& EPA
United States
Environmental Protection
Agency
Office Of Air Quality
Planning And Standards
Research Triangle Park, NC 27711
EPA-453/R-99-010
November 1999
Air
TRIM
Total Risk Integrated Methodology
STATUS REPORT
Environmental Fate,
Transport, & Ecological
Exposure Module
Exposure-Event Module
(TRIM.ExDO
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EPA-453/R-99-010
TRIM
Total Risk Integrated Methodology
STATUS REPORT
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
November 1999
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Disclaimer
This report has been reviewed and approved for publication by the U.S. Environmental
Protection Agency's Office of Air Quality Planning and Standards. Mention of trade names or
commercial products is not intended to constitute endorsement or recommendation for use.
NOVEMBER 1999 i TRIM STATUS REPORT
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ACKNOWLEDGMENTS
Acknowledgments
As described in this report, the Office of Air Quality Planning and Standards (OAQPS) of
the U.S. Environmental Protection Agency is developing the Total Risk Integrated Methodology.
The principal individuals and organizations in the TRIM development effort and in the
preparation of this report are listed below. Additionally, valuable technical support for report
development was provided by ICF Consulting.
Robert G. Hetes, EPA, Office of Air Quality Planning and Standards
Deirdre L. Murphy, EPA, Office of Air Quality Planning and Standards
Ted Palma, EPA, Office of Air Quality Planning and Standards
Harvey M. Richmond, Office of Air Quality Planning and Standards
Amy B. Vasu, EPA, Office of Air Quality Planning and Standards
Deborah Hall Bennett, Lawrence Berkeley National Laboratory
Rebecca A. Efroymson, Oak Ridge National Laboratory
Steve Fine, MCNC-North Carolina Supercomputing Center
Dan Jones, Oak Ridge National Laboratory
John Langstaff, EC/R Incorporated
Bradford F. Lyon, Oak Ridge National Laboratory
Thomas E. McKone, Lawrence Berkeley National Laboratory & University of California, Berkeley
Randy Maddalena, Lawrence Berkeley National Laboratory
Michael P. Zelenka, ICF Consulting
NOVEMBER 1999 iii TRIM STATUS REPORT
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ACKNOWLEDGMENTS
The following EPA individuals reviewed a previous draft of this document.
EPA Models 2000 TRIM Review Team
Robert F. Carousel
National Exposure Research Laboratory
Office of Research and Development
*S. Steven Chang
Office of Emergency and Remedial
Response
Office of Solid Waste and Emergency
Response
Ellen Cooler
National Exposure Research Laboratory
Office of Research and Development
Stan Durkee
Office of Science Policy
Office of Research and Development
Harvey Holm
National Exposure Research Laboratory
Office of Research and Development
John S. Irwin
Office of Air Quality Planning and
Standards
Office of Air and Radiation
Team Leader
Linda Kirkland
National Center for Environmental Research
and Quality Assurance
Office of Research and Development
Matthew Lorber
National Center for Environmental
Assessment
Office of Research and Development
Haluk Ozkaynak
National Exposure Research Laboratory
Office of Research and Development
William Petersen
National Exposure Research Laboratory
Office of Research and Development
Ted W. Simon
Region 4
Amina Wilkins
National Center for Environmental
Assessment
Office of Research and Development
Review by Other Program Offices
Pam Brodowicz, Office of Air and Radiation, Office of Mobile Sources
William R. Effland, Office of Pesticide Programs
John Girman, Office of Air and Radiation, Office of Radiation and Indoor Air
Steven M. Hassur, Office of Pollution Prevention and Toxics
Terry J. Keating, Office of Air and Radiation, Office of Policy Analysis and Review
Russell Kinerson, Office of Water
Stephen Kroner, Office of Solid Waste
David J. Miller, Office of Pesticide Programs
NOVEMBER 1999
IV
TRIM STATUS REPORT
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PREFACE
Preface
This document, the 1999 Total Risk Integrated Methodology (TRIM) Status Report, is
part of a series of documentation for the overall TRIM modeling system. The purpose of this
report is to provide a summary of the status of TRIM and all of its major components, with
particular focus on the progress in TRIM development since the 1998 TRIM Status Report (U.S.
EPA 1998e). EPA plans to issue status reports on an annual basis while TRIM is under
development.
The detailed documentation of TRIM's logic, assumptions, algorithms, equations, and
input parameters is provided in comprehensive Technical Support Documents (TSDs) for each of
the TRIM modules. The purpose of the TSDs is to provide full documentation of how TRIM
works and of the rationale for key development decisions that were made. To date, EPA has
issued TSDs for the Environmental Fate, Transport, and Ecological Exposure module
(TRIM.FaTE TSD, U.S. EPA 1999i and U.S. EPA 1999J, which supersedes an earlier version,
U.S. EPA 1998f) and the Exposure-Event module (TRIM.Expo TSD, U.S. EPA 1999h). When
the Risk Characterization module (TRDVI.Risk) is developed, EPA plans to issue a TSD for it.
The TSDs will be updated as needed to reflect future changes to the TRIM modules.
In addition to status reports and TSDs, EPA intends to develop detailed user guidance for
the TRIM computer system. The purpose of such guidance will be to define appropriate (and
inappropriate) uses of TRIM and to assist users in applying TRIM to assess exposures and risks
in a variety of air quality situations.
Comments and suggestions are welcomed. The OAQPS TRIM team members, with their
individual roles and addresses, are provided below.
TRIM Coordination Deirdre L. Murphy
REAG/ESD/OAQPS
MD-13
RTF, NC 27711
[murphy.deirdre@epa.gov]
TRIM.FaTE AmyB. Vasu
REAG/ESD/OAQPS
MD-13
RTF, NC 27711
[vasu.amy@epa.gov]
TRIM.Expo Ted Palma Harvey M. Richmond
REAG/ESD/OAQPS HEEG/AQSSD/OAQPS
MD-13 MD-15
RTF, NC 27711 RTF, NC 27711
[palma.ted@epa.gov] [richmond.harvey@epa.gov]
NOVEMBER 1999 v TRIM STATUS REPORT
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PREFACE
TRIM.Risk Robert G. Hetes
REAG/ESD/OAQPS
MD-13
RTF, NC 27711
[hetes.bob@epa.gov]
NOVEMBER 1999 vi TRIM STATUS REPORT
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ACRONYMS
ACRONYMS
APEX Air Pollutant Exposure Model
ATFERM Agency Task Force on Environmental Regulatory Modeling
B(a)P Benzo(a)pyrene
CAA Clean Air Act
CalTOX California Multimedia Total Exposure Model for Hazardous Waste Sites
CART Classification and regression tree
CMAQ Community Multi-Scale Air Quality
CRARM Presidential/Congressional Commission on Risk Assessment and Risk
Management
CO Carbon monoxide
EC50 Effective concentration at 50 percent response
EC10 Effective concentration at 10 percent response
EPA United States Environmental Protection Agency
HAP Hazardous air pollutant
HAPEM4 Hazardous Air Pollutant Exposure Model Version 4.0
HAPEM-MS Hazardous Air Pollutant Exposure Model for Mobile Sources
HEM Human Exposure Model
HI Hazard Index
HQ Hazard Quotient
GIS Geographic Information System
GUI Graphical User Interface
I/O API Environmental Decision Support System/Models 3 Input/Output Applications
Programming Interface
IEM Indirect Exposure Methodology
IEM2M Indirect Exposure Methodology for Mercury
ISCST3 Industrial Source Complex, Short Term Version 3
IUBK Intake, Uptake, Biokinetic Model
Kaw Air/water partition coefficient
Koa Octanol/air partition coefficient
Kow Octanol/water partition coefficient
LC50 Lethal concentration at 50 percent response
LOAEC Lowest observed adverse effect concentration
LSODE Livermore Solver for Ordinary Differential Equations
MATC Maximum acceptable toxicant concentration
MPE Multiple Pathways of Exposure
NAAQS National ambient air quality standard
NAS National Academy of Sciences
NATA National Air Toxics Assessment
NOAEC No observed adverse effect concentration
OAQPS EPA Office of Air Quality Planning and Standards
PAH Polycyclic aromatic hydrocarbon
pNEM Probabilistic NAAQS Exposure Models
pNEM/CO Probabilistic NAAQS Exposure Model for Carbon Monoxide
RfC Reference concentration
RfD Reference dose
NOVEMBER 1999
VII
TRIM STATUS REPORT
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ACRONYMS
RIA Regulatory impact analysis
SAB Science Advisory Board
SET AC Society of Environmental Toxicology and Chemistry
SHEDS Stochastic Human Exposure and Dose Simulation
SRA Society for Risk Analysis
TCCR Transparency, clarity, consistency, and reasonableness
TRIM Total Risk Integrated Methodology
TRDVI.Expo TRIM Exposure-Event module
TRDVI.FaTE TRIM Environmental Fate, Transport, and Ecological Exposure module
TREVI.Risk TRIM Risk Characterization module
TSD Technical Support Document
URE Unit risk estimate
WASP Water Quality Analysis Simulation Program
NOVEMBER 1999 viii TRIM STATUS REPORT
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TABLE OF CONTENTS
TABLE OF CONTENTS
Disclaimer i
Acknowledgments iii
Preface v
Acronyms vii
1 Introduction 1-1
1.1 Goals and Objectives for TRIM 1-2
1.2 TRIM Design 1-3
1.2.1 Description of TRTM.FaTE 1-6
1.2.2 Description of TRIM.Expo 1-6
1.2.3 Description of TRTM.Risk 1-7
1.3 TRIM Development 1-8
1.3.1 Initial Development Activities 1-9
1.3.2 Recent Activities 1-10
1.3.3 Future Activities 1-10
1.4 Phasing TRIM Into OAQPS' Set of Modeling Tools 1-12
2 May 1998 Science Advisory Board Review and Agency Responses 2-1
2.1 Is the Overall TRIM Conceptual Approach Appropriate? 2-2
2.2 Is the Spatial Compartmental Mass Balance Approach Commensurate with
Quantifying Uncertainty and Variability in a Scientifically Defensible
Manner? 2-3
2.3 Is the Overall Conceptual Approach Represented in the TRTM.FaTE Module
Appropriate? 2-4
2.4 The TRIM Approach is Designed to be Flexible and to Allow For a Tiered
Approach 2-6
2.4.1 Is the TRTM.FaTE Module Appropriate From a Scientific
Perspective? 2-6
2.4.2 Is the TRTM.FaTE Module an Appropriate Tool For Use in Providing
Information For Regulatory Decision-making? 2-8
2.5 Does the TRTM.FaTE Module, As It Has Been Conceptualized, Address
Some of the Limitations Associated with Other Models? 2-8
2.6 Does the TRTM.FaTE Module, As It Has Been Conceptualized and
Demonstrated to Date, Facilitate Future Integration With Appropriate Data
Sources and Applications? 2-11
3 Treatment of Uncertainty and Variability in TRIM 3-1
3.1 Objectives of the Integrated Uncertainty Analysis 3-1
3.2 General Steps in an Analysis of Uncertainty and Variability 3-2
3.3 Overview of the Approach Selected for TRIM 3-4
4 Revisions and Additions to TRLVLFaTE 4-1
4.1 Ability to Account for Metals 4-1
4.2 Ability to Model Fate and Transport of Chemical Transformation Products . . 4-1
4.3 Ability to Account for Seasonality 4-2
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TABLE OF CONTENTS
4.4 Other Additions and Improvements to Algorithms 4-4
4.4.1 Abiotic Algorithms 4-4
4.4.1.1 Dispersive Transport Between Surface Water Compartments . . 4-4
4.4.1.2 Diffusion and Advection With Soil Compartments 4-4
4.4.1.3 Diffusive Transport Between Surface Water and Sediment
Compartments 4-5
4.4.2 Biotic Algorithms 4-5
4.4.3 Chemical- or Chemical Class-Specific Algorithms 4-6
4.5 Interface with External Models 4-7
4.6 Methodology for Determining Parameters of the Modeling Environment .... 4-7
4.7 Overview of the Uncertainty and Variability Analysis Approach Selected for
TRIM 4-9
4.8 Model Evaluation 4-10
5 Current Status of TRIM.FaTE 5-1
5.1 Compartment Types 5-1
5.2 Links and Algorithms 5-2
5.2.1 Abiotic Links and Algorithms 5-2
5.2.2 Biotic Links and Algorithms 5-3
5.2.3 Chemical-specific Algorithms 5-8
6 Evaluation Plan for TREVLFaTE 6-1
6.1 Background 6-1
6.2 Model Evaluation 6-2
6.3 Conceptual Model Evaluation 6-4
6.3.1 Definition and General Approach 6-4
6.3.2 TRTM.FaTE-Specific Activities 6-5
6.4 Mechanistic and Data Quality Evaluation 6-5
6.4.1 Definition and General Approach 6-5
6.4.2 TRTM.FaTE-Specific Activities 6-7
6.5 Structural Evaluation 6-10
6.5.1 Definition and General Approach 6-10
6.5.2 TRTM.FaTE-Specific Activities 6-11
6.6 Overall Performance Evaluation 6-12
6.6.1 Definition and General Approach 6-12
6.6.2 TRTM.FaTE-Specific Activities 6-13
6.7 Summary of TRIM.FaTE Evaluation Activities 6-17
7 TREVLFaTE Mercury Case Study 7-1
7.1 Objectives 7-1
7.2 Case Study Chemical Selection 7-2
7.3 Case Study Site Selection 7-2
7.4 Case Study Evaluation Activities 7-3
7.4.1 Mechanistic and Data Quality, and Structural Evaluations 7-3
7.4.2 Performance Evaluation 7-5
7.4.2.1 Comparison with Other Models 7-5
7.4.2.2 Comparison with Measurement Data 7-6
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TABLE OF CONTENTS
8 Development of TRIM.Expo 8-1
8.1 Purpose of Developing TRIM.Expo 8-1
8.2 Overview of TRIM.Expo 8-2
8.3 Conceptualization of TRIM.Expo 8-5
8.4 Functional Attributes of TRIM.Expo 8-6
8.4.1 Dimensions of the Exposure Assessment Problem 8-6
8.4.2 Design Features of TRIM.Expo 8-7
8.5 Approach Used in Developing TRIM.Expo 8-8
8.6 Summary Review of Existing Exposure Models and the Uniqueness
of TRIM.Expo 8-10
8.6.1 Overview of Current Models 8-10
8.6.2 Rationale and Need for Developing TRIM.Expo 8-12
9 General Description and Conceptual Design of TRIM.Risk 9-1
9.1 Background on Risk Characterization 9-1
9.2 Purpose of TRIM.Risk 9-3
9.3 Design Goals of TRIM.Risk 9-5
9.4 Overview of TRIM.Risk 9-6
9.4.1 Documentation of Assumptions and Input Data 9-6
9.4.2 Risk Calculation and Analysis 9-6
9.4.2.1 Human Health Risks 9-7
9.4.2.2 Environmental Risk 9-9
9.4.3 Presentation of Results 9-11
9.4.3.1 Risk Descriptors for Human Health 9-11
9.4.3.2 Presentation of Ecological Risk Assessment Results 9-12
9.4.3.3 Uncertainty 9-13
9.4.3.4 Outputs 9-13
9.5 Current Status and Future Plans for TRIM.Risk 9-14
10 Development of TRIM Computer Framework 10-1
10.1 Architecture 10-1
10.1.1 TRIM Core 10-3
10.1.2 Projects 10-3
10.1.3 TRIM Modules 10-3
10.1.4 Libraries 10-4
10.1.5 External Data Sources, Importers, and Exporters 10-4
10.1.6 Analysis and Visualization Tools 10-5
10.2 Implementation Approaches and Technologies 10-5
10.3 Using TREVI.FaTE Version 1.0 10-7
10.4 Implementation Status 10-9
11 References 11-1
NOVEMBER 1999 xi TRIM STATUS REPORT
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TABLE OF CONTENTS
Appendices
A. Glossary
B. Review of Methods for Conducting Uncertainty Analyses
C. Input Values Being Developed for TREVLFaTE Mercury Case Study
D. Summary of Available Monitoring Data for TRLVLFaTE Mercury Case Study
NOVEMBER 1999 xii TRIM STATUS REPORT
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CHAPTER 1
INTRODUCTION
1. INTRODUCTION
The Office of Air Quality Planning and Standards (OAQPS) of the U.S. Environmental
Protection Agency (EPA, or the Agency) has the responsibility for the hazardous and criteria air
pollutant programs described by sections 112 and 108 of the Clean Air Act (CAA). Several
aspects of these programs require evaluation of the health risks and environmental effects
associated with exposure to these pollutants.1 In response to these risk-related mandates of the
CAA, and the scientific recommendations of the National Academy of Sciences (NAS) (NRC
1994), the Presidential/Congressional Commission on Risk Assessment and Risk Management
(CRARM) (CRARM 1997), as well as EPA guidelines and policies, OAQPS recognized the need
for improved fate and transport, exposure, and risk modeling tools that:
• Have multimedia assessment capabilities;
• Have human health and ecological exposure and risk assessment capabilities;
• Can perform multiple pollutant assessments (e.g., ability to assess mixtures of pollutants,
ability to track chemical transformations);
• Can explicitly address uncertainty and variability;
• Have the ability to easily perform analyses iteratively, moving from the use of simpler
assumptions and scenarios to more detailed assessments; and
• Are readily available and user-friendly, so that they can be used by EPA, as well as by a
variety of Agency stakeholders.
In 1996, OAQPS embarked on a multi-year effort to develop the Total Risk Integrated
Methodology (TRIM), a time series modeling system with multimedia capabilities for assessing
human health and ecological risks from hazardous and criteria air pollutants.
The main purpose of this Status Report is to summarize the work performed during the
second developmental phase of TRIM. The first phase, which included the conceptualization of
TRIM and implementation of the TRIM conceptual approach through development of a
prototype of the first TRIM module, TRIM.FaTE (U.S. EPA 1998e), was reviewed by EPA's
Science Advisory Board (SAB) in May 1998 (U.S. EPA 1998a). The second developmental
phase has included refining TRIM.FaTE and developing a model evaluation plan, initiating
development of the second module (TRDVI.Expo), and conceptualizing the third module
(TREVI.Risk). In addition, progress has been made on developing overarching aspects, such as
the computer framework and an approach to uncertainty and variability. Consistent with the
integral role of peer review in the TRIM development plan, the current Status Report and
1 Hazardous air pollutants (HAPs) include any air pollutant listed under CAA section 112(b); currently,
there are 188 air pollutants designated as HAPs. Criteria air pollutants are air pollutants for which national ambient
air quality standards (NAAQS) have been established under the CAA; at present, the six criteria air pollutants are
paniculate matter, ozone, carbon monoxide, nitrogen oxides, sulfur dioxide, and lead.
NOVEMBER 1999 1-1 TRIM STATUS REPORT
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Technical Support Documents (TSDs) were subjected to review by representatives from the
major program offices at EPA and an EPA Models 20002 review team prior to this SAB
advisory.
The goals and objectives, design, and development activities for TRIM are summarized in
the following sections of Chapter 1, and certain aspects of development are expanded upon in
later chapters of the report. Detailed descriptions of the TREVI.FaTE and TRTM.Expo modules
are presented in accompanying TSDs (U.S. EPA 19991, U.S. EPA 1999J, U.S. EPA 1999h).
1.1 GOALS AND OBJECTIVES FOR TRIM
The TRIM modeling system is intended to represent the next generation of human and
environmental exposure and risk models for OAQPS. For example, TRIM is expected to be a
useful tool for performing exposure and/or risk assessments for the following CAA programs:
the Residual Risk Program (CAA section 112[f]); the Integrated Urban Air Toxics Strategy
(CAA section 112[k]); studies of deposition to water bodies and mercury emissions from utilities
(CAA sections 112[m] and 112[n]); petitions to delist individual HAPs and/or source categories
(CAA sections 112[b][3] and 112[c][9]); review and setting of the national ambient air quality
standards (NAAQS) (CAA section 109); and regulatory impact analyses (RIA).
The goal in developing TRIM is to create a modeling system, and the components of that
system, for use in characterizing human health and ecological exposure and risk in support of
hazardous and criteria air pollutant programs under the CAA. The goal in designing TRIM is to
develop a modeling system that is: (1) scientifically defensible, (2) flexible, and (3) user-friendly.
(1) Characteristics of the TRIM components important to their scientific defensibility include
the following.
• Conservation of pollutant mass. The modeled pollutant(s)' mass will be conserved
within the system being assessed, wherever appropriate and feasible, including during
intermedia transfers. For pollutants where transformation is modeled, the mass of the
core substance (e.g., mercury for methylmercury as well as divalent mercury) within the
modeling simulation will be preserved.
• Ability to characterize parameter uncertainty and variability. For critical
parameters, the impacts of parameter uncertainty and variability on model outputs will be
tracked and, where feasible, differentiated.
• Capability for multiple pollutant, multiple media, multiple exposure pathway
assessment. The TRIM modeling system is being designed to facilitate assessment of
2 Following the report of the Agency Task Force on Environmental Regulatory Modeling (U.S. EPA
1994a), the Agency conducted the Models 2000 Conference in December 1997. This conference has led to renewed
emphasis on Agency-wide coordination of model development and the proposal for the implementation of a Council
on Regulatory Environmental Modeling (CREM) to facilitate and promote scientifically-based, defensible
regulatory computer models. The charter for CREM has been reviewed by SAB and is being updated for
implementation by the Agency.
NOVEMBER 1999 1-2 TRIM STATUS REPORT
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CHAPTER 1
INTRODUCTION
risks posed by aggregate exposures to single or multiple chemicals from multiple sources
and via multiple exposure pathways.
(2) To ensure flexibility, the features of TRIM include the following.
• Modular design. Major components of TRIM will be independent and can be used
individually, with outside information or models, or in combination. Only those model
components necessary for evaluating the particular pollutants, pathways, and/or effect
endpoints of interest need be employed in an assessment.
• Flexibility in temporal and spatial scale. Exposure and risk assessments will be
possible for a wide range of temporal and spatial scales, including hourly to daily or
yearly time steps, and from local (10 kilometers (km) or less) to greater spatial scales
(depending on the module).
• Ability to assess human and ecological endpoints. Impacts to humans and/or biota can
be assessed.
(3) To ensure that TRIM will be user-friendly for a variety of groups, including EPA, state
and local agencies, and other stakeholders, TRIM will have the following characteristics.
Easily accessible. The TRIM modeling system will be accessible for use with a personal
computer (PC). The system may be available for download from the Internet and
accessible through an Agency model system framework (e.g., Models-3 (U.S. EPA
1999g)).
• Well-documented. Guidance materials for use of the TRIM modeling system will be
provided through a user's guide, with a focus on the modular aspects of the modeling
system, limitations of the modeling system, and appropriate uses, user responsibilities,
and user options.
• Clear and transparent. The graphical user interface of the TRIM computer framework
will provide transparency and clarity in the functioning of the TRIM modules, and output
from the risk characterization module will document modeling assumptions, limitations,
and uncertainties.
1.2 TRIM DESIGN
The current TRIM design (Figure 1-1) includes three individual modules. The
Environmental Fate, Transport, and Ecological Exposure module, TRIM.FaTE, accounts for
movement of a chemical through a comprehensive system of discrete compartments (e.g., media,
biota) that represent possible locations of the chemical in the physical and biological
environments of the modeled ecosystem and provides an inventory, over time, of a chemical
NOVEMBER 1999 1-3 TRIM STATUS REPORT
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CHAPTER 1
INTRODUCTION
Figure 1-1
Conceptual Design of TRIM
o
Air Quality Models
(e.g., ISC3, AERMOD;
Other Multimedia Models
(e.g., MEND-TOX)
Environmental Fate,
Transport, and
Ecological Exposure
(TRIM.FaTE)
I Monitoring Dataj—
Inputs
e.g.,
Physical, Chemical
Properties
f Site-:
specific Data
CIS Data
Temporal and
Spatial Distributior
of Pollutant
Concentrations
Inputs
e.g.,
Activity Data
(e.g., CHAD)
Population Data
(e.g., 1990 BOG)
Indoor/Outdoor 7
Concentration Ratios^
[media concentrations
re levant to human
exposures]
Exposure Event
(TRIM.Expo)
Dosimetry Models
(e.g., CO, Pb models)
\
Temporal and Spatial
Distribution of
Exposures within
Exposed Human
Population
Temporal and Spatia
Distribution of Doses
within Exposed
Population
Human Health
Dose-response
Assessment
(e.g., RfC, URE)
Risk
Characterization
(TRIM.Risk)
[media and biota
concentrations and biota
pollutant intake rates
relevant to ecological
exposures]
Ecological Effects /
Assessment I
(e.g., endpoints, criteria) V
Documentation of assumptions and input datja
Quantitative risk and exposure
characterization (human and ecological)
Measures of uncertainty and variability
Description of limitations (graphical/tabular/
GIS presentation)
NOVEMBER 1999
1-4
TRIM STATUS REPORT
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CHAPTER 1
INTRODUCTION
throughout the entire system. In addition to providing exposure estimates relevant to ecological
risk assessment, TREVI.FaTE generates media concentrations relevant to human pollutant
exposures that can be used as input to the Exposure-Event module, TREVLExpo. In
TRUVI.Expo, human exposures are evaluated by tracking population groups referred to as
"cohorts" and their inhalation and ingestion through time and space. In the Risk Characterization
module, TRIM.Risk, estimates of human exposures or doses are characterized with regard to
potential risk using the corresponding exposure- or dose-response relationships. The TRIM.Risk
module is also being designed to characterize ecological risks from multimedia exposures. The
output from TRIM.Risk will include documentation of the input data, assumptions in the
analysis, and measures of uncertainty, as well as the results of risk calculations and exposure
analysis.
An overarching feature of the TRIM design is the analysis of uncertainty and variability.
A two-stage approach for providing this feature to the user has been developed. The first stage
includes sensitivity analyses that are useful in identifying critical parameters, while more detailed
uncertainty and variability analyses using Monte Carlo methods (e.g.., for refined assessment of
the impact of the critical parameters) are available in the second stage. The uncertainty and
variability feature augments the TRIM capability for performing iterative analyses. For example,
the user may perform assessments varying from simple deterministic screening analyses using
conservative default parameters to refined and complex risk assessments where the impacts of
parameter uncertainty and variability are assessed for critical parameters.
Additionally, the modular design of TRIM allows for flexibility in both its development
and application. Modules can be developed in a phased approach, with refinements being made
as scientific information and tools become available. Furthermore, the user may select any one
or more of these modules for an assessment depending on the user's needs. For example, when
performing a human health risk assessment for an air pollutant for which multimedia distribution
is not significant, TREVI.Expo may be applied using ambient concentration data or the output
from an air quality model external to TRIM; the output from TRDVI.Expo may then be used as
input to TRIM.Risk to perform the desired risk analyses. In the case of a multimedia air
pollutant, such as mercury, the user may choose to run all three TRIM modules to assess both
human and ecological risks posed by multipathway exposures from multiple media.
Overview descriptions of the TRIM modules are provided in Sections 1.2.1 through 1.2.3,
the status and plans for development are presented in Section 1.3, and plans for application
appear in Section 1.4. A summary of the previous SAB comments and OAQPS responses is
presented in Chapter 2. The approach for handling uncertainty and variability in TRIM is
described in Chapter 3. Certain aspects of the TREVI.FaTE module are addressed in greater detail
in Chapters 4 through 7, and additional details on TREVI.Expo and TRIM.Risk are provided in
Chapters 8 and 9, respectively. Chapter 10 discusses the computer framework that is being
implemented for the TRIM system. In addition, the TREVI.FaTE and TREVI.Expo TSDs provide
more detailed explanations of those modules.
NOVEMBER 1999 1-5 TRIM STATUS REPORT
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1.2.1 DESCRIPTION OF TRIM.FaTE
The first TRIM module to be developed, TRIM.FaTE, is a spatial compartmental mass
balance model that describes the movement and transformation of pollutants over time, through a
user-defined, bounded system that includes both biotic and abiotic components (compartments).
The TRIM.FaTE module predicts pollutant concentrations in multiple environmental media and
in biota and pollutant intakes for biota, all of which provide both temporal and spatial exposure
estimates for ecological receptors (i.e.., plants and animals). The output concentrations from
TRIM.FaTE also can be used as inputs to a human exposure model, such as TRUVI.Expo, to
estimate human exposures.
Significant features of TRIM.FaTE include: (1) the implementation of a truly coupled
multimedia model; (2) the flexibility to define a variety of scenarios, in terms of the links among
compartments as well as the number and types of compartments, as appropriate for the desired
spatial and temporal scale of assessment; (3) the use of a transparent approach to chemical mass
transfer and transformation based on an algorithm library that allows the user to change how
environmental processes are modeled; (4) an accounting for all of the pollutant as it moves
among the environmental compartments; (5) an embedded procedure to characterize uncertainty
and variability; and (6) the capability to provide exposure estimates for ecological receptors. The
TRIM.FaTE module is the most fully developed of the TRIM modules at this time, and this
development has produced a library of algorithms that account for transfer of chemical mass
throughout an environmental system, a database of the information needed to initialize these
algorithms for a test site, and a working computer model.
1.2.2 DESCRIPTION OF TRIM.Expo
The TRIM.Expo module, similar to most human exposure assessment models, provides
an analysis of the relationships between various chemical concentrations in the environment and
exposure levels of humans. Because multiple sources of environmental contamination can lead
to multiple contaminated media, including air, water, soil, food, and indoor air, it is useful to
focus on the contaminated environmental media with which a human population will come into
contact. These media typically include the envelope of air surrounding an individual, the water
and food ingested by an individual, and the layer of soil and/or water that contacts the surface of
an individual. The magnitude and relative contribution of each exposure pathway must be
considered to assess total exposure to a particular chemical. Currently, the focus of TRIM.Expo
development is on inhalation and ingestion exposure; however, dermal exposure will be added
later.
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The exposure analysis process
consists of relating chemical
concentrations in
environmental media (e.g., air, surface
soil, root zone soil, surface water) to
chemical concentrations in the exposure
media with which a human or population
has contact (e.g.., air, tap water, foods,
household dusts, and soils). The initial
prototype for TRIM.Expo will predict
exposure by tracking the movement of a
population cohort through locations
where chemical exposure can occur
according to a specific activity pattern.
In a typical application, TRTM.FaTE
could be used to provide an inventory of
chemical concentrations across the
ecosystem at selected time intervals (e.g.,
days, hours). For chemicals that are not
persistent and/or bioaccumulative,
processed air monitoring data or air
dispersion modeling results can be
substituted for TRTM.FaTE output data.
The TRIM.Expo module would then use
these chemical concentration data,
combined with the activity patterns of the
cohorts, to estimate exposures. The
movements are defined as an exposure-event sequence that can be related to time periods for
which exposure media concentrations are available (e.g., from TRDVI.FaTE, ambient data, and/or
dispersion modeling results). Each exposure event places the population cohort in contact with
one or more environmental media within a specified microenvironment (e.g., inside a home,
along a road, inside a vehicle) in an exposure district for a specified time interval. In addition to
the location assignments, the exposure event would provide information relating to the potential
for pollutant uptake, such as respiration rate and quantity of water consumed. The TRIM.Expo
module is intended to contribute to a number of health-related assessments, including risk
assessments and status and trends analyses.
1.2.3 DESCRIPTION OF TRIM.Risk
Risk characterization is the final step in risk assessment and is primarily used to integrate
the information from the other three key steps (i.e., hazard identification, dose-response
assessment, exposure assessment). Within the TRIM framework, TRIM.Risk, the risk
characterization module, will be used to integrate the information on exposure (human or
ecological receptor) with that on dose-response or hazard and for providing quantitative
descriptions of risk and some of the attendant uncertainties. The TRIM.Risk module will provide
decision-makers and the public with information for use in developing, evaluating, and selecting
TRIM.Expo KEY TERMS
Cohort - A group of people within a population with
the same demographic variables who are assumed
to have similar exposures.
Activity pattern - A series of discrete events of
varying time intervals describing information about
an individual's lifestyle and routine. The information
contained in an activity pattern typically includes the
locations that the individual visited (usually
described in terms of microenvironments), the
amount of time spent in those locations, and a
description of what the individual was doing in each
location (e.g., sleeping, eating, exercising).
Microenvironment - A defined space in which
human contact with an environmental pollutant takes
place and which can be treated as a well-
characterized, relatively homogeneous location with
respect to pollutant concentrations for a specified
time period.
Exposure district - A geographic location within a
defined physical or political region where there is
potential contact between an organism and a
pollutant and for which environmental media
concentrations have been estimated either through
modeling or measurement.
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appropriate air quality standards and risk management strategies. The purpose of TRIM.Risk is
to integrate information from other TRIM modules and to facilitate the preparation of a risk
characterization. The TRIM.Risk module will, therefore, be able to summarize or highlight the
major points from each of the analyses conducted in the other TRIM modules. Where possible,
the TRIM.Risk module will do so in an automated manner. In general, TRIM.Risk will (1)
document assumptions and input data, (2) conduct risk calculations and data analysis, and (3)
present results and supporting information.
Current and proposed EPA guidance on risk characterization will guide the development
of TRIM.Risk. The TRIM.Risk module will be developed in a phased approach similar to other
TRIM modules. Ideally, TRIM.Risk will provide all of the information required to prepare a full
risk characterization. However, the type and variability of information needed for this purpose
are vast. Therefore, the type of information generated by TRIM.Risk will evolve over time as the
Agency gains experience and has the resources to implement more flexibility. For example, early
versions of TRIM.Risk will be limited to preparing summaries of input data and results, without
supporting text. However, as the Agency gains experience, it may be possible to incorporate
generic language to more fully describe the information required for a full risk characterization.
Many EPA risk assessments will be expected to address or provide descriptions of (1) individual
risk,3 including the central tendency and high-end portions of the risk distribution, (2) population
risk, and (3) risk to important subgroups of the population such as highly exposed or highly
susceptible groups or individuals, if known. Some form of these three types of descriptors will
be developed within TRIM.Risk and presented to support risk characterization. Because people
process information differently, it is appropriate to provide more than one format for presenting
the same information. Therefore, TRIM.Risk will be designed so that the output can be
presented in various ways in an automated manner (e.g., Chart Wizard in Microsoft® Excel),
allowing the user to select a preferred format.
1.3 TRIM DEVELOPMENT
In the development of TRIM, existing models and tools are being relied upon where
possible. Adopting or incorporating existing models or model components into a tool that meets
OAQPS' needs is preferable as it is usually the most cost-effective approach. Consequently,
review of existing models and consideration of other current modeling efforts is an important
part of TRIM development activities. Reviews of relevant models existing at the initiation of
development activities for each module are described in the TRDVI.FaTE and TRDVI.Expo TSDs.
Additionally, OAQPS is closely following several current activities as they relate to TRIM.
3 The phrase individual risk as used here does not refer to a risk estimate developed specifically for a single
member of a population. Rather, it refers to the estimate of risk for a subgroup of a population that is presented as
an estimate of the risk faced by a person rather than by the population as a whole.
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Current Agency model development activities relevant to TRIM development include the
recently published updated guidance on assessing health risks associated with indirect exposure
to combustor emissions (U.S. EPA 1999d). This guidance, previously referred to as the Indirect
Exposure Methodology (IEM), is now called the Multiple Pathways of Exposure (MPE) method.
In addition, the multimedia model, FRAMES-HWTR, has recently been developed by the Agency
to support a specific risk assessment need regarding hazardous chemicals released from land-
based waste management units. The FRAMES-HWTR model has been developed as part of a
focused fast-track (two-year) effort to support a risk-based regulation regarding disposal of
hazardous waste (HWTR99).4 Another model of interest for multimedia pollutants is the
Stochastic Human Exposure and Dose Simulation (SHEDS) model (e.g., Ozkaynak et al. 1999).
The OAQPS will be carefully considering the various aspects of MPE, FRAMES-HWIR, and
SHEDS with regard to OAQPS needs, as well as compatibility with or future improvements or
evaluations of TRIM. As TRIM is intended to be a dynamic method, developmental activities
will consider and respond as appropriate to newly available methods and scientific information.
A current major Agency research project involves the design and development of a
flexible software system to simplify the development and use of air quality models and other
environmental decision support tools. This system, called Models-3, is designed for applications
ranging from regulatory and policy analysis to understanding the complex interactions of
atmospheric chemistry and physics (U.S. EPA 1999g). The June 1999 release of Models-3
contains a Community Multi-Scale Air Quality (CMAQ) modeling system for urban- to regional-
scale air quality simulation of tropospheric ozone, acid deposition, visibility, and fine particles.
The long-term goal is to extend the system to handle integrated cross-media assessments and
serve as a platform for community development of complex environmental models. In
recognition of the availability of Models-3 over the longer term, OAQPS has designed and is
developing the TRIM computer framework to be compatible with the Models-3 system.
1.3.1 INITIAL DEVELOPMENT ACTIVITIES
The first phase of TRIM development included the conceptualization of TRIM and the
implementation of the TRIM conceptual approach through the development of a prototype of the
first TRIM module, TREVI.FaTE (U.S. EPA 1998e). The progress on TREVI.FaTE included the
development of (1) a conceptual design for the module; (2) a library of algorithms that account
for chemical mass transfer throughout the ecosystem; (3) a database to initialize the algorithms
for a test site; and (4) a working prototype in spreadsheet format.
Consistent with Agency peer review policy (U.S. EPA 1998b) and the 1994 Agency Task
Force on Environmental Regulatory Modeling (U.S. EPA 1994a), internal and external peer
review are an integral part of the TRIM development plan. Following the first phase of TRIM
development, OAQPS submitted TRIM to SAB under their advisory method of review (U.S.
EPA 1998a). In May 1998 in Washington, DC, the Environmental Models Subcommittee
(Subcommittee) of the Executive Committee of SAB reviewed the TRIM project. The SAB
The FRAMES-HWIR documentation is scheduled for public release in fall 1999.
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Subcommittee was charged with assessing the overall conceptual approach of TRIM and the
specific approach of TRIM.FaTE.
The SAB Subcommittee reported that the development of TRIM and the TRIM.FaTE
module was conceptually sound and scientifically based (U.S. EPA 1998a). The SAB
Subcommittee provided specific recommendations related to six specific charge questions. The
SAB recommendations are detailed in Chapter 2 along with brief responses, and changes to
TRIM.FaTE based in part on the SAB recommendations are highlighted in Chapter 4 of this
report.
1.3.2 RECENT ACTIVITIES
During the most recent developmental phase of TRIM, progress has been made in many
areas, including a change to the overall modular design of TRIM. As shown in Figure 1-1, the
TRIM design now includes three modules: TRIM.FaTE, TREVI.Expo, and TREVI.Risk. The
design presented to SAB in May 1998 included three other modules (Pollutant Uptake,
Biokinetics, and Dose/Response). In recognition of the flexibility of the TRIM design, which
provides an ability to rely on a variety of input data and outside models, OAQPS decided not to
include the development of these modules in the TRIM design at this time.
In consideration of SAB comments, TRIM.FaTE was refined, including the development
of new and updated capabilities, as well as the development and limited testing of
methodologies for model set-up, uncertainty and variability analysis, and evaluation. In addition,
OAQPS developed a conceptual plan for TREVI.Expo, initiated work on a prototype of
TRDVI.Expo (initially focusing on inhalation), and developed a conceptual design for TRDVI.Risk.
Furthermore, the overall computer framework for TRIM was designed and implemented in a PC-
based platform, and substantial progress was made in installing TRIM.FaTE into this framework.
Changes and additions to TRIM.FaTE are discussed in more detail in Chapter 4. The
development of TRDVI.Expo is discussed in Chapter 8, and the conceptual plan for TRDVI.Risk is
described in Chapter 9. In addition, the TRIM.FaTE and TREVI.Expo TSDs provide more
detailed explanations of these modules.
The current TRIM documentation has gone through internal Agency peer review, which
involved reviewers across the Agency, including the major program offices, the Office or
Research and Development, and staff involved in the Agency's Models 2000 efforts. The current
SAB advisory will be the second on TRIM development activities.
1.3.3 FUTURE ACTIVITIES
Following the 1999 SAB advisory, improvements will be made to the uncertainty and
variability approach, TRDVI.Expo prototype, and TREVI.Risk conceptual plan. These revisions
are scheduled to be completed in 2000. As needed, refinements will be made to the TRIM.FaTE
evaluation plan, and completion of the bulk of those activities are also scheduled for 2000. The
Agency has planned for a substantial amount of progress on each of the TRIM modules for 2000
and 2001, as described below.
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TREVLFaTE. Future work on TRIM.FaTE will include model evaluation activities and
additional development of the module to accommodate additional chemicals. The
TRIM.FaTE module is expected to be available for limited external use late in 2000 and
to be publicly released in 2001.
TREVLExpo. Future work on TRDVI.Expo in 2000 will include the further development
of ingestion algorithms, incorporation of EPA's Air Pollutant Exposure Model (APEX)
coding into the TRIM platform followed by adjustments to APEX to include ingestion
algorithms, a test case of the inhalation pathway, and a test case of inhalation and
ingestion pathways. Over the longer term, addition of the dermal pathway to the module
will be initiated.
TRIM.Risk. Development of TREVI.Risk will begin after SAB comments are received
on the conceptual design. Module development will include identification of data needs
and formatting of data outputs. Programming for a TRIM.Risk prototype is expected to
be completed in 2000.
TRIM computer framework. Further development of the TRIM computer framework,
including incorporation of the TREVI.Expo (inhalation) module, will take place during
2000. Features to be refined during this time frame include limited geographic
information system (GIS) or mapping capabilities. Additionally, long-range
comprehensive GIS planning will occur. Development of user guidance materials is
planned for 2000 (see text box).
In addition to consulting with Agency scientists during future TRIM development (i.e.,
USER GUIDANCE
Development of the TRIM user's guide is scheduled to begin in 2000, along with a plan for
training activities. The OAQPS recognizes the importance of developing detailed user guidance that
will assist users in defining, for a particular modeling application, the spatial and temporal resolution,
compartments and linkages, and parameters and initial conditions. For example, the TRIM.FaTE
guidance will likely emphasize the value of performing several different preliminary simulations in
verifying the adequacy of the parcel and compartment specifications for the desired application.
Similarly, detailed users guidance will be developed for TRIM.Expo to assist users in defining
cohorts, study areas, exposure districts, and microenvironments, as well as various parameters and
exposure factors.
It also will be important for the guidance to note the responsibility of the user in defining the
simulation as appropriate to the application. For example, in TRIM.FaTE, default values will likely
be made available with the model for a variety of parameters ranging from physiological
characteristics of various biota to physical characteristics of abiotic media; the user will need to
consider appropriateness of these values or others (e.g., site-specific data) for their application.
While the TRIM modules are intended to provide valuable tools for risk assessment, and their
documentation and guidance will identify, as feasible, uncertainties and limitations associated with
their application, the guidance will emphasize that their appropriate use and the characterization of
uncertainties and limitations surrounding the results are the responsibility of the user.
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peer involvement), in late 2000 or early 2001, OAQPS will seek both internal and external peer
review of new aspects following the next phase of TRIM development. In addition to the SAB,
which provides the Agency with reviews, advisories, and consultations, other external peer
review mechanisms consistent with Agency policy (U.S. EPA 1998b) include the use of a group
of independent experts from outside the Agency (e.g., a letter review by outside scientists), an ad
hoc panel of independent experts, and peer review workshops. The OAQPS intends to seek the
peer review mechanism appropriate to the importance, nature, and complexity of the material for
review.
1.4 PHASING TRIM INTO OAQPS' SET OF MODELING TOOLS
As mentioned earlier, TRIM is intended to support assessment activities for both the
criteria and hazardous air pollutant programs of OAQPS. As a result of the greater level of effort
expended by the Agency on assessment activities for criteria air pollutants, these activities are
generally more widely known. To improve the public understanding of the hazardous air
pollutant (or air toxics) program, the Agency published an overview of the air toxics program in
July 1999 (U.S. EPA 1999e). Air toxics assessment activities (National Air Toxics Assessment,
or NAT A) are described as one of the program's key components.5 The NATA includes both
national- and local-scale activities. The TRIM system is intended to provide tools in support of
local-scale assessment activities, including
multimedia analyses.
One of the Agency's most immediate
j f T-nTTv/r • Au -n -j i-n- i - A human health or ecological assessment
needs for TRIM comes in the Residual Risk Qf mu|timedia mu|tipat Jay risks
associated with mercury emissions from
one or several local sources could be
performed using all three modules in the
TRIM system.
An assessment of human health risks
associated with air emissions of a criteria
air pollutant (e.g., ozone) or one or several
volatile HAPs in a metropolitan area could
be developed using an external air model
or ambient concentration data from fixed-
multimedia environmental distribution. site monitors coup|ed with TRIM.Expo and
Program, in which there are statutory
deadlines within the next two to nine years
for risk-based emissions standards decisions.
As described in the Residual Risk Report to
Congress (U.S. EPA 1999f), TRIM is
intended to improve upon the Agency's
ability to perform multipathway human health
risk assessments and ecological risk
assessments for HAPs with the potential for
Another important upcoming use for TRIM is
EXAMPLES OF TRIM APPLICATIONS
TRIM.Risk.
5 Within the air toxics program, these activities are intended to help EPA identify areas of concern (e.g.,
pollutants, locations, or sources), characterize risks, and track progress toward meeting the Agency's overall air
toxics program goals, as well as the risk-based goals of the various activities and initiatives within the program, such
as residual risk assessments and the Integrated Urban Air Toxics Strategy. More specifically, NATA activities
include expansion of air toxics monitoring, improvements and periodic updates to emissions inventories, national-
and local-scale air quality modeling, multimedia and exposure modeling (including modeling that considers
stationary and mobile sources), continued research on health effects of and exposures to both ambient and indoor
air, and use and improvement of exposure and assessment tools. These activities are intended to provide the
Agency with improved characterizations of air toxics risk and of risk reductions resulting from emissions control
standards and initiatives for both stationary and mobile source programs.
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in exposure assessment in support of the review of the ozone NAAQS. The TRIM.Expo and
TRIM.Risk modules augmented with external air quality monitoring data and models are
intended to support this type of criteria pollutant assessment as well as risk assessments for non-
multimedia HAPs.
Consistent with the phased plan of TRIM development, the application of TRIM will also
be initiated in a phased approach. With the further development of the TRIM modules in 2000
and 2001, EPA will begin to use the modules to contribute to or support CAA exposure and risk
assessments. These initial applications also will contribute to model evaluation. The earliest
TRIM activities are expected to include the use of TRDVI.FaTE side-by-side (at a comparable
level of detail) with the existing multimedia methodology6 in risk assessments of certain
multimedia HAPs (e.g., mercury) under the Residual Risk Program. As TRIM.Expo is
developed to accommodate inhalation modeling of HAPs and after it has undergone testing,
OAQPS plans to initially run it side-by-side (at a comparable level of detail) with EPA's existing
inhalation exposure model, HEM (Human Exposure Model (U.S. EPA 1986b)). When
TRIM.Risk has been completed, it will be used, as appropriate, in both criteria and hazardous air
pollutant risk assessments.
In later years, OAQPS intends to use TRIM and the TRIM modules in a variety of
activities including (1) residual risk assessments using TREVI.FaTE, TRIM.Expo, and
TRIM.Risk, in combinations appropriate to the environmental distribution characteristics of the
HAPs being assessed; (2) urban scale assessments on case study cities as part of the Integrated
Urban Air Toxics Strategy; and (3) exposure and risk assessments of criteria air pollutants (e.g.,
ozone, carbon monoxide) in support of NAAQS reviews.
6 In support of the Mercury Report to Congress (U.S. EPA 1997a) and the Study of Hazardous Air
Pollutant Emissions from Electric Utility Steam Generating Units — Final Report to Congress (U.S. EPA 1998d),
the Agency relied upon the Indirect Exposure Methodology, which has recently been updated and is now termed the
Multiple Pathways of Exposure methodology (U.S. EPA 1999d). This methodology is being used in initial
assessment activities for the Residual Risk Program (U.S. EPA 1999f).
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2. MAY 1998 SCIENCE ADVISORY BOARD REVIEW AND
AGENCY RESPONSES
On May 5 and 6, 1998, the Environmental Models Subcommittee (Subcommittee) of the
Executive Committee of EPA's Science Advisory Board (SAB) held a meeting in Washington,
DC to perform an early review of TRIM. At the time of the SAB review, only the conceptual
approach for TRIM and a prototype of TRDVI.FaTE had been developed. The Agency requested
an early review of TRIM and the TRIM.FaTE prototype to ensure that the development of TRIM
was conceptually sound and scientifically defensible as well as consistent with Agency
objectives. Additional reviews of TRIM and its modules will be conducted by SAB over the next
few years.
During the May 1998 SAB review, there were six charge questions related to TRIM and
TRIM.FaTE.
1. Is the overall conceptual TRIM approach appropriate, given the underlying science, EPA
policy, and regulatory needs (i.e., what are the strengths and weaknesses)?
2. The TRIM approach is designed for the explicit treatment of uncertainty and variability,
including both model uncertainty and parameter uncertainty. Is the spatial compartmental
mass balance approach commensurate with quantifying uncertainty and variability in a
scientifically defensible manner?
3. The TRIM.FaTE module is the environmental fate, transport, and exposure component of
TRIM. Is the overall conceptual approach represented in the TRIM.FaTE module
appropriate, given the underlying science, EPA policy, and regulatory needs (i.e., what
are the strengths and weaknesses of the approach)?
4. The TRIM approach is designed to be flexible and to allow for a tiered approach, to
function as a hierarchy of models, from simple to complex, as needed.
(a) As implemented at this time, is the TRIM.FaTE module, with its three-dimensional,
spatial compartmental mass conserving approach to predicting the movement of pollutant
mass over time, appropriate from a scientific perspective?
(b) Is the TRIM.FaTE module, as designed, an appropriate tool, when run either at a
screening level or for a more refined analysis, for use in providing information for
regulatory decision-making? Given the module design (i.e., the potentially large number
of model parameters and associated uncertainty and variability), is TRIM.FaTE suitable
to support regulatory decisions?
5. Does the TRIM.FaTE module, as it has been conceptualized, address some of the
limitations associated with other models (e.g., non-conservation of mass, steady-state
approach, inability to quantify uncertainty and variability, limited range of receptors and
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processes considered)? Are there other limitations that the TRIM.FaTE module should
address?
6. Does the TRIM.FaTE module, as it has been conceptualized and demonstrated to date,
facilitate future integration with appropriate data sources (e.g., GIS) and applications
(e.g., multipathway exposure assessment for humans)?
The SAB responded to each of these questions and provided EPA with recommendations for
improvements in the next versions of TRIM modules and TRIM.FaTE in particular (U.S. EPA
1998a). The SAB comments and Agency responses under each of the six charge questions are
summarized below.
Overall, SAB found the development of TRIM and the TRIM.FaTE module to be
conceptually sound and scientifically based. The SAB recommended that the TRIM team seek
input from users before and after the methodology is developed to maximize its utility;
understand the potential uses of TRIM to guard against inappropriate uses; provide
documentation of recommended and inappropriate applications; provide training for users; test
the model and its subcomponents against current data and models to evaluate its ability to
provide realistic results; and apply terminology consistently.
2.1 IS THE OVERALL TRIM CONCEPTUAL APPROACH
APPROPRIATE?
COMMENT: The SAB found the conceptual approach for TRIM to be technically
defensible and appropriate for use in regulatory decision-making, but noted that because the
system is evolving, it is unclear how the overall methodology will address the spectrum of
regulatory questions. The SAB cited the flexibility of TRIM to be a strength, but also
recommended that care be exercised to guard against developing unnecessarily complex or
inconsistent modeling applications.
RESPONSE: The OAQPS agrees with the need to maintain the focus of TRIM uses on
practical applications, recognizing that it is not intended to be a research model. The Office
intends to provide clear documentation for those applications for which TRIM is an appropriate
tool. Preparation of users guidance materials is planned for the next phase of TRIM
development.
COMMENT: The SAB noted that the largest challenge facing TRIM is the lack of
available data for estimating fate, transport, exposure, and risk processes, possibly limiting the
ability of TRIM to model many chemicals and hindering model validation efforts. Therefore,
SAB recommended identifying and acquiring significant additional field data (e.g., air
monitoring data, soil samples) to estimate modeling parameters and to "validate" the model
components and other aspects of the modeling system.
RESPONSE: The OAQPS developed a strategy for model evaluation for TRIM, and the
TRIM.FaTE module specifically, and identified existing data sets for potential use in
implementing the approach, as discussed further in Section 4.8 and Chapters 6 and 7. This effort
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should facilitate the comparison of model results with "real world" environmental concentration
data. Such a comparison is a key element of the TREVI.FaTE mercury case study described in
Chapter 7. In addition, OAQPS recognizes the need for evaluation of specific model components
and other aspects of the modeling system. Therefore, adjustments to the TRIM.FaTE module
were made to increase the transparency of the module and to more readily allow for the testing of
individual components within the module.
2.2 IS THE SPATIAL COMPARTMENTAL MASS BALANCE
APPROACH COMMENSURATE WITH QUANTIFYING
UNCERTAINTY AND VARIABILITY IN A SCIENTIFICALLY
DEFENSIBLE MANNER?
COMMENT: In its review, SAB noted that, at that stage, it was not possible to indicate
whether the spatial compartmentalization would be a significant source of uncertainty in
generating predictions using TRIM, and added that this issue should be kept in perspective
relative to other potential error sources. The SAB recommended that OAQPS conduct a
thorough review of the available literature on sensitivity and uncertainty analysis prior to making
choices on the specific approaches for incorporating sensitivity and uncertainty analysis into
TRIM. Furthermore, SAB recommended that TRIM developers clarify how the analysis of
uncertainty and sensitivity will be incorporated into TRIM and how it will be presented as part of
the overall assessment. The SAB also stated that the role and limitations of sensitivity and
uncertainty analysis be clearly recognized and acknowledged by TRIM developers and users.
RESPONSE: As part of the TRIM.FaTE evaluation plan (see Chapter 6), OAQPS is
conducting structural evaluations on the effect of spatial configuration on model results. For
example, the impact of compartment size, shape, and location on model outputs will be analyzed.
These results will be considered along with those of other analyses as part of the TRIM.FaTE
evaluation process.
The OAQPS has continued to review the available literature on sensitivity and
uncertainty analysis for model parameters (see Appendix B), developed a general approach for
uncertainty and variability analysis in TRIM (see Chapter 3), and developed a specific approach
for incorporating sensitivity and uncertainty analysis capabilities into the TRIM.FaTE module -
recognizing the roles and limitations of sensitivity and uncertainty analysis - as discussed further
in Section 4.7 and in Chapter 6 of TRIM.FaTE TSD Volume I. The proposed approach reflects a
balance between the additional effort needed in developing the module and the added value to the
module. This approach includes adding the capability to present the results of uncertainty and
variability analysis as part of an assessment using TRIM.FaTE. Plans for an uncertainty and
variability analysis for a simplified environmental scenario are described in Chapter 6 of the
TRIM.FaTE TSD Volume I and Chapter 7 of this report.
In the TRIM modeling system, the uncertainty and variability outputs from one module
(e.g., TRIM.FaTE) will be carried through to the other modules (e.g., TRIM.Expo, TRIM.Risk).
This feature will insure that TRIM outputs include measures of uncertainty and variability, which
are important to the characterization of risks for the Agency's decision-making process.
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COMMENT: The SAB noted that validation of TRIM is a difficult issue because TRIM
will never be capable of (in)validation in the classical sense. Rather, the notion of model
"validation" should be seen as a matter of designing a tool appropriate for the given (predictive)
task. Accordingly, SAB recommended that history matching and qualitative peer review should
not be set aside and that the Agency should watch for new methods for quantitatively assuring
the quality of models as tools for fulfilling specified predictive tasks.
RESPONSE: A model evaluation plan has been developed for TRIM that will use a
wide range of model evaluation tools to assess the quality, reliability, and relevance of TRIM and
TRIM.FaTE (see Section 4.8 and Chapter 6). This plan includes some reliance on history
matching and qualitative peer review. As new methodologies are developed and reviewed within
the scientific community, OAQPS will be assessing their acceptance and usefulness for assuring
the quality of TRIM and TRIM.FaTE.
2.3 IS THE OVERALL CONCEPTUAL APPROACH REPRESENTED IN
THE TRIM.FHTE MODULE APPROPRIATE?
COMMENT: The SAB found that the TRIM.FaTE module is conceptually sound and
aims at an appropriate level of complexity. The Subcommittee noted several strengths of the
TRIM.FaTE module, including (1) meeting the requirements of scientific and technical
defensibility, (2) flexibility, (3) ability to address exposures relevant to human health and
ecological risk assessments, and (4) user friendliness. Limitations that were noted include (1) the
use of confusing and contradictory terminology, (2) difficulty in understanding the difference
between applications of the module in a screening capacity versus a more in-depth analysis
mode, (3) the predisposition toward first-order, linear algorithms representing the fate and
transport of chemicals, (4) the emphasis on the steady-state distribution of contaminants, and (5)
the constraints and computational overhead associated with the spreadsheet software relied on for
Prototype IV of TRIM.FaTE. The SAB also recommended providing examples of applications
of the module and developing a user's guide that describes the proper use, strengths, and
limitations of the TRIM.FaTE module.
RESPONSE: Recognizing the inconsistent and sometimes conflicting terminology used
to describe the TRIM.FaTE prototype presented to SAB, OAQPS revised the terminology to be
more consistent with other multimedia models. These revisions should help decrease confusion
for both experienced and novice fate and exposure modelers. The new set of terms, which is
used consistently throughout this report and the TRIM.FaTE TSD, is included in the glossary of
each document.
One of the design objectives for the TRIM modeling system has been the ability to use it
in performing iterative analyses. That is, the user is able to select the necessary level of analysis,
ranging from a simple analysis, for which less site-specific data are required and which will run
more quickly, to one needed for a more detailed risk assessment. For example, the more simple
analysis, providing a more imprecise, general idea of pollutant distribution, may be sufficient for
priority setting or other similar scoping activities (e.g.., in a screening analysis for which
conservative default input parameters could be used). This allows the Agency to focus a more
detailed analysis, where the impacts of parameter uncertainty may be assessed qualitatively for
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critical parameters, on situations where a more refined assessment is needed (e.g., human health
risk assessments to support environmental regulation).
Although it may appear that there is a predisposition toward using first-order, linear
algorithms in TRUVI.FaTE because of the use of LSODE (the Livermore Solver for Ordinary
Differential Equations, a calculation tools used within TREVI.FaTE), the model is capable of
using more complex, non-linear chemical mass transfer algorithms. The application of these
higher order algorithms is limited, however, due to a lack of understanding in the scientific
community regarding these chemical processes. Furthermore, the use of complex algorithms is
sometimes limited because of the need to balance accuracy of outputs with the time needed to
run the model.
It is probable that SAB's observation that TREVI.FaTE focuses on steady-state
distributions of chemicals is due to the sample results that were presented at the May 1998 SAB
meeting, which were virtually all steady-state results. However, the original and current intent of
TREVI.FaTE is to develop a model that produces dynamic results. In the past year, the majority
of work on TREVI.FaTE focused on presenting dynamic results.
The Agency's development of a computer framework for TRIM is described in Chapter
10. Consistent with the SAB recommendation, Version 1.0 of the framework has been
developed primarily, but not entirely, in the Java programming language. Some parts are
implemented in Fortran and others in C. The C programming language and Fortran are used in
situations where existing code in those languages provides required functionality and where high
computational efficiency is needed, such as solving systems of equations. Java provides
portability across different hardware and operating systems and offers a good combination of
speed of development, robustness, and support for object-oriented designs.
The model evaluation activities described in Chapter 6 and the experience gained through
the mercury case study described in Chapter 7 will assist in describing appropriate uses of
TREVI.FaTE and in identifying model limitations. The findings from these and other tests will be
used to develop guidance for users of TREVI.FaTE, including guidance on the interpretation of
results using linear algorithms. In addition, OAQPS is developing plans for providing training
on the uses of TRIM and the individual modules.
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2.4 THE TRIM APPROACH IS DESIGNED TO BE FLEXIBLE AND TO
ALLOW FOR A TIERED APPROACH
2.4.1 IS THE TRIM.FaTE MODULE APPROPRIATE FROM A SCIENTIFIC
PERSPECTIVE?
COMMENT: The SAB noted in its review of the prototype of TRIM.FaTE that it had
not been checked against a detailed set of observed, spatially varying "real world" environmental
concentration data. In addition, SAB stated that because of its highly aggregate representation of
environmental compartments, it is unlikely that TRIM.FaTE can be effectively used to address
fully variable three-dimensional spatial analyses and cited several other models that may offer
greater value for certain applications.
RESPONSE: As discussed in Section 2.1, OAQPS developed a model evaluation plan
and is conducting a case study using mercury which will include evaluating model results in
comparison to monitoring data from a specific site. The TRIM system with its assumption of
uniform distribution within a compartment may not effectively address fully variable three-
dimensional spatial analyses within a single compartment. However, with TRIM'S features
promoting flexibility, it may be able to represent spatial variability within a single medium
through the use of multiple compartments. The degree to which the spatial variability within a
medium can be captured is dependent on the number of compartments into which that medium
can be divided and the number of compartments that can be modeled. Recognizing the
importance of this issue, one part of the TRIM.FaTE evaluation effort is to assess, through tests
of varying spatial aggregation, the simulation of three-dimensional aspects.
It also should be noted that the Agency does not intend to rely solely on TRIM.FaTE in
evaluating the multimedia impacts of air pollutants in support of regulatory and policy decisions.
For those pollutants believed to have multimedia impacts, TRIM.FaTE analyses, including
analyses of uncertainty and variability, along with other relevant information (including any
limitations of the analyses), are intended to be used to inform those decisions. For pollutants for
which a particular medium is dominant and for which transport and concentration gradients
within that medium dominate the fate and exposure outcome, applicable single media, process-
based models may be used to support decision-making.
COMMENT: The SAB noted that TRIM.FaTE lacked the ability to handle processes
such as diffusive/dispersive transfer perpendicular to the longitudinal direction. Specifically,
they cited the omission of dispersion phenomena throughout the module as an important issue
that may limit the applicability and credibility of TRIM.FaTE.
RESPONSE: The EPA recognizes the need to incorporate such processes into the fate
and transport module of TRIM and has conducted additional investigation into how this might be
defensibly addressed within the current model architecture. At this time, these investigations
have resulted in the inclusion of additional dispersion algorithms in surface water, as well as
implementation of methods for including the results of external air models that do consider
dispersion processes in TRIM.FaTE. However, due to the coupled relationship between
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compartments in TRIM.FaTE and the state of the science in characterizing air dispersion within
grid models, dispersion and diffusion algorithms for air transport have not been included.
The structure of the currently implemented air model in TRIM.FaTE is that of a grid
model, although it deviates from the traditional grid model used in air simulations for
photochemical assessments in that the air compartment volume elements can be unequal in size
and extent. Grid models have limitations with respect to characterizing dispersion. The
homogeneous assumption used in grid models results in artificial (numerical) dispersion that
tends to simulate the dilution of the material in the grid cell. For the typical grid cell on the order
of several kilometers in size, it is this "artificial" dilution that is of much larger magnitude than
the expected dispersion term. Thus, inclusion of an additional dispersion term may tend to over-
dilute the plume. Further, the largest surface impacts can result from nonhomogeneous
conditions (e.g., asymmetric vertical mixing in convective conditions).
Due to these limitations, special dispersion characterizations are necessary with grid
models, involving parameterizations for subgrid processes such as diffusion. Research indicates
that there are few horizontal and vertical dispersion characterizations for grid models currently
available. Further, it has been reported that numerical diffusion can dominate the physical
diffusion predicted by these characterizations, especially in stable conditions (Nguyen et al.
1997). A method of addressing asymmetric vertical mixing during convective conditions has
been explored by Pleim and Chang (1992). This approach will be investigated for inclusion in
the TRIM.FaTE algorithm library.
An alternative to incorporating a more sophisticated air model into TRIM.FaTE is to
import the results of such a model. The details for how this alternative can be accomplished are
described in Appendix B of TRIM.FaTE TSD Volume I. As discussed in Appendix B, this
approach has other limitations; notably, either the linkage between the external model and
TRIM.FaTE is in one direction only and, hence, conservation of chemical mass is lost, or the
external model must be linked with TRIM.FaTE in such a way that chemical transfer can occur
in both directions. The difficulty of the latter will depend on the particular external model
considered, but it is likely that it would generally require a substantial effort to implement. This
is because the user must not only perform the practical tasks associated with computer
programming, but also must ensure that no fundamental assumptions or concepts inherent to
either model are violated. This could occur, for example, if there is overlap between the models
in how they address other processes that are not an explicit component of the model linkage itself
(e.g., the external model may be treating deposition using general inputs for vegetative cover, and
the user must implement additional checks to ensure that these inputs are consistent with the
vegetative compartments used within TRIM.FaTE).
For cases where the lack of air dispersion modeling cannot be accepted, it is suggested
that an external air model be used, the results of which would then be used as one of the inputs
for TRIM.FaTE. The details of how this can be implemented have been developed (see Section
4.5, and Appendix B of the TRIM.FaTE TSD Volume I) and demonstrated using a common
regulatory air model (Industrial Source Complex, Short Term Version 3, or ISCST3) (U.S. EPA
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1995c). This approach is not limited to using air models alone, as the same method will work for
any compartment type.
COMMENT: The SAB recommended that TRIM.FaTE be constructed to permit
disaggregation of the component results and that the module be further studied to build
confidence in the overall predictive ability of the model.
RESPONSE: As noted in Section 2.1, OAQPS recognizes the importance of the
evaluation process for specific TRIM.FaTE components and other aspects of the modeling
system and, therefore, made adjustments to TRIM.FaTE to allow for easier testing of individual
components of the module.
COMMENT: The SAB suggested that tracking and accounting within the TRIM.FaTE
module is needed to isolate its predictions and to permit benchmark comparison with data sets
and other models. The Subcommittee noted that this would permit scrutiny of TRIM.FaTE
transformation algorithms and the parameters that are used within this component of the TRIM
model.
RESPONSE: The accessibility of the algorithm library for TRIM.FaTE permits scrutiny
of the transformation and transfer algorithms selected for each modeling simulation. The initial
process models and default parameters within TRIM.FaTE have been selected upon
consideration of those available in existing models and the current modeling literature. The
evaluation strategy proposed for TRIM.FaTE includes mechanistic evaluations to assess the
individual process models. For example, OAQPS is performing a comparison of the TRIM.FaTE
air transport component to a widely used EPA air dispersion model, ISCST3.
2.4.2 IS THE TRIM.FaTE MODULE AN APPROPRIATE TOOL FOR USE IN
PROVIDING INFORMATION FOR REGULATORY DECISION-MAKING?
COMMENT: The SAB was unable to assess the appropriateness of the module as a
decision-making tool because additional testing and evaluation are necessary.
RESPONSE: The OAQPS is conducting additional testing and evaluation of the
TRIM.FaTE module, including testing against environmental concentration data and
comparisons of outputs to other model results (see Chapters 6 and 7), and believes that
TRIM.FaTE will be a useful tool that can provide information in support of regulatory decision-
making.
2.5 DOES THE TRIM.FaTE MODULE, AS IT HAS BEEN
CONCEPTUALIZED, ADDRESS SOME OF THE LIMITATIONS
ASSOCIATED WITH OTHER MODELS?
COMMENT: While TRIM.FaTE includes the mass conserving feature for chemicals
undergoing first-order linear mass transfer and transformation processes, SAB found it unclear as
to how TRIM.FaTE can be adapted for chemicals that are subject to non-linear higher-order
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processes. The SAB recommended that additional methods and guidance be developed to assist
users in selecting the appropriate level of spatial and temporal resolution necessary to obtain
adequate precision and accuracy in the results.
RESPONSE: Thus far in the development of TRTM.FaTE, only first-order linear
methods have been implemented for all fate and transport processes. The degree of additional
effort required to incorporate non-linear and/or higher-order methods will depend on the types of
methods of interest. For example, implementation of the types of equations used to model non-
linear kinetics will be straightforward, as the original system of differential equations can be
used, after adding the non-linear product terms. More care will be needed for incorporating
methods for estimating gradients within what are currently assumed to be homogeneous
compartments. The equation solving method used in TRTM.FaTE, LSODE, allows non-linear
mass transfers to easily be set up numerically. The primary limitation TRIM.FaTE has for
addressing such processes is a result of a lack of appropriate data, not a result of limitations in
technical capability.
As part of the TRIM.FaTE mercury case study (see Chapter 7), OAQPS is conducting
analyses to examine the level of spatial and temporal resolution necessary to obtain adequate
precision and accuracy in the results for various Agency needs. The results of these analyses will
assist OAQPS in the development of users guidance for the TRIM.FaTE module that will assist
users in selecting an appropriate level of spatial and temporal resolution (see Chapter 5 of
TRIM.FaTE TSD Volume I). The results of such testing and initial model applications will
inform the guidance development process.
COMMENT: The SAB found that the flow model for air transport was highly
simplified and recommended further evaluation of available air models and selection of
additional process modules or components for incorporation into TRIM.FaTE.
RESPONSE: The EPA recognizes the need to incorporate more sophisticated methods
for modeling air transport in TRIM.FaTE. Two primary means of doing so have been
investigated since the May 1998 SAB review. The first option consists of incorporating
algorithms for addressing dispersion/diffusion directly within the TRIM.FaTE algorithm library
itself. The second option consists of ensuring that it is possible to use the results of an external
air model that addresses these processes. Each of these approaches has drawbacks that limit its
applicability within a coupled model such as TRIM.FaTE.
Incorporating horizontal and vertical air dispersion/diffusion algorithms directly within
TRIM.FaTE was pursued using a method utilizing both lateral and vertical Pasquill-Gifford
plume dispersion coefficients. However, review indicated that such methods are not preferable at
this time (see the second response in Section 2.4.1). The alternative (i.e., incorporating the
results of an external air model that more appropriately addresses dispersion) has other
limitations; notably, either the linkage between the external model and TRIM.FaTE is in one
direction only and, hence, conservation of chemical mass is lost, or the external model must be
linked with TRIM.FaTE in such a way that chemical transfer can occur in both directions. The
difficulty of the latter will depend on the particular external model considered, but it is likely that
it would generally require a substantial effort to implement. This is because the user must not
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only perform the practical tasks associated with computer programming, but also must ensure
that no fundamental assumptions or concepts inherent to either model are violated. This could
occur, for example, if there is overlap between the models in how they address other processes
that are not an explicit component of the model linkage itself (e.g., the external model may treat
deposition using general inputs for vegetative cover, and the user must implement additional
checks to ensure that these inputs are consistent with the vegetative compartments used within
TRIM.FaTE).
The users guidance materials to be developed in the next TRIM development phase will
caution users to carefully consider which external air models should be used as input to
TRIM.FaTE. External models for various media can be used in lieu of the TRIM.FaTE
algorithms; however, strong caution should be placed on the use of external models that
themselves may not conserve mass (e.g., Gaussian plume models), but whose use may be
dictated or preferred for regulatory reasons.
COMMENT: The SAB noted that the predictive capability of the module is limited
because of the gross transfer of mass between sources, receptors, and sinks. Therefore, it
recommended comparing results from TRIM.FaTE to results from existing "single-media linked
models" to establish the advantages and limitations of TRIM.FaTE.
RESPONSE: As part of the evaluation plan described in Chapter 6, OAQPS is testing
TRIM.FaTE using monitoring data to compare model results to both "real world" observations
and other model outputs, including those from the Agency's Indirect Exposure Methodology
(IEM, now termed Multiple Pathways of Exposure or MPE), which is a methodology that relies
on a one-way transport process through a series of linked models or algorithms. In addition,
outputs from the TRIM.FaTE air modeling component are being compared to outputs from
ISCST3. The ISCST3 is the air model relied upon in the MPE methodology.
COMMENT: With regard to uncertainty and sensitivity analyses, SAB recommended
reviewing the literature on sensitivity and uncertainty analysis (see Section 2.2 for additional
details).
RESPONSE: As noted in Section 2.2, after reviewing the literature (see Appendix B),
OAQPS developed a proposed approach to incorporate sensitivity and uncertainty analysis
capabilities into the TRIM computer framework. This approach is described in Chapter 3. In
addition, implementation of the approach in the TRIM.FaTE module is summarized in Section
4.7 and described in more detail in Chapter 6 of TRIM.FaTE TSD Volume I.
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2.6 DOES THE TRIM.FaTE MODULE, AS IT HAS BEEN
CONCEPTUALIZED AND DEMONSTRATED TO DATE,
FACILITATE FUTURE INTEGRATION WITH APPROPRIATE
DATA SOURCES AND APPLICATIONS?
COMMENT: The SAB found that TRIM.FaTE could conveniently and effectively be
integrated with data sources such as GIS, but that coupling of TRIM.FaTE with other more
complex models that generate continuous spatial gradients may be problematic.
The SAB noted that the results from TRIM.FaTE would not be directly usable for human
health assessments because TRIM.FaTE does not generate distributions of indoor air pollutants,
which are the most important input for TREVI.Expo.
RESPONSE: The TRIM.FaTE module was never intended to solely support human
health assessments, but only to generate estimates of concentrations in the various environmental
and biotic media. The exposure component is critical for any human health assessment.
Distributions of indoor air pollutants are not generated in TRIM.FaTE because it was determined
that on a total mass basis, the indoor environment represents a negligible reservoir of mass of air
pollutants. However, OAQPS recognizes the importance of indoor air pollutant concentrations to
human exposure. For that reason, indoor air concentrations will be generated within the
TRIM.Expo module by accounting for penetration of pollutants in the ambient air (obtained from
output of TRIM.FaTE, from other air models, or from analysis of ambient monitoring data)
indoors as well as significant indoor sources. Therefore, distributions of indoor air
concentrations and exposures will be generated within the TRIM.Expo module.
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3. TREATMENT OF UNCERTAINTY AND VARIABILITY IN
TRIM
This chapter summarizes the approach for assessing uncertainty and variability in the
TRIM modules. Additional background on how this method was selected is provided in
Appendix B of this report. A specific discussion of the approach for TRTM.FaTE is presented in
Chapter 6 of TSD Volume I. The following text box presents definitions for the key terms used
in this chapter to explain the uncertainty and variability analysis framework for TRIM.
KEY TERMS FOR UNCERTAINTY AND VARIABILITY ANALYSIS
Variability
Variability represents the diversity or heterogeneity in a population or parameter, and is sometimes
referred to as natural variability. An example is the variation in the heights of people. Variability
cannot be reduced by using more measurements or measurements with increased precision (taking
more precise measurements of people's heights does not reduce the natural variation in heights).
However, it can often be reduced by a more detailed model formulation (e.g., modeling people's
heights in terms of age will reduce the unexplained variability of heights).
Uncertainty
Uncertainty refers to the lack of knowledge regarding the actual values of physical model input
variables (parameter uncertainty) and of physical systems (model uncertainty). For example,
parameter uncertainty results when non-representative sampling (to measure the distribution of
parameter values) gives sampling errors. Model uncertainty results from simplification of complex
physical systems. Uncertainty can be reduced through improved measurements and improved
model formulation.
Sensitivity
Sensitivity refers to the rate of change of the model output with respect to changes in an input
parameter.
3.1 OBJECTIVES OF THE INTEGRATED UNCERTAINTY ANALYSIS
Development of the TRIM framework involved development of an approach to estimate
uncertainty and variability in a manner that allows for integration between the TRIM modules
and for tracking the uncertainty and variability through the modules. The TRIM approach for
uncertainty and variability analysis is intended to accomplish the following objectives:
Propagation of variability, uncertainty, and parameter dependencies throughout TRIM in
an integrated manner, tracking uncertainty and variability jointly and separately;
Characterization of uncertainty and variability of model results with respect to parameter
distributions and correlations, and calculate summary measures of the uncertainty and
variability of model results that clearly convey the important aspects of model uncertainty
and variability;
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• Identification of critical parameters and correlations through sensitivity analyses;
• Information to guide data improvement efforts (including setting priorities for gathering
data to develop distributions of parameters), guide model simplification efforts, and
support temporal and spatial aggregation choices;
Results that can support risk management decision-making; and
Estimation of uncertainty and variability within a reasonable amount of computer
processing time.
3.2 GENERAL STEPS IN AN ANALYSIS OF UNCERTAINTY AND
VARIABILITY
The analysis of uncertainty and variability in a risk assessment takes place through a
series of functional steps. Some steps may be skipped and others may be incorporated in more
sophisticated or refined iterations. Underlying the risk assessment is a mechanistic model of the
risk process. In the case of the TRJM.FaTE module of TRIM, the model algorithms represent the
physical and chemical processes that transfer chemical mass through different compartments of a
physical system. As a first step, the mechanistic model can be evaluated in a deterministic
manner for its sensitivity to changes in its variable inputs. For example, one very simple
sensitivity analysis looks at the percent change in the model output (e.g., risk) given percent
changes in model inputs (e.g., emissions, wind velocities, air-to-soil deposition rates, soil
density). This technique does not require information about the range of values of the input
variables, but requires only a selection of possible single values from which local deviations are
calculated. This univariate analysis can be expanded to look at pairs of input variables, thereby
taking into account dependencies or interactions between variables. Such simple analyses of
model sensitivity are valuable because they can be used quickly and easily to identify the
variables that have the greatest potential to "influence" the model results, based on the
relationships in the model and the selected set of values.
The second step of the analysis is collecting information about the ranges and likelihoods
of the values the variables might take. After the ranges are estimated for the variables, it is
possible to conduct a deterministic scenario analysis by selecting a set of values for each variable
and using the mechanistic model to calculate risk for each possible combination of the selected
values. A probability tree can be created by adding information about the likelihood of each
scenario. Either approach can be implemented to identify the most important variables on the
basis of the combination of model response sensitivity and indeterminancy (i.e., lack of
knowledge of the actual or "true" values) in the variables. A variety of approaches are available
to estimate the ranges and likelihoods of the values for the input variables. Examples include
direct measurement of the physical system and elicitation of expert judgment. Further processing
of the input data may be desirable to fit analytical forms, such as normal or lognormal probability
distributions, or to estimate statistics, such as the mean and variance, from the information about
the dispersion in the variable values. The bootstrap is a statistical resampling method that can
assist with this step (Efron 1980).
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The propagation of uncertainty and variability in a very simple mechanistic model may be
conducted through combinatorial methods, including the sensitivity analyses described above,
discrete probability trees, and analytical approaches such as the method of moments, Taylor
series expansions, and differential analysis. An alternative to these approaches is Monte Carlo
simulation, using either simple or stratified random samples from the input probability
distributions to approximate the output distribution for risk. The Monte Carlo approach was
selected for TRIM uncertainty and variability analysis. The selection of the Monte Carlo
approach does not exclude the use of the other approaches. In fact, Monte Carlo was selected
because it provides flexibility. If input values can be tracked along with the results of each
iteration, these data can be used in the other approaches.
If the mechanistic model is very complex, computational resources may be conserved by
"modeling the model." One option for this approach is to develop a response surface by
simulating a very large number of scenarios, as in the combinatorial approach described above,
and fitting a surface to the results using regression techniques. This response surface model can
be substituted for the mechanistic model when propagating uncertainty and variability.
Alternative methods for "modeling the model" include generalized linear models and other
regression models; the class of fuzzy logic, neural networks, and genetic algorithms; and a
technique known as classification and regression trees (CART). Any of these approaches can be
used to reduce the form of the mechanistic model to dramatically reduce the time required to
compute the risk results for large numbers of scenarios or samples in a Monte Carlo application.
These approximations to the model are called "reduced form models." Drawbacks to using
reduced form models include inaccuracies (because they are only approximations) and the
restriction of not extrapolating outside of the scope of the simulations performed to build the
reduced form model.
A final step in the analysis of uncertainty and variability is the interpretation of the
relationship between the distributions of the results of the model and the distributions of the
model inputs. Distributions of model results can be prepared and presented as part of the risk
characterization module, TRTM.Risk (see Chapter 9), either directly or interpreted in terms of
identifying important assumptions and parameters, which are also presented in TREVI.Risk.
Much like the deterministic tests of the sensitivity of the model to local changes in the inputs,
tests can be constructed to identify the probabilistic importance of the indeterminate variables.
The first step in accomplishing this is to calculate a sensitivity score composed of the elasticity,
the coefficient of variation, and the ratio of nominal input and output values. This score
considers the range of uncertainty in the variables and the change in output per change in input,
and it also identifies variables with both relatively high sensitivity in the model and high
dispersion. Additional approaches for calculating and presenting measures of uncertainty include
rank correlation, analysis of deviance, confidence intervals, distributions of the model outputs,
and joint distributions of the model parameters and outputs.
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3.3 OVERVIEW OF THE APPROACH SELECTED FOR TRIM
There are numerous expositions in the published literature describing techniques and
applications of analysis of uncertainty and variability. Several techniques are used in a wide
range of disciplines, including ecological risk assessment, manufacturing, aerospace applications,
and nuclear physics. Respected references related to risk analysis include Beck (1987), Cohrssen
and Covello (1989), Cullen and Frey (1999), Morgan and Henri on (1990), National Research
Council (1994, 1996), and Vose (1996).
In agreement with EPA guidance on probabilistic assessments, OAQPS chose a staged
approach for analysis of uncertainty and variability in TRIM. The use of a staged approach has
advantages for models as complex as TRIM. The first stage consists of analyses that are
comparatively easy to implement, identifying influential parameters and giving an
importance-ranking of parameters, which are useful for narrowing down the number of
parameters to be analyzed in the uncertainty and variability analysis. This first stage is
considered a sensitivity and screening analysis. The second stage involves uncertainty and
variability analyses of increasing detail and complexity. Figure 3-1 illustrates this staged
approach and how the functional parts fit together. This approach provides the TRIM user with
options to perform a sensitivity analysis or a combined sensitivity and uncertainty/variability
analysis, where the sensitivity results guide the selection of parameters for the uncertainty and
variability analysis. The user also has the option to perform only the uncertainty and variability
analysis, if the user has identified specific parameters to analyze.
The sensitivity and screening analysis calculates the importance of parameters with
respect to how the model results change when the parameters vary, varying parameters singly or
in pairs. This process provides for a first-order determination of the more influential parameters
and allows further analysis to focus on the key parameters.
The screening component of this approach is performed to narrow down the scope of the
second-stage detailed analysis, in terms of the number of parameters to be treated, by identifying
influential parameters which should be retained for further analyses. This is a critical step toward
the goal of producing a economical representation of uncertainty and variability, excluding less
influential terms and parameters and still capturing all of the significant features of TRIM
uncertainty and variability.
A Monte Carlo approach was selected for the second stage, the detailed uncertainty and
variability analyses. Monte Carlo methods for analysis of model uncertainty use statistical
sampling techniques to derive statistics that characterize uncertainty. Essentially, a Monte Carlo
approach entails performing many model runs with model inputs randomly sampled from
specified distributions for the model inputs. Using a two-dimensional Monte Carlo simulation,
uncertainty and variability can be modeled separately. These model runs can be set up to
characterize the propagation of uncertainty and variability of the model input parameters, taking
into account distributions of parameter uncertainty and variability and parameter dependencies.
These simulations provide uncertainties of model outputs in terms of distributions of model
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Figure 3-1
Uncertainty and Variability Analysis Framework
(Illustrated for TRIM.FaTE Module)
Measurements
Bootstrap
Expert Elicitation
Input |
Distributions I
STAGE 1
TRIM.FaTE
Sensitivity Simulations
Sensitivity and
Screening Analysis
Sensitivity
Elasticity
Sensitivity Score
Analysis of Sensitivity Results
Selection of
Influential Parameters
STAGE 2
TRIM.FaTE
Monte Carlo Simulations
Process Simulation Results
Rank Correlation
Measures
Distributions
Analysis of Results
Note: Results from Stage 2 Monte Carlo analyses can
be used to support other analyses, including response
surfaces, classification and regression trees, generalized
regression analyses, and combinatorial analysis/
probability trees.
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outputs, joint distributions of model inputs and outputs, and summary scalar measures. These are
the core data from which information about uncertainty and variability can be extracted.
While the importance of characterizing uncertainty and variability explicitly and
separately is well recognized (NRC 1994, CRARM 1997, U.S. EPA 1997 c), this does not imply
that OAQPS will do so for all cases and all parameters. As stated above and described below,
TRIM will have the capability of evaluating and tracking uncertainty and variability separately
via two-dimensional Monte Carlo simulation. However, the parameters for which this will be
done will be determined on a case-specific basis. The OAQPS intends to characterize
uncertainty and variability separately only for critical parameters and where appropriate based on
the underlying science and data.
The analysis of uncertainty and variability requires estimates of the distributions of
parameters, reflecting both the uncertainty and variability of the parameters in question. In
addition, estimates of dependencies (correlations) between parameters would enable a more
detailed analysis to be performed. However, typically data are not collected or measured in a
way that allows for separating uncertainty and variability for most parameters. When a
parameter distribution is available, it is rarely separated into components of uncertainty and
variability. For some parameters, such as body weight, the inherent variability within a
population has been characterized through the use of large surveys and precise measurement
methods. However, emission rates, such as stack gas sampling, have been measured in such a
way that separating uncertainty and variability is not supported by the data and would require
more of a numeric exercise to tease out variability based on assumptions which may themselves
introduce unspecified uncertainty. In some cases, this exercise may result in introducing
uncertainty to a greater degree than the variability that is estimated.
This is a current limitation of the Monte Carlo approach which can be addressed over
time by developing distributions for parameters to which the model is most sensitive. It should
also be noted that OAQPS intends to conduct probabilistic analysis in a tiered approach in
accordance with EPA guidance (U.S. EPA 1997b). First, critical parameters are identified
through sensitivity analysis, distributions are developed, and correlations are identified only for
the more critical parameters. Therefore, distributions are not needed for all parameters for either
composite uncertainty or uncertainty and variability separately.
The analysis of the TRIM predictions of risk involves the propagation of uncertainty
through the TRIM modules. This can be accomplished by conducting a two-stage analysis of
uncertainty and variability sequentially for each of the TRIM modules. The distributions of
outputs are passed from one module to the next to propagate distributional information to
succeeding modules.
Because the amount of data produced from Monte Carlo simulations is voluminous, the
full results will be archived and a reduced set will be retained to feed the next module. The
output values from each of the TRIM modules as well as the model inputs (parameter values) for
each Monte Carlo simulation will be saved. This large amount of information could be passed
along to the next module for subsequent uncertainty analysis, but the amount of data would
increase drastically from one module to the next. As illustrated by Figure 3-2, the output data
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(BJAj, A2...AJ) from Module I (i.e., TRJM.FaTE) also are part of the input data to Module II
(i.e., TRJM.Expo), and a portion of the input data to Module II (Bj) is dependent on the input
parameters to Module I (Aj). The notation BJAj, A2...AJ indicates the dependence of the module
outputs Bj on the module inputs Al3 A2, ...A,,. Module n also has input data from different
sources, as represented by B2 through Bn. Similarly, the output data from Module II (CJBjIAjH)
are dependent, in part, on the input parameters to both Module I and Module n. Therefore, it is
important to track the input parameters to each TRIM module. However, Figure 3-2 only depicts
the information flow for a single simulation, and the actual volume of information would be
multiplied by the number of Monte Carlo simulations performed (potentially thousands).
To reduce the size and complexity of the flow of uncertainty and variability information
between TRIM modules, these results will be summarized in the form of nonparametric
probability distributions that can be passed to the next module, where each distribution to be
passed is characterized nonparametrically by its percentiles. Figure 3-3 illustrates how input
parameters and output data will be tracked as distributions.
Another way in which the amount of information to be tracked through the modules can
be reduced is through the use of the screening and sensitivity stage analysis described above.
The simulation could be run with this feature to select the critical parameters in each module to
be tracked for the more detailed uncertainty analysis; all other parameters would be set at their
central tendency value in the more detailed analysis run. To further reduce the volume of
information, after summarizing the results from one module as probability distributions, the
transmission of information to the next module is filtered to select the most critical parameters
(e.g., those that account for 95 percent of the variance of the uncertainty and variability).
The Agency has begun testing the two-stage approach to uncertainty and variability
described in this section as part of the TRTM.FaTE mercury case study (described in Chapter 7).
These tests involve the uncertainty analysis for one module and not for a sequence of modules, as
depicted by the outlined area in Figure 3-3. When the TRIM modules are linked together, each
module after the first will treat its inputs from the previous module in the same way its other
inputs are treated, as deterministic values with uncertainty and variability distributions.
Therefore, no modification to the approach that OAQPS is testing for TRTM.FaTE is required for
the remaining modules, and the extension to other modules will be straightforward.
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Figure 3-2
Information Flow for a Single Monte Carlo Simulation of TRIM
Module I
(i.e., TRIM.FaTE)
A1
A2
An
s
Module II
(i.e., TRIM. Expo)
BI
B2
A,
A2
An
Bn
\
\
Module III
(i.e., TRIM. Risk)
Legend
Input parameters to Module I
Output data from Module I; input parameter to Module I
Input parameters to Module II
Output data from Module II; input parameter to Module
Input parameters to Module III
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Figure 3-3
Nonparametric Probability Distribution Method for
Information Flow in Monte Carlo Simulation
Module I
(i.e., TRIM.FaTE)
Module II
(i.e., TRIM.Expo)
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4. REVISIONS AND ADDITIONS TO TRIM.FaTE
Since May 1998, the Agency has implemented numerous revisions and additions to the
TRIM.FaTE module, both in response to SAB comments (see Chapter 2) and as part of the
ongoing model development. This chapter summarizes the major revisions and additions,
including their basis and current status. As appropriate, revisions and additions to TRIM.FaTE
will be assessed in the uncertainty and sensitivity analyses along with the model evaluation
activities that are being conducted on this module. Technical terms used in this chapter are
defined in the glossary (Appendix A) and in a text box at the beginning of Chapter 5. More
detailed information on the aspects of the TRIM.FaTE module that are discussed below is
presented in Volume I of the TRIM.FaTE TSD.
4.1 ABILITY TO ACCOUNT FOR METALS
Prior to May 1998, the chemical fate and transport algorithms developed for and included
in TRIM.FaTE were specific to nonionic organic compounds, with phenanthrene and
benzo(a)pyrene as the example chemicals for which all the necessary parameter values were
obtained and used in the algorithms. The transfer coefficients used in these algorithms rely upon
the concept of fugacity for modeling some types of chemical transfers; however, the concept of
fugacity cannot generally be applied to metals and other inorganic compounds. Because
addressing the impacts of metals and other inorganic compounds is a priority for OAQPS and to
demonstrate that the TRIM.FaTE methodology is not restricted to modeling the fate and transport
of organic compounds, algorithms have been added to TRIM.FaTE prototype V that address the
fate and transport of inorganic compounds. The new algorithms were developed and included in
TRIM.FaTE specifically for mercury and mercury compounds, but many of these algorithms can
be used for other metals and inorganic compounds. Thus, TRIM.FaTE now has the flexibility to
model fate and transport of organic and inorganic chemicals (assuming the
chemical property values required as inputs are available or can be estimated).
4.2 ABILITY TO MODEL FATE AND TRANSPORT OF CHEMICAL
TRANSFORMATION PRODUCTS
The transformation of chemical substances in the environment can have a profound effect
on their potential for dispersion, persistence, accumulation, and exposure. Chemical
transformations, which may occur as a result of biotic (e.g., microbial degradation) or abiotic
(e.g.., oxidation, hydrolysis) processes, can significantly reduce the concentration of a substance
or alter its structure in such a way as to enhance or diminish its toxicity. For example,
nitrogenous compounds, which are largely represented by aliphatic and aromatic amines, are of
particular interest due to their potential genotoxic activities; transformation processes such as
photolytic transformation and oxidation and reduction reactions can lead to the interconversion
of these compounds between their related condensation products (e.g., azo compounds) and
oxidation products (e.g., primary amines). Such transformations may prolong the persistence of
these compounds in the environment and determine their genotoxic potencies (Layton et al.
1993).
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Prior to May 1998, chemical transformation was represented in the TRIM.FaTE
prototypes via the use of reaction sinks, and the fate of the transformed chemical was not tracked.
That is, the mass of the chemical being transformed diminished over time, as appropriate, but the
fate and transport of the newly created transformation product(s) was not tracked over time.
Algorithms have been added to TRIM.FaTE prototype V to model reversible chemical
transformation processes (e.g., the transformation of elemental mercury (Hg) to divalent mercury
(Hg2+) and then back to elemental). These algorithms were developed specifically for three
species of mercury (i.e., elemental, divalent, methyl), although the general framework is
applicable for any case in which first-order transformation is appropriate. This additional feature
provides TRIM.FaTE with the ability to model the fate and transport of chemical transformation
products, in addition to the disappearance of the chemical being transformed.
4.3 ABILITY TO ACCOUNT FOR SEASONALITY
Although few multimedia fate and transport models include seasonal components, these
are desirable for two reasons: (1) for the model to be applicable to regions in the U.S. where
below-freezing temperatures occur, and (2) for model runs with durations extending beyond a
single growing season. However, model realism gained by accounting for seasonality must be
balanced with the burden on the user to collect site-specific data. Therefore, only selected
seasonal algorithms have been implemented in TRIM.FaTE at this time.1
Since May 1998, two principal seasonal components have been added to TRIM.FaTE:
litterfall algorithms and plant uptake of chemicals. The algorithms that have been implemented
reflect the seasonality in the following ways:
• During litterfall, which is assumed to be either continuous for one month or one year
(depending on the vegetation type), the mass of chemical that is in and on the leaves is
transferred to the surface soil compartments; and
Uptake of chemicals by plants occurs only between the day of last and first frost.
A third seasonal process, harvesting (i.e., removal of pollutant mass from the system), may be
easy to implement in TRIM.FaTE; however, the module has not yet been tested in agricultural
regions where this process would be relevant.
Additional seasonal processes may be considered in future improvements to TRIM.FaTE.
In addition to evaluating the significance of the process to pollutant transfers among media
within the modeling system (and the resultant media concentrations), an important part of this
consideration will be the extent of modeling revisions needed for implementation in TRIM.FaTE.
For example, some seasonal processes would require that the mass and volume of a compartment
change during a model run (e.g., growth dilution), and the current implementation of TRIM.FaTE
does not include changes to compartment mass or volume with time. Methods may be devised to
accommodate this, as in the case of litterfall, which as implemented in TRIM.FaTE does not
1 Seasonal weather patterns are accounted for in the meteorological data inputs.
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involve a change in the mass of the leaves within the compartment. Instead, for litterfall, uptake
of the chemical ceases in the winter, and the chemical is transferred from the leaf compartments
to the surface soil compartments.
Examples of seasonal processes that may affect pollutant distribution within the modeling
system include the following.
• The dynamics of snow. The dynamics of snow accumulation and snowmelt and the
timing of chemical transfers from snow to water may affect long-term chemical exposure
estimates.
Growth of organisms. The dilution of chemical concentrations in an organism because
of its growth may affect predicted organism concentrations and dose rates.
Litterfall to surface water. Litterfall to streams and lakes may affect the dynamics of
chemical behavior in surface water.
• Transformation of chemicals in litter. The transformation of chemicals in leaf litter
may occur at a different rate from that in surface and root zone soil; however, little
information on these processes is available at this time.
• Senescence of plant foliage. Senescence of plant leaves can result in altered gas
exchange with leaves, altered rates of chemical transformation in leaves, lowered water
content of leaves, and altered uptake rates of chemicals from soil.
Blooming of algae. The timing and rate of growth of algae, not incorporated in the
current version of TRTM.FaTE, may affect the assumed exposure of aquatic organisms to
chemicals. Furthermore, the sedimentation of algae following a bloom would affect the
mass of a chemical in the sediment.
Dietary changes of wildlife. Some wildlife species change diets at different times of the
year, affecting chemical exposure estimates.
Habitat use. Some wildlife species hibernate, winter sleep, or migrate from the
contaminated region during winter. These seasonal differences in habitat use could
decrease exposure to chemical contaminants.
• Excretion periods. Excretion of chemical body burdens by egg-laying and lactation
occur during spring and summer seasons. These seasonal excretions may affect chemical
body burdens and exposure levels of organisms.
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4.4 OTHER ADDITIONS AND IMPROVEMENTS TO ALGORITHMS
This section highlights major changes and additions to the TRIM.FaTE algorithms.
Detailed information on TRIM.FaTE algorithms is presented in Volume II of the TRIM.FaTE
TSD.
4.4.1 ABIOTIC ALGORITHMS
ABIOTIC COMPARTMENT TYPES IN
TRIM.FaTE
Air
Surface Soil
Root Zone Soil
Vadose Zone Soil
Surface Water
Sediment
Ground Water
The TRIM.FaTE module addresses chemical
fate and transport within and between seven different
abiotic compartment types (see adjacent text box).
Many of the current abiotic algorithms were included
in an earlier prototype of TRIM.FaTE. Since May
1997, however, several additions and improvements
have been made.
4.4.1.1 Dispersive Transport Between
Surface Water Compartments
The current implementation of the TRIM.FaTE methodology retains the assumption that
chemical mass is homogeneous within compartments. However, algorithms have been
developed for addressing dispersive transport between surface water compartments (i.e., surface
water to surface water). This addresses a limitation of previous TRIM.FaTE prototypes because
dispersion may be an important mechanism of transfer of some chemicals. In surface water
compartments (see Chapter 4 of the TRIM.FaTE TSD Volume II), the algorithms are based on
the methods used in the Water Quality Analysis Simulation Program (WASP) (Ambrose et al.
1995). Thus, TRIM.FaTE can now model the transport of chemicals by both dispersive and
advective processes between surface water compartments.
4.4.1.2 Diffusion and Advection With Soil Compartments
During the last year, EPA developed a new approach for constructing air-to-soil and soil-
to-soil chemical transport algorithms for TRIM.FaTE. This approach provides a simple but
reliable method for simulating the transport of chemicals in soil. The new algorithm applies to
three soil compartment types: (1) surface soil, (2) root zone soil, and (3) vadose zone soil. These
different soils can be represented by two or more soil compartment types for the purpose of
assessing chemical mass transfer. Two types of chemical transport are considered by the soil
algorithm: (1) diffusion and (2) advection. The top soil layer (i.e.., surface soil compartment)
exchanges chemical mass with the lowest compartment of the atmosphere (see Section 5.3.1.1)
by a combination of diffusion and several advection processes - wet deposition, dry deposition,
and resuspension. Each soil layer also can have one or more transformation processes. The
specific links for soil compartments for which TRIM.FaTE includes algorithms are discussed in
Chapter 5.
Quantifying the exchange of chemical mass between air and soil and among soil layers
depends strongly on the concentration gradient within the soil layers. The Agency recognized
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that any algorithm that properly addresses chemical mass transfer from air and into soil must
account for the concentration gradient. Therefore, EPA developed an exact analytical solution by
applying the appropriate boundary conditions to the transport/transformation equations as
presented by Jury (1983). The Agency then developed a simplified form of this solution as the
basis for an equivalent mass exchange algorithm that is applicable to each soil layer. Differential
equations describing the dispersion, diffusion, advection, and transformation of chemicals
distributed among air and soil layers were developed and solved analytically in one dimension.
An evaluation of the mathematical behavior of the analytical solution resulted in the development
of an approximate mathematical form, which uses a series of exponential functions to represent
the variation of concentration with depth. Unlike the analytical solution, which requires fixed
boundary conditions, these simple forms can be dynamically linked to other compartments in a
multimedia fate model. The new algorithm makes it possible to calculate a characteristic soil
penetration depth for each chemical based on the chemical's diffusion and degradation rates in
various soil types. To confirm the accuracy of the simple model, several chemical property sets
were used to compare results of the simple model against the analytical solution. The Agency
only needed to conduct testing with a few chemical property sets because the equations are
normalized for the chemical-specific soil penetration depth. Therefore, TRIM.FaTE can now be
used to assess the penetration of chemicals from air into soil and provide results that are
comparable to those obtained from more complex models. The current restriction on this
approach is that the chemical concentration in air must be greater than the concentration in the
gas phase of the vadose zone soil.
4.4.1.3 Diffusive Transport Between Surface Water and Sediment Compartments
Diffusive transport of chemicals between sediment and surface water compartments in
both directions has been addressed in the current TRIM.FaTE prototype using standard methods
as discussed in the Water Quality Analysis Simulation Program (WASP) (Ambrose et al. 1995).
Inclusion of these algorithms in TRIM.FaTE is important because diffusive exchange of a
chemical between surface water and sediment can be a primary means of transport for some
chemicals. The methods adopted from WASP allow for the specification of a diffusive water
flow velocity, allowing the movement of a chemical between sediment and sediment pore water
in the dissolved phase to be simulated. Thus, TRIM.FaTE is capable of modeling the chemical
transport between sediment and surface water compartments (in either direction), including
sediment pore water.
4.4.2 BIOTIC ALGORITHMS
In addition to the abiotic compartment types and algorithms, the TRIM.FaTE module
includes numerous biotic compartment types and algorithms related to terrestrial and aquatic
plants and animals. The biotic algorithms in TRIM.FaTE represent chemical transfers to and
from biotic and abiotic compartments, primarily through diffusion, advection, and dietary uptake
processes (see Chapter 5 for additional information on biotic compartment types and algorithms).
Since May 1998, a number of changes and additions to the biotic algorithm library in
TRIM.FaTE have been implemented, including the following:
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• Several biotic algorithms were added to the library to estimate the accumulation of
chemicals by new biotic compartment types, including algae, soil arthropods, plant
foliage (as distinct from leaf surface), and plant leaf surfaces (as distinct from plant
foliage);
Minor improvements were made to several existing biotic algorithms, such as diffusion of
chemicals into plant foliage, particle washoff from plant leaf surfaces, and litterfall;
• Several alternative biotic algorithms were added to the library to estimate the
accumulation of chemicals by various biotic compartment types, including fish, plant
roots, and earthworms; and
• An algorithm was added to the library to represent the link between plant stems and
leaves.
4.4.3 CHEMICAL- OR CHEMICAL CLASS-SPECIFIC ALGORITHMS
Chemical- or chemical class-specific algorithms are included in TRIM.FaTE to model
chemical fate and transport processes that are specific to a particular chemical or chemical class
and that cannot be as accurately represented in the more generic abiotic or biotic algorithms.
Because of the specificity of chemical- and chemical class-specific algorithms, the TRIM.FaTE
module will be most useful and cost-effective if only a small number of chemical-specific
algorithms are included (i.e., if the algorithms included are applicable to a broad range of
chemicals so new algorithms do not often have to be developed for new applications). At this
time, a few chemical-specific biotic algorithms are necessary because certain chemical
parameters (e.g., rate constants, partition coefficients) are only applicable to certain chemicals
and chemical classes. For example, biotic uptake of a specific chemical may be dependent on
particular environmental parameters. The goal, however, is to implement generic algorithms for
all important transport and transformation processes, supplemented by class-specific algorithms
(e.g., for metals) as needed, and to minimize the use of chemical-specific algorithms to chemicals
and processes where a real benefit can be realized. In the current version of TRIM.FaTE,
algorithms are included that (1) are applicable to high priority chemicals, such as mercury and
polycyclic aromatic hydrocarbons (PAHs), (2) pose issues in multiple environmental media, and
(3) are not addressed by other EPA models.
The previous prototype of TRIM.FaTE included some chemical class-specific algorithms
for PAHs in support of the TRIM.FaTE test case. To test the ability of TRIM.FaTE to model
metals, the Agency added some chemical-specific algorithms for mercury. For example,
TRIM.FaTE now includes an algorithm to model the transformation of methylmercury to
divalent mercury in plant leaves and stems. More detailed information on the mercury-specific
algorithms included in the current prototype is presented in Appendix A of Volume II of the
TRIM.FaTE TSD.
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4.5 INTERFACE WITH EXTERNAL MODELS
Over the past year, the Agency developed and implemented methods for incorporating the
results from an external fate and transport model (e.g., an air model, such as ISCST3) within the
current TREVI.FaTE framework. This provides additional flexibility because, for example,
external model data can be used in place of certain TREVI.FaTE algorithms during a simulation.
These methods are described in detail in Appendix B of the TRTM.FaTE TSD Volume I. These
methods have been implemented in prototype V of TRTM.FaTE in two ways: (1) the results of
an external air model (e.g., ISCST3) can be used as input data, and (2) the concentration can be
fixed in specified compartments during a simulation. In either case, certain TREVI.FaTE
algorithms are bypassed and essentially "replaced" by model results or fixed concentrations.
As discussed in Appendix B of TREVI.FaTE TSD Volume I, there are limitations with this
approach. Either the linkage between the model and TREVI.FaTE is in one direction only and,
hence, conservation of chemical mass is lost, or the external model must be linked with
TREVI.FaTE in such a way so that chemical transfer can occur in both directions. The difficulty
of the latter option will depend on the particular external model considered, but it is likely that it
would generally require a substantial effort to implement. This is because the user must not only
perform the practical tasks associated with computer programming, but also must ensure that no
fundamental assumptions or concepts inherent to either model are violated. Such a violation
could occur, for example, if there is overlap between the models in how they address other
processes that are not an explicit component of the model linkage itself (e.g., the external model
may be treating deposition using general inputs for vegetative cover, and the user must
implement additional checks to ensure that these inputs are consistent with the vegetative
compartments used within TREVI.FaTE).
The user guidance materials to be developed in the next TRIM development phase will
caution users to carefully consider which external air models should be used as input to
TREVI.FaTE. External models for various media can be used in lieu of the TREVI.FaTE
algorithms; however, strong caution should be placed on the use of external models that
themselves may not conserve mass (e.g., Gaussian plume models), but whose use may be
dictated or preferred for regulatory reasons.
4.6 METHODOLOGY FOR DETERMINING PARAMETERS OF THE
MODELING ENVIRONMENT
While previous prototypes of TREVI.FaTE allowed for specification of the parameters of
the modeling environment (e.g., scale and spatial resolution, and selection of parcels, volume
elements, and compartments), it did not provide a structured process for the user to follow. As a
first step in designing this feature, the Agency has developed a consistent general stepwise
procedure for setting up a simulation using TREVI.FaTE. These general steps are described in
detail in Chapter 5 of TREVI.FaTE TSD Volume I and summarized below. Appendix C of
TREVI.FaTE TSD Volume I provides, as an example, more detailed discussion for one step:
defining the parcels used in setting up the spatial configuration of a model application.
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1. Define the analysis objective. As part of the problem definition phase (i.e., the first step
in developing a TRUVI.FaTE simulation), the user defines the objective of the analysis,
including the chemical(s) of concern, the potentially exposed population(s), and the
health and/or ecological effects endpoint(s) to be assessed.
A parcel is a planar (i.e., two-
dimensional) geographical area used to
subdivide a modeling region. Parcels,
which can be virtually any size or shape,
are the basis for defining volume
elements. There can be air, land, and
surface water parcels.
2. Define parcels. The user considers factors
including the likely pattern of transport and
transformation of each chemical of concern
(i.e., where significant concentration
gradients are likely to occur), the location of
natural boundaries, and locations of key
receptors to help determine the appropriate
level of complexity (e.g., size of modeling
region, location, size, and number of parcels) for the simulation. The TRTM.FaTE module
is intended for local-scale assessments of multimedia pollutant distribution.
3 Designate volume elements and compartments. After parcels have been defined and
boundaries established, the user designates volume elements and then defines abiotic and
biotic compartments. Abiotic compartments include air, surface soil, root zone soil,
vadose zone soil, surface water, sediment, and ground water. The depths of each abiotic
compartment can be based on generic values, chemical-specific values (e.g., whether a
chemical is likely to penetrate deeply into the soil), or site-specific values (e.g., the
average depth of a modeled pond). The landscape property values assigned to
compartments (e.g., fraction organic carbon, amount of particles in the air) can be based
on generic values or site-specific values. Biotic compartments include terrestrial and
aquatic organisms; only plant compartments are required to be included in a simulation.
A user can perform an assessment for an entire trophic group or for a particular animal
species of concern.
4. Select links and algorithms. Following the establishment of TREVI.FaTE compartments
for a given simulation, the appropriate links and algorithms are selected to model mass
transfer and transformation. This step may include specifying the data or data source and
the algorithms to use or may in some cases require a user to add algorithms to the
algorithm library.
5. Determine specifications for the simulation. The last step of TREVI.FaTE setup is
preparing the simulation, which involves specifying the simulation time-step, the
chemical properties of each modeled chemical, the initial distribution of the chemical
mass in the compartments, the data for each modeled source, all site data needed by the
selected algorithms, and the output time period(s) of interest.
The steps above describe the general process for setting up a simulation using
TREVI.FaTE. The flexible, user-friendly design of TREVI.FaTE provides the user with the ability
to perform simulations in an iterative fashion. That is, the user is able to select the necessary
level of analysis, ranging from a simple analysis, for which less site-specific data are required
and which will run more quickly, to one needed for a more detailed risk assessment. For
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example, the more simple analysis, providing a more imprecise, general idea of pollutant
distribution, may be sufficient for setting priorities or other similar scoping activities (e.g., in a
screening analysis for which conservative default input parameters could be used). This allows
the user to focus a more detailed analysis, where the impacts of parameter uncertainty may be
assessed for critical parameters, on situations where a more refined assessment is needed (e.g.,
human health risk assessments to support environmental regulation or other environmental
control actions).
4.7 OVERVIEW OF THE UNCERTAINTY AND VARIABILITY
ANALYSIS APPROACH SELECTED FOR TRIM
In accordance with National Academy of Sciences guidance (NRC 1994), current EPA
risk characterization guidance (U.S. EPA 1995a, U.S. EPA 1995b), and updated guidance being
developed (e.g., U.S. EPA 1998c), EPA is developing TRIM to allow for stochastic modeling so
that uncertainty and variability can be explicitly characterized. This involves the development of
an approach to estimate uncertainty and variability within TRIM in a manner that allows for
integration between the TRIM modules and for tracking the uncertainty and variability through
the modules. At this time, an overall uncertainty and variability analysis approach has been
developed for TRIM, as described in Chapter 3 of this report. Chapter 6 of TRIM.FaTE TSD
Volume I describes the specific approach being implemented for TRIM.FaTE.
Following a review of current peer reviewed literature and assessment of the available
options for uncertainty and variability analyses (see Appendix B), the Agency selected a staged
approach for analysis of uncertainty and variability in TRIM, which has advantages for models as
complex as TRIM. This approach provides the user with the option to include one of two stages
of uncertainty and variability analyses in the simulation. The first stage, consisting of analyses
that are comparatively easy to implement, identifies influential parameters and gives an
importance-ranking of parameters. This information is useful for narrowing down the number of
parameters to be analyzed in a more complex uncertainty and variability analysis. This first stage
can be considered a sensitivity and screening analysis. The second stage involves uncertainty
and variability analyses of increasing detail and complexity. For TRIM, a Monte Carlo approach
was selected for this stage. This approach entails performing numerous model runs with model
inputs randomly sampled from specified distributions for the model inputs. Figure 3-1 illustrates
this staged approach for TRIM.FaTE.
As work on TRIM.FaTE and the other TRIM modules progresses, EPA plans to continue
to evaluate new uncertainty analysis techniques for applicability to improving the current
methodology. For example, methods using Fourier transforms, such as the Fourier Amplitude
Sensitivity Test (Saltelli et al. 1999), will be evaluated in this context.
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CHAPTER 4
REVISIONS AND ADDITIONS TO TRIM.FATE
4.8 MODEL EVALUATION
In its May 1998 review, SAB recognized the challenge in developing a methodological
framework for evaluating a model such as TRIM.FaTE. In developing the evaluation plan for
TRIM.FaTE, the Agency has attempted to design an approach that contains the essential
ingredients for judging the acceptability of TRIM.FaTE for its intended uses, while allowing
enough flexibility to accommodate new methods that become available or changes in direction
indicated by knowledge gained through the evaluation process. Chapter 6 presents a detailed
description of the model evaluation plan designed for TRIM.FaTE.
The evaluation plan for TRIM.FaTE includes four types of model evaluation activities,
described below.
• Conceptual model evaluation activities focus on whether the model is conceptually
sound. This type of evaluation begins in the early stages of model development.
• Mechanistic and data quality evaluation activities focus on the algorithms and
assumptions used in the model. They determine whether the individual process models
and input data used are scientifically sound, and if they properly "fit together."
• Structural evaluation activities focus on how changes in modeling complexity affect
model performance. They address, for example, the effects of varying the level of both
temporal and spatial resolution.
• Performance evaluation activities focus on whether the output of the full model is
relevant, reliable, and useful. They involve comparing modeling results to some type of
benchmark (e.g., monitoring data, other model results, expert judgment).
The first three types of evaluation focus primarily on model inputs (e.g., theory and data) and
processing (e.g., process models, assumptions and algorithms, model setup), while the fourth
focuses mainly on the information that comes out of the model (e.g., comparing overall model
outputs to environmental monitoring data).
The model evaluation plan designed for TRIM.FaTE must be flexible. Results from
initial evaluation efforts are posing new questions and leading to additional review, analysis, and
testing. A number of evaluation activities have been completed or are underway (e.g., code
verification, model documentation, peer review, case studies, sensitivity analysis), while others
are still in the conceptual or planning stages.
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CHAPTER 5
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5. CURRENT STATUS OF TRIM.FaTE
As discussed in Chapter 4, EPA has implemented many changes and additions to
TRIM.FaTE since May 1998. This chapter summarizes the current status of the TRIM.FaTE
module, including the compartment types that are addressed and the links and processes that are
represented by the algorithms included in Prototype V.
TRIM.FaTE HISTORY
Prototype I was designed in 1997 to test the mass transfer methodology and the Livermore Solver
for Ordinary Differential Equations (LSODE) utility. Air, soil, ground water, surface water, and fish
compartment types were included. Chemical reaction was not simulated. The runs produced
estimates of benzene mass throughout the system.
Prototype II, also developed in 1997, included more spatial detail in the types and number of
compartments. It included multiple volume elements for soil and air compartment types and
included plant and sediment compartments. Prototype II was developed using benzo(a)pyrene
(B(a)P) as an example chemical. The links between compartments had multiple-phase (i.e., gas,
liquid, solid) mass transfers.
Prototype III, developed later in 1997 using B(a)P as an example chemical and greater complexity
than previous prototypes, included a focus on code and input data structure refinements. This
prototype was primarily developed to incorporate lessons learned from earlier prototypes, including
a refined set of algorithms, and to set up the module for a case study model run using Prototype IV.
Prototype IV, developed in 1998 using B(a)P as an example chemical, was designed to be applied
to an actual site rather than for evaluation simulations with generic inputs like the earlier prototypes.
Prototype IV was used to evaluate the likely limits of TRIM.FaTE with respect to the number of land
parcels and the length of time steps used.
Prototype V, the current prototype, addresses the issues identified by the SAB in their May 1998
advisory and includes additional and revised fate and transport algorithms. This prototype was
designed to be applied to an actual site for a metal contaminant (i.e., mercury) rather than an
organic contaminant, as was the case for Prototype IV.
Version 1.0 is a computer framework that is intended to support all of TRIM, although only
TRIM.FaTE is currently implemented. While the prototype versions of TRIM.FaTE were developed
using Microsoft Visual Basic™, Fortran, and Microsoft Excel™ software, Version 1.0 was developed
using Java, C, and Fortran in a manner that allows it to run on multiple operating systems, including
Windows and UNIX. Version 1.0 also provides improved management of multiple modeling
scenarios and is more user-friendly and reliable. Version 1.0 is designed for assessments of any
chemical, although it includes some specific algorithms for B(a)P and mercury.
5.1 COMPARTMENT TYPES
The TRIM.FaTE module includes both abiotic and biotic compartment types. The seven
abiotic compartment types that are included in Prototype V of TRIM.FaTE are air, surface soil,
root zone soil, vadose zone soil, surface water, sediment, and ground water. Biotic
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compartment types are generally defined by
trophic group. Terrestrial plant
compartment types include leaves, stems,
leaf surfaces, and roots. Table 5-1 lists the
24 biotic compartment types that currently
are included in TRIM.FaTE.
5.2 LINKS AND
ALGORITHMS
Algorithms are used to model the
transport of chemicals from one
compartment to another (i.e.., through links)
and the transformation of chemicals from
one form to another within a compartment.
Prototype V of TRIM.FaTE includes
abiotic, biotic, and chemical- and chemical
class-specific algorithms. Many algorithms
are currently included in the algorithm
library of TRIM.FaTE, and users can add
algorithms to the library as needed.
5.2.1 ABIOTIC LINKS AND
ALGORITHMS
Abiotic algorithms in TRIM.FaTE
represent the transfer of chemicals from
one abiotic compartment to other
compartments of the same or different
compartment type. The links between
abiotic compartment types and the
processes modeled by abiotic algorithms
are provided in Table 5-2. The major
changes and additions to abiotic algorithms
since May 1998 are discussed in Chapter 4,
and detailed information on abiotic
algorithms is provided in Chapters 3
through 7 of the TRIM.FaTE TSD Volume
II.
KEY TERMS
A chemical is a unit whose mass is being
modeled. A chemical can be any element or
compound, or even group of compounds,
assuming the necessary parameters (e.g.,
molecular weight, diffusion coefficient in air) are
defined.
A compartment is a homogeneous unit of space
characterized by its physical composition and
within which it is assumed, for modeling
purposes, that all chemical mass is in equilibrium.
A compartment type is a specific kind of
compartment, such as an air compartment type or
a mule deer compartment type. Compartment
types are distinguished from each other by the
way they exchange chemical mass with other
compartment types.
An abiotic compartment type is one consisting
primarily of a non-living environmental medium
(e.g., air, soil) for which TRIM.FaTE calculates
chemical masses and concentrations; it may also
contain biota, such as the microorganisms
responsible for chemical transformation.
A biotic compartment type is one consisting of
a population or community of living organisms
(e.g., bald eagle, benthic invertebrate), or in the
case of terrestrial plants, portions of living
organisms (e.g., stems, leaves), for which
TRIM.FaTE calculates chemical masses and
concentrations.
A volume element is a bounded three-
dimensional space that defines the location of
one or more compartments. This term is
introduced to provide a consistent method for
organizing objects that have a natural spatial
relationship.
A link is a connection that allows the transfer of
chemical mass between any two compartments.
Each link is implemented by an algorithm or
algorithms that mathematically represent the
mass transfer.
A source is an external component that
introduces chemical mass directly into a
compartment.
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Table 5-1
Compartment Types in TRIM.FaTE
Abiotic Compartment Types
Air
Surface soil
Root zone soil
Vadose zone soil
Surface water
Sediment
Ground water
Biotic Compartment Types
Leaf
Leaf surface
Stem
Root
Algae
Macrophyte
Water column herbivore
Water column omnivore
Water column carnivore
Benthic invertebrate
Benthic omnivore
Benthic carnivore
Terrestrial omnivore
Semiaquatic piscivore
Terrestrial herbivore
Semiaquatic predator/scavenger
Terrestrial insectivore
Semiaquatic herbivore
Terrestrial predator/scavengera
Semiaquatic insectivore
Semiaquatic omnivore
Terrestrial ground-invertebrate feeder
Flying insect
Soil detritivore
a Includes terrestrial carnivores (e.g., hawks).
5.2.2 BIOTIC LINKS AND ALGORITHMS
Biotic compartments in TRIM.FaTE are linked, using biotic algorithms, to abiotic
compartments through two principal chemical processes: diffusion and advection. For example,
in the process of ingestion, chemicals are advected in the air or diet to a mammal or bird. Active
uptake of chemicals that mimic nutrients is possible but not represented mechanistically in
TRIM.FaTE.
Examples of links between biotic compartment types and between abiotic and biotic
compartment types in TRIM.FaTE are shown in Figure 4-1 in the TRIM.FaTE TSD Volume I.
Many of these links also are summarized below in Table 5-3, which shows the links between
biotic compartment types and between abiotic and biotic compartment types and the processes
that are modeled by biotic algorithms.
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Table 5-2
Links and Processes Addressed for Abiotic Compartment Types
Links Between Compartment Types
Receiving
Air
Surface Soil
Root Zone Soil
Vadose Zone Soil
Surface Water
Sediment
Ground Water
Air Advection Sink
Surface Water Advection Sink
Sediment Burial Sink
Sending
Air
Surface Soil
Surface Water
Surface Soil
Root Zone Soil
Air
Root Zone Soil
Surface Soil
Vadose Zone Soil
Vadose Zone Soil
Root Zone Soil
Surface Water
Surface Soil
Air
Sediment
Surface Water
Surface Water
Vadose Zone Soil
Air
Surface Water
Sediment
Processes Addressed
Bulk Advection
Diffusion
Resuspension
Diffusion
Diffusion
Erosion
Runoff
Diffusion
Diffusion
Dry Deposition
Wet Deposition
Diffusion
Percolation
Diffusion
Percolation
Diffusion
Diffusion
Percolation
Diffusion
Percolation
Bulk Advection
Dispersion
Erosion
Runoff
Dry Deposition
Wet Deposition
Diffusion
Resuspension
Pore Water Diffusion
Abiotic Solids Settling
Pore Water Diffusion
Recharge
Percolation
Bulk Advection Beyond
Bulk Advection Beyond
System Boundary
System Boundary
Solids Advection Beyond System Boundary
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Table 5-3
Links and Processes Addressed For Biotic Compartment Types
Links Between Compartment Types
Receiving
Leaf Surface
Surface Soil
Leaf
Air
Stem
Root
Stem
Soil Detritivore
Root Zone Soil
Flying Insect
Terrestrial Ground-Invertebrate Feeder
Sending
Air (Particulates)
Air (Rain Water)
Leaf
Leaf Surface
Leaf
Terrestrial Ground-Invertebrate Feeder
Terrestrial Herbivore
Terrestrial Omnivore
Terrestrial Insectivore
Semiaquatic Omnivore
Predator/Scavenger
Semiaquatic Insectivore
Semiaquatic Herbivore
Semiaquatic Piscivore
Leaf Surface
Air
Stem
Leaf
Root Zone Soil
Root Zone Soil (Water Phase)
Leaf
Root Zone Soil
Root
Soil Detritivore
Sediment
Soil Detritivore
Surface Soil
Air
Processes Addressed
Dry Deposition b
Wet Deposition b
Diffusion/Advection
Particle Washoff"
Litterfall b
Litterfall b
Excretion a
Uptake a
Diffusion/Advection
Uptake a
Uptake a
Uptake a
Equilibrium Partitioning
Uptake a
Diet"
Inhalation b
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Links Between Compartment Types
Receiving
Terrestrial Herbivore
Terrestrial Omnivore
Terrestrial Insectivore
Semiaquatic Omnivore
Predator/Scavenger
Semiaquatic Insectivore
Semiaquatic Herbivore
Semiaquatic Piscivore
Surface Water
Sending
Leaf
Leaf Surface
Surface Soil
Air
Leaf
Leaf Surface
Soil Detritivore
Surface Soil
Air
Soil Detritivore
Air
Benthic Invertebrate
Soil Detritivore
Herbivorous Fish
Omnivorous Fish
Carnivorous Fish
Surface Soil
Air
Terrestrial Herbivore
Terrestrial Omnivore
Terrestrial Insectivore
Soil Detritivore
Benthic Invertebrate (Insect)
Benthic Invertebrate (Insect)
Benthic Invertebrate
Leaf
Terrestrial Omnivore
Terrestrial Herbivore
Terrestrial Insectivore
Herbivorous Fish
Omnivorous Fish
Carnivorous Fish
Semiaquatic Omnivore
Semiaquatic Insectivore
Semiaquatic Herbivore
Semiaquatic Piscivore
Algae
Macrophyte
Water Column Herbivorous Fish
Processes Addressed
Diet"
Inhalation b
Diet"
Inhalation b
Diet"
Inhalation b
Diet"
Inhalation b
Diet"
Diet"
Diet"
Diet"
Excretion
Equilibrium Partitioning3
Equilibrium Partitioning ac
Elimination bd
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Surface Water (continued)
Algae
Macrophyte
Benthic Invertebrate
Sediment
Water Column Herbivorous Fish c
Water Column Herbivorous Fish d
Water Column Omnivorous Fish c
Water Column Omnivorous Fish d
Water Column Carnivorous Fish c
Water Column Carnivorous Fish d
Benthic Omnivorous Fish c
Benthic Omnivorous Fish d
Benthic Carnivorous Fish c
Benthic Carnivorous Fish d
Water Column Omnivorous Fish
Water Column Carnivorous Fish
Benthic Omnivorous Fish
Benthic Carnivorous Fish
Surface Water
Surface Water
Sediment
Benthic Invertebrate
Algae
Algae
Surface Water
Herbivorous Fish
Herbivorous Fish
Surface Water
Water Column Omnivorous Fish
Water Column Omnivorous Fish
Surface Water
Benthic Invertebrate
Benthic Invertebrate
Surface Water
Benthic Omnivorous Fish
Benthic Omnivorous Fish
Surface Water
Equilibrium Partitioning 3C
Elimination bd
Equilibrium Partitioning 3C
Elimination bd
Equilibrium Partitioning 3C
Elimination bd
Equilibrium Partitioning 3C
Elimination bd
Uptake a
Uptake a
Uptake a
Equilibrium Partitioning a
Diet"
Diet"
Gill filtration3
Diet"
Diet"
Gill filtration 3
Diet"
Diet"
Gill filtration 3
Diet"
Diet"
Gill filtration3
Diet"
Diet"
Gill filtration3
1 Uptake, filtration, or partitioning which includes diffusion, advection, and/or active accumulation by organism.
bAdvection processes.
c Equilibrium model for bioaccumulation by fish.
d Bioenergetic model for bioaccumulation by fish.
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An alternative way to describe the chemical transfer processes and types of links handled by
TRIM.FaTE is as follows:
Diffusion of gaseous forms of elements into and out of plants following the concepts of
conductance and resistance;
• Deposition of particles to the leaf surface;
• Equilibrium partitioning of chemicals from one environmental medium to another, using
the time-to-equilibrium (e.g., plant roots, soil detritivores, benthic invertebrates, algae,
macrophytes, herbivorous fish, omnivorous fish, carnivorous fish); and
• Ingestion, inhalation, and excretion by terrestrial and semiaquatic wildlife.
All biotic transfer algorithms in TRIM.FaTE represent first-order chemical transfers
between compartments. As for the abiotic compartments, there is no gradient of mass within a
single compartment. For example, all of the plant leaves or benthic invertebrates within a single
volume element have a homogeneous chemical concentration at any simulation time step. In
addition, EPA developed mechanisms in TRIM.FaTE that allow the user to turn off or on
particular algorithms at certain times (e.g.., at night, on a certain date such as the date of first or
last frost).
5.2.3 CHEMICAL-SPECIFIC ALGORITHMS
As discussed in Chapter 4, TRIM.FaTE Prototype V includes some chemical- and
chemical class-specific fate and transport algorithms for processes that are specific to particular
chemicals and chemical classes. For such chemical classes, TRIM.FaTE can substitute the
specific algorithms for certain of the more generic abiotic or biotic algorithms. Currently,
TRIM.FaTE includes chemical-specific algorithms for three forms of mercury (elemental,
divalent, methyl). Appendix A of the TRIM.FaTE TSD Volume II provides detailed information
on these algorithms. In addition, TRIM.FaTE is designed to allow users to add chemical- and
chemical class-specific algorithms to the algorithm library, as necessary.
Chemical-specific algorithms can also represent the transformation of chemicals from one
form to another within a compartment. At this time, these algorithms consist of using input
transformation rates.
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CHAPTER 6
EVALUATION PLAN FOR TRIM.FATE
6. EVALUATION PLAN FOR TRIM.FaTE
TRIM.FaTE is a predictive environmental fate and transport model designed to support
decisions on programmatic policy and regulation for multimedia air pollutants. These decisions
can have far reaching human health, environmental, and economic implications. It is important
that an assessment of how well the model is expected to perform the tasks for which it was
designed is incorporated within the model development process. In other words, the
trustworthiness of models used to determine policy or to attest to public safety should be
ascertained (Oreskes et al. 1994). This chapter describes the role of model evaluation in
developing an assessment of model quality and acceptability in support of regulatory decisions.
The chapter provides background on the evolution of model validation terminology and concepts
as well as previous Agency efforts (Section 6.1). The chapter then provides an introduction to
model evaluation (Section 6.2) and presents an evaluation plan for TRIM.FaTE using four basic
components (Sections 6.3 through 6.6). Finally, the Agency's progress in implementing the plan
to date is described (Section 6.7).
6.1 BACKGROUND
Most of the early efforts to establish the quality of models used in supporting policy
decisions focused on model validation. The term validation does not necessarily denote an
establishment of truth, but rather the establishment of legitimacy (Oreskes et al. 1994).
However, common practice has been not consistent with this restricted sense of the term, and the
term validation has been commonly used in at least two ways: (1) to indicate that model
predictions are consistent with observational data, and (2) to indicate that the model is an
accurate representation of physical reality (Konikow and Bredehoeft 1992). The ideal of
achieving - or even approximating - truth in predicting the behavior of natural systems is
unattainable (Beck et al. 1997). As a result, the scientific community no longer accepts that
models can be validated using ASTM standard E 978-84 (i.e., comparison of model results with
numerical data independently derived from experience or observation of the environment) and,
therefore, be considered to be "true" (U.S. EPA 1998g). It is unreasonable to equate model
validity with its ability to correctly predict the future (unknowable) true behavior of the system.
A judgment about the validity of a model is a judgment on whether the model can perform its
designated task reliably (i.e., minimize the risk of an undesirable outcome (Beck et al. 1997)).
The current approach used by the Agency is to replace model validation, as though it
were an endpoint that a model could achieve, with model evaluation, a process that examines
each of the different elements of theory, mathematical construction, software construction,
calibration, and testing with data (U.S. EPA 1998g). Therefore, the term evaluation will be used
throughout this report to describe the broad range of review, analysis, and testing activities
designed to examine and build consensus about a model's performance.
Over the last 10 years, the Agency has been considering model acceptance or model use
acceptability criteria for selection of environmental models for regulatory activities. The
Agency's efforts in this area are a result of SAB recommendations in 1989 that "EPA establish a
general model validation protocol and provide sufficient resources to test and confirm models
with appropriate field and laboratory data" and that "an Agency-wide task group to assess and
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guide model use by EPA should be formed" (U.S. EPA 1989). In response, EPA formed the
Agency Task Force on Environmental Regulatory Modeling (ATFERM). This cross-agency task
force was charged to make "a recommendation to the Agency on specific actions that should be
taken to satisfy the needs for improvement in the way that models are developed and used in
policy and regulatory assessment and decision-making" (Habicht 1992). In its March 1994
report, ATFERM recommended the development of "a comprehensive set of criteria for model
selection (that) could reduce inconsistency in model selection and ease the burden on the regions
and states applying the models in their programs," and they drafted a set of "model use
acceptability criteria" (U.S. EPA 1994a).
More recently, an Agency white paper work group was formed to re-evaluate the
recommendations in the 1994 ATFERM report. As a result, in 1998, EPA drafted the White
Paper on the Nature and Scope of Issues on Adoption of Model Use Acceptability Guidance
(U.S. EPA 1998g), which recommends the use of updated general guidelines on model
acceptance criteria (to maintain consistency across the Agency) and the incorporation of the
criteria into an Agency-wide strategy for model evaluation that can accommodate differences
between model types and their uses. The work group also recommended the initial use of a
protocol developed by the Agency's Risk Assessment Forum to provide a consistent basis for
evaluation of a model's ability to perform its designated task reliably. The White Paper was
reviewed by SAB in February 1999, and it is currently being revised in respond to SAB
comments. The proposed approach for evaluation of TRDVI.FaTE, as described in the evaluation
plan presented here, is intended to be consistent with the Agency's current thinking on
approaches for gaining model acceptability.
In its May 1998 review of TRDVI.FaTE, SAB recognized the challenge in developing a
methodological framework for evaluating a model such as TRDVI.FaTE. Further, SAB suggested
that "novel methodologies may become available for quantitatively assuring the quality of
models as tools for fulfilling specified predictive tasks" (U.S. EPA 1998a). In developing the
evaluation plan for TRDVI.FaTE, the Agency has attempted to incorporate the essential
ingredients for judging the acceptability of TRDVI.FaTE for its intended uses, while retaining the
flexibility to accommodate new methods that become available or changes in direction indicated
by knowledge gained through the evaluation process.
6.2 MODEL EVALUATION
Model evaluation is necessary to increase the acceptance of a model. It is not a one-time
exercise but a continuing and critical part of model development and application. Several model
evaluation methods have emerged in recent years (Dennis et al. 1990, Hodges and Dewar 1992,
U.S. EPA 1994b, Cohn and Dennis 1994, Eisenberg et al. 1995, Spear 1997, Schatzmann et al.
1997, Beck and Chen 1999, Arnold et al. 1998, Chen and Beck 1998). All of these methods can
be placed into one of two basic categories: (1) those that focus on the performance or output
from the model, and (2) those that test the internal consistency (Beck et al. 1997, Beck and Chen
1999) or scientific credibility (Eisenberg et al. 1995) of the model. These methods range from
objectively matching model output with monitoring data to more subjective and abstract quality
measures (e.g., expert judgment, peer review). The focus of model evaluation activities will
change during the life of a model. As a model matures, less emphasis will be placed on peer
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review and internal consistency checks and more resources will be directed toward evaluating
how well the model satisfies both its original design objective and the specific modeling
objectives of individual users.
Model evaluation can be viewed as a consensus building process (Figure 6-1) including
three aspects as identified by Beck et al. (1997) - (1) model composition, (2) model performance,
and (3) task specification - and recognized in the Agency's December 1998 White Paper (U.S.
EPA 1998g).
Figure 6-1
Conceptual Representation of the Model Evaluation Process
Increasing Acceptability
Model
Composition
Model
Performance
Task
Specification
Conceptual model development
and review
Code verification
Model documentation
Performance evaluation
through a wide range of
applications and analyses
Continued structural and
sensitivity analysis
Round robin analysis
Peer review
Sensitivity analysis
Hypothetical case studies
Model-to-model comparison
The evaluation plan for TRIM.FaTE is presented in the following four sections of this
chapter, which correspond to different (but overlapping) types of model evaluation activities:
Conceptual model evaluation;
Mechanistic and data quality evaluation;
Structural evaluation; and
Performance evaluation.
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The first three primarily focus on the information that goes into the model (e.g., theory and data);
how this information is synthesized (e.g., process models, assumptions, and algorithms); and
how the finished model is set up (e.g., relevant level of complexity). The fourth focuses mainly
on the information that comes out of the model (e.g., comparing overall model outputs to various
kinds of benchmarks).
The model evaluation plan designed
initial TRIM.FaTE evaluation efforts are
posing new questions and leading to
additional review, analysis, and testing.
The various evaluation activities
performed on TRIM.FaTE increase the
experience and understanding that will
ultimately lead to a judgment about its
quality, reliability, relevance, and
acceptability. The activities that
are currently part of the consensus
building process for TRIM.FaTE are
described in the following sections. A
number of these activities have been
completed or are underway (e.g., code
verification, model documentation, peer
review, case studies, sensitivity analysis),
while others are still in the conceptual or
planning stages.
for TRIM.FaTE must be flexible. Results from
EVALUATION THROUGHOUT MODEL
DEVELOPMENT
As noted in the text, model evaluation is being
performed in conjunction with model development.
The evaluation activities performed to date have
used the most current Prototype (i.e., I through V)
of TRIM.FaTE available at the time. Activities
since the May 1998 SAB meeting have focused on
Prototype V. These evaluation activities are fully
applicable to TRIM.FaTE Version 1.0, which is
being built from the same simulation algorithms
and data as Prototype V. After verification that
Version 1.0 produces identical results to Prototype
V, Version 1.0 will become the focus of future
model evaluation activities.
6.3 CONCEPTUAL MODEL EVALUATION
6.3.1 DEFINITION AND GENERAL APPROACH
Conceptual model evaluation activities
focus on the theory and assumptions
underlying the model. These activities
seek to determine if the model is
conceptually sound.
Conceptual model evaluation is initiated in
the early stages of model development. During the
process of framing the problem and designing the
conceptual model, the appropriate level of
modeling complexity (e.g., what to include and
what to exclude), the availability and quality of
information that will be used to run the model (i.e.,
input data), and the theoretical basis for the model should be evaluated. A literature review
should be undertaken to identify and evaluate the state-of-the-science for processes to be
included in the model, as well as to compile and document the initial set of values that will be
used as model inputs.
Examples of conceptual model evaluation activities include:
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• Literature review;
• Model documentation; and
• Peer review of problem definition and modeling concepts and approaches.
6.3.2 TRIM.FaTE-SPECIFIC ACTIVITIES
Considerable progress has been made in developing, documenting, evaluating, and
refining TRIM.FaTE, including the following.
An initial literature review identifying the state-of-the-science and the rationale for
development of TRIM.FaTE has been completed (U.S. EPA 1997b, U.S. EPA 1997c),
and the problem and design objective have been clearly defined (U.S. EPA 1998e).
Model documentation has been extensive. TRIM Status Reports have been published in
1998 (US EPA 1998e) and 1999 (this document), and presentations have been made at
scientific meetings including the Society of Environmental Toxicology and Chemistry
(SETAC) annual meetings in 1997 (McKone et al. 1997a, Zimmer et al. 1997, Efroymson
et al. 1997) and 1998 (Vasu et al. 1998) and the Society for Risk Analysis (SRA) in 1997
(Vasu et al. 1997, Guha et al. 1997, Lyon et al. 1997, Bennett et al. 1997, McKone et al.
1997b, Johnson et al. 1997). A detailed Technical Support Document for TRIM.FaTE is
available (U.S. EPA 1999i, U.S. EPA 1999J), updated from a previous version (U.S. EPA
1998f).
A May 1998 review by the SAB has been published (U.S. EPA 1998a). Additional
evaluations of the conceptual model will continue to be reported in peer reviewed
journals and will be subject to additional SAB consultation and review.
As refinements to TRIM.FaTE are made and as new applications are performed, conceptual
model evaluation will continue.
6.4 MECHANISTIC AND DATA QUALITY EVALUATION
6.4.1 DEFINITION AND GENERAL APPROACH
Multimedia fate models are built around a
series of process models (i.e.., algorithms or groups
of algorithms) that make up the mechanics of the
model. Many individual process models are taken
directly from the literature and have been tested
previously for performance and peer reviewed. The
prior testing and review provides a degree of
confidence that the process model correctly captures
the behavior of the processes it is intended to model.
New process models and assumptions are often introduced during model development; these new
components need to be evaluated individually to ensure that they are working properly.
NOVEMBER 1999 6-5 TRIM STATUS REPORT
Mechanistic and data quality
evaluation activities focus on the
specific algorithms and assumptions
used in the model. These activities seek
to determine if the individual process
models and input data used in the model
are scientifically sound, and if they
properly "fit together."
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Mechanistic and data quality evaluations help to elucidate the internal workings of the
model and, when necessary, provide a basis to refine process models and assumptions that play a
critical role in the calculations. Sensitivity analysis methods are used to identify important model
inputs during mechanistic evaluations and to identify the process models having the greatest
influence on the model output. For example, alternative algorithms for the same process can be
modeled and the results compared. Similarly, each time the model is used for a new kind of
application, a sensitivity analysis may be appropriate to identify inputs, algorithms, and
assumptions that have the greatest influence on the model outcome in that application. The
quality and reliability of these influential factors directly affect the quality and reliability of the
outcome from the analysis (Maddalena et al. 1999, Taylor 1993). When feasible, these
influential factors should be refined to provide the best inputs to the analysis or, at the very least,
identified as a potential source of uncertainty in the outcome.
Some mechanistic and data quality evaluation activities consider the model in its entirety.
Process models are typically developed and tested in controlled or simplified systems. Therefore,
how well these individual process models will perform in a fully coupled system is unknown.
Mechanistic and data quality evaluations are designed and used to measure certain bounded
indices of performance (e.g., mass balance, appropriate and realistic mass transfer rates, relative
concentrations within reasonable bounds). In addition, algorithms or routines that are used in a
model to manipulate the data or to solve a system of equations (e.g., LSODE, the differential
equation solver used in TRTM.FaTE) need to be tested during the mechanistic evaluation to
ensure proper performance.
Examples of mechanistic and data quality evaluation activities include:
Computer code verification;
Verification of generic algorithms adapted for and used within a model;
Literature review to determine the extent of prior process model testing;
• Peer review of model components;
Sensitivity analysis to identify important process models;
Mass or molar balance checks;
Performance evaluation of new and existing individual process models and of multiple
process models in a linked system (e.g., compare with existing models or with
measurements, when available);
Comparison of alternative process models (e.g., equilibrium versus bioenergetic model
for fish bioaccumulation of mercury);
• Data acquisition and evaluation (e.g., data quality or reliability relative to the other inputs
and assumptions), and development and documentation of default input data;
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• Distribution development for input data to support probabilistic analysis; and
• Generic sensitivity analysis to help identify parameters that are most influential on model
results, as well as potential data limitations (i.e.., model inputs that need further
refinement or that are potential sources of uncertainty in the analysis).
6.4.2 TRIM.FaTE-SPECIFIC ACTIVITIES
Prototype V (i.e., spreadsheet-based model) is the current working version of
TRIM.FaTE, and Version 1.0 (i.e., Java-based platform) is under development (see Chapter 10).
One of the features of TRIM.FaTE Prototype V that aids in mechanistic and data quality
evaluation is its web-based output functions. There is an option to create a "full-recursive
output," which documents the mass flow, as well as the associated transfer factors, to and from
each compartment. The equation for each transfer factor can be viewed on a separate web page,
and any calculated quantities used in that equation can then be viewed on additional pages. In
this manner, checks can be made to ensure that the equations are input properly, and that the
computer code is correctly calculating intermediate values. Analyses have been conducted on
various parts of the code using this function.
In addition to the standard computer code verification efforts, performance of the generic
code used to solve the differential equations in TRIM.FaTE (i.e., LSODE) has been reviewed.
Mass and molar balance checks are incorporated in the model for non-transforming organic
compounds and mercury to allow for the quick assessment of model performance under a range
of conditions.
Prior to conducting detailed evaluations of TRIM.FaTE's process models, numerous
model runs were performed. It was determined that there was too much information in a
complete run to evaluate whether the model was producing results that are logical, internally
consistent, and reasonable. Thus, a "shakedown" phase of the model evaluation was begun using
a set of hypothetical chemicals with a broad range of chemical properties. These hypothetical
chemicals were designed to systematically probe the model across the broadest range of fate
scenarios. The environment in its simplest form can divided into three major phases (i.e., solid,
aqueous, and gaseous). The relative solubility of a chemical in each of these phases indicates
much about where and how the chemical will partition when released to the environment. These
three solubilities can be represented by two fundamental partition coefficients, Kow (i.e.,
octanol/water partition coefficient) and Kaw (i.e., non-dimensionalized Henry's Law constant,
air/water partition coefficient).
A simple, level three (steady-state) mass balance model was used to identify the
environmental phases for a randomly generated set of 500 "pseudochemicals" plotted in Figure
6-2. From this plot, the regions of parameter space that result in predominantly (>90 percent)
single medium pollutants or multimedia pollutants can be identified. Two chemicals from each
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Figure 6-2
Single Medium and Multimedia Chemical Regions for 500 "Pseudochemicals'
10
I
O) -5
O
-10
-15
-20
-4
-2
2 4
LogK
8
• > 90%
• > 90%
A > 90%
x Multimedia*
O Selected "Shakedown"
Chemicals
10
OW
Multimedia defined as not more than 80% of total mass
in any single medium. Chemicals between 80% and
90% of total mass in any single medium were excluded
from selection as "shakedown" chemicals.
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single medium pollutant class and three from the multimedia pollutant class were selected for use
as the initial shakedown evaluation set. This approach is particularly useful when performing
diagnostic evaluations because the set of pseudochemicals provides insight into possible reasons
for unexpected model outcomes. For example, if the model predicts an unusually high
concentration in plants for the gas phase chemicals while the aqueous, solid, and multimedia
phase chemicals seem reasonable, a problem in one of the diffusion algorithms would be
suspected. Often, the model is run with only a subset of the available compartment types to
focus on a particular algorithm or set of algorithms. To date, this group of shakedown chemicals
has been used to evaluate and debug the soil algorithms, the plant algorithms, and the general
biotic algorithms. These pseudochemicals will continue to be used to further evaluate the
process models in TRUVI.FaTE and the model as a whole.
Tests are being performed or designed to evaluate process models that, according to the
literature review, have not been thoroughly tested, as well as for approaches and algorithms
developed specifically for TRUVI.FaTE. Examples of process models that have been identified
for evaluation include the particle/plant leaf algorithms, the soil flux model, and the air transport
algorithms. Approaches and algorithms that are related to seasonality (e.g., snow, plant growth,
senescence) will be evaluated so that they can be incorporated into TREVI.FaTE, if appropriate.
When different models are available for the same process (e.g., bioaccumulation in fish),
model-to-model evaluations may be performed at a process model level to test the overall
performance of TREVI.FaTE using different input algorithms. As one example of this, EPA is
comparing the air transport component of TREVI.FaTE to a widely used EPA air dispersion
model, ISCST3 (U.S. EPA 1995c). In addition, measured concentrations that are available for a
single medium or multiple adjacent media (e.g., water and sediment, or water and fish) will be
used, where available, to test single or multiple process models.
Data acquisition and the careful evaluation of model inputs are ongoing. To date, the
majority of effort has focused on compiling an initial set of model inputs for a small set of test
chemicals (i.e., phenanthrene, benzo(a)pyrene, mercury) and environmental settings (U.S. EPA
1998f; also Chapter 7 and Appendix C of this document).
In addition, sensitivity analysis techniques are being used to provide a first-order
determination of the most influential parameters in TREVI.FaTE. The sensitivity of model
outputs to changes in individual parameters is assessed by performing a series of simulations
where each parameter is varied with the other parameters held constant. This does not take into
account parameter dependencies or synergistic effects, but is an efficient way to perform an
initial assessment of the relative influence of the parameters. This information supports model
evaluation by providing a prioritized list of parameters on which to focus the evaluation efforts.
To take full advantage of the probabilistic capabilities of TRIM, some inputs will need to
be described using probability distributions that separate uncertainty and variability. The
uncertainty and variability analysis methodology that has been developed for TREVI.FaTE is
further described in Section 4.7 and in TREVI.FaTE TSD Volume I, Chapter 6. Following the
implementation of this methodology, sensitivity analyses are being performed to help identify
potential influential factors and data limitations. One of the key functions of the uncertainty
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analysis methodology is to evaluate the importance, in terms of both uncertainty and variability,
of specific model inputs and of model components in relation to other inputs and components.
This gives insight into priorities for reducing uncertainty and for focusing efforts on the
improved representation of model inputs. The ability to rank input parameters in order of their
influence on the uncertainty and variability of the model results is an important component of
establishing such priorities.
As refinements to TRTM.FaTE are made and as new applications are performed, data
quality evaluation will continue to be revisited. Sensitivity analysis can be used to identify
inputs, algorithms, and assumptions that have the greatest influence on the model outcome in
specific applications. When feasible, influential factors may be refined to provide the best inputs
to the analysis or identified as a potential source of uncertainty in the outcome.
6.5 STRUCTURAL EVALUATION
6.5.1 DEFINITION AND GENERAL APPROACH
Judging the reliability of a model requires an „ ... . . .
. 1- r-i 1 11 11 Structural evaluation activities focus
understanding or how the model responds to changes
on how changes in modeling complexity
affect model performance. These
activities seek to determine how the
model will respond to being set up
differently for different applications.
in complexity (i.e., changes in the modeling
structure). Both temporal and spatial changes can be
made to the model structure. Structural evaluation
addresses these kinds of changes and provides
valuable information about the performance and
behavior of the model under a range of conditions,
improving the ability to judge the model's quality and reliability. Ideally, these evaluations can
help determine the optimal model structure to balance the level of complexity needed to create
reliable outputs with the simplifications that can make the model easier and more practical to use.
If the model is less complex, it is easier to perform additional analysis, such as uncertainty and
sensitivity analysis, and is more practical to apply to specific sites and situations. Structural
evaluation can provide insight and guidance for future model applications, and it is a very useful
input to developing user guidance.
A large number of well designed runs is necessary to evaluate the way a model performs
under different conditions. These structural evaluations combine sensitivity analysis
methodology with model-to-model comparisons. For a structural evaluation, the model is set up
for an application, using either real or hypothetical data. Changes are then made to the structure
(e.g., spatial elements are split or recombined; time steps are changed; compartment shapes,
sizes, and locations are altered), and the model outcomes are compared (i.e., the model is
compared to itself under various set-up conditions).
Structural evaluations encompass a series of comparisons designed to measure the
model's response to various changes, which can include:
• Different run duration and/or time step values;
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• Varying spatial configurations;
• Changes in initial and boundary chemical concentrations;
• Changes in the source and/or target locations; and
• Other changes in complexity (e.g., including/excluding biota, using average precipitation
versus discrete rain events).
6.5.2 TRIM.FaTE-SPECIFIC ACTIVITIES
TRIM.FaTE is intended to be used in a wide range of modeling applications (e.g., various
chemicals, environmental settings, exposure conditions). Because TRIM.FaTE can be used at
various levels of complexity, it is important to understand the level of complexity needed for a
particular analysis and the stability of model output when the system structure is changed. Given
the complexity of the "real world" and the large number of inputs used in TRIM.FaTE, a
complete set of structural evaluations cannot be identified and performed. The focus of structural
evaluation activities for TRIM.FaTE will be responsiveness to changes in model complexity with
respect to both temporal and spatial scales and the types of compartments included.
Several structural evaluation activities have been identified for TRIM.FaTE, including the
following.
• Response of abiotic compartments to the exclusion/inclusion of biota. It has typically
been assumed that the mass of a chemical in biota compared to the mass in abiotic
compartments (e.g., soil, water, air) is not large enough to influence the overall chemical
mass balance. However, if both the flux into the biotic compartment and the reaction
rates within the compartment are rapid enough, the biota can potentially influence the
mass balance even when a relatively small volume of biota is present (Maddalena 1998).
Testing will be done to measure the model response to biota inclusion to determine when
and to what extent biota need to be included in mass balance calculations.
• Response to temporal scales of analysis and to aggregate inputs. Detailed
meteorological data are available and will be used in a simplified scenario, as part of the
mercury case study (see Chapter 7), to test the model's response to aggregation of input
data over various time periods. By running the model with varying degrees of input
aggregation, the level of input detail required to achieve a specified level of detail in the
output can be determined.
• Response to changes in the size, shape, and location of compartments. As part of the
mercury case study (see Chapter 7), EPA plans to examine the effect of varying spatial
configurations on TRIM.FaTE results. This will include changing the size of
compartments in multiple dimensions to determine the most appropriate way to grid the
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model, as well as adding compartments at the edges of the model region to examine the
boundary effects around the model system (i.e., flux of chemical mass into or out of the
system).
Results from initial structural evaluation analyses would likely lead to further testing (i.e.,
diagnostic evaluations). Different tests could be designed and executed until a clear
understanding of the behavior of TRUVI.FaTE at different levels of complexity emerges. This
understanding will ultimately be incorporated into a user's manual to provide guidance on setting
up the model at an appropriate level of complexity for a given application. For practical reasons,
it is important to limit the complexity of model setup to that which is needed to produce
acceptable modeling results.
6.6 OVERALL PERFORMANCE EVALUATION
6.6.1 DEFINITION AND GENERAL APPROACH
Performance evaluation activities
focus on the output of the full model.
These activities seek to determine if the
output is relevant, reliable, and useful.
Unlike the other types of model evaluation
discussed above, which focus on specific aspects of
the model (e.g., inputs, process models),
performance evaluation focuses on the model as a
whole. Performance evaluation compares modeling
results to some type of benchmark (e.g., monitoring
data, other modeling results). Generally, various performance evaluation analyses are conducted
in a similar manner, with only the source of the comparison data changing. The optimized
model, as modified based on all prior evaluations, is used for performance evaluation.
Matching model output to monitoring data is often considered the most desirable form of
performance evaluation. Although comparing model output to measured values provides useful
information on the model, history matching experiments provide only part of the overall picture
of the model's quality, reliability, and relevance (Beck et al. 1997). Several other forms of
performance evaluation exist. In addition to monitoring data, output of another model and expert
opinion and judgment about how output should look can be used as comparison benchmarks in
performance evaluation.
Moreover, each evaluation provides an opportunity to use the model. In addition to the
ultimate findings of the performance evaluation itself, the experience gained through these
exercises contributes to an overall understanding of the model, which ultimately enables both
model developers and users to judge the quality of the model.
A different form of performance evaluation is the "round-robin" experiment (Cowan et al.
1995), in which several different users independently set up the model and generate output using
the same data for a particular case study (e.g., site description, chemical properties). Model
outputs are then compared, and the users' experiences are reviewed to identify weaknesses and
ambiguities in the program's user interface and other user guidance that could lead to errors
inapplying the model. The lessons learned can then be incorporated into user guidance to help
prevent user errors and inappropriate model applications.
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Examples of performance evaluation activities include:
• Comparison of model output to monitoring data (e.g., concentrations in environmental
media and biota, exposure markers);
• Model-to-model comparison;
• Round-robin experiments; and
• Some forms of regional sensitivity analysis (i.e.., output is tested against knowledge about
a plausible bound).
6.6.2 TRIM.FaTE-SPECIFIC ACTIVITIES
An extensive review of the literature was undertaken following SAB's initial comments
on the importance of model evaluation for the TRIM project (U.S. EPA 1998a). The review
focused on identifying potential data sets for use in evaluating the performance of TRTM.FaTE.
The usefulness of some of the reported environmental measurements was limited because in
many cases the source of the chemical contamination was not well characterized. Several studies
were identified that report chemical measurements in multiple environmental media (Table 6-1).
The majority of these studies focus on measuring the current chemical concentrations in the
environment with little emphasis on temporal variability or trends. Several of the studies were
designed to assess multimedia partitioning (e.g., atmospheric partitioning among the gas, aerosol,
and water phases) or to investigate specific environmental processes such as the transfer rate
across an environmental interface. Although historical emission patterns can potentially be
reconstructed for certain chemicals using sediment chronology (Cowan et al. 1995), little effort
has gone into matching historical emissions to multimedia environmental concentrations.
None of the studies identified during EPA's literature review provides complete and
concurrent information on chemical concentrations in the five major environmental media (i.e.,
air, water, sediment, soil, biota) along with the associated source term(s) and environmental
characteristics (e.g., meteorology, hydrology, landscape properties). Although some of these
studies can and will be used to evaluate certain aspects of the model, it is important not to
overvalue these results when judging the overall quality of the model.
As noted above, comparisons of TRIM.FaTE outputs to monitoring data are difficult
because complete multimedia data sets from well-characterized systems (e.g., known source,
meteorology, and landscape) to use in a performance evaluation are not currently available.
However, limited data sets are becoming available through the literature (see Table 6-1) and
through unpublished sources (e.g., multimedia monitoring by state or local agencies). These
smaller data sets will allow TRIM.FaTE's output to be evaluated and compared with
measurements, at least to some degree.
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Table 6-1
Multiple Environmental Media Studies
Chemical
Benzo(a)pyrene,
other PAHs
Benzo(a)pyrene,
PAHs (4), PCBs
Chlorpyrifos
Dioxins
Mercury
Speciation?
NA
NA
NA
NA
In mammals
and
earthworms
only
None
None
None
Source
Urban
Petrochemical
factories
Not specified
Urban
Chloralkali plant
Lithium
separation
facility
Chloralkali plant
Chloralkali plant
Location
Florence, Italy
Stenungsund,
Sweden
Chesapeake Bay
Bolsover, Derbyshire,
England
Great Britain
Oak Ridge, TN
India
India
Media Measured
• Air particulate
• Plant
• Plant
• Soil
• Air
• Water
• Air (including deposition
rate)
• Plant
• Air
• Earthworm
• Grass
• Soil
• Wood mouse and vole
organs
• Earthworm
• Grass
• Mouse
• Shrew
• Soil
• Goat
• Some plant species parts
• Sheep
• Soil
• Aquatic plant
• Crop plant
• Soil
• Sediment
• Water
Sampling Frequency
Once
Once
1993 (four times per
year from eight
stations)
Once
Once
Once
Once
Once
Study
Ignestietal. (1992)
Thomas et al. (1984)
McConnelletal. (1997)
Jones and Duarte-
Davidson (1997), Duarte-
Davidson et al. (1997)
Bulletal. (1977)
Talmage and Walton
(1993)
Shaw and Panigrahi
(1986)
Lenkaetal. (1992)
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Chemical
Mercury
(continued)
Metals,
pesticides, PAHs
MTBE
Organochlorines
Speciation?
None
None
Some
methyl mercury
Total, methyl,
dissolved
gaseous
NA
NA
NA
NA
Source
Chloralkali plant
Cinnabar, mining
Chloralkali plant
Urban/runoff
Not specified
Not specified
Multiple
estimated
Not specified
Location
Italy
Italy
Saltmarsh ecosystem
near Brunswick, GA
Chesapeake Bay and
streams
Two different regions
in US
Northeastern US
California
Lake Baikal, Russia
Media Measured
• Air
• Soil
• Plant
• Air
• Rain water
• Plant
• Soil
• Surface water sediment
• Birds
• Fish
• Invertebrates
• Mammals
• Plant Parts
• Sediment
• Precipitation
• Sediment
• Water
• Air (indoor and outdoor)
• Biologic fluid
• Food (market basket)
• Soil
• Air (indoor and outdoor)
• Biologic fluid
• Food (market basket)
• Soil
• Air
• Ground water
• Surface water
• Fish
• Seal
• Water (dissolved and
particulate)
• Zooplankton
Sampling Frequency
Once
Once
Once
Several single event
measurements (1995
through 1997)
Single measurements
per household (early
1990s)
Longitudinal study of
several households
(early 1990s)
1997-98 and prior
1993 (August -
September)
Study
Maserti and Ferrara
(1991)
Ferrara et al. (1991)
Gardner et al. (1978)
Mason et al. (1999,
1997a,b)
U.S. EPA(1999a),
Sexton etal. (1995)
U.S. EPA(1999a),
Sexton etal. (1995)
University of California
(1998)
Kucklicketal. (1996)
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Chemical
Organochlorines
(continued)
Organochlorines,
PAHs
PAHs(10)
Total PAH
Speciation?
NA
NA
NA
NA
NA
Source
Not specified
Urban
Not specified
Urban
Road
Location
Lake Superior
Lake Michigan
Chesapeake Bay and
streams
Indiana
Australia
Media Measured
• Aquatic biota (19, from
amphipod to lake trout)
• Precipitation
• Air (vapor and aerosol)
• Atmospheric deposition
• Diffusive exchange
• Water (dissolved and
suspended particles)
• Plankton
• Wet deposition
• Air (particulate)
• Gas
• Plant
• Air (particulate)
• Grass
• Soil
Sampling Frequency
Summer 1994 (at
multiple sites)
Summer 1994 (at
multiple sites)
October 1990 -August
1992 (at multiple sites
over, in, and adjacent
to lake)
Every 20-30 days for
several months
Once
Study
Kucklick and Baker
(1998)
Offenberg and Baker
(1997)
Koand Baker (1995),
Leister and Baker (1994)
Simonich and Hites
(1994)
Yangetal. (1991)
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An important aspect of the plan for performance evaluation of TRIM.FaTE is a detailed
case study of a mercury-emitting industrial facility, which was chosen in part because of the
availability of multimedia monitoring data and concurrent emission estimates from a local
source. The mercury case study site also is playing a critical role in the mechanistic and data
quality, and structural evaluations being done, as well as serving as the basis for a variety of
sensitivity analyses. Chapter 7 describes the mercury case study, including the available
environmental and biotic measurement data, in more detail.
The previous prototype of TRIM.FaTE was compared with two similar models, CalTOX
(McKone 1993a, McKone 1993b, McKone 1993c) and SimpleBox (van de Meent 1993, Brandes
et al. 1997). The pollutants modeled for this comparison were PAHs (U.S. EPA 1998f). More
recently, outputs from TRIM.FaTE are being compared to outputs from the EPA's ISCST3 and
IEM2M models, as part of the mercury test case (see Chapter 7). ISCST3 will be used to
generate air deposition and concentration data that will be used in IEM2M to estimate
multimedia concentrations of mercury. These concentrations will be compared to TRIM.FaTE
outputs that will be modeled using consistent inputs, as well as to TRIM.FaTE outputs from an
analysis where the air depositions and concentrations from ISCST3 are incorporated into
TRIM.FaTE (in essence, substituting for TRIM.FaTE's air transport component). As part of the
mercury test case, TRIM.FaTE outputs (e.g., ranges of predicted environmental media and biotic
concentrations of mercury) will also be compared to the available measurement data for mercury
in environmental media and biota. The predicted ranges of model results used for these
comparisons will be based on the results of TRIM.FaTE uncertainty and variability analyses, as
described in Chapter 6 of TRIM.FaTE TSD Volume I.
Although most model-to-model comparisons are performed on a scenario-specific basis, a
more informative approach may be to compare models across a range of conditions using
multiple regression or data mining software (Helton et al. 1989, Spear et al. 1994). In the future,
more robust forms of model-to-model comparison may be considered for TRIM.FaTE.
Sensitivity analyses are often used in performance evaluations to identify the part of the
model that is actually being tested. Given the large number of inputs used in multimedia models
such as TRIM.FaTE, it is not always obvious which processes and algorithms are participating in
the calculation. TRIM features for uncertainty and variability analysis (see Chapter 3), standard
sensitivity analysis methods, and regional sensitivity or parameter space analysis methods (Beck
and Chen 1999, Spear 1997) may be used to understand and communicate the results from
performance evaluations and to improve the ability to assimilate the results from all the
evaluation efforts.
6.7 SUMMARY OF TREVLFaTE EVALUATION ACTIVITIES
The TRIM.FaTE evaluation plan, as described in this chapter, includes a variety of
activities designed to build consensus about the model's performance and increase acceptance of
the model for its intended applications. A few of these activities have been completed, many are
in progress, and several others are in the planning stages. Table 6-2 summarizes key elements of
the evaluation plan by providing examples of TRIM.FaTE evaluation activities to date as well as
examples of planned future activities.
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Table 6-2
Summary of TRIM.FaTE Evaluation Activities
Type of
Evaluation
Conceptual
Model
Evaluation
Mechanistic
and Data
Quality
Evaluation
Evaluation Activity
Literature review
Model documentation
Peer review of
modeling concepts
and approaches
Computer code
verification
Performance
evaluation of process
models that are
components of
TRIM.FaTE
Comparison of
alternative process
models
Data acquisition and
evaluation/
development and
documentation of
default input data
Generic sensitivity
analysis of input
parameters
Examples of Progress to Date
Extensive during model
conceptualization and early
development
Status Reports and comprehensive
TSDs in 1998 and 1999,
presentations at scientific meetings
Reviewed by SAB in 1998; full
internal EPA review and SAB
advisory in 1999
Extensive during development for
Prototypes I to V and Version 1.0;
performed review of LSODE;
compared Prototype V and Version
1.0 results for some test cases;
developed automated tests of
internal functions for Version 1.0
Compared TRIM.FaTE to CalTOX
output for nine "pseudochemicals"
(i.e., varying K^/KgJ in a simple
scenario (i.e., air, water, soil);
compared TRIM.FaTE to ISCST3
for air transport of mercury
Compared chemical flux across
soil/air interface with results from
Jury model; comparing chemical
transfer from soil to root with
physically based model
Compiled an initial set of data for
test chemicals (phenanthrene,
benzo(a)pyrene, mercury) and
environmental settings
Some analyses of Prototypes I to
IV; initial analyses to determine
elasticities of >100 parameters for
Prototype V using mercury case
study
Examples of Future Plans
Perform targeted reviews when
adding or refining algorithms
Update and expand documentation
throughout development; develop
user guidance
Periodic internal and external peer
review
Complete comparisons between
Prototype V and Version 1.0 results
and reconcile any differences;
develop and evaluate additional
Version 1.0 internal tests
Continue performance evaluation
for process models (e.g.,
particle/plant leaf algorithm, soil flux
model)
Compare Koa (i.e., octanol/air
partition coefficient) aerosol model
with the Junge model; perform
model-to-model evaluations for
bioaccumulation in fish models
Continue data acquisition and
evaluation (e.g., other chemicals
and environmental settings)
Assess the most influential input
parameters as part of future
evaluations and applications
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EVALUATION PLAN FOR TRIM.FATE
Type of
Evaluation
Structural
Evaluation
Overall
Performance
Evaluation
Evaluation Activity
Analysis of time step
resolution and other
time-related aspects
of modeling as part of
case study
Analysis of varying
spatial configurations
as part of case study
Analysis of changes
in complexity
Regional sensitivity
analysis
Model-to-model
comparison
Comparison to
monitoring data
Examples of Progress to Date
Very limited analysis
Limited analysis for air component
only
Compared TRIM.FaTE for a
simplified mercury case study
scenario with and without biota
None to date
Compared early prototypes to
CalTOX and SimpleBox; have
begun comparisons with
ISCST3/IEM2M for mercury case
study
Have begun multimedia
comparisons for mercury case study
Examples of Future Plans
Perform detailed analyses;
characterize variance due to
temporal resolution changes in
inputs; ensure that time-averaged
output sufficiently maps the
temporally resolved output
Perform detailed analyses to
characterize how robust the model
is to spatial configuration changes
Identify issues to be addressed
when setting up the model for an
application
Identify regions of parameter space
that are critical to certain model
outcomes as part of future
evaluations and applications
Complete ISCST3/IEM2M for
mercury case study comparisons
Complete mercury case study;
identify other test chemicals and
sites, as needed
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CHAPTER 7
TRIM.FATE MERCURY CASE STUDY
7. TRIM.FaTE MERCURY CASE STUDY
As discussed in Chapter 6, systematic model evaluation is an important step in
determining if a model performs as expected. Model evaluation activities have been undertaken
for the TRIM.FaTE module starting with the early model prototypes and have included case
studies with organic chemicals. Consistent with SAB recommendations, OAQPS has continued
its model evaluation activities for TRIM.FaTE Prototype V. As described in Chapter 6, OAQPS
is performing a variety of evaluation activities, including a case study for mercury at a chlor-
alkali plant in the U.S. This case study, currently in progress, will begin with data quality,
mechanistic, and structural evaluations, which will improve understanding of the most important
model processes and inputs and of the effects of varying the model's spatial and temporal
resolution. After gaining an understanding of and confidence in the model's structure and
performance, the case study will proceed to compare TRIM.FaTE outputs with environmental
and biotic measurement data for the selected site as well as with outputs from other models. The
case study site and conditions also serve as the basis for extensive testing, troubleshooting, and
sensitivity analysis of TRIM.FaTE. This chapter provides summary information on the mercury
case study, including the study objectives, information on selection of the study chemical and test
site, and an overview of the evaluation activities. In the future, EPA may perform additional case
studies and apply TRIM.FaTE to other chemicals (e.g., dioxins) and other locations.
7.1 OBJECTIVES
The specific objectives of the TRIM.FaTE mercury case study are three-fold:
Demonstrate that TRIM.FaTE can be
used effectively for metals and other
inorganic chemicals;
Demonstrate that TRIM.FaTE can
account for reversible transformation
of chemicals and can track the
environmental fate of transformation
products; and
Test TRIM.FaTE and compare the
results with measured data, as well
as against modeled results from
IEM2M (EPA's Indirect Exposure
Methodology, as modified for
mercury and applied in the Mercury
Study Report to Congress (U.S. EPA
1997a)).
MERCURY
Mercury is one of the 188 HAPs listed under
section 112(b) of the CAA, is one of 33 HAPs
being addressed by the Integrated Urban Air
Toxics Strategy under section 112(k) (U.S. EPA
1999e), is a pollutant of concern under the
section 112(m) Great Waters program (U.S.
EPA 1999b), and is one of the seven specific
pollutants listed for source identification under
section 112(c)(6). In addition, the findings of
the Mercury Study Report to Congress (U.S.
EPA 1997a) indicate that mercury air emissions
may be deposited to water bodies, resulting in
mercury uptake by fish. According to that
report, ingestion of mercury-containing fish is a
critical environmental pathway of concern for
mercury-related health effects in humans,
particularly developmental effects in children.
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7.2 CASE STUDY CHEMICAL SELECTION
As part of the evaluation process for TRIM, EPA must test TRIM.FaTE with both organic
and inorganic pollutants because of their distinctly different multimedia fate and transport
properties. The EPA selected PAHs for an organic chemical test case, and the methodology and
results of that testing were reported in the 1998 TRIM Status Report (U.S. EPA 1998e). The
Agency selected mercury as an inorganic chemical for testing TRIM.FaTE because of its fate and
transport properties (e.g., transformation to multiple chemical species), the concern for
multipathway exposure (particularly through ingestion offish), and the potential health effects
associated with exposure.
7.3 CASE STUDY SITE SELECTION
After selecting mercury for this case study, the Agency evaluated different stationary
sources of mercury that are significant on a national basis. The four types of stationary sources
with the highest total national air emissions of mercury, based on the findings of the Mercury
Study Report to Congress (U.S. EPA 1997a), are - in order of highest to lowest mercury
emissions - electric utility plants, municipal waste combustors, medical waste incinerators, and
chlor-alkali plants. Electric utility plants, which are addressed in section 112(n) of the CAA, are
still undergoing evaluation by EPA for possible regulation of mercury air emissions. For
municipal waste combustors and medical waste incinerators, national air emission standards have
been promulgated under section 129 of the CAA, and these standards are expected to result in
large reductions of mercury air emissions.
Chlor-alkali plants were selected for further assessment in the TRIM.FaTE case study
because they are a large source of mercury air emissions and are not yet regulated for HAP
emissions. In addition, these plants are more likely than other major mercury emission sources to
pose localized health concerns as a result of their lower stack heights and relatively high
estimated level of fugitive emissions.
The Agency selected a single chlor-alkali plant for the mercury case study after evaluating
data availability for several sites. At the time of the site selection, 14 chlor-alkali plants were in
operation in the United States. Mercury air emission estimates were available for all 14 plants;
however, data on mercury levels in environmental media and biota were available for only two of
the plants. Fish tissue, water quality, and air quality data had been collected for one of the two
plants, but ultra-clean techniques were not used for collecting and analyzing the water samples.
For the second plant, air quality, soil, fish tissue, sediment, and additional biotic data had been
collected and analyzed. In addition, accumulation of mercury in environmental media and biota
near the second plant was possible because the plant has been in operation since 1967. Because
the data set for the second plant was more complete, of higher quality, and readily available for
use, that chlor-alkali plant was selected for the mercury case study. A simplified map of the site
area showing delineation of the parcels used for the case study is provided in Figure 7-1 (for a
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TRIM.FATE MERCURY CASE STUDY
general discussion of the process of defining parcels, volume elements, and compartments for a
TRIM.FaTE application, see Chapter 5 of TRIM.FaTE TSD Volume I).1
7.4 CASE STUDY EVALUATION ACTIVITIES
As part of the TRIM.FaTE mercury case study, several different types of analyses are
being performed that correspond with the types of evaluations (i.e., mechanistic and data quality,
structural, performance) described in Chapter 6. These analyses are described below. The model
input values being developed for the TRIM.FaTE mercury study are documented in Appendix C.
Some of these values will likely be revised over the course of the case study as better information
is acquired.
7.4.1 MECHANISTIC AND DATA QUALITY, AND STRUCTURAL EVALUATIONS
Evaluating the quality of the input data for a given model application is an iterative
process. A literature search is completed to determine the value and identify any available
information on the predicted uncertainty or variability associated with that value. The current
values resulting from our search are listed in Appendix C. Then, a sensitivity analysis will be
performed for all of the parameters to evaluate how the uncertainty in an input value influences
the model output. If a model input is very uncertain and this uncertainty significantly influences
the model output, more research may be completed to refine that input value. Additionally, the
stage 2 Monte Carlo analysis (described in Chapter 3 of this report and Chapter 6 of TRIM.FaTE
TSD Volume I) will be performed on these critical input parameters.
Evaluating the model's internal mechanisms (i.e., mechanistic evaluation) involves
assessing selected chemical fate and transport algorithms used in the model. In addition to
assessing selected components of the model, intermediate processes, such as flows between
compartments, will be assessed to ensure that the model accurately represents the current
understanding of physical and chemical processes. It also must be confirmed that the algorithms
work effectively together within the model. Because of the number of compartment types and
links included in TRIM.FaTE, this will be a complex process.
One mechanistic evaluation being performed is a comparison of the TRIM.FaTE air
component with a commonly used EPA air dispersion model, ISCST3 (U.S. EPA 1995c).
Specifically, the concentration and deposition results from ISCST3 are being compared to the
concentrations and total deposition fluxes estimated for the air compartments in TRIM.FaTE to
provide insight into how the methodology for modeling transport and fate in TRIM.FaTE
compares to the conventional Gaussian plume methodology used in ISCST3.
Another type of evaluation being performed in the TRIM.FaTE mercury case study is an
assessment of the influence of the structural representation of the system being modeled. Some
of the key assumptions in any TRIM.FaTE application, including this case study, involve
1 While the case study site is a real facility and site-specific data are being used to the extent available, the
name and location of the site are being kept confidential.
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TRIM.FATE MERCURY CASE STUDY
Figure 7-1
Simplified Parcel Layout for TRIM.FaTE Mercury Case Study2
t
2 This diagram shows the initial set of surface water (i.e., river, pond) and soil (i.e., all other) parcels for
the TRIM.FaTE mercury case study; the air parcels are slightly different. The Agency also plans to use more
complex parcel layouts as the case study progresses.
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TRIM.FATE MERCURY CASE STUDY
determination of the simulation time step, the background and boundary concentrations, the
spatial representation of the modeled system, and the compartment types selected for modeling.
Examples of structural evaluation include the following:
Understanding the effect of temporal variability, by assessing the impact of the
temporal resolution of the meteorological and source emissions data on model outputs;
Understanding the effect of spatial configuration, by comparing results obtained using
a variety of spatial layouts; and
• Determining the effect of external boundaries on internal compartments, by
assessing, for example, whether wind direction changes result in elevated concentrations
in the air advected back into the system.
After the mechanistic and data quality, and structural evaluations are performed for the
case study site, and greater understanding of and confidence in the model has been gained, the
performance evaluation will begin.
7.4.2 PERFORMANCE EVALUATION
Model performance evaluation, as described in Chapter 6, can include comparisons of
model outputs to outputs from other models and to available measurement data for a specific site.
Both types of performance evaluations will be performed as part of the TREVI.FaTE mercury case
study.
7.4.2.1 Comparison with Other Models
The objective of this part of the case study is to model environmental media and biota
concentrations of mercury using ISCST3 and IEM2M for comparison to the fate and transport
results from the TRTM.FaTE simulations. These models were selected as comparison
benchmarks because they (or in case of IEM2M, the core model from which it was derived, IEM;
see Section 2.1 of TREVI.FaTE TSD Volume I for more discussion of IEM) have been
extensively reviewed and widely used by EPA to estimate air and multimedia fate and transport
of air toxics for regulatory applications. Furthermore, IEM2M was applied previously by
OAQPS in the Mercury Study Report to Congress (U.S. EPA 1997a). When possible, the inputs
used for ISCST3/IEM2M will be identical to the TREVI.FaTE inputs in order to provide results
that are most appropriate for comparison. In some cases, such as the spatial representation of the
modeled system, this will not be possible because of fundamental differences in modeling
approaches, and assumptions will be necessary to maximize the similarities between the models
as applied.
Annual average air concentrations and deposition rates predicted by ISCST3 will be used
as the chemical source inputs to the IEM2M fate and transport algorithms. Environmental media
and biota concentrations predicted by IEM2M will be used for comparison to the corresponding
TREVI.FaTE outputs. They also will be compared to a second set of TREVI.FaTE outputs
generated using ISCST3 results as inputs instead of the built-in TREVI.FaTE air component. The
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TRIM.FATE MERCURY CASE STUDY
fate and transport of three forms of mercury (i.e., elemental, divalent, and methylmercury) will be
tracked and compared. Because mercury speciation affects its fate and transport properties and
because the speciation of the chlor-alkali source emissions is not known with certainty, two sets
of simulations will be performed: (1) assuming that source emissions are composed of 100
percent elemental mercury (in gaseous form), and (2) assuming that source emissions are
composed of 70 percent elemental mercury (in gaseous form) and 30 percent divalent mercury (in
particulate form).
7.4.2.2 Comparison with Measurement Data
The objective of this part of the case study is to model environmental media and biota
concentrations of mercury for the test site and compare the modeled outputs to the available
monitoring data. Comparisons of multimedia model results to monitoring data are challenging
because it is difficult to match modeling conditions to site conditions. However, these
comparisons are useful analyses in the early stages of model evaluation and may lead to
diagnostic assessments.
The parcels being modeled for the test site were constructed, in part, based on the
available monitoring data so that data comparisons would be most relevant and meaningful. The
results (i.e., concentrations in environmental media and biota) from the TRTM.FaTE simulations
will be compared to available measurement data for the chlor-alkali plant vicinity. Appendix D
provides details on the abiotic and biotic monitoring data sets that are available for use in the
TRUVI.FaTE mercury case study. For each data set, Appendix D includes the following
information: environmental medium, number of data points (including the number of duplicates
and measurements below the detection limit), measurement endpoint(s) and units, sampling
date(s), sample location(s), purpose of monitoring, range of values, mean and standard deviation
of values, and other information (if relevant).
Some of the monitoring data sets are from on-site sampling that was conducted as part of
site investigations in 1995 and 1997. In many cases, the on-site data sets also include at least one
measurement from an off-site reference location. Most of the sample collection and analysis for
the site investigations was performed by a contractor or by the facility. Several of the on-site
sediment and surface water data sets that were available are not summarized in Appendix D
because the data appear to represent mercury concentrations in waste streams, rather than
mercury in the environment as a result of air emissions. There are also data sets from off-site
locations, including additional data collected during the site investigations and data collected by
independent researchers during monitoring efforts not related to the facility. Most of the
available measurement data, however, are for total mercury, rather than being speciated into the
various forms, which will limit direct comparisons to the speciated mercury results from
TRJM.FaTE.
The abiotic environmental media data sets include both on-site and off-site monitoring
data sets. The on-site monitoring data sets include five data sets for surface and subsurface soil
measurements from various locations. The off-site monitoring data sets include ambient air
mercury concentration measurements from three monitors within 7,000 feet of the facility;
NOVEMBER 1999 7-6 TRIM STATUS REPORT
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TRIM.FATE MERCURY CASE STUDY
surface water measurements from the adjacent river both downstream and upstream of the
facility; and sediment measurements from four nearby ponds and lakes.
The biotic data sets also include on-site and off-site monitoring data sets. Deer mouse
and earthworm tissue measurements from a variety of locations comprise the on-site data. Off-
site data sets include various mercury concentration measurements in loons, including local level
(e.g., juvenile blood concentrations, adult male blood concentrations, egg concentrations from
nearby ponds) and state level (e.g., state average and individual site-specific juvenile, male adult,
and female adult blood concentrations; state average egg concentrations) data. The off-site data
sets also include measurements of mercury concentrations in skinless fillets of white perch from
nearby ponds; mercury concentrations in short-tailed shrew tissues; mercury concentrations in eel
tissues from the adjacent river; and a single measurement of the mercury concentration in a river
minnow from the adjacent river. For full details on each of these on-site and off-site data sets,
refer to Appendix D.
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CHAPTER 8
DEVELOPMENT OF TRIM.Expo
8. DEVELOPMENT OF TRIM.Expo
The TRIM.Expo module of TRIM is an exposure-event model that is being developed to
assist in assessing health risks related to chemical exposures. The exposure assessment process
consists of relating chemical concentrations in environmental media (e.g., air, surface soil, root
zone soil, surface water) to chemical concentrations in the exposure media with which a human
population has contact. This model uses concentrations of chemicals in different environmental
media over time to provide information such as the number of individuals in a population that are
exposed to various levels of chemicals over various time periods of interest. The TRIM.Expo
module can function as an integral part of TRIM, using the output data from TRTM.FaTE as input
data, or it can function independently of TRIM, using other environmental fate models or
monitoring data as input data.
8.1 PURPOSE OF DEVELOPING TRIM.Expo
The TRIM.Expo module is intended to contribute to a number of health-related
assessments, including risk assessments and status and trends analyses. The TRIM.Expo
module, like most exposure models,
provides a key step in the analysis of the
link between various chemical sources
and potential human health risks.
Multiple sources of environmental
contaminated media, including air, water,
soil, food, and dust. When considering
human exposure, it is necessary to focus
on the more immediate contact or
,. i • i • i i .1 concentrations.
exposure media, which include the
EXPOSURE
Exposure is the contact between a target organism
, • .• i i, u- i and a poutant at the outer boundary of the
contamination lead to multiple . ,.,. . ,. , *, .. , ,
^ organism, quantified as the amount of pollutant
available at the boundary of the receptor organism
per specified time period. As an example, inhalation
exposure over a period of time may be represented
by a time-dependent profile of the exposure
envelope of air surrounding a human
receptor, the water and food ingested, and
the layer of soil and/or water that contacts the skin surface. The magnitude and relative
contribution of each exposure pathway must be considered to assess the total exposure of a
particular chemical to humans.
Human exposures to air pollutants can result from contact with contaminated air, water,
soil, and food. Such exposures may be dominated by contact with a single environmental
medium or may reflect concurrent or successive contacts with multiple media. The nature and
extent of such exposures depend largely on two elements: (1) human factors and (2) the
concentrations of a chemical in the exposure media. Human factors include all behavioral,
sociological, and physiological characteristics of an individual that directly or indirectly affect his
or her contact with the substances of concern. Important factors in this regard include contact
rates with food, air, water, and soil. Activity patterns, which are defined by an individual's or a
group of people's allocation of time spent participating in different activities at various locations,
are also significant because they directly affect the magnitude of exposures to substances present
in different indoor and outdoor environments. The information on activity patterns is taken from
measured data collected during field surveys of individuals' daily activities, the amount of time
NOVEMBER 1999 8-1 TRIM STATUS REPORT
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CHAPTER 8
DEVELOPMENT OF TRIM.Expo
spent engaged in those activities, and the locations where the activities occur. Therefore, from an
exposure assessment standpoint, the principal goal is to estimate or measure an individual's or
group's exposure as a function of relevant human factors and the measured and/or estimated
chemical concentrations in the contact or exposure media. This is a challenge due to the paucity
of information regarding many of the human factors that affect exposure. Therefore, a tiered
approach is being used for the initial development of TRIM.Expo. Specifically, model
development is being focused on applications where the input parameters are most critical and
where input data exist.
With respect to population groups, the exposure-event module within TRIM.Expo uses
the concept of a cohort. Cohorts are subsets of a population grouped so that the variation of
exposure within a cohort is much lower than the variation between or among cohorts. This
approach is used because available data are not adequate to estimate the exposure of each
individual in a population; therefore, information about people who are expected to have similar
exposures is aggregated together to make more efficient use of the limited data. The cohorts are
assumed to include individuals with exposures that can be characterized by the same probability
distribution for key characteristics. The demographic variables used to describe a cohort are
selected to minimize the differences between individuals within the cohort. The model selects an
individual from the appropriate cohort and uses that individual's activity pattern data to create an
exposure-event sequence for that day. Currently, TRIM.Expo accounts for variability within a
cohort through multiple runs of the model for the exposure duration under study. As new
statistical techniques are developed, future versions of TRIM.Expo will be modified to use the
best available approaches for characterizing time/activity pattern data. At the present time,
however, the current method of using cohorts is a useful technique for modeling the exposures of
a large population in the absence of adequate time/activity pattern data (see the TRIM.Expo TSD
for a more detailed discussion on cohorts).
The TRIM.Expo module was designed to allow flexibility in the user's ability to select a
cohort's characteristics. The demographic variables (e.g., age, gender, work status) that
characterize a cohort can be modified by a user of TRIM.Expo providing that there are data
available. Hence, the cohorts' characteristics can be chosen for individualized studies on a site-
specific or case-specific basis.
Using exposure modeling approaches instead of exposure monitoring studies has several
advantages: (1) direct monitoring of the exposure of humans to chemicals (i.e., personal exposure
monitoring) is expensive, and (2) direct monitoring of exposures resulting from large numbers of
pollutants can present large logistical and analytical difficulties. Therefore, OAQPS has
determined that exposure modeling, such as using TRIM.Expo, is useful for estimating exposures
to air pollutants and may be used in conjunction with limited personal exposure monitoring data.
8.2 OVERVIEW OF TRIM.Expo
Emissions of chemicals to air can (depending on the characteristics of an individual
chemical) lead to contamination of multiple environmental media, including ambient outdoor air,
indoor air, surface and ground water, soil, food, and dust. The more immediate contact or
exposure media, which include the envelope of air surrounding a receptor, the water and food
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CHAPTER 8
DEVELOPMENT OF TRIM.Expo
ingested, and the layer of soil and/or water that contacts the skin surface, should be the main
focus of an exposure assessment. Furthermore, the magnitude, duration, and frequency of
exposures via each exposure route (inhalation, ingestion, or dermal contact) must be considered
to assess the aggregate exposure to a particular chemical.
As shown in Figure 8-1, the TRIM exposure assessment process consists of relating
chemical concentrations in environmental media (e.g., air, surface soil, root zone soil, surface
water, vegetation, ground water) to chemical concentrations in the immediate exposure/contact
media with which a human population has direct contact (e.g., personal air, tap water, foods,
house dust, soil). The TRIM.Expo module simulates the movement of an individual and/or a
subset of the human population (i.e., a cohort) according to activity patterns, through locations
(i.e., microenvironments) of varying chemical concentrations, thus allowing the estimation of
exposures to the various cohorts within the population.
In a typical TRIM application, TRTM.FaTE data may be used to provide an inventory of
chemical mass across an ecosystem for selected time steps (e.g., hours, days, years), or
monitoring data may be substituted for TRUVI.FaTE data. Alternatively, concentration estimates
from an air dispersion model may be used if inhalation is the only exposure route of interest and
either (1) the chemical is not persistent, or (2) the impacts of only current emissions are of
interest. The TRIM.Expo module uses these data, combined with the characteristics and
movements of individuals and/or cohorts, to estimate exposures. The movements are defined
through a sequence of exposure events that corresponds to the time steps modeled by
TRUVI.FaTE. Each exposure event places the individual or cohort in contact with one or more of
the exposure media for a specified time. Besides the individual's or cohort's sequence of
locations, other characteristics that relate to exposure and uptake, such as the respiration rate or
the water consumption rate, are also tracked over time.
Current development of TRIM.Expo includes incorporation of the Probabilistic National
Ambient Air Quality Standards Exposure Models (pNEM) (Johnson et al. 1992, Johnson et al.
1999) and Hazardous Air Pollutant Exposure Model (HAPEM4)1 into the TRIM.Expo platform
for short-term and long-term inhalation exposures, respectively; incorporation of ingestion
algorithms based on the EPA Indirect Exposure Methodology (IEM)2 (U.S. EPA 1999d) and the
California Total Exposure Model for Hazardous Waste Sites (CalTOX) (McKone 1993a,
McKone 1993b, McKone 1993c); and the performance of test cases for inhalation and ingestion
pathways. These test cases will undergo an SAB review.
1 The development and testing of HAPEM4 were recently completed. The developers are in the process of
producing a report and accompanying Programmer and User Guides.
2 The EPA now refers to this as Multiple Pathways of Exposure (MPE) methodology.
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Input Data
General
- Human activity
patterns
- Human intake
patterns
- Other
Air
Water
Soil/Dust
Sediment
Figure 8-1
Conceptual Diagram of TREVLExpo
Model Components
Air
- Inhalation
- Dermal
Indoor Model
- Residence (incl. resuspens.)
- Vehicles
- Work (incl.resuspension)
- School (incl. resuspension)
- Other
Outdoor Model
- Along road
- Self-service gas station
- Parking garages
- Resuspens. from human activity
- Other
Water
- Drinking
- Showering/bathing
- dermal
- inhalation
- ingestion
- Recreation (e.g., swimming)
- dermal
- inhalation
- ingestion
Soil/Dust
- Inhalation
- Ingestion
- Dermal
Food
Ingestion:
-Fish
- recreational
- commercial
- Meat/Poultry
- home
- commercial
- Vegetables/Fruits/Grains
- home
- commercial
• Dairy products
- home
- commercial
- breast milk
Outputs
For any exposure route & for
various population groups,
distribution of people and
occurrences of exposures
at the appropriate temporal
scale, tracked with ventilation
rate/intake dose.
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8.3 CONCEPTUALIZATION OF TRIM.Expo
The TRIM.Expo module is built around the concept of simulating a series of exposure
events. Exposure events are human activities that bring individuals in contact with a
contaminated exposure medium within a specified microenvironment at a given geographic
location. In TRIM.Expo, exposure of each individual or cohort is determined by a sequence of
exposure events specific to the individual or cohort. The exposure-event sequence is a
chronologically-ordered series of events that identifies the locations and amount of time spent in
those locations. Each exposure-event sequence consists of a series of events with durations
ranging from one to 60 minutes. Each exposure event assigns the cohort to a particular
combination of exposure district, microenvironment, and activity (e.g., cooking, playing, resting).
An exposure district is a geographic location within a defined physical or political region, where
there is potential contact between an organism and a pollutant, and for which environmental
media concentrations have been estimated either though modeling or measurement. A
microenvironment is a location defined by a specific chemical concentration where exposure may
occur. The following important attributes of an exposure event are used to estimate the
corresponding exposure concentrations and potential doses:
• Chemical concentration in an environmental medium (e.g., ambient outdoor air, surface
water, soil);
• Any significant intermedia transfer to the exposure medium (e.g., from soil to house dust to
air in an indoor microenvironment);
• Chemical concentration in an exposure medium (e.g., personal air, tap water);
• Duration of contact with the exposure medium;
• Number of contacts with the exposure medium; and
• Time scale of interest.
The TRIM.Expo algorithms will use this information to estimate the exposure
concentration at each time step to create an exposure time series or profile. By combining the
exposure concentration and the breathing rate at each time step, TRIM.Expo will also create a
potential dose profile. Depending upon the health effects associated with the chemical of
interest, the exposure and potential dose profile may be used to derive several metrics, such as
the number of person-hours of exposure to concentrations above a threshold value, the sum of the
concentrations that exceed a threshold value, the average of concentrations that exceed a
threshold value, or the maximum concentration corresponding to an averaging time of interest for
the simulation period.
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8.4 FUNCTIONAL ATTRIBUTES OF TRIM.Expo
The goal of the TRIM project is to develop a framework that is scientifically defensible,
flexible, and user-friendly; that can address the broad range of risk assessments required under
the various CAA programs/provisions; and that supports the regulatory decision-making process
for these programs. TRIM is intended to be part of a new generation of environmental risk and
exposure models for OAQPS. It will eventually be a predictive environmental model of
chemical transfers to human health endpoints that is flexible and applicable to both criteria
pollutants and HAPs, while incorporating multimedia, multipathway estimates of exposure and
dose. To be successful, TRIM must address the wide range of spatial and temporal scales,
endpoints, and pathways of interest to specific CAA programs. To meet these goals, TRIM.Expo
will include the following functional attributes:
• Indoor and outdoor environments;
• Indoor and outdoor sources;
• Portable, modular, and flexible algorithms;
• Explicit treatment of uncertainty; and
• Explicit treatment of variability.
8.4.1 DIMENSIONS OF THE EXPOSURE ASSESSMENT PROBLEM
Three important dimensions of the exposure assessment problem are considered: (1) the
route of exposure, (2) the time scale of an exposure event relevant to the pollutant's associated
effects, and (3) dependence of exposure on the location of the exposed subject (i.e., how strongly
or weakly dependent is exposure on the location of the exposure subject?). Addressing these
three issues has the greatest impact on the structure of the exposure model (e.g., on the exposure
media included, the degree of spatial resolution, and the level of temporal and spatial
aggregation). For example, consider a model used to assess inhalation exposures to chemicals
with health effects that depend on the number and duration of contacts above a threshold
concentration. This model requires a compilation of short-term exposure events and must
provide relatively detailed information on the location of the exposed individual. In contrast, an
exposure model used to assess ingestion contact with a chemical that has health effects that
depend primarily on the lifetime cumulative intake of that chemical would require much different
temporal and spatial detail about the exposed individuals. In this case, rather than tracking the
detailed time/location profile of the exposed cohort, it is more important to know the location of
the exposed cohort's food or water supply and the cumulative intake of food or water from a
specific supply.
The primary routes of exposure to environmental chemicals are inhalation, ingestion, and
dermal contact. The primary time scales for exposure assessment models vary from short-term
resolution (e.g., minutes to hours and days) to long-term resolution (e.g., days to months and
years). Short-term resolution allows for the assessment of both cumulative intake and the
number and duration of peak exposure events. Long-term resolution allows primarily for the
assessment of cumulative intake. The quantitative distinction between short-term and long-term
depends to some extent on the pharmacokinetics (i.e., uptake and distribution) and toxicokinetics
of the chemical substance. Location dependence specifies the level of detail required for the
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time-activity budget of an exposed individual. For example, to address inhalation exposures
where chemical concentration varies significantly from among several districts in which the
exposed cohort lives and differs strongly between indoor and outdoor microenvironments,
location dependence is high. But, if the properties of the chemical are such that concentrations
are similar in almost all microenvironments, location dependence is lower. For ingestion
exposures to a chemical in ground water that is distributed throughout a region, the location of
the exposed cohort is much less important than the source of the cohort's drinking water.
Three primary attributes are used to describe an exposure problem using TRIM.Expo: (1)
exposure route, (2) exposure time scale, and (3) the degree of location dependence. This set of
attributes gives rise to a broad set of exposure problems, such as short-term inhalation exposure
with strong location dependence, long-term ingestion exposure with weak location dependence,
and short-term dermal contact exposure. The general exposure-event function used in
TRIM.Expo has a form that can be adapted across the broad range of problems defined by these
attributes. In some situations, aggregating among two or more sets of exposure model attributes
may be necessary (i.e.., combining long-term ingestion exposures that are weakly location
dependent with short-term inhalation exposures that are strongly location dependent). The
TRIM.Expo module is designed to make such aggregation possible.
8.4.2 DESIGN FEATURES OF TRIM.Expo
Although exposures to some types of exposure media, such as commercial foodstuffs, are
not location or time dependent, most of the chemical exposures addressed by TRIM.Expo are
associated with particular locations. Also, because of spatial and temporal differences in
contamination of exposure media, tracking the locations and activities of individuals or cohorts
through time and space to estimate their exposure is important. This requires methods for
logging both time-activity-specific locations of individuals or cohorts and the time-specific
concentrations of chemicals in relevant exposure media. The process of combining these three
different types of information (i.e., location, activity, and concentration) is the exposure
characterization process. The exposure characterization process can be short-term (i.e., over
hours or days) or long-term (i.e., over months or years). The critical issue of the exposure
characterization process is to identify appropriate and transparent methods to combine
concentration information with activity tracking (i.e., tracking locations and activities at different
times) to assess short- and long-term exposures. To develop the exposure characterization
process for TRIM.Expo, the following attributes that define an exposure event were identified
and ranked:
• Route of exposure;
Time/space scale of the chemical concentration;
• Time scale of the health effects;
• Duration of the exposure event;
• Contributing environmental medium;
• Exposure medium; and
• Demographic characteristics of the exposed individual (e.g., age, gender).
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The route of exposure refers to the way the chemical can enter the receptor during the
exposure event (i.e., by inhalation, ingestion, or dermal uptake). The route of potential uptake
(i.e., absorption into the body) is a very important attribute of an exposure event. The health
effects of an exposure may vary significantly among these three routes. Both the exposure
medium and exposure-related activity are often strongly associated with a particular intake route.
For example, air is associated with the inhalation route, and the inhalation rate varies
significantly with activity location. Water, food, and soil are associated with the ingestion route
and with eating and hand-to-mouth activities.
The time scale and spatial scale of the chemical concentration variation provide critical
insight on time and space resolution needed in constructing an exposure event. If a chemical
shows little spatial variation in concentration over a large region, even if the concentrations vary
with time, there is little need for large numbers of geographic regions in an assessment.
Similarly, for a chemical whose concentrations do not vary significantly in time, even if they
show large spatial variation, using longer time steps may be possible than that needed for a
chemical whose concentrations vary more quickly in time. However, the time scale of exposure
associated with health effects for a particular pollutant also strongly effects the temporal
resolution required of the exposure-event model. Some chemicals, such as most of the criteria
air pollutants, require the estimation of the number and duration of peak exposure events. For
hazardous air pollutants with acute health effects, exposures may need to be aggregated over
periods as short as one hour or less. For many hazardous air pollutants, only long-term
cumulative exposure may need to be characterized.
The durations of the exposure events and human activities are important considerations in
the structure of the exposure-event model. Other factors that affect the structure of the
exposure-event model are the demographic characteristics of an exposed individual or population
group, such as age or gender, that may influence both their activity pattern and their health
response to exposure. Other characteristics, such as proximity to particular emission sources or
health status, also may be important. The interconnected nature of the relationships among the
locations, microenvironments, environmental media, intermedia transfers, exposure media, and
cohorts within TRIM.Expo is illustrated in Figure 8-2.
8.5 APPROACH USED IN DEVELOPING TRIM.Expo
The TRIM.Expo module will model exposures from the inhalation, ingestion, and dermal
contact routes. For the first prototype, however, the exposure routes are limited to inhalation and
ingestion. Dermal contact will be addressed as a longer-term goal of TRIM.Expo.
The TRIM.Expo TSD includes a comprehensive discussion of currently available
exposure models. These models were reviewed to determine whether they would be suitable for
the exposure modeling needs of OAQPS. Although no single model or set of models has been
identified that meets all the requirements for the exposure modeling needs of OAQPS, many of
the concepts and components developed for existing models have been used in TRIM.Expo. For
the inhalation pathway, the structure from EPA's pNEM/CO (probabilistic NAAQS Exposure
Model for Carbon Monoxide) and HAPEM4 models
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Figure 8-2
Relationships Among Locations, Microenvironments, Environmental Media,
Intermedia Transfers, Exposure Media, and Cohorts
Exposure media
Cohort
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have been adopted for short-term and long-term exposures, respectively. These constructs use
activity patterns to track population groups/cohorts as they move among exposure media. The
pNEM/CO model also includes a mass balance treatment of the relationship between the
environmental medium (i.e.., outdoor air) and the exposure medium (i.e.., indoor air), as well as
the characterization of uncertainty and variability. For the ingestion pathway, algorithms from
EPA's IBM and CalTOX have been adopted. The CalTOX model can model multimedia
transport and transformation of chemicals, and multipathway exposure for humans. The IBM
incorporates current EPA guidance for addressing exposures via inhalation, ingestion, and dermal
contact.
8.6 SUMMARY REVIEW OF EXISTING EXPOSURE MODELS AND
THE UNIQUENESS OF TRIM.Expo
This section provides a brief review of currently available and emerging exposure
modeling approaches. The Agency critically evaluated each of the exposure models described in
this section, assessing their strengths and limitations. Based on this review, none of the models
adequately meets the modeling needs of OAQPS (see Chapter 1 for a discussion of OAQPS'
needs). The review in this section, however, highlights the unique features of these models that
can be included in TRIM.Expo to meet the modeling needs.
8.6.1 OVERVIEW OF CURRENT MODELS
In general, the models that most closely meet the design goals for TRIM.Expo
development are the focus of this section. These include models that can calculate short-term
exposures (i.e., one hour or shorter) and that can be adapted to evaluate long-term exposures as
well. The models should also be able to explicitly treat variability and uncertainty. Other
desirable model attributes meeting OAQPS' needs are the inclusion of multiple media, the use of
a mass balance approach for estimating indoor air concentrations, and the ability to track exertion
rates concurrent with exposure. For inhalation, this means providing estimates of the respiration
rate (also called the ventilation or breathing rate) for various activities. Additional useful
features include accounting for indoor air emission sources and the ability to include geographic
mobility (e.g.., commuting) in the exposure simulation.
The development of TRIM is designed to focus on the processes that have the greatest
impact on chemical fate and transport and on human exposure. To have the same scientific basis
as the rest of the TRIM system, TRIM.Expo needs to incorporate the same attributes, including
(1) mass conservation; (2) the ability to characterize uncertainty and variability; (3) the capability
to assess multiple chemicals, multiple media, and multiple exposure pathways; and (4) the ability
to perform iterative analyses at varying levels of complexity. Hence, these four design attributes
are the basis for critically comparing the strengths and limitations of current exposure models
and for determining the features that will be used in TRIM.Expo development.
No single model exists that can meet all of the needs of OAQPS for a multimedia,
multichemical exposure model. However, several models use methodologies that can be adopted
in the development of TRIM.Expo. One model that has many of the desirable attributes is the
pNEM/CO (Johnson et al. 1992, Johnson et al. 1999). Although this model is for a single
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medium only (i.e., air), it incorporates many of the features needed for the inhalation component
of TRIM.Expo. The pNEM/CO benefits from having most of its input variables chosen
stochastically. This stochastic approach allows both variability and uncertainty to be
incorporated into the model operation. The pNEM/CO treats human exposure as a time series of
the convergence of human activities occurring in a particular microenvironment and air quality in
those microenvironments. The model also is designed to provide estimates of the intake dose
associated with exposures. In addition to the other criteria listed above, pNEM/CO is well
documented and is already being used by OAQPS as an input to regulatory decision-making.
Furthermore, the pNEM/CO has undergone review.
The disadvantages of the pNEM/CO model in its current form are that it is difficult to
execute and cannot be readily updated and calibrated as more data becomes available.
Furthermore, the pNEM/CO model, as with all of the pNEM models, is a single pollutant, single
media model.
For modeling the non-inhalation routes of exposure, the CalTOX model, developed at the
Lawrence Berkeley National Laboratory, includes many features needed for estimating indirect
routes of exposure (McKone 1993a, McKone 1993b, McKone 1993c). The CalTOX model can
calculate multipathway exposures for organic chemicals and some metals. In addition, the model
is stochastic and can quantify the variability and uncertainty in the exposure calculations. The
CalTOX model consists of two main components: (1) a multimedia transport and transformation
model and (2) a multipathway human exposure model. The CalTOX model has 23 exposure
pathways encompassing all three routes of human exposure, which are used to estimate average
daily doses within a human population near a hazardous air pollutant release site. The exposure
assessment process consists of relating contaminant concentrations in the multimedia model
compartments to pollutant concentrations in the media with which a human population has
contact (e.g., personal air, tap water, foods, house dust). This provides explicit treatment of the
differentiating environmental media pollutant concentrations and the pollutant concentrations to
which humans are exposed. In addition, all input variables are taken from distributions that are
provided with the model.
The CalTOX model is limited in the extent of the environmental settings for which it can
be applied. For example, it has limited effectiveness for settings where there is a large ratio of
surface water area to land area. In addition, it was developed for a limited range of pollutants
(i.e., only organic chemicals). As a result, CalTOX does not provide adequate flexibility in the
environmental settings nor the chemical classes it models. Also, CalTOX does not allow spatial
tracking of a pollutant, hence it is not directly applicable to the TRIM approach.
The Hazardous Air Pollutant Exposure Model (previously called the Hazardous Air
Pollutant Exposure Model for Mobile Sources, or HAPEM-MS) has undergone many
enhancements in recent years (Johnson et al. 1993, Palma et al. 1996). The latest version of the
HAPEM is designated HAPEM4. It allows exposure to population cohorts to be simulated at the
census tract level. This is a much finer spatial resolution than was previously possible with the
model. It also means that calculation of population exposures no longer needs to rely solely on
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data from fixed-site monitors. This is an important step in being able to estimate exposures to
HAPs because widespread monitoring networks for these chemicals are not available.
The HAPEM4 calculates long-term average exposure concentrations in order to address
exposures to pollutants with carcinogenic and other long-term effects. Thus, HAPEM4 does not
preserve the time-sequence of exposure events when sampling from the time/activity database.
This means that information to evaluate possible correlations in exposures to different pollutants
due to activities that are related in time is not preserved. Also, the model does not include any
measures of the ventilation rate associated with an activity, so that there is no ability to calculate
the potential dose received when engaging in various activities.
The IBM includes fate and transport algorithms, exposure pathways, and exposure
algorithms. It focuses on procedures for estimating the indirect (i.e., non-inhalation) human
exposures and health risks that can result from the transfer of chemicals from air to soil,
vegetation, and water bodies. The IBM addresses exposures via inhalation, ingestion of food,
water, and soil, and dermal contact. The methodology has undergone extensive scientific peer
review.
The IBM has limitations, however, related to the design goals for TRIM. The
methodology can be applied only to pollutants that are emitted to air. Another important
limitation of IBM is that it does not provide a detailed time-series estimation of media
concentrations and resultant exposures. Also, the methodology does not provide for the
flexibility needed by OAQPS in site-specific applications or in estimating population exposures.
Significant site-specific adjustments must be made to allow for spatially tracking the relationship
between concentrations and exposures. Much of the focus of the methodology is on evaluating
specific receptor scenarios (e.g.., recreational or subsistence fisher) that may be indicative of
high-end or average exposures, but it does not readily allow for modeling the distribution of
exposures within a population.
The models summarized in this section provide background information for some of the
most commonly used exposure models currently available. More detailed information about
these and the other exposure models that were evaluated can be found in the TRIM.Expo TSD.
8.6.2 RATIONALE AND NEED FOR DEVELOPING TRIM.Expo
The TRIM.Expo module is intended to contribute to a number of health-related
assessments, including risk assessments and status and trends analyses. The TRIM.Expo module
provides a key step in the analysis of the potential for various pollutant sources to contribute to
human and ecological health risks. Multiple sources of environmental contamination can lead to
multiple contaminated environmental media, including air, water, soil, food, and dust. When
considering human exposure, it is necessary to focus on the more immediate contact or exposure
media, which include(s) the envelope of air surrounding a human receptor, the water and food
ingested, and the layer of soil and/or water that contacts the skin surface. The magnitude and
relative contribution of each exposure pathway must be considered to assess the total exposure of
a particular pollutant to humans.
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The TRIM development is designed to focus on the processes that have the greatest
impact on chemical fate and transport and on human exposure. Besides the four design attributes
for TRIM.Expo (see Section 8.6.1), OAQPS determined that the model must also (1) address
varying time steps (one hour or greater) and provide sufficient spatial detail at varying scales, (2)
have the "transparency" needed to be practical to a large and diverse group of users, (3) be
modular in design, and (4) be easily accessible.
The summary review of multimedia models presented here, and described in more detail
in Chapter 3 and Appendix B of the TRIM.Expo TSD, showed that none of the currently
available models offers all of the design features needed by OAQPS for multimedia exposure
assessments. Although some models incorporate individual features, none of these, separately or
in combination with other models, can function to provide an integrated system that meets the
modeling requirements previously described. In addition, most models are limited in the type of
media and environmental processes addressed. No model currently exists that addresses the
broad range of chemicals and environmental fate and transport processes that are anticipated to
be encountered by OAQPS and other stakeholders when evaluating the risks from the multitude
of hazardous air pollutants and criteria air pollutants. Therefore, the developers of TRIM have
constructed a new model framework that is distinct from the other multimedia models and
unique among the current suite of EPA exposure models.
Another reason for developing TRIM.Expo is that none of the currently available
exposure models that OAQPS investigated is a sufficiently integrated multimedia model that
accounts for inherent "feedback" loops in the exposure continuum and that provides the temporal
and spatial resolution needed for estimating human exposures. It is not known to what extent
modeled exposure estimates would differ between the currently available models and a truly
integrated multimedia exposure model. However, models that are not fully coupled have long
been considered to lack scientific credibility. Therefore, OAQPS has determined that it is
necessary to undertake efforts to develop a truly coupled multimedia exposure model.
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GENERAL DESCRIPTION AND CONCEPTUAL DESIGN OF TRIM.RISK
9. GENERAL DESCRIPTION AND CONCEPTUAL DESIGN
OF TRIM.Risk
The National Academy of Sciences (NAS) has defined risk characterization as a
description of the nature and magnitude of human or ecological risk and the attendant
uncertainties (NRC 1983). Risk characterization is the final step in risk assessment and is
primarily used to integrate the information from the other three key steps (i.e., hazard
identification, dose-response assessment, exposure assessment). Within the TRIM framework,
the risk characterization module (TRIM.Risk) will be used to integrate the information on
exposure (to human and ecological receptors) with that on dose-response or hazard and to
provide quantitative descriptions of risk and the attendant uncertainties. The TRIM.Risk module
will provide decision-makers and the public with information for use in developing, evaluating,
and selecting appropriate air quality standards and risk management strategies. The sources of
input data for TRIM.Risk can be other TRIM modules, including model assumptions, inputs, and
results, or outside information sources or models.
9.1 BACKGROUND ON RISK CHARACTERIZATION
In general, the Agency's risk characterization guidance described below addresses two
essential elements of a full characterization of risk. First, the characterization should address
qualitative and quantitative features of the assessment. That is, in addition to quantitative
estimates of risk, a full risk characterization should clearly describe (1) the hazard information
and associated relevant issues, (2) the dose-response relationship used, and (3) what is known
about the principal paths, patterns, and magnitudes of exposure. Furthermore, for each of these
three items, the characterization should describe any assumptions, the rationale behind these
assumptions, and the effect of reasonable alternative assumptions on the conclusions and
estimates. The second essential element of a full risk characterization is the identification and
discussion of any important uncertainties. As noted by the Agency's Deputy Administrator in
issuing the Agency's initial risk characterization policy memo "... scientific uncertainty is a fact
of life (and)... a balanced discussion of reliable conclusions and related uncertainties enhances,
rather than detracts, from the overall credibility of each assessment..." The uncertainty
discussion is important for several reasons (Habicht 1992):
• Information from different sources carries different kinds of uncertainty, and knowledge
of these differences is important when uncertainties are combined for characterizing risk,
allowing for decisions to be made about expending resources to acquire additional
information to reduce the uncertainties; and
• Uncertainty analysis provides the decision-maker and the public with clear and explicit
statements of the implications and limitations of a risk assessment and of the related
uncertainties.
Each step of the analysis phase of risk assessment (i.e., hazard identification, dose-
response assessment, exposure assessment) should include its own summary characterization
section. Because every risk assessment has many uncertainties and involves many assumptions,
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the challenge in characterizing risk for decision-makers, whose time is limited and who may not
be risk experts, is to convey that small subset of key strengths and limitations that are crucial to
the assessment outcome. When integrated, they identify the fundamental, irreducible set of key
points that must be communicated to characterize adequately any risk assessment. Therefore, the
risk characterization should provide the following:
• A clear description of the key strengths and weaknesses;
• A brief "bottom-line" statement about the risks, including the assessor's confidence in
any estimate(s) of risk and in the conclusions; and
Information that allows the reader to grasp easily what is known about the nature,
likelihood, and magnitude of any risk.
For each step of the analysis phase of risk assessment, the assessor should identify the
following items:
Available studies and their robustness (e.g., have the findings been repeated in an
independent laboratory?);
• Assumptions and extrapolations used and the residual uncertainties;
• Use of defaults, policy choices, and any risk management decisions;
• Quality of the data used for the risk assessment (e.g., experimental, state-of-the art,
generally accepted scientific knowledge); and
Quantitative data presented in an easily understandable form, such as tables and graphics.
At EPA, risk characterization takes many different forms depending on the nature of the
risk assessment. The level of detail in each risk characterization varies according to the type of
assessment for which the characterization is written and the audience for which the
characterization is intended. The goal of risk characterization is to clearly communicate the
strengths and limitations of the risk assessment so it can be put into context with the other
information critical to evaluating options for rules, regulations, and negotiated agreements (e.g.,
economics, social values, public perception, policies) in the decision-making stage.
The general content of risk characterization is defined by the NAS and, to a limited
degree, in each of the EPA risk assessment guidelines (e.g., U.S. EPA 1996a). More specifically,
however, the Agency issued its first policy for risk characterization in 1992 (Habicht 1992). This
policy was intended to strengthen the reporting of the Agency's risk assessment results.
Previously, risk information was sometimes presented to the decision-maker and the public in a
form reduced to a simple point-estimate of risk. Such "short-hand" approaches did not fully
convey the range of information used in developing the assessment because the numbers alone do
not provide an accurate picture of the assessment.
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More recently, the Agency updated its policy and issued guidance for the preparation of
risk characterizations (Browner 1995, U.S. EPA 1995a, U.S. EPA 1995b). The policy called for
all risk assessments performed at EPA to include a risk characterization to ensure that the risk
assessment process is transparent and that the risk assessments are clear, reasonable, and
consistent with other risk assessments of similar scope prepared by programs across the Agency.
In issuing the policy and guidance, the Administrator emphasized the importance of a set of core
values to guide risk characterization activities. These core values are transparency, clarity,
consistency, and reasonableness (TCCR).
To implement the policy, an Agency-wide document, the Risk Characterization
Handbook, is being developed (U.S. EPA 1998c). The previously issued policy and guidance, as
well as the Risk Characterization Handbook under development, will be used to guide the design
and implementation of the TRUVI.Risk module. Therefore, this chapter includes text drawing
from specific discussions and recommendations outlined in these documents along with a
description of how TREVI.Risk will conceptually address these recommendations.
9.2 PURPOSE OF TRIM.Risk
In order to develop a full risk characterization, information from each of the risk
assessment components needs to be characterized separately. These individual characterizations
carry forward the key findings, assumptions, strengths, and limitations, and provide a
fundamental set of information that must be conveyed in an informative risk characterization.
The purpose of the TRIM.Risk module is to summarize and integrate key information from other
TRIM modules in addition to other information sources (Figure 9-1) and to facilitate the
preparation of a risk characterization. In general, TRIM.Risk will (1) document assumptions
and input data, (2) perform risk calculations and data analysis, and (3) present results and
supporting information. Where possible, these actions will be automated. It should be noted that
while TRIM.Risk is the module with the primary purpose of preparing information to support
risk characterization, the guiding principles for risk characterization are also being followed in
the development of other TRIM modules (e.g., documenting setup, runs, output), which will
facilitate the development of TRIM.Risk.
It is anticipated that TRIM.Risk will be developed in a phased approach similar to other
TRIM modules. Ideally, the TRIM.Risk module will provide all of the information required to
prepare a full risk characterization. However, the type and variability of information needed for
this purpose is vast. Therefore, the type of information generated by TRIM.Risk will evolve over
time as the Agency gains experience and has the resources to implement more flexibility. For
example, early versions of TRIM.Risk will be limited to preparing quantitative summaries of
input data and results, without supporting text. However, as the Agency gains experience, it may
be possible to incorporate language to more fully describe the information required for a full risk
characterization.
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Figure 9-1
Conceptual Diagram of the TRIM.Risk Module
Environmental Fate,
Transport, and
Ecological Exposure
(TRIM.FaTE)
Assumptions
Input Data
Measures of uncertainty
and variability
Distribution of ecological
exposures
• media
concentrations
• biota concentrations
• biota doses
Exposure Event
(TRIM.Expo)
Assumptions
Input Data
Measures of uncertainty
and variability
Distribution of human
exposures
• population
• subpopulation
Dose-response or Exposure-
response Relationships
(e.g., O3 vs. effect) or Values
(e.g., RfC, RfD)
Risk
Characterization
(TRIM.Risk)
Ecological Effects
Concentrations (media,
body burden, doses)
Documentation of assumptions and input data
Quantitative risk and exposure characterization (human
and ecological)
Measures of uncertainty and variability
Description of limitations (graphical/tabular/GIS
presentation)
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The purpose of TRIM.Risk is to provide information to risk managers, the public, and
stakeholders to support decision-making. To be effective, TRIM.Risk must communicate
information that is readily understandable. Specifically, TRIM.Risk is responsible for conveying
the information for a specific risk assessment. However, because risk assessments are often used
to inform choices between policy alternatives, care will be taken to insure that outputs from
TRIM.Risk are formatted to facilitate comparisons (including statistical comparisons) between
alternatives.
9.3 DESIGN GOALS OF TRIM.Risk
As described in Chapter 1, EPA has established specific goals for the design of TRIM
which can be used to measure progress and performance of either the overall modeling system or
its individual components. These overall design features of scientific defensibility, flexibility,
and accessibility (user-friendliness) apply to the TRIM.Risk module as well. How TRIM.Risk
will meet these major design goals is summarized below.
• Scientific defensibility. The scientific defensibility of TRIM.Risk will be assured by
adherence to the applicable risk characterization guidance (U.S. EPA 1995a, U.S. EPA
1998c) and by full utilization of the abilities of the other TRIM modules to describe
uncertainty and variability surrounding their outputs. Consistent with the Agency's
guidance for risk characterization to clearly communicate the key strengths and
weaknesses of any assessment, the TRIM.Risk module will have the capability to present
the variety of important information generated by any of the other TRIM modules. The
capability of addressing uncertainty and variability in an integrated manner is critical to
presenting risk information beyond deterministic single-point estimates of risk, which is
essential in a full characterization of risk. Furthermore, the integrated uncertainty and
variability analysis capabilities of the TRIM modules also enhance the ability to identify
critical assumptions and data and determine their contributions to overall uncertainty.
• Flexibility. The flexibility designed within the TRIM framework will be maintained in
TRIM.Risk. Specifically, TRIM.Risk will accommodate and present information for the
variety of spatial and temporal scales of analysis possible for other TRIM modules. The
value of any risk characterization lies in its ability to convey useful and, most
importantly, understandable information to risk managers. An OAQPS evaluation of
information needs of risk managers found that because different people process
information differently, it is appropriate to provide more than one format for presenting
the same information (U.S. EPA 1993). As a result, TRIM.Risk will be designed in such
a way that using a specific user interface, outputs may be presented in user-specified
formats (e.g., tables, charts, graphics).
• Accessibility. As with all TRIM modules, TRIM.Risk will be publicly available and
easily obtainable by all interested parties, along with user guides, and will be designed to
be user-friendly.
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9.4 OVERVIEW OF TRIM.RISK
Current and proposed EPA guidance on risk characterization are serving as the basis for
designing TRIM.Risk. Therefore, the major elements identified in the guidance with respect to
TCCR will be explicitly addressed in TRIM.Risk and are described below. In addition, some
discussion is provided on how TRIM.Risk will provide such information and conduct its three
primary functions: (1) documenting assumptions and input data, (2) risk calculation and data
analysis, and (3) presentation of results.
9.4.1 DOCUMENTATION OF ASSUMPTIONS AND INPUT DATA
One purpose of a full risk characterization is to inform the risk manager and others of
why EPA assessed the risk the way it did in terms of the available data, the analysis used,
uncertainties, alternative analyses, and science policy choices. Risk characterization is not only
about science, but also about making clear that current scientific knowledge does not provide all
that is needed to perform the analysis, and consequently science policy judgments must be made.
Every risk assessment involves a multiplicity of choices and options, and the Agency's Policy for
Risk Characterization (U.S. EPA 1995b) calls for a highly visible presentation of the explanation
for these choices. When appropriate, a recognition and discussion of how others have assessed
the same risks should be included.
The computer framework of TRIM (described in Chapter 10) provides an excellent
opportunity for documenting assumptions and input data. The algorithm library and parameter
database approach used in the TRIM.FaTE and TRIM.Expo modules allows for easy
documentation of the algorithms and parameters used in an analysis. Although each module
contains default inputs and algorithms, the user can replace these values with alternatives to
support site-specific analysis or alternative assumptions. To provide transparency in interpreting
results, the TRIM modules will be self-documenting (see Chapter 10), with the ability to catalog
the data and algorithms used for every model run, thereby identifying any changes in parameters
or algorithms. Therefore, it can be readily determined if differences between model runs are
attributable to differences in parameters or algorithms. The algorithm library and parameter
database also have comment fields, which provide the opportunity for articulating the rationale
for such changes. In addition, the design of user interfaces for each model run within individual
modules will document the major assumptions of the analysis.
9.4.2 RISK CALCULATION AND ANALYSIS
A variety of risk calculations and analyses is performed by the Agency in risk
assessments for the hazardous and criteria air pollutant programs. The TRIM.Risk module is
intended to perform this full spectrum of analyses to support characterizations of both human
health and environmental risks.
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9.4.2.1 Human Health Risks
Because cancer and noncancer dose-response assessment have traditionally been different
(i.e., assumption of threshold for noncancer versus no threshold for cancer), the current methods
for risk assessment also differ and are discussed separately below. In some cases, available data
and information do not support the estimation of quantitative estimates of risk. In those cases,
the risk characterization may rely on data analyses that summarize risks in a semi-quantitative or
qualitative manner, such as comparing exposure concentrations to exposure levels of concern.
Quantification of Cancer Risks
Cancer risk is defined as the predicted excess probability of contracting cancer over a 70-
year period (i.e., assumed human lifespan) following exposure to a pollutant at the estimated
concentration for a specified time period. This estimated risk focuses on the additional risk of
cancer predicted from the exposure being analyzed, beyond that due to any other factors.
Individual cancer risks or population cancer risks associated with an exposure can be calculated
by multiplying the individual or population exposure estimate, respectively, by the unit risk
estimate (URE). Estimates of risk to an individual are usually expressed as a probability
represented in scientific notation as a negative exponent of 10. For example, an additional risk of
contracting cancer of one chance in 10,000 (or one additional person in 10,000) is written as
IxlO'4.
In quantitative risk assessment, population risk is an estimate that applies to the entire
population within the given area of analysis. The population risk often is expressed as a
predicted annual cancer incidence, which is the annual number of excess cancer cases predicted
in the exposed population. Each estimated exposure level is multiplied by the number of
individuals exposed to that level and by the URE. This provides a prediction of risk for that
group after a 70-year exposure to that level. The risks for each exposure group are summed to
provide the number of excess cancer cases predicted for the entire exposed population. This 70-
year risk estimate can be divided by 70 to estimate the predicted annual incidence in units of
cancer cases per year.
People often are exposed to multiple chemicals rather than a single chemical. For
analysis of cancer risk from multiple chemical exposures, TREVI.Risk will be consistent with the
Agency's Guidelines for the Health Risk Assessment of Chemical Mixtures (U.S. EPA 1986a). In
developing TRDVI.Risk, activities to update these guidelines (e.g., U.S. EPA 1999c) will be
followed closely to ensure consistency.
In those few cases where cancer potency values are available for the chemical mixture of
concern or for a similar mixture, risk characterization can be conducted on the mixture using the
same procedures used for a single compound. However, cancer dose-response assessments
usually are available only for individual compounds within a mixture. In such cases, based on
the assumption that the risks associated with the individual chemicals in the mixture are additive,
the cancer risks predicted for individual chemicals are sometimes added to estimate total risk.
The following equation estimates the predicted incremental individual cancer risk for
simultaneous exposures to several carcinogens:
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RiskT = Riskj + Risk2 + ....+ Risk;
where:
RiskT = the total cancer risk (expressed as a probability of contracting cancer over
a lifetime)
Risk; = the risk estimate for the ith substance.
As described in the proposed revisions to the guidelines for carcinogen risk assessment
(U.S. EPA 1996b), when sufficient information is known on the mode of action for a pollutant,
dose-response may be better defined by a non-linear relationship. In cases of non-linearity, risk
is not extrapolated as the probability of an effect at low doses. In these cases, a margin of
exposure analysis is used to evaluate concern for levels of exposure. The margin of exposure is
the "point of departure" from the health effects data divided by a human environmental
exposure(s) of interest - either actual or hypothetical. Exposures may be of interest because they
are associated with actual or projected exposure scenarios or because they are levels that may
result from alternative control actions. The risk manager decides whether a given margin of
exposure is acceptable within a given regulatory program context. The risk assessment provides
an analysis with supporting information and advice to assist the decision-maker in considering
aspects of the exposure scenarios at issue in light of the mode of action. A margin of exposure
analysis presents all of the pertinent hazard and dose-response factors together. The TREVI.Risk
module will be designed to provide analyses and output consistent with the revised guidelines for
carcinogen risk assessment.
Analysis ofNoncancer Risks
Unlike cancer risk characterization, noncancer risks for hazardous air pollutants currently
are not expressed as a probability of an individual suffering an adverse effect (e.g., reproductive,
neurological, behavioral). Instead, the potential for noncancer effects often is evaluated by
comparing an exposure estimate over a specified period of time (e.g., lifetime) with a health
reference value, such as a reference concentration (RfC). "Risk" for noncancer effects is
quantified by comparing the exposure to the reference level (or benchmark) as a ratio. The
resultant Hazard Quotient (HQ) is expressed as:
HQ = Exposure/Benchmark.
Exposures or doses below the benchmark (HQ<1) are not likely to be associated with adverse
health effects. With exposures increasingly greater than the reference level (i.e., HQs
increasingly greater than 1), the potential for adverse effects increases. The HQ, however, should
not be interpreted as a probability. Comparisons of HQs across substances may not be valid, and
the level of concern does not increase linearly as exposures approach or cross the reference level.
This is because reference levels are derived using different methods and because the slope of the
dose-response curve above the benchmark can vary depending on the substance.
As with the evaluation of cancer risks described above, analysis of mixtures in
TREVI.Risk will be consistent with Agency guidelines (U.S. EPA 1986a, U.S. EPA 1999c). In
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screening-level assessments for such cases, a Hazard Index (HI) approach is sometimes used.
This approach is based on the assumption that even when individual pollutant levels are lower
than the corresponding reference levels, some pollutants may work together such that their
potential for harm is additive and the combined exposure to the group of chemicals poses harm.
The assumption of dose additivity is most appropriate to compounds that induce the same effect
by similar modes of action (U.S. EPA 1986a). The HI (for a mixture of i compounds) is
calculated as:
HI = HQ1+HQ2 + ...+ HQi.
As with risk measures for individual pollutants, the HI should not be interpreted as a
probability of effect, nor as strict delineation of "safe" and "unsafe" levels (U.S. EPA 1999f, U.S.
EPA 1986a). Rather, the HI is a rough measure of potential for risk and needs to be interpreted
carefully. Although the HI approach may be appropriate for a screening-level study (U.S. EPA
1999f), it is important to note that application of the HI equation to compounds that may produce
different effects or that act by different mechanisms could overestimate or underestimate the
potential for effects. Calculating a separate HI for each noncancer endpoint of concern when
mechanisms of action are known to be the same is scientifically more appropriate (U.S. EPA
1999f, U.S. EPA 1986a).
It should be noted that, in some instances, the noncancer toxicity of a particular pollutant
is well characterized, either because the biokinetics and toxicokinetics are well known or because
substantial information on dose- or exposure-response relationships are well known. In these
circumstances, probabilistic risk estimates similar to those described for cancer risks above may
be possible. For example, risk assessments for criteria air pollutants, and potentially future risk
assessments for hazardous air pollutants, utilize a variety of dose- or exposure-response tools in
place of the RfC or RfD values. For example, risk assessments for carbon monoxide (CO)
include a step in which a population distribution of response (i.e.., carboxyhemoglobin production
in the blood) is modeled from the population distribution of CO exposures. In ozone risk
assessments, population distributions of exposure are modeled against an exposure-response
relationship (derived from either controlled human exposures or epidemiological analyses) to
predict the distribution of responses in the exposed population or subpopulation. In the case of
lead risk assessments, exposure estimates are entered into the IUBK (Intake, Uptake, Biokinetic)
model to predict blood levels of lead, which can be compared to levels of concern in the risk
characterization step.
9.4.2.2 Environmental Risk
Some components of environmental risk assessment are integral to the assessment of
human health risks. For example, the concentrations of pollutants in the environment and their
fate and transport can represent a significant part of human exposure assessment. In addition,
laboratory animal toxicity data are often used to extrapolate effects of chemical exposures on
humans. However, because ecosystems consist of living and non-living entities linked together
in numerous interdependent relationships, the scope of an environmental risk assessment can
range from very simple to very broad and complex and must be defined at the outset. As an
assessment moves from the level of the individual organism to species or populations of species,
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communities of several species, and to whole ecosystems, the level of complexity increases. To
an even greater degree than for human health, environmental risk assessments rely on qualitative
information or expert judgments.
Individual and Population Levels
When the scope of an environmental risk assessment is set at the level of an individual
organism within a species or an entire population or subpopulation of that species (e.g..,
threatened or endangered species, sentinel species), the assessment may use types of information
and tools analogous to those used for human health risk assessments. In some cases, animal
toxicity data developed for human health risk assessments may be directly applicable to the
animal species of concern (e.g., when species-specific toxicity values, such as EC50, EC10, LC50,
NOAEC, LOAEC, MATC, already exist).
The TRTM.Risk module will have the ability to compare these ecological toxicity values
or endpoints with the outputs of TRTM.FaTE (or another source of data) - including (1)
concentration of pollutant in relevant media, such as air, soil, water, sediments, (2) tissue
concentrations or body burdens in organisms based on ingestion, dermal contact or absorption, or
inhalation, and (3) the dose or amount entering organism per unit time. This information can
then be used to derive hazard quotients or display the distributions of exposures relative to
toxicity values or endpoints.
Because of the paucity of ecological toxicity data for most species, however,
extrapolation from one species to the other and from laboratory to field conditions is required,
introducing significant uncertainties into the calculation of risk. With respect to animals, a
primary effect of concern is mortality. However, because most ecological species live in a much
more competitive environment than humans, noncancer effects (e.g., reproductive, neurological,
behavioral, growth) can also play a large role in individual and species survival (e.g., reduced
ability to avoid predators, defend territory, attract a mate), though they are much more difficult to
measure.
Because populations are made up of individual organisms, if enough individuals of a
species are adversely affected by exposure to a chemical, the population also will be adversely
affected. In order to evaluate population effects from data on individuals, it is necessary to know
what kind of life history strategy is employed by that species. In addition to direct effects of
exposure, an organism may be indirectly affected by the presence of a toxic chemical in the
environment (e.g., through effects on a prey species or on some other aspect of the environment
that reduces habitat quality). The EPA's water quality criteria for the protection of aquatic life
are an example of an indicator as to the suitability of the aquatic habitat for certain species as
well as providing information to assist in the evaluation of the potential for ecosystem impacts.
As with humans, other species are often exposed to multiple chemicals simultaneously or
in close temporal proximity so that there may be interactions occurring between them (e.g.,
synergistic effects, antagonistic effects). Although little is known about these interactions in the
field, where information does exist for chemical mixtures, it can be used in the same way as that
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for a single compound. Where information does not exist about chemical interactions, it may be
necessary to make assumptions in order to assess the risk posed by mixtures.
Communities and Whole Environments
Although TRIM.Risk will have the ability to provide distributions of hazard quotients
around the modeled site for species of concern, it is expected that substantial additional
information will be needed in order to sufficiently characterize risks occurring from HAP
exposure at the community and ecosystem levels. Such a refined analysis may require
information such as detailed descriptions of the particular ecosystem in which the exposures are
occurring; the temporal and spatial scales of the exposures; the significance of the effect of the
exposure in the larger landscape; and the ecosystem services and functions affected. Some of
this information may be available from TRIM.FaTE or by accessing GIS databases. Thus, the
complete ecological risk characterization would combine the outputs of TRIM.Risk with other
relevant information in a weight-of-evidence approach.
9.4.3 PRESENTATION OF RESULTS
As stated above, there are two elements required for a full characterization of risk. First,
the characterization must address qualitative and quantitative features of the assessment, namely
clearly identify assumptions (covered under documentation of assumptions and inputs above) as
well as quantitative estimates of risk. Second, the characterization must identify any important
uncertainties in the assessment as part of a discussion on confidence in the assessment.
TRIM.Risk, in presenting results, will address these two points.
9.4.3.1 Risk Descriptors for Human Health
The Agency's Guidance for Risk Characterization (U.S. EPA 1995 a) recommends that
EPA risk assessments address or provide descriptors of (1) individual risk, to include the central
tendency and high-end portions of the risk distribution, (2) population risk, and (3) important
subgroups of the populations such as highly exposed or highly susceptible groups or individuals,
if known. Assessors may also use additional descriptors of risk as needed when these add to the
clarity of the presentation. With the exception of assessments where particular descriptors
clearly do not apply, some form of these three types of descriptors should generally be developed
and presented for EPA risk assessments.
• Individual Risk. Individual risk descriptors are intended to estimate the risk borne by
individuals within a specified population or subpopulation. These descriptors are used to
answer questions concerning the affected population, the risk levels of various groups
within the population, and the average or maximum risk for individuals within the
populations of interest.
• Population Risk. Population risk descriptors are intended to estimate the extent of harm
for the population as a whole. This typically represents the sum of individual risks within
the exposed population. Two important population risk descriptors should be estimated
and presented (Habicht 1992): (1) the probabilistic number of health effect cases
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estimated in the population of interest over a specified time period; and (2) the percentage
of the population, or the number of persons, above a specified level of risk or range of
health benchmark levels.
• Highly Exposed or Highly Susceptible Subpopulations. Risk descriptors also may be
developed for specific segments of the exposed population. These include highly
exposed and highly susceptible groups (U.S. EPA 1995a). Use of a risk descriptor for
highly exposed subgroups is useful when there is expected to be a subgroup experiencing
significantly greater exposures than those of a larger population (e.g., high fish
consumers, children playing outdoors all day). Use of a risk descriptor for highly
susceptible subgroups is useful when the susceptibility to the health effect being assessed
is expected to be significantly greater for a specific population subgroup than it is for the
larger population. For example, upon exposure to a chemical, pregnant women, elderly
people, children, and people with certain illnesses or nutritional status may each be more
sensitive than the population as a whole.
Consistent with Agency guidance, TREVI.Risk will provide central tendency and high-end
estimates of risk. Use of several descriptors, rather than a single descriptor, will result in a more
complete picture of risk that corresponds to the range of different exposure conditions
encountered by various populations exposed to most environmental chemicals. Central tendency
estimates of risk are intended to give a characterization of risk for the typical situation in which
an individual is likely to be exposed. This may be either the arithmetic mean risk (i.e., average
estimate) or the median risk (i.e., median estimate) and should be clearly labeled (Habicht 1992).
High-end estimates of risk are intended to estimate the risk that is expected to occur in a small
but definable segment of the population. The intent is to "convey an estimate of risk in the upper
range of the distribution, but to avoid estimates which are beyond the true distribution.
Conceptually, high-end risk means risk above about the 90th percentile of the population
distribution, but not higher than the individual in the population who has the highest risk"
(Habicht 1992).
9.4.3.2 Presentation of Ecological Risk Assessment Results
In the problem formulation stage of ecological risk assessment, the specific analyses that
will be performed for the assessment are identified. Depending on how these analyses are
framed, the assessment could focus on either population risk or ecosystem risk. The TRTM.Risk
module will be designed with the flexibility for the user to specify the focus of the assessment
and the relevant risk analyses. The results will be presented in a form relevant to the specific
focus (e.g., a presentation of population risk or ecosystem risk information).
To present outputs for ecological risk, in some cases (e.g., with endangered or indicator
species) HQs may be useful by themselves, where the distribution of HQs may be graphically
displayed on a map of the study area. In most cases, however, a weight-of-evidence approach
will be needed. In these cases, a suite of GIS maps showing different layers of information could
be used by experts to evaluate the meaning and context of the HQ. These GIS maps might
include media concentrations for both single and multiple HAPs, land use, terrain/topography,
soil types, hydrology, distributions of flora/fauna, distributions of endangered species, and
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temporal variations (e.g., between years, seasons). In the case of contamination by or exposure
to multiple HAPs, GIS overlays might help with the identification of ecological "hotspots" that
might not be identified by evaluating the pollutants separately. In addition to GIS maps,
graphical displays of distributions of effects within a population would be useful. In cases where
TRUVI.Risk is used for a simple screening exercise, site-specific information would not be needed
and TREVI.Risk can provide more simple outputs.
9.4.3.3 Uncertainty
Uncertainty can be introduced into a risk assessment at every step in the process. Even
using the most accurate data with the most sophisticated models, uncertainty is inherent in the
process because risk assessment is a complex process. The degree to which all types of
uncertainty need to be quantified and the amount of uncertainty that is acceptable vary,
depending on the purpose and intended use of the risk assessment. For a screening-level
analysis, a high degree of uncertainty often is acceptable, provided that conservative assumptions
are used to bias potential error toward protecting human health or the environment. Similarly,
the concentrations at a specific location in a region-wide or nationwide assessment will be more
uncertain than the concentrations at a specific location in a site-specific assessment because there
is more variability in the input parameters for larger scale assessments.
9.4.3.4 Outputs
Because there is more than one audience for each risk assessment, there will probably be
more than one risk characterization for a risk assessment. Different types of risk assessment also
vary in length and degree of detail, and each risk characterization is as simple or complex as the
assessment from which it is derived. While the full risk characterization is written for the type of
assessment conducted, as it is presented to various audiences, the characterization product should
be tailored to that audience. For fellow risk assessors and other scientists, the full
characterization is most appropriate. If the risk characterization is presented to non-technical
colleagues and to those whose time is limited (e.g., managers), it should shortened and focused,
but the characterization should always include the fundamental, irreducible set of key points that
must be communicated to characterize adequately the essence of any risk assessment.
OAQPS recognizes that individuals process information differently and it is, therefore,
appropriate to provide more than one format for presenting the same information. Therefore,
each TRIM module will be designed so that the output can be presented in various ways in an
automated manner (e.g., Chart Wizard in Excel), allowing the user to select a preferred format.
The TRDVI.Risk module will provide quantitative estimates of risk for both human and
ecological risks. At a minimum, the following risk measures will be presented as outputs of
TREVI.Risk.
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EXAMPLES OF RISK MEASURES TO BE INCLUDED IN TRIM.Risk
Human Risks
Cancer
Noncancer
Ecological Risks
Distributions of excess cancer, MOE within exposed population
(Note: deterministic values may be used for screens)
Estimate of predicted cancer incidence
Distribution of HQ or HI within exposed population
(Note: deterministic values may be used for screens)
Distribution of exposure (dose) relative to exposure (dose) levels of concern
Distribution of probability of effect within exposed population (estimated
incidence)
Distribution of concentration/criteria (similar to HQ or HI)
Distribution of probability of effect within population
9.5 CURRENT STATUS AND FUTURE PLANS FOR TRIM.Risk
At present, only the conceptual design of TRIM.Risk has been developed. Development
of a TRIM.Risk prototype will begin after SAB comments are received on the conceptual design.
Module development will include identification of data needs and formatting of data outputs.
Programming for a TRIM.Risk prototype is expected to be completed in 2000.
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10. DEVELOPMENT OF TRIM COMPUTER FRAMEWORK
This chapter describes the computer framework that will be used for each of the TRIM
modules and that is currently being implemented for TRTM.FaTE. Therefore, much of this
discussion is specific to TRTM.FaTE, but it can be generalized to all of the TRIM modules.
Additional information about these aspects of TRIM.FaTE Version 1.0 can be found in Appendix
F of Volume I of the TRIM.FaTE TSD and in Fine et al. (1998a, 1998b).
The development of TRIM.FaTE Version 1.0 began in 1998 and was completed in
September 1999. Version 1.0 differs from the prototype in several ways. Specifically, Version
1.0 (1) is compatible with operating systems beyond Microsoft Windows, such as UNIX, (2)
provides improved management of multiple modeling scenarios, and (3) is easier to use and more
reliable. Similar to Prototype V of TRIM.FaTE, TRIM.FaTE Version 1.0 provides users with the
following options:
Define the parameters of each specific assessment, including time period, geographic
region, pollutants, environmental media, and populations of interest;
• Choose appropriate pollutant fate and transport algorithms for use in assessments;
• Select modeling parameters, including emissions sources, characteristics of the
environment (e.g., air temperature, soil permeability), and simulation time step (e.g..,
hourly, daily);
• Identify and access input data sets, and identify and create output data sets;
• Execute the assessment; and
• Export results.
10.1 ARCHITECTURE
As shown in Figure 10-1, the TRIM computer system architecture is complex but
flexible, allowing it to be applied in developing each of the different TRIM modules. The
architecture components used to describe TRIM are classified as those that primarily provide (1)
functionality (rectangles), and (2) data (ovals). However, each of the components except for
external data sources provide both functionality and data. The architectural components that
have been implemented to some degree in Version 1.0 are depicted with shadows. This figure is
designed to represent the relationships within the TRIM computer framework, rather than the
data flow within the system. Therefore, the word along an arrow forms a sentence where the
verb on the arrow connects the two architecture components at the end of an arrow. For
example, in the upper left hand corner of the figure, the TRIM Core "invokes" Analysis and
Visualization Tools. Each of the TRIM components shown in Figure 10-1 are described below.
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Figure 10-1
TRIM Computer System Architecture
. Reference
Contain
TRIM Core
Map display
User interface coordination
Property editor
Data Input/Output
Plug-in management
Sensitivity & uncertainty calculations
Utility functions
Projects
OutdoorEnvironment
OutdoorEnvironment editor
Affected populations
Run characteristics
Legend
Primarily
Functionality
Shadow indicates that
some functionality is
present in Version 1.0.
Note: Each arrow
summarizes how TRIM
components interact.
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10.1.1 TRIM CORE
The TRIM Core component primarily provides services required by multiple architectural
components or integrates those components. The following items are included in the Core.
A mapping tool shows volume elements and associated information, such as predicted
chemical concentrations, and is based on an off-the-shelf software component that
provides some GIS-like capabilities. The mapping tool allows users to view geospatial
data from external sources, such as soil type layers generated by a GIS and stored in a
SHAPE file, with an overlay of TRIM information.
A simple graphical user interface allows the user to invoke TRIM modules, such as
TRIM.FaTE, and that maintains lists of open windows.
• A property editor enables users to edit and view property values, where a property value
describes an attribute (e.g., molecular weight) of an entity that is simulated by the
module, such as a chemical or compartment or volume element. Examples of attributes
for which property values are used include air temperature, scavenging coefficients, and
chemical reaction rates.
• A management system allows user to plug in data importers and exporters.
• An analysis feature calculates sensitivity, uncertainty, and variability of outputs using
TRIM modules (Note: this may not be supported in Version 1.0).
Utility functions, such as routines that assist with data storage and retrieval, are used by
TRIM modules.
10.1.2 PROJECTS
Projects in TRIM are used to store all information pertinent to an individual assessment.
A project contains "scenarios," where each scenario contains a description of the outdoor
environment being simulated, populations being studied, and model parameters, such as the
simulation time step. Each project also displays the information it contains and allows the user
to change that information. In some cases, the information display and manipulation functions of
a project rely on a TRIM Core functionality, such as the property editor.
10.1.3 TRIM MODULES
Each TRIM module, such as TRIM.FaTE, is a component that allows for simulation or
analysis. Where required, modules also provide specialized graphical user interfaces that support
their functionality. Version 1.0 includes only the TRIM.FaTE module. Future TRIM versions
will include additional TRIM modules.
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10.1.4 LIBRARIES
A substantial amount of relatively static information is required to conduct assessments of
multimedia chemical fate and transport and subsequent exposures and effects on selected
populations. For instance, static information includes the measured properties of chemicals that
change infrequently or the boundaries of a study region that might stay constant for years.
Because of the static nature of this information and because a large amount of static information
may be needed for a single assessment, users can store such information in TRIM libraries.
Users can then easily reuse selected information from a library in future projects. Changes may
be made to the library over time to ensure that the most current science is used in assessments.
However, when a user creates a project that accesses information from a library, a copy of the
information is made to protect the project from future changes to the library.
The TRTM.FaTE module uses a number of chemical fate and transport algorithms that
compute chemical transfer coefficients between and chemical transformation coefficients within
compartments. As new chemicals, ecosystems, and relationships are studied, new algorithms
will be required. In anticipation of this need, TRTM.FaTE has been designed to allow users to
add algorithms. The algorithms are stored in libraries and can be applied to various projects, as
designated by the user. Specifically, a user can manually assign algorithms stored in libraries to
links or can request that TRTM.FaTE assign applicable algorithms based on the compartments
that are connected by a link. For instance, some algorithms might only be applicable for transfer
from surface water to fish. Even when TRTM.FaTE assigns algorithms, the user can review the
assignments and make changes before the simulation starts. Before or after a simulation, the user
can export the simulation scenario and its results (if available) to a set of HTML files. These
HTML files show which algorithms were used for each link and the formulation of each
algorithm.
10.1.5 EXTERNAL DATA SOURCES, IMPORTERS, AND EXPORTERS
Given the diversity of potential applications of TRIM, data required to address those
applications, and formats used for storing that data, it is difficult to construct a computer
framework that provides all potentially required capabilities. The TRIM architecture addresses
this issue in several ways.
The architecture allows the user to add data importers and exporters in a relatively easy
manner, as needed. Data importers read non-TRIM data sets and create and/or set appropriate
TRIM objects and properties. For instance, Version 1.0 contains a data importer that can read a
text file describing volume elements and can create the corresponding elements in a TRIM
project. Another data importer can read a textual description of algorithms, compartments,
chemicals, and sources and can create the corresponding objects in a TRIM library. Data
exporters can write TRIM configurations and results in a format that is suitable for use by
another computer program or for interactive review. Version 1.0 can export the configuration of
a simulation scenario and its results to HTML files and simulation results to a text file that can be
imported by Microsoft® Excel. Future data importers and exporters could provide many other
capabilities. Examples include reading data produced by a GIS (e.g., SHAPE files) and
interpolating values to TRIM volume elements, writing results in a format that could be further
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processed by a GIS, importing information directly from a web site or database, and transferring
results to a statistical package that is executing concurrently with TRIM. To provide additional
flexibility, future versions of TRIM may allow knowledgeable users to apply data importers and
exporters that users develop without modifying TRIM.
The TRDVI.FaTE module, in specific, allows users to provide environmental data in
binary files that can be read as needed by a TRIM.FaTE simulation. This streamlines the use of
large data sets, such as hourly temperatures or concentrations over a 30-year period. Binary files
can also be used for storing TRIM.FaTE results. The TRIM Core supports reading data from and
writing data to file formats that are based on the Environmental Decision Support
System/Models-3 Input/Output Applications Programming Interface (I/O API) (Coats 1998).
The I/O API format can be easily read and written from several programming languages, is
platform-independent, is suitable for large data sets, is self-describing (i.e., contains information
about variables and time periods contained in the file), and is computationally efficient.
10.1.6 ANALYSIS AND VISUALIZATION TOOLS
Version 1.0 does not include any analysis or visualization tools. Instead, simulation
results can be easily exported to Microsoft® Excel or other analysis packages. In the future,
TRIM will include some analysis and visualization capabilities and may allow users to develop
and plug in additional capabilities.
10.2 IMPLEMENTATION APPROACHES AND TECHNOLOGIES
The TRIM is being developed using an object-oriented approach. There has been much
discussion in the software engineering literature, such as Booch (1993), on the benefits of this
approach, including increased software extensibility, reusability, and maintainability. The
essence of object-oriented software development is that concepts, such as a volume element, are
represented as a unit that contains internal data (e.g., the boundaries of a volume element) and
operations on the data (e.g., computation of volume), and that one class of objects (e.g., volume
element with vertical sides) can be a specialization of another class of objects (e.g., volume
element). Being able to specialize classes of objects allows general functionality to be shared by
several specialized classes. The TRIM's representation of the outdoor environment (with volume
elements that contain compartments) and the development of associated graphical user interfaces
are well suited for an object-oriented treatment.
The TRIM is being developed in an iterative manner. The major components and
responsibilities of a class of objects are understood before implementation, but some details may
need to be resolved as implementation proceeds. Prior to implementation, graphical user
interface mock-ups and significant new capabilities are shown to potential users. During
implementation, the design is modified as needed. This user-oriented development approach
helps highlight potential problems before undesirable approaches become embedded in the
system. Furthermore, the object-oriented, open-ended structure of TRIM is intended to make
future changes and additions a relatively simple process.
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For Version 1.0 of TRIM, simpler and/or more reliable approaches were used in
preference to faster and/or less resource-intensive approaches. In cases where simple approaches
did not have adequate performance or significantly limited the potential for future changes, more
complex approaches were used. Operations that caused noticeable speed or resource problems
were optimized as time and resources permitted.
The TRIM computer framework and TRDVI.FaTE module have been developed primarily,
but not entirely, in the Java programming language. Some parts of TRIM.FaTE, such as the
differential equation solver, and other TRIM modules, such as TRIM.Expo, ultimately will be
implemented in the FORTRAN programming language. Advantages of using Java include the
following.
• Java code is portable across different hardware and operating systems. This is especially
important for graphical user interfaces, which will comprise a large fraction of the TRIM
code and which can be difficult to develop for multiple platforms.
• Java offers a combination of speed of development, dependable system behavior, and
support for object-oriented designs.
Java is supported by multiple vendors, often leading to competitive pressures to improve
development tools. In addition, it reduces the likelihood that one vendor's product
strategy or financial problems will negatively affect TRIM development.
Java provides built-in support for multithreading (i.e., allowing multiple operations to
proceed simultaneously) and networking (i.e., communicating with software on remote
computers, such as extracting simulation properties from a web-based data repository).
The disadvantages of using Java as the primary programming language for TRIM include
the following.
• Programs written in Java typically execute more slowly than programs written in C++ or
BASIC. However, as the technologies for compiling and executing Java programs
advance, the execution time for Java programs should decrease.
• Fewer plug-in components (e.g., mapping tools) and libraries (e.g., matrix manipulation)
are available for Java than are available for languages such as C++ or BASIC on
Windows. However, the number of plug-in components available for Java is continuing
to grow.
Java development tools are not as mature (e.g., fewer tools, lower performance, greater
probability of system errors) as tools for other languages, but that situation is improving.
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10.3 USING TRIM.FHTE VERSION i.o
Version 1.0 of TRIM.FaTE as completed in September 1999. This section provides a
general discussion of how a user would set up and run a simulation using Version 1.0.
After starting TRIM.FaTE Version 1.0, the user can create a new project or library or
open an existing project or library. A library populated with objects must be created by the user
before any meaningful work can be performed with a project. Note that when TRIM.FaTE is
distributed in the future, some pre-loaded libraries will be included. From the library window
(shown in Figure 10-2), the user can choose to create new or examine existing algorithms,
chemicals, compartments, point sources, or property types. TRIM.FaTE Version 1.0 also allows
the user to import objects from text files rather than creating objects from the Graphical User
Interface (GUI).
Properties are an important concept in TRIM.FaTE Version 1.0 because they store
information about the objects in the system. Examples of properties for a chemical include
melting point, vapor pressure, and molecular weight. Examples of properties for an algorithm
include the receiving compartment type, the sending compartment type, and whether it
transforms a chemical. Each property references a property type that defines the type of data
(e.g., real number, date and time, true or false), a default value, a description, units, and for
numeric data types recommended minimum and maximum values. In TRIM.FaTE Version 1.0, a
GUI component, the Property Editor (shown on the right sides of Figures 10-2 and 10-3), is used
throughout the system to add properties to objects and to view and edit the values of properties.
For some properties in Figure 10-1, the value field contains "." These are special
properties for which the value is a formula. An example of the use of formulas as properties is
specifying how transfer factors are calculated for algorithm objects.
After creating a library that contains the algorithms, chemicals, compartments, and point
sources to be used in the simulations, a project can be created with scenarios that will run the
simulations. New projects are created with one scenario by default, and additional scenarios can
be added as needed. Generally, the scenarios in a project are related in some manner. Libraries
are attached to projects and serve as sources of objects for the scenarios. Typically, after creating
a scenario, the user sets its properties. These include the begin and end times for and the time
step for the simulation. After setting the properties, volume elements can be imported from a text
file into the scenario. In a later version of TRIM.FaTE, a GUI will be available for defining and
viewing volume elements.
The outdoor environment window (shown in Figure 10-3) is organized as a set of tabbed
panes that allow the user to define the sources, chemicals, compartments, links, and algorithms
that comprise the outdoor environment. The general procedure for populating the outdoor
environment is to copy objects from libraries into the scenario's outdoor environment, and then
to customize the objects as needed. Abiotic compartments are automatically added to the
outdoor environment when the volume elements are imported, whereas biotic compartments can
be manually added and deleted from the Compartments tab. A function called "Smart Add" is
available to intelligently add biotic compartments to the abiotic compartments based on their
properties. The volume elements, compartments, and links in the outdoor environment are
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displayed in an outline form that can be expanded and collapsed to display varying levels of
detail. Links can be created manually using the Links tab or automatically using the "Smart
Link" function. With "Smart Link," links are created between adjacent or co-located
compartments if algorithms that connect their compartment types exist in the project's libraries.
Algorithms on links can be viewed and added or removed manually from the Algorithms tab.
After the properties for the scenario are set and the sources, chemicals, compartments,
links, and algorithms are assigned to the outdoor environment, it is possible to run a simulation.
The Verify button on the scenario window can be used before running the simulation to ensure
that all necessary information is available (i.e., all properties needed by the simulation have
values). The Run button on the window is used to start the simulation. After the simulation is
executed, the results can be exported to HTML and to text files that can be imported by
Microsoft® Excel or another spreadsheet program.
Figure 10-2
Library Window of TRIM.FaTE Version 1.0
Library: samplelib.trl
File Edit Help
Contents Compartments T i
Advection Sink ;
•Air -Default 11
Deer \
Fish j
0 ro u n dwate r - D efa u It \
r ropemes ror uompanmem NX - ueraun
New] Del Desc PType
i Properly Name
; u e m ethyl ati o n K ate
D em ethyl ati on Rate
DustDensity
DustLoad
ElHii^^^^^^^^^^^^^^^BI i FractlonMass Sorbed
Root zone- default \
Root zone- Forest ;
Surface soil- Forest \
Vadose zone - default \
Select... Properties
Open... Delete
New Duplicate...
iFractionMass Sorbed
|FraetionMass_Sorbed
FractionMass_Sorbed
i 4
"A. -w
Form Show
i Chemical
elemental ...
MethylMerc...
l8enzo(A)Py,..|
Divalent M...
'Elemental ...
MethylMerc...
. • ..
Value
u.u
0.0
1400.0
6.15E-8
*See befow*
«See below*
«See below*
I Unite
***^
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CHAPTER 10
DEVELOPMENT OF TRIM COMPUTER FRAMEWORK
Figure 10-3
Outdoor Environment Window of TRIM.FaTE Version 1.0
Outdoor Environment: air only
Edit View Help
Sources ] Chemicals Compartments
Links I Algorithms |
Outdoor Environment
(Compartments
NW Lower Air
_j Air-Default in NWL
NW Upper Air
_j Air-Default in NWL
NE Lower Air
j Air- Default in NE L<
~
j Air -Default in NE U |
8W Lower Air j
S | Air- Default in SWL J
-
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REFERENCES
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APPENDIX A
GLOSSARY
Abiotic Compartment Type
Activity Patterns
Biotic Compartment Type
Chemical
Cohort
APPENDIX A
Glossary
A compartment type consisting primarily of a non-living
environmental medium (e.g., air, soil) for which TREVI.FaTE
calculates chemical masses and concentrations; it may also
contain biota, such as the microorganisms responsible for
chemical transformation (see also compartment type).
A series of discrete events of varying time intervals describing
information about an individual's lifestyle and routine. The
information contained in an activity pattern typically includes
the locations that the individual visited (usually described in
terms of microenvironments), the amount of time spent in those
locations, and a description of what the individual was doing in
each location (e.g., sleeping, eating, exercising). All of the
information for an activity pattern is gathered during an
"activity pattern survey," usually through the use of
questionnaires or diaries. Each activity pattern survey is
designed to collect information on activities needed for a
particular study or purpose. Activity patterns are also referred
to as "time/activity patterns."
A compartment type consisting of a population or community
of living organisms (e.g., bald eagle, benthic invertebrate), or in
the case of terrestrial plants, portions of living organisms (e.g.,
stems, leaves), for which TREVI.FaTE calculates chemical
masses and concentrations (see also compartment type).
A unit whose mass is being modeled by TREVI.FaTE. A
chemical can be any element or compound, or even group of
compounds, assuming the necessary parameters (e.g.,
molecular weight, diffusion coefficient in air) are defined.
A group of people within a population who are assumed to
have similar exposures and whose demographic variables are
taken from the same probability distribution during a specified
exposure period.
The use of cohorts is useful when modeling the exposures of a
large population. Since adequate data on the exposures of each
individual in a population does not exist, information about
people who are expected to have similar exposures are
aggregated together in order to make better use of the limited
data that is available.
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APPENDIX A
GLOSSARY
Compartment
Compartment Type
Cohorts can be defined for each application or situation. In the
latest pNEM/CO model, for example, cohort exposure was
taken to be a function of demographic group, location of
residence, location of work place, and type of cooking fuel
(natural gas or other). Specifying the home and work district of
each cohort provided a means of linking cohort exposure to
ambient CO concentrations. Specifying the demographic group
provided a means of linking cohort exposure to activity
patterns which vary with age, work status, and other
demographic variables. Specifying the type of cooking fuel
provided a means of linking cohort exposure to proximity to a
particular emission source. In some analyses, cohorts are
further distinguished according to factors relating to time spent
in particular microenvironments. In the pNEM analyses, the
population-of-interest is divided into a set of cohorts such that
each person is assigned to one and only one cohort.
A homogeneous unit of space characterized by its physical
composition and within which it is assumed, for modeling
purposes, that all chemical mass is in equilibrium.
A specific kind of compartment, such as an air compartment
type or a mule deer compartment type. Compartment types are
distinguished from each other by the way they exchange
chemical mass with other compartment types.
Conceptual Model Evaluations Evaluations focused on the theory and assumptions underlying
the model. These activities seek to determine if the model is
conceptually sound.
Criteria Air Pollutants
Air pollutants for which national ambient air quality standards
(NAAQS) have been established under the Clean Air Act
(CAA); at present, the six criteria air pollutants are particulate
matter, ozone, carbon monoxide, nitrogen oxides, sulfur
dioxide, and lead.
Exposure
Exposure District
The contact between a target organism and a pollutant at the
outer boundary of the organism. Exposure may be quantified
as the amount of pollutant available at the boundary of the
receptor organism per specified time period. As an example,
inhalation exposure over a period of time may be represented
by a time-dependent profile of the exposure concentrations.
A geographic location within a defined physical or political
region where there is potential contact between an organism
and a pollutant, and for which environmental media
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APPENDIX A
GLOSSARY
Exposure Event
Functionality
Hazardous Air Pollutant
Link
Mechanistic and Data
Quality Evaluations
Microenvironment
Model Evaluation
Parcel
Performance Evaluations
Scenario
APPENDIX A
GLOSSARY
concentrations have been estimated either through modeling or
measurement.
A human activity that results in contact with a contaminated
medium within a specified microenvironment at a given
geographic location.
The capability to perform computational operations.
Any air pollutant listed under Clean Air Act (CAA) section
112(b); currently, there are 188 air pollutants designated as
Hazardous Air Pollutants (HAPs).
A connection that allows the transfer of chemical mass between
any two compartments. Each link is implemented by an
algorithm or algorithms that mathematically represent the mass
transfer.
Evaluations focused on the specific algorithms and
assumptions used in the model. These activities seek to
determine if the individual process models and input data used
in the model are scientifically sound, and if they properly "fit
together."
A defined space in which human contact with an environmental
pollutant takes place and which can be treated as a
well-characterized, relatively homogeneous location with
respect to pollutant concentrations for a specified time period.
The broad range of review, analysis, and testing activities
designed to examine and build consensus about a model's
performance.
A planar (i.e., two-dimensional) geographical area used to
subdivide a modeling region. Parcels, which can be virtually
any size or shape, are the basis for defining volume elements.
There can be air, land, and surface water parcels.
Evaluations focused on the output of the full model. These
activities seek to determine if the output is relevant, reliable,
and useful.
A specified set of conditions (e.g.., spatial, temporal,
environmental, source, chemical) used to define a model setup
for a particular simulation or set of simulations.
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APPENDIX A
GLOSSARY
Sensitivity
Simulation
Source
Structural evaluations
Uncertainty
Variability
Volume Element
The rate of change of the model output with respect to changes
in an input parameter.
A single application of a model to estimate environmental
conditions, based on a given scenario and any initial input
values needed.
An external component that introduces chemical mass directly
into a compartment.
Evaluations focused on how changes in modeling complexity
affect model performance. These activities seek to determine
how the model will respond to being set up differently for
different applications.
The lack of knowledge regarding the actual values of model
input variables (parameter uncertainty) and of physical systems
(model uncertainty).
The diversity or heterogeneity in a population or parameter;
sometimes referred to as natural variability.
A bounded three-dimensional space that defines the location of
one or more compartments. This term is introduced to provide
a consistent method for organizing objects that have a natural
spatial relationship.
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
APPENDIX B
Review of Methods for Conducting Uncertainty Analyses
As part of the TRIM model development process, the Agency has conducted and will
continue to conduct uncertainty and variability analyses. To ensure that the most appropriate
methods were selected for use in this effort, EPA conducted a literature search to identify the full
range of the available methods and developed a set of selection criteria against which to compare
those methods. This appendix describes the selection criteria and the review of available
methods for use in connection with uncertainty and variability analyses for TRIM. The method
that was selected is a two-stage approach consisting of a sensitivity/screening analysis followed
by a detailed analysis of uncertainty and variability, as described in Chapter 3 and Section 4.7 of
this report and in Chapter 6 of the TRIM.FaTE TSD Volume I.
B.I CRITERIA FOR METHOD SELECTION
The primary objectives for the overall approach for TRIM uncertainty and variability
analyses were articulated as detailed criteria that characterize the desirable and undesirable
features of the candidate uncertainty and variability analysis methods. These criteria were used
to distinguish the available methods according to how well they might serve the objectives. Most
of the criteria described below are necessary or highly desirable to support the analysis of
uncertainty and variability for TRIM. The required criteria for a method to be used are listed
first, followed by additional criteria that are desirable but not absolutely necessary for a method
to be used in TRIM uncertainty and variability analyses.
B.1.1 REQUIRED CRITERIA
• Estimate uncertainty and variability separately, and maintain this separation
throughout risk characterization and across module interfaces. Uncertainty and
variability have different meanings, and uncertainty may be reducible, while variability is
not. The method should follow the distributions of both uncertainty and variability
through the model, yielding uncertainty and variability distributions of the model outputs.
The modular design of TRIM poses challenges for the propagation and analysis of
uncertainty and variability, and it is important that the selected method be able to fit
smoothly and accurately within the modular design and be able to transfer information
between connected TRIM modules.
• Evaluate sensitivity of both specific model inputs and model components. In addition
to the changes in the results which occur when the values of input variables are changed,
it is useful to examine the changes in the results which occur when different algorithms
are used in the mechanistic model.
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Evaluate uncertainty and variability importance. One of the key functions of the
analysis methodology is to evaluate the importance, in terms of both uncertainty and
variability, of specific model inputs and model components in relation to other inputs and
components. This type of analysis provides insight into priorities for reducing uncertainty
and allocating resources to the improved representation of uncertain and variable inputs.
The ability to rank input parameters in order of their influence on the uncertainty of the
model results is an important component of this function.
Identify and represent correlations and other interdependencies. Physical processes
or relationships can dictate that certain variable values change in concert with other
variable values. The correlations can be weak or strong and can be negative or positive.
Ignoring these relationships can introduce error into the risk assessment effort. Tracking
these relationships can add substantial complexity and increase the computational
resources necessary for the risk assessment effort. These correlations can exist between
model input parameters or can be introduced within the model. The selected method
should represent the input parameter correlations and follow these through the model with
the structural correlations introduced by the model. It also should be able to identify
correlations between sets of model inputs and outputs and between sets of intermediate
variables.
Treat tails of distributions. Some methods focus more on the central region of
probability distributions and are less accurate in their treatment of extreme values. For
risk assessment applications, accuracy in the tails of the probability distributions is very
important. Therefore, the selected method should adequately treat infrequent but
important events.
Evaluate relevant temporal and spatial scales. The selected method should facilitate
comparison of the results when different temporal or spatial scales are used, as the
appropriate scale to use for modeling can directly impact model uncertainties.
Limit computational requirements. The method should be computationally efficient
and should not require excessive effort to set up or run.
Handle complex mathematical relationships. Some available methods are designed for
and more suited to models with simple mathematical constructs and cannot feasiblely
handle more complex models. Other methods are designed to handle very
mathematically complex models. The selected method for TRIM must be able to handle
complex models.
Be easily automated. Propagation and analysis of uncertainty and variability can require
considerable effort, often in the form of large numbers of repetitions of model runs for
numerical techniques and large numbers of algebraic manipulations for analytical
techniques. The ability to automate the procedures is highly desirable to minimize effort
and can provide the added advantage of reducing human errors.
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
B.1.2 DESIRABLE CRITERIA
• Use input distributions directly without further approximations or need to fit input
distributions to standard analytical forms. When the values for the input variables
arise from certain specific types of physical processes or have certain natural properties,
they may be accurately represented by standard analytical forms (e.g., normal or
lognormal probability density functions). When conditions are less than ideal, fitting the
values to such distributions can introduce unnecessary error into the risk assessment
effort, and the use of nonparametric representations may be preferable. Some of the
methods evaluated assume analytical forms, while other methods handle a wide variety of
representations of probability distributions. Some available methods require
discretization of continuous distributions to be applied. The selected method should be
able to handle the basic types of distributions that will be encountered.
• Increase the level of precision in a simple manner. Most of the uncertainty
methodologies have some means of increasing the precision of uncertainty analysis
results. For some methods, however, the procedures for doing so are substantially more
difficult or require substantially more effort. The method selected for TRIM needs to
provide a balance between precision and necessary effort.
• Support investigation of model behavior and changes to the model. Insight into the
structure of the model can reveal the effects of model changes and why changes affect
uncertainty as they do. Obtaining this insight typically involves analysis of the
intermediate calculations performed within the model and keeping track of the
uncertainties of the intermediate values calculated.
• Reduce opportunities for human error in the uncertainty and variability analysis.
Complex models, large numbers of variables, significant amounts of hand processing for
data entry, debugging, and programming of transfers across modules among other
processes all create significant opportunities for error. A desirable method for uncertainty
analysis will not increase the opportunities for error and, ideally, will expose errors of this
type.
• Maintain ability to track causal links and support auditing. This feature involves
maintaining an audit trail of processing within the model, keeping track of where
significant changes occur through the model data flow. Primarily useful during the
development of an application, this ability supports the interpretation of the meaning of
results as well as the need to check for errors. This desired characteristic can be partially
fulfilled by retaining intermediate results (e.g., input and output distributions) and
tracking linkages within the model.
• Provide capabilities for additional analysis. A single method may not be able to
perform all of the envisioned types of uncertainty analyses, and other methods might be
used to perform specific analyses in conjunction with the primary method selected. It is
desirable for a method to be able to interface with other analysis tools.
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
• Support model evaluation efforts. A major effort during the development of TRIM is
model evaluation (see Chapter 6 of this report), and it would be advantageous for the
method to be capable of supporting this effort.
• Build a reduced form model. While not a method for analysis of uncertainty per se,
some methods used in connection with uncertainty analyses are able to produce a
"reduced form model" (an approximation to the simulation model which runs much faster
while giving results reasonably consistent with the results of the full simulation model).
This can be advantageous when the uncertainty method requires thousands of simulations
by using the reduced form model for some of the simulations.
B.2 DESCRIPTION OF AVAILABLE METHODS
Table B-l presents descriptions of a number of available methods for analysis of model
uncertainty and variability that were identified by EPA in its literature review. For each method,
the strengths, weaknesses, and applicability for TRIM uncertainty analysis are summarized, with
particular focus on TRTM.FaTE. The recommended uses of the methods reviewed, which are
appropriate for a TRIM uncertainty and variability analysis, are summarized in Table B-2 and are
described below.
B.2.1 THE CORE UNCERTAINTY ANALYSIS METHOD
The core uncertainty analysis method selected for use in TRIM must be able to handle
propagation of uncertainty and variability of the model input parameters, taking into account
distributions of parameter uncertainty and variability and parameter dependencies. The method
will be used to provide uncertainties of model outputs in terms of distributions of model outputs,
joint distributions of model inputs and outputs, and summary scalar measures.
For performing a thorough uncertainty and variability analysis, the Monte Carlo method
has a number of advantages over other methods described in Table B-l, and therefore it has been
selected as the core uncertainly analysis method for TRIM. The primary advantages of this
method are the reduction of the number of simulations required, the ability to use different ways
of specifying parameter distributions, the ability to handle very complex models, and the
propagation of variability, uncertainty, and parameter dependencies through the model that are
reflected in the distributions of model outputs. The Monte Carlo method is a widely used
method, with numerous papers and other publications describing the method and how to apply it
(Frey and Rhodes 1996, Morgan and Henri on 1990, Thompson et al. 1992, U.S. EPA 1997, Vose
1996, SRA 1993).
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Table B-1
Available Methods for Analysis of Model Uncertainty and Variability
Method
Monte Carlo
Method of
Moments
(Taylor Series
Expansion)
Technique
Monte Carlo uses multiple
iterations of random samples
from model input distributions.
Four sampling techniques
include:
• Simple Monte Carlo;
• Latin Hypercube Sampling
(LHS);
• Midpoint LHS; and
• Importance sampling.
The method of moments
calculates partial derivatives of
the mathematical expressions
in the model and uses these to
calculate effects of perturbing
the data. The Taylor series
expansion is a case of this
method involving only first order
derivatives. Analytical
expressions for partial
derivatives for each equation
are coded into the model as a
companion set of equations.
Distributions of interest can be
propagated through the model
using derivatives to transform
input distribution parameters to
output distribution parameters.
Assumptions
• There are no specific
assumptions; it works
with any reasonable
data set and does not
require distributional
assumptions other than
reasonably complete
distributions and
correlations.
• Distributions can be
adequately
parameterized by their
means, variances, and
other moments;
distribution type/shape
known a priori.
• All parameters are
independent of each
other.
• Mathematical
expressions in the
model are continuously
differentiable.
Strengths
• Varied parameter distributions
can be specified.
• It can handle correlations,
dependencies, and complex
model algorithms.
• Complexity is linear with the
number of parameters.
• It can find confidence bounds
for estimates of output
distributions.
• Additional iterations increase
precision.
• It provides insight into behavior
of the model.
• It is widely used and accepted
by the scientific community.
• A well-known method, it has
been extensively applied to a
variety of applications.
• It can be effective for models
where analytic expressions for
all derivatives can be derived
and coded.
Weaknesses
• Large numbers of
parameters and
correlations between
parameters can require
many iterations and
large computing time
requirements. (Note: a
reduced form of the
model can be used for
some applications that
significantly reduces the
required computing
time.)
• Techniques are usually
very complex.
• Errors can be easily
introduced when coding
expressions for
calculating derivatives.
• Computation of
derivatives can be
intractable for all but
fairly simple models.
• Methods are not
appropriate for some
normality assumptions
and distributional forms.
Applicability to
TRIM
Highly applicable:
listed strengths are
all desirable for
TRIM.FaTE
uncertainty analysis,
and any of the four
Monte Carlo
techniques described
would be applicable
to TRIM
requirements.
Not easily applicable:
TRIM.FaTE
algorithms are too
complex to apply this
method feasiblely.
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Method
Differential
Sensitivity
Analysis
(DSA)
Classification
and
Regression
Trees (CART)
Technique
DSA calculates partial
derivatives of the mathematical
expressions in the model (like
method of moments, but uses
additional techniques to reduce
intractability of computing
derivatives in complex models).
Two classes of DSA methods
exist: (1) specialized numerical
procedures to efficiently
calculate derivatives, and (2)
symbolic differentiation
methods to obtain derivatives
through the use of computer
programs that also generate
code.
CART uses a nonparametric,
binary tree method to
simultaneously treat ordered
and categorical data in the
same problem. It produces tree
structures rather than predictive
algorithms.
Assumptions
• Functions in model are
continuously
differentiable.
• Output distributions and
the distributions
propagated throughout
the model are normal.
• No specific
assumptions; it works
with any reasonable
data set and does not
require distributional
assumptions other than
reasonably complete
distributions and
correlations
Strengths
• It can be a powerful tool for
local model sensitivity analysis
when coupled with automated
procedures for generating code
required to calculate
derivatives
• It can obtain derivatives even
for complex models
• It is applicable to large, high-
dimensional data sets, non-
homogeneous data, and sets
with missing data.
• It can account for masking of
variables in ranking parameters
according to their influence on
the classification of outcomes.
• It is robust with respect toward
outliers.
• It uses conditional information
and dependencies in the data
• Graphical tree structure
produced can be relatively
easy to understand and
interpret.
Weaknesses
• Procedures for
calculating derivatives
can be difficult and time-
consuming to implement
• Smoothness
assumptions may be
violated by step-
functions or discrete
distributions used in risk
assessment
• May not be able to
integrate correlations
unless DSA method is
nested within a method
that can
• Complexity of results for
complex models can
hinder interpretation of
analysis.
Applicability to
TRIM
Not easily applicable
for all cases;
additional code would
need to be developed
to handle any
discrete distributions
due to the
smoothness
assumptions of DSA.
Possibly applicable
through three uses:
• Reduced-form
model that allows
for faster
simulations;
• A tool to examine
model sensitivity
to individual
parameters; or
• A method that
provides initial
importance-
ranking of
parameters, taking
into account
dependencies.
NOVEMBER 1999
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Method
Bootstrap
Method
Response
Surface
Estimation
Technique
Bootstrap uses Monte Carlo
algorithms to sample the data
for multiple replications in order
to obtain a confidence interval
or other measure of accuracy
for an estimated statistic. It is
used primarily to assess the
accuracy of estimated
parameters and model
predictions.
Response Surface Estimation
uses sets of inputs and outputs
from numerous model runs in a
general regression model to
produce a sample response
surface that functions as an
estimate of the actual model
response surface. The fitted
response surface can be used
to predict outcomes of the
model given values of the input
parameters. Usually, factorial-
type designs are used to
specify the model runs used to
fit the surface. Model inputs
can be sampled carefully to
give higher importance to more
influential parameters and to
ensure full representation of
distribution tails.
Assumptions
• Sufficient data are
available to generate
distributions (specific
amount of data
required depends on
the particular
application).
• Simulations performed
to develop the surface
will provide adequate
coverage of the
parameter-outcome
space at resolutions
that will capture
variability and
dependencies.
• Continuity and
boundedness of
response surface
function exists.
• Response surface
should not be extended
beyond the region
represented by the
simulations.
Strengths
• It can be used to assess
accuracy for complex
procedures relatively easily.
• It can be applied parametrically
or nonparametrically
• It can use importance sampling
methods to improve efficiency
of estimating tail probabilities.
• With careful setup, it can be
used with data having
dependencies.
• It is widely used, well
understood, and simple to
interpret.
• It is useful as an approximation
of a reduced form of a complex
model.
• It provides an overview of how
a model responds to variations
in the input values.
Weaknesses
• It can give incorrect
results if the
nonparametric empirical
distribution function
deviates from the true
distribution function in
the tails
• It can give downwardly
biased estimates when
used for assessment of
model prediction
accuracy. (Note:
modified bootstrap
methods can help to
alleviate this problem.)
• It requires a very large
number of model runs to
estimate the fitted
response surface,
particularly if there are
many correlated
parameters.
• It can be tedious to
estimate the accuracy of
the sample response
surface with respect to
the full model response
surface; this usually
requires a second set of
simulations .
Applicability to
TRIM
Possibly applicable:
it can be used for
estimating statistics
of the input
parameters (e.g.,
mean, variance,
distributions, and
confidence intervals).
It can serve as a
reduced form
replacement for
TRIM.FaTE for fast
simulations. Also, it
could be used for
sensitivity analyses in
the initial ranking of
model inputs
according to
contributions to
model variability.
NOVEMBER 1999
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Method
Combinatorial
Scenarios /
Discrete
Probability
Trees
Generalized
Linear Models
(GLM)
Neural
Networks
(artificial)
Technique
It produces combinatorial
scenarios by selecting a small
set of values for each input
parameter and then running the
model for all physically possible
combinations of these
parameters to form a grid on
the response surface of the
model. A discrete probability
tree then analyzes results of
these scenarios by using
information about each
parameter's probability.
This class of classical linear
models includes the use of
techniques like analysis of
variance (ANOVA), linear
regression, logit/probit, log-
linear, and multinomial
response models. These
models can calculate an
uncertainty partitioning based
on deviance or the generalized
Pearson chi-squared statistic.
Neural Networks consist of a
collection of techniques for
estimating functions. Networks
are composed of a large
number of simple processing
elements and connections
between the elements.
Elements operate only on local
information and store
information via a training
process where the network
"learns" about the data using
observed data. The trained
network can then make
predictions of model outputs.
Assumptions
• Chosen scenarios are
representative of a
range of scenarios.
• It uses second-order
moment assumptions,
primarily on how
moments vary with
respect to each other.
• For neural networks
without smoothing, no
assumptions made
regarding parameter
distributions or
complexity of the
model. Networks with
smoothing limit the
complexity of the fitted
model.
Strengths
• For models with few
parameters, it provides insight
into the structure of the
influence of parameter
uncertainty and variability on
the model outcomes.
• Tree diagrams present results
relatively clearly for models
with few parameters.
• Restrictive assumptions on the
form of distributions are not
needed.
• It handles both discrete and
continuous covariates.
• It is relatively easy to apply.
• It provides models that are
flexible and non-linear.
• With sufficient training data, it
can approximate any
reasonable function of any
degree of complexity.
• Complexity of the network can
be controlled.
• As the number of model input
parameters increases,
computational complexity of
the network does not increase
exponentially.
Weaknesses
• Number of combinatorial
scenarios increases
exponentially with the
number of parameters,
leading to extremely long
analysis times for
models with many
variables
• As a parametric method,
it does not have the
flexibility of
nonparametric methods
like classification and
regression tree methods.
• It has a tendency to
overfit by using too many
parameters.
• There are high time
requirements for the
analyst to properly apply
the appropriate neural
network techniques.
• For many applications, it
offers no advantage over
more simple standard
statistical methods.
• Poor model performance
results if a small amount
of data is available for
training the network.
Applicability to
TRIM
Not applicable: the
large number of
uncertain parameters
and the complexity
inherent to
TRIM.FaTE renders
the use of these
methods
computationally
infeasible.
May be applicable as
a first-order type
analysis of
importance of
parameters, taking
into account
interactions between
pairs of parameters.
Applicability
uncertain: probably
not enough data to
make use of neural
networks directly for
TRIM.FaTE
uncertainty
procedures. Method
may be applicable for
developing a reduced
model that would
execute faster than
TRIM.FaTE;
however, other
approaches may be
more favorable for
the analysis due to
the weaknesses.
NOVEMBER 1999
TRIM STATUS REPORT
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
Table B-2
Recommended Uses of Applicable Methods
Method
Direct Sensitivity
Calculations
Monte Carlo
Classification and
Regression Trees
Response Surfaces
Combinatorial Methods
Generalized Linear
Models
Uncertainty
Analysis
Methods
•
Sensitivity
Analysis
Methods
•
•
Supplementary
Analysis
Methods
•
•
•
•
Reduced Form
Models
•
Compared to the Monte Carlo approach, the other methods reviewed in Table B-l would
not function as well for the core uncertainty analysis method for TRDVLFaTE, for different
reasons. Taylor series expansion and methods of moments only treat local sensitivity analyses
and are not feasible for this application due to the complexity of the TRTM.FaTE algorithms.
The number of uncertain parameters and the complexity of TRIM.FaTE also make the use of
combinatorial methods (e.g., discrete probability trees) computationally infeasible for a full
analysis. Neural network approaches require more simulations than Monte Carlo and operate on
an approximation to the model. Treatment of parameter and model dependencies in a neural
network approach is not straightforward and would entail development of additional techniques,
as would the use of differential sensitivity analysis. The remaining methods reviewed (i.e.,
response surface estimation, bootstrap, generalized linear models, and classification and
regression trees (CART)) do not adequately address the primary requirements for uncertainty
analysis (e.g., propagation of uncertainty).
B.2.2 SENSITIVITY ANALYSIS METHODS
Sensitivity analysis and screening analysis are fast techniques for measuring changes in
model results relative to changes in input parameters. These analyses provide a first-order
determination of the influential parameters that will need to be included in the detailed
uncertainty analysis. Sensitivity analyses often are conducted not only for the full model, but
also for each modular component within the model. They also can be employed to uncover
anomalies and verify that the model is performing as expected.
In addition to directly calculating parameter sensitivities (e.g., elasticity and normalized
sensitivity scores), response surface estimation and generalized linear models (GLM) can be used
to characterize model sensitivity. These approaches require that a set of model simulations be
performed that vary the parameter values according to some experimental design. Response
surfaces involving many parameters are difficult to display and interpret and require more
NOVEMBER 1999
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
simulations than direct calculation and therefore are not appropriate for TRIM sensitivity
analysis. Measures of parameter importance are the primary result of sensitivity analysis and can
be presented visually using graphs.
B.2.3 SUPPLEMENTARY ANALYSIS METHODS
Much of the uncertainty and variability analysis will involve direct computation of
measures of uncertainty and variability and summaries of relationships between different input
parameter variability/uncertainty distributions as well as dependencies of the input distributions
with model output distributions. Some of the methods described in Table B-l can be used to
perform supplementary analyses of results of the Monte Carlo simulations. The use of these
methods can provide a better understanding of the uncertainty and variability process and can
provide additional measures. These methods include response surface estimation, combinatorial
scenarios, discrete probability trees, GLM, and CART.
B.2.4 REDUCED FORM MODELS
Some of the available methods can be used to construct reduced form models (i.e..,
simplified "models of the model") that, in the context of an uncertainty analysis, could be used
for uncertainty propagation simulations. By substantially reducing the computational burden of
model runs, the use of a reduced form model allows for a larger number of parameters to be
treated and for more detailed results to be generated. This analysis would only need to be
performed if the computational requirements for the detailed analysis using the full model are
excessive. Reduced form models can be built using a variety of non-linear regression methods.
CART could be used for this because it typically produces good models of non-linear systems, it
is relatively easy to implement, it provides insight into the model itself, and the way that CART
functions is not difficult to conceptualize and explain.
NOVEMBER 1999 B-10 TRIM STATUS REPORT
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APPENDIX B
REVIEW OF METHODS FOR CONDUCTING UNCERTAINTY ANALYSES
References for Appendix B
Frey H.C. and D.S. Rhodes. 1996. Characterizing, simulating, and analyzing variability and
uncertainty: An illustration of methods used and air toxics emissions sample. Human and
Ecological Risk Assessment. 2(4):762.
Morgan and Henri on. 1990. Uncertainty: A guide to dealing with uncertainty on quantitative
risk and policy analysis. New York, NY: Cambridge University Press.
SRA. 1993. Society for Risk Analysis. Workshop on the Application of Monte Carlo Modeling
to Exposure and Risk Assessment. The Annual Meeting of the Society for Risk Analysis,
Savannah, Georgia.
Thompson, K.M., D.E. Burmaster, and E.A.C. Crouch. 1992. Monte Carlo techniques for
quantitative uncertainty analysis in public health assessments. Risk Analysis. 12(1).
U.S. EPA. 1997. Guiding principles for Monte Carlo analysis. EPA/603/R-97/001. Office of
Research and Development.
Vose, D. 1996. Quantitative risk analysis: A guide to Monte Carlo simulation. West Sussex,
England: John Wiley & Sons Ltd.
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
APPENDIX C
Input Values Being Developed for TRIM.FaTE Mercury Case Study
Input Parameter
Input Units
Value3
Reference
SOURCE DATA
(for each source)
Location of source
Height of emission source
Emission rate
(for each chemical)
UTM coordinates
(x,y)
m
g/s
[specific coordinates]
specified by height of source
compartment
1.75E-03
supplied by state agency
supplied by state agency
supplied by state agency
BACKGROUND DATA
(for each modeled chemical)
Background concentration in
each compartment
Soil: ng/m3
Water: ng/1
Air: ng/m3
Soil: 100 [98%Hg(2), 2%MHg]
Water: 1000 [90%Hg(2), 10%MHg]
Air: 1 [100%Hg(0)l
based on ranges reported in U.S. EPA 1997
METEOROLOGICAL DATA
Horizontal wind speed
Horizontal wind direction
Vertical wind speed
Air temperature
Precipitation
Mixing height
Relative humidity
m/s
degrees
m/s
°K
m/day
m
unitless
varies over time
varies over time
varies over time
varies over time
varies over time
varies over time
varies over time
NCDC 1997
NCDC 1997
NCDC 1997
NCDC 1997
NCDC 1997
NCDC 1997
NCDC 1997
SPATIAL DATA
Height of each air VEb
Surface soil depth
(for each surface soil VE)
Root zone depth
(for each root zone VE)
Vadose zone depth
(for each vadose zone VE)
m
m
m
m
Equal to mixing height
0.01
0.55
0.76
NCDC 1997
professional judgment
McKoneetal. 1998
McKoneetal. 1998
NOVEMBER 1999
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Ground water layer depth
(for each aquifer layer VE)
Surface water depth
(for each surface water VE)
Sediment layer depth
(for each sediment layer VE)
Input Units
m
m
m
Value3
3
ponds: 3.0
rivers: [being developed]
ponds: 0.05
rivers: [being developed]
Reference
professional judgment
supplied by state agency
based on default from U.S. EPA 1997
ABIOTIC ENVIRONMENTAL SETTING DATA
Air
(assumed same for all air compartments)
Atmospheric dust load
Dust density
Dry deposition velocity of air
particulates
Washout ratio
Surface area per volume of
particles
Junge C
Density of air
Fraction organic matter on
particulates
Diffusion coefficient of
water in air
Boundary layer thickness in
air above soil
kg[dust] / m3|air|
kg[dust] / m3[dust]
m/day
[mass chem/volume
rain] /[ mass
chem/volume air]
m2[area] /
m3 [particles]
m-Pa
g/cm3
unitless
mVd
m
6.15E-08
1.40E+03
5.00E+02
2.00E+05
5.71E-04
1.72E-01
1.20E-03
2.00-01
2.16E+00
5.00E-03
Bidleman 1988
Bidleman 1988
McKone et al. 1998
U.S. EPA 1997
Bidleman 1988
Pankow 1987
U.S. EPA 1997
Harner and Bidleman 1998
Riederer 1995
McKone et al. 1998
Surface Soil
(assumed same for all surface soil compartments)
Water content
Air content
Soil material density
Organic carbon fraction
volume [water] /
volume [compartment]
volume [air] /
volume [compartment]
kgfsoil] / m3[soil]
unitless
1.60E-01
4.38E-01
2.60E+03
1.66E-02
McKone etal. 1998
McKone etal. 1998
McKone etal. 1998
McKone etal. 1998
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Air soil boundary thickness
Default depth of runoff water
Fraction of area available for
vertical diffusion
Fraction of area available for
erosion
Fraction of area available for
runoff
Input Units
m
m
m2[area available] /
m2 [total]
m2[area available] /
m2 [total]
m2[area available] /
m2 [total]
Value3
5.00E-03
5.00E-03
l.OOE+00
l.OOE+00
l.OOE+00
Reference
Thibodeaux 1996
approximated from a typical runoff rate and number of rain
events
area assumed rural
area assumed rural
area assumed rural
Root Zone
(assumed same for all root zone compartments)
Water content
Air content
Soil material density
Organic carbon fraction
volume [water] /
volume [compartment]
volume [air] /
volume [compartment]
kgfsoil] / m3[soil]
unitless
1.61E-01
3.60E-01
2.60E+03
1.66E-02
McKone et al. 1998
McKone et al. 1998
Siever 1986
McKone et al. 1998
Vadose Zone
(assumed same for all vadose zone compartments)
Water content
Air content
Soil material density
Organic carbon fraction
volume [water] /
volume [compartment]
volume [air] /
volume [compartment]
kgfsoil] / m3[soil]
unitless
1.60E-01
2.16E-01
2.60E+03
1.28E-03
Ground Water
assumed same for all ground water com
Porosity
Solid material density in
aquifer
Organic carbon fraction
volume [total pore
space] /
volume [compartment]
kg[soil] / m3[soil]
unitless
2.00E-01
2.60E+03
l.OOE-02
McKone etal. 1998
McKone etal. 1998
McKone etal. 1998
McKone etal. 1998
partments)
McKone etal. 1998
McKone etal. 1998
Schwarzenbach et al. 1993
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
Surface Water
(depends on water body type - values provided have been developed for an initial simple water body scenario)
Flush rate
Suspended sediment
concentration
Evaporation of water
Current velocity
Organic carbon fraction in
suspended sediments
Suspended sediment density
Boundary layer thickness
above sediment
Drag coefficient for water
body
Viscous sublayer thickness
PH
Chloride concentration
flushes/year
kg[sediment] /
m3 [water column]
m3 [water] /
m2 [area] -day
m/s
unitless
kg[sediment] /
m3 [sediment]
m
unitless
m
unitless
mg/L
4.31
0.8E-03
9.45E-05
0
0.02
2600
2.00E-02
0.0011
4
6.5
56
supplied by state agency
Schwalen and Kiefer 1996
van der Leeden et al. 1990
professional judgment
McKone et al. 1998
McKone et al. 1998
McKone et al. 1998
Ambrose etal. 1995
Ambrose et al. 1995
supplied by state agency
supplied by state agency
Sediment
(depends on associated water body type)
Organic carbon fraction
Solid material density in
sediment
Porosity of the sediment zone
Benthic solids concentration
unitless
kg[sediment] /
m3 [sediment]
volume [total pore
space] /
volume [sediment
compartment]
kg[sediment] /
m3 [sediment
compartment]
2.00E-02
2.60E+03
2.00E-01
2.08E+03
McKone etal. 1998
McKone etal. 1998
McKone etal. 1998
professional judgment
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
ABIOTIC CHEMICAL-SPECIFIC DATA
(for each chemical)
General to All Media
Molecular weight
Octanol-water partition
coefficient (Kow)
Melting point
Water solubility
Henry's Law constant
Diffusion coefficient in pure
air
Diffusion coefficient in pure
water
g/mol
L [water] / L[octanol]
°K
mol / m3
Pa-m3 / mol
m2 / day
m2 / day
Hg(0) Hg(2) MHg
2.01E+02 2.01E+02 2.16E+02
4.15E+00 3.33E+00 1.70E+00
2.34E+02 5.50E+02 4.43E+02
3.00E-04 3.30E+02 NA
7.10E-03 7.10E-10 4.70E-07
4.78E-01 4.78E-01 4.56E-01
5.54E-05 5.54E-05 5.28E-05
U.S. EPA 1997
Mason etal. 1996
CARB 1994
CARB 1994
U.S. EPA 1997
U.S. EPA 1997
U.S. EPA 1997
Surface Soil
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
I/day
I/day
I/day
I/day
l.OOE-03
0.06
1.25E-05
l.OOE-08
range reported in Porvari and Verta (1995) is 2E-4 to 1E-3
/day; value is average maximum potential methylation rate
constant under anaerobic conditions
range reported in Porvari and Verta (1995) is 3E-2 to 6E-2
/day; value is average maximum potential demethylation rate
constant under anaerobic conditions
value used for untilled surface soil (2cm), 10% moisture
content, in U.S. EPA 1997; general range is
(0.0013/day)*moisture content to (0.000 l/day)*moisture
content for forested region (Lindberg 1996; Carpi and
Lindberg 1997)
small default nonzero value (0 assumed in U.S. EPA 1997)
Root Zone
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
I/day
I/day
l.OOE-03
0.06
range reported in Porvari and Verta (1995) is 2E-4 to 1E-3
/day; value is average maximum potential methylation rate
constant under anaerobic conditions
range reported in Porvari and Verta (1995) is 3E-2 to 6E-2
/day; value is average maximum potential demethylation rate
constant under anaerobic conditions
NOVEMBER 1999
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APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
Input Units
I/day
I/day
Value3
3.25E-06
l.OOE-08
Reference
value used for tilled surface soil (20cm), 10% moisture content
in U.S. EPA 1997 (Lindberg 1996; Carpi and Lindberg, 1997)
small default nonzero value (0 assumed in U.S. EPA 1997)
Vadose Zone
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
I/day
I/day
I/day
I/day
l.OOE-03
0.06
3.25E-06
l.OOE-08
range reported in Porvari and Verta (1995) is 2E-4 to 1E-3
/day; value is average maximum potential methylation rate
constant under anaerobic conditions
range reported in Porvari and Verta (1995) is 3E-2 to 6E-2
/day; value is average maximum potential demethylation rate
constant under anaerobic conditions
value used for tilled surface soil (20cm), 10% moisture content
in U.S. EPA 1997 (Lindberg 1996; Carpi and Lindberg 1997)
small default nonzero value (0 assumed in U.S. EPA 1997)
Ground Water
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
I/day
I/day
I/day
I/day
l.OOE-03
0.06
3.25E-06
l.OOE-08
range reported in Porvari and Verta (1995) is 2E-4 to 1E-3
/day; value is average maximum potential methylation rate
constant under anaerobic conditions
range reported in Porvari and Verta (1995) is 3E-2 to 6E-2
/day; value is average maximum potential demethylation rate
constant under anaerobic conditions
value used for tilled surface soil (20cm), 10% moisture content
in U.S. EPA 1997 (Lindberg 1996; Carpi and Lindberg, 1997)
small default nonzero value (0 assumed in U.S. EPA 1997)
Surface Water
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
I/day
I/day
l.OOE-03
0.0130
value used in U.S. EPA 1997; range is from 1E-4 to 3E-4/day
(Gilmour and Henry 1991)
average of range of 1E-3 to 2.5E-2/day from Gilmour and
Henry (1991)
NOVEMBER 1999
C-6
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
Input Units
I/day
I/day
Value3
7.50E-03
l.OOE-08
Reference
value used in U.S. EPA 1997; reported values range from less
than 5E-3/day for depths greated than 17m, up to 3.5/day (Xiao
et al. 1995; Vandal et al. 1995; Mason et al. 1995a; Amyot et
al. 1997)
small default nonzero value (0 assumed in U.S. EPA 1997)
Sediment
Methylation rate constant for
Hg(2) to MHg
Demethylation rate constant
forMHgtoHg(2)
Reduction rate constant for
Hg(2) to Hg(0)
Oxidation rate constant for
Hg(0) to Hg(2)
I/day
I/day
I/day
I/day
l.OOE-04
0.0501
l.OOE-06
l.OOE-08
value used in U.S. EPA 1997; range is from 1E-5 to lE-l/day
(Gilmour and Henry 1991)
average of range of 2E-4 to IE- I/day from Gilmour and Henry
(1991)
inferred value based on presence of Hg(0) in sediment
porewater (U.S. EPA 1997; Vandal et al. 1995)
small default nonzero value (0 assumed in U.S. EPA 1997)
ABIOTIC FLOW DATA
Total erosion rate from soil
Erosion rates between soil
and soil
Erosion rates between soil
and surface water
Total runoff rate from soil
Runoff rates between soil
and soil
Runoff rates between soil
and surface water
Percolation rates between
soil and soil
Surface water flow between
surface water compartments
Recharge from ground water
to surface water
kg[soil] / m2 [area] -day
kg[soil] / m2 [area] -day
kg[soil] / m2 [area] -day
m3 [water] / m2[area]-
day
m3 [water] / m2[area]-
day
m3 [water] / m2[area]-
day
m3 [water] / m2[area]-
day
m3 [water] / m2[area]-
day
m3 [water] / m2[area]-
day
2.89E-04
parcel-specific
parcel-specific
1.01E-03
parcel-specific
parcel-specific
0.2 x rainfall rate
ponds: 0
rivers: NA
being developed
van der Leeden et al. 1990
professional judgment
professional judgment
van der Leeden et al. 1990
professional judgment
professional judgment
professional judgement
professional judgment
professional judgment
NOVEMBER 1999
C-7
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Deposition of suspended
sediment in the water column
to the sediment bed
Resuspension of sediment
from the sediment bed to the
water column
Burial rate of sediment in the
sediment bed
Input Units
kg[sediment] /
m2 [area] -day
kg[sediment] /
m2 [area] -day
kg[sediment] /
m2 [area] -day
Value3
1.3E+01
l.OOE-04
calculated assuming net solid
deposition to sediment is zero
Reference
McKone et al. 1998
McKone et al. 1998
professional judgment
BIOTIC ENVIRONMENTAL SETTING DATA
(for each relevant compartment)
ANIMALS - AQUATIC
Water Column Carnivore - Bass
Body weight (BW)
Fraction lipid weight
Biomass per area
Population per area
Ventilation rate
Fraction of food diet
comprised of fish omnivores
Fraction of food diet
comprised of fish herbivores
Fraction of food diet
comprised of fish carnivores
Fraction of food diet
comprised of mayfly nymph
kg
unitless
km/m2
#/m2
ml / min / kg
unitless
unitless
unitless
unitless
2.00E+00
l.OOE-01
5.97E-04
calculated
5.00E+02
l.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
professional judgment
value from Thomann 1989
mean of data from 1 1 lakes in Kelso and Johnson (1991)
biomass per area divided by body weight of individual
low end of range, 500-6000, in Thomann 1989
value set based on definition of trophic levels
value set based on definition of trophic levels
value set based on definition of trophic levels
value set based on definition of trophic levels
Water Column Herbivore - Bluesill
Body weight (BW)
Fraction lipid weight
Biomass per area
Population per area
Ventilation rate
Fraction of food diet
comprised of phytoplankton
(algae)
kg
unitless
kg/m2
#/m2
ml / min / kg
unitless
2.50E-02
l.OOE-01
1.65E-03
calculated
5.00E+02
l.OOE+00
professional judgment
value from Thomann 1989
based on data from 1 1 lakes in Kelso and Johnson (1991)
biomass per area divided by body weight of individual
low end of range, 500-6000, in Thomann 1989
value set based on definition of trophic levels
NOVEMBER 1999
C-8
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Fraction of food diet
comprised of macrophyte
Fraction of diet_mayfly
Input Units
unitless
unitless
Value3
O.OOE+00
O.OOE+00
Reference
value set based on definition of trophic levels
value set based on definition of trophic levels
Water Column Omnivore - Channel Catfish
Body weight (BW)
Fraction lipid weight
Biomass per area
Population per area
Ventilation rate
Fraction of food diet
comprised of macrophyte
Fraction of food diet
comprised of mayfly nymph
Fraction of food diet
comprised of omnivore
Fraction of food diet
comprised of fish herbivores
kg
unitless
kg/m2
#/m2
ml / min / kg
unitless
unitless
unitless
unitless
5.00E-01
0.1
1.67E-04
calculated
500
O.OOE+00
O.OOE+00
O.OOE+00
l.OOE+00
professional judgment
value from Thomann 1989
mean of data from 1 1 lakes in Kelso and Johnson (1991)
biomass per area divided by body weight of individual
low end of range, 500-6000, in Thomann 1989
value set based on definition of trophic levels
value set based on definition of trophic levels
value set based on definition of trophic levels
value set based on definition of trophic levels
Benthic Omnivore
Body weight (BW)
Fraction lipid weight
Biomass per area
Population per area
Ventilation rate
Fraction of diet comprised of
benthic invertebrates
kg
unitless
kg/m2
#/m2
ml / min / kg
unitless
0.500
0.1
1.39E-03
calculated
500
l.OOE+00
professional judgment
value from Thomann 1989
mean of data from 1 1 lakes in Kelso and Johnson (1991)
biomass per area divided by body weight of individual
low end of range, 500-6000, in Thomann 1989
value set based on definition of trophic levels
Benthic Carnivore
Body weight (BW)
Fraction lipid weight
Biomass per area
Population per area
Ventilation rate
Fraction of diet comprised of
benthic omnivores
kg
unitless
kg/m2
#/m2
ml / min / kg
unitless
2.0
0.1
7.14E-04
calculated
500
l.OOE+00
professional judgment
value from Thomann 1989
mean of data from 1 1 lakes in Kelso and Johnson (1991)
biomass per area divided by body weight of individual
low end of range, 500-6000, in Thomann 1989
value set based on definition of trophic levels
NOVEMBER 1999
C-9
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
PLANTS - AQUATIC
Macronhvte
Biomass per area
Density of macrophytes
kg/m2
kg/m3
0.1
1000
Bonaretal. 1993
professional judgment
Phvtonlankton - Alsae
Diameter of algae
Average cell density (per vol
cell, not water)
Algae growth rate
Algae density in water
column
Algae carbon content
(fraction)
Algae water content
(fraction)
|jm
g/|jm3
I/day
g[algae]/L[water]
unitless
unitless
2.5
l.OOE-12
0.7
2.50E-03
4.65E-01
9.00E-01
Mason etal. 1995b
Mason et al. 1995b, Mason et al. 1996
Hudson et al. 1994 as cited in Mason et al. 1995b
derived from Millard et al. 1996
APHA 1995
APHA 1995
ANIMALS - TERRESTRIAL
Soil Detritivore - Earthworm
Density per soil area,
deciduous forest
Density per soil area,
coniferous forest
Density per soil area,
grass/herb
Density
Water content of worm
kg[worm] / m2 [area]
kg[worm] / m2 [area]
kg[worm] / m2 [area]
kg[worm] / L[volume]
mass fraction
4.50E-02
2.20E-02
5.00E-03
l.OOE+00
8.40E-01
avg of oak and beech values in Satchell 1983
pine forest in Satchell 1983
avg of 0.0032 and 0.0075, range on grassland in Tennessee,
Lee 1985
professional judgment
U.S. EPA 1993
Soil Detritivore - Soil Arthropod
Body weight (BW)
Biomass per area
kg
kg/m2
1.31E-04
3.01E-04
grasshopper, Porter et al. 1996
grasshopper, Porter et al. 1996
Terrestrial Ground-Invertebrate Feeder - Black-canned Chickadee
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
kg
#/m2
kg[soil]/kgBW-day
unitless
unitless
1.08E-02
3.50E-05
O.OOE+00
5.90E-02
6.70E-01
Dunning 1993
avg of 0.2 and 0.3 /ha in British Columbia, Smith 1993
assumed, rarely observed on ground, Smith 1993
Calder and Braun 1983
Calder and Braun 1983
NOVEMBER 1999
C-10
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised on plants
Fraction of food diet
comprised of benthic
invertebrates
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
unitless
Value3
4.09E-01
8.00E-01
7.40E-01
3.00E-01
7.00E-01
l.OOE+00
O.OOE+00
Reference
Lasiewski and Calder 1971
Lasiewski and Calder 1971
calculated from Bell 1990, Dunning 1993
Sample et al. 1993, Smith 1993, Martin et al. 1951
Sample et al. 1993, Smith 1993, Martin et al. 1951
professional judgment
professional judgment
Semiaauatic Piscivore - Kingfisher
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of water column
herbivore
Fraction of food diet
comprised of water column
omnivore
Fraction of food diet
comprised of benthic
omnivore
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
unitless
unitless
1.48E-01
4.00E-07
O.OOE+00
5.90E-02
6.70E-01
4.09E-01
8.00E-01
7.40E-02
0.16
0.16
0.325
5.00E-01
5.00E-01
Dunning 1993
Fry and Fry 1992
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
Alexander 1977
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
professional judgment
professional judgment
Semiaauatic Predator/Scavenger - Bald easle
Body weight (BW)
Population per area
Soil ingestion rate
kg
#/m2
kg[soill/kgBW-day
4.74E+00
1.30E-08
O.OOE+00
Dunning 1993
supplied by state agency
professional judgment
NOVEMBER 1999
C-ll
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of mouse
Fraction of food diet
comprised of chickadee
Fraction of food diet
comprised of water column
herbivore
Fraction of food diet
comprised of water column
omnivore
Fraction of food diet
comprised of water column
carnivore
Fraction of food diet
comprised of benthic
omnivore
Fraction of food diet
comprised of benthic
carnivore
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
unitless
unitless
unitless
unitless
unitless
unitless
Value3
5.90E-02
6.70E-01
4.09E-01
8.00E-01
7.40E-02
2.30E-01
l.OOE-01
0.11
0.11
0.11
0.17
0.17
5.00E-01
5.00E-01
Reference
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
U.S. EPA 1995
professional judgment
professional judgment
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
professional judgment
professional judgment
Semiaaautic Piscivore - Common Loon
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
4.13E+00
4.90E-08
O.OOE+00
5.90E-02
6.70E-01
4.09E-01
8.00E-01
2.30E-01
Dunning 1993
W. Jakubas, Maine Dept Inland Fisheries & Wildlife
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
Barr 1996
NOVEMBER 1999
C-12
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Fraction of diet comprised of
water column herbivore
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
unitless
Value3
l.OOE+00
5.00E-01
5.00E-01
Reference
assumed based on approximate size range of fish consumed
professional judgment
professional judgment
Semiaauatic Omnivore - Mallard
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of plant
Fraction of food diet
comprised of benthic
invertebrate
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
unitless
1.13E+00
9.30E-06
3.30E-03
5.90E-02
6.70E-01
4.09E-01
8.00E-01
l.OOE-01
6.65E-01
3.35E-01
5.00E-01
5.00E-01
Nelson and Martin 1953
average of 0.012 and 0.174/ha, North Dakota, U.S. EPA 1993
Beyer etal. 1994
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
Heinz etal. 1987
Martin etal. 1951
professional judgment
professional judgment
professional judgment
Terrestrial Predator/Scavenger - Red-tailed Hawk
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of soil arthropod
Fraction of food diet
comprised of chickadee
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
1.13E+00
7.00E-07
O.OOE+00
5.90E-02
6.70E-01
4.09E-01
8.00E-01
1.20E-01
4.00E-02
2.57E-01
North America, Dunning 1993
average of range 0.0034 and 0.01 for Colorado, U.S. EPA
1993
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
Preston and Beane 1993
approximate from Sherrod 1978
approximate from Sherrod 1978
NOVEMBER 1999
C-13
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Fraction of food diet
comprised of mouse
Fraction of food diet
comprised of short tailed
shrew
Fraction of food diet
comprised of vole
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
unitless
unitless
unitless
Value3
3.03E-01
2.00E-01
2.00E-01
l.OOE+00
O.OOE+00
Reference
approximate from Sherrod 1978
approximate from Sherrod 1978
approximate from Sherrod 1978
professional judgment
professional judgment
Terrestrial Insectivore - Tree Swallow
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of benthic
invertebrate
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
2.01E-02
7.00E-04
O.OOE+00
5.90E-02
6.70E-01
4.09E-01
8.00E-01
1.98E-01
l.OOE+00
l.OOE+00
O.OOE+00
Dunning 1993
DeGraafetal. 1981
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Lasiewski and Calder 1971
Lasiewski and Calder 1971
Preston and Beane 1993
professional judgment
professional judgment
professional judgment
Terrestrial Herbivore - Meadow Vole
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
4.41E-02
6.00E-03
2.30E-03
9.90E-02
9.00E-01
5.46E-01
8.00E-01
9.70E-02
Reich 1981
average of 0.011/m2, Klaas et al. 1998, and 0.0015/m2, Getz
1961
Beyer etal. 1994
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
mean for Microtus spp., Dark et al. 1983
NOVEMBER 1999
C-14
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Fraction of food diet
comprised of plant
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
unitless
Value3
l.OOE+00
l.OOE+00
O.OOE+00
Reference
professional judgment
professional judgment
professional judgment
Terrestrial Predator/Scavenger - Long-tailed Weasel
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of mouse
Fraction of food diet
comprised of vole
Fraction of food diet
comprised of shrew
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[food]/kgBW-day
unitless
unitless
unitless
unitless
unitless
1.47E-01
6.50E-06
O.OOE+00
9.90E-02
9.00E-01
5.46E-01
8.00E-01
7.35E-02
5.00E-01
2.50E-01
2.50E-01
l.OOE+00
O.OOE+00
Mumford and Whitaker 1982
average of 6-7/ha, Svendsen 1982
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
calc from Brown and Lasiewski 1972, Golley 1961, U.S. EPA
1993
professional judgment
professional judgment
professional judgment
professional judgment
professional judgment
Semiaauatic Omnivore - Mink
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of mouse
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
8.32E-01
5.75E-05
O.OOE+00
9.90E-02
9.00E-01
5.46E-01
8.00E-01
1.40E-01
2.30E-01
Mumford and Whitaker 1982
avg of 0.01 and 0. 1/ha for general US, U.S. EPA 1993
professional judgment
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
mink in captivity, Bleavins and Aulerich 1981
Hamilton 1940, Sealander 1943, Korschgen 1958, Burgess and
Bider 1980
NOVEMBER 1999
C-15
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Fraction of food diet
comprised of vole
Fraction of diet comprised of
water column herbivore
Fraction of diet comprised of
water column omnivore
Fraction of diet comprised of
benthic omnivore
Fraction of food diet
comprised of benthic
invertebrate
Fraction of food diet
comprised of chickadee
Fraction excretion to soil
Fraction excretion to water
Input Units
unitless
unitless
unitless
unitless
unitless
unitless
unitless
unitless
Value3
2.30E-01
7.00E-02
7.00E-02
1.50E-01
1.70E-01
8.00E-02
5.00E-01
5.00E-01
Reference
Hamilton 1940, Sealander 1943, Korschgen 1958, Burgess and
Bider 1980
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
Hamilton 1940, Sealander 1943, Korschgen 1958, Burgess anc
Bider 1980
Hamilton 1940, Sealander 1943, Korschgen 1958, Burgess anc
Bider 1980
professional judgment
professional judgment
Terrestrial Omnivore - White-footed Mouse
Body weight
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of worm
Fraction of food diet
comprised of plant
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/kgBW-day
unitless
unitless
unitless
unitless
kg[foodl/kgBW-day
unitless
unitless
unitless
unitless
2.12E-02
3.15E-03
2.00E-02
9.90E-02
9.00E-01
5.46E-01
8.00E-01
1.51E-01
5.00E-01
5.00E-01
5.00E-01
5.00E-01
North America, Silva and Downing 1995
average of range 6-57/ha, Wolff 1985
Beyer etal. 1994
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
Green and Millar 1987
professional judgment
professional judgment
professional judgment
professional judgment
Terrestrial Herbivore - White-tailed Deer
Body weight (BW)
Population per area
kg
#/m2
7.48E+01
4.60E-05
Silva and Downing 1995
12-80/ha, forest avg from Smith 1987, Torgerson and Porath
1984,Wishart 1984,Cook 1984
NOVEMBER 1999
C-16
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of plant
Fraction excretion to soil
Fraction excretion to water
Input Units
kg[soill/ kg BW-day
L[water] / kg BW-day
Lfwater] / kg BW-day
unitless
unitless
kg[foodl/ kg BW-day
unitless
unitless
unitless
Value3
l.OOE-03
9.90E-02
9.00E-01
5.46E-01
8.00E-01
5.00E-02
l.OOE+00
l.OOE+00
O.OOE+00
Reference
Beyer etal. 1994
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
Mautzetal. 1976
professional judgment
professional judgment
professional judgment
Semiaauatic Omnivore - Raccoon
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of benthic
invertebrate
Fraction of diet comprised of
water column herbivore
Fraction of diet comprised of
water column omnivore
Fraction of diet comprised of
benthic omnivore
Fraction of food diet
comprised of worm
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/ kg BW-day
Lfwater] / kg BW-day
Lfwater] / kg BW-day
unitless
unitless
kg[food]/ kg BW-day
unitless
unitless
unitless
unitless
unitless
unitless
unitless
6.35E+00
2.15E-05
9.40E-02
9.90E-02
9.00E-01
5.46E-01
8.00E-01
5.20E-01
6.90E-01
3.00E-02
3.00E-02
4.00E-02
2.10E-01
5.00E-01
5.00E-01
Lotze and Anderson 1979
average of range 0.023 to 0.2/ha, Lotze and Anderson 1979
Beyer etal. 1994
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
calc from Teubner and Barrett 1983, Tyson 1950, U.S. EPA
1993
representing molluscs, Crustacea, Tyson 1950
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
assumed based on approximate trophic levels of several
consumed fish species
coastal mudflats of SW Washington, Tyson 1950
professional judgment
professional judgment
NOVEMBER 1999
C-17
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
Terrestrial Ground-Invertebrate Feeder - Short-tailed Shrew
Body weight (BW)
Population per area
Soil ingestion rate
Water a
Water b
Inhalation a
Inhalation b
Food ingestion rate
Fraction of food diet
comprised of worm
Fraction of food diet
comprised of soil arthropod
Fraction excretion to soil
Fraction excretion to water
kg
#/m2
kg[soill/ kg BW-day
L[water] / kg BW-day
Lfwater] / kg BW-day
unitless
unitless
kg[foodl/ kg BW-day
unitless
unitless
unitless
unitless
2.20E-02
6.10E-04
6.11E-02
9.90E-02
9.00E-01
5.46E-01
8.00E-01
4.70E-01
5.85E-01
4.15E-01
l.OOE+00
O.OOE+00
0.015-0.029 kg reported for Manitoba, Silva and Downing
1995
average value for Maine, A. Fuller, U. Maine
Talmage and Walton 1993
Calder and Braun 1983
Calder and Braun 1983
Stahl 1967
Stahl 1967
Barrett and Stueck 1976
Whitaker and Ferraro 1963, slugs represented by earthworms,
Ithaca, NY
Whitaker and Ferraro 1963
professional judgment
professional judgment
PLANTS - TERRESTRIAL
Deciduous Forest Leaf
Water content
Lipid content
Correction exponent, octanol
to lipid
Volume of wet leaf weight
per unit area
Density of wet leaf
Mass of leaf per unit area
Dry mass of leaf per unit area
Leaf wetting factor
1 -sided leaf area index0
unitless
kg/kg wet weight
unitless
m3/m2
kg/m3
kg[fresh leaf] /
nf[area]
kg[dry leaf] / nf|areal
m
m2[leaf] /m2[area]
8.00E-01
2.24E-03
7.60E-01
calculated
8.20E+02
6.00E-01
calculated
3.00E-04
3.40E+00
Paterson et al. 1991
European beech, Riederer 1995
from roots, Trapp 1995
calculated
Paterson et al. 1991
calc from LAP, leaf thickness (Simonich and Hites 1994),
density of wet foliage
calculated
1E-04 to 6E-04 for different crops and elements, Muller and
Prohl 1993
Harvard Forest, dom. red oak and red maple,
http://cdiac.esd.ornl.gov/
NOVEMBER 1999
C-18
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Vegetation attenuation factor
(to calc interception fraction)
Particle washoff rate constant
Length of leaf
Input Units
unitless
11 day
m
Value3
2.90E+00
5.76E+01
l.OOE-01
Reference
grass/hay, Baes et al. 1984
conifer leaves, McCune and Lauver 1986
professional judgment
Coniferous Forest Leaf
Water content
Lipid content
Correction exponent, octanol
to lipid
Volume of wet leaf weight
per unit area
Density of wet leaf
Mass of leaf per unit area
Dry mass of leaf per unit area
Leaf wetting factor
1 -sided leaf area index0
Vegetation attenuation factor
(to calc interception fraction)
Particle washoff rate constant
Length of leaf
unitless
kg/kg wet weight
unitless
m3/nf
kg/m3
kg[fresh leaf] /
m2|area]
kg[dry leaf] / nf|areal
m
m2[leaf] /m2|areal
unitless
I/ day
m
8.00E-01
2.24E-03
7.60E-01
calculated
8.20E+02
2.00E+00
calculated
3.00E-04
5.00E+00
2.90E+00
5.76E+01
l.OOE-02
Paterson et al. 1991
European beech, Riederer 1995
from roots, Trapp 1995
calculated
Paterson et al. 1991
calc from LAP, leaf thickness (Simonich and Hites 1994),
density of wet foliage
calculated
1E-04 to 6E-04 for different crops and elements, Muller and
Prohl 1993
rep. value for conifers, Ned Nikolov, Oak Ridge National Lab
grass/hay, Baes et al. 1984
conifer leaves, McCune and Lauver 1986
professional judgment
Herb/Grassland Leaf
Water content
Lipid content
Correction exponent, octanol
to lipid
Volume of wet leaf weight
per unit area
Density of wet leaf
Mass of leaf per unit area
Dry mass of leaf per unit area
unitless
kg/kg wet weight
unitless
m3 /m2
kg/m3
kg[fresh leaf] /
m2|area]
kg[dry leaf] / m2|area]
8.00E-01
2.24E-03
7.60E-01
calculated
8.20E+02
l.OOE+00
calculated
Paterson et al. 1991
European beech, Riederer 1995
from roots, Trapp 1995
calculated
Paterson et al. 1991
calc from LAP and Maddelena 1998
professional judgment
NOVEMBER 1999
C-19
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Leaf wetting factor
1 -sided leaf area index0
Vegetation attenuation factor
(to calc interception fraction)
Particle washoff rate constant
Length of leaf
Input Units
m
m2[leaf] /m2[area]
unitless
11 day
m
Value3
3.00E-04
5.00E+00
2.90E+00
5.76E+01
5.00E-02
Reference
1E-04 to 6E-04 for different crops and elements, Muller and
Prohl 1993
range for old field about 4 to 6, R. J. Luxmoore, Oak Ridge
National Lab.
grass/hay, Baes et al. 1984
conifer leaves, McCune and Lauver 1986
professional judgment
Root - Nonwoodv Only
Wet density of root
Water content of root
Lipid content of root
Correction exponent for the
differences between octanol
and lipids
Total volume of dry roots in
domain per unit area
Areal density grass/herb
kg/m3
unitless
kg/kg wet weight
unitless
m3 /m2
kg/m2
8.30E+02
0.8
1.10E-02
0.76
calculated
1.40E+00
soybean, Paterson et al. 1991
professional judgment
calculated
Trapp 1995
calculated
temperate grassland , Jackson et al. 1996
Stem - Nonwoodv Onlv
Density
Water content of stem
Lipid content
Volume of wet stem per unit
area
Density of phloem fluid
Density of xylem fluid
Volume of wet weight in
domain per unit area
Flow rate of transpired water
per leaf area
Fraction of transpiration flow
rate that is phloem rate
Correction exponent between
foliage lipids and octanol
g/cm3
unitless
kg/kg wet weight
m3/m2
kg/m3
kg/cm3
m3/m2
m3[water]/m2 [leaf]
unitless
unitless
9.00E-01
8.00E-01
2.24E-03
10% of volume of foliage
l.OOE+03
9.00E+02
0.4 times volume of foliage per unit
area
4.80E-03
0.05
7.60E-01
professional judgment
Paterson et al. 1991
leaves of European beech, Riederer 1995
professional judgment
professional judgment
professional judgment
professional judgment
Crank etal. 1981
Paterson et al. 1991
Trapp 1995
NOVEMBER 1999
C-20
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
TEMPORAL ENVIRONMENTAL SETTING DATA
Site-specific
Day of first frost
Day of last frost
unitless
unitless
Sept 30
May 12
Deciduous Forest
Litterfall begin date
Litterfall end date
Uptake by leaf, end date
Uptake by root (herb/grass),
end date
LAP = 0, date
Uptake by leaf, begin date
LAP = default value, date
Litterfall rate constant
unitless
unitless
unitless
unitless
unitless
unitless
unitless
11 day
Hampden, ME, value for northeastern US
Hampden, ME, value for northeastern US
and Grassland
Day 273 (Sept 30)
Day 302 (Oct 29)
Day 273 (Sept 30)
Day 273 (Sept 30)
Day 273 (Sept 30)
Day 132 (May 12)
Day 132 (May 12)
1.50E-01
assumed equivalent to date of first frost (W. W. Hargrove, U.
of Term., pers. comm., 2/99)
professional judgment
assumed equivalent to date of first frost (W. W. Hargrove, U.
of Term., pers. comm., 2/99)
assumed equivalent to date of first frost (W. W. Hargrove, U.
of Term., pers. comm., 2/99)
assumed equivalent to date of first frost (W. W. Hargrove, U.
of Term., pers. comm., 2/99)
assumed equivalent to date of last frost (W. W. Hargrove, U. oi
Term., pers. comm., 2/99)
assumed equivalent to date of last frost (W. W. Hargrove, U. oi
Term., pers. comm., 2/99)
assumes 99% of leaves have fallen during 30 days of litterfall
Coniferous Forest
Uptake by leaf, end date
Uptake by leaf, end date
Litterfall rate constant
unitless
unitless
11 day
Day 303 (Oct 30)
Day 102 (Apr 12)
being developed
assumed to be one month after date of first frost
assumed to be one month before date of last frost
2-10 yr turnover, WM Post, Oak Ridge Natl
BIOTIC CHEMICAL-SPECIFIC DATA
ANIMALS - AQUATIC
Water-column Carnivore - Bass
Carnivore-omnivore partition
coefficient
Alpha for carnivore
U*.
Assimilation efficiency
kg/kg
unitless
day
percent
Hg(0)
NA
NA
NA
NA
Hg(2)
8.81E-02
9.50E-01
4.38E+04
9
MHg
3.50E+00
9.50E-01
4.38E+04
90
1 trophic level transfer, Hg(2) - Watras and Bloom 1992, MHg
- Lindqvst et al. 1991
professional judgment
derived from Lindqvist et al. 1991
Hg(2): Trudel and Rasmussen 1997; MHg: Odin et al. 1995
NOVEMBER 1999
C-21
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
Water-column Herbivore - Bluegill
Herbivore-algae partition
coefficient
Alpha for herbivore
talntia
Assimilation efficiency
kg/kg
unitless
day
percent
Hg(0)
NA
NA
NA
NA
Hg(2)
1.41E-01
9.50E-01
5.48E+02
9
MHg
1.20E+01
9.50E-01
5.48E+02
90
zooplankton intermediate trophic level, Hg(2) - Watras and
Bloom 1992; MHg - Lindqvst et al. 1991
professional judgment
derived from Lindqvist et al. 1991
Hg(2): Trudel and Rasmussen 1997; MHg: Odin et al. 1995
Water-column Omnivore - Channel Catfish
Omnivore-herbivore partition
coefficient
Alpha for omnivore
talnha
Assimilation efficiency
kg/kg
unitless
days
percent
Hg(0)
NA
NA
NA
NA
Hg(2)
8.81E-02
9.50E-01
1.46E+03
9
Benthic Invertebrate (re
Benthic invertebrate-
sediment partition coefficient
Alpha for omnivore
ta,nha
kg/kg
unitless
days
Hg(0)
NA
NA
NA
Hg(2)
8.24E-02
9.50E-01
1.40E+01
MHg
3.50E+00
9.50E-01
1.46E+03
90
zooplankton to planktiverous fish, Hg(2) - Watras and Bloom
1992; MHg - Lindqvst et al. 1991
professional judgment
derived from Lindqvist et al. 1991
Hg(2): Trudel and Rasmussen 1997; MHg: Odin et al. 1995
presented bv Mavflv)
MHg
5.04E+00
9.50E-01
1.40E+01
Hg(0) - assumed based on Hg(2) value; Hg(2) and MHg -
Saouteretal. 1991
professional judgment
experiment duration from Saouter 1991
Benthic Carnivore (represented bv Largemouth Bass)
Carnivore-omnivore partition
coefficient
Alpha for omnivore
talnha
Assimilation efficiency
kg/kg
unitless
day
percent
NA
NA
NA
NA
8.81E-02
9.50E-01
4.38E+04
9
3.50E+00
9.50E-01
4.38E+04
90
zooplankton to planktiverous fish, Hg(2) - Watras and Bloom
1992; MHg - Lindqvst et al. 1991
professional judgment
derived from Lindqvist et al. 1991
Hg(2): Trudel and Rasmussen 1997; MHg: Odin et al. 1995
Benthic Omnivore (ren resented bv Channel Catfish)
Omnivore-invertebrate
partition coefficient
Alpha for omnivore
ta,nha
Assimilation efficiency
kg]/kg
unitless
day
percent
Hg(0)
NA
NA
NA
NA
Hg(2)
8.81E-02
9.50E-01
1.46E+03
8
MHg
3.50E+00
9.50E-01
1.46E+03
80
zooplankton to planktiverous fish, Hg(2) - Watras and Bloom
1992; MHg - Lindqvst et al. 1991
professional judgment
derived from Lindqvist et al. 1991
Hg(2): Trudel and Rasmussen 1997; MHg: Odin et al. 1995
NOVEMBER 1999
C-22
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
PLANTS - AQUATIC
Macronhvte
Macrophyte-water partition
coefficient
Alpha for macrophyte
talntia
L/g
unitless
days
Hg(0)
NA
NA
NA
Hg(2)
8.83E-01
9.50E-01
1.80E+01
MHg
4.40E+00
9.50E-01
1.80E+01
Elodea densa, Ribeyre and Boudou 1994
professional judgment
experiment duration from Ribeyre and Boudou 1994
Phvtonlankton - Algae
DOW
Uptake rate
unitless
Hm'-d-'-L
Hg(0)
NA
NA
Hg(2)
depends on
pH and Cl
cone
4.00E-11
MHg
depends on
pH and Cl
cone
7.07E-11
look-up table of pH and Cl concentrations derived from graph
in Mason et al. 1996
assumes radius = 2.5mm, Mason et al. 1995b, Mason et al.
1996
ANIMALS - TERRESTRIAL
Soil Detritivore - Earthworm
Earthworm-soil partitition
coefficient, dry
talnh:, for worm <->• soil
Alpha for worm <->• soil
mg/kg per mg/kg
day
unitiess
Hg(0)
NA
2.10E+01
9.50E-01
Hg(2)
3.60E-01
2.10E+01
9.50E-01
MHg
3.60E-01
2.10E+01
9.50E-01
Bulletal. 1977
assumed same as earthworms, Janssen et al. 1997
specified
Soil Detritivore - Soil Arthropod
Arthropod-soil partition
coefficient
ta]nha for arthropod <->• soil
Alpha for arthropod <->• soil
kg/kg wet weight
day
unitiess
Hg(0)
NA
2.10E+01
9.50E-01
Hg(2)
4.60E-01
2.10E+01
9.50E-01
MHg
2.90E+00
2.10E+01
9.50E-01
Hg(2) - median from Talmage and Walton 1993; MHg -
median from Nuorteva and Nuorteva 1982
assumed same as earthworms, Janssen et al. 1997
professional judgment
Terrestrial Ground-Invertebrate Feeder - Black-canned Chickadee
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
I/day
I/day
l.OOE+00
9.00E-02
professional judgment
calculated from rats in Takeda and Ukita 1970
NOVEMBER 1999
C-23
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
I/day
I/day
I/day
unitless
unitless
unitless
Value3
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Semiaauatic Piscivore - Kingfisher
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
I/day
I/day
I/day
I/day
I/day
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
NOVEMBER 1999
C-24
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
unitless
unitless
unitless
Value3
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Semiaauatic Predator/Scavenger - Bald Easle
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
NOVEMBER 1999
C-25
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
Semiaauatic Piscivore - Common Loon
First-order transformation
rate constant for
Hg(0)->-Hg(2)
First-order transformation
rate constant for
First-order transformation
rate constant for
First-order transformation
rate constant for
Hg(2)->-MHg
First-order transformation
rate constant for
Hg(2)->-Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Semiaauatic Omnivore - Mallard
First-order transformation
rate constant for
Hg(0)->-Hg(2)
First-order transformation
rate constant for
First-order transformation
rate constant for
Hg(0)->MHg
I/day
I/day
I/day
l.OOE+00
9.00E-02
O.OOE+00
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
NOVEMBER 1999
C-26
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
I/day
I/day
unitless
unitless
unitless
Value3
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Predator/Scavenger - Red-tailed Hawk
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
I/day
I/day
I/day
I/day
I/day
I/day
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
NOVEMBER 1999
C-27
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
unitless
unitless
Value3
0.4
0.75
Reference
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Insectivore - Tree Swallow
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Herbivore - Meadow Vole
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
I/day
I/day
l.OOE+00
9.00E-02
professional judgment
calculated from rats in Takeda and Ukita 1970
NOVEMBER 1999
C-28
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
I/day
I/day
I/day
unitless
unitless
unitless
Value3
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Herbivore - Long-tailed Vole
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
I/day
I/day
I/day
I/day
I/day
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
NOVEMBER 1999
C-29
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
unitless
unitless
unitless
Value3
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Predator/Scavenger - Long-tailed Weasel
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
NOVEMBER 1999
C-30
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Input Units
Value3
Reference
Semiaauatic Omnivore - Mink
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation of Hg(0)
Assimilation efficiency for
inhalation of Hg(2)
Assimilation efficiency for
inhalation of MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Omnivore - White-footed Mouse
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
I/day
I/day
I/day
l.OOE+00
9.00E-02
O.OOE+00
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
NOVEMBER 1999
C-31
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
I/day
I/day
unitless
unitless
unitless
Value3
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Herbivore - Mule Deer/Black-tailed Deer
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
I/day
I/day
I/day
I/day
I/day
I/day
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
NOVEMBER 1999
C-32
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
unitless
unitless
Value3
0.4
0.75
Reference
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Herbivore - White-tailed Deer
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
I/day
I/day
I/day
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Semiaauatic Omnivore - Raccoon
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
I/day
I/day
l.OOE+00
9.00E-02
professional judgment
calculated from rats in Takeda and Ukita 1970
NOVEMBER 1999
C-33
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
Input Parameter
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
I/day
I/day
I/day
unitless
unitless
unitless
Value3
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
professional judgment
professional judgment
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
Terrestrial Ground-Invertebrate Feeder - Short-tailed Shrew
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
MHg^Hg(2)
First-order transformation
rate constant for
Hg(0)^MHg
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for
Hg(2)^Hg(0)
I/day
I/day
I/day
I/day
I/day
l.OOE+00
9.00E-02
O.OOE+00
O.OOE+00
O.OOE+00
professional judgment
calculated from rats in Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
NOVEMBER 1999
C-34
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE CASE STUDY
Input Parameter
First-order transformation
rate constant for
MHg^Hg(O)
Assimilation efficiency for
inhalation for Hg(0)
Assimilation efficiency for
inhalation for Hg(2)
Assimilation efficiency for
inhalation for MHg
Input Units
I/day
unitless
unitless
unitless
Value3
O.OOE+00
0.75
0.4
0.75
Reference
professional judgment
human, ATSDR 1997, Teisinger and Fiserova-Bergova 1965
value for dog, U.S. EPA 1997
assume same as value of Hg(0)
PLANTS - TERRESTRIAL
Leaf
First-order transformation
rate constant for
Hg(0)^Hg(2)
First-order transformation
rate constant for
Hg(2)^MHg
First-order transformation
rate constant for MHg
^Hg(2)
Washout ratio Hg(2) vapor
Washout ratio Hg(0) vapor
Washout ratio Hg particulate
I/day
I/day
I/day
unitless
unitless
unitless
l.OOE+06
O.OOE+00
3.00E-02
1.60E+06
1.20E+03
5.00E+05
value used for rate constants that are judged to be close to
instantaneous
assumed from Gay 1975, Bache et al. 1973, Lindberg pers
comm
calc from Bache et al. 1973
U.S. EPA 1997 based on Petersen et al. 1995
U.S. EPA 1997 based on Petersen et al. 1995
U.S. EPA 1997 based on Petersen etal. 1995
Root
Alpha for root <->• root-zone
soil
Uh.
Dry root/root-zone-soil
partition coefficient
unitless
day
mg/kg per mg/kg
Hg(0) Hg(2)
9.50E-01 9.50E-01
NA 2.10E+01
NA 9.00E-01
MHg
9.50E-01
2.10E+01
6.00E+00
professional judgment
professional judgment
Hg(2) - geom mean Leonard et al. 1998, John 1972, Hogg et al.
1978; MHg - assumed, based on Hogg et al. 1978
Stem
Transpiration stream
concentration factor
kg/m3 per kg/m3
0 0.2
0.5
calculation from Norway spruce, Scots pine, Bishop et al. 1998
NOVEMBER 1999
C-35
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE CASE STUDY
Input Parameter
Transfer factor from leaf to
leaf surface (Hg)
Transfer factor from leaf
surface to leaf (Hg
paniculate)
Input Units
Value3
Reference
Leaf Surface
I/day
I/day
2.00E-03
2.00E-01
calculated (1% of transfer factor from leaf surface to leaf)
professional judgment
a NA = not applicable, parameter does not apply to this species of mercury
b VE = volume element
0 LAI = leaf area index
NOVEMBER 1999
C-36
TRIM STATUS REPORT
-------
APPENDIX C
INPUT VALUES BEING DEVELOPED FOR TRIM.FATE MERCURY CASE STUDY
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APPENDIX C
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APPENDIX C
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APPENDIX C
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NOVEMBER 1999 C-44 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
APPENDIX D
Summary of Available Monitoring Data for
TREVLFaTE Mercury Case Study
The test site used to compare TRIM.FaTE module outputs to environmental monitoring
data was selected, in part, due to the amount and kinds of monitoring data available for
comparison. This appendix provides a summary of the mercury monitoring data that are
currently available for the selected test site. The data sets are organized into primarily on-site
and primarily off-site monitoring efforts, with abiotic data sets presented first followed by biotic
data sets. Note that several of the data sets that are listed as on-site include one or two off-site
reference location measurements.
Note: Data sources are not provided because they could reveal the location and identity of the
case study site.
D. 1 ON-SITE MONITORING DATA
D.I.I ON-SITE SOIL MONITORING DATA
Environmental Medium: Surface soil
Number of Data Points: 11 data points from 11 locations
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): June 6-7, 1995, October 27, 1997
Sample Location(s): 10 data points from 10 on-site and 1 data point from a reference location
Purpose of Monitoring: 1995 and 1997 site investigations
Range: 0.18 - 10.3 mg/kg, dry weight
Mean and Standard Deviation: 5.05 ± 3.47 mg/kg, dry weight (median = 4.8 mg/kg, dry
weight)
Other Information: These data correspond in time and location to the earthworm monitoring
data (see below) and overlap with 6 of the 61 surface soil samples listed below. Some of these
samples maybe contaminated by on-site point source releases.
NOVEMBER 1999 D-l TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Environmental Medium: Surface and subsurface soil
Number of Data Points: 113 data points of which 61 are from 0 - 0.5 feet including 2 not
analyzed (NA) and 6 duplicates, and 52 are from 1-1.5 feet including 2 ND at detection level of
0.1 mg/kg, 4 NA, and 4 duplicates
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): August 16, 1994, August 17, 1994, August 18, 1994, May 3, 1995, May 4,
1995, June 6, 1995, June 7, 1995
Sample Location(s): On-site from 56 locations
Purpose of Monitoring: 1995 and 1997 site investigations
Range: (1) At 0 - 0.5 feet: 0.14 - 310 mg/kg
(2) At 1 - 1.5 feet: < 0.1 - 80 mg/kg
Mean and Standard Deviation: (1) At 0 - 0.5 feet: 30.1 ± 47.6 mg/kg (median =13 mg/kg)
(2) At 1 - 1.5 feet: 12.4 ± 16.3 mg/kg (median = 6.2 mg/kg)
Other Information: These data also include measurement of soil pH in some cases. Some of
these samples maybe contaminated by on-site point source releases.
Environmental Medium: Surface soil
Number of Data Points: 56 data points including 6 duplicates from 50 different sampling sites
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): September/October 1997
Sample Location(s): On-site from 50 locations
Purpose of Monitoring: 1997 site investigation
Range: < 0.20 - 310 mg/kg dry weight
Mean and Standard Deviation: 5.37 ± 11.4 mg/kg, dry weight (median =1.2 mg/kg, dry
weight)
Other Information: Some of these samples maybe contaminated by on-site point source
releases.
NOVEMBER 1999 D-2 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Environmental Medium: Surface and subsurface soil.
Number of Data Points: 33 data points from 4 locations, including 19 from 0 - 0.5 feet, 8 from
0 - 0.2 feet, and 6 from 1-1.5 feet
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): November 3, 1997, November 6, 1997, November 14, 1997
Sample Location(s): On-site from 14 sites clustered around 1 location, 11 sites clustered around
another location, and 4 sites each clustered around 2 other locations
Purpose of Monitoring: Delineation soil sampling for 1997 site investigation
Range: (1) 0 - 0.5 feet: 0.1 - 42.5 mg/kg, dry weight
(2) 0 - 0.2 feet: 4.5 - 126.9 mg/kg, dry weight
(3) 1-1.5 feet: 0 - 6.4 mg/kg, dry weight
Mean and Standard Deviation: (1) 0 - 0.5 feet: 8.3 ± 12.6 mg/kg, dry weight
(2) 0 - 0.2 feet: 23.8 ± 41.8 mg/kg, dry weight
(3) 1-1.5 feet: 3.5 ± 2.5 mg/kg, dry weight
Other Information: Some of these samples maybe contaminated by on-site point source
releases.
Environmental Medium: Subsurface soil
Number of Data Points: 107 data points, including 25 data points from 2 cores in different
locations in 1995 including 3 duplicate samples and 9 ND, and 82 data points from 8 cores in
different locations in 1997 including 8 duplicate samples and 2 ND. Data are provided in 2 foot
intervals from 0 feet to up to 57 feet in some cores.
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): 1995 and 1997
Sample Location(s): On-site from 10 different locations
Purpose of Monitoring: 1995 and 1997 site investigations
Range: 0.00 - 239.30 mg/kg (for entire data set, regardless of depth and year)
Mean and Standard Deviation: Due to the nature of this data set, a mean and standard
deviation were not calculated.
NOVEMBER 1999 D-3 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Other Information: Some of these samples maybe contaminated by on-site point source
releases.
D.1.2 ON-SITE BIOTA MONITORING DATA
Environmental Medium: Deer mouse (Peromyscus maniculatus)
Number of Data Points: 9 data points from 9 locations
Measurement Endpoint(s) (Units): (1) Total Mercury concentration (mg/kg, wet weight, whole
body)
(2) Percent moisture (%)
Sampling Date(s): June 1995
Sample Location(s): (1)7 from on-site locations
(2) 2 from an off-site reference location
Purpose of Monitoring: 1995 Site Investigation
Range: (1) On-site: 0.06 - 0.198 mg/kg, wet weight; 70.4 - 77.1 % moisture
(2) Off-site: 0.016 - 0.087 mg/kg, wet weight; 73.5 - 77.3 % moisture
Mean and Standard Deviation: (1) On-site: 0.100 ± 0.063 mg/kg, wet weight; 74.2 ± 2.3
% moisture
(2) Off-site: 0.0515 ± 0.050 mg/kg, wet weight; 75.4 ± 2.7
% moisture
Environmental Medium: Earthworm (Species not specified)
Number of Data Points: 11 data points from 11 locations
Measurement Endpoint(s) (Units): (1) Total mercury concentration (mg/kg, wet weight)
(2) Percent moisture (%)
Sampling Date(s): June 6-7, 1995, October 27, 1997
Sample Location(s): 10 data points on-site and 1 data point from a reference location
Purpose of Monitoring: 1995 and 1997 site investigations
Range: (1) On-site: 0.087 - 2.82 mg/kg, wet weight; 84.3 - 88.6%
(2) Off-site: 0.044 mg/kg, wet weight; 87.9 % (single values)
NOVEMBER 1999 D-4 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Mean and Standard Deviation: (1) On-site: 0.044 mg/kg, wet weight; 87.9 % (single value,
no standard deviation)
(2) Off-site: 0.982 ± 0.79 mg/kg, wet weight; 86.3 ± 1.2 %
Other Information: These data correspond in time and location to one set of soil monitoring
data (see above)
D.2 OFF-SITE MONITORING DATA
D.2.1 OFF-SITE AIR MONITORING DATA
Environmental Medium: Ambient air
Number of Data Points: Approximately 9,000 data points from 3 continuous monitoring
stations. Data quality flags are included indicating automatic calibration, power failure, valid
measurement, standard addition, maintenance and manual calibrations, equipment failure or
malfunction, no peak (i.e., below detection limit), overload (beyond analyzer range), and suspect
data (based on quality assurance measures).
Measurement Endpoint (Units): One-hour average total gaseous mercury (ng/m3)
Sampling Date(s): September 4, 1998 to January 9, 1999 (hourly samples throughout period)
Sample Location(s): (1) Approximately 1,500 feet southeast of facility
(2) approximately 4,300 feet north-northwest of the facility
(3) approximately 6,400 feet north-northwest of the facility
Purpose of Monitoring: To provide data to the state environmental agency as a result of a
consent agreement enforcement order
Range: (1) 0.834 - 157 ng/m3
(2)0.993 -25.8 ng/m3
(3) 0.565 - 14.8 ng/m3
Mean and Standard Deviation: (1) 9.96 ± 15.52 ng/m3 (includes values with all types of
data flags)
(2) 2.46 ±2.15 ng/m3 (includes values with all types of
data flags)
(3) 1.85 ± 1.66 ng/m3 (includes values with all types of data
flags)
Other Information: Corresponding meteorological data are also available from an on-site
monitoring station, including approximately 1,680 data points each for (1) average hourly wind
speed (mph), (2) average hourly wind direction (°N), (3) average hourly ambient temperature
NOVEMBER 1999 D-5 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR MERCURY CASE STUDY
(°C), and (4) average hourly solar radiation (W/m2) from 1 continuous monitoring station from
November 1, 1998 to January 9, 1999. Another data set of meteorological parameters that can be
used as inputs to TREVI.FaTE is available from a continuous monitoring station in Portland, ME.
This data set includes approximately 8,760 hourly averaged measurements from each year from
1990 to 1995 for wind speed (m/s), wind direction (degrees), rural and urban mixing height (m),
precipitation rate (mm/hour), precipitation type (unitless), ambient temperature (°K), stability
class (unitless), friction velocity (m/s), monin-obukhov length (m), and surface roughness length
(m).
D.2.2 OFF-SITE SURFACE WATER MONITORING DATA
Environmental Medium: Surface water
Number of Data Points: 5 data points in 5 locations
Measurement Endpoint(s) (Units): Total mercury concentration (ug/L) (unfiltered samples)
Sampling Date(s): June 1995
Sample Location(s): (1)2 samples located in adjacent river upstream of facility
(2) 3 samples located in adjacent river downstream of facility
Purpose of Monitoring: 1995 site investigation
Range: (1) Upstream: 0.00359 - 0.00529 ug/L
(2) Downstream: 0.000646 - 0.0703 ug/L
Mean and Standard Deviation: (1) Upstream: 0.004 ± 0.001 ug/L (median = 0.004 ug/L)
(2) Downstream: 0.034 ± 0.033 ug/L (median = 0.027 ug/L)
Environmental Medium: Surface water
Number of Data Points: 50 data points plus 6 not analyzed from 14 locations at ebb tide, flood
tide, high tide, and low tide
Measurement Endpoint(s) (Units): Total mercury concentration (ng/L)
Sampling Date(s): August 18-19, 1997
Sample Location(s): In adjacent river
Purpose of Monitoring: 1995 site investigation
Range: 4.09 (flood tide) - 173 (ebb tide) ng/L
NOVEMBER 1999 D-6 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Mean and Standard Deviation: 15 ± 37.71 ng/L
D.2.3 OFF-SITE SEDIMENT MONITORING DATA
Environmental Medium: Sediment
Number of Data Points: 1 data point based on a single measurement each from 4 different off-
site ponds and lakes, including measurements from the pond that is part of the mercury case
study
Measurement Endpoint(s) (Units): Total mercury concentration in the upper 2 cm of the
sediment in the deepest part of the waterbody (mg/kg, dry weight)
Sampling Date(s): (1) September 19, 1996
(2) September 26, 1996
(3) September 20, 1996
(4) September 20, 1996
Sampling Location(s): four nearby offsite lakes and ponds. Deepest part of each waterbody.
Purpose of Monitoring: To determine if lakes and ponds are measurably affected by small, local
air emission sources of mercury
Range: N/A (single value provided)
Mean and Standard Deviation: (1) 0.319 mg/kg (no SD available)
(2) 0.157 mg/kg (no SD available)
(3) 0.201 mg/kg (no SD available)
(4) 0.132 mg/kg (no SD available)
D.2.4 OFF-SITE BIOTA MONITORING DATA
Environmental Medium: Juvenile loon
Number of Data Points: 1 data point from 1 location
Measurement Endpoint(s) (Units): Blood total mercury concentration (ppm, wet weight) from
single loon
Sampling Date(s): July 1998
Sample Location(s): Pond located southeast of the facility that is part of the mercury case study
Purpose of Monitoring: Mercury risk assessment
NOVEMBER 1999 D-7 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR MERCURY CASE STUDY
Range: N/A
Mean and Standard Deviation: 1.3 ppm, wet weight (single value, no standard deviation)
Environmental Medium: Loon egg
Number of Data Points: 3 data points from 1 location
Measurement Endpoint(s) (Units): Total average mercury concentration (ppm, wet weight)
from multiple measurements
Sampling Date(s): June 1998
Sample Location(s): Pond located east of the facility
Purpose of Monitoring: Mercury risk assessment
Range: 1.6 - 1.8 ppm, wet weight
Mean and Standard Deviation: 1.73 ± 0.12 ppm, wet weight
Other Information: The sediment mercury concentration in this pond is 0.319 mg/kg.
Environmental Medium: Loon egg
Number of Data Points: 1 state average based on a sample size of 43
Measurement Endpoint(s) (Units): Total mercury concentration (ppm, wet weight)
Sampling Date(s): June 1998
Sample Location(s): Ponds and lakes in facility's state
Purpose of Monitoring: Mercury risk assessment
Range: N/A
Mean and Standard Deviation: 0.93 ± 0.55 ppm
Environmental Medium: Adult male loon
Number of Data Points: (1)6 data points from 6 locations
(2) 1 state average based on a sample size of 67
NOVEMBER 1999 D-8 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Measurement Endpoint(s) (Units): Blood total mercury concentration (ppm, wet weight)
Sampling Date(s): July - August, 1997-1998
Sample Location(s): Ponds and lakes in facility's state
Purpose of Monitoring: Mercury risk assessment
Range: (1) 0.61 - 3.71 ppm, wet weight for 6 locations
(2) Not provided
Mean and Standard Deviation: (1) 2.62 ± 1.23 ppm, wet weight for 6 locations
(2) 2.5 ± 1.1 ppm, wet weight for state average
Environmental Medium: Adult female loon
Number of Data Points: (1) 1 data point from 1 location
(2) 1 state average based on a sample size of 64
Measurement Endpoint(s) (Units): Mercury total blood concentration (ppm, wet weight)
Sampling Date(s): July 1998
Sample Location(s): Ponds and lakes in facility's state
Purpose of Monitoring: Mercury risk assessment
Mean and Standard Deviation: (1) 1.16 ppm, wet weight (single value, no standard
deviation) for 1 location
(2) 2.1 ± 1.5 ppm, wet weight for state average
Range: (1) N/A for 1 location
(2) Not provided
Environmental Medium: Juvenile loon
Number of Data Points: (1) 5 data points from 5 locations
(2) 1 state average based on a sample size of 52
Measurement Endpoint(s) (Units): Total mercury blood concentration (ppm, wet weight)
Sampling Date(s): July - August, 1997 - 1998
Sample Location(s): Ponds and lakes in facility's state
NOVEMBER 1999 D-9 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR MERCURY CASE STUDY
Purpose of Monitoring: Mercury risk assessment
Range: (1) 0.01 - 0.64 ppm, wet weight for 5 locations
(2) N/A
Mean and Standard Deviation: (1) 0.22 ± 0.24 ppm, wet weight for 5 locations
(2) 0.22 ± 0.29 ppm, wet weight for state average
Environmental Medium: White perch
Number of Data Points: 35 mercury concentration and fish length data points from 4
waterbodies, including (1) 10 data points from 1 pond, (2) 8 data points from 1 pond, (3) 11 data
points from 1 pond, and (4) 6 data points from 1 lake
Measurement Endpoint(s) (Units): (1) Total mercury concentration in skinless fillet
(mg/kg, wet weight)
(2) Fish length (mm)
Sampling Date(s): (1) September 19, 1996
(2) September 20, 1996
(3) September 26, 1996
(4) September 20, 1996
Sample Location(s): (1) Southeast of facility
(2) East of facility
(3) East of facility
(4) East of facility
Purpose of Monitoring: To determine if lakes and ponds are measurably affected by small, local
air emission sources of mercury
Range: (1) 0.50 -1.31 mg/kg, wet weight and 240 - 350 mm in length
(2) 0.28 - 0.72 mg/kg, wet weight and 135 - 270 mm in length
(3) 0.60 - 2.20 mg/kg, wet weight and 186 - 305 mm in length
(4) 0.32 - 0.53 mg/kg, wet weight and 185 - 202 mm in length
Mean and Standard Deviation: (1) 0.98 ± 0.25 mg/kg, wet weight and 308 ± 32 mm in
length
(2) 0.45 ± 0.14 mg/kg, wet weight and 224 ± 48 mm in
length
(3) 1.07 ± 0.43 mg/kg, wet weight and 231 ± 34 mm in
length
(4) 0.41 ± 0.08 mg/kg, wet weight and 195 ± 8 mm in
length
NOVEMBER 1999 D-10 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR TRIM.FATE MERCURY CASE STUDY
Environmental Medium: Short-tailed Shrew (Blarina brevicaudd)
Number of Data Points: 1 data point from 1 location
Measurement Endpoint(s) (Units): (1) Total mercury concentration (mg/kg, wet weight, whole
body)
(2) Percent moisture (%)
Sampling Date(s): June 1995
Sample Location(s): Off-site reference location
Purpose of Monitoring: 1995 Site Investigation
Range: N/A
Mean and Standard Deviation: (1) 0.064 mg/kg, wet weight (single value, no standard
deviation)
(2) 73.4 % (single value, no standard deviation)
Environmental Medium: Eel (Anguilla rostratd)
Number of Data Points: 15 data points, including (1) 6 from upstream of the site and (2) 9 from
downstream of the site
Measurement Endpoint(s) (Units): (1) Total mercury concentration (mg/kg, dry weight, fillets)
(2) Percent moisture (%)
(3) Total mercury concentration (mg/kg, wet weight, fillets)
(4) Eel weight (grams)
(5) Eel length (cm)
Sampling Date(s): June 6, 1995
Sample Locations: One location each in the river upstream and downstream of the facility
Purpose of Monitoring: 1995 site investigation
Range: (1) Upstream: 1.08 - 4.49 mg/kg, dry weight; 74.9 - 80.5 % moisture; 0.271 -
0.876 mg/kg, wet weight; 50 - 200 g; 30 - 46 cm
(2) Downstream: 1.2 - 3.64 mg/kg, dry weight; 70.5 - 81.6 % moisture; 0.259 -
0.678 mg/kg, wet weight; 50 - 375 g; 28 - 56 cm
Mean and Standard Deviation: (1) Upstream: 2.5 ± 1.1 mg/kg, dry weight; 78 ± 2 %
moisture; 0.53 ± 0.2 mg/kg, wet weight; 110 ± 56.01 g; 37
± 5.72 cm
NOVEMBER 1999 D-l 1 TRIM STATUS REPORT
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APPENDIX D
SUMMARY OF AVAILABLE MONITORING DATA FOR MERCURY CASE STUDY
(2) Downstream: 2.14 ± 0.81 mg/kg, dry weight; 78 ± 3 %
moisture; 0.46 ± 0.14 mg/kg, wet weight; 75 ± 101.64 g; 33
± 8.92 cm
Environmental Medium: River minnow (killyfish) (Fundulus heteroclitus)
Number of Data Points: 1 data point from 1 location
Measurement Endpoint(s) (Units): Total mercury concentration (mg/kg, dry weight, composite
whole body)
Sampling Date(s): August 1, 1995
Sample Location(s): In river downstream of the facility
Purpose of Monitoring: 1995 site investigation
Range: N/A
Mean and Standard Deviation: 0.447 mg/kg, dry weight (single value, no standard deviation)
NOVEMBER 1999 D-12 TRIM STATUS REPORT
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