M.L. Tuchman. 1993. Investigation of
Contaminant Transport from the Saginaw Confined
Disposal Facility. J. Great Lakes Res., 19(1): 158-174.
212
-------
Freeman, K., F. Mitchell, M. Velleux, and D. Endicott.
1992. Changes to the LLRS Implementations of WASP4
and TOXI Specific to the Lower Fox River Applications.
Report to the U.S. Environmental Protection Agency,
Office of Research and Development, ERL-Duluth, Large
Lakes Research Station, Grosse lie, Michigan. 7 pp.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1988. Confined Disposal Facility Far-Field Modeling
Project Report: An Application to Saginaw Bay. Internal
Report. U.S. Environmental Protection Agency, Office of
Research and Development, ERL-Duluth, Large Lakes
Research Station, Grosse He, Michigan. 11 pp.
Presentations
Velleux, M.L., D. Endicott, and K. Freeman. 1993. A
Mass Balance Model for Estimating Contaminant Export
from the Lower Fox River to Green Bay. 36th Conference
on Great Lakes Research, International Association for
Great Lakes Research, St. Norbert College, DePere,
Wisconsin. June 4-10, 1993.
Velleux, M.L., D. Endicott, and J. DePinto. 1991. A
Mass Balance Analysis of Contaminant Transport and Fate
in the Lower Fox River. 34th Conference on Great Lakes
Research, International Association for Great Lakes
Research, State University of New York at Buffalo,
Buffalo, New York. June 3-6, 1991.
Martin, J.L., M. Velleux, and K. Rygwelski. 1989.
Screening Level PCB of Model of Green Bay, Lake
Michigan. 32nd Conference on Great Lakes Research,
International Association for Great Lakes Research,
University of Wisconsin, Madison, Wisconsin. May 30-
June 2, 1989.
Velleux, M.L., J. Martin, J. Rathbun, and R. Kreis, Jr.
1989. Predicted and Observed Impacts of Contaminant
Transport from the Saginaw Bay Diked Facility. Tenth
Annual Meeting of the Society of Environmental
Toxicology and Chemistry, Toronto, Ontario, Canada.
October 28-November 2, 1989.
Velleux, M.L., D.D. Endicott, and W.L. Richardson.
1989. Predicted Water Quality Impacts of CDF Leakage
on Saginaw Bay. 32nd Conference on Great Lakes
Research, International Association for Great Lakes
Research, University of Wisconsin, Madison, Wisconsin.
May 30-June 2, 1989.
213
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Thomas M. Cole
Research Hydrologist
U.S. Army Engineer Waterways Experiment Station
CEWES-ES-Q
3909 Halls Ferry Road
Vicksburg, Mississippi 39180-6199
(601) 634-3283
Fax:(601)634-3129
tcole@lasher.wes.army.mil
Role in the Lake Michigan Mass Balance
Project
Implement the QUICKEST/ULTIMATE higher-order
transport scheme into the IPX water quality model and link
the POM to IPX.
Education
B.S., Aquatic Biology/Chemistry, Southwest Texas State
University, 1978
M.S., Biology/Computer Science, Southwest Texas State
University, 1982
Completed coursework for Ph.D., Environmental
Engineering/Mathematics, Texas Technological
University, 1987
Completed coursework for Ph.D., Environmental
Engineering, Portland State University, 1996
Training
Lake and Reservoir Water Quality Modeling, Duke
University, one week 1987
Cray Supercomputer Training, one week, 1988
Modeling of Transport, Fate, and Bioaccumulation of
Toxic Substances in Surface Water, Manhattan College,
one week, 1994
Awards
Meritorious Civilian Service Award, 1991
Commander and Director's Research and Development
Achievement Award, WES, 1991
Outstanding Planning Achievement Award, Baltimore
District, 1991
Outstanding Planning Achievement Award, North Atlantic
Division, 1991
Department of the Army Research and Development
Award, 1992
Technology Transfer Award, USEPA, 1995
Wesley H. Homer Award, American Society of Civil
Engineers, Journal of the Environmental Engineering,
1995.
Publications
Cole, T.M. 1997. Application of CE-QUAL-W2 to J.
Strom Thurmond Reservoir. To be published as WES TR.
Draft submitted to sponsor and returned for revisions.
Publication in 1997.
Tillman, D.H., T.M. Cole, and B. Bunch. 1997. Detailed
Reservoir Water Quality Modeling (CE-QUAL-W2),
Alabama-Coosa-Tallapoosa/Apalachicola-Chattahoochee-
Flint (ACT/ACF) Comprehensive Water Resource Study.
To be published as WES TR. Draft submitted to sponsor,
returned for revisions, and revisions complete. Publication
in 1997.
Li, S.G., T. Cole, F. Ruan, and D.B. McLaughlin. 1996.
A Generalized Analytical Testing Technique for
Hydrologic Models. In Proceedings of International
Conference on Computational Methods in Water
Resources, pp. 19-26, Cancun, Mexico. July 22-26,1996.
Tillman, D.H. and T.M. Cole. 1996. Simulation of
Richard B. Russell and J. Strom Thurmond Reservoirs for
Pump-Storage Using CE-QUAL-W2. jto Water Quality
'96: Proceedings of the llth Seminar, Seattle,
Washington, February 1996.
Cole, T.M. 1995. Review of Water Quality Monitoring
and Recommendations for Water Quality Modeling of the
Lower St. John's River. U.S. Army Corps of Engineers,
Waterways Experiment Station, Vicksburg, Mississippi.
Miscellaneous Paper EL-95-3.
Cole, T.M. and E.M. Buchak. 1995. CE-QUAL-W2: A
Two-Dimensional, Laterally Averaged, Hydrodynamic and
Water Quality Model, Version 2.0 User Manual. U.S.
Army Corps of Engineers, Waterways Experiment
Station, Vicksburg, Mississippi. Instruction Report EL-
95-1, 352 pp.
214
-------
Cole, T.M., M.L. Schneider, J.G. Skogerboe, R.E. Heath,
and H.O. Turner. 1995. Temperature and Dissolved
Oxygen Simulations for the Upper Missouri River
Reservoirs. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi.
Miscellaneous Paper EL-95-7, 255 pp.
Cerco, C.F. and T.M. Cole. 1994. Three-Dimensional
Eutrophication Model of Chesapeake Bay; Volume 1,
Main Report. U.S. Army Corps of Engineers, Waterways
Experiment Station, Vicksburg, Mississippi. Technical
Report EL-94-4, 652 pp.
Cole, T.M. 1994. The Future Role of Sophisticated
Models in Reservoir Management. Lake Res. Manag.,
9(2).
Cole, T.M. 1994. CE-QUAL-W2, Version 2.0. WOTS
Bull., Vol. E-94-1.
Harberg, M., D. Latka, T. Cole, J. Nestler, and G.
Ploskey. 1994. Development of Fisheries Models for the
Missouri River System. In Proceedings of the 21st
Annual Conference of the Water Resources Planning and
Management Division, American Society of Civil
Engineers, Denver, Colorado.
Tillman, D.H. and T.M. Cole. 1994. Bluestone Phase 2
Temperature and Dissolved Oxygen Modeling Study. U.S.
Army Corps of Engineers, Waterways Experiment Station,
Vicksburg, Mississippi. Miscellaneous Paper EL-94-2.
Tillman, D.H. and T.M. Cole. 1994. Bluestone Modeling
Study. In Water Quality '94: Proceedings of the 10th
Seminar, Savannah, Georgia, February 1994.
Cerco, C.F. and T.M. Cole. 1993. Three Dimensional
Eutrophication Model of Chesapeake Bay. J. Environ.
Engin, 119:1006-1025.
Cerco, C.F. and T.M. Cole. 1992. Overview of
Chesapeake Bay Water Quality Model. Mar. Environ.
Res.
Chapman, R. S. and T.M. Cole. 1992. Improved Thermal
Predictions in CE-QUAL-W2. Proceedings of Water
Forum '92, American Society of Civil Engineers,
Baltimore, Maryland, August 1992.
Cerco, C.F., and T.M. Cole. 1991. Thirty-Year Simula-
tion of Chesapeake Bay Eutrophication. In: M.
Spaulding, K. Bedford, A. Blumberg, R. Cheng, and C.
Swanson (Eds.), Estuarine and Coastal Modeling, pp. 116-
126. Proceedings of the Second International Conference,
American Society of Civil Engineers.
Cerco, C.F. and T.M. Cole. 1991. Thirty-Year Simulation
of Chesapeake Bay Dissolved Oxygen. In Lee and
Cheung (Eds.), Proceedings of the International Sympo-
sium on Environmental Hydraulics, pp. 771-776.
Cole, T.M. and H.H. Hannan. 1990. Dissolved Oxygen
Dynamics. In Thornton, Kimmel, and Payne (Eds.),
Reservoir Limnology - Ecological Perspectives, Chapter
3, John Wiley and Sons, Incorporated, New York, New
York.
Cerco, C.F. and T.M. Cole. 1989. Calibrating the
Chesapeake Bay Water Quality Model, hi - M. Spaulding
(Ed.), Estuarine and Coastal Circulation and Pollutant
Transport Modeling: Model-Data Comparison, pp. 192-
199. American Society of Civil Engineers.
Ramsey, R.H., Y. Liu, and T.M. Cole. 1985. Water
Quality Results from Selected Recharge Units. In
Aquifer Recharge from Playa Lakes Research Status - Fall,
1985. Water Resources Center, Texas Technological
University, Lubbock, Texas.
Waide, J.B., M.S. Dortch, and T.M. Cole. 1984. Two-
Dimensional Reservoir Model. EWQOS Inform. Exch.
Bull., Vol. E-84-3.
Cole, T.M. 1982. Application of the LARM Two-
Dimensional Computer Model to Canyon Reservoir.
Masters' Thesis, Southwest Texas State University, San
Marcos, Texas.
Cole, T.M. and H.H. Hannan. 1981. Application of the
LARM Computer Model to Canyon Reservoir. Report to
the U.S. Army Corps of Engineers, Waterways Experiment
Station, Vicksburg, Mississippi. Contract DACW39-81-
M-0822.
Hannan, H.H. and T.M. Cole. 1979. Water Quality
Analysis of Canyon Reservoir Data. Report to the U.S.
Army Corps of Engineers, Waterways Experiment Station,
Vicksburg, Mississippi. Contract DACW39-79-M-2987.
215
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Mark S. Dortch
Professional Activities and Awards
Supervisory Research Civil Engineer, GS-15
Chief, Water Quality and Contaminant Modeling Branch
Environmental Processes and Effects Division
Environmental Laboratory
U.S. Army Corps of Engineers
Waterways Experiment Station
ES-Q
3909 Halls Ferry Road
Vicksburg, Mississippi 39180-6199
(601)634-3517
Fax:(601)634-3129
dortchm@exl.wes.army.mil
Education
B.S., Aerospace Engineering, Mississippi State University,
1971
M.S., Engineering, Mississippi State University, 1972
Ph.D., Civil Engineering, Colorado State University, 1990
Expertise
Water quality and contaminant modeling of surface water
Transport processes and numerical modeling of transport
Linkage of hydrodynamic and water quality models
Groundwater contaminant transport modeling
Wetland water quality treatment
Professional Expertise
Research Hydraulic Engineer, WES Hydraulics
Laboratory, 1972-1983. Physical model studies of
hydraulic structures; studies of reservoir hydrodynamics,
stratified flow, and mixing; and numerical reservoir
thermal modeling studies.
Chief, Water Quality and Contaminant Modeling Branch,
WES Environmental Laboratory, 1983-Present. Water
quality and contaminant modeling of all types of surface
water systems; lead the development of simulators for
subsurface in-situ contaminant remediation.
Member of ASCE (Fellow Grade), AGU, and Sigma Xi
Associate Editor of ASCE Journal of Hydraulic
Engineering, 1990-1994
Produced 90 technical publications
Registered Professional Engineer in Mississippi
Herbert D. Vogel WES Engineer Award, 1991
North Atlantic Division, USAGE, Outstanding Planning
Achievement Award, 1991
Department of Army Meritorious Civilian Service Award,
1991
Commanders Research and Development Achievement
Award, 1991
Department of Army Research and Development
Achievement Award, 1992
Training
Technical Report Writing (OPM, three days, 1973)
Water Quality Modeling for Rivers and Reservoirs (HEC,
one week, 1976)
Mathematical Modeling of Environmental Systems
(Manhattan College, one week, 1974)
Value Engineering (OPM, two days, 1975)
Radiological Monitoring (OPM, two days, 1978)
Statistical Hydrology (Colorado State University, one
week, 1978)
Technical Writing (Shipley Associates, three days, 1981)
Supervision and Group Performance (three days, 1983)
Dale Carnegie Course (WES-EL through contractor, five
days, 1985)
Workshop for First Line Managers (Mississippi Research
and Development Center, three days, 1985)
Several computer short courses (WES, one to two days
each, 1970s-1980s)
Lake and Reservoir Water Quality Modeling (Duke
University, one week, 1987)
WES Management Seminar (WES through contractor, two
days, 1987)
Modeling Fate of Toxic Substances (University of
Colorado, three days, 1988)
Oil Spill Modeling (San Diego, California, three days,
1991)
Groundwater Contaminant Transport Modeling
(University of Vermont, three days, 1991)
MINTEQA2 Metals Speciation Equilibrium Modeling
(WES by USEPA, three days, 1991)
216
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Groundwater Flow and Transport Modeling (University of
Colorado, one week, 1991)
Multiphase Flow and Transport Modeling in Porous Media
(WES, three days, 1991)
Hazardous/Radioactive Waste Management, WERC
Videoconference Training Series (four 4-hour satellite
video series, 1991)
World Oil Spill Model (WOSM) training course (ASA,
Inc., three days, Narragansett, Rhode Island, 1992 and
1993)
Labor Relations Short Course (WES, three days, 1992)
Developmental Assignment (CERD-C, Washington, D.C.,
four months, 1993)
Executive Development Seminar (Arlington, Virginia, four
days, 1995)
Leadership Development Program, Conducted by Center
for Creative Leadership (San Diego, California, six days,
1996)
CE Executive Development Program (graduated
November 1996)
Introduction to Neural Networks (WES, three days, 1996)
Introduction to HPC Parallel Processing (WES, one day,
1996)
Role in the Lake Michigan Mass Balance
Project
Serve as WES oversight for work being conducted with
Mr. Thomas Cole and Dr. Ray Chapman.
Publications
Li, Y., A.J. Mehta, K. Hatfield, and M.S. Dortch. 1997.
Modulation of Constituent Release Across the Mud-Water
Interface by Water Waves. Water Res. Res., 33(6): 1409-
1418.
Hall, R.W. and M.S. Dprtch. 1995. New Jersey
NearshoreHypoxia during the Summer 1976. Proceedings
of the Fourth International Conference on Estuarine and
Coastal Modeling, San Diego, California. October 26-18,
1995.
Dortch, M.S. and C.F. Cerco. 1993. Chesapeake Bay
Water Quality Model. In - Tom Patin (Ed.), Management
of Bottom Sediments Containing Toxic Substances,
Proceedings of the 16th U.S.-Japan Experts Meeting on
Management of Contaminated Sediments, October 1993,
Kitakyushu, Japan.
Dortch, M.S., R.S. Chapman, and S.R. Abt. 1992.
Application of Three-Dimensional, Lagrangian Residual
Transport. J. Hydr. Engin., 118(6):831-848.
Dortch, M.S. and B.H. Johnson. 1992. Hydrodynamics
for Water Quality Models. Jji Marshall Jennings and
Nani Bhowmilk (Eds.), Hydraulic Engineering: Saving a
Threatened Resource - In Search of Solutions, Proceedings
of Water Forum 92, pp. 145-150. American Society of
Civil Engineers, Baltimore, Maryland, August 1992.
Dortch, Mark S. 1991. Long-Term Water Quality
Transport Simulations for Chesapeake Bay. In - J.H.W.
Lee and Y.K. Cheung (Eds.), Proceedings of the
International Symposium on Environmental Hydraulics,
pp. 765-769, University of Hong Kong, Hong Kong,
December 16-18, 1991. A.A. Balkema Publishers,
Rotterdam.
Dortch, M.S. 1990. Three-Dimensional, Lagrangian
Residual Transport Computed from an Intratidal
Hydrodynamic Model. Doctoral Dissertation, Department
of Civil Engineering, Colorado State University, Fort
Collins, Colorado.
Dortch, M.S., R.S. Chapman, J.M. Hamrick, and T.K.
Gerald. 1989. Interfacing 3-D Hydrodynamic and Water
Quality Models of Chesapeake Bay. In Malcolm
Spaulding (Ed.), Proceedings of Conference on Estuarine
and Coastal Modeling, pp. 182-191. American Society of
Civil Engineers, Newport, Rhode Island, November 1989.
Dortch, Mark S. 1988. Approach for 3-D, Time-Varying
Hydrodynamic and Water Quality Model of Chesapeake
Bay. In Steven R. Abt and Johannes Gessler (Eds.),
Hydraulic Engineering, Proceedings of the 1988 National
Conference, pp. 920-925. Hydraulic Division, American
Society of Civil Engineers, Colorado Springs, Colorado,
August 8-12, 1988.
217
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Appendix C
Revision Control System
Introduction
The following is a brief overview of the UNIX commands
needed for using the Revision Control System or RCS
code management. Several example commands are
provided below. However, this document is not intended
as a comprehensive manual of RCS. More detailed
discussions of RCS and source code revision control in
general can be found elsewhere. (See, for instance, Daniel
Gilly's discussion of RCS and SCCS in UNIX in a
Nutshell: System V Edition Oreilly, 2nd Edition, June
1992.)
Unix Directories
By convention, each application, i.e., each model or utility
program, as a directory associated with it. The
relationship between the user's development directory and
the RCS repository for the respective application is shown
in the figure below. The UNIX command for creating a
symbolic link to the repository directory is shown in the
figure.
Parent directory of source code archive:
/usr/users/model/dev/FDCHAIN
User development directory
/usr/u2/dde/FDCHAIN
Subdirectory with revision files: RCS/
Symbolic link to repository: RCS
LLRS Implementation: File Names
The following conventions are being followed for
application source files:
FORTRAN Modules i.e., subroutines and
functions are each given separate files. File
names corresponding to FORTRAN modules are
given the suffix .F (earlier versions of
FDCHAIN, IPX, and UT were given the file
suffix .FORTRAN. Please refer to
http://hobbes.grl.epa.gov/MODELING/dev.html
for a discussion of the development of these
applications (internal to LLRS, will be available
on internet when MED-Duluth, Web Page is
established in the future). FORTRAN include
files containing parameter definitions and
common blocks are given the file suffix .inc,
while cpp header file names are given the suffix
.h.
218
-------
All standard C function file names are given the
suffix .c. C include files are given the standard
file suffix .h.
Repository s-files i.e., those contained in the
subdirectory RCS have the additional file
suffix,.v appended to their names.
The Cheat Sheet
rlog Commands
The following commands are used to show revision
information:
RCS Command Explanation
RCS Command
Explanation
rlog RCS/*
rlog -R RCS/*
rlog RCS/filename
rlog filename
rlog -r 1.1.1 filename
rlog -R -L RCS/*
rlog _R _L -
lusername RCS/*
rlog-dOl-June-
1996/<31-
December-1996
filename
Show detailed revision
information for all source files
Show a all revision files
Show revision information for
source file filename
Show revision information for
source file filename
Show revision information for
branch 1.1.1 of filename
Show revision information for
all files that do not have locks
set
Show files locked by the user
username
Show all revisions made to
filename between 1-June-1996
and 31 -December-1996. Note
the backslash
co Commands
The following commands are used to check out or retrieve
files from the RCS repository:
co filename
co -q filename
co-r 1.1.1 filename
Co -11.1.1 filename
Co -u 1.1.1 filename
Check out source file filename
from the default branch
Check out source file filename
quietly (no diagnostics) from
the default revision branch
Check out the latest revision
of filename from branch 1.1.1
Check out and lock the latest
revision of filename from
branch 1.1.1
Check out and unlock the
latest revision of filename
from branch 1.1.1. Note: you
must already have a lock on
the corresponding revision
ci Commands
The following commands are used to check in files to the
RCS repository. All of these commands assume that the
revision corresponding to the modified file has already
been locked by the user.
RCS Command Explanation
ci filename Check in source file filename
ci -q filename Check in source file filename
quietly (no diagnostics)
ci -r 1.1.1 Check in source file to the
filename revision branch 1.1.1. This is
usually not necessary.
Ci -f filename Force the check in of a source
file filename. Check in is not
normally done if no
modifications were made.
Ci -1 filename Check in the source file filename,
then check out and lock again
ci -u filename Check in the source file filename,
then check out (unlocked) again
219
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rcsdiff Commands
Because the RCS s-files contain both the source and its
revision information, direct comparison of a revised source
file with an RCS s-file is impractical. The RCS command
rcsdiff allows the user to compare a checked out version of
a file with any previous revision of that file, for the
purpose of identifying recent modifications to the source.
RCS Command Explanation
rcsdiff filename
rcsdiff -r 1.1.1
filename
Rcsdiff-r 1.1.1.3
filename
Rcsdiff-r 1.1.1.3-
r 1.1.1.4 filename
Show differences between the
user file filename and its most
recent revision in the default
branch
Show differences between the
user file filename and the last
revision in the branch 1.1.1
Show differences between the
user file filename and the
specific revision 1.1.1.3
Show differences between
revisions 1.1.1.3 and 1.1.1.4 of
the source file filename
An Example Session
In the example below, the user makes modifications to a
source file for the FDCHAIN application. The symbolic
link to the FDCHAIN RCS repository only needs to be
made if it is not already present. The user reviews the
revision information for a specific file, and checks out two
files. The user intends to modify the files being checked
out, and locks them at check out time. Only one file is
modified, but the user desires that both files be checked in
as new revisions. The modified file can be checked in
normally; however, the unmodified file must be checked
in using the -f flag, otherwise no new revisions will be
registered by the source code management. The user than
reviews the revision information for the two files.
%cd
% cd FDCHAIN
development
% In -s ~model/dev/
FDCHAIN/RCS
% flog bioengi.F
% co-16.0.1 bioengi.F
%co-16.0.1bioeng2.F
% vibioengl.f
% rcsdiff bioengi.F
% ci bioengi.F
%ci-fbioeng2.F
% rlog bioengi.F
bioengi.F
# Go to home directory.
# Go to FDCHAIN
directory
# Make symbolic link to
repository
# See revisions for a file
# Check out and lock first file
# Check out and lock second file
# Edit the file
# Compare with last checked in
revision
# Check in first file
# Force check in of second file
# See revision information of
both files
Conclusion
A brief review of the RCS directory structure and
commands has been provided. Example commands for
examining revision information, retrieving and checking
in source, and examining differences between source
revisions has been provided. Review of the RCS
administrator's command res has been intentionally
avoided as that utility, and its functions are not generally
pertinent to the users' interaction with the source code
management system.
220
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Appendix D
Project Approvals
Branch/Team/Project Management Approvals
William L. Richardson
LMMBP Modeling Workgroup Chair
Signature
Date
Douglas D. Endicott
CBSSS Team Leader
Signature
Date
Russell G. Kreis, Jr.
CBSSS Chief
Signature
Date
Health and Safety Approvals
Eric S. Mead
SHEMP Manager
NOT APPLICABLE
Signature
Date
Eric S. Mead
Radiation Safety Officer
NOT APPLICABLE
Signature
Date
Eric S. Mead
Chemical Assessment Committee Chair
NOT APPLICABLE
Signature
Date
Quality Assurance Approvals
Allan R. Batterman
QA Manager
Signature
Date
221
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Jose A. Serrano
Quality of Science Committee Chair Signature Date
Animal Care and Use Approvals
Virginia Snarski NOT APPLICABLE
Animal Care and Use Committee Chair Signature Date
Senior Management Approvals
Vacant
Acting Associate Director of Science Signature Date
Steven P. Bradbury
Acting Division Director Signature Date
Note: When all above signatures are obtained the QAPP has been completely reviewed and approved.
Project QAPP Concurrence
Louis Blume
QA Manager, GLNPO Signature Date
Great Lakes National Program Office Approval
Paul Horvatin
Division Director Signature Date
222
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Subproject Approvals
The following signatures signify that their portions of this document are current and accurate to the best of their
knowledge.
Victor J. Bierman, Jr.
Limno-Tech, Inc.
Signature
Date
Ellen Cooter
USEPA-AMD, RTF
Signature
Date
Thomas Cole
USACOE-WES
Signature
Date
Robert Day
MDEQ
Signature
Date
Gerald Keeler
University of Michigan
Signature
Date
Keri Hornbuckle
SUNY at Buffalo
Signature
Date
Mark Velleux
WDNR
Signature
Date
David Schwab
NOAA-GLERL
Signature
Date
223
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Appendix E
Model Development and Progress
As stated in the main report, many of the sub-models are progress of this work, modelers are now being asked to
being developed as part of the project. They are all at maintain a status sheet indicating the various model levels
different stages of development which is difficult to and stages of their work. Those wishing to receive this
communicate in a report that has taken months to prepare report should make their request by sending an E-mail
and finalize. By the time this report is published, the message to William Richardson: wlr@lloyd.grl.epa.gov or
models will have progressed even further. To call (734) 692-7611.
communicate to managers, participants, and reviewers the
224
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Appendix F
Lake Michigan Mass Balance Project Committees, Workgroups,
and Personnel
(At the time QAPP was Approved - November 1994
Through February 1995)
Executive Steering Committee:
Barry DeGraaf, Acting Director, Water Division, USEPA,
Region 5, Chicago, Illinois
Lloyd Eagan, Air Division, WDNR, Madison, Wisconsin
Christopher Grundler, Director, USEPA, GLNPO,
Chicago, Illinois
Steven Hedtke, Director, USEPA, MED-Duluth, Duluth,
Minnesota
Melissa McCullough, USEPA, Office of Air Quality
Planning and Standards, Washington, D.C.
Richard Powers, Assistant Chief, Michigan Department of
Natural Resources, Surface Water Quality Division,
Lansing, Michigan
QA Program Plan Cooperators:
Daniel Bauer, USGS, Water Resources Division,
Middleton, Wisconsin
Brian Eadie, NOAA, GLERL, Ann Arbor, Michigan
John Gannon, USGS, National Biological Survey, Ann
Arbor, Michigan
Paul Horvatin, USEPA, GLNPO, Chicago, Illinois
Technical Coordinating Committee:
Paul Horvatin, Co-Coordinator, USEPA, GLNPO,
Chicago, Illinois
Brian Eadie, Sediment Co-chairperson, NOAA, GLERL,
Ann Arbor, Michigan
Robert Day, Chairperson, Tributary Load Committee,
Michigan Department of Natural Resources, Lansing,
Michigan
William Richardson, Chairperson, Modeling Workgroup,
USEPA, CBSSS, LLRS, Grosse lie, Michigan
John Gannon, Biota Co-chairperson, USGS, NBS, Ann
Arbor, Michigan
Louis Blume, Chairperson, QA and Data Coordinator,
USEPA, GLNPO, Chicago, Illinois
225
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Appendix G
Quality Systems and Implementation Plan (QSIP)
Project Title
Estimation of Contaminant Loading From Monitored and
Unmonitored Tributaries to Lake Michigan for the USEPA
Lake Michigan Mass Balance Study.
Principle Investigator
David Hall, USGS, 8505 Research Way, Middleton,
Wisconsin 53562 (608) 821-3875.
EPA Project Officer
Mr. Glenn Warren, USEPA, GLNPO, 77 West Jackson,
Chicago, Illinois 60604-3590.
Submitted
October 23, 1998.
Project Planning and Organization
Introduction
The USEPA requires that all environmental projects
mandated or funded by the USEPA develop a reviewed
and approved quality assurance (QA) program as
summarized in a written QA Project Plan (QAPP). The
purpose of the QAPP is to demonstrate that: intended
measurements are appropriate for achieving project
objectives; quality control procedures are sufficient for
obtaining data of known and adequate quality; and such
data will be defensible if challenged technically or legally
(USEPA, 1991). A Quality Systems and Implementation
Plan (QSIP) may be used to describe specific aspects of a
project as a supplement to the project QAPP. This QSIP
describes methods used to compute loads for both
monitored and unmonitored tributaries to Lake Michigan
in support of the USEPA LMMBP.
Background
Annex 2 of the 1972 GLWQA (amended in 1978, 1983,
and 1987) between the United States and Canada called
for development of LaMPs for each of the Great Lakes.
The LaMPs document approaches to reduce inputs of toxic
chemicals and other pollutants to each Great Lake. The
LMMBP was developed in 1993 as part of the LaMP for
Lake Michigan. The primary objective of the LMMBP
was to provide an information base from which to guide
federal, state, and local toxic load reduction efforts in the
Lake Michigan basin (USEPA, 1997). An overview of the
LMMBP has been published in the Lake Michigan Mass
Budget/Mass Balance Workplan (USEPA, 1995).
Additional information describing the LMMBP can be
found on the USEPA Mass Balance Internet homepage at
"http ://w w w .epa. go v/grlakes /Immb''.
The Lake Michigan Tributary Project (LMTMP) is a sub-
project of the LMMBP. The overall objective of the
LMTMP is to obtain estimates of contaminant loading to
Lake Michigan from all tributaries, both monitored and
unmonitored. The LMTMP was supported by the USEPA
and was conducted as a cooperative effort between the
USGS, the Wisconsin Department of Environmental
Quality, the Michigan Department of Environmental
Quality, the Wisconsin State Laboratory of Hygiene, the
University of Wisconsin Water Chemistry Program, and
Rutgers University.
As part of the LMTMP, eleven major tributaries to Lake
Michigan (Figure 1, Table 1) were sampled from April
1994 through October 1995. Tributaries monitored were
226
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89
Monitored tributary
M Unmonitored tributary
Basin area of
monitored tributaries
^ Hydrologic unit boundary
Figure 1. Lake Michigan tributaries.
the Menominee, Fox, Sheboygan, and Milwaukee Rivers
in Wisconsin; the Grand Calumet River in Indiana; and the
St. Joseph, Kalamazoo, Grand, Muskegon, Pere
Marquette, and Manistique Rivers in Michigan.
Discharge was monitored at each tributary, and a total of
405 samples were collected from the eleven sites for
water-quality analysis. Hall et al. (1998) have published
ancillary data collected during sampling including pH,
dissolved oxygen, conductance, and temperature. The
remainder of this QSIP will describe methods for
computing contaminant loads for both monitored and
unmonitored tributaries to Lake Michigan
Tributary Load Computation Objectives
Specific contaminant loading objectives of this project are
as follows:
1. Compute loads of point and non-point source
constituents from 11 monitored tributaries to Lake
Michigan including:
227
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Table 1. Identifier Numbers, Station Names, and Station
Numbers of Monitored Tributaries to Lake Michigan.
Identifier
Number
(From
Figure 1)
1
2
3
4
5
6
7
8
9
10
11
USGS
Station
Number
04067651
040851385
040860041
04087170
04092750
04102533
04108660
04120250
04122150
04122500
04057005
USGS Station Name
Menominee River, at
mouth, at Marinette, WI
Fox River, Oil Tank
Depot at Green Bay, WI
Sheboygan River, at
mouth, at Sheboygan, WI
Milwaukee River, at
mouth, at Milwaukee, WI
Grand Calumet River, at
Indiana Harbor, IN
St. Joseph River at St.
Joseph, MI
Kalamazoo River at New
Richmond, MI
Grand River at Grand
Haven, MI
Muskegon River at
Muskegon, MI
Pere Marquette River at
Scottville, MI
Manistique River at
Manistique, MI
atrazine and degradates deisopropylatrazine and
deethylatrazine,
filtered and unfiltered mercury,
nutrients, including total phosphorus, dissolved
phosphorus, total nitrogen, Kjeldahl nitrogen,
ammonia, dissolved nitrate plus nitrite, and silica,
other parameters including total solids, particulate
organic carbon, dissolved organic carbon,
chloride, calcium, magnesium, conductivity,
alkalinity, and hardness,
total PCBs and 34 selected PCB congeners,
rrans-nonachlor.
2. Estimate loads of predominately non-point source
contaminants from selected unmonitored tributaries to
Lake Michigan, including:
atrazine and degradates deisopropylatrazine and
deethylatrazine,
filtered and unfiltered mercury,
nutrients, including total phosphorus, dissolved
phosphorus, total nitrogen, Kjeldahl nitrogen,
ammonia, dissolved nitrate plus nitrite, and silica,
frans-nonachlor,
other parameters, including total solids,
particulate organic carbon, dissolved organic
carbon, chloride, calcium, magnesium,
conductivity, alkalinity, and hardness.
3. Estimate loads of predominately point-source PCBs
and PCB congeners from selected unmonitored
tributaries to Lake Michigan, including
total PCBs and 34 selected PCB congeners.
Personnel Descriptions
David Hall - David Hall is a Hydrologist with the USGS,
Water Resources Division, in Middleton, Wisconsin. He
earned a BA in Geology from Humboldt State University
in 1985, Master of Environmental Pollution Control
degree from Penn State University, Capitol Campus, in
1994, and completed additional coursework in water
chemistry and hydrogeology at University of Wisconsin-
Madison, 1994-96. His research experience includes
228
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modeling of the partitioning, transport, and fate of organic
chemicals in the environment, evaluation of agricultural
best-management practices, characterizations of nonpoint-
source water and air pollution at field-site and regional
scales, and statistical analyses of water quality.
Dale Robertson Dr. Robertson is currently a Research
Hydrologist with the USGS, Water Resources Division, in
Middleton, Wisconsin. His research interests include
developing regional load estimates, determining how
different sampling strategies effect load estimates, and
examining the influence of environmental factors,
watershed management strategies, and in-lake management
alternatives on the water quality of rivers and lakes. He
has recently completed a project with USEPA to estimate
high-flow and long-term average annual nutrient and
sediment loading to Lake Michigan and Lake Superior.
He is currently working on a project, funded by the
National Water Quality Assessment (NAWQA) program
of the USGS and the WDNR to determine how different
sampling strategies affect load estimates in small streams.
He is also working with the WDNR and the University of
Wisconsin to estimate regional loading of trace metals to
Lake Michigan and determine the allocation of this load to
urban and watershed sources.
Method Description
Researcher Responsibilities
David Hall is responsible for all project loading activities,
computation of contaminant loads, compilation and
reporting of load data, maintenance of the loading data
database, and publication of project reports.
Dr. Dale Robertson will act as a consultant in all project
tasks, and will specifically provide guidance on the
application of methods used to extrapolate loads from the
11 monitored tributaries to unmonitored portions of the
basin.
Methods of Load Computation
Task 1: Compute loads of point and non-point source
contaminants from 11 monitored tributaries to Lake
Michigan, including:
atrazine and degradates deisopropylatrazine and
deethylatrazine,
filtered and unfiltered mercury,
nutrients, including total phosphorus, total nitrogen,
Kjeldahl nitrogen, ammonia, dissolved nitrate plus
nitrite and silica,
other parameters including total solids, particulate
organic carbon, dissolved organic carbon, chloride,
calcium, magnesium, conductivity, alkalinity, and
hardness,
total PCBs and 34 selected PCB congeners,
frarcs-nonachlor.
Eleven tributaries in Wisconsin, Michigan, and Indiana
were monitored for discharge and water quality from April
1, 1994 through October 31, 1995. Daily loads of
contaminants discharged from each tributary during this
19-month period are to be estimated using the Beale Ratio
Estimator method, which produces error estimates
associated with loads. Additionally, loads for a 24-month
period from January 1, 1994 through December 31, 1995
will be estimated for input to the LMMBP model by an
innovative combination of the Beale model output with
output from the USGS Estimator Regression Model.
LMTMP Project Results
The Lake Michigan Tributary Monitoring Project results
will consist of Beale-model daily contaminant loads and
associated error for the 19-month monitored period from
April 1, 1994 through October 31, 1995. In summary,
Beale model output divides each year of discharge and
concentration data into a variable number of strata of
averaged daily loads (same average load for each day
within the stratum). The total number of strata is
determined by an algorithm that minimizes the error
associated with the total annual load.
As an extension of the Beale approach, average daily
values within each stratum may be converted to discrete
daily values (i.e., a different load for each day) by
multiplying the average daily load for each stratum by the
ratio of discharge on each day divided by the average
discharge for the stratum (R. Peter Richards, Heidelberg
College, oral communication, June 1998). Thus, days with
greater discharge within a stratum have a larger daily load,
229
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which is consistent with the Beale model assumption that
contaminant flux increases with increased discharge.
Additional Loads Produced as Input to the Lake
Michigan Mass Balance Project Model
For the purposes of producing tributary-load input data for
the USEPA LMMBP model, data from the 19-month
period will be used to provide estimates of daily
contaminant loads for two additional periods: January 1,
1994 through March 31, 1994, and November 1, 1995
through December 31, 1995. Data for these additional
periods are required to enable comparisons of tributary
loading data collected in other facets of the LMMBP
including open-lake monitoring, biological monitoring,
and atmospheric monitoring that extended for a 24-month
period from January 1, 1994 through December 31, 1995
(Douglas Endicott, USEPA, LLRS, oral communication,
February 1998).
Because the Beale Ratio Estimator model does not
produce a mathematical formula or other means from
which to extrapolate the monitoring-period data to
unmonitored periods, a procedure was developed to use
the Beale-model daily loads and regression-model loads
from the monitored period to "adjust" regression-produced
daily loads from the unmonitored period (Dave Dolan, JJC,
oral communication, June 1998; R. Peter Richards,
Heidelberg College, oral communication, June 1998).
An adjustment coefficient will be computed by dividing
the sum of Beale-model daily loads for the period April 1,
1994 through October 31, 1995 by the sum of the
Estimator Regression Model loads for the same period.
The adjustment coefficient will then be multiplied by each
daily load produced by the selected regression model for
each of the two unmonitored periods to produce
"corrected" daily loads. For example, if the Beale model
was producing a sum of daily loads greater than the sum of
regression model daily loads for the monitored period, the
adjustment coefficient would be greater than one, and the
adjustment multiplication would linearly increase each
regression-daily load in each of the two unmonitored
periods.
The final series of daily contaminant loads used as model
input will therefore consist of the following series of daily
loads:
January 1, 1994 through March 31, 1994: adjusted
regression-model daily loads
April 1, 1994 through October 31, 1995: adjusted
Beale-model daily loads
November 1, 1995 through December 31, 1995:
adjusted regression-model daily loads.
Selection of the Most Appropriate Regression Model
The USGS Estimator regression software enables the user
to evaluate different models using various combinations of
simple and transformed variables such as flow, time, and
constants. For example, a standard set of variables for a
regression on a data series that may demonstrate a trend
and also possibly display seasonality is log-flow, log-flow
squared, decimal time, decimal-time squared, sine, and
cosine terms (Timothy Cohn, USGS, Reston, Virginia,
written communication, June 1998). In cases where this
suggested set of terms produces an unacceptable output,
such as a poor r-squared, unacceptable residual
distributions, or any negative daily loads, simpler models
can be constructed from fewer terms such as log-flow or
square-root flow, with or without a constant. For the
purposes of this project, the regression model with the
largest r-squared value, the most acceptable residual
distributions, and the output most similar in magnitude to
Beale-model output will be selected.
Task 2: Estimate loads of predominately non-point source
constituents from selected unmonitored tributaries to Lake
Michigan, including:
atrazine and degradates deisopropylatrazine and
deethylatrazine,
filtered and unfiltered mercury,
nutrients, including total phosphorus, dissolved
phosphorus, total nitrogen, Kjeldahl nitrogen,
ammonia, dissolved nitrate plus nitrite, and silica,
frans-nonachlor,
other parameters including total solids, paniculate
organic carbon, dissolved organic carbon, chloride,
calcium, magnesium, conductivity, alkalinity, and
hardness.
230
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Unit-Area Yields
Unit-area yields of constituents calculated from the 11
monitored tributaries will be extrapolated to 25 additional
unmonitored tributaries with basins larger than 325 km-
squared (Figure 1, Table 2) in the Lake Michigan basin to
obtain an estimate of the total tributary loads input to Lake
Michigan. Locations of both the monitored and
unmonitored tributaries are illustrated in Figure 1 from
Robertson (1997). The unit-area yields from the
monitored basin with the most similar environmental
factors will be multiplied by the area of the selected
unmonitored basin to obtain loads. Distributions of daily
loads from the unmonitored areas will be assumed to
resemble the daily load distribution from the monitored
tributary.
Surficial deposit and land use data will be used to select
the most similar monitored basin (Figure 1, Table 1) from
which to extrapolate yield data to each unmonitored basin
(Figure 1, Table 1). Geographic information system
(GIS/ARC/INFO) coverages of surficial deposits and land
use that will be used to define basin characteristics for the
extrapolation procedure have been published in
Robertson (1997). The generalized coverage of surficial
deposits in the Lake Michigan basin was obtained from
quatenary geologic maps published by Richmond and
Fullerton (1983), Farrand and Bell (1982), and Hobbs and
Goebel (1982). The land use coverage was digitized from
the National Atlas of the United States of America (USGS,
1970).
The combination of the basin areas of the 11 monitored
tributaries and the 25 unmonitored tributaries with basin
areas greater than 325 km-squared (Table 2) comprise
approximately 87 percent of the land area draining into
Lake Michigan (Robertson, 1996). Areas of the 25
selected tributaries will be enlarged to encompass smaller
basins (less than 325 km-squared) drained by numerous
small tributaries where basin boundaries may be poorly
defined and land use and physical properties of the basins
may be poorly resolved, thereby obtaining representation
of the entire unmonitored area of Lake Michigan.
PCB loads will be estimated for each of the 25
unmonitored rivers listed above (Table 1). Where
discharge data exist for an unmonitored tributary for the
period of interest, the existing record will be used in load
Table 2. Unmonitored Tributaries With Basin Areas
Greater than 325 km2 and Location Identifiers Used in
Figure 1.
Unmonitored Tributary Identifiers on Figure 1
Cedar
Peshtigo
Oconto
Pensaukee
Duck
Kewaunee
East Twin
West Twin
Manitowoc
Root
Black (SH)
Black (HD)
Pigeon
White
Pentwater
Big Sable
Manistee
Betsie
Boardman
Jordan
Sturgeon
Whitefish
Rapid
Escanaba
Ford
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
w
X
Y
computations. Where no discharge data exist, unit-area
water yields will be extrapolated from the most similar
monitored basin to the unmonitored basins.
Bed sediment concentrations of PCBs in Lake Michigan
tributaries were published in Robertson (1997) and were
obtained by Robertson from the USEPA (K. Klewin,
USEPA, written communication, 1994). Sediments were
sampled either at the river mouth or at the harbor at the
river mouth. For each river where sediment chemistry data
are available, the median PCB concentration of all samples
will be used in the load calculations. Sediment PCB data
from the eleven monitored sites will be used to develop a
regression model relating sediment concentration to water
concentrations. This model will be used to translate the
existing database of sediment PCB concentrations in the
231
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unmonitored tributaries to water concentrations to enable
the estimation of tributary loads.
Where possible, PCB congener distributions from
published literature will be used to assist in determining
the loads of individual congeners. Where no PCB
concentration data exist for an unmonitored tributary,
concentrations will be assumed to be at or near zero.
Record Usage and Management
Data Records
All data generated by the USGS will be recorded in
electronic format. All databases are backed up either to
floppy disks or 8-mm tape, and will be stored at USGS
offices in Middleton, Wisconsin.
Records Management System
A master directory, LMMBP, will be created to hold all
data. Separate subdirectories will be created for FINAL
results. A complete description of the data directory
structure will be included in a 'readme' file located in the
master directory.
Records Validation
Computer files are manually validated by visually
checking approximately 10% of the data records for
accuracy, and by inspection of data plots. Additionally,
project results will be reviewed by various personnel as
necessary prior to, and after, data submission to the
USEPA.
Record Identification, Indexing, and Retention
After completion of the project, all electronic data will be
archived on tape or on disks. Electronic archived data and
printed materials will be retained for five years after the
end of the project.
Records Distribution and Storage
Only final data records will be distributed outside the
USGS. These records will be prepared and carefully
reviewed by David Hall before distribution and reporting.
Interim storage of preliminary data records is described
above. Data releases to non-USEPA agencies or to the
general public will be cleared through Mr. Glenn Warren,
USEPA GLNPO prior to release.
References
Farrand, W.R. and D.L. Bell. 1982. Quaternary Geology
of Southern Michigan and Northern Michigan.
Department of Geological Sciences, University of
Michigan, Ann Arbor, Michigan. 2 pp.
Hall, D.W., T.E. Behrendt, and P.E. Hughes. 1998.
Temperature, pH, Conductance, and Dissolved Oxygen in
Cross-Sections of Eleven Lake Michigan Tributaries. U.S.
Geological Survey, Open-File Data Report OFDR 98.
Hobbs, H.C. and I.E. Goebel. 1982. Geologic Map of
Minnesota. Quaternary Geology. Minnesota Geological
Survey, University of Minnesota. State Map Series S-l.
Richmond, G.M. and D.S. Fullerton. 1983. Quaternary
Geologic Map of Lake Superior, 4 Degree x 6 Degree
Quadrangle. U.S. Geological Survey, Quaternary Atlas of
the United States, Scale 1:1,000,000.
Robertson, D.M. 1997. Regionalized Loads of Sediment
and Phosphorus to Lakes Michigan and Superior: High
Flow and Long-Term Average. J. Great Lakes Res.,
23(4):416-439.
U.S. Environmental Protection Agency. 1991.
Preparation Aids for the Development of Category I
Quality Assurance Project Plans. U.S. Environmental
Protection Agency, Office of Research and Development,
Cincinnati, Ohio. EPA-600/8-91/003.
U.S. Environmental Protection Agency. 1995. Lake
Michigan Mass Budget/Mass Balance Workplan. U.S.
Environmental Protection Agency, Great Lakes National
Program Office, Chicago, Illinois. Version 2, 155 pp.
U.S. Environmental Protection Agency. 1997. Lake
Michigan Mass Balance Study (LMMB) Methods
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