^ PRO!*
Total Risk Integrated Methodology
TRIM.FaTE Technical Support Document
Volume I: Description of Module
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EPA-453/R-02-011a
September 2002
TRIM
Total Risk Integrated Methodology
TRIM.FaTE
Technical Support Document
Volume I: Description of Module
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Emissions Standards & Air Quality Strategies and Standards Divisions
Research Triangle Park, North Carolina
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DISCLAIMER
DISCLAIMER
This document has been reviewed and approved for publication by the U.S.
Environmental Protection Agency. It does not constitute Agency policy. Mention of trade
names or commercial products is not intended to constitute endorsement or recommendation for
use.
SEPTEMBER 2002 i TRIM.FATE TSD VOLUME I
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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 (EPA) is developing the Total Risk Integrated
Methodology. Individuals and organizations who have been involved in the TREVI.FaTE
development effort and in the preparation of this report are listed below.
Steve Fine, EPA, Office of Research and Development
Robert G. Hetes, EPA, Office of Air Quality Planning and Standards
John Langstaff, EPA, Office of Air Quality Planning and Standards
Thomas McCurdy, EPA, Office of Research and Development
Deirdre L. Murphy, EPA, Office of Air Quality Planning and Standards
Ted Palma, EPA, Office of Air Quality Planning and Standards
Harvey M. Richmond, EPA, 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
David Burch, ICF Consulting
Rebecca A. Efroymson, Oak Ridge National Laboratory
Alison Eyth, MCNC-Environmental Modeling Center
Baxter Jones, ICF Consulting
Daniel S. Jones, Oak Ridge National Laboratory
Mark Lee, MCNC - Environmental Modeling Center
Bradford F. Lyon, University of Tennessee
Thomas E. McKone, Lawrence Berkeley National Laboratory & University of California, Berkeley
Margaret E. McVey, ICF Consulting
Randy Maddalena, Lawrence Berkeley National Laboratory
SEPTEMBER 2002 iii TRIM.FATE TSD VOLUME I
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ACKNOWLEDGMENTS
The following external experts reviewed a previous draft of this document.
Science Advisory Board Reviewers
Chair
Mitchell Small, Professor, Department of Civil Engineering & Public Policy,
Carnegie Mellon University, Pittsburgh, PA
Members
Steven M. Bartell, Senior Associate,
Cadmus Group, Inc., Oak Ridge, TN
Calvin Chien, Senior Environmental Fellow,
E.I. DuPont Company,
Wilmington, DE
Kai-Shen Liu, Epidemiologist, California
Department of Health Services,
Environmental Health Laboratory Branch,
Berkeley, CA
Paulette Middleton, Associate Director,
Environmental Science and Policy Center,
RAND Corporation, Boulder, CO
Ishwar Murarka, Chief Scientist and
President, ISHInc., Cupertino, CA
CONSULTANTS
M. Bruce Beck, Professor & Eminent
Scholar, Warnell School of Forest
Resources,
University of Georgia, Athens GA
Linfield Brown, Professor, Department of
Civil and Environmental Engineering,
Tufts University, Medford, MA
Arthur J. Gold, Professor, Department of
Natural Resources Science,
University of Rhode Island, Kingston, RI
Helen Grogan, Research Scientist, Cascade
Scientific, Inc., Bend, OR
Wu-Seng Lung, Professor, Department of
Civil Engineering,
University of Virginia, Charlottesville, VA
Jana Milford, Associate Professor,
Department of Mechanical Engineering,
University of Colorado, Boulder, CO
Thomas Theis, Professor & Chair,
Department of Civil and Environmental
Engineering, Clarkson University,
Potsdam, NY
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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
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PREFACE
PREFACE
This document, the TRIM.FaTE Technical Support Document, is part of a series of
documentation for the overall Total Risk Integrated Methodology (TRIM) modeling system.
The detailed documentation of TRIM's logic, assumptions, algorithms, equations, and input
parameters is provided in comprehensive Technical Support Documents (TSDs) and/or user's
guidance for each of the TRIM modules. This report, which supersedes earlier versions (U.S.
EPA 1998a, U.S. EPA 1999g,h), documents the Environmental Fate, Transport, and Ecological
Exposure module of TRIM (TRIM.FaTE) and is divided into two volumes. The first volume
provides a description of terminology, model framework, and functionality of TRIM.FaTE, and
the second volume presents a detailed description of the algorithms used in the module.
Comments and suggestions are welcomed. The OAQPS leads on the various modules are
provided below with their individual roles and addresses.
TRIM coordination
and TRIM.FaTE
TRIM.Expo
[Inhalation]
TRIM.Expo
[Ingestion]
TRIM.Risk
Deirdre L. Murphy
REAG/ESD/OAQPS
C404-01
RTF, NC 27711
[murphy.deirdre@epa.gov]
Ted Palma
REAG/ESD/OAQPS
C404-01
RTF, NC 27711
[palma.ted@epa.gov]
Amy B. Vasu
REAG/ESD/OAQPS
C404-01
RTF, NC 27711
[vasu.amy@epa.gov]
Terri Hollingsworth
REAG/ESD/OAQPS
C404-01
RTF, NC 27711
[hollingsworth.terri@epa.gov]
Harvey M. Richmond
HEEG/AQSSD/OAQPS
C404-01
RTF, NC 27711
[richmond.harvey@epa.gov]
SEPTEMBER 2002
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ACRONYMS
ACRONYMS
B(a)P Benzo(a)pyrene
CAA Clean Air Act
CalTOX California Department of Toxic Substance Control's Risk Computerized Model
CART Classification and regression tree
CDF Cumulative distribution function
CRARM Presidential/Congressional Commission on Risk Assessment and Risk
Management
DOE United States Department of Energy
EPA United States Environmental Protection Agency
GIS Geographic Information System
HAP Hazardous air pollutant
IEM Indirect Exposure Methodology
I/O API Environmental Decision Support System/Models 3 Input/Output Applications
Programming Interface
ISMCM Integrated Spatial Multimedia Compartment Model
LHS Latin Hypercube Sampling
LSODE Livermore Solver for Ordinary Differential Equations
MC Monte Carlo
MCM Multimedia Compartment Model
MEPAS Multimedia Environmental Pollutant Assessment System
MEVIS Multimedia Integrated Modeling System
MMSP Multimedia, multipathway, and multireceptor simulation process
MPE Multiple Pathways of Exposure
NAAQS National ambient air quality standard
NAS National Academy of Sciences
NATA National Air Toxics Assessments
OAQPS EPA Office of Air Quality Planning and Standards
OSWER EPA Office of Solid Waste and Emergency Response
PAH Polycyclic aromatic hydrocarbon
PDF Probability distribution function
RIA Regulatory impact analysis
SAB Science Advisory Board
SMCM Spatial Multimedia Compartment Model
TRIM Total Risk Integrated Methodology
TRIM.Expo TRIM Exposure-Event module
TRIM.FaTE TRIM Environmental Fate, Transport, and Ecological Exposure module
TRIM.Risk TRIM Risk Characterization module
TSD Technical Support Document
USGS United States Geological Survey
WASP Water Quality Analysis Simulation Program
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TABLE OF CONTENTS
TABLE OF CONTENTS
DISCLAIMER i
ACKNOWLEDGMENTS iii
PREFACE vii
ACRONYMS ix
TABLE OF CONTENTS xi
1. INTRODUCTION 1-1
1.1 Goals and Objectives for TRIM 1-2
1.2 TRIM Design 1-4
1.2.1 Description of TRIM.FaTE 1-6
1.2.2 Description of TRIM.Expo 1-6
1.2.3 Description of TRIM.Risk 1-7
1.3 TRIM Development 1-8
1.4 Phasing TRIM into OAQPS' Set of Modeling Tools 1-9
2. INTRODUCTION TO TREVLFaTE 2-1
2.1 Review of Existing Fate and Transport Models 2-1
2.2 Approach to Developing TRIM.FaTE 2-6
2.3 Key Capabilities of TRIM.FaTE 2-8
2.3.1 Truly Coupled Multimedia Framework 2-9
2.3.2 Scalable Complexity 2-9
2.3.3 Flexible Algorithm Library 2-10
2.3.4 Full Mass Balance 2-10
2.3.5 Embedded Procedure For Uncertainty and Variability Analysis 2-11
2.3.6 Exposure Model for Ecological Receptors 2-11
3. OVERVIEW OF TREVLFaTE CONCEPTS AND TERMINOLOGY 3-1
3.1 Basic TRIM.FaTE Terminology 3-1
3.2 Compartment Types 3-6
3.2.1 Abiotic Compartment Types 3-7
3.2.2 Biotic Compartment Types 3-7
3.3 Links 3-10
3.4 Sources 3-11
3.5 Time-Related Terms and Concepts 3-14
3.5.1 Basic Time Terminology 3-14
3.5.2 Other Time-related Concepts 3-15
4. CONCEPTUAL DESIGN AND MASS BALANCE FRAMEWORK FOR
TREVLFaTE 4-1
4.1 Conceptual Design 4-1
4.2 Mass Balance Concepts and Equations 4-1
4.3 Phases 4-11
4.4 Fate, Transformation, and Transport Processes 4-12
4.4.1 Advection 4-12
4.4.2 Dispersion 4-12
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TABLE OF CONTENTS
4.4.3 Diffusion 4-13
4.4.4. Biotic Processes 4-13
4.4.5 Reaction and Transformation 4-14
5. APPLICATION OF TRIM.FaTE 5-1
5.1 General Process for a TRIM.FaTE Simulation 5-1
5.2 Problem Definition 5-3
5.2.1 Determining Source(s) and Chemical(s) to be Modeled 5-3
5.2.2 Determining Scale and Spatial Resolution 5-3
5.2.2.1 Specifying the Modeling Region 5-4
5.2.2.2 Specifying Parcels 5-4
5.2.2.3 Specifying Volume Elements 5-6
5.2.3 Determining Compartments 5-7
5.2.3.1 Abiotic Compartments 5-7
5.2.3.2 Biotic Compartments 5-7
5.2.4 Determining Simulation Period 5-7
5.3 Determining Links/Algorithms 5-8
5.4 Simulation Set-up 5-13
5.4.1 Chemical Properties 5-14
5.4.2 Initial and Boundary Conditions 5-14
5.4.3 Source Data 5-15
5.4.4 Environmental Setting Data 5-15
5.4.4.1 Meteorological Data 5-15
5.4.4.2 Other Environmental Setting Data 5-15
5.4.5 Defining Time Steps 5-16
5.5 Simulation Implementation 5-16
5.6 Analysis of Results 5-17
6. TREATMENT OF UNCERTAINTY AND VARIABILITY 6-1
6.1 Sensitivity Analysis 6-2
6.2 The Monte Carlo Approach for Uncertainty and Variability Analyses 6-6
6.2.1 Two-stage Monte Carlo Design 6-7
6.2.2 Distributions of Input Parameters 6-9
6.2.3 Latin Hypercube Sampling 6-9
6.2.4 Treatment of Tails of Distributions 6-10
6.2.5 Tracking Information Between Modules 6-10
6.2.6 Computational Resources 6-12
6.2.7 Spatial and Temporal Resolution and Aggregation 6-12
6.2.8 Specification of Probability Distributions of Model Inputs 6-12
6.3 Presentation of Uncertainty Results 6-13
7. REFERENCES 7-1
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TABLE OF CONTENTS
APPENDICES
A. Glossary A-l
B. Integrating External Models or Measured Data into TRIM.FaTE B-l
B. 1 Compromises that Must Be Made in Order to Use Externally Derived
Compartment Concentrations B-l
B.2 Implementation Details in the Case of First-Order Transfers - Case of Constant
Inputs B-2
C. Prototypes I-IV C-l
C.I Computer Implementation of Prototypes C-l
C.2 Prototype Development C-2
C.2.1 Prototype I C-2
C.2.2 Prototype II C-4
C.2.3 Prototype III C-6
C.2.4 Prototype IV C-8
C.3 Prototype Features C-8
C.3.1 Abiotic Compartments C-8
C.3.2 Biotic Compartments C-8
C.3.3 Links C-10
C.4 PAH-Specific Values Used in Testing of Prototype IV C-12
C.4.1 Transformation of PAHs by Plants C-12
C.4.1.1 Metabolism in Plants C-12
C.4.1.2 Photolysis on the Plant Surface C-13
C.4.2 Distribution, Elimination, and Transformation of PAHs in Wildlife . C-14
C.4.3 Uptake of PAHs by Benthic Infauna C-15
C.5 References C-17
D. TRIM.FaTE Computer Framework D-l
D.I Software Architecture D-2
D.I.I Architecture of the Prototypes D-2
D.1.2 Version 2.5 Architecture D-3
D.l.2.1 TRIM.FaTE Core D-3
D.l.2.2 Project D-5
D.l.2.3 Libraries D-5
D.I.2.4 External Data Sources, Importers, and Exporters .... D-5
D.I.2.5 Analysis and Visualization Tools D-6
D.2 Implementation Approaches D-6
D.3 Implementation Language D-7
D.3.1 Prototypes D-7
D.3.2 Version 2.5 D-7
D.4 Embedded Functions D-8
D.4.1 Advective Transport between Air Compartments D-8
D.4.2 Interfacial Area between Volume Elements D-8
D.4.3 Area of Volume Elements D-9
D.4.4 Volume of Volume Elements D-9
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TABLE OF CONTENTS
D.4.5 Boundary Contribution D-9
D.4.6 Other Mathematical Functions D-9
D.5 References D-9
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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) 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
and 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 first
developmental phase of TRIM, 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 1998b), was reviewed by EPA's Science Advisory
Board (SAB) in May 1998 (U.S. EPA 1998c). The second developmental phase included
refining TRIM.FaTE and developing a model evaluation plan, initiating development of the
second module (TREVI.Expo), and conceptualizing the third module (TRIM.Risk). In addition,
progress was 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 draft Status Report and draft Technical Support Documents (TSDs)
documenting this phase (U.S. EPA 1999a-f) were subjected to review by representatives from
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.
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the major program offices at EPA and an EPA Models 20002 review team prior to this SAB
advisory. Since review of the draft documents, TRIM.FaTE activities have focused on model
evaluation, implementation of the uncertainty analysis feature, and documentation of associated
libraries.
This Technical Support Document (TSD) provides technical documentation and support
for TRIM.FaTE, the environmental fate, transport, and ecological exposure module of the TRIM
modeling system. The TSD is divided into two volumes. Volume I provides a description of the
terminology, model framework, and functionality of TRIM.FaTE. Specifically, Chapter 2
provides an overview of the development and features of TRIM.FaTE, Chapters 3 and 4 discuss
the TRIM.FaTE terminology and conceptual design, Chapter 5 provides a general description of
how the conceptual design is implemented in TRIM.FaTE, and Chapter 6 explains the treatment
of uncertainty and variability in TRIM.FaTE. A glossary of key terms related to TRIM.FaTE is
presented in Appendix A. Volume II of the TSD presents detailed descriptions of the equations
used in the TRIM.FaTE module.
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 has been 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 has been 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
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 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.
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core substance (e.g., mercury for methylmercury and 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, and multiple exposure pathway
assessment. The TRIM modeling system is being designed to facilitate assessment of
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 has the following characteristics.
Easily accessible. The TRIM modeling system is accessible for use with a personal
computer (PC). The system will be available for download from the Internet and
accessible through an Agency model system framework (e.g.., Multimedia Integrated
Modeling System (MIMS)).
Well-documented. In addition to Technical Support Documents, Users Guidance is
available to assist with setting up and running the model, and interpreting results.
Clear and transparent. The graphical user interface of the TRIM computer framework
provides transparency and clarity in the functioning of the TRIM modules, and output
from the modules will include information on modeling assumptions, limitations, and
uncertainties.
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1.2 TRIM DESIGN
The TRIM design (Figure 1-1) includes three individual modules. The Environmental
Fate, Transport, and Ecological Exposure module, TREVLFaTE, accounts for movement of a
chemical through a comprehensive system of discrete compartments (e.g., media and 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 throughout the entire
system. In addition to providing exposure estimates relevant to ecological risk assessment,
TRIM.FaTE generates media concentrations relevant to human pollutant exposures that can be
used as input to the Exposure-Event module, TREVLExpo.3 In TRIM.Expo, human exposures
are evaluated by tracking either randomly selected individuals that represent an area's population
or 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 designed to characterize ecological risks
from multimedia exposures. The output from TRIM.Risk is intended to 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 to assess the
overall precision of the model. The uncertainty and variability feature augments 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 have been developed in a phased approach, and refinements can be
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, TRIM.Expo may be applied without the need to run TRIM.FaTE,
using ambient concentration data or the output from an air quality model external to TRIM. The
output from TRIM.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.
3 A farm food chain (FFC) model is also available as a method for deriving livestock and produce
contaminant concentrations from soil and air concentrations and air deposition outputs from TRIM.FaTE.
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Figure 1-1
Conceptual Design of TRIM
Air Quality Models
(e.g., ISC3, AERMOD)
Other Multimedia Models
(e.g., MEND-TOX)
Environmental Fate,
Transport, and
Ecological Exposure
(TRIM.FaTE)
Inputs
Physical, Chemical
Properties
f Site-:
specific Data
GIS Data
I Monitoring Data)
Temporal and
Spatial Distribution
of Pollutant
Concentrations
Inputs
(Activity Data 7
(e.g., CHAD) {
(Population Data 7
(e.g., 2000 BOC) \
Indoor/Outdoor 7
Concentration Ratios
[media concentrations
relevant to human
exposures]
Exposure Event
(TRIM.Expo)
[Ingestion] [Inhalation]
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)
[HH] [ECO]
[media and biota
concentrations and biota
pollutant intake rates
relevant to ecological
exposures]
Ecological Effects
Assessment
(e.g., endpoints, criteria)
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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1.2.1 DESCRIPTION OF TRUVLFaTE
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 TRIM.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.
Additional details regarding TRIM.FaTE are covered in the chapters that follow.
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 may be added
in the future.
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,
drinking water, foods, household dusts, and soils).
TRIM.Expo is currently comprised of two components, one for inhalation exposure and
one for ingestion exposure. The inhalation component of TRIM.Expo predicts inhalation
exposures of many individuals randomly selected to represent an area's population by tracking
the movement of each individual through locations where chemical exposure can occur
according to specific activity patterns. In a typical application, the inhalation component can
combine either processed air monitoring data or air dispersion modeling results with the activity
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patterns of the individuals and micro-
environment concentration relationships
to estimate exposures. The movements
of an individual are defined as an
exposure-event sequence that can be
related to time periods for which
exposure concentrations are estimated.
Each exposure event places the
individual 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 also can
provide information relating to the
potential for pollutant uptake (e.g.,
respiration rate). Exposures associated
with these events are aggregated to
predict an exposure concentration for the
time period of interest.
The primary purpose of the
ingestion component of TRIM.Expo is
for the assessment of ingestion exposure
to air pollutants that are persistent and/or
bioaccumulative. The ingestion
component calculates the ingestion exposure (in units of mg of constituent per kg of body weight
per day) to human receptor groups from media and food concentrations. In a typical application,
the ingestion component can accept TRIM.FaTE output data or other pollutant concentration
data for media and biota to estimate human exposure. TRIM.FaTE can be used to provide an
inventory of chemical concentrations in environmental media and in biota across the ecosystem
at selected time intervals (e.g., days, hours). A farm food chain module is also available to
provide livestock and produce contaminant estimates from air and soil concentrations and air
deposition estimates provided by TRIM.FaTE or from an external file.
In addition to directly providing human population exposure estimates, 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 assessment and to provide quantitative
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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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 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 and associated tools will provide the
capability to summarize or highlight the major points from each of the analyses conducted in the
TRIM modules. In general, this will include: (1) documentation of assumptions and input data,
(2) risk calculations and data analysis, and (3) presentation of results and supporting information.
Current and proposed EPA guidance on risk characterization will guide the development
of TRIM.Risk and associated tools. 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 types 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,4 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.,
similar to the Chart Wizard in Microsoftฎ 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. Consequently, review of existing models and consideration of other current modeling
efforts has been an important part of TRIM development activities. Reviews of relevant models
existing at the initiation of development activities for TRIM.FaTE are described in Chapter 2 of
this document. Additionally, EPA has incorporated TRIM into EPA's MIMS, which is
essentially a modeling framework that accommodates linkages among multiple models and the
sharing of common tools and data.
Consistent with Agency peer review policy (U.S. EPA 1998d) and the 1994 Agency Task
Force on Environmental Regulatory Modeling (U.S. EPA 1994a), internal and external peer
review has been an integral part of the TRIM development plan. Following the first phase of
TRIM development, OAQPS submitted TRIM (U.S. EPA 1998a, U.S. EPA 1998b) to SAB
under their advisory method of review. In May 1998 in Washington, DC, the Environmental
4 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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Models Subcommittee (Subcommittee) of the Executive Committee of SAB reviewed the TRIM
project, assessing the overall conceptual approach of TRIM and the specific approach of
TRIM.FaTE. The Subcommittee reported that the development of TRIM and the TRIM.FaTE
module was conceptually sound and scientifically based (U.S. EPA 1998c) and provided specific
recommendations related to six specific charge questions. These recommendations are detailed
in Chapter 2 of the 1999 TRIM Status Report (U.S. EPA 1999b), along with brief responses.
Changes to TRIM.FaTE based in part on the SAB recommendations are highlighted in Chapter 4
of the 1999 TRIM Status Report.
TRIM was again submitted to SAB advisory review in December 1999, when the
Subcommittee met to review progress on TRIM (U.S. EPA 1999a,b, U.S. EPA 1999g,h). In
providing comments and recommendations the Subcommittee found "ongoing efforts to develop
TRIM as a flexible, state-of-the-art system for evaluation multimedia chemical fate, transport,
exposure and risk, to be effective and innovative" (U.S. EPA 2000). In consideration of SAB's
comments and recommendations, TRIM.FaTE was refined, including the development of new
and updated capabilities and the development and limited testing of methodologies for model
set-up, uncertainty and variability analysis, and evaluation. This TRIM.FaTE documentation has
also gone through internal Agency peer review, involving reviewers across the Agency from
major program offices, the Office of Research and Development, and staff involved in the
Agency's Models 2000 efforts, as well as the SAB reviews described above.
In addition to consulting with Agency scientists during future TRIM development (i.e.,
peer involvement), OAQPS will, as appropriate, seek peer review of new aspects in 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
1998d) 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 (HAP) (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 such as National Air
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Toxics Assessments (NATA) are described as one of the program's key components.5 NATA
include 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 EXAMPLES OF TRIM APPLICATIONS
needs for TRIM comes in the Residual Risk
Program, in which there are many upcoming
statutory deadlines for risk-based emissions
standards decisions. As described in the
Residual Risk Report to Congress (U.S. EPA
1999 f), 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
, ,. ,,..,. volatile HAPs in a metropolitan area could
for multimedia environmental distribution. be deveioped using an external air model or
A human health or ecological assessment of
multimedia, multipathway 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
ambient concentration data from fixed-site
monitors coupled with TRIM.Expo and
TRIM.Risk.
Another important upcoming use for TRIM is
in exposure assessment in support of the
review of the ozone National Ambient Air
Quality Standards (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. The 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 TRIM.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
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.
In support of the Mercury Report to Congress (U.S. EPA 1997) and the Study of Hazardous Air Pollutant
Emissions from Electric Utility Steam Generating Units -Final Report to Congress (U.S. EPA 1998e), the Agency
relied upon the Indirect Exposure Methodology, which has recently been updated and is now titled 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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1986)). When TREVI.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 TRIM.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.
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2. INTRODUCTION TO TRIM.FaTE
Implementation of the TRIM system began with development of the TRIM
Environmental Fate, Transport, and Ecological Exposure module (TRIM.FaTE), a flexible
multimedia fate and transport model designed to estimate pollutant concentrations in various
environmental compartments (i.e., media and organisms). These media and biota concentrations,
as well as estimates of pollutant intake by organisms, provide measures of ecological exposure in
various biota on a temporal and spatial scale. The media and biota concentrations also provide
temporally and spatially varying inputs for a human exposure model such as TRIM.Expo, which
can model population cohorts through space and time.
Prior to and during the initial development of TRIM.FaTE, EPA reviewed the features of
existing multimedia models and approaches in order to build on, rather than duplicate, previous
efforts. In these reviews, the Agency focused on how the existing models addressed the
following characteristics desired for TRIM.FaTE:
Ability to address varying time steps (of one hour or greater) and provide sufficient
spatial detail at varying scales (site-specific to urban scale);
Conservation of pollutant mass within the system being assessed;
Transparency, as needed for use in a regulatory context; and
Performance as a truly coupled multimedia model rather than a set of linked single
medium models.
As a result of the Agency's reviews of other models (Section 2.1), OAQPS concluded (as
described in Section 2.2) that in order to meet the Office's needs for assessing human health and
ecological risks of exposure to criteria and hazardous air pollutants, it was necessary to develop
a new, truly coupled, multimedia modeling framework. In developing TRIM.FaTE, the Agency
has incorporated several features that improve upon the capabilities of the existing models
considered during the review. These key features are summarized in Section 2.3. A complete
description of the TRIM.FaTE computer framework is presented in Appendix D.
2.1 REVIEW OF EXISTING FATE AND TRANSPORT MODELS
Efforts to assess human exposure from multiple media date back to the 1950s, when the
need to assess human exposure to global radioactive fallout led rapidly to a framework that
included transport through and transfers among air, soil, surface water, vegetation, and food
chains (Wicker and Kirchner 1987). Efforts to apply such a framework to non-radioactive
organic and inorganic toxic chemicals have been more recent and have not as yet achieved the
level of sophistication that exists in the radioecology field. In response to the need for
multimedia models in exposure assessment, a number of multimedia transport and
transformation models have been recently developed.
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Thibodeaux (1979, 1996) proposed the term "chemodynamics" to describe a set of
integrated methods for assessing the cross-media transfers of organic chemicals. The first
widely used multimedia compartment modeling approaches for organic chemicals were the
"fugacity" models proposed by Mackay (1979, 1991) and Mackay and Paterson (1981, 1982).
Cohen and his co-workers applied the concept of multimedia compartment modeling as a
screening tool with the Multimedia Compartment Model (MCM) (Cohen and Ryan 1985),
followed by the Spatial MCM (SMCM) (Cohen et al. 1990), and more recently with the
Integrated SMCM (ISMCM), which allows for non-uniformity in some compartments (van de
Water 1995). Another multimedia screening model, called GEOTOX (McKone and Layton
1986), was one of the earliest multimedia models to explicitly address human exposure. The
CalTOX program (McKone 1993a,b,c) has been developed for the California EPA as a set of
spreadsheet models and spreadsheet data sets to assist in assessing human exposures to toxic
substance releases in multiple media. More recently, SimpleBOX (van de Meent 1993, Brandes
et al. 1997) has been developed for the National Institute of Public Health and the Environment
in the Netherlands to evaluate the environmental fate of chemicals.
In 1996, EPA undertook a review of existing models and approaches as an initial step in
the TRIM development effort. The resulting report, entitled Evaluation of Existing Approaches
for Assessing Non-Inhalation Exposure and Risk with Recommendations for Implementing TRIM
(Mosier and Johnson 1996), examined several multimedia models. Two additional EPA studies
conducted in 1997 (IT 1997a,b) updated the 1996 study.
The initial literature searches identified several models/approaches for multimedia,
multipathway modeling, including EPA's Indirect Exposure Methodology (IEM), the California
Department of Toxic Substance Control's Multimedia Risk Computerized Model (CalTOX), the
Dutch model SimpleBOX, the Integrated Spatial Multimedia Compartmental Model (ISMCM),
and the Multimedia Environmental Pollutant Assessment System (MEPAS).
A brief summary of the key multimedia models that were evaluated for applicability to
the TREVI.FaTE effort follows. Other models that were reviewed are documented in the
previously mentioned background reports (Mosier et al. 1996, IT 1997a, IT 1997b).
Indirect Exposure Methodology (IEM). The version of IEM reviewed by OAQPS
during initial TRIM development efforts consisted of a set of multimedia fate and
exposure algorithms developed by EPA's Office of Research and Development. This
methodology was, and remains today, a significant Agency methodology for multimedia,
multipathway modeling for pollutants for which indirect (i.e., non-inhalation) impacts
may be important (i.e., organic and inorganic pollutants that tend to be long-lived,
bioaccumulating, non- (or at most semi-) volatile, and more associated with soil and
sediment than with water).
An interim document describing this methodology was published in 1990 (U.S. EPA
1990), a major addendum was issued in 1993 (U.S. EPA 1993), and an updated guidance
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document was published in 1999 (U.S. EPA 1999d).1 The IBM assessed by OAQPS had
undergone extensive scientific review, including review by SAB, which was useful in
focusing efforts in the development of TRIM. The SAB identified several limitations of
IBM that were pertinent to its application to the design qualifications for TRIM (U.S.
EPA 1994b). Concurrently with IBM development, EPA also developed and applied a
closely related set of multimedia models in a variety of dioxin assessments (U.S. EPA
1994c).
Descriptions of fate and transport algorithms, exposure pathways, receptor scenarios, and
dose algorithms are presented in the existing IEM documentation. The IEM includes
procedures for estimating the indirect human exposures and health risks that could result
from the transfer of emitted air pollutants to soil, vegetation, and water bodies. The
methodology addresses exposures via inhalation, food, water, and soil ingestion, and
dermal contact.
During its review of the IEM methodology, OAQPS identified several limitations in the
IEM approach relative to the TRIM.FaTE design criteria and OAQPS' needs. For
example, the version of IEM evaluated could be applied only to chemicals that were
emitted to the air. This limited its ability to provide assessment of media concentrations
resulting from air emissions when other pollutant sources might have a significant impact
on the results. However, IEM is an evolving and emerging methodology that has moved
EPA beyond analyzing the potential effects associated with only one medium (air) and
exposure pathway (inhalation) to the consideration of multiple media and exposure
pathways. It was crucial in the initial development of TRIM, and remains true today, that
a sense of continuity be maintained between IEM and TRIM methodologies.
The IEM was designed to predict long-term, steady-state impacts from continuous
sources, not short-term, time series estimates. It consists of a one-way process through a
series of linked models or algorithms and requires annual average air concentrations and
wet and dry deposition values from air dispersion modeling external to IEM. As a result,
IEM could not provide detailed time-series estimation (e.g., for time steps less than one
year) of media concentrations and concomitant exposure, could not maintain full mass
balance, and, because it was not a truly coupled multimedia model, did not have the
ability to model "feedback" loops between media or secondary emissions (e.g., re-
emission of deposited pollutants). Furthermore, IEM did not provide for the flexibility
OAQPS considered as necessary in site-specific applications or in estimating population
exposures. Significant site-specific adjustment would be necessary to allow for spatially
1 Since OAQPS' initial review and consideration of IEM in 1996, the methodology and its documentation
have undergone several important changes. A draft revised document addressing SAB and public comments on the
1993 Addendum was released for review in 1998 (U.S. EPA 1998f). The IEM2M was derived from IEM and
applied by OAQPS to estimate exposures to mercury for the Mercury Study Report to Congress (U.S. EPA 1997).
The Agency's Office of Solid Waste and Emergency Response (OSWER) has adapted IEM and compiled detailed
information on many of IBM's input parameters and algorithms in the Human Health Risk Assessment Protocol for
Hazardous Waste Combustion Facilities (U.S. EPA 1998g), which has been applied to assess exposures and risks for
many hazardous waste combustion facilities. The most up-to-date version of the general IBM methodology was
published in late 1999 (U.S. EPA 1999d). The updated documentation no longer refers to the methodology as IBM;
it is now referred to as the Multiple Pathways of Exposure (MPE) methodology.
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tracking differences in concentrations and exposures. Much of the focus of IBM was on
evaluating specific receptor scenarios (e.g., recreational or subsistence fisher) that may be
indicative of high-end or average exposures, but the model was not designed to model the
range of exposures within a population (e.g., IBM could not estimate population exposure
distributions). More recent advances (Rice et al. 1997) addressed some of these issues to
some degree, but at the time of OAQPS review, these were not fully implemented.
Therefore, while IBM met its own design criteria quite well (e.g., could adequately
estimate long-term average exposure media concentrations in the vicinity of an air source
for contaminants for which indirect impacts were important), it did not fully meet the
needs of OAQPS at the time of initial TRIM development for the reasons noted above.
California Department of Toxic Substance Control's Multimedia Risk
Computerized Model (CalTOX). First issued in 1993 (McKone 1993a,b,c) and updated
in 1995, CalTOX was developed as a spreadsheet model for California's Department of
Toxic Substance Control to assist in human health risk assessments that address
contaminated soils and the contamination of adjacent air, surface water, sediment, and
ground water. CalTOX consists of two component models: a multimedia transport and
transformation (i.e., fate and transport) model, which is based on both conservation of
mass and chemical equilibrium; and a multipathway human exposure model that includes
ingestion, inhalation, and dermal uptake exposure routes. CalTOX is a fully mass
balancing model that includes add-ins to quantify uncertainty and variability.
The version of CalTOX reviewed by OAQPS was a dynamic multimedia transport and
transformation model that could be used to assess time-varying concentrations of
contaminants introduced initially to soil layers or for contaminants released continuously
to air, soil, or water. The CalTOX multimedia model was a seven-compartment regional
and dynamic multimedia fugacity model. The seven compartments were: (1) air, (2)
surface soil, (3) plants, (4) root-zone soil, (5) the vadose-zone soil below the root zone,
(6) surface water, and (7) sediment. The air, surface water, surface soil, plants, and
sediment compartments were assumed to be in quasi-steady-state with the root-zone soil
and vadose-zone soil compartments. Contaminant inventories in the root-zone soil and
vadose-soil zone were treated as time-varying state variables. Contaminant
concentrations in ground water were based on the leachate from the vadose-zone soil.
The multipathway exposure model reviewed at the time of initial TRIM development
encompassed 23 exposure pathways to estimate average daily doses within a human
population in the vicinity of a hazardous substances release site. The exposure
assessment process consisted of relating contaminant concentrations in the multimedia
model compartments to contaminant concentrations in the media with which a human
population has contact (e.g., personal air, tap water, foods, household dusts/soils). The
explicit treatment of differentiating environmental media pollutant concentration and the
pollutant concentration to which humans are exposed favorably distinguished CalTOX
from many other exposure models. In addition, all parameter values used as inputs to
CalTOX were distributions, described in terms of mean values and a coefficient of
variation, rather than point estimates (central tendency or plausible upper values) such as
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most other models employed. This stochastic approach allowed both sensitivity and
uncertainty to be directly incorporated into the model operation.
As indicated in the literature review reports, the CalTOX model appeared to be the most
promising existing model for application to the TRIM effort. Several of the
mathematical concepts and derivations used by the developers of CalTOX could have
been directly applied to meet the TRIM goals. However, OAQPS identified several
limitations to CalTOX that prevented it from being entirely imported into the TRIM
approach. These limitations resulted from the need to go beyond the intended
applications for CalTOX; for example, for landscapes in which there is a large ratio of
land area to surface water area, or for a limited range of chemicals (e.g., non-ionic
organic chemicals in a liquid or gaseous state). As a result, the model did not provide
adequate flexibility in environmental settings and chemical classes (e.g., volatile metals
such as mercury) to be suitable for OAQPS' needs. The most significant of these
limitations, in terms of application to TRIM, was the fact that the CalTOX model, as it
existed at the time of initial TRIM efforts, did not allow spatial tracking of a pollutant as
was required in the TRIM approach.
SimpleBOX. Based on OAQPS' review, SimpleBOX was identified as a steady-state,
non-equilibrium partitioning, mass balance model (van de Meent 1993, Brandes et al.
1997). It consisted of eight compartments, three of which were soils of differing use and
properties. It also produced quasi-dynamic (non-steady-state) output by using an external
numerical integrator. The model was developed as a regional scale model for the
Netherlands, so its default characteristics represented the Netherlands. SimpleBOX used
the classical concentration concept to compute the mass balance (van de Meent 1993).
While its goals were comparable to TRIM to the extent that it simulated regional
systems, its coarse spatial and temporal complexity and lack of exposure media
concentration estimates caused it to fall short of TRIM's goals.
Integrated Spatial Multimedia Compartmental Model (ISMCM) At the time of
initial TRIM efforts, ISMCM had been under development at the School of Engineering
and Applied Science at University of California Los Angeles for approximately 15 years
(van de Water 1995). A newer version of ISMCM, called MEND-TOX, was under
evaluation by the EPA Office of Research and Development's National Exposure
Research Laboratory.
OAQPS review found that the version of ISMCM available during early TRIM efforts
considered all media, biological and non-biological, in one integrated system and
included both spatial and compartmental modules to account for complex transport of
pollutants through an ecosystem. Assuming mass conservation, ISMCM was able to
predict transport based on a sound mechanistic description of environmental processes,
including estimation of intermedia transfer factors.
One of the limiting factors of the ISMCM system, with regard to use in the TRIM
system, was that it was not structured to incorporate uncertainty and variability directly
into the model operation. Another of the limitations of the ISMCM model within the
context of the goals for TRIM was the fact that the links and compartments (spatial
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configuration) of this model were predetermined (van de Water 1995). Thus, ISMCM
was apparently not designed from the start with the flexibility to meet the goals of TRIM.
Multimedia Environmental Pollutant Assessment System (MEPAS). MEPAS was
developed at the U.S. Department of Energy's (DOE) Pacific Northwest Laboratory to
assess risks from mixed (i.e.., chemical and radioactive) wastes at DOE facilities.
OAQPS review during initial TRIM efforts showed that this model consisted of single-
media transport models linked together under appropriate boundary conditions and
considered four primary types of pollutant pathways (ground water, overland, surface
water, and atmospheric) in evaluating human exposure. MEPAS also contained an
exposure and risk module. The model was unique in its ability to estimate multipathway
risks for chemicals and radionuclides. The nature of its algorithms made it a screening
tool, rather than a detailed assessment tool. The model was updated periodically and the
latest version of MEPAS (Version 3.1) evaluated by OAQPS during its review contained
an uncertainty and variability analysis module (Buck et al. 1995).
Based on OAQPS review during initial TRIM efforts, the mathematical design of this
model did not include mass balance and could not be integrated into TRIM. As with
IEM, MEPAS represented a "linked" model system that utilized a one-way process
through a sequence of models that individually describe a specific environmental process
or medium. These types of models are not mass conservative and did not allow for the
temporal tracking of the pollutants and concomitant exposure necessary to meet the needs
of TRIM.
Based on the results of these review efforts, OAQPS concluded that while certain
features of existing models were desirable for TRIM, none of the models reviewed at that time
would fully address the needs for the TRIM modeling system. Therefore, OAQPS determined
that it would be necessary to develop an improved fate and transport modeling tool. Reasons for
this conclusion are discussed in the next section.
2.2 APPROACH TO DEVELOPING TRIMFaTE
During initial TRIM development, OAQPS determined that the currently existing
OAQPS fate and transport models for hazardous and criteria air pollutants did not address
multimedia exposures, and currently existing OAQPS HAP models did not adequately estimate
temporal and spatial patterns of exposures. Adopting or incorporating existing models into a
tool that meets OAQPS' needs represented the most cost-effective approach to developing the
tools needed to support regulatory decision-making related to hazardous and criteria air
pollutants. Based on the OAQPS review of existing multimedia models and modeling systems
(described in Section 2.1), there was no single fate and transport model that met the needs of
OAQPS (outlined in Chapter 1) and that could be adopted as part of TRIM. Most models were
limited in the types of media and environmental processes addressed. Simply, no single model at
that time could address the broad range of pollutants and environmental fate and transport
processes anticipated to be encountered by OAQPS in evaluating risks from hazardous and
criteria air pollutants. In addition, it was unlikely that one individual model could be developed
to address this wide range of concerns. Therefore, the TRIM framework was constructed to
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emphasize a modular design. The lack of a flexible multimedia fate and transport model was
identified as a major limitation and was the focus of the first phase implementation efforts for
TRIM.
Multimedia models existing at the time of initial TRIM development could be divided
into two basic categories: "linked" single medium model systems and mass-conserving models.
Mass-conserving models could be further classified as fugacity-based, concentration-based, or
inventory-based models depending on the choice of state variable (i.e., fugacity, concentration,
or inventory). OAQPS concluded that the linked single medium and mass-conserving models
each had their own strengths and limitations.
"Linked" single medium modeling systems were identified as those composed of several
independent single medium models. The linked system typically calculated fate and transport by
running a single medium model (e.g., an atmospheric model) and using the output from each
time step as the input for the corresponding time step of another single medium model (e.g., a
soil or surface water model). There were several highly sophisticated single medium models to
choose from when constructing a linked system. However, the linked design did not assure
conservation of mass because the dynamic feedback loops and secondary pollutant transfers
were not treated in a fully coupled manner. In addition, the level of detail provided by the linked
model system could not be easily adjusted to suit the needs of different modeling objectives.
Mass-conserving multimedia models were developed to fully account for the distribution
of mass within a compartmentalized system. The fugacity type multimedia models were
introduced by Mackay (1979, 1991) as screening tools to assess the relative distribution of
chemicals in air, water, sediment, and soil. Although the fugacity concept provides a convenient
method for quantifying the multimedia fate of chemicals (Cowen et al. 1995),2 models that use
fugacity as the state variable are limited in application only to organic chemicals.
Concentration-based models like Simple Box and inventory-based models like CalTOX could
technically handle inorganic chemicals, but temporal and spatial resolution would be limited by
the rigid compartmentalized structure or boxes used to represent the environmental media.
Spatial compartmental models (e.g., ISMCM) represented the closest current models to an
integrated multimedia system. However, as previously described, ISMCM did not meet the
TRIM design criterion for a flexible architecture.
In general, none of the multimedia models that existed at the time TRIM development
began were sufficiently coupled to account for inherent feedback loops or secondary emissions
or releases to specific media, or were able to provide the temporal and spatial resolution critical
in estimating exposures. While the degree to which results would differ between existing linked
models and a truly coupled multimedia model was unknown, non-coupled multimedia models
were generally considered to lack scientific credibility. Therefore, OAQPS determined it was
necessary to undertake efforts to develop a truly coupled multimedia model.
2 Fugacity is a method of expressing the escape tendency of a substance using units of pressure. If this
concept is considered as a measure of chemical intensity, the chemical potential of a substance in any phase can be
expressed as a fugacity, using the gas phase of that substance as a reference phase. When two or more phases are at
equilibrium, the fugacities of the substance's phases are equal; therefore, the fugacity concept can be used to
determine the fractional distribution of mass within different phases in a compartment.
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Another model, the multimedia, multipathway and multireceptor risk assessment (3MRA,
formerly FRAMES-HWIR), was also under development during the initial TRIM development
phase. Development on this model has continued through the present time as a methodology for
EPA to support risk assessment decisions made regarding waste concentration limits for
chemicals in industrial waste management units. 3MRA is a framework system which includes,
along with several site-specific databases and processors, a multimedia, multipathway, and
multireceptor simulation processor (MMSP) for fate and transport and exposure modeling.
MMSP is itself made up of 18 individual modules (e.g., air, watershed, human exposure).
Similar to IBM, the 3MRA methodology includes procedures for estimating the indirect human
exposures and health risks that could result from the transfer of emitted air pollutants to soil,
vegetation, and water bodies. This model is designed to predict long-term impacts from
continuous sources, using a one-way process through a series of linked models and algorithms
incorporating IBM concepts. The methodology addresses exposures via inhalation, food, water,
and soil ingestion, and dermal contact. However, like IEM, 3MRA is not a truly coupled, mass-
balanced multimedia model.
2.3 KEY CAPABILITIES OF TRIM.FaTE
As mentioned above, several key characteristics were identified as essential to the design
of TRIM.FaTE:
Ability to address varying time steps (of one hour or greater) and provide sufficient
spatial detail at varying scales (site-specific to urban scale);
Conservation of pollutant mass within the system being assessed;
Transparency, as needed for use in a regulatory context; and
Performance as a truly coupled multimedia model rather than a set of linked single
medium models.
To accommodate these characteristics, the Agency developed a new model framework
that expanded upon the mass balance and compartmental framework used by CalTOX and the
system of equations used in ChemCan3 and SimpleBOX to produce a modeling system that
incorporates a flexible level of spatial and temporal resolution while maintaining a complete
multimedia mass balance. Development of the TRIM.FaTE framework required the TRIM team
to design several features not available in existing multimedia models at the time of initial TRIM
development. These key features included:
Implementation as a truly coupled multimedia model framework;
3 ChemCan is a steady-state fugacity balance model, designed for Health Canada, intended to assist in
human exposure assessment. The model estimates average concentrations in air, fresh surface water, fish, sediments,
soils, vegetation, and marine near-shore waters.
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The adaptability to match a simulation to the spatial and temporal scales needed for a
broad range of pollutants and geographical areas;
The use of a unified approach to mass transfer, based on an algorithm library that allows
the user to change mass transfer relationships between compartments without creating a
new modeling scenario;
An accounting of the pollutant mass distributed within, as well as entering and leaving,
the environmental system;
An embedded procedure to characterize uncertainty and variability; and
The capability to be used as an exposure model for ecological receptors.
This section summarizes these features along with some of the potential limitations of the
TRIM.FaTE framework.
2.3.1 TRULY COUPLED MULTIMEDIA FRAMEWORK
One of the significant distinguishing features of the TRIM.FaTE methodology is the
attention paid to possible interactions between media. The transfer of chemical mass between
compartments is not restricted to a one-way process, which is common for many "linked"
multimedia models. Instead, TRIM.FaTE allows the user to simulate the movement of a
chemical in any direction for which transfer can occur. Without this functionality, a multimedia
model can never be truly mass conservative and cannot adequately address feedback loops and
secondary pollutant movement (e.g., revolatilization and transport). The lack of a full mass
balance and the functionality to account for feedback loops and secondary pollutant movement
are generally considered significant sources of uncertainty in the application of "linked" models.
The use of a truly coupled multimedia framework for TRIM.FaTE can reduce this important area
of uncertainty.
2.3.2 SCALABLE COMPLEXITY
The current TRIM.FaTE methodology allows the user a great deal of flexibility in the
design of any particular model application, both spatially and temporally. The functionality to
account for varying degrees of temporal resolution is common among multimedia models.
Conversely, the spatial flexibility provided in TRIM.FaTE is unique among multimedia models
because it allows the user to vary the resolution significantly over the modeled region. For
example, initially the user may define only a few homogeneous regions for the model area.
After inspecting the results of the initial analysis, the user could subdivide those regions where
more resolution is desired. This assists the user in not including more resolution than is
necessary for a particular application, resulting in more efficiency in modeling. Although some
applications of TRIM.FaTE may resemble a simple fugacity-based compartmental model, it also
can be scaled to simulate time-series and spatial resolutions that current fugacity-type models
could not handle.
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General recommendations for designing the spatial extent and subdivision of a modeling
region based on existing TRIM.FaTE test cases will be incorporated into TRIM.FaTE User
Guidance (EPA 2002a). However, the flexibility inherent to TRIM.FaTE allows the user a
nearly limitless number of unique layout options for a single modeling application, including
untested approaches and potentially problematic configurations. Consequently, the user must be
careful to maintain a balance between the advantages and the uncertainties of spatial complexity.
2.3.3 FLEXIBLE ALGORITHM LIBRARY
The manner in which the chemical mass transfer algorithms have been implemented in
TRIM.FaTE is unique among multimedia models. Rather than storing the equations only in
computer code, which is not readable by the user at run time, the equations are stored in a form
that allows the user to inspect the equations, variables in the equations, and values for the
variables for almost any calculated term at run time. It is possible for the user to trace the
calculation of almost any of the chemical mass transfers, which can be useful when trying to
explore an unexpected result. For most models, the user cannot be sure how faithfully the
equations documented have been implemented, or how synchronized the documentation is with
the code. With the TRIM.FaTE methodology, these problems can be substantially alleviated.
Another advantage in the algorithm implementation is the potential to choose from a set
of algorithms for each of the types of chemical mass transfers. The primary benefit would be in
performing sensitivity analyses when there are uncertainties regarding the model approach for
some transport or transformation processes. If there were several different algorithms available
for a given process, the user could perform analyses using the different algorithms, thus allowing
decision-makers to consider the impact of algorithms selection on predicted values.
2.3.4 FULL MASS BALANCE
One of the design features of TRIM.FaTE that sets it apart from many other multimedia
models is that it incorporates a full mass balance. In order to maintain a full mass balance, all
environmental media need to be modeled simultaneously, rather than sequentially. This allows
the model to properly account for all of the pollutant mass as it moves from within and between
media. This approach is in contrast to the methodology used in a set of linked models. With
linked models, it is difficult to model the time-fluctuating diffusive transport between the various
media. Furthermore, a series of interactions between more than two media is difficult to capture.
With TRIM.FaTE, all of the model compartments are fully coupled such that the exact
amount of mass that travels between compartments is accounted for explicitly and continuously.
Additionally, diffusion between compartments follows the time-dependent mass in each
compartment. As a result, in contrast to many other models, TRIM.FaTE considers time varying
concentration for diffusion and thus can provide a more accurate algorithm for diffusive mass
transfer among multiple compartments. That is, there is a continuous feedback system adjusting
the relative mass exchange among the compartments.
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2.3.5 EMBEDDED PROCEDURE FOR UNCERTAINTY AND VARIABILITY
ANALYSIS
The overall TRIM model framework has been developed to allow for probabilistic
modeling such that variability and uncertainty can be explicitly and separately characterized.
This has involved the development of an approach to estimate variability and uncertainty within
TRIM, in a manner that allows for: (1) integration among the three TRIM modules; (2) tracking
the variability and uncertainty through the modules; and (3) feasible computational processing.
The implementation of this approach for uncertainty analysis is integrated within the
TRIM.FaTE module, as opposed to operating as a separate shell around the module.
TRIM.FaTE handles some of the calculations internally and passes information to the
uncertainty system during a simulation. This close interfacing of the uncertainty software with
the model allows for greater flexibility in terms of what can be tracked and also dramatically
reduces the processing time required.
The key features of this approach to variability and uncertainly analysis are joint and
separate tracking of variability and uncertainty, characterization of variability and uncertainty of
model results with respect to parameter distributions and correlations, and identification of
critical parameters and correlations. In addition to providing information to support decision-
making, analyses of variability and uncertainty in TRIM will help to guide data and model
improvement efforts.
2.3.6 EXPOSURE MODEL FOR ECOLOGICAL RECEPTORS
TRIM.FaTE is also unique in its ability to estimate exposure for ecological receptors.
Several measures of ecological exposure are used in exposure-response models: concentrations
of chemicals in environmental media; body burdens or tissue levels of chemicals in the organism
of concern; and doses to the organism of concern (mass of chemical per mass of organism per
unit time). TRIM.FaTE can output chemical mass in all compartments at each time step, thus
providing body burden estimates for ecological receptors. TRIM.FaTE is also designed to divide
the compartmental chemical mass by the volume or mass of a compartment to estimate
concentrations in soil, sediment, water, air, or biota. Additionally, TRIM.FaTE can output
chemical intake for organisms of interest at the desired temporal and spatial scale.
Body burdens or tissue concentrations are useful measures of exposure because they
integrate exposure from all routes. Dietary exposure is already determined for mammals, birds,
and fish by TRIM.FaTE, and exposure to plants from both air and soil is calculated. However, if
body burden-response models are not available for particular pollutants, models may be
available that relate effects to concentrations in environmental media. These concentrations are
available directly from the TRIM.FaTE output as well. Models that relate doses to toxicity may
also be used, and doses may be calculated using any averaging time that is equal to or shorter
than the length of the TRIM.FaTE simulation. Given the range of ecological exposure measures
directly available from TRIM.FaTE, a user will rarely be limited in the options for exposure-
response models that may be used in an ecological risk assessment.
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TRIM.FaTE does not currently estimate the concentrations of chemicals in vertebrate
organs; therefore, models that relate toxicity to organ concentration are not easily implemented.
However, given the inherent flexibility of TRIM.FaTE, the user, if armed with prerequisite
information to describe pollutant movement among the internal compartments of a particular
animal, could implement a scenario in TRIM.FaTE to make use of such models.
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3. OVERVIEW OF TRIM.FaTE CONCEPTS AND
TERMINOLOGY
The TRIM.FaTE methodology integrates OAQPS' needs and multimedia modeling
concepts into a unique model that serves as an integral part of the TRIM system. This chapter
provides an overview of the terminology central to the TRIM.FaTE module. Note that the
Glossary presented in Appendix A also provide brief definitions for key terms related to
TRIM.Fate. An understanding of the terminology and concepts presented in this chapter is
crucial to understanding the remainder of this report.
Because the terminology used in the world of environmental modeling can have multiple
meanings and implications, it is essential in the initial steps of any model conceptualization that
the terminology is clearly defined within the model framework. The terminology for multimedia
modeling is particularly
complicated because multimedia
models are, by nature,
multidisciplinary. Thus,
terminology can be especially
confusing because a single term
can have different meanings in
different disciplines. Three
general modeling terms are
defined, for the purposes of
TRIM.FaTE, in the adjacent text
box to provide a consistent basis
for the discussion in this section
and the remainder of the
document.
GENERAL MODELING TERMS
Scenario: A specified set of conditions (e.g., spatial,
temporal, environmental, source, chemical)
used to define a TRIM.FaTE model set-up for a
particular simulation or set of simulations.
Simulation: A single application of TRIM.FaTE to estimate
chemical transport and fate, based on a given
scenario and any initial input values needed.
Project: A TRIM.FaTE computer framework for saving
one or more scenarios and all of the data
properties for the scenarios that pertain to a
single model application.
The primary objective of the TRIM.FaTE module is to estimate the fate and transport of a
chemical pollutant or pollutants over time through a modeled environment. Because the term
"pollutant" can have various meanings, the modeled unit of chemical mass in TRIM.FaTE is
referred to as a chemical. Within the context of TRIM.FaTE, a chemical is simply defined as a
unit whose mass is being modeled by TRIM.FaTE. A chemical can be any element or
compound, or even a group of compounds, assuming the necessary parameters (e.g., molecular
weight, diffusion coefficient in air) are defined. Examples of chemicals that may be modeled in
TRIM.FaTE are polycyclic aromatic hydrocarbons (PAHs), methylmercury, elemental mercury,
and benzene.
3.1 BASIC TRIM.FaTE TERMINOLOGY
In the TRIM.FaTE module, chemicals are contained within compartments. The term
"compartment" is in some ways similar to what is referred to as a "medium" in environmental
fate and transport modeling literature. However, the term "medium" was considered too limited
in its scope because it generally invokes images of abiotic systems such as soil or air, while
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TRIM.FaTE includes both abiotic and biotic systems. Therefore, the term compartment was
adopted for TRIM.FaTE because it captures the flexibility of the TRIM.FaTE module in that it
refers to both abiotic and biotic systems. Simply, a compartment is defined as the TRIM.FaTE
modeling unit that contains chemical mass. Within TRIM.FaTE, chemical mass is transported
between and transformed within compartments. A specific compartment is characterized by its
physical and spatial composition and its relationship to other compartments. It is assumed, for
modeling purposes, that all chemical mass within a compartment is homogeneously distributed
and is in phase equilibrium. Multiple chemicals can exist within a compartment, and the various
phases that compose a compartment (gases, liquids, solids) are generally assumed to be in
equilibrium with respect to chemical partitioning (see TRIM.FaTE TSD Volume II for
exceptions). Compartments can be either biotic, such as a deer compartment, or abiotic, such as
a surface water compartment. Furthermore, two compartments could have identical
compositions and only be distinguished by their location in the modeled environment; they are
still separate compartments. It is important to note that biotic compartments do not refer to an
individual organism, but instead to the population of that organism (or in some cases community,
or other subgroup) within a specified spatial volume or area.
The term compartment type is used to denote a particular kind of compartment, such as
an air compartment type or a soil detritivore compartment type. Compartment types are typically
distinguished from each other by their basic characteristics and the way they exchange chemical
mass with other compartment types. A compartment type can be thought of as a "template" for a
specific, spatially defined and located compartment. A specific compartment is defined for a
modeling scenario by first
adding a copy of that
"template" to the scenario
and then defining its
location and establishing its
site-specific properties.
Compartments of the same
type are distinguished from
each other in a given
scenario by their location
and sometimes also by the
values that define their
composition at a particular
location. For example, two
different surface soil
compartments may have
organic carbon fractions of
0.015 and 0.01,
respectively, but they are
both described by the
compartment type called
"surface soil."
SINKS
A sink is a special kind of compartment type that accounts for
chemical mass no longer available for transport or uptake within a
TRIM.FaTE simulation. Like other compartment types, sinks are
linked to compartments, but sinks do not have a volume and, by
definition, they do not have any loss processes associated with
them (i.e., they "receive" mass but do not "send" mass). There are
three types of sinks: advection sinks, flush rate sinks, and
degradation/reaction sinks. Advection sinks are linked to the
outside edges of certain compartments (e.g., air compartments) to
account for chemical mass removed from the entire modeling
region via advection from these compartments. Flush rate sinks
are needed for each water body that "flushes" or releases water
out of the modeled domain (e.g., a river that flows across the
boundary of the modeled region, or a lake that flushes its contents
outside of the region). Degradation/reaction sinks are needed
when modeling chemicals that degrade or are transformed into
products whose fate after degradation/transformation is not tracked
by the model.
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In general, compartment types are classified as either abiotic or biotic (see also the text
above describing sinks). An abiotic compartment type consists primarily of a non-living
environmental medium1 (e.g., air, soil) for which TREVI.FaTE calculates chemical masses and
concentrations. A biotic compartment type consists 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. Abiotic and biotic compartment types are discussed in more detail in Section 3.2.
Each compartment is associated with (contained within) a volume element. 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 locating and
organizing objects that have a natural spatial relationship. In applications of TREVI.FaTE thus
far, one (and only one) abiotic compartment is contained within any one volume element, and
each volume element is identified by the abiotic compartment type it contains (e.g., surface soil
volume element, ground water volume element). In addition to the abiotic compartment,
numerous biotic compartments may be contained within a single volume element (e.g., multiple
terrestrial biota compartment types along with a surface soil compartment may be contained
within a surface soil volume element). Animals are typically associated with surface soil or
surface water compartment types depending on where they usually reside and/or feed. For
example, mallards generally feed in the water, so the mallard compartment type, when included
in modeling, is placed along with the surface water compartment in one or more surface water
volume elements. Terrestrial biota included in modeling are placed with the surface soil
compartment in one or more surface soil volume elements. It should be noted, however, that
animals are not restricted to feeding only in the volume element where they are located. The
user may designate that a particular biotic compartment (e.g., bald eagle) obtains its diet from
compartments (e.g., mouse) in more than one volume element.
The size and shape of volume elements for a given TRIM.FaTE application depend on
the needs of the user. For example, if the user is most interested in the range of impacts of a
chemical over a given water body, the water body could be divided into a number of volume
elements with depth, length, and width. Typically, the higher the desired resolution, the greater
the number and more complicated the shapes of the volume elements.
Figure 3-1 shows the basic spatial relationships between chemicals, compartments, and
volume elements. This figure shows that chemicals are contained within compartments, and
compartments are associated with volume elements. Figure 3-2 demonstrates how multiple
compartments can exist within a single volume element. Because the air compartment is the
only abiotic compartment within the volume element in Figure 3-1, this volume element is
referred to as an air volume element. Likewise, the volume element in Figure 3-2 is referred to
as a surface water volume element. Figure 3-3 applies the concepts presented in Figures 3-1 and
3-2 by dividing a hypothetical environment into volume elements and defining the compartments
to be modeled within this framework.
1 When chemical transformation or degradation is modeled, an abiotic compartment may implicitly contain
biota in the form of the microorganisms responsible for chemical transformation.
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Figure 3-1
Simple TRIM.FaTE System3
Volume Element
Compartment (Air)
Chemical
a Chemicals shown in this figure, and all subsequent similar figures, are units of mass of the same chemical instead of
multiple chemicals.
Figure 3-2
Multiple Compartments within a Single Volume Element
Volume Element
Chemical
Compartment (Water)
Chemical
Compartment (Fish)
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Figure 3-3
Example of a Hypothetical Environment (Multiple Volume Elements, Multiple
Compartments)
Air Volume
Element
Compartment: \.'
Air in Air Volume
Element
Compartment:
Mallard in
Surface Water
Volume Element
Compartment: -
Deer in Surface Soil
Volume Element
Surface Water
Volume Element
Compartment:
Surface Water in
Surf ace Water
Volume Element
Compartment:
Fish in Surface Water
Volume Element
Compartment:
Macrophytes in
Surface Water
Volume Element
Composite
Compartment:
Leaf and Particles on
Leaf Compartments
(Deciduous Forest)
in Surface Soil
Volume Element
Surface Soil
/Volume Element
Compartment:
Surface Soil in
Surface Soil
Volume Element
Compartment:
Worms in Surface Soil
Volume Element
Root Zone Soil
Volume Element
Compartment:
Root Zone Soil
in Root Zone Soil
Volume Element
Vadose Zone Soil
Volume Element
Compartment:
Vadose Zone Soil in
Vadose Zone Soil
Volume Element
Compartment: Sediment
Sediment in Volume
Sediment Element
Volume Element
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Abiotic compartments (e.g., surface soil, air) generally possess the same dimensions (i.e.,
height, length, and width), and hence the same volume, as the containing volume element. Note
that a sediment compartment may include both the sediment particles and the interstitial pore
water, with the chemical in phase equilibrium between them. The total volume of this sediment
compartment would consist of water (with a volume fraction equal to x) and sediment particles
(with a volume fraction equal to (1 - x)). Similarly, surface water compartments include both
particulate and dissolved phases, and air compartments contain particulate and vapor phases (see
Section 4.3).
Although biotic
compartments are associated
with volume elements as a part
of the TRIM.FaTE modeling
structure, they do not "take up
space" within a volume
element. Concentrations in
biotic compartments are
calculated on the basis of
biomass (refer to TRIM.FaTE
TSD Volume II for more
detailed information). For the
purposes of estimating
chemical mass and
concentration in the abiotic
compartment, the volumes
associated with biotic
compartments are considered
insignificant compared with
the volumes of the associated
abiotic compartment (e.g., air,
soil, water). See the text box
above for two examples of the conceptual relationship between biotic compartments and volume
elements for the hypothetical environment shown in Figure 3-3.
3.2 COMPARTMENT TYPES
The openness and flexibility of TRIM.FaTE give the user wide latitude in defining the
compartment types for any particular modeling scenario. The discussion in the two sections that
follow describes compartment type implementations that have been used and evaluated thus far.
TRIM.FaTE provides the user the capability to define scenarios using these or alternate
compartment type strategies. With this flexibility, however, comes the responsibility to consider
the ramifications of the selected strategy. Observations of complexity evaluations described in
the TRIM.FaTE Evaluation Report (USEPA 2002b) and discussion in TRIM.FaTE User
Guidance (USEPA 2002a) may be of assistance, particularly in considering the strategy for
including biota in a TRIM.FaTE scenario.
Biotic Compartments and Volume Elements:
Examples from Figure 3-3
The fish compartment is associated with the Surface Water
Volume Element and is assumed to exist as a population evenly
spread throughout the surface water compartment within that
volume element. Characteristics for the fish population are
defined by the user (e.g., average body weight, number offish
per square meter of water body). However, the volume occupied
by the biomass of the fish is not calculated, and the volume
fraction of the surface water compartment that consists offish is
not estimated or subtracted from the volume of water in the
compartment for any of the calculations in TRIM.FaTE.
The mallard compartment is also associated with the Surface
Water Volume Element, and some of the same population
characteristics (e.g., average body weight, number of individuals
per area) are defined by the user. The mallard population is
assumed to feed on both aquatic invertebrates (associated with
the same volume element) and terrestrial plants (associated with
one or more neighboring volume elements). Thus, in terms of
feeding habits, the mallard population is not physically restricted
to the volume element with which it is associated.
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ABIOTIC COMPARTMENT TYPES IN
TRIM.FaTE
Air Ground Water
Surface Soil Surface Water
Root Zone Soil Sediment
Vadose Zone Soil
3.2.1 ABIOTIC COMPARTMENT TYPES
Abiotic compartment types consist
primarily of a non-living environmental medium.
The adjacent text box lists the abiotic compartment
types currently included in TRIM.FaTE. Within
the TRIM.FaTE system, chemical mass is initially
transported to biotic compartments based on their
relationships with abiotic compartment types. In
addition to the abiotic compartment types listed
here, compartment types defined within the
TRIM.FaTE system that are not associated with
biota also include advection and flush rate sinks
(to account for mass transported outside of the modeled region) and reaction/degradation sinks
(to account for the results of chemical transformation reactions when the fate of the reaction
product is no longer tracked by TRIM.FaTE). Sinks are described in more detail in the text box
in Section 3.1 and in the discussion of the TRIM.FaTE mass balance framework (Chapter 4).
The properties of abiotic compartment types are set by the user who can give them
differing or identical values throughout the modeling region. For example, parameter values for
all surface soil compartments in a modeling scenario can be assumed to be the same. However,
if site-specific data are available for different compartments in a scenario, these data can be
entered as compartment-specific properties. For example, where multiple water bodies are
represented in a scenario, the user may need to specify differing values for the surface water and
sediment compartment properties (e.g., current velocity, water pH, sediment particle density).
3.2.2 BIOTIC COMPARTMENT TYPES
Biotic compartment types generally are differentiated from one another based on their
links with other compartment types. Thus, each biotic compartment type represents a different
trophic/taxonomic group or has a different route of uptake. The compartment types are further
distinguished by the ecosystem (i.e.., terrestrial or aquatic) in which the biotic compartment feeds
or is located. Mammalian and avian wildlife, soil detritivores, fish, aquatic (benthic)
invertebrates, and aquatic plants are considered different compartment types because they belong
to unique trophic groups and/or occupy different ecosystems. Those groups can be further
subdivided based on their habitat (e.g., terrestrial, semi-aquatic) and/or general feeding patterns
(e.g., herbivore, omnivore). For example, some wildlife species are considered to be entirely
terrestrial (e.g., deer), while others are considered to be semi-aquatic (e.g., mink), feeding on
prey from both aquatic and terrestrial habitats. The different biotic compartment types that have
been used to date in TRIM.FaTE are listed in Table 3-1.
For terrestrial and semi-aquatic wildlife (i.e., birds and mammals), one or more species
can represent each trophic/functional group. For example, a single species, the short-tailed
shrew, might represent terrestrial ground invertebrate feeders for the terrestrial portions of the
modeling region. For a terrestrial predator trophic group, both the red-tailed hawk and the
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Table 3-1
Biotic Compartment Types Defined for TREVLFaTE
Biotic Compartment Type
Representative Subgroup or Species
Aquatic Plants
Macrophyte
[User input] a
Benthic Fauna
Benthic invertebrate
Benthic omnivore
Benthic carnivore
[User input] a
[User input] a
[User input]3
Water-Column Fauna
Water-column herbivore
Water-column omnivore
Water-column carnivore
[User input] a
[User input] a
[User input] a
Semi-Aquatic Fauna
Semi-aquatic piscivore
Semi-aquatic predator/scavenger
Semi-aquatic aerial insectivore (i.e., feeding on
adults of emergent insects such as mayflies,
mosquitos, damselflies)
Semi-aquatic omnivore
Belted kingfisher
Common loon
Mink
Bald eagle
Tree swallow
Mallard
Raccoon
Terrestrial Plants b
Plant leaf
Particle on leaf
Plant stem
Plant root
Defined as appropriate for coniferous or
deciduous forest, grass/herb fields, or
agricultural systems.
Terrestrial Fauna
Terrestrial omnivore
Terrestrial insectivore
Terrestrial predator/scavenger
American robin
White-footed mouse
Black-capped chickadee
Long-tailed weasel
Red-tailed hawk
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Biotic Compartment Type
Terrestrial vertebrate herbivore
Terrestrial ground-invertebrate feeder
Flying insect c
Soil detritivore
Representative Subgroup or Species
Black-tailed deer
Bobwhite quail
Long-tailed vole
Meadow vole
Mule deer
White-tailed deer
Short-tailed shrew
Trowbridge shrew
American woodcock
Mayfly
Earthworm
Soil arthropod
a In applications to date, the fish compartments have been employed to represent trophic niches arising either from a
benthic (i.e., sediment-based) or water-column contaminant source. In addition, the term omnivore has been a
misnomer in the applications to date, because the water-column "omnivore" has fed only on the water-column
herbivore, and the benthic "omnivore" only on benthic invertebrates. Thus, these compartment types have not been
parameterized using the concept of a single representative species that might feed on organisms from more than one
trophic level or from both the benthic and water-column environments. Rather, the total biomass for a single
representative fish species that feeds from both benthic and water-column sources has been divided into two
compartments for that species: one that feeds from benthic sources and one that feeds from water-column sources,
respectively. The water-column "herbivore" also is a misnomer in that zooplankton are implicitly included in the
diet and represent an intermediate trophic level between the phytoplankton and the fish herbivore. Further
discussion of this is provided in the TRDVLFaTE Evaluation Report and the TRIM.FaTE TSD, Volume II, Chapter 6.
As mentioned previously, however, alternate strategies may be evaluated and employed, as appropriate.
b Terrestrial plant parts constitute different compartment types even though they are not different trophic groups.
0 For applications thus far, flying insects have been represented by the benthic invertebrate compartment type, thus
essentially assuming that the concentration of chemical in the adult flying insect is the same as the concentration of
chemical in the aquatic nymph from which it emerged.
long-tailed weasel might be included in one or more surface soil volume elements of the
modeling region. In that case, the diet of each of those species would be specified and
compartments representing the prey species would also need to be included in the model. The
diet offish and wildlife can range from a single diet item (e.g., 100 percent algae for an
herbivorous fish) to multiple diet items (e.g., 5 percent shrews, 20 percent mice, 25 percent
ducks, 25 percent benthic invertebrates, and 25 percent water-column herbivorous fish for mink).
The selection of wildlife compartments and assignment of corresponding diet, biomass, and
other parameter values will depend on the location of the site, the habitat types found at the site,
the area covered by the modeling region, and other factors as described in the TRIM.FaTE User
Guidance.
There are similarities between constructing an aquatic food web and constructing a food
web for terrestrial wildlife. The user can specify fish compartments appropriate to the site and
can specify the diet for each fish compartment. A given fish compartment might be
parameterized to represent an entire trophic group, or one or more species considered
representative of that group can be used to assign values to the parameters for one or more
compartments. Biomass relevant to the distribution of the pollutant being modeled should be
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accounted for. For example, the entire biomass of aquatic organisms in the surface water body
being modeled should be accounted for in the set of aquatic compartments implemented to
ensure appropriate chemical partitioning in the aquatic ecosystem.
In some cases, composite compartment types may be implemented within the
TRIM.FaTE framework. A composite compartment type is a group of different compartment
types that are consistently interconnected. Each individual compartment within a composite
compartment requires the presence of other compartments. Conceptually, a composite
compartment type may be considered as a term of convenience for the user rather than a
structural component of the TRIM.FaTE system; that is, TRIM.FaTE distributes mass between
the individual compartments, not into the compartment type as a single unit. Currently, plant
composite compartment types have been defined within TRIM.FaTE, with root, stem, leaf, and
particle on leaf2 compartments comprising the plant composite compartment type (given the
difficulty in modeling woody stems and roots, only the leaf and particle on leaf compartments
are currently included in the coniferous and deciduous forest composite compartments). Note
that different types of plant communities (e.g., deciduous forest, grass and herb community) may
be represented by separate composite compartments.
Given the flexibility of the TRIM.FaTE framework, the user can design and implement
their own approach to modeling pollutant movement through biotic compartments. This can
include custom definitions of compartment types, feeding habits, biomass densities, and other
parameters. As mentioned previously, with this flexibility comes an inherent responsibility to
consider the ramifications of a given design and consequently its appropriateness for the
objectives of the assessment. For example, although the animals in an ecosystem are not
considered to play a large role in the distribution of pollutant mass within that ecosystem (i.e..,
the balance will occur in abiotic media), distribution of pollutant mass among the various
animals in TRIM.FaTE is influenced by the relationships among them (e.g., predator-prey,
relative abundance, dietary preferences).
3.3 LINKS
There are two general processes that can affect the presence of chemical mass within a
given compartment:
(1) the transfer of chemical mass from and/or to another compartment; and
(2) the transformation of one chemical to another chemical within a compartment.
In order to evaluate the occurrence and magnitude of these processes, the relationships,
or links, between and within the compartments must be determined. A link is defined as a
"connection" between compartments (for transfer processes) or within a compartment (for
2 The "particle on leaf' compartment type represents those particles deposited from air onto a leaf surface
via wet and dry deposition. The chemical mass that is associated with the "particle on leaf' compartment can be
transferred into the leaf or blown off the leaf surface, or can remain associated with the particles on the surface of the
leaf.
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transformation processes3) that allows one of these processes to occur. Each link contains an
algorithm or multiple algorithms that mathematically represent the mass transfer or
transformation. Figure 3-4 expands on the concepts presented in Figure 3-2 by showing links
representing transfers between compartments in different volume elements. The figure
demonstrates that chemicals in the air compartment can be transferred to the water compartment
via a link.
The algorithms that mathematically represent mass transfer or transformation are
assigned to links which specify the compartments involved in the transfer and the direction of
transfer. This concept is demonstrated in Figure 3-5 where chemical mass is transferred between
the water and air compartments and between the water and fish compartments, but not between
the fish and air compartments. For a transformation process, the link exists within a
compartment to allow transformation between two chemicals (parent chemical and reaction
product) within the same compartment (see Figure 3-6).
In the TRIM.FaTE framework, links provide for the organization and application of
algorithms that describe chemical mass transfer among, and transformation within,
compartments. Links "contain" the algorithms describing the one or more processes governing
mass transfer across that link, as well as any properties specific to the link itself (but not those
properties of the compartments or chemicals involved). For example, a link between two
particular surface soil compartments may contain algorithms for advection of mass via erosion
and runoff processes as well as information on the fraction of overland advective flow from one
soil compartment to another. A soil-to-shrew link may contain an algorithm for intake of
chemical mass via soil ingestion by the shrew. The algorithms contained on both of these links
would also use properties of the sending and receiving compartments and the chemicals being
modeled (e.g., erosion rate, liquid volume of soil, ingestion rate of soil by the shrew, chemical
partition factors) to calculate transfer factors.
3.4 SOURCES
The set of all compartments in a TRIM.FaTE modeling scenario is assumed to contain all
of the chemical mass within the system being modeled, excluding sources and boundary
contributions. A source is an external component that introduces chemical mass directly into an
abiotic compartment. A common example of a source would be the factory emissions of a
chemical into an air compartment. TRIM.FaTE is designed to accommodate single or multiple
source scenarios. Currently, sources in TRIM.FaTE can introduce chemical mass into air
compartments only.
3 Note that in TRIM.FaTE, chemical transformations that result in degradation products or metabolites of a
parent chemical whose fate will not be tracked in the modeling scenario are considered "degradation" processes (see
Chapter 4). Thus, chemical mass can be lost from the system of modeled compartments in two ways: (1) mass
transferred into advection or flush rate sinks, and (2) mass transferred into degradation/reaction sinks due to
degradation processes.
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Figure 3-4
Two Linked Compartments in Separate Volume Elements
Transfer
1 inks
Volume Element
Compartment (Air)
Chemical
4
Volume Element
Compartment (Water)
Chemical
Figure 3-5
Three Linked Compartments in Two Volume Elements
Transfer
Links
Chemical
Volume Element
Compartment (Air)
Chemical
Volume Element
Compartment (Water)
Transfer Links
Compartment (Fish)
Chemical
Figure 3-6
Transformation Links Between Chemicals within a Compartment
Chemical A
Transfer Links
Volume Element
*
Compartment (Fish)
Transformation Links
*
Chemical B
Compartment (Water)
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TRIM.FaTE differentiates between sources emitting within the modeling region (e.g., air
emissions from a facility stack) and sources of chemicals derived from outside the scenario
modeling boundaries; the latter are referred to as boundary contributions (or boundary
conditions) within the TRIM.FaTE framework. Sources emit chemicals directly to the primary
abiotic compartment within which the source emission point(s) is located, as specified by
coordinates (including height) assigned to the source. The emission rate of chemical entering
this compartment (either a constant or time-varying rate) is specified by the user. A boundary
contribution may be set for any outside interface of a compartment located on the outer sides of
the modeled region. For air, a boundary concentration (which may be set by the user for the
outer volume elements) establishes the concentration of a chemical in the air on the external (i.e.,
non-modeled) side of the modeled region. Chemical mass enters the outside compartments via
air advection algorithms that depend on wind speed, wind direction, and boundary concentration.
Although currently implemented in TRIM.FaTE only for air compartments, boundary
contributions could be set for other compartments as well (e.g., flowing surface water
compartments).
In addition, chemical mass can be included in the starting conditions for a scenario based
on initial concentrations set by the user for abiotic or biotic compartments included in the
TRIM.FaTE modeling scenario. The addition of mass to a scenario as an initial concentration
in one or more compartments is a one-time event and represents concentrations in environmental
media just before the simulation period begins. Outside of the TRIM.FaTE framework and
terminology, this concept is often referred to as "background" concentrations. However, within
the TRIM.FaTE framework, the word background is not used; the term "initial concentration" is
used. A related TRIM.FaTE feature that can be useful for evaluation and diagnostic purposes is
the ability to fix the concentration in any compartment to a user-specified value for the duration
of the simulation.
Figure 3-7 adds to Figure 3-5 by showing a source emitting into the air compartment.
Figure 3-7
Linked TRIM.FaTE System with a Source
Chemical
Transfer Chemical
Links |
/"
% '
.
(^>*-ป ->->-ป()
J
Source '
1 ' *
\
\
1
V 1
1 X
***/
T
"^
J
1
Volume Element
Compartment (Water)
Transfer Links
Compartment (Fish)
Chemical
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3.5 TIME-RELATED TERMS AND CONCEPTS
3.5.1 BASIC TIME TERMINOLOGY
TRIM.FaTE is a dynamic model (i.e., the model accepts time-varying inputs and
produces time-varying outputs), which makes it important that the time-related terminology is
clearly defined and consistently used. There are three main time-related terms that are central to
understanding how TRIM.FaTE relates input data, fate and transport calculations, and model
outputs: simulation period, input data time step, and output time step Definitions of these
terms for the purposes of TRIM.FaTE are given below.
For a given TRIM.FaTE model run, the Simu|ation period is the length of time
simulation period (or modeled time period) is
the entire length of time over which the
simulation occurs and compartment masses and
concentrations are calculated - in other words,
., .. -jr. ., , i . i . matrix of transfer factors.
the time period from the beginning date and time
over which the simulation occurs.
Input data time step dictates the points in
time at which TRIM.FaTE calculates a new
Output time step is the frequency at which
results are provided by the model.
of the simulation until the ending date and time.
The simulation period is set by the user through
specification of the simulation begin and end
date and time. For most anticipated applications
of TRIM.FaTE, the simulation period is expected to be one year or longer. Source emissions can
occur for either all or part of the simulation period (i.e.., source modeling periods can be shorter
than the simulation period), or even none of the simulation period if the analysis objective is to
assess boundary conditions only (see Section 3.4).
The input data time step dictates the points in time at which TRIM.FaTE calculates new
transfer factors (i.e.., a new transition matrix as described in Section 4.2). The input data time
step refers to the interval between changes in value of any time-varying input (e.g., wind speed
data may be provided at hourly intervals). The input data time step (or time increments by which
time-varying input data change) is recognized by the model during the run and can vary both
across and within data types. That is, values for a particular data input can even be provided for
erratically-spaced times during the simulation period (e.g., Day 1, Day 2, Day 5, Day 7, Day 8,
Day 9). In moving through the simulation time period, the model recalculates the relevant parts
of the matrix of transfer factors with each occurrence of a change in input data. This up-to-date
matrix is then relied on by TRIM.FaTE for its calculation of the distribution of chemical among
the compartments.
The model can output results for any hourly time interval greater than or equal to one
hour. The user specifies the frequency at which results are output (sometimes referred to as
"output time step") through the use of two model parameters: simulation time step and number
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of simulation steps per output. Setting the
simulation time step to 12 hours and the number of
simulation steps per output step at 2, the model
will write out results at 24-hour intervals
,, , , ,, , , , c ... ,, time step of 1 hour, and the number of
throughout the simulation period. Setting the , " . .
simulation time steps per output equal to
For a modeling scenario with
100 compartments, a 30-year (262,800
hour) simulation period, a simulation
2, there would be 13,140,000 (100 x
262,800 + 2) reported values each for
moles, mass, and concentration results
for each chemical modeled.
number of simulation steps per output step to 1
would produce results at 12-hour intervals (i.e.,
that specified by the simulation time step). That
is, the product of the two parameters is the time
interval between model outputs (i.e., the output
time step). The results at each output time are
those derived for that time point (i.e., a
"snapshot"), and are not an average over any time period. The time series results output from
TRIM.FaTE can also be averaged over various time periods of interest (e.g., each day, month,
year) using an averaging feature of TRIM.FaTE.
TRIM.FaTE also can be run in a steady-state mode. In this mode, there are no time-
varying inputs, no simulation period, and no output time step; the model's run time is negligible
compared to the dynamic modeling mode. In the steady-state mode, TRIM.FaTE calculates
single values for chemical moles, mass, and concentration for each compartment. These values
approximate the levels that the chemical would reach if the dynamic form of the model was run
for a long enough period of time to allow all mass "inputs" and "outputs" to balance for each
compartment (i.e., to reach a steady-state).
3.5.2 OTHER TIME-RELATED CONCEPTS
Much of TRIM.FaTE's data are, in reality, time-varying and can be treated as such by the
model, although it is expected that typically most would be treated as being constant over time.
However, inputs such as meteorological data (e.g., wind speed and direction), stream flow, and
source emissions rate typically would be treated as time-varying in TRIM.FaTE model runs
where sufficient data are available. Values for time-varying parameters can be provided to
TRIM.FaTE in any time increment selected by the user, generally based on data availability and
run time considerations. For example, meteorological data (e.g., wind speed, precipitation
measurements) may be available at hourly increments, and source emission rate data may be
available at monthly increments; TRIM.FaTE can readily accommodate these different input
data time increments. However, using smaller time increments for time-varying input data can
increase the model's run time.
TRIM.FaTE can model certain processes that vary by season and time of day. Including
seasonal changes in chemical fate and transport modeling can be advantageous for at least two
reasons: (1) a seasonally dependent model can be applied to regions where below-freezing
temperatures occur, and (2) model runs can be implemented for durations greater than a single
growing system. However, model realism gained by accounting for seasonality must be
balanced with the burden on the user to collect site-specific data on seasonal processes.
Two seasonal processes are currently implemented for plants in TRIM.FaTE: litterfall,
which allows chemical mass to be transferred from the leaves to the surface soil, and growing
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season, which allows uptake of chemicals by plants only between the day of last and first frost.4
These processes are implemented by enabling or disabling certain links and the associated
transfer algorithms depending on the date. Additional seasonal processes may be considered in
future refinements to TREVLFaTE (e.g., dynamics of snow accumulation and snowmelt, timing
and rate of growth of algae during and following a bloom, dietary and habitat changes by
wildlife). In addition, day-night is simulated in TRIM.FaTE to allow for different exchanges
between the plants and air or soil during the day and night. These processes are implemented by
enabling or disabling certain links depending on the time of day.
4 In addition, seasonality in weather patterns is accounted for by the use of time-varying meteorological and
streamflow data.
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4. CONCEPTUAL DESIGN AND MASS BALANCE
FRAMEWORK FOR TRIM.FaTE
This chapter, building on the definitions and spatial and temporal concepts discussed in
Chapter 3, presents the overall logic and mathematical framework implemented in TRIM.FaTE
for expressing transport and transformation of chemicals in a multimedia environment.
Specifically, this chapter discusses and illustrates the mass balance approach and describes the
processes simulated in TRIM.FaTE. The specific algorithms used to implement the approach are
documented in Volume II of the TRIM.FaTE TSD.
4.1 CONCEPTUAL DESIGN
TRIM.FaTE calculates, given an initial mass inventory and mass inputs over time from
one or more sources and from boundary conditions, the mass of one or more chemicals being
modeled in each compartment in the modeled system for each simulation time step. With the
estimated chemical mass and total volume or mass for each compartment, TRIM.FaTE can
calculate the concentration of each chemical in each compartment for each output time step.
The development of TRIM.FaTE began with a conceptual diagram of the relationships
and processes that affect chemical transport within the environment. The current version of this
diagram is shown in Figure 4-1. In this figure, biotic compartments are represented by
rectangles and abiotic compartments are represented by ovals. The arrows illustrate the potential
chemical transfers between each of the components of the ecosystem. Chemical transformations,
which may occur within compartments in TRIM.FaTE, are not included in Figure 4-1.
4.2 MASS BALANCE CONCEPTS AND EQUATIONS
TRIM.FaTE has been developed with an emphasis on maintaining a mass balance within
the modeling system. That is, all inputs and outputs of the chemical of interest are tracked to
ensure that the results are not unrealistic with regard to the relationship between the amount of
that chemical being introduced to the system and the amounts predicted to be retained in and lost
from the system. More specifically, it means that the amounts of the chemical of interest
predicted to be retained in and lost from the system always sum to the amount of chemical
introduced into the system. When applied to a specific compartment (e.g., soil, or a mouse
population), the mass balance approach implies that, over a given time period, the amount of the
chemical of interest in the compartment at the end of the period is equal to the sum of the amount
of that chemical in the compartment at the beginning of the period plus the gains of the chemical
that occurred during the time period, minus the chemical that was lost from the compartment
during the time period.
It should be noted that in order to accommodate transformation of a chemical and
modeling of the fate of the transformation product chemicals, the mass balance aspect of
TRIM.FaTE is achieved for the chemical entity of interest (i.e., the "core chemical"). Inherent to
the application of this approach is that the core chemical, which may be a part of the chemical
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Figure 4-1
Simplified Conceptual Diagram for TRIM.FaTEa
.
LEGEND
I BMC
J Subd.vi
O
Compartment
Type
Compartment
Type
^ Chemical Mass Trawler
^ Food Chain Transfer
Benthic
Fauna
Water
Column
Fauna
Aquatic
Plants
a Sinks are not shown. Chemical transformation processes are not depicted but may occur in all compartment types. Transformation products may be tracked
within compartments in TRIM.FaTE or transferred out of compartments to reaction/degradation sinks.
b Includes surface soil, root zone soil, and vadose zone soil compartment types.
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introduced into the system rather than the whole chemical (e.g., the X molecule of a hypothetical
chemical of formula XY2), is an entity that maintains its chemical integrity throughout the
modeling system. To date, certain mercury compounds are the only chemicals for which
TRIM.FaTE applications have incorporated transformation among chemicals that are explicitly
modeled. In this case, the mercury atom (in elemental or ionic form) is the core chemical for
which mass balance is maintained within the system, and the model predicts distribution of that
core chemical occurring as elemental, divalent, and methyl mercury within the system. That is,
the mass of mercury atoms (regardless of its speciation or the compound of which it may be a
part) that is introduced to the system is in balance with the sum of the mass and number of
conserved units predicted to remain in and predicted to exit from the system.
To date, TRIM.FaTE has been implemented primarily for first-order linear transfer and
transformation processes. Therefore, the following discussion is limited to algorithms of that
type. It is important to note that higher order nonlinear methods can also be implemented within
the TRIM.FaTE modeling structure.
First-order transfers between compartments are described by transfer factors, referred to
as T-factors. T-factors are in units of inverse time and are not dependent on the sending and
receiving chemicals or the amount of chemical in the sending compartment. The T-factor is the
instantaneous flux of chemical normalized to the amount of that chemical in the sending
compartment. The actual instantaneous flux (i.e., the amount of chemical passing across an
interface per unit time) can be calculated using the T-factor and the amount of chemical in the
sending compartment.1
First-order transformation of chemicals is described by a reaction rate, referred to as R.
Conceptually similar to (and mathematically identical to) the T-factor, R is measured in units of
inverse time. Due to the structure of TRIM.FaTE, a chemical molecule cannot be transferred to
a different compartment and transformed simultaneously. Thus, when both a transformation and
a transfer occur, two separate processes are modeled in TRIM.FaTE.2 Transformations occur for
chemicals within a compartment, and the reaction products (and remaining parent compound)
may subsequently be transferred to another compartment or out of the system. A transformation
in which a chemical of interest is converted to a chemical species for which transport and fate
are not modeled is handled by transferring the chemical moles out of the system of
compartments to a degradation/reaction sink. This kind of transformation is treated as an
irreversible loss from the system.
1 T-factors are actually applied to the moles of the chemical in the compartment, rather than mass, to
facilitate the modeling of transformation. Although some of the transfer (e.g., advection, diffusion) and degradation
algorithms used in TRIM.Fate may be based on mass relationships, there is no mathematical impact of applying the
T-factors to moles instead of mass when the sending and receiving chemical are the same. The algorithms involving
inter-transformation processes between chemicals are based on mole relationships.
2 When there are multiple processes being modeled for a chemical in a given compartment (e.g., multiple
transfer processes, simultaneous transfer and transformation), the processes are modeled separately but calculated
concurrently based on the same starting number of moles (i.e., the number of moles present in a compartment at the
start of the series of calculations for a particular point in time).
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A chemical also can be transferred to a sink via advective transport that moves the
chemical beyond the physical boundaries of the modeled system (e.g., moles of chemical that are
blown out of the modeled area with the air or flushed out with surface water). This kind of sink
is referred to as either an advection sink or a flush rate sink depending on the physical processes
involved in transferring the chemical. The transfer of chemical to a sink by either advective
transport or degradation/reaction is irreversible because after the chemical is transferred to a
sink, it can no longer move to any other compartments.
Although TREVI.FaTE models chemical transfers among compartments and chemicals
(where transformation is involved) on the basis of moles, it also provides the results in units of
mass or concentration by multiplying the resultant moles in each compartment by the molecular
weight of each complete chemical being modeled, or the weight of the entity of interest (e.g.,
methyl mercury mass and concentration results can be provided either in units of methyl mercury
or units of methylmercury, as mercury). Figure 4-2 illustrates the mass to moles to mass
conversions in TRIM.FaTE.
A simplification of a first-order transfer process involving a single chemical (i.e., no
transformation between chemicals) is shown in the top part of Figure 4-3 for a system of one
chemical, two compartments, and two degradation/reaction sinks, where degradation is treated as
an irreversible loss. For this system, it can be seen that:
Chemical gains for compartment A = Sa+TbaNb (1)
Chemical losses for compartment A = TabNa+RaNa (2)
Chemical gains for compartment B = TabNa (3)
Chemical losses for compartment B = TbaNb+RbNb (4)
where:
Na = amount of chemical in compartment A, units of moles
Nb = amount of chemical in compartment B, units of moles
Sa = chemical source outputting to compartment A, units of moles/time
Tab = transfer factor for movement of chemical from compartment A to
compartment B, units of /time
Tba = transfer factor for movement of chemical from compartment B to
compartment A, units of /time
Ra = reaction loss of chemical in compartment A, units of /time
Rh = reaction loss of chemical in compartment B, units of /time.
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Figure 4-2
Chemical Mass/Mole Conversions in TRIM.FaTE
(Example for a single chemical)
Chemical enters modeling system via initial
conditions, boundary contributions, or
source inputs (units of mass).
TRIM.FaTE converts mass to moles
(divides mass by molecular weight).
TRIM.FaTE estimates chemical transfers for each
compartment overtime; specifically, for each
simulation time step, TRIM.FaTE calculates
number of moles of chemical in each compartment
(i.e., applies transfer factors to numbers of moles).
To output results in user-specified units,
TRIM.FaTE converts moles of chemical to mass
(multiplies moles by molecular weight) and
concentration for each compartment.
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Figure 4-3
Example of First-order Transfer Processes for Two Compartments, One Chemical3
Source
Compartment A
Amount of chemical in
compartment = N
Compartment B
Amount of chemical in
compartment = Nb
Sink,
Sink.
dNb/
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As discussed earlier, the constraint that mass balance must be preserved means that, over
any time interval, the change in amount of chemical in a compartment is equal to the gains minus
the losses over the time interval. For compartments A and B, this change in amount is the
difference in equations (1) and (2), and (3) and (4), respectively. The instantaneous change in
amount with respect to time is the derivative with respect to time, denoted by dN/dt. Thus, the
mass balance constraint, when applied to the simple system of the two compartments A and B
discussed here, yields a system of two linked differential equations:
dNa
~^= Sa+TbaNb-(Ra+Tab)Na (6)
dNh
-ฑ= TabNa-(Rb+Tba)Nb (7)
Additional terms are needed to properly account for the mass that is accumulated in the
degradation/reaction sinks. Thus, two additional compartments are added to the system as sinks
and serve as the repository of the chemicals after a reaction creates a form of the chemical that is
no longer modeled in the system. After the chemical is transferred to a sink, it cannot move to
any other compartments within the system. Sinka and Sinkb denote the mass in the degradation/
reaction sinks for compartments A and B, respectively. Now the complete system can be
described using equations (6) and (7) above along with (8) and (9) presented below:
dSink
= RaNa (8)
dt
dSink
(9)
This system of equations (equations (6) through (9)) is shown in matrix form in Equation (5) at
the bottom of Figure 4-3.
For TRIM.FaTE, systems of linear ordinary differential equations are solved using the
Livermore Solver for Ordinary Differential Equations (LSODE) (Radhakrishnan and Hindmarsh
1993), a Fortran program freely available via several online numerical algorithm repositories.
The use of LSODE in TRIM.FaTE is described in more detail in Appendix C of this document.
If the fate of the chemical created by a transformation reaction is to be modeled
explicitly, and the necessary algorithms and input data are available for this transformation
product, then the mass balance approach described above can be modified accordingly to include
this transformation product. Figure 4-4 shows a generalization of the previous example to the
case where the transformed chemical is modeled in addition to the parent chemical. In this case,
in addition to the reaction rate, T-factors are added for the transformed chemical to account for
additional possible transfers between compartments.
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Figure 4-4
Example of First-order Transfer Processes for Two Compartments, Two Chemicals"
Compartment A
Chemical 1
Amount of chemical in
compartment = N (1)
Chemical 2
Amount of chemical in
compartment = N (2)
Chemical 1
Amount of chemical in
compartment = Nb(1)
Chemical 2
Amount of chemical in
compartment = Nb(2)
Compartment B
a This example illustrates the transformation and independent physical movement of two chemicals that can
transform into each other.
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Using the same approach used for the system in Figure 4-3, the more complex system in
Figure 4-4 can be expressed as a set of four differential equations, as follows:
dN (1)
a = -T WN (1) - R l'2N (1) + T WN (1) + R 2'1N (2) + S (1) 00)
dt ab a a a ba b a a a \ )
dN,m
lba ^ * b Aib
at
dN '
where:
(1) AT (1) rri (1) AT (1) 7} 1,2 AT (1) 7} 2,1 AT (2) / 1 1 \
/V / /V r\ /V T- /\ /V (III
''a ฑba ^b ^b ^ b rJVfe-'Vfe V11/
J'2A/ (1) - T (2) N (2) - R 2'1N (2) + 7" (2) A/" (2) H2)
a ah a a a ba b \ )
l,2N (1) + T (2)N (2) _ j, (2)N (2) _ R 2,1 N (2) ,^
O GO Ct DO. OOO V J
Na(1) = amount of core chemical 1 in compartment A, units of moles
Na(2) = amount of core chemical 2 in compartment A, units of moles
Nb(1) = amount of core chemical 1 in compartment B, units of moles
Nb(2) = amount of core chemical 2 in compartment B, units of moles
Tab(1) = transfer factor for movement of chemical 1 in compartment A to
compartment B, units of /time
Tab(2) = transfer factor for movement of chemical 2 in compartment A to
compartment B, units of /time
Tba(1) = transfer factor for movement of chemical 1 in compartment B to
compartment A, units of /time
Tba(2) = transfer factor for movement of chemical 2 in compartment B to
compartment A, units of /time
Ra(1'2) = reaction/transformation of chemical 1 to chemical 2 in compartment A,
units of /time
Ra(2il) = reaction/transformation of chemical 2 to chemical 1 in compartment A,
units of /time
Rb(1'2) = reaction/transformation of chemical 1 to chemical 2 in compartment B,
units of /time
Rb(2J) = reaction/transformation of chemical 2 to chemical 1 in compartment B,
units of /time
Sa(1) = source for chemical 1 outputting to compartment A, units of moles/time
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This system of equations can be summarized in matrix form as follows:
dN8(2)/<#
a)
ab
1,2
T,
Rla
0
--ha
r(i)
Lba
+ Rlh2) 0
o -(Tฎ +
R
Nf
0
0
0
(14)
Applying this same approach to a general system with M compartments (including all
sinks), and allowing the transfer factors and source terms to depend on time as well, results in a
system of linked differential equations of the form:
^N = A(f)N(t)
at
Sฎ, N(to) = N0
(15)
where:
N(t) =
A(t) =
an M-dimensional time-dependent vector whose /'th entry is the moles of
chemical in the /'th compartment
an MX M-dimensional time-dependent matrix
an M-dimensional time-dependent vector accounting for the source terms
contributing to each compartment.
The vector NQ is the initial distribution of chemical moles among the compartments. The
vector N(t) is the distribution of chemical moles among the compartments over time. The
matrix A(t) is referred to as the transition matrix for the system. This term is borrowed from
Markov theory (Schneider and Barker 1989), although the model is not strictly a Markov
process. The transition matrix is used in TRIM.FaTE to solve for the T-factors at each time
step. The vector s(f) accounts for pollutant sources emitting the chemical to specific
compartments over time.
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PHASES CURRENTLY
IMPLEMENTED IN TRIM.FaTE
Air
vapor/gas
suspended participate
4.3 PHASES
Chemicals in the environment can exist in
various phases (e.g., liquid, gas, solid). The
chemical mass in each phase is tracked in
TRIM.FaTE because transfer factors are phase-
dependent (e.g.., the transfer factor for suspended
particulate deposition from air is applied only to the
chemical mass in the particulate phase of air). There
are multiple environmental phases modeled within
the abiotic compartments in TRIM.FaTE. A
chemical's phase distribution is generally assumed to
be at chemical equilibrium within a compartment
(see Volume II of the TSD for exceptions). For
example, chemical mass sorbed to suspended
particulate matter (solid phase) exists in equilibrium
along with chemical mass present as vapor (gas
phase) in an air compartment. The adjacent text box
lists the phases currently implemented in
TRIM.FaTE for each abiotic compartment type.3
In any compartment, the total amount of
chemical present is made up of the sum of the
amounts in the different phases. Because chemical
equilibrium among phases is generally assumed, the ratios of the concentrations in the individual
phases are constant over time for a given chemical. The fraction of the chemical that is in each
phase in a compartment is calculated in TRIM.FaTE using either partitioning or fugacity
concepts. Both concepts are mathematical approaches to describe the same physical phenomena.
Partitioning coefficients are empirical relationships that show the distribution of a specific
chemical between two phases. Fugacity is a measure of a chemical's potential to remain in any
particular phase based on its chemical properties. Transfer between two phases is dependent on
the fugacity capacity of a chemical, or the tendency for the chemical to "escape," or transfer, to
another phase. Details and calculations for the implementation of phases in TRIM.FaTE are
presented in Chapter 2 of Volume II of the TRIM.FaTE TSD.
Soil
soil pore water
vapor/gas
soil solids
Surface water
water
suspended solids-algae
suspended solids-other
Sediment
sediment pore water
sediment solids
Groundwater
soil pore water
soil solids
3 Includes surface, root zone, and vadose
zone soil compartment types.
Algae are included as a phase within surface water rather than a separate compartment type because of the
limited data available to explicitly model chemical movement between algae and surface water at the time
TRIM.FaTE was being developed for mercury. Given the available uptake algorithm for algae, uptake was assumed
to occur by passive diffusion and could thus be most simply represented by equilibrium partitioning.
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4.4 FATE, TRANSFORMATION, AND TRANSPORT PROCESSES
In TRIM.FaTE, the following physical, chemical, and biological processes are
implemented as first-order mathematical processes for modeling the transfer and transformation
of chemicals:
Advection;
Dispersion;
Diffusion;
Biotic processes; and
Reaction and transformation.
Qualitative descriptions of these five types of processes are presented in the subsections
below. More detailed explanation of the mathematical representation of these processes and
documentation of all of the currently implemented algorithms are presented in Volume II of the
TRIM.FaTE TSD.
Fate and transport algorithms developed for early prototypes of TRIM.FaTE were
specific to nonionic organic compounds (i.e., phenanthrene and benzo(a)pyrene). Transfer
factors used in the algorithms contained in these prototypes relied 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. In order to address the fate and
transport of metals and other inorganic compounds using TRIM.FaTE, non-fugacity-based
algorithms can be developed. The current version of TRIM.FaTE includes algorithms
specifically developed for mercury and mercury compounds as well as several individual PAHs;
it is expected that many of these algorithms can be used for other metals and organic and
inorganic compounds as well. The flexibility of the TRIM.FaTE framework allows for the
development of additional algorithms specific to other chemicals.
4.4.1 ADVECTION
An advective process is one in which a chemical is transported within a given medium
that is moving from one compartment to another. Mackay (1991) refers to this as a "piggyback"
process, in which a chemical is "piggybacking" on material that is moving from one place to
another for reasons unrelated to the presence of the chemical. Advective processes are modeled
using first-order methods in TRIM.FaTE. Mathematically, all that is required to calculate the
advective flux (i.e.., mass per time traveling across an interface) is the velocity of the moving
phase and the amount of the chemical that is in the moving phase. Examples of advective
processes included in TRIM.FaTE for transport of a chemical include erosion from a surface soil
compartment to a surface water compartment, runoff of water from a surface soil compartment to
a surface water compartment, and transport from one air compartment to another due to the
wind.
4.4.2 DISPERSION
Dispersion refers to the "spreading out" of a chemical during advective transport and can
result in movement in any direction relative to the direction of advective flow (including
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perpendicular to the flow). Dispersion can be partially explained by Brownian motion, which is
the movement of small particles as they are randomly bombarded by the molecules of the
surrounding medium. In the current version of TRIM.FaTE, dispersion is explicitly addressed
(as a first-order process) in transfers between surface water compartments using the methods
presented in the Water Quality Analysis Simulation Program (WASP) water transport model
(Ambrose et al. 1995).
Horizontal dispersion between air compartments is not currently modeled in TRIM.FaTE
due to the complexity of accurately modeling this process in a compartment-based model.
Currently, vertical dispersion is not modeled either. For a compartment-based model like
TRIM.FaTE, however, the assumption that chemicals are instantaneously distributed evenly
throughout a compartment volume results in three-dimensional "numerical dispersion" in the air
compartments. The effective dilution that results from this assumption is generally much larger
than the effects of dispersion in air.
4.4.3 DIFFUSION
In a diffusive process, a chemical is transported from one compartment to another as a
result of differences in chemical concentration between the two compartments at their interface.
Examples of diffusive processes in TRIM.FaTE include exchange between air compartments and
soil or surface water compartments, exchange between adjacent soil compartments, and
exchange between sediment compartments and surface water compartments. Models for
diffusion frequently use non-first-order methods; however, these can often be approximated with
first-order methods. All diffusive processes are currently modeled in TRIM.FaTE using
first-order methods. Diffusion rates are based on the chemical concentrations in adjacent
compartments at the beginning of each simulation time step.
4.4.4 BIOTIC PROCESSES
The transport of chemicals to biota (i.e., into biotic compartments) consists of advective
and diffusive processes, though the former term is rarely used by biologists. For example,
chemicals deposit onto plant leaves with particles in air, an advective process; chemicals also
diffuse into plant leaves from air and from the plant surface. The uptake of chemicals from soil
or water in soil by plant roots or earthworms is treated as diffusion, though water carries the
chemical into the plant (advection). Similarly, chemicals are assumed to enter macrophytes and
benthic invertebrates by diffusion. The major advective process for the transport of chemicals
into animals is food intake by fish, birds, and mammals. The transport of chemicals out of biota
and into the surrounding abiotic compartment is also modeled within TRIM.FaTE by the same
types of processes (e.g., diffusion by benthic invertebrates, excretion by mammals).
The only transport process within biota (i.e., within the biological system in the biotic
compartments) that is included in TRIM.FaTE is advective transport within the composite leaf
compartment between the leaves and the plant stem via xylem and phloem fluids. Modeling the
distribution of chemicals among organs in fish and wildlife is not currently a feature of
TRIM.FaTE; however, this increased complexity could be accommodated within the current
TRIM.FaTE framework.
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4.4.5 REACTION AND TRANSFORMATION
Reaction and transformation processes in the abiotic media include biodegradation (i.e.,
the biotically mediated breakdown of the chemical in abiotic compartments), photolysis,
hydrolysis, and oxidation/reduction. In biota, metabolic processes can transform the chemical to
another chemical of concern tracked in the system or can break down (i.e., degrade) the chemical
into metabolites that are not followed in the system. Transformation is modeled in TRIM.FaTE
as a reversible reaction using first-order rate equations, as discussed in Section 4.2. In all
TRIM.FaTE applications to date, transformation is modeled as a bi-directional process between
two inter-transforming chemicals that occurs via a pair of transformation algorithms, whereas
degradation is modeled as an irreversible first-order reaction. The first-order transformation and
degradation rates may be process-specific, or they may incorporate more than one of the
processes involved. First-order rate constants are equivalently described in TRIM.FaTE as
transformation half-lives (i.e.., a rate constant can be mathematically converted to a half-life and
vice-versa). Depending on the algorithm and compartments involved, the mass of chemical
transformed may be either lost from the system (i.e., transferred to a sink) or tracked as a
different chemical.
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5. APPLICATION OF TRIM.FaTE
This chapter illustrates the application of the concepts presented in Chapters 3 and 4 by
briefly explaining each of the main steps necessary to set up and perform a simulation with
TRIM.FaTE. It explains the methods associated with key steps in the modeling process,
provides a general sense of the level of effort associated with performing a TRIM.FaTE
simulation, and summarizes the inputs and outputs of the model. Note that the text here is
simply a summary based on the applications of TRIM.FaTE to date and the associated
chemicals, algorithms, compartment specifications, and properties. Future applications may
expand upon these or implement alternatives. However, it is expected that the basic framework
for model application will remain as presented here.
This chapter is not intended to serve as a user's guide to the model; rather, it provides a
broad understanding of how TRIM.FaTE works by explaining in general terms how it is used to
simulate the multimedia fate and transport of air emissions from one or more sources. For
detailed information on installing and navigating TRIM.FaTE software, applying the model for a
specific scenario, and other aspects of operating TRIM.FaTE, refer to the TRIM.FaTE User
Guidance.
5.1 GENERAL PROCESS FOR A TRIM.FaTE SIMULATION
TRIM.FaTE is designed to be an iterative and flexible model. Although there are a
number of steps that the user must include in the development and application of a TRIM.FaTE
simulation, some of the order in which these steps are completed is flexible. The general process
is shown in Figure 5-1. The boxes on the left side of the figure categorize the basic steps
involved in the completion of TRIM.FaTE simulation: problem definition, specification of links,
simulation set-up, model execution, and analysis of results. In an actual application, progression
through the modeling process might not be quite as linear as that shown in the figure. However,
all of the tasks identified in the basic steps must be addressed during the course of developing
and executing a TRIM.FaTE simulation.
The vertical arrows linking the steps under the "Process Flow" heading represent the
typical order of events in an application. The arrows to the left indicate that model application
might involve multiple iterations as the modeling scenario is refined. Primary components of the
analysis are illustrated under the "Primary Tools" heading and linked with arrows to the relevant
steps in the "Process Flow" column. To focus on key aspects of the TRIM.FaTE approach, only
selected tools are shown. There are other tools that may be relied upon that are not indicated in
this figure (e.g., sensitivity analysis and Monte Carlo features, steady state solver).
SEPTEMBER 2002 5-1 TRIM.FATE TSD VOLUME I
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Figure 5-1
TRIM.FaTE Modeling Sequence
PROCESS FLOW
PRIMARY TOOLS
&
.5
'c
Q.
O
Q.
Q.
ro
in
ro
Define Problem
Specify chemicals and
sources to be modeled
Specify modeling region
Specify volume elements
Specify compartments
Specify simulation period
Specify Links/Algorithms
Specify links between
compartments
Specify algorithms where
required
Set Up Model Run
Set initial and boundary
conditions
Specify input data (i.e.,
properties for all objects3)
Set simulation and output
time steps
Perform Simulation
MODEL:
Updates property values for each
simulation or data time step
Calculates transfer factor for
each link
Calculates moles distribution in
system compartments at
simulation time steps
Converts moles to mass and
concentration
Saves moles, masses, and
concentrations for output time
steps
I
DATA
Spatial data
Meteorological and other
environmental setting
data
Chemical properties
Source terms and
"background" data
Data preprocessors
ALGORITHM LIBRARY
GENERAL CALCULATION TOOLS
Differential equation solver
Partial differential equation solver
Analyze Results
POST-PROCESSING TOOLS
Results averagers
Visualization tools
The term objects refers to sources, chemicals, volume elements, compartments, and the scenario.
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5.2 PROBLEM DEFINITION
TRIM.FaTE is not intended for use in assessments involving air pollutant emissions
where the air is the medium of interest; rather, it is intended for applications involving air
pollutant emissions where the assessment focus is on non-air media. The first step in the
TRIM.FaTE modeling process is articulating a clear statement of the problem and then
translating that into the TRIM.FaTE structure. In defining the problem for a TRIM.FaTE
application, the chemical(s), source(s), primary spatial features of the ecosystem, and simulation
period are defined. Using the nomenclature presented in Chapter 3, the volume elements and
compartments within the volume elements are specified.
The TRIM.FaTE problem definition is based directly on the underlying objectives of the
fate and transport/exposure/risk assessment to be performed. As recommended in recent EPA
and other risk guidance, a clear problem formulation should be developed for the fate and
transport/exposure/risk assessment before any modeling begins. This is particularly important
for highly flexible models like TRIM.FaTE.
5.2.1 DETERMINING SOURCE(S) AND CHEMICAL(S) TO BE MODELED
The sources and chemicals to be included in the TRIM.FaTE simulation are identified in
the first step of the problem definition process. Generally, this identification flows directly from
the overall problem formulation for the fate and transport/exposure/risk assessment. This step
must be performed first because the chemical(s) and location of the source(s) can influence how
the region to be modeled is delineated and subdivided. In determining the chemicals to be
modeled, the user should consider the objective of the modeling exercise, the available emissions
information, and the potential effects and receptors of concern. The user must decide, given the
objectives and resources available for the analysis, which sources and chemicals should be
included in the modeling analysis. The process of identifying chemicals of concern and
associated sources should be referenced to existing EPA guidance when possible.
5.2.2 DETERMINING SCALE
AND SPATIAL RESOLUTION
This section introduces the
considerations for defining the overall
modeling scale and the level of
spatial complexity (i.e., location, size,
shape, and number of parcels) in a
TRIM.FaTE analysis. Definitions for
the important spatial terms used in
this section are discussed in more
detail in Chapter 3 and are
summarized in the adjacent text box.
After the initial scenario is
constructed and a simulation has been
completed, the preliminary results
A parcel is a planar (i.e., two-dimensional), horizontal
geographical area used to subdivide the modeling
region. Parcels, which are polygons of virtually any size
or shape, are the basis for defining volume elements
and do not change for a given scenario. There can be
separate parcels for air and for the land surface (soil or
surface water).
A volume element is a bounded three-dimensional
space that defines the location of one or more
compartments.
A compartment is defined as a unit of space
characterized by its homogeneous physical composition
and within which it is assumed, for modeling purposes,
that all chemical mass is homogeneously distributed
and is in phase equilibrium.
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need to be evaluated to confirm that an appropriate level of resolution has been used. An
example of a general approach for determining appropriate scale and spatial resolution as well as
suggestions for defining compartments are included in the TRIM.FaTE User Guidance.
It is important to note that TRIM.FaTE is designed to model the fate and transport of air
pollutant emissions into non-air media. Scenarios may be created that range from general and
conceptual (e.g., an environment consisting of air, soil, and a water body) to more detailed and
site-specific (e.g., capturing major land and water topographic variation in a 25 square mile
area). The compartment-based design, with its assumptions of homogeneity within
compartments, is not intended to provide fine scale resolution of predicted pollutant
concentrations. When such detail is desired for particular media (e.g., a water body), it may be
appropriate to also consider use of a detailed model specific to that medium (e.g., a water quality
model). As noted previously, for assessments of air pollutant emissions where the air is the
medium of interest, users should rely on an air quality model.
5.2.2.1 Specifying the Modeling Region
The first step in determining the scale and spatial resolution of a TRIM.FaTE scenario is
to determine the scale of the modeling region (i.e., external spatial boundaries of the analysis).
In this step, the user specifies the geographical extent of the area to be modeled. A user should
consider factors such as mobility of the modeled chemical(s), location of source(s), location of
sensitive populations, and background concentrations of the chemical(s). A scoping step using
air dispersion modeling may be appropriate to evaluate the predicted spatial pattern of
deposition. When the predominant wind direction is variable, the modeling region may need to
extend beyond the region of primary interest to account for the possibility of pollutant mass
leaving and re-entering the system. After the scale of the modeling region has been chosen, one
must consider whether that scale is appropriate when compared to other sources of model
uncertainty.
5.2.2.2 Specifying Parcels
Once the scale of the analysis is determined, the next step is to specify the spatial
resolution of the modeling region using two-dimensional parcelsessentially polygonsthat
subdivide the modeling region. Parcels need to be defined for the air and for the land surface
(i.e., soil and surface water); these two sets of parcels do not need to have the same spatial layout
(i.e., their individual and combined boundaries do not need to line up). A larger number of
parcels in a given scenario can be used to provide higher spatial resolution and/or greater areal
coverage. It is noted however, that more parcels correspond to greater resource requirements,
both in terms of model set-up and data collection as well as model run time.
Beyond complexity and resource considerations, there are three principal technical
considerations for determining the parcels for a TRIM.FaTE scenario:
The likely pattern of transport and transformation of each chemical of concern (i.e.,
where significant concentration gradients are likely to be);
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The locations of natural and land use boundaries; and
The locations of important environmental or biological receptors. These can include
populations of receptors (e.g., human or ecological cohorts) or landscape components
(e.g., lake, agricultural area) of interest.
For the chemical(s) of interest, three important factors in determining the likely pattern of
transport and transformation are the atmospheric transport of chemical mass, rapidity of
chemical transport in media other than air, and transformation or degradation of the chemical(s)
in the environment. Understanding the atmospheric transport of the chemical(s) of interest is
useful in developing both the modeling scale and spatial resolution. Because air pollutants travel
more rapidly in air than any other medium, insight into atmospheric transport can provide the
user with a general idea of the extent of chemical transport and thus can be useful in determining
the modeling scale. Furthermore, this information can provide the user with a picture of the
general path of chemical transport, helping the user determine where higher spatial resolution
may be beneficial.
Information about the mobility and transformation or degradation of the chemical(s) of
interest in soils and water, when combined with information on land use, can provide additional
insight into the transport in media other than air. It can be helpful in refining the scale of the
scenario as well as providing additional input to help determine the spatial resolution of the
modeling region.
Natural boundaries are also an important consideration in developing the parcel layout.
For example, an airshed boundary can be defined by a combination of geographical and
meteorological conditions such as large valleys where inversion layers and diurnal wind patterns
may result in a confined and relatively well mixed air mass within the area for extended periods
of time. Airshed boundaries can also include smaller valleys when meteorological conditions
produce a long residence time for the air mass in the bounded region. Airshed boundaries are
useful in providing information about the scale of the overall modeling region (i.e., external
boundaries of the system).
Watersheds are also useful in determining the scale of the system as well as the size and
location of surface parcels within the system, especially if chemical concentrations in a particular
lake or stream are of interest. Watershed boundaries can be identified from existing references
(e.g., United States Geological Survey (USGS) applications) or approximated from
topographical maps by tracing ridgelines and noting the origin and direction of flow for streams
and rivers. The size and location of a watershed can influence the transfer of chemical to water
bodies within it.
Land use and land cover data should be considered in defining land parcels. That is, it
may be appropriate for the parcel layout to capture the pattern of land use and cover, including
forest type, rangeland, or agriculture lands,1 as land use homogeneity can be desirable within
1 For example, it is useful to consider variation in vegetation type which may be important for the
subsequent step of assigning vegetation and other biotic compartments.
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parcels. Land use or land cover can also correspond with a particular receptor of interest (e.g.,
ecological receptor(s) in a coniferous forest, or residents on small farms). The location of the
receptor(s) is important because it allows the user to focus the analysis on the area(s) of interest,
thus allowing resolution to be coarse in areas that are not expected to affect the chemical
concentrations near the receptor(s) and resolution to be finer in areas that may have a greater
impact.
The illustrative approach to specifying parcels described in the TRIM.FaTE User
Guidance provides a starting point for any given analysis objective for which TREVI.FaTE is
used. The approach is intended to impart some consistency and transparency into the spatial set-
up process while maintaining an adequate level of flexibility.
5.2.2.3 Specifying Volume Elements
After the parcels have been determined for a scenario, the volume elements
corresponding to those parcels are specified. This step involves specifying the appropriate
vertical dimension and resolution of the modeling region. Whereas parcels only represent the
modeling region in two dimensions (i.e., a horizontal plane), volume elements add the
component of depth, thus representing the modeling region in three dimensions (the location and
horizontal two-dimensional planar shape of a volume element correspond exactly to the relevant
parcel). The volume elements are determined based on various factors, including knowledge of
mixing heights in air, average depth of water bodies or approximate levels of stratification, and
typical demarcations in the soil horizon (or demarcations of interest to a particular assessment).
The specification of volume elements represents the final step in specifying the spatial
dimensions of the modeling region.
EXAMPLES OF VOLUME ELEMENTS
USED IN TRIM.FaTE
Air Surface Water
Surface Soil Sediment
Root Zone Soil Ground Water
Vadose Zone Soil
TRIM.FaTE allows the specification of
multiple layers of volume elements, when the
additional complexity and resource
requirements of doing so are offset by the desire
for greater spatial resolution. In addition, the
user must ensure that algorithms to model the
transfer of chemical mass between multiple
layers (or multiple vertically stacked volume
elements) are included in the library. Although
use of a single volume element (i.e., no vertical stacking of volume elements), may be sufficient
for the application at hand, use of multiple vertically stacked volume elements of the same type
(e.g., air, surface water) may be useful in particular assessments. For example, instead of one set
of volume elements for the vadose zone (i.e., one layer), additional vertical resolution can be
gained by dividing the vadose zone into two or more vertically stacked layers of volume
elements. Without such assignment of multiple volume elements of the same type, however,
TRIM.FaTE currently accommodates vertical variation in soil through the use of separate
volume elements of different types for the three major soil zones (i.e., surface, root zone, and
vadose zone) and the availability in the TRIM.FaTE library of algorithms describing vertical
transport among them.
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5.2.3 DETERMINING COMPARTMENTS
5.2.3.1 Abiotic Compartments
Abiotic compartment types are assigned as appropriate to each volume element (e.g., air
compartments in air volume elements, surface water compartments in surface water
compartments). At least one abiotic compartment must be contained within each volume
element. Although not currently implemented, the model framework supports multiple abiotic
compartments within a volume element. In most cases, the determination of abiotic
compartments is an implied step because these compartments are simply defined by the abiotic
medium designation of the volume element.
5.2.3.2 Biotic Compartments
Of the available biotic compartment types, the user is only required to run the model with
those that significantly influence the overall mass balance of the chemical in the modeled
scenario and those that significantly affect concentration in the compartments of interest in the
assessment. In applying the model to PAHs, for example, plant biomass significantly influences
the mass balance in the system. Thus, it would not be appropriate to run a PAH application of
the model without the plant compartment types, even if the only results of interest for a
particular application were concentrations in abiotic compartments. In addition to plants, the
TRIM.FaTE scenario should include those biotic compartment types that significantly affect
chemical concentrations in the compartments of interest to the assessment.
The flexibility of TRIM.FaTE can accommodate a variety of designs for
compartmentalization of biota for a modeling scenario. For example, a user can perform a
TRIM.FaTE assessment for a whole trophic group using parameter values for a single species
selected to be representative of all of the species in the group. In addition, a user can choose
particular animal species of concern (e.g., threatened or endangered populations) and
parameterize the model for those species.
5.2.4 DETERMINING SIMULATION PERIOD
After determining the sources and chemicals to be modeled, the user determines the
appropriate simulation period by considering the modeling objectives (per the problem
definition), the lifetime of the modeled source(s), the persistence and mobility of the modeled
chemical(s), and the effects of concern. The user should also consider resource limitations when
determining the simulation period because this parameter directly affects the computing time.
In addition to the dynamic modeling mode, TRIM.FaTE includes a feature for obtaining a steady
solution. This is desirable for some assessments or for parts of some assessments, and provides
substantial savings in computing time over a multi-year dynamic simulation.
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5.3 DETERMINING LINKS/ALGORITHMS
The second step shown in Figure 5-1 is to assign the links, as appropriate, between the
compartments and sinks specified for a given scenario (the compartment types currently
available in TRIM.FaTE are listed in Chapter 3). Generally, for abiotic compartment types, the
user will want to link all adjacent compartments to each other and link compartments at the edge
of the modeling region to advection sinks. Biotic compartment types need to be linked to the
appropriate abiotic compartments, as well as to other biotic compartments, depending on their
relationships to each other in terms of the transfer of chemical mass. For example, each biotic
compartment should be linked to all compartments that comprise its diet or in any way provide it
a source of chemical mass (e.g., terrestrial biota need to be linked to air compartments for
inhalation of chemical), as well as to all compartments to which that biotic compartment
provides chemical mass (e.g., via excretion or by acting as a dietary component). TRIM.FaTE
has a "SmartLink" feature that facilitates setting links, particularly among abiotic compartments.
However, it is still necessary, particularly for biotic compartments, to carefully identify what
links should be in place and confirm their placement or assignment within the TRIM.FaTE
scenario.
The system of links is one of the most critical components of TRIM.FaTE. This
component is critical because the links provide for the assignment of the algorithms describing
the processes that drive chemical transfer and transformation. By specifying a link between two
compartments, it is assumed that one or more algorithms2 exist by which to estimate the transfer
of chemical through the link via the prevailing processes. If an algorithm is not in the algorithm
library, then it must be "added" so that it can be accessed by the underlying software. Methods
for adding additional algorithms to the library will be described in the TRIM.FaTE User
Guidance. Tables 5-1 and 5-2 present examples of links between abiotic and biotic compartment
types, respectively, which are supported by non-transforming mass transfer algorithms in the
current TRIM.FaTE library. In addition, Table 5-3 presents links that are implemented if the
user chooses to apply the equilibrium model for bioaccumulation by fish (see Volume II of the
TRIM.FaTE TSD for more information regarding this model for fish). To support the modeling
of chemical transformation (i.e., transportation of mass between different chemicals within the
same compartment), TRIM.FaTE includes a feature that automatically creates links for each
compartment with itself. At this time, the TRIM.FaTE library includes transformation
algorithms for mercury species.
TRIM.FaTE also has the flexibility to use model results from single-medium models
(e.g., ISC) in place of some of the internal links and algorithms. In this case, the output from the
external model would replace the calculations of fate and transport within the specific medium.
It is noted, however, that this could have ramifications on the mass balance feature of
2 Where multiple process operate to transport chemical across a link (e.g., deposition and diffusion between
an air compartment and a soil compartment), multiple algorithms may be active (i.e., enabled) on a single link.
Additionally, there may be more than one algorithm, derived by different methods, which could be used to describe
chemical movement across a link via the same process. In that case, the user must identify the algorithm preferred
for the process in the current scenario and disable the other(s).
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Table 5-1
Abiotic Compartment Type Links and Mass Transfer Processes
(Table includes non-transformation related examples supported by the current
TRIM.FaTE algorithm library.)
Links Between Compartment Types
Sending
Air
Surface Water
Sediment
Surface Soil
Root Zone Soil
Receiving
Air
Surface Soil
Surface Water
Air Advection Sink
Air
Surface Water
Sediment
Surface Water Advection
Sink
Surface Water
Sediment Burial Sink
Air
Surface Soil
Root Zone Soil
Surface Water
Surface Soil Advection
Sink
Surface Soil
Root Zone Soil
Vadose Zone Soil
Mass Transfer Processes Addressed
Bulk Advection
Dry Deposition (of particles)
Wet Deposition (of particles and vapors)
Diffusion
Dry Deposition (of particles)
Wet Deposition (of particles and vapors)
Diffusion
Bulk Advection Beyond System Boundary
Diffusion a
Bulk Advection
Dispersion
Suspended Sediment Deposition
Pore Water Diffusion
Bulk Advection Beyond System Boundary
Resuspension
Pore Water Diffusion
Solids Advection Beyond System Boundary
Diffusion a
Resuspension
Erosion
Runoff
Percolation
Diffusion
Erosion
Runoff
Erosion Beyond System Boundary
Runoff Beyond System Boundary
Diffusion
Percolation (if multiple root zone layers)
Percolation
Diffusion
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Links Between Compartment Types
Sending
Vadose Zone Soil
Groundwater
Receiving
Root Zone Soil
Vadose Zone Soil
Ground Water
Surface Water
Mass Transfer Processes Addressed
Diffusion
Percolation (if multiple vadose zone layers)
Percolation
Bulk Advection (recharge)
1 Includes volatilization.
Table 5-2
Biotic Compartment Type Links and Mass Transfer Processes
(Table includes non-transformation related examples supported by the current TRIM.FaTE algorithm library.")
Links Between Compartment Types
Sending
Air
Air (Particulates)
Air (Rain Water)
Plant Leaf
Particle on Leaf
Plant Stem
Plant Root
Receiving
Plant Leaf
Terrestrial and Semi-aquatic Wildlife
(birds and mammals)
Particle on Leaf
Leaf
Particle on Leaf
Surface Soil
Air
Terrestrial Vertbrate Herbivore and
Omnivore
Semi-aquatic Herbivore
Plant Stem
Surface Soil
Plant Leaf
Terrestrial Herbivore and Omnivore
Semi-aquatic Herbivore and Omnivore
Plant Leaf
Root Zone Soil
Mass Transfer
Processes Addressed
Uptake b
Inhalation c
Wet Deposition c
Dry Deposition c
Wet Deposition c
Diffusion
Litterfall c
Diffusion/Advection
Dietc
Dietc
Uptake b (advection only)
Particle Washoff c
Litterfall c
Uptake b (diffusion only)
Dietc
Dietc
Uptake b (advection only)
Equilibrium Partitioning
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Links Between Compartment Types
Sending
Surface Soil
Root Zone Soil
Surface Water
Macrophyte
Sediment
Terrestrial and Semi-aquatic
Predator/Scavenger
Terrestrial Vertebrate Herbivore
Terrestrial Omnivore, Insectivore, and
Ground-invertebrate Feeder
Soil Detritivore
Receiving
Terrestrial Ground-invertebrate Feeder
Terrestrial Vertebrate Herbivore and
Omnivore
Semi-aquatic Herbivore
Plant Stem
Plant Root
Soil Detritivore
Plant Stem
Macrophyte
Fish6
Water-column Herbivore
Surface Water
Benthic Invertebrate (Flying Insect)
Herbivore f
Surface Soil
Semiaquatic Omnivore
Terrestrial and Semi-aquatic
Predator/Scavenger
Surface Soil
Terrestrial Predator/Scavenger
Semi-aquatic Piscivore
Surface Soil
Surface Soil (soil arthropod)
Root Zone Soil (earthworms)
Terrestrial Ground-invertebrate Feeder
(earthworms)
Terrestrial Insectivore (arthropods)
Terrestrial Omnivore, Semi-aquatic
Omnivore, and Terrestrial
Predator/Scavenger (worms and
arthropods)
Mass Transfer
Processes Addressed
Dietc
Dietc
Dietc
Uptake b (advection only)
Uptake b (equilibrium
partitioning)
Uptake b
Uptake b (advection only)
Equilibrium Partitioning b
Gill Exchange bd
Diet (algae as phase of
surface water) c
Equilibrium Partitioning b
Uptake b
Excretion b
Dietc
Diet cg
Excretion b
Dietc
Dietc
Excretion b
Equilibrium Partitioning
Equilibrium Partitioning
Dietc
Dietc
Dietc
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Links Between Compartment Types
Sending
Water-column Herbivore and Omnivore
Water-column Carnivore
All Semi-aquatic Wildlife
Benthic Omnivore
Benthic Carnivore
Benthic Invertebrate
Flying Insect (emerged from benthic larval
insect) f
Receiving
Semi-aquatic Omnivore
Semi-aquatic Piscivore
Water-column Carnivore
Surface Water
Semi-aquatic Predator/Scavenger
Semi-aquatic Omnivore
Semi-aquatic Piscivore
Surface Water
Semi-aquatic Predator/Scavenger
Surface Soil
Surface Water
Benthic Carnivore
Surface Water
Surface Water
Semi-aquatic Omnivore
Sediment
Benthic Omnivore
Semiaquatic Predator/Scavenger
Semi-aquatic Insectivore
Mass Transfer
Processes Addressed
Dietc
Dietc
Diet cd
Elimination cd
Dietc
Dietc
Dietc
Elimination cd
Dietc
Excretion b
Excretion b
Diet cd
Elimination cd
Elimination cd
Dietc
Equilibrium Partitioning b
Dietc
Dietc
Dietc
a Examples of links among aquatic fish compartments that are supported by the time-to-equilibrium model for
bioaccumulation by fish are not shown here; they are presented in Table 5-3.
b Uptake, filtration, or partitioning which includes diffusion, advection, and/or active accumulation and elimination by
organism.
c Advection processes.
d Bioenergetic model for bioaccumulation by fish.
e "Fish" include the following compartment types: benthic omnivore, benthic carnivore, water-column herbivore,
water-column omnivore, and water-column carnivore.
f The flying insect compartment is modeling an insect with an aquatic benthic larval phase; assumes insect emerges
and becomes a flying adult invertebrate with the same body concentration as aquatic larval phase.
9 The presence of the terrestrial herbivore to terrestrial predator/scavenger link will depend on the species or groups
selected to represent these compartment types for particular site (e.g., a mouse could be prey for red-tailed hawks or
mink, but a deer might not be prey for any predators).
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Table 5-3
Biotic Compartment Type Links and Mass Transfer Processes:
Time-to-Equilibrium Model for Bioaccumulation by Fish a
(Table includes non-transformation related examples supported by the current TRIM.FaTE algorithm library.)
Links Between Compartment Types
Sending
Surface Water (algae as phase of
surface water)
Water-column Herbivore
Sediment
Benthic Invertebrate (Flying
Insect) Herbivore"
Water-column Herbivore
Water-column Omnivore
Water-column Carnivore
Benthic Invertebrate
Benthic Omnivore
Benthic Carnivore
Receiving
Water-column Herbivore
Surface Water (algae as phase of
surface water)
Benthic Invertebrate (Flying
Insect) Herbivore b
Sediment
Water-column Omnivore
(Water-column Herbivore)
Water-column Carnivore
(Water-column Omnivore)
Benthic Omnivore
(Benthic Invertebrate)
Benthic Carnivore
(Benthic Omnivore)
Mass Transfer Process Addressed
Equilibrium Partitioning
(Equilibrium Partitioning)
Equilibrium Partitioning
Equilibrium Partitioning
Equilibrium Partitioning
(Equilibrium Partitioning)
Equilibrium Partitioning
(Equilibrium Partitioning)
Equilibrium Partitioning
(Equilibrium Partitioning)
Equilibrium Partitioning
(Equilibrium Partitioning)
a In the current library, the algorithms implementing this model are limited to use with mercury species.
b The flying insect compartment represents an insect with an aquatic benthic larval phase; assumes insect emerges
and becomes a flying adult with the same body concentration as aquatic larval phase.
TRIM.FaTE. A description of how external models can be integrated with TRIM.FaTE is
presented in Appendix B.
5.4 SIMULATION SET-UP
The third step shown in Figure 5-1 is providing the relevant input data for the simulation.
This involves specifying the chemical properties of each modeled chemical, the initial
distribution of chemical mass in the compartments, the data for each modeled source,
environmental data needed by the selected algorithms, and the simulation and output time steps.
Note that some properties must be specified before links can be made. The role of these inputs
in estimating the chemical fate and transport is briefly explained in this section. A complete list
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of all the parameters for the currently implemented algorithms is presented in Appendix D of
Volume II of the TRIM.FaTE TSD.
5.4.1 CHEMICAL PROPERTIES
To estimate the fate and transport of chemical
mass through the system, certain properties for each
modeled chemical must be specified. The list of
chemical properties that are necessary for a given
simulation varies depending on the chemical (e.g.,
organic chemicals differ from metals) and the media
and biota modeled. Several examples of abiotic and
biotic chemical properties are listed in the adjacent
text box.
5.4.2 INITIAL AND BOUNDARY
CONDITIONS
ILLUSTRATIVE EXAMPLES OF
ABIOTIC CHEMICAL PROPERTIES
half-life or degradation rate (in each
environmental medium)
Henry's Law constant
melting point
molecular weight
ILLUSTRATIVE EXAMPLES OF
BIOTIC CHEMICAL PROPERTIES
half-life or rate of metabolic
degradation (for each modeled
animal or plant species)
chemical transformation rates (in
modeled plant leaves and animal
species)
elimination rate (for modeled animal
species)
For each compartment and chemical in a
scenario, the user has the option of specifying an
initial concentration. In addition, the user can specify
boundary concentrations for volume elements that
include an external interface (e.g., air volume
elements located around the outer edge of the
modeled domain). Boundary contribution algorithms
for air compartments in outer volume elements can then be applied to account for continuing
contribution of chemical to the modeled region from external sources (e.g., the cumulative effect
of non-local sources). Default values of zero may be assumed for boundary concentrations for
pollutants that have a relatively short half-life in the air or if the objective of the simulation is to
assess the effects of a modeled source (or sources) in the absence of "background." If the
objective is to assess "cumulative" exposures, or if results of the analysis are to be compared
with measurement data, however, the initial concentration and boundary contribution features
may be essential. Refer to Section 3.5 for additional discussion of initial and boundary
conditions in TRIM.FaTE.
Additionally, the user can specify concentrations (in one or more compartments of any
type) to be held constant (or fixed) throughout the simulation period. This feature is useful for
evaluation or diagnostic simulations.
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5.4.3 SOURCE DATA
Source data must be specified for each source
modeled in the scenario. Conceptually, there is no
limit on the number of different sources that can be
modeled. The adjacent text box lists the variables that
must be defined for each source.
5.4.4 ENVIRONMENTAL SETTING DATA
SOURCE INPUTS
source location
emission height
chemical-specific emission rate
Whereas initial and boundary conditions and source data specify the location and influx
of chemical mass in the system, environmental setting data are needed to estimate the fate and
transport of that mass throughout the modeled system. There are two general types of
environmental data necessary for TRIM.FaTE to estimate mass transfers in abiotic media:
meteorological data and other environmental setting data. The level of desired refinement in the
simulation dictates the appropriate data (i.e., ranging from site-specific data to default values).
Each general type of input data is briefly described below.
5.4.4.1 Meteorological Data
Meteorological data are required for many of the transport-related algorithms. For
example, the air advection algorithms rely on wind data, and some of the deposition algorithms
from air to surface soil and surface water rely on
precipitation data. The meteorological and related inputs
needed for TRIM.FaTE are listed in the adjacent text box
(vertical wind velocities are also needed when modeling
multiple air layers). Meteorological data at any (or various)
time intervals can be used in TRIM.FaTE (e.g., hourly wind
speed and direction data). Preprocessors are available to
convert the available meteorological data to the format
required for TRIM.FaTe. For example, TRIMet is a data
preprocessor developed for use with TRIM.FaTE to process
the necessary meteorological data and convert them into a
single, specifically formatted, meteorological input file.
METEOROLOGICAL INPUTS
horizontal wind speed
horizontal wind direction
air temperature
precipitation rate
mixing height
day/night
beginning and end of litter
fall
frost date
5.4.4.2 Other Environmental Setting Data
Other environmental setting data are needed to define the characteristics of the biotic and
abiotic compartment types that TRIM.FaTE uses to estimate the transport and transformation of
chemical mass in the system. For example, data on atmospheric dust load are needed for
TRIM.FaTE to estimate dry deposition of airborne particles to soils and surface water; data on
sediment porosity (i.e.., water content) are required to estimate mass transfers between a sediment
compartment and its overlying surface water compartment. In addition, some data are also
required to define properties of links between compartments, such as the fraction of total surface
runoff from one soil compartment that enters into an adjacent soil or water compartment. Input
data values can be provided to TRIM.FaTE as point estimates, or, when using the stochastic
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sampling feature for uncertainty analyses, as
distributions (with appropriate associated
characteristics such as mean and standard
deviation). The adjacent text box presents
some examples of both biotic and abiotic
environmental setting data that may be
necessary for a TREVI.FaTE simulation.
5.4.5 DEFINING TIME STEPS
The final inputs necessary to begin a
TRIM.FaTE simulation are the time steps. The
simulation time step specifies a minimum
frequency at which the model will calculate
transfer factors and chemical mass exchange
between (and transformation within)
compartments.3 The output time step (defined
by a combination of the simulation time step ^^^^^^^^^^^^^^^^^^^^^^^
and the number of simulation steps per output)
determines the points in time at which the amount of chemical in each compartment will be
reported as an output as moles, mass, or concentration. Post-processors may be used to
aggregate these results over specified averaging periods. For example, the results using an
output time step of one hour may be averaged to produce the mean daily or monthly
concentrations of the pollutant in each compartment. Refer to Section 3.2 for a more detailed
discussion of basic timing issues and Section 5.6 for more information on analysis of results.
5.5 SIMULATION IMPLEMENTATION
ILLUSTRATIVE EXAMPLES OF ABIOTIC
ENVIRONMENTAL SETTING DATA
atmospheric dust load (for air compartment
type)
soil density (for all soil compartment types)
current flow velocity (for surface water
compartment type)
ILLUSTRATIVE EXAMPLES OF BIOTIC
ENVIRONMENTAL SETTING DATA
population per area (for all animal
compartment types)
biomass per area (for all plant
compartment types)
food ingestion rate (for all animal
compartment types)
The next step is the actual running of the model, where the movement of the chemical(s)
through the compartments is simulated for each calculation time step for the specified simulation
period. The exact manner in which this is performed depends on the algorithms selected. For
each link, a call is made by the model to the algorithm library to determine the transfer factors
that mathematically describe the potential exchange of chemical mass across an interface
between two compartments or a compartment and a sink. If all algorithms involve only first-
order processes, then movement of the chemical will be simulated with a system of linked
differential equations, the solution of which is found using a differential equation solver (e.g.,
LSODE). For more complicated algorithms, other tools would be necessary (e.g.., a method of
solving partial differential equations).
As described in Section 3.5.1, the model also calculates the chemical inventory at each time point when a
new value is encountered for any inputs.
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The basic TRIM.FaTE outputs are
described in the adjacent text box. The
concentration estimates in the abiotic
compartments and uptake rates (i.e., doses)
for biotic compartments can be used to
estimate ecological risks (see Section 2.3.6).
The concentration estimates in the abiotic
and biotic compartments can be output to an
exposure model (e.g., TRIM.Expo) to
estimate human exposure.
A separate part of the application of
TRIM.FaTE at a site involves analysis of
uncertainty and variability for a simulation.
The concepts and processes involved with
that analysis are discussed in Chapter 6, Treatment of Uncertainty and Variability.
5.6 ANALYSIS OF RESULTS
TYPES OF OUTPUTS
TRIM.FaTE provides several different types of
output to a user. The main TRIM.FaTE outputs
are the moles, mass, and concentration in each
compartment at each reporting time step.
TRIM.FaTE can also output all algorithms used,
all input values, and transfer factors for each
transfer of mass. In addition, TRIM.FaTE can
output certain intermediate calculated values,
such as the calculated chemical mass moving
across the interface between two volume
elements, that can be used for evaluating the
performance of the model.
After completion of a simulation, the user must interpret the model output. This can be a
daunting task because of the quantity of output data TRIM.FaTE produces. For example, for an
analysis that models the fate and transport of three chemicals in 100 compartments for 30 years,
with simulation and reporting time steps of one hour, the model would produce nearly 79 million
values each for moles, mass, and concentration (3 x 100 x 30 x 8,760). Even using a reporting
time of once per day (i.e., 24-hour reporting time step) would result in over three million output
values. If the user wanted to also examine the intermediate model calculations, the output data
set could grow even larger. Because output data from a multimedia fate and transport model can
be used in many ways, different users will have different needs for the model's output. Post-
processors can be used to present the output in forms that are useful to the decision-makers, such
as the maximum concentration in the modeling region or in specific compartments, the average
concentration in an environmental medium, and long-term time trends of environmental
concentrations.
TRIM.FaTE also includes tools that facilitate analysis of results by summarizing and
condensing output data through spatial, tabular, and graphical methods. Examples of three tools
currently implemented in TRIM.FaTE are presented here.
The Averager can generate averages of TRIM.FaTE outputs in any multiple of the
output time step as well as in monthly and annual increments. It can also limit the
compartments included in the averaged file by excluding selected results columns (each
column represents the results for a single compartment). These functions are useful for
reducing the size of output files for further off-line analysis in a separate program (e.g.,
spreadsheet or other quantitative analysis program).
The Graphical Results Viewer presents model results (moles, mass, or concentration)
for each compartment type on a map of the parcels by using different colors to represent
incremental gradations in the results for a specific chemical. Results can be presented
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for any time increment that is output by the model or generated using the Averager. The
results can also be animated over a time series (e.g., to show changes in monthly average
concentration over the course of a multi-year run).
The Aggregator can produce tables in HTML, text, or comma-delimited formats that
combine columns of output data in different ways for producing combined or
comparative statistics. Functions available for combining results columns include sum,
average, difference, ratio, and percent difference. For example, the user can use this tool
to combine columns for similar chemicals (e.g., to calculate total mercury for a particular
compartment type) or to compare two columns (e.g., to compare results for two PAHs
and calculate differences in concentration).
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6. TREATMENT OF UNCERTAINTY AND VARIABILITY
This chapter summarizes the approach for assessing uncertainty and variability in
TRIM.FaTE, which follows the general approach for TRIM as described in Chapter 3 of the
TRIM Status Report (USEPA 1999). Additional background on how this method was selected is
provided in Appendix B of the TRIM Status Report (USEPA 1999). The following text box
presents definitions for the key terms used in this chapter to explain the uncertainty and
variability analysis framework for TRIM.FaTE.
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 (e.g., taking more
precise measurements of people's heights does not reduce the natural variation in heights). However,
it can often be accounted for by a more detailed model formulation (e.g., modeling peoples' heights in
terms of age will reduce the unexplained variability due to variation of heights).
Uncertainty
Uncertainty refers to the lack of knowledge regarding the actual values of 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 analysis
Sensitivity analyses assess the effect of changes in individual model input parameters on model
predictions. This is usually done by varying one parameter at a time and recording the associated
changes in model response. One primary objective of a sensitivity analysis is to rank the input
parameters on the basis of their influence on or contribution to the variability in the model output.
Uncertainty analysis
Uncertainty analysis involves the propagation of uncertainties and natural variability in a model's inputs
to calculate the uncertainty and variability in the model outputs. It can also involve an analysis of the
uncertainties resulting from model formulation. The contributions of the uncertainty and variability of
each model input to the uncertainty and variability of the model predictions are explicitly quantified.
The EPA chose a staged approach for analysis of uncertainty and variability. The use of
a staged approach has advantages for models as complex as TRIM.FaTE. The first stage,
consisting of sensitivity analyses that are comparatively easy to implement, identifies influential
parameters and generates an importance-ranking of parameters. The results of this stage are
useful for narrowing down the number of parameters to be analyzed in the second-stage
uncertainly and variability analysis and are also useful in evaluating model structure and
modeling assumptions. The second stage involves uncertainty and variability analyses of
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increasing detail and complexity. Figure 6-1 illustrates this staged approach for the TRIM.FaTE
module and how the functional parts fit together.
6.1 SENSITIVITY ANALYSES
The sensitivity analysis provides a quantitative characterization of the sensitivity of the
model results to variations in the model input parameters. A ranking of sensitivity results can be
used to provide a first-order determination of the most influential parameters that will need to be
included in the detailed uncertainty analysis. Assessment of whether it is reasonable that
parameters would have the influence they do in the model can also aid in evaluating model
structure and modeling assumptions.
The TRIM.FaTE sensitivity feature allows the user to choose a set of parameters to vary,
the compartments in which to vary them, and the compartments and chemicals for which the
results are of interest. A parameter can be varied in parallel across all compartments of a
specific type (e.g., vary the organic carbon content in surface soil simultaneously for all surface
soil compartments, where the varying parameter values would match across all surface soil
compartments) or independently in specific compartments (e.g., vary organic carbon content in
surface soil separately for each compartment). One simulation of the user's design (e.g., a 5-year
dynamic run, or a steady-state analysis) is completed per selected parameter for comparison to
the base simulation of the same design. All parameter values in that simulation would match the
values in the base simulation except for the selected parameter which is set equal to a new value.
In the current version of TRIM.FaTE, the parameters to be varied must be constants or
time-varying values such as meteorological data (i.e., parameters that are specified by formulas
in the model cannot be varied). The amount by which each selected parameter is varied
(represented by Ap) is specified by the user, and may be a small fixed percentage (e.g., one to ten
percent) of the nominal parameter value or a small fixed percentage of a measure of the spread of
values the parameter typically addresses. One can use the standard deviation or a range of
percentiles (e.g., the range from the 10th to the 90th percentile). A simulation for each parameter
is required for this analysis; thus, 2,000 simulations would be needed to examine 2,000
parameters.
For the selected compartment and chemical outputs of interest, the system will calculate
the sensitivity score for each parameter based on a specified result (e.g., the average
concentration values for the last year of the simulations). Thus, the user may run simulations of
the TRIM.FaTE model with the parameters being varied singly, with the model results
summarized to show the sensitivity to parameters and to identify the most influential parameters.
The results of a sensitivity analysis are applicable to a particular location and for the
range of conditions (i.e., parameter space) simulated, and may not apply to conditions outside of
this. To generate more broadly applicable sensitivity results, the sensitivity analysis can be
performed for a number of different "nominal" base simulations representing distinct modeling
regimes (e.g., summer and winter time periods, wet and arid locations).
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Figure 6-1
Uncertainty and Variability Analysis Framework
(Illustrated for TRIM.FaTE Module)
Measurements
Bootstrap
Expert Elicitation
Input
Distributions
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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Varying parameters so that they are both larger and smaller by Ap (i.e., varying by ฑAp
instead of just +Ap) doubles the number of simulations required to complete the analysis, but
allows the user to calculate the local nonlinearity of the effect of varying a parameter on the
model results (i.e.., the nonlinear impact of varying parameters around a given value, for values
close to the original value). These results are reported as second order terms in the sensitivity
measures to show the extent of local nonlinearity for parameters. Non-local nonlinearities (i.e.,
the nonlinear effects of wider variations of a parameter value) are quantified by increasing Ap to
be in the range of 10 to 100 percent of the nominal values or spread of the parameters.
The results of these simulations are processed to produce measures of the importance of
the parameters in the sense of how the model results change when the parameters are changed.
The measures of parameter sensitivity and ranking automatically computed in sensitivity
analyses are the sensitivity, the elasticity, and the sensitivity score. The user can set up
sensitivity simulations to calculate the nominal range sensitivity if desired. We define these
measures following Morgan and Henri on (1990).
The sensitivity of a model output to a parameter is the rate of change of the output with
respect to changes in the parameter. Denoting the parameter as p and the model output as y, the
sensitivity (at a particular value pฐ of p) is conventionally defined as the partial derivative dy/dp,
evaluated at pฐ. This measure describes how the model responds to small changes in the
parameter p for values of p that are close to pฐ, keeping all other parameters fixed, and is referred
to as a "local" measure.
We calculate the sensitivity by:
Sensitivity = " = (14)
A/? A/?
where Ap is a small change in the parameter value and:
Ay = y(pฐ+Ap)-y(p0) (15)
The nominal range sensitivity is used to assess changes in the model outputs resulting
from large variations in input parameters. The effects on model outputs of varying each input
parameter from the low end to the high end of the range of values for the parameter, are
calculated in essentially the same way as the local sensitivity:
A,- -ID c v v. g o
Nominal Range Sensitivity = ^ (16)
Phigh ~ Plow
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The sensitivity can be interpreted as the slope of the tangent to the response surface y(p)
at the point pฐ (Figure 6-2). Note that the calculated value of the sensitivity depends both on the
nominal parameter value pฐ and the amount of change Ap. The sensitivity to a parameter can be
quite different at different values pฐ of the parameter. It can be useful to vary both Ap and pฐ to
see how the sensitivity depends on them.
Figure 6-2
Illustration of Sensitivity in One Dimension
The elasticity is defined as the ratio of the relative change in the model output y to a
specified relative change in a parameter p:
Elasticity = /
yฐ Pฐ
(17)
where Ap/pฐ is a fixed relative change. For example, if the specified parameter change is one
percent (Ap/pฐ = 0.01), then the elasticity is the percent change in y due to a one percent change
in the parameter p, evaluated at a particular value pฐ of p.
The sensitivity score is the elasticity weighted by a normalized measure of the variability
of the parameter which takes the form of a normalized range or normalized standard deviation of
the parameter. The sensitivity score for the model input parameter p with respect to the model
output y is defined as:
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Sensitivity Score = *
ng)
yฐ
where:
Ay/Ap = change in output y per change in input p
o/\i = coefficient of variation of p (standard deviation/mean)
pฐ/yฐ = ratio of nominal values of the input and output
Other normalized measures of the variation of the parameter can be used in place of the
coefficient of variation (e.g., the range of p divided by the mean).
6.2 THE MONTE CARLO APPROACH FOR UNCERTAINTY AND
VARIABILITY ANALYSES
A Monte Carlo approach with Latin Hypercube Sampling (LHS) is available within
TRIM.FaTE for characterizing and analyzing the uncertainty and variability of the TRIM.FaTE
outputs, with respect to the model inputs and parameters. The primary advantages of Monte
Carlo methods for this type of analysis are the generality with which they can be applied, the
lack of assumptions required, and their computational efficiency. Particular strengths of a Monte
Carlo approach relevant to TRIM uncertainty and variability analyses include the following:
Monte Carlo (MC) can be used to analyze many parameters.
MC handles different ways of specifying parameter distributions.
MC can treat correlations and dependencies.
MC allows for tracking the propagation of uncertainty and variability through model
components at any level.
MC gives estimates of confidence bounds for the estimates of the output distributions.
MC allows precision to be increased easily by performing additional iterations.
LHS is an efficient sampling scheme, reducing the number of simulations required. (MC
with LHS has computational complexity linear with the number of parameters or model
inputs that are being analyzed.)
MC handles complex algorithms in the model without increased difficulty.
MC is flexible and will accommodate future additional analyses without major
restructuring.
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MC output is compatible with a number of methods for specific analyses of uncertainty
and variability, including response surfaces, regression models, classification and
regression trees (CART), ranking methods, and combinatorial analysis.
MC is widely used, is generally accepted in the scientific community, and can be
explained to a lay audience.
A significant limitation results from the fact that the analysis of uncertainty and
variability requires estimates of parameter distributions that reflect both uncertainty and
variability individually, and information on the distributions for parameters is not available for
most parameters. Estimates of dependencies (i.e., correlations) between parameters would
enable a more detailed analysis to be performed, although this is of lesser importance. However,
when a parameter distribution has been developed, it is rarely separated into components of
uncertainty and variability. This limitation of the Monte Carlo approach can be addressed by
developing distributions for the parameters to which the model shows the greatest sensitivity.
Distributions are not needed for all parameters.
6.2.1 TWO-STAGE MONTE CARLO DESIGN
Two-stage Monte Carlo designs are used to characterize uncertainty and variability
separately. This is not currently implemented in TRIM.FaTE, and is being considered for a
future version. Joint uncertainty and variability Monte Carlo simulations are generated based on
sampling from an uncertainty distribution and a variability distribution for each parameter, with
the uncertainty distributions sampled in an outer loop and the variability distributions sampled in
an inner loop. For each uncertainty realization (outer loop sample) there is a specified
distribution of variability (for each parameter) from which several samples are drawn to
represent variability in the inner loop. These several samples represent one variability
realization. Figure 6-3 illustrates the structure of this two-stage Monte Carlo design.
As an example, suppose there are Nu samples drawn from the uncertainty distributions,
and that for each uncertainty sample there are Nv variability samples. The cumulative
distribution function (of a model output) representing variability for that uncertainty sample can
be estimated from these Nv variability samples and statistics can be calculated (e.g., mean,
percentiles, variance). For each of these statistics, there are Nu values, corresponding to the Nu
uncertainty samples. These are then used to calculate a cumulative distribution function for each
statistic, representing the uncertainty distribution for that statistic.
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Figure 6-3
Two-stage Monte Carlo Approach
TRIM.FaTE
Base
Parameter
File
TRIM FaTE
Inner Loop
Outer Loop
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6.2.2 DISTRIBUTIONS OF INPUT PARAMETERS
The Monte Carlo approach requires specification of probability distributions for each
parameter being analyzed for its role in the overall uncertainty of the model. In general,
distributions can be specified as parametric forms of probability distribution functions (PDFs) or
cumulative distribution functions (CDFs), as nonparametric PDFs or CDFs, or as sets of data
points from which samples are drawn. At the present time the distributions supported by
TRIM.FaTE are the uniform, normal, lognormal and triangular distributions. Future
enhancements to TRIM.FaTE will include an expansion of the types of distributions which can
be specified by the user.
Distributions for parameter variability and for parameter uncertainty are required for
those parameters to be analyzed; TRIM does not use "default" distributions where there is no
information. Parameters without any specification of distributions are treated as if they are
known exactly.
6.2.3 LATIN HYPERCUBE SAMPLING
There are four sampling techniques that are widely used in Monte Carlo methods for
generating random samples from parameter distributions: simple random sampling, Latin
hypercube sampling (LHS), midpoint LHS, and importance sampling. Randomness is an
important feature of these methods for sampling, since it allows one to directly estimate the
precision of the statistics estimated using the Monte Carlo approach.
Both the simple random sampling and LHS techniques are available in TRIM.FaTE. The
preferred sampling technique is LHS, which employs a stratified random sampling without
replacement scheme that is very efficient for sampling, especially for multiparameter models
(Iman and Shortencarier 1984, Iman and Helton 1987, Helton and Davis 2000). Importance
sampling strategies also will be used in conjunction with LHS to obtain better coverage of
distribution tails or extreme values. The strata for LHS are chosen to be intervals partitioning
the range of each parameter, in such a way that the parameter has equal probability of realization
within each interval. Then a sample is selected randomly from each of the intervals. To
illustrate this, say there are k intervals used for each parameter. A random sample is selected
from within each interval, and this is repeated for each parameter, yielding k samples for each
parameter. Then, k multivariate samples are constructed by randomly pairing up the samples for
each parameter. These k sets of parameter values (each set containing a value for each
parameter) are referred to as the Latin hypercube sample.
If there are correlations among the parameters, there is a technique for sampling within
the LHS framework so that the sample reflects the correlations (Iman and Conover 1982, Iman et
al. 1985). This treatment of correlation is based on rank-order correlation (Kendall and Gibbons
1990) and has desirable properties. It can be used with any distribution and with any sampling
scheme, and it does not change the marginal distributions of the parameters. This is being
considered for inclusion in a future version of TRIM.FaTE.
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6.2.4 TREATMENT OF TAILS OF DISTRIBUTIONS
As noted above, for certain influential parameters an importance sampling technique will
be incorporated to obtain adequate sampling coverage of extreme values of these parameters.
Importance sampling refers to a class of sampling techniques that takes into account the areas of
a distribution that are important to the analysis, providing enhanced detail in these areas.
Importance sampling is often used when increased accuracy in one or both tails of a distribution
is desired. These techniques are being considered for a future version of TRIM.FaTE.
6.2.5 TRACKING INFORMATION BETWEEN MODULES
There are two levels at which tracking of information related to uncertainty analysis
occurs; the first is within each TRIM module, and the second is from one TRIM module to the
next. The information passed from one TRIM module to the next (e.g., from TRIM.FaTE to
TRIM.Expo) needs to provide enough detail to allow for continuation of the Monte Carlo
propagation of uncertainty and variability in the next module. Information on the joint
distributions of a TRIM module's inputs and outputs is required to do this, for both uncertainty
and variability. This is accomplished by maintaining a record of the input parameter values used
for each Monte Carlo simulation of a TRIM module. Simulations of one module are randomly
selected for input to Monte Carlo simulations for a succeeding module, while keeping track of
the input values for all simulations.
For example, take the first module to be TRIM.FaTE and the following module to be
TRIM.Expo. TRIM.Expo takes as inputs some of the results generated by TRIM.FaTE, in
addition to other input parameters. Suppose that 100 Monte Carlo simulations of TRIM.FaTE
are performed and, following this, TRIM.Expo is going to be run for 300 Monte Carlo
simulations. Figure 6-4 provides an illustration of this example. For each of the TRIM.Expo
Monte Carlo simulations, one of the TRIM.FaTE simulations is randomly selected and the
results of this simulation used for input to TRIM.Expo. There are other input parameters also
input to TRIM.Expo, and some of these might be sampled from uncertainty and/or variability
distributions as part of the Monte Carlo process. Using the notation of Figure 6-4, suppose the ith
TRIM.FaTE simulation is selected for the jth Monte Carlo simulation of TRIM.Expo. For this
simulation, TRIM.FaTE inputs { AJ result in outputs {B;} which are then input to TRIM.Expo.
The other inputs to the jth Monte Carlo simulation of TRIM.Expo are denoted as {Cj}, and the
results of this simulation are denoted as {Dj}. Then, each of the 300 simulations of TRIM.Expo
are tagged with the indices ij andy to respectively track the corresponding TRIM.FaTE and
TRIM.Expo input values, for j = 1 to 300. The index /takes values from 1 to 100, but is indexed
by j so that the TRIM.FaTE inputs used for the jth TRIM.Expo simulation are tracked.
The same process would be used for a module following TRIM.Expo, where the 300
TRIM.Expo simulations would be tagged by ip j = 1 to 300.
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Figure 6-4
Example of Propagation of Uncertainty and Variability Between TRIM Modules
Inputs for 100
TRIMFaTE
MC simulations
A
100
Aj represents the collection of inputs
for TRDVLFaTE simulation i, and B,
represents the collection of outputs
for that simulation.
Inputs for 300 TRIM.Expo MC simulations
Outputs for 100
TRDVLFaTE
MC simulations
[ci C2 C3
B!
B2
BIOO
C3ooJ
TREVLExpo] - ^
Output from TRIM.FaTE
simulation i; input to
TRIM.Expo simulationj
Dy represents the collection of
outputs from the TRIM.Expo
simulation with inputs B- and Cj
Outputs for 300 TRIM.Expo
MC simulations
D2
D,
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Care must be taken to ensure that the model input values are consistent in sequences of
runs. For example, if there is an input to TRIM.FaTE which is also an input to TRIM.Expo, then
the value for the TRIM.Expo simulation should be the same as the TRIM.FaTE input value.
Similar consistency constraints should be imposed if joint variables are highly correlated or
related by a functional relationship.
6.2.6 COMPUTATIONAL RESOURCES
Although the Monte Carlo technique is very efficient, Monte Carlo simulations of
TRIM.FaTE require substantial computer processing time, especially when treating more than a
few parameters. The available computational resources can be a limiting factor in the scope of
the analysis performed. Consequently, the more detailed analyses may have to restrict their
scope to small numbers of parameters being jointly varied.
Computer processing time for both the uncertainty propagation and tracking and the
TRIM.FaTE model depends on the definition of the TRIM.FaTE modeling scenario, in terms of
the numbers of compartments, time steps, length of simulation, chemicals, and so forth. It also
depends on the number of parameters and number of model outputs analyzed, the sizes of the
Monte Carlo samples (which relates to the number of simulations), and the level of detail of the
analysis.
Uncertainty analyses may be conducted running TRIM.FaTE in a steady-state mode,
which requires drastically less processing time than the dynamic modeling mode. In the steady-
state mode, TRIM.FaTE calculates single values for chemical moles, mass, and concentration for
each compartment. These values approximate the steady-state levels that the chemical would
reach if the dynamic form of the model was run for a long enough period of time to allow all
chemical mass inputs and outputs to balance for each compartment (i.e.., to reach a steady-state).
6.2.7 SPATIAL AND TEMPORAL RESOLUTION AND AGGREGATION
Estimation of the effects of spatial and temporal aggregation on uncertainty and
variability could be accomplished by sensitivity analyses of Monte Carlo results. For analysis of
spatial aggregation, the user could set up a small number of TRIM.FaTE scenarios with
increasing levels of spatial resolution (decreasing levels of aggregation), and run the same set of
simple Monte Carlo simulations for each scenario. Comparison of the Monte Carlo output
distributions for the scenarios would show the impact of the aggregation on uncertainty and
variability for the scenarios modeled. Similarly, the effects on model output uncertainty of
temporal aggregation could be assessed by comparing uncertainty results from scenarios with
different levels of temporal aggregation.
6.2.8 SPECIFICATION OF PROBABILITY DISTRIBUTIONS OF MODEL INPUTS
The need for distributions for the input parameters is discussed above. Implementation
of this Monte Carlo approach employs a data file that specifies the distributions of uncertainty
and variability for each parameter. For each parameter, this file contains the distribution name
(e.g., lognormal) and the parameters or data that complete the specification of the distribution. A
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distribution for variability and a distribution for uncertainty is required for each parameter. As
described in Section 6.2.1, both variability and uncertainty distributions are used a two-stage
Monte Carlo analysis.
There are often physical constraints on values of parameters and intermediate quantities
in the model; for example, mass is always non-negative. These can have implications for how
parameter distributions are set. The specified distributional forms should satisfy the physical
constraints as well as reflect the distributions indicated by the available data.
6.3 PRESENTATION OF UNCERTAINTY RESULTS
When a model has many inputs and is complex, as TRIM.FaTE is, the analyst will make
use of methods that are simple and give a first-order picture of uncertainty, as well as more
complex methods giving a more refined, detailed analysis of uncertainty. There are several ways
to form summary measures and present the uncertainty and variability of a modeling system.
Loosely speaking, "measures" are one or a small number of descriptive statistics, such as the
sensitivity score, or the 10th, 50th, and 90th percentiles of a distribution. In addition to summary
measures, ways of presenting the results include graphs of distributions of model outputs, tree
diagrams, other graphs, and tables of statistics.
Results from Monte Carlo simulations are collected in a data file which can be accessed
with other analysis software, such as graphical and statistical software, to analyze and present
the results of the uncertainty and variability analysis. In the future, the overall TRIM framework
or TRIM.Risk may be used to generate these results:
Sensitivity
Sensitivity score
Elasticity
Probability density functions
Cumulative distribution functions
Confidence intervals
Tables of statistics
Rank order correlation
Correlation matrix
Scatter plots, scatter plot matrix
The first three of these are produced from results of the sensitivity simulations; the remainder
can be produced from the Monte Carlo results.
SEPTEMBER 2002 6-13 TRIM.FATE TSD VOLUME I
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CHAPTER 7
REFERENCES
7. REFERENCES
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Program, WASPS. Part A: Model documentation. Athens, GA: U.S. EPA National Exposure
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Brandes, L. I, H. den Hollander, and D. van deMeent. 1997. SimpleBOXv 2.0. Netherlands:
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Buck, J.W., G. Whelan, J.G. Droppo, Jr., D.L. Strenge, K.J. Castleton, J.P. McDonald, C. Sato,
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CRARM. 1997. Commission on Risk Assessment and Risk Management. Risk assessment and
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Cohen, Y., W. Tsai, S.L. Chetty, and G.J. Mayer. 1990. Dynamic partitioning of organic
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Cohen, Y. and P. A. Ryan. 1985. Multimedia modeling of environmental transport:
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Cowen, E.G., D. Mackay, T.C.J. Feihtel, D. van de Meent, A. DiGuardo, J. Davies, and N.
Mackay. 1995. The multi-media fate model: A vital tool for predicting the fate of chemicals.
Pensacola, FL: SETAC Press.
Helton, J.C. and Davis, F.J. "Sampling-Based Methods," in Sensitivity Analysis, ed. Saltelli, A.,
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Iman, R.L. and J.C. Helton. 1987. An investigation on uncertainty and sensitivity analysis
techniques for computer models. Risk Analysis. 8(1):71.
Iman, R.L., M.J. Shortencarier, and J.D. Johnson. 1985. A FORTRAN 77 program and user's
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SAND83-0044. Albuquerque, NM: Sandia National Laboratories.
Iman, R.L. and M.J. Shortencarier. 1984. A FORTRAN 77 program and user's guide for the
generation of latin hypercube and random samples for use with computer models. NUREG/CR-
3624. SAND83-2365. Albuquerque, NM: Sandia National Laboratories.
Iman, R.L. and W.J. Conover. 1982. A distribution-free approach to inducing rank correlation
among input variables. Communications on Statistics: Simulation and Computing. 11(3):311-
334.
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IT. 1997'a. IT Corporation. Overview and key features of the Integrated Spatial Multimedia
Compartmental Model (ISMCM). An addendum to evaluation of existing approaches for
assessing non-inhalation exposure and risk with recommendations for implementing TRIM.
Contract No. 68-D-30094, Work Assignment 3-11. Prepared for U.S. Environmental Protection
Agency, April 1997.
IT. 1997b. IT Corporation. Model comparison update for TRIM. Contract No. 68-D-30094,
Work Assignment 4-18, Task 2 Report. Prepared for U.S. Environmental Protection Agency,
November 1997.
Kandall, M. and J.D. Gibbons. 1990. Rank correlation methods. New York, NY: Oxford
University Press.
Mackay, D. 1991. Multimedia environmental models: The fugacity approach. Chelsea, MI:
Lewis Publishers.
Mackay, D. and S. Paterson. 1982. Fugacity revisited. Environmental Science and Technology.
16:654-660.
Mackay, D. and S. Paterson. 1981. Calculating fugacity. Environmental Science and
Technology. 15:1006-1014.
Mackay, D. 1979. Finding fugacity feasible. Environmental Science and Technology. 13:
1218-1223.
Mardia, K.V., J.T. Kent, and J.M. Bibby. 1979. Multivariate analysis. London: Academic
Press.
McKone, T. E. 1993a. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part I: Executive summary. Laboratory.UCRL-CR-111456PtI. Livermore, CA: Lawrence
Livermore National.
McKone, T. E. 1993b. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part II: The dynamic multimedia transport and transformation model. UCRL-CR-111456PtII.
Livermore, CA: Lawrence Livermore National Laboratory.
McKone, T. E. 1993c. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part III: The multiple-pathway exposure model. UCRL-CR-111456PtIII. Livermore, CA:
Lawrence Livermore National.
McKone, T.E. and D.W. Layton. 1986. Screening the potential risk of toxic substances using a
multimedia compartment model: Estimation of human exposure. Regul. Toxicol. Pharmacol.
6:359-380.
Morgan, G.M. and M. Henrion. 1990. Uncertainty: A guide to dealing with uncertainty on
quantitative risk and policy analysis. New York, NY: Cambridge University Press.
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CHAPTER 7
REFERENCES
Mozier, J.W. and T.R. Johnson. 1996. Evaluation of existing approaches for assessing
non-inhalation exposure and risk with recommendations for implementing TRIM. 68-D-30094,
Work Assignment 2-9. Prepared by IT Corporation for U.S. Environmental Protection Agency,
April 1996.
NRC. 1994. National Research Council. Science and judgment in risk assessment.
Washington, DC: National Academy Press.
Ozkaynak, H., M.. Zufall, J. Burke, J. Xue, and J. Zidek. 1999. A probabilistic population
exposure model for PM10 and PM2 5. Presented at 9th Conference of the International Society of
Exposure Analysis. Athens, Greece, September 5-8, 1999.
Radhakrishnan, K. and A.C. Hindmarsh. 1993. Description and use of LSODE, the livermore
solver for ordinary differential equations. LLNL UCRL-ID-113855.
Rice, G., R. Hetes, J. Swartout, Z. Pekar, and D. Layland. 1997. Methods for assessing
population exposures to combustor emissions. Presentation at the 1997 Society for Risk
Analysis Annual Meeting, Washington, DC.
Schneider, H. and G.P. Barker. 1989. Matrices and Linear Algebra. 2nd ed. New York, NY:
Dover Publications, Inc.
Thibodeaux, L. J. 1996. Environmental chemodynamics: Movement of chemicals in air, water,
and Soil. 2nd ed. New York, NY: J. Wiley & Sons.
Thibodeaux, L. J. 1979. Chemodynamics, environmental movement of chemicals in air, water,
and soil. New York, NY: John Wiley and Sons.
U.S. EPA. 2002a. U.S. Environmental Protection Agency. TRIM.FaTE User Guidance (Draft).
Office of Air Quality Planning and Standards.
U.S. EPA. 2002b. U.S. Environmental Protection Agency. Evaluation of TRIM.FaTE Volume
I: Approach and Initial Findings. EPA-453/R-02-012. Office of Air Quality Planning and
Standards. September.
U.S. EPA. 2000. An SAB advisory on the agency's "total risk integrated methodology" (TRIM).
Science Advisory Board. EPA-SAB-EC-ADV-00-004. Environmental Models Subcommittee of
the Science Advisory Board. May.
U.S. EPA. 1999a. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: TREVI.Expo Technical Support Document. External Review Draft. EPA-453/D-
99-001. Research Triangle Park, NC: Office of Air Quality Planning and Standards. November.
U.S. EPA. 1999b. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: Status Report. EPA-453/D-99-010. Office of Air Quality Planning and
Standards. November.
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CHAPTER 7
REFERENCES
U.S. EPA. 1999c. U.S. Environmental Protection Agency. Systems Installation and Operation
Manual for the EPA Third-Generation Air Quality Modeling System (Models-3 Version 3.0).
Atmospheric Modeling Division, National Exposure Research Laboratory, Research Triangle
Park, NC and EPA Systems Development Center (A contractor operated facility), Science
Applications International Corporation, Arlington, VA. June.
U.S. EPA. 1999d. Methodology for assessing health risks associated with multiple pathways of
exposure to combustor emissions. National Center for Environmental Assessment. EPA 600/R-
98/137. Office of Research and Development.
U.S. EPA. 1999e. U.S. Environmental Protection Agency. National Air Toxics Program: The
Integrated Urban Strategy. Federal Register 64: 38705-38740. July 19.
U.S. EPA. 1999f. U.S. Environmental Protection Agency. Residual Risk Report to Congress.
Office of Air Quality Planning and Standards, Research Triangle Park, NC. March.
U.S. EPA. 1999g. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: TRIM.FaTE Technical Support Document Volume I: Description of Module.
External Review Draft. EPA-453/D-99-002A. Research Triangle Park, NC: Office of Air
Quality Planning and Standards. November.
U.S. EPA 1999h. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: TRIM.FaTE Technical Support Document Volume II: Description of Chemical
Transport and Transformation Algorithms. External Review Draft. EPA-453/D-99-002B.
Research Triangle Park, NC: Office of Air Quality Planning and Standards. November.
U.S. EPA. 1998a. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: Technical support document for the TRIM.FaTE Module. Draft. EPA-452/D-
98-001. Office of Air Quality Planning and Standards.
U.S. EPA. 1998b. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: Implementation of the TRIM conceptual design through the TRIM.FaTE Module.
Status Report. Draft. EPA-452/R-98-001. Office of Air Quality Planning and Standards.
U.S. EPA. 1998c. U.S. Environmental Protection Agency. Advisory on the Total Risk
Integrated Methodology (TRIM). EPA-SAB-EC-ADV-99-003. Science Advisory Board.
U.S. EPA. 1998d. U.S. Environmental Protection Agency. Risk characterization handbook.
Draft. EPA 100-B-98-OOX. Science Policy Council.
U.S. EPA. 1998e. U.S. Environmental Protection Agency. Study of Hazardous Air Pollutants
from Electric Utility Steam Generating Units - Final Report to Congress. EPA 453/R-989-004a.
February.
U.S. EPA. 1998f U.S. Environmental Protection Agency. Methodology for assessing health
risks associated with multiple pathways of exposure to combustor emissions. Update to
EPA/600/6-90/003. EPA 600/R-98/137. National Center for Environmental Assessment.
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CHAPTER 7
REFERENCES
U.S. EPA. 1998g. U.S. Environmental Protection Agency. Human health risk assessment
protocol for hazardous waste combustion facilities. Peer Review Draft. EPA530-D-98-001 A.
Office of Solid Waste and U.S. EPA Region 6, Multimedia Planning and Permitting Division.
U.S. EPA. 1997. U. S. Environmental Protection Agency. Mercury study report to congress
(Volume I - VIII). EPA-452/R-97-005. Office of Air Quality Planning and Standards and
Office of Research and Development.
U.S. EPA. 1994a. U.S. Environmental Protection Agency. Report of the Agency Task Force on
environmental regulatory modeling. Guidance, support needs, draft criteria and charter. EPA
500-R-94-001. Washington, DC: Office of Solid Waste and Emergency Response.
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methodology for assessing health risk associated with indirect exposure to combustor
emissions". EPA-SAB-1AQC-94-009B. Washington, DC: Science Advisory Board.
U.S. EPA. 1994c. U.S. Environmental Protection Agency. Estimating exposure to Dioxin-like
compounds. Volume II. Site-specific Assessment Procedures. External Review Draft.
EPA/600/6-88/005Cc.
U.S. EPA. 1993. U.S. Environmental Protection Agency. Addendum to methodology for
assessing health risks associated with indirect exposure to combustor emissions. External
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Assessment.
U.S. EPA. 1990. U.S. Environmental Protection Agency. Methodology for assessing health
risks associated with indirect exposure to combustor emissions. Interim Final. EPA/600/6-
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U.S. EPA. 1986. U.S. Environmental Protection Agency. Users Manual for the Human
Exposure Model (HEM). Research Triangle Park, NC: Office of Air Quality Planning and
Standards. EPA-540/5-86-001. June.
van de Meent, D. 1993. SIMPLEBOX: A generic multimedia fate evaluation model. Report
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van de Water, R.B. 1995. Modeling the transport and fate of volatile and semi-volatile organics
in a multimedia environment. M.S. Thesis. Los Angeles, CA: University of California.
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radionuclide ingestion after fallout deposition. Health Phys. 52:717-737.
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APPENDIX A
GLOSSARY
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APPENDIX A
GLOSSARY
APPENDIX A
GLOSSARY
Abiotic Compartment Type
Advective Process
Biotic Compartment Type
Boundary Condition
Chemical
Compartment
Compartment Type
Composite Compartment
Type
A compartment type 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 (see also compartment type).
A process by which a chemical can be transported within a
given medium that is moving from one compartment to
another.
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 TRIM.FaTE calculates
chemical masses and concentrations (see also compartment
type).
A user-defined setting that establishes the concentration of a
chemical on the external (i.e., non-modeled) side of the
modeled domain; chemical mass can enter a compartment
across this interface via advective processes.
A unit whose mass is being modeled by TRIM.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.
The TRIM.FaTE modeling unit that contains chemical mass.
Chemical mass is transported between and transformed within
compartments; a specific compartment is characterized by its
physical and spatial composition and its relationship to other
compartments.
A specific kind of compartment, such as an air compartment
type or a racoon compartment type. Compartment types are
distinguished from each other by their basic properties and the
way they exchange chemical mass with other compartment
types.
A group of different compartment types that are consistently
interconnected. Individual compartments within a composite
compartment require the presence of other compartments.
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APPENDIX A
GLOSSARY
Diffusive Process
Dispersion
Fugacity
Initial Condition
Link
Model Evaluation
Modeling Region
Output Time Step
Parameter
Parcel
Phase
A process by which a chemical is transported from one
compartment to another as a result of the magnitude and
direction of the concentration differences between two
compartments at the interface between the two locations.
The "spreading out" of a chemical during advective transport.
May result in movement of the chemical perpendicular to the
direction of advective flow.
A measure of the tendency of a substance to escape by some
chemical process from the phase in which it exists.
The user-defined chemical concentration in a compartment at
the beginning of a scenario; represents concentrations in
environmental media just before a source begins to emit
chemicals in the modeled scenario.
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.
The broad range of review, analysis, and testing activities
designed to examine and build consensus about a model's
performance.
The region of space through which the transport and
transformation of the modeled chemical(s) is estimated.
A length of time over which the compartment masses and
concentrations calculated at each simulation time step are
summarized and reported by the model.
A model input that defines a variable in an algorithm (e.g.,
emission rate, half-life, biomass).
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.
The physical state of material within a compartment; in
TRIM.FaTE, the various phases in which a chemical exists
within a compartment are assumed to be in equilibrium with
respect to chemical partitioning.
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Source
Project
Reporting Time Step
Scenario
Sensitivity
Simulation
Simulation Period
Simulation Time Step
Sink
Source
Transfer Factor (T-factor)
APPENDIX A
GLOSSARY
Within TRIM.FaTE, a chemical source that is emitted directly
to the primary abiotic compartment within which the source is
located (e.g., a stack at a facility).
A computer framework for saving one or more scenarios and
all of the data properties for the scenarios that pertain to a
single site (i.e.., bounded modeling region).
The user-specified time interval at which the model results
(e.g.., compartment moles) are saved and reported in output
files by the model.
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.
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.
The entire length of time for which the model is run and
compartment masses and concentrations are calculated - in
other words, the time period from the beginning of the
simulation until the end.
The time increment at which the model calculates (and re-
calculates iteratively throughout the simulation period) a new
inventory of compartment masses and concentrations.
A special kind of compartment type that accounts for chemical
mass no longer available for transport or uptake within a
scenario. There are three types of sinks: advection sinks, flush
rate sinks, and degradation/reaction sinks.
An external component that introduces chemical mass directly
into a compartment.
A quantitative factor in units of inverse time that describes, for
a first-order transfer between two compartments, the
instantaneous flux of modeled chemical per amount of the
chemical in the sending compartment.
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APPENDIX A
GLOSSARY
TRIM.FaTE
TRIM.Expo
TRIM.Risk
Uncertainty
Variability
Volume Element
The TRIM Environmental Fate, Transport and Ecological
Exposure module that accounts for movement of a chemical
through a comprehensive system of discrete compartments that
represent possible locations of the chemical in the physical and
biological environment and provides an inventory, over time,
of a chemical throughout the entire system.
The TRIM Exposure-Event module that measures human
exposures by tracking population groups referred to as
"cohorts" and their inhalation and ingestion through time and
space.
The TRIM Risk Characterization module that characterizes
human exposures or doses with regard to potential risk using
the corresponding exposure- or dose-response relationships.
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
an abiotic compartment and provides a frame of reference for
one or more biotic 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
INTEGRATING EXTERNAL MODELS OR MEASURED DATA INTO
TRIM.FaTE
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APPENDIX B
INTEGRATING EXTERNAL MODELS OR MEASURED DATA INTO TRIM.FATE
APPENDIX B
INTEGRATING EXTERNAL MODELS OR MEASURED
DATA INTO TRIM.FaTE
At some point, it may be desirable to use either measured data or the output of other
models with the TRIM.FaTE model. In either case, there are two basic forms that the data can
take: (1) fluxes into certain compartments (e.g., deposition rates), or (2) calculated/specified
concentrations or chemical mass in certain compartments. The first case is the simpler of the
two, as it requires only adding additional source terms to the affected compartments. The second
case has more of an impact on the system of equations used in the modeling, and is the focus of
this Appendix.
B.I COMPROMISES THAT MUST BE MADE IN ORDER TO USE
EXTERNALLY DERIVED COMPARTMENT CONCENTRATIONS
The incorporation of externally derived compartment concentrations into any multimedia
model dictates that compromises be made with regard to preserving chemical mass balance. The
basic problem is the loss of chemical mass from the compartments that send a chemical to the
compartment for which an externally derived compartment concentration is used. Since the
receiving compartment will not receive the chemical, it is effectively lost from the system. The
only way to avoid this loss of chemical mass is to modify all of the links to the "constant"
compartments so that this exchange does not take place (e.g., disable resuspension from surface
soil to the air domain if the results of an air model are to be used).
While we will know mathematically how much mass has been lost through these
processes, the chemical lost will not be allowed to participate in any further exchanges with
other compartments. Whether this is acceptable or not depends on the attitude of the user, but
this compromise is unavoidable if they are to use externally derived compartment
concentrations.
B.2 IMPLEMENTATION DETAILS IN THE CASE OF FIRST-ORDER
TRANSFERS - CASE OF CONSTANT INPUTS
In this section, explicit details are provided for incorporating externally derived
compartment concentrations for selected compartments in the case where these concentrations
are constant for the time period of interest. The general case where the values from externally
derived compartment concentrations change with time can be addressed by sequentially using
this method over the different intervals over which the values from the externally derived
compartment concentrations are constant.
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APPENDIX B
INTEGRATING EXTERNAL MODELS OR MEASURED DATA INTO TRIM.FATE
For a given time interval in which the parameters are constant with time, the system of
differential equations to be solved is:
A TT ..
= AN + s
dt
(1)
where N is the vector of the mass of chemical(s) in each compartment, given by:
N =
(2)
where n/t) is the mass of a particular chemical in a compartment/chemical pair, m is the total
number of compartment/chemical pairs; A is the matrix of transfer factors describing transport
between compartment/chemical pairs:
A =
a
\\
a
22
a
ml
(3)
and s* is the vector of sources terms for each chemical in each compartment.
The fact that chemical mass balance is preserved implies that the matrix A satisfies two
basic conditions:
a > 0, if /' * and a <0
(4)
(5)
Using externally derived compartment concentrations is equivalent to fixing the
concentration/chemical mass in some compartments. This can be done by solving a reduced
system of differential equations, using constant values for the applicable terms nt(t). This can be
conceptualized as using "virtual sources" for the relevant compartment/chemical pairs, with each
(time-dependent) virtual source set so that the mass of chemical is constant.
For example, if there is only one chemical being considered, and we want the
concentration/mass to be fixed in the first compartment, say n1(t)=M1, then dn/dt=0, and the
original system becomes:
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APPENDIX B
INTEGRATING EXTERNAL MODELS OR MEASURED DATA INTO TRIM.FATE
d
dt
MI'
n2(f)
nm(f)
=
11 12 13 1/Ti
21 22 23 """ 2/w
a 7 ct ~, a -, ... #
7W7 Wz 7WJ WW
M/
H20)
ซw(0
-
51
52
Sm
(6)
Since the derivative of a constant is zero, examining the first row of the above system shows
that:
0
(7)
1=1
i.e., the virtual source st(t) in the first compartment is given by:
1=1
= ~Man - E
(8)
The terms nt(t) for />7 can be determined by solving the system of differential equations
obtained by eliminating the first row, and using n,(t)=M:
^
J?
ซ2(0
<*
22
a
a
ml
2m
a
m3
ฐ22 ฐ23 - a2m
- "
M
n2(t)
s2 + Ma2l
Ma
ml
(9)
This system of differential equations is of the same form as the original equation, and can
be solved using the same solver used for the original equation. However, it can also be rewritten
as a system of the same size as the original system by adding the differential equation dn/dt=0,
n1(0)=M; this results in the system (with initial condition):
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APPENDIX B
INTEGRATING EXTERNAL MODELS OR MEASURED DATA INTO TRIM.FATE
dt
n2(f)
000
0 a22 a23
0 a
0
a
2m
1)
n2(0)
ปซป
M
ซ-(<>)
(10)
Note that the mass lost from the system to the compartments which are to be held
constant is accounted for, as condition (5) is still satisfied for the diagonal elements of the matrix
in equation (10), where the coefficients a;>are used in the sum.
When more than one of the w/s is constant, this same method can be used. In general, if
the Mi compartment/chemical pair is to be constant (say with value Mk\ then one puts zeros in
the Mi row and Mi column, and adds the term Mk ank to the wth row of the source term vector.
This is done for every compartment/chemical pair which is to be constant. For example, applied
to equation (2), one would obtain the following:
d_
dt
0 0
".(0
Ma
MlPm,k
ซ2(0)
ซm(0)
M
ซ2(0)
(11)
The method described in this section fits seamlessly into the general process of
calculating transition matrices and source terms prior to calling LSODE. In general, one uses all
zeros for any row that is to be constant, adds the extra flux terms to the source term vector, and
calls LSODE as is done normally.
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APPENDIX C
PROTOTYPES I - IV
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APPENDIX C
PROTOTYPES I - IV
APPENDIX C
PROTOTYPES I - IV
This appendix provides a description of the process of applying the TRIM.FaTE
methodology (Chapter 4) to cases of increasing complexity (referred to as "prototypes"). These
prototypes were developmental applications of the TRIM.FaTE modeling framework that were
used to test the model as additional phases, compartment types, processes, algorithms, and other
aspects of the TRIM.FaTE methodology were added to the model. Four early prototypes (i.e., I
through IV) were developed for preliminary testing purposes. Section 1 of this appendix
discusses the computer implementation of the prototypes; Section 2 describes the development
process for each prototype; Section 3 addresses the features of the prototypes, including the
types of compartments and links simulated; and Section 4 discusses the chemical-specific
parameters and values used in prototype 4. The goals of this appendix are to: (1) illustrate the
flexibility of TRIM.FaTE for application at different levels of spatial and temporal resolution;
(2) illustrate how different multimedia configurations with TRIM.FaTE are set up; and (3)
document the historical development of TRIM.FaTE leading up to Prototype V. More
documentation of Prototypes I - IV, including a detailed description of the testing performed
using Prototype IV, is presented in the initial TRIM.FaTE Status Report (U.S. EPA 1998a) and
Technical Support Document (U.S. EPA 1998b).
Based on the lessons learned from testing and application of Prototypes I - IV and the
1998 comments by EPA's Science Advisory Board, EPA developed Prototype V, the first
application of TRIM.FaTE at an actual site for a metal contaminant (i.e., mercury). For detailed
information regarding testing and application of Prototype V, the reader is referred to the 1999
TRIM.FaTE Status Report (U.S. EPA 1999a) and Technical Support Documents (U.S. EPA
1999b,c).
C. 1 COMPUTER IMPLEMENTATION OF PROTOTYPES
The concepts discussed in Chapter 4 have been implemented in all the prototypes using a
combination of Microsoft Visual Basic, Fortran, and Microsoft Excel software. An
object-oriented architecture was implemented using Visual Basic 5 application environment
imbedded within Excel 97 to model the hierarchy of components of TRIM.FaTE. This hierarchy
includes volume elements, compartment types, compartments, links, and sources. The coding
architecture is not tied to any specific ecosystem configuration. A preliminary algorithm library
that utilized this coding architecture was also implemented.
If all transport processes are simulated as a first-order process, the result is a system of
linear ordinary differential equations, as explained in Section 4.2. This system must be solved to
determine the redistribution of chemical mass as a function of time. For TRIM.FaTE, this
system is solved using the Livermore Solver for Ordinary Differential Equations (LSODE)
(Radhakrishnan and Hindmarsh 1993), a Fortran program freely available via several online
numerical algorithm repositories.
SEPTEMBER 2002 C-l TRIM.FATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
The LSODE subroutine solves systems of first-order ordinary differential equations of
the form (Hindmarsh 1983):
= F(t,y),y(t0) =
where y is an n-dimensional time-dependent vector, i.e.,
The system of differential equations can be stiff or non-stiff. In the stiff case, it treats the
Jacobian matrix (Schneider and Barker 1989) as either a full or banded matrix. It uses Adams
(Schneider and Barker 1989) methods (predictor-corrector) in the non-stiff case, and backward
differentiation formula methods in the stiff case. The linear systems that arise are solved by
direct methods. LSODE supersedes the older GEAR and GEARB packages.
The only restriction on the size of the system of differential equations is that imposed by
computer memory. This code was modified so that it could be accessed by Visual Basic 5 in
Excel 97. Another Fortran code was used, in a similar manner, to determine the steady-state
solution to the system of linear differential equations (Barrodole and Stuart 1981).
Microsoft Excel spreadsheets were used for general preprocessing, postprocessing, and
data storage (additional databases for spatial data were also created using Visual Basic and
accessed by Excel). Excel spreadsheets also served as a convenient interface to the Visual Basic
and Fortran subroutines.
The approach taken for testing the methodology made it possible to investigate the
implications of draft algorithms and to work on the development of a flexible system for
addressing conceptual site models with many compartments. The pre- and postprocessing for the
ultimate implementation of TRTM.FaTE may require a more sophisticated platform. However,
with some modification, much of the Visual Basic code, and all of the Fortran code, can be used
in other computer programming languages.
C.2 PROTOTYPE DEVELOPMENT
Multiple prototypes were developed with increasing complexity to model the movement
of a chemical through an ecosystem. This section describes features of the prototypes in
increasing order of complexity.
C.2.1 PROTOTYPE I
Prototype I (PI) was designed to test the mass transfer methodology (Section 4.2) and the
LSODE utility. Air, surface, soil, ground water, surface water, and fish compartment types were
simulated in PI as illustrated in the conceptual site model shown in Figure C-l. PI includes a
uniform volume source emission of benzene into the air compartment volume. Benzene was
selected because most of its transfer factors were readily available from CalTOX (Maddalena et
al. 1995).
SEPTEMBER 2002 C-2 TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Figure C-l
Conceptual Site Model for Prototype I
..1,000m..
1,000m
Air Cell
SIT
Fish
Soil Zone
90m
GW
SEPTEMBER 2002
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TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Some transfer factors were derived independently of CalTOX for the air to air sink, soil
to ground water, fish to water, and water to fish transfers. The remaining factors were taken
directly from CalTOX. The dimensions of the terrain were adapted from CalTOX to facilitate
comparison of results. Chemical reaction was not simulated in this prototype.
The runs produced estimates of benzene mass throughout the system, and no problems
were experienced in running the LSODE subroutine. The resulting mass distribution of benzene
in various compartments was examined qualitatively to ensure that the numerical routines were
producing stable and realistic solutions. A quantitative analysis of the results was not performed
because the input parameters were selected only to test the implementation infrastructure. The
results were approximately commensurate with theoretical expectations with no unstable or
anomalous values. These results prompted further testing of the modeling approach on a more
complex ecosystem.
C.2.2 PROTOTYPE II
Prototype II (P2) includes more spatial detail sophistication than PI in both the types and
number of compartments used. Unlike PI, P2 included multiple volume elements for both the
soil and air compartment types and included the use of plant and sediment compartments. In
addition, the links between compartments had multiple-phase (i.e., gas, liquid, and solid) mass
transfers. P2 included a volume source emission of benzo(a)pyrene (B[a]P) into only one of the
air compartment volumes. This made possible a very simple representation of spatial transport.
B(a)P was selected as a test chemical for this and subsequent prototypes because of its
persistence in the environment and because it is a HAP (a chemical of concern in the CAA). The
derivation of the transfer factors is described in detail in the second volume of this document.
The conceptual site model for P2 is shown in Figure C-2.
Multiple-phase (liquid, gas, and solid) transport within a compartment was introduced in
P2. The phases are assumed to be at chemical equilibrium, with the ratios of the concentrations
in the individual phases constant.
P2 was run for four different conditions that included constant source terms under
pristine conditions, an artificially lower organic carbon partitioning coefficient (Koc) value for
B(a)P, a constant source term with non-pristine conditions in surface water, and a time-varying
source- term condition. In all cases, under steady-state conditions, most of the B(a)P
accumulated in the plants, with minimal penetration into the subsurface. In the water column,
most of the B(a)P was found in the sediment sink, with minimal accumulation seen in the fish
compartment. Decrease of the Koc value resulted in corresponding increase in mass in
subsurface soil. Only the air compartment type seemed to be responsive to the varying source-
term condition.
SEPTEMBER 2002 C-4 TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Figure C-2
Conceptual Site Model for Prototype II
Turbulent
layer
5 m
5 m
2.5km
air over land
soil
plants
1 m
root
I
2 m
2 m
groundwater
2.5km
air over water
5 km
lake
olayered '
fish
inte
1 m
stitial sediment
sediment
1 km
5 km
10 m deep
SEPTEMBER 2002
C-5
TRIM.FATE TSD VOLUME i
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APPENDIX C
PROTOTYPES I - IV
The transfer factors and steady-state outputs of P2 were compared to runs performed on CalTOX
(Maddalena et al. 1995). Most of the transfer factors used in P2 were very similar to those in
CalTOX; the mass distributions of B(a)P were similar in air, soil, and surface water
compartments and differed by three orders of magnitude in plant, sediment, and ground water
(aquifer) compartments. This led to refinement of the TREVI.FaTE algorithms for plant and
sediment compartment types. The difference in the ground water masses was due to the fact that
both TRIM and CalTOX have a simple approximations to model transport in ground water.
C.2.3 PROTOTYPE III
Prototype III (P3) focuses on code and input data structure refinements because the code
and input data are significantly more complex than either PI or P2. P3 was developed both to
incorporate lessons learned from P2, which has a refined set of abiotic algorithms, and to set up
the TREVI.FaTE model for the case study model run Prototype IV (P4). P3 includes a conceptual
site that approaches the spatial scale (approximately 10-kilometer [km] radius) of the ecosystem
used for the testing the full prototype (P4). The conceptual site model for P3 is shown in Figure
C-3. The vertical dimensions of individual air compartments are not indicated because these
dimensions were allowed to vary with time according to a set of specified meteorological
conditions. The soil and surface water compartments were split into finer grid structures relative
to P2, and several new biotic algorithms were added. The source term simulated in P3 was a
volume-source emission of B(a)P into only one of the four air compartments. This was used to
make an approximation to a continuous point-source release.
The differences of P3 relative to P2 include:
Addition of terrestrial earthworm, kingfisher, and mouse compartment types;
Addition of aquatic food-web system;
Addition of macrophyte compartment type;
Addition of compartments with varying heights for air;
Division of soil compartments horizontally;
Introduction of "thermoclines" and refinement of mixing for surface water;
Refinement of plant algorithms;
Refinement of soil diffusion algorithms;
Addition of erosion in the soil compartment types;
Refinement of ground water algorithm;
Introduction of flexible code design; and
Introduction of temporal variation for a few key input parameters.
As in the case of P2, several runs were performed for P3. The results showed that the
plant, macrophyte, and sediment compartments are major sinks of B(a)P in the environment.
The model showed that B(a)P mass distribution in the environment is sensitive to total
macrophyte volume in the water column. The model results were extremely responsive in most
compartments to varying source-term conditions. Comparisons of P3 outputs with CalTOX
outputs showed that B(a)P mass distributions in the ecosystem being simulated were in closer
agreement than was seen in the case of P2. This was believed to be a result of refining the
SEPTEMBER 2002 C-6 TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Figure C-3
Conceptual Site Model for Prototype III
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SEPTEMBER 2002
C-7
TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
algorithms as previously stated and implied that the prototype was appropriate for application to
a more complicated test case.
C.2.4 PROTOTYPE IV
Whereas PI through P3 used generic inputs and were intended for evaluation simulations,
P4 was designed to be applied to an actual site. PI through P3 were used to develop and test the
TRIM.FaTE algorithms. P4 was developed and used to illustrate and evaluate the likely limits of
TRIM.FaTE with respect to the number of land parcels and length of time steps used. This
prototype had the shortest plausible time step (1 hours), a large number of land units in the
planar view (20 parcels), and 21 different biotic compartment types. This level of detail resulted
in several hundred compartments, including abiotic and biotic compartments, and the sinks
necessary to account for transformation and transport losses outside of the system boundary. To
test the model using a realistic ecosystem, P4 was applied to an area in the northwestern region
of the United States. A detailed description of the compartment layout used, the abiotic and
biotic compartment types modeled, and model evaluations carried out for P4 is included in the
1998 TRIM.FaTE Status Report (U.S. EPA 1998a) and Technical Support Document (U.S. EPA
1998b).
C.3 PROTOTYPE FEATURES
The specific features modeled in the prototypes are discussed in this section. Section 3.1
presents the abiotic compartment types modeled; Section 3.2 includes the biotic compartment
types modeled; and Section 3.3 discusses the abiotic and biotic links associated with the
prototypes.
C.3.1 ABIOTIC COMPARTMENTS
In PI (Figure C-l), the air, soil, and surface water each consist of a single volume
element and compartment. Ground water was simulated simply as a sink to the soil
compartment. P2, as shown in Figure C-2, divides the air into four volume elements (two upper
air and two lower air layers); divides the soil into four volume elements (surface soil, root zone,
and vadose zones one and 2); and simulated ground water, surface water, and sediment as single
volume elements. In P3, (Figure C-3) the air consists of six volume elements (two lower air and
two upper air over soil, and a lower air and upper air over surface water); the soil was divided
into 32 volume elements (eight surface soil, eight root zone, eight vadose zone 1, and eight
vadose zone 2); ground water and surface water were both simulated with two volume elements;
and sediment was simulated as a single volume element. P4 simulates 129 abiotic volume
elements. Parcels were defined in P4 and divided vertically based on compartment type. The
129 abiotic compartments associated with the parcels in P4 are summarized in Table C-l.
C.3.2 BIOTIC COMPARTMENTS
In PI and P2, a single fish species is modeled and only uptake and loss of chemical
through the gills is simulated. In the transition to P3 and P4, the number of biotic water column
compartments was expanded from a single fish species to an aquatic food web represented by
SEPTEMBER 2002 C-8 TRIM.FATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
several feeding trophic levels (compartment types). Bioaccumulation by herbivores, as well as
omnivores and carnivores, is accommodated within the P3 and P4 simulations. It is important to
note, however, that the trophic level representations were simplified to reflect primary uptake
and loss from a single representative species from each trophic level.
Both P3 and P4 include terrestrial wildlife as compartments. Wildlife may be exposed to
chemicals through food, soil, and water ingestion, and through inhalation of chemicals in air.
Elimination of chemicals from body tissues may occur through metabolic breakdown of the
chemical and excretion through urine, feces, milk (mammals only), and eggs (birds only).
Terrestrial and semiaquatic biota were not considered in PI and P2. Two species were
introduced in P3: a white-footed mouse (Peromyscus leucopus) and the belted kingfisher
(Ceryle alcyon). These species were selected because they are taxonomically dissimilar
(mammal versus bird) and represent differing compartment types (terrestrial omnivore and
semiaquatic piscivore, respectively). P4 simulated a more complex terrestrial, aquatic, and
semiaquatic system, as summarized in Table C-2.
Table C-l
Types of Abiotic Compartments and Number of Volume Elements Modeled
Compartment
Type
Air
Soil
Surface Water
Sediment
TOTAL NUMBER
Number of Volume Elements9
P1
1 -Air
1 - Soil (general)
1 - Ground water
1 - Surface Water
NA
4 Volume Elements
P2
2 - Upper Air Layer
2 - Lower Air Layer
1 - Surface Soil
1 - Root Zone
1 - Vadose Zone 1
1 - Vadose Zone 2
1 - Ground water
1 - Surface Water
1 - Interstitial Water
1 - Sediment
12 Volume Elements
P3
3 -Upper Air Layer
3- Lower Air Layer
8 - Surface Soil
8 - Root Zone
8 - Vadose Zone 1
8 - Vadose Zone 2
2 - Ground water
1 - Upper Surface Water
Layer
1 - Lower Surface Water
Layer
1 - Interstitial Water
1 - Sediment
44 Volume Elements
P4
20 -Upper Air Layer
20- Lower Air Layer
14- Surface Soil
14- Root Zone
14- Vadose Zone 1
14- Vadose Zone 2
14- Ground water
1 - Upper Lake Layer
1 - Lower Lake Layer
5 - River Segments
6- Interstitial Water
6 - Sediment
129 Volume Elements
' Reaction and advection sinks are not listed in this table.
SEPTEMBER 2002
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TRDVLFATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Table C-2
Biotic Compartments Modeled
Compartment
Aquatic
Ecosystem
Terrestrial
Ecosystem
Semi-Aquatic
Ecosystem
P1
Single Fish
Species
NA
NA
P2
Single Fish
Species
NA
NA
P3
Macrophytes (Benthic
Herbivores)
Aquatic Herbivores
Aquatic Omnivores
Aquatic Carnivores
White-footed Mouse (Omnivore)
Earthworm (Soil Detritovore)
Plant Leaves, Roots, Xylem and
Stem
Belted Kingfisher (Piscivore)
P4
Macrophytes (Benthic Herbivores)
Mayfly (Benthic Herbivores)
Bluegill (Modeled as Herbivore)
Channel Catfish (Omnivore)
Bass (Carnivore)
Mallard (Herbivore)
Raccoon (Omnivore)
Tree Swallow (Insectivore)
White-footed Mouse (Omnivore)
Earthworm (Soil Detritovore)
Black-capped Chickadee (Insectivore)
Red-tailed Hawk (Predator)
Long-tailed Weasel (Predator)
Black-tailed Deer (Herbivore)
Long-tailed Vole (Herbivore)
Mink (Piscivore)
Trowbridge Shrew (Ground
Invertebrate Feeder)
Insects
Plant Leaves, Roots, Xylem and Stem
Belted Kingfisher (Piscivore)
Wetland Plant Leaves, Roots, Xylem
and Stem
P3 and P4 also simulated pollutant transfer to earthworms. The concentration in
earthworms was assumed to be in equilibrium with the solid, liquid, and vapor-phase
concentrations of the chemical in the root zone compartments.
Plants were introduced to the TRIM.FaTE framework in P2. The plant component of the
ecological model implemented for P2, P3, and P4 is comprised of leaves, roots, xylem, and stem.
Plants are divided into these compartment types because: (1) the literature suggests that
concentrations of non-ionic organic chemicals in foliage are primarily related to those in air and
that concentrations in roots are generally related to those in soil (with stems serving as the
conduit between the two), and (2) herbivores may eat part but not all of a plant. Each
compartment type was assumed to be homogeneously-mixed. The plant algorithms implemented
in P2 through P4 are applicable for mature plants only, and did not address plant growth.
C.3.3 LINKS
If mass can move from one compartment to another compartment without first moving
through intervening compartments, then the two compartments are considered "linked." Each
link is associated with an algorithm that determines the direction and rate of mass flow between
the two compartments. Links may be between compartments in adjacent volume elements or
compartments within a volume element. At a given spatial location, and within a single volume
element, more than one compartment may exist and linkages may exist between these
compartments.
SEPTEMBER 2002
C-10
TRIM.FATE TSD VOLUME i
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APPENDIX C
PROTOTYPES I - IV
Table C-3 shows examples of generalized links applied in PI through P4. This table is
generic and can be used in conjunction with Tables C-l and C-2 to define a specific link. For
example, in P2 through P4, transfer of a pollutant can occur from an upper air compartment to
adjacent upper air compartments and to a lower air compartment. This is represented in Table C-
by the air (sending compartment) to air (receiving compartment) link. A more complex example
is the links associated with the kingfisher from the semi-aquatic ecosystem. As a receiving
compartment, pollutant(s) can transfer to the kingfisher from air (i.e., lower air), soil (i.e.,
surface soil), surface water (i.e., upper lake layer), and aquatic (i.e., bluegill) ecosystems.
The links from sending compartments to sinks are not shown in Table C-3. Sinks refer to
the compartments of pollutant mass leaving the modeled ecosystem through a reaction or
physical process(es).
Table C-3
Examples of Links Associated with Compartments Types
Sending Compartment Types
Air
Soil
Ground water
Surface Water
Sediment
Terrestrial Ecosystem
Aquatic Ecosystem
Semi-aquatic Ecosystem
Receiving Compartment Types
Air
Soil
Surface Water
Terrestrial Ecosystem
Semi-aquatic Ecosystem
Air
Soil
Ground water
Surface Water
Terrestrial Ecosystem
Semi-aquatic Ecosystem
Ground water
Surface Water
Surface Water
Sediment
Aquatic Ecosystem
Semi-aquatic Ecosystem
Terrestrial Ecosystem
Surface Water
Aquatic Ecosystem
Terrestrial Ecosystem
Air
Soil
Aquatic Ecosystem
Semi-aquatic Ecosystem
Terrestrial Ecosystem
Surface Water
Terrestrial Ecosystem
Air
Soil
Surface Water
SEPTEMBER 2002
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APPENDIX C
PROTOTYPES I - IV
C.4 PAH-SPECIFIC VALUES USED IN TESTING OF PROTOTYPE IV
This section discusses the testing approach for chemical specific parameters and values.
More detailed descriptions of algorithms associated with many of the parameters discussed in
this section can be found in TRIM.FaTE TSD Volume II.
C.4.1 TRANSFORMATION OF PAHs BY PLANTS
C.4.1.1 Metabolism in Plants
Few studies of metabolism of organic chemicals in plants exist. Exceptions include
metabolism of: atrazine by poplar trees (Burken and Schnoor 1997); pentachlorophenol in
soybean and spinach (Casterline et al. 1985); trichloroethylene in carrots, spinach, and tomatoes
(Schnabel et al. 1997); PCBs in plants (reviewed in Puri et al. 1997); and bromacil, diclobenil,
nitrobenzene, and 1,3-dinitrobenzene in soybean plants and barley roots. Metabolic rate
constants were only calculated in the first paper. Investigations of the metabolism of poly cyclic
aromatic hydrocarbons in plants include: metabolism of phenanthrene and anthracene by tomato
and wheat (Harms 1996), metabolism of anthracene and benz[a]anthracene in bush bean
(Edwards 1988), metabolism of anthracene by soybean (Edwards et al. 1982), metabolism of
anthracene in bush bean (Edwards 1986), and metabolism of various PAHs by bush bean (in
progress, T. McKone, personal communication, August 1997). The first two papers are
somewhat useful for the calculation of a metabolic rate constant, and the ongoing study by
McKone may prove most useful when completed. Unfortunately, the two papers are dynamic
studies with PAH taken up through the soil and air and degraded gradually, perhaps at a first-
order rate, and with metabolites present in the nutrient solution that could also be taken up.
Thus, it is difficult to calculate the metabolic rate constant. Harms (1996) provides
radioactivity (percentage of applied) of parent compound (phenanthrene or anthracene) and
metabolites in culture medium; parent compound, metabolites, and nonextractable residue in
shoots; and parent compound, metabolites, and nonextractable residue in roots after five days of
exposure. If it is assumed that a) non-extractable residues reflect the measured proportion of
parent compound to metabolite, b) metabolites produced in aseptic culture medium were
produced by roots rather than by shoots, c) metabolites did not move between plant organs, and
d) that most of the measured parent compound was in the plant for the majority of the five days
(the rate of uptake may have been rapid because of the application of phenanthrene in
liposomes), a simple calculation of a first-order metabolic rate constant can be made. (Although
these are poor assumptions, it is notable that the order of magnitude variability in rate constants
for metabolism of phenanthrene in shoots of two plant species (below) is probably greater than
errors associated with the above assumptions.)
Thus, a calculation of a lower bound on the first-order metabolic rate constant can be
made. The equation used is In (N/N0) = -kt, where N is the radioactivity of the metabolite pool
after five days and N0 is the sum of the radioactivity of the parent compound pool and metabolite
pool after five days (assumed to be the total radioactivity of the parent compound in the plant
close to the beginning of the experiment). If the calculation is made, the rate constants are:
0.008/d for phenanthrene in tomato leaf and stem, 0.08/d for phenanthrene in wheat leaf and
SEPTEMBER 2002 C-12 TRIM.FATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
stem, 0.24/d for phenanthrene in tomato root, and 0.28 for phenanthrene in wheat root. The half-
lives range from 2.5 to 90 days.
Similarly, a calculation of a lower bound on the first-order metabolic rate constant for
benzo(a)pyrene can be made using results from uptake and metabolism of benz(a)anthracene by
bush beans in nutrient solution (Edwards 1988). The PAH was added continually to solution to
maintain a constant concentration. In a previous experiment it was determined that most of the
benz(a)anthracene absorbed by roots was taken up within one day. After 30 days 25 percent of
the radioactivity was parent compound and 14 percent was in the form of metabolites; the
distribution of the parent compound and metabolites in the plant is presented in the paper. Using
the same assumptions as above, low estimates of the rate constants are: 0.015/d for
benzo(a)pyrene in root, 0.19/d for the PAH in stem and 0.12/d for the chemical in foliage.
Randy Maddalena and Tom McKone of Lawrence Berkeley Laboratory investigated the
uptake of anthracene, fluoranthene, phenanthrene, and pyrene from air by leaves of bushy beans.
The following calculation is based on a personal communication from Tom McKone in
September 1997. These compounds appear to have reaction rates on the order of 0.1 to 0.3 /day
(half-life of three to 10 days) and thus are somewhat higher than the low estimate of the rate
constant for phenanthrene metabolism in leaves described above.
It is expected that metabolism in plants is estimated within an order of magnitude in
TRIM.FaTE. The parameters in Table C-4 should be used for phenanthrene and benzo(a)pyrene
or as defaults for other PAHs. Different numbers may be chosen in the future as additional
information is obtained. As the root and leaf compartment types are connected, the rate constant
for the stem is likely to change.
Table C-4
First-order Metabolic Rate Constants (d"1)
Chemical
Phenanthrene
Benzo(a)pyrene
Root
0.3
0.02
Stem
0.08
0.2
Leaf
0.2
0.2
C.4.1.2 Photolysis on the Plant Surface
The process of photolysis on the plant surface was not implemented in the PAH test case
of TRIM.FaTE because the leaf and leaf surface were not separate compartment types. In future
runs of the model for PAHs, photolysis on the leaf surface may be included. Few investigations
of the photolysis of contaminants on plant foliage have been undertaken. An exception is the
photodegradation of 2,3,7,8-tetrachlorodibenzodioxin sorbed to grass foliage (k = 0.0156 hr-1).
It is assumed that photolysis of organic contaminants on the leaf surface occurs at a rate that is
somewhat less than that of PAHs sorbed to particulate matter in air; PAHs on leaves are
probably exposed to a lower light intensity than those in air. Thus, the rate constant on leaf
surfaces is assumed to be one-half of the rate constant of photolysis of PAHs on particulates in
SEPTEMBER 2002 C-13 TRIM.FATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
air. Kamens et al. (1987) provides measurements of the rate constant for benzo(a)pyrene when
the chemical is present at a loading of 30 to 350 ng/mg participates (0.0211 min"1) and when the
PAH is present at a loading of 1000 to 2000 ng/mg particulates (0.009 min"1). Their more
general equation for determining the rate constant (in min"1) for the 30 to 350 ng/mg loading case
is:
In k = -1.355 - 1.279(1/1) + 0.831(ln(I)) + 0.816(ln[H2O]),
where:
I = the average solar intensity (cal/cm2/min)
[H2O] = water vapor concentration in g/m3
Kamens et al. (1987) have not investigated photolysis of 3-ringed PAHs such as
phenanthrene. Behymer and Kites (1988) suggest that photolysis is independent of PAH
structure for substrates with a carbon content greater than five percent. In an experiment in
which fifteen fly-ash substrates were irradiated using a mercury vapor lamp (17.6 W/m2), they
investigators measured photolytic rate constants for phenanthrene ranging from <0.00069 hr"1 to
0.0050 hr"1, with a mean of 0.0019 hr"1. The mean rate constant for benzo(a)pyrene was
measured at 0.0035 hr"1. Thus, this measurement is more than an order of magnitude lower than
the numbers in the Kamens study (note that they are presented in min"1).
Without knowledge of solar intensity (and with lots of uncertainty), the following rates
are suggested for photolysis of contaminants on a leaf surface during the daytime hours: 0.03 hr-1
for benzo(a)pyrene and 0.001 hr"1 for phenanthrene.
C.4.2 DISTRIBUTION, ELIMINATION, AND TRANSFORMATION OF PAHs IN
WILDLIFE
The toxicological literature was reviewed to identify models or parameters to describe the
absorption, metabolism, and excretion of phenanthrene in both avian and mammalian species.
No data were found to describe the toxicokinetics of phenanthrene in birds. Although models to
describe the toxicokinetics of phenanthrene in mammals were not found, data suitable for
estimating absorption, metabolism, and excretion rates following oral exposure were available.
These data, and rate estimates developed from them, are outlined below. Phenanthrene appears
to be readily absorbed, metabolized, and eliminated by mammals. Rahman et al. (1986) orally
dosed rats with single one mg dose radiolabeled phenanthrene. Eight hours post dose, 72.74
percent of the initial radio label dose had been recovered in bile or urine, suggesting an
assimilation efficiency of approximately 73 percent.
Chang (1943) orally exposed rats to an experimental diet containing one percent
phenanthrene and by oral gavage of 11 or 13 mg phenanthrene. Amount of parent compound
excreted in feces was measured. Because excretion rates were comparable regardless of the
mode of exposure, results from both dietary and gavage exposure were pooled. Rats excreted
four to seven percent (mean equals 5.75 percent) of the original dose. Conclusions from this
study are limited by the small sample size used in limited description of the methods employed.
SEPTEMBER 2002 C-14 TRIM.FATE TSD VOLUME I
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Chu et al. (1992) exposed both rats and guinea pigs to doses of radiolabeled
phenanthrene of 10 mg/kg/d via gavage. After 48 hours, rats and guinea pigs had excreted 52
percent and 47 percent of the initial radiolabel. In rats, 90 percent of the excreted radiolabel was
in urine and 10 percent in feces; among guinea pigs, 95 percent of the excreted radiolabel was in
urine and five percent in feces . Of the radiolabel in the urine both species, 95.8 percent and
95.7 percent consisted of metabolites of phenanthrene and 4.2 percent and 4.3 percent of
unmetabolized phenanthrene in rats and guinea pigs, respectively.
Female rats were orally or dermally exposed to phenanthrene, either as phenanthrene
alone or as phenanthrene adsorbed to sandy or clay soil (Kadry et al. 1995). Absorption was
greatest for pure phenanthrene as compared to phenanthrene adsorbed to soil. Percent absorption
of the initial dose ranged from 55.7 percent to 65.3 percent and 0.7 percent to one percent for
oral and dermal pathways, respectively. After 72 hours, 47.6 percent to 52.4 percent of the
initial oral dose was recovered in urine; 27.8 percent to 22.1 percent was recovered in feces.
After 96 hours, 36.2 percent to 48.4 percent of the initial dermal dose was recovered in urine; 8.6
percent to 14 percent was recovered in feces.
The results of these studies are listed and summarized in Table C-5. From these data, the
mean excretion (Eu), metabolic (Em), and absorption efficiencies for phenanthrene are 3.2
percent, 63.4 percent, and 33.8.0 percent, respectively. The first-order rate constants for
metabolism range from 0.1 day"1 to one day"1. Because no data were found for assimilation for
water, soil, or food, assimilation via all pathways is assumed to be equal, e.g., Aa=Aw=As=As.
Because no data were found concerning uptake and elimination of phenanthrene by birds,
parameters developed for mammals should be used. Due to physiological differences between
birds and mammals, use of mammalian values for birds will contribute significant uncertainty to
the final tissue residue estimate. No studies data were found to enumerate elimination of
phenanthrene via lactation (E,) or elimination via egg production (Ee). However, transfer of
contaminants from the diet to milk or eggs may be estimated using models reported in Travis and
Arms (1988) and McKone (1993a, 1993b, 1993c).
.4.3 UPTAKE OF PAHs BY BENTHIC INFAUNA
Uptake of PAHs is based on the water to benthic infauna transfers presented in Section
7.3.2.1 of TSD Volume II. Uptake of contaminants from water is primarily based on respiratory
processes. (Stehly et al. 1990) have found that the clearance rate of B(a)P and phenanthrene
from water by the mayfly is analogous to the clearance rate of oxygen during respiration. The
uptake of these two PAHs can, therefore, be estimated similarly to the ratio of oxygen clearance
to the volume of water passing over respiratory surfaces. With a known or assumed volume of
water passing over respiratory membranes with known concentrations of B(a)P and
phenanthrene, the extraction efficiency of these PAHs can be calculated. Generic algorithms in
Section 7.4.2.1 of TSD Volume II were adapted from Stehly et al. (1990) for estimating PAH
uptake and loss within the benthic invertebrate, based on the clearance rate driven by the volume
of water cleared and the bioaccumulation factor (BCF). Uptake rates, as measured by a
clearance rate constant, as well as the bioconcentration factor for 30, 60, and 120-day-old
mayflies for B(a)P and phenanthrene, were provided by Stehly et al. (1990).
SEPTEMBER 2002 C-15 TRIM.FATE TSD VOLUME I
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APPENDIX C
PROTOTYPES I - IV
Table C-5
Summary of Assimilation, Metabolism, and Elimination Data for Phenanthrene
Percent of Total Dose Excreted
as Phenanthrene or Metabolites
(percent)
72.74
52a
47b
75.4C
76.2d
74e
Days
0.33
2
2
3
3
3
First-order Excretion Rate
(day1)
3.9
0.37
0.32
0.47
0.48
0.45
Percent of Total
Dose Metabolized'
(percent)
69.68
49.82
44.99
72.23
73
70.9
First-order Metabolic Rate
Constant (day1)
3.6
0.34
0.30
0.43
0.44
0.41
Reference
Rahman et al. 1986
Chuetal. 1992
Chuetal. 1992
Kadryetal. 1995
Kadryetal. 1995
Kadryetal. 1995
arats
b guinea pigs
0 pure phenanthrene
d phenanthrene adsorbed to sandy soil
e phenanthrene adsorbed to clay soil
f assumes that 95.8 percent of total excreted dose is not phenanthrene, based on Chu et al. (1992)
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PROTOTYPES I - IV
C.5 REFERENCES
Barrodale, I. and G. F. Stuart. 1981. ACM transactions on mathematical software. September.
Behymer, T.D. and R.A. Kites. 1988. Photolysis of polycyclic aromatic hydrocarbons adsorbed
on fly ash. Environ. Sci. Technol. 22:1311-1319.
Burken, J.G. and J.L. Schnoor. 1997. Uptake and metabolism of atrazine by poplar trees.
Environ. Sci. Technol. 31:1399-1406.
Casterline, J.L., Jr., N. M. Barnett, and Y. Ku. 1985. Uptake, translocation, and transformation
of pentachlorophenol in soybean and spinach plants. Environ. Res. 37:101-118.
Chang, L.H. 1943. The fecal excretion of polycyclic hydrocarbons following their
administration to the rat. J. Biol. Chem. 151:93-99.
Chu, I, K.M.E. Ng, P.M. Benoit, and D. Moir. 1992. Comparative metabolism of phenanthrene
in the rat and guinea pig. J. Environ. Sci. Health. 827:729-749.
Edwards, N.T. 1988. Assimilation and metabolism of polycyclic aromatic hydrocarbons by
vegetation - An approach to this controversial issue and suggestions for future research. In:
Cooke, M. and A. J. Dennis, eds. Polynuclear aromatic hydrocarbons: A decade of progress.
Tenth International Symposium. Columbus, OH: Batelle Press, pp. 211-229.
Edwards, N.T. 1986. Uptake, translocation and metabolism of anthracene in bush bean
(Phaseolus vulgaris L.). Environ. Toxicol. Chem. 5:659-665.
Edwards, N.T., B.M. Ross-Todd, and E.G. Garver. 1982. Uptake and metabolism of 14C
anthracene by soybean (Glycine max). Environmental and Experimental Botany.
Harms, H.H. 1996. Bioaccumulation and metabolic fate of sewage sludge derived organic
xenobiotics in plants. The Science of the Total Environment. 185:83-92.
Hindmarsh, A.C. 1983. ODEPACK, A systematized collection of ode solvers. In: R.S.
Stepleman et al., eds. Scientific computing. North-Holland, Amsterdam, pp. 55-64.
Kadry, A.M., G.A. Skowronski, R.M. Turkall, and M.S. Abdel-Rahman. 1995. Comparison
between oral and dermal bioavailability of soil-adsorbed phenanthrene in female rats. Toxicol.
Lett. 78:153-163.
Kamens, R.M., Z. Guo, J. N. Fulcher, and D.A. Bell. 1987. Influence of humidity, sunlight, and
temperature on the daytime decay of polyaromatic hydrocarbons on atmospheric soot particles.
Environ. Sci. Technol. 22:103-108.
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PROTOTYPES I - IV
Maddalena, R.L., I.E. McKone, D.W. Layton, and D.P.H. Hsieh. 1995. Comparison of multi-
media transport and transformation models: Regional fugacity mode vs. CalTOX. Chemosphere.
30:869-889.
McKone, T. E. 1993a. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part I: Executive summary. Laboratory.UCRL-CR-111456PtI. Livermore, CA: Lawrence
Livermore National.
McKone, T. E. 1993b. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part II: The dynamic multimedia transport and transformation model. UCRL-CR-111456PtII.
Livermore, CA: Lawrence Livermore National Laboratory.
McKone, T. E. 1993c. CalTOX, A multimedia total-exposure model for hazardous-wastes sites
Part III: The multiple-pathway exposure model. UCRL-CR-111456PtIII. Livermore, CA:
Lawrence Livermore National.
Puri, R. K., Y. Qiuping, S. Kapila, W. R. Lower, and V. Puri. 1997. Plant uptake and
metabolism of poly chlorinated biphenyls (PCBs). In: Wang, W., J. W. Gorsuch, and J. S.
Hughes, eds. Plants for environmental studies. Boca Raton, FL: Lewis Publishers, pp. 481-513.
Radhakrishnan, K. and A.C. Hindmarsh. 1993. Description and use of LSODE, the Livermore
Solver for Ordinary Differential Equations. LLNL UCRL-ID-113855.
Rahman, A., J.A. Barrowman, and A. Rahimtula. 1986. The influence of bile on the
bioavailability of polynuclear aromatic hydrocarbons from the rat intestine. Can. J. Physiol.
Pharmacol. 64:1214-1218.
Schnabel, W.E., A.C. Dietz, J.G. Burken, J.L. Schnoor, and P.J. Alvarez. 1997. Uptake and
transformation of trichloroethylene by edible garden plants. Wat. Res. 31:816.
Schneider, H. and G.P. Barker. 1989. Matrices and Linear Algebra, 2nd ed. New York, NY:
Dover Publications, Inc.
Stehly, G.R., P.P. Landrum, M.G. Henry, and C. Klemm. 1990. Toxicokinetics of PAHs in
Hexagenia. Environmental Toxicology and Chemistry. 9(2): 167-174.
Travis, C.C. and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and vegetation.
Environ. Sci. Technol. 22:271-274.
U.S. EPA. 1998a. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: Implementation of the TRIM conceptual design through the TREVI.FaTE Module:
Status Report. EPA-452/R-98-001. Office of Air Quality Planning and Standards.
U.S. EPA. 1998b. U.S. Environmental Protection Agency. The Total Risk Integrated
Methodology: Technical support document for the TRIM.FaTE Module. EPA-452/D-98-001.
Office of Air Quality Planning and Standards.
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PROTOTYPES I - IV
U.S. EPA. 1999a. U.S. Environmental Protection Agency. TRIM: Total Risk Integrated
Methodology: Status Report. EPA-453/R-99-010. Office of Air Quality Planning and
Standards.
U.S. EPA. 1999b. U.S. Environmental Protection Agency. TRIM: Total Risk Integrated
Methodology: TREVI.FaTE Technical Support Document, Volume I: Description of Module.
EPA-453/D-99-002A. Office of Air Quality Planning and Standards.
U.S. EPA. 1999c. U.S. Environmental Protection Agency. TRIM: Total Risk Integrated
Methodology: TREVI.FaTE Technical Support Document, Volume II: Description of Chemical
Transport and Transformation Algorithms. EPA-453/D-99-002B. Office of Air Quality
Planning and Standards.
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TRIM.FATE COMPUTER FRAMEWORK
APPENDIX D
TRIM.FaTE COMPUTER FRAMEWORK
The TRIM.FaTE computer framework provides the infrastructure required to conduct and
analyze TRIM.FaTE simulations. The framework allow users to:
Define the issue to be studied, including time period, geographic region, pollutants,
media, and populations of interest;
Specify and choose algorithms that will be used for simulations;
Specify modeling parameters, including emissions sources, characteristics of the
environment (e.g., air temperature and soil permeability), and simulation time step;
Identify data sets to be used and created;
Execute the simulation;
Perform sensitivity and Monte Carlo studies; and
Export and process results.
The development of the TRIM.FaTE framework began with the creation of a series of
prototypes. These prototypes served as test beds for evaluating approaches and allowed changes
to be quickly implemented and tested. The lessons learned from Prototypes I through V were
incorporated into Version 1.0 of the TRIM.FaTE framework with the addition of features that
increase the usefulness of the system, such as management of multiple modeling scenarios,
portability between Windows and UNIX, and improved ease of use and robustness. The Version
1.0 framework has served as the basis for all subsequent versions. The current version, Version
2.5, was released in July 2002 and includes expanded analysis tools and stochastic modeling
capabilities.
This description of the TRIM.FaTE computer framework generally covers both the
prototypes and Version 2.5 with indications where necessary that descriptions apply to only one
of the implementations. As the software architecture and implementation for Version 1.0 and all
subsequent versions are very similar, only the Version 2.5 will be described in this chapter.
Additional information about the architecture and design of TRIM.FaTE Version 1.0 can be
found in Fine et al. (1998a,b).
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D.I SOFTWARE ARCHITECTURE
Bass et al. (1998) provide the following definition of software architecture:
The software architecture of a program or computing system is the structure or
structures of the system, which comprise software components, the externally
visible properties of those components, and the relationships among them.
As the prototypes and Version 2.5 have different architectures, they are described separately in
this section.
D.I.I ARCHITECTURE OF THE PROTOTYPES
The prototypes were implemented in an object-oriented manner, with almost all
important quantities implemented as objects/classes. These include:
parcels;
volume elements;
compartments;
chemicals;
links;
algorithms;
parameters (input parameters and calculated parameters);
runs; and
projects.
In the prototypes, a project was constructed in a hierarchical fashion: first a parcel was
created, then volume elements were added "to" the parcel, and then compartments were added to
the volume elements. Links were created manually or automatically determined based on the
spatial adjacency information of the project.
When a run was initiated, the needed transition matrices, source term vectors, and initial
condition vector were constructed from the modeled system. This process utilized the link
topology and algorithms associated with each link, in addition to the source specified for
particular compartments and the implied source terms calculated based on any boundary air
concentrations specified. The transition matrices and associated source term and initial
condition vectors were used in successive calls to the differential equation solver (i.e., LSODE),
after which the predicted chemical mass in each compartment was available.
An expression evaluator was also included within the design of the prototypes. This is
used to evaluate almost all algorithms and other needed calculated quantities (e.g., distribution
coefficients in soil for organics, which were calculated from properties of the chemical and the
soil compartment). The expressions themselves were stored as strings, using an object-oriented
syntax consistent with the overall object model used. These expressions were "compiled" when
a run was performed, with the objects needed to calculate each expression obtained for
subsequent calculation. This allowed flexible naming of variables and the creation of numerous
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TRIM.FATE COMPUTER FRAMEWORK
intermediate terms that helped provide insight into the finer details of a particular run. Further, it
significantly improved the quality of output reports that could be produced. For example,
detailed reports could be generated that showed the exact equations used to calculate a given
quantity, as well as the values of the terms used in its calculation. The successful
implementation of such a system in the prototype made it possible to seriously consider, and
ultimately decide upon, implementing a similar capability in the TRIM.FaTE Version 1.0 and
subsequent versions.
D.1.2 VERSION 2.5 ARCHITECTURE
As shown in Figure D-l, the TRIM.FaTE computer system architecture is complex yet
flexible. The architecture components used to describe TRIM are classified as those that
primarily provide (1) functionality (rectangles), and (2) those that primarily provide data (ovals).
However, each of the components except for external data sources provide both functionality and
data. The architectural components that are implemented to some degree in Version 2.5 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
D-l are described below. Note that this framework is used only for TRIM.FaTE. The remaining
modules will be integrated together into EPA's Multimedia Integrated Modeling System
(MIMS) (Fine et al. 2002).
D.l.2.1 TREVLFaTE Core
The TRIM.FaTE Core primarily provides services required by multiple TRIM
components or to integrate those components. The following functions are provided by the
Core:
Coordination of TRIM graphical user interface components. This includes allowing the
user to invoke TRIM modules, such as TRIM.FaTE, and maintaining lists of open
windows.
Allowing users to edit and view property values, where a property is a parameter or
attribute (e.g., molecular weight) that describes an entity used by a model, such as a
compartment or volume element. Properties include air temperature, scavenging
coefficients, and chemical reaction rates.
Interpretation of formulas used to describe property values. TRIM.FaTE allows users to
write formulas using a simple language for conveying expressions, developed for
TRIM.FaTE.
Calculation of sensitivity and uncertainty.
Utility functions used by TRIM.FaTE, such as routines to assist with common graphical
user interface (GUI) and geometric operations.
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TRIM.FATE COMPUTER FRAMEWORK
Figure D-l
TREVLFaTE Computer System Architecture
Algorithms H
Invokes
Applies
TRIM.FaTE Core
User interface coordination
Property editor
Data input/output
Formula processing
Sensitivity and uncertainty calculations
Utility functions
Map display
Simulation management
T
Opens
Invokes
Simulates
Projects
Scenario
Scenario GUI
References
Libraries
Reads
Reads
Reads/Writes
Reads
Reads/Writes
External Data
Sources
External Model
Ouput
Internet Data
Sources
References
Legend
Shadow indicates that
the functionality or
data is included in
Version 2.5
Note: Each arrow
summarizes either how
TRIM.FaTE components
interact or how one
component is a special
case of another.
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Utility functions used by TRIM.FaTE, such as routines to assist with common graphical
user interface (GUI) and geometric operations (e.g., computing parcel overlaps and
adjacencies, low-level routines used to represent points, lines, and polygons such as those
used to define parcels and volume elements).
Spatial presentation ("mapping") of TRIM.FaTE compartment results with respect to
volume elements. Future versions will be able to display arbitrary supplemental
information supplied by the user, such as soil types. The mapping tool will also allow
users to specify the X-Y extent of TRIM.FaTE volume elements.
Coordination of simulation functions, including management of algorithms that compute
chemical transfer coefficients between and transformation coefficients within conceptual
compartments and controls that start and stop simulations, display error messages, and
manage complex sets of simulations.
D.I.2.2 Project
All information pertinent to an environmental study is stored in a "project." Each project
is responsible for displaying the information it contains and allowing the user to change the
information, in some cases relying on a TRIM.FaTE Core functionality such as the property
editor. A project can contain one or more "scenarios," where each scenario contains a complete
description of a model run (or set of related runs), a description of the outdoor environment
being simulated, populations being studied, and model parameters, such as the simulation time
step.
D.l.2.3 Libraries
A substantial amount of relatively static information is required to conduct studies of
multimedia fate and transport and effects on selected populations. For instance, the measured
properties of chemicals change infrequently. Also, the boundaries of a study region might stay
constant for years. In addition, many of the characteristics of and relationships between
compartments are defined using formulas that do not change for a given simulation. Users can
store such information in TRIM object libraries. They can then easily reuse selected information
from a library in future projects. When information from a library is used in a project, a copy is
made of the information, which protects the project from future changes to the library.
D.l.2.4 External Data Sources, Importers, and Exporters
It will be common for TRIM.FaTE users to access or create data sets beyond TRIM.FaTE
projects. Some data sets may be too large to be conveniently stored in projects, while other data
sets already exist in non-TRIM formats. TRIM.FaTE provides several methods for accessing
external information. The TRIM.FaTE Core accepts user inputs and will read and write data in
files in the standard TRIM.FaTE format. These files are currently delimited ASCII text files that
can be imported into spreadsheets. In the future, output directly to a database will be considered.
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TRIM.FaTE is modular and allows new importers and exporters to be developed quickly
and easily. Current data importers read data sets created outside of TRIM.FaTE, create
TRIM.FaTE objects as needed, and set appropriate TRIM.FaTE properties. For example, an
importer could read files containing measurements of surface air temperature and set properties
in ground-level TRIM.FaTE air domains. Data exporters provide TRIM.FaTE results in a form
that is suitable for use by another program or for interactive review. This could include comma-
delimited files that could be imported into a spreadsheet and tabular results for people to review
and HTML formatted files that allow the user to view links, transfer factors, and other
information for a time step of a specific scenario.
D.l.2.5 Analysis and Visualization Tools
Version 2.5 includes several analysis and visualization tools. The first group of tools,
which includes the Graphical Results Viewer and the Food Web Viewer, are used to display
results and simulation setup details graphically. The second group of tools, which includes the
Averager, the Aggregator, and the Transposer, are post-processing tools used to process
TRIM.FaTE moles, mass, and concentration output files. In addition, simulation results can be
easily exported to Excel or other analysis packages. In the future, TRIM.FaTE may include
additional analysis and visualization capabilities.
D.2 IMPLEMENTATION APPROACHES
The computer framework has been developed using an object-oriented approach. There
has been much discussion in the software engineering literature (e.g., Booch 1993) on the
benefits of this approach, including increased software extensibility, reuse, and maintainability.
The essence of object-oriented software development is that concepts, such as a volume element,
are represented as units that contain internal data (e.g.., the boundaries of a volume element) and
operations on that data (e.g., compute volume). Additionally, 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. TRIM.FaTE's view of the environment (with volume
elements that contain compartments) and the development of associated graphical user interfaces
are well suited for an object-oriented treatment.
TRIM.FaTE is being developed in an iterative manner. The major components and
responsibilities of a class of objects are understood before implementation, but some details are
worked out as implementation proceeds. During implementation, the design is modified as
needed. The object-oriented, open-ended structure of TRIM is intended to make future changes
and additions a relatively simple process. For Version 2.5 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 will not have adequate performance or will significantly limit
the potential for future changes, more complex approaches were used. As time permits,
operations that cause noticeable speed or resource problems will be optimized.
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D.3 IMPLEMENTATION LANGUAGE
Due to the different objectives of the framework prototypes and Version 2.5, different
development languages were chosen. The rationale for each choice is described below.
D.3.1 PROTOTYPES
Microsoft's Visual Basic was used as the primary tool with which to implement the
prototypes. This was due to a number of factors:
Ease of use with Microsoft Excel, to which all members on the team had access (for early
prototypes);
Object-oriented features of language, while limited1, simplify a dynamic, iterative
architecture development cycle; and
Straightforward to call needed Fortran codes (e.g., differential equation solver, linear
equation solver, triangulation).
D.3.2 VERSION 2.5
The Version 2.5 computer framework was developed primarily, but not entirely, in the
Java programming language. Some parts of TRIM.FaTE, such as the differential equation
solver, are implemented in FORTRAN, and other parts, such as the polygon overlay algorithm,
are implemented in C.
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 TOIM
code and which can be difficult to develop for multiple platforms.
Java offers a good combination of speed of development, robustness, and support for
object-oriented designs.
Java is supported by multiple vendors. This often leads to competitive pressures to
improve development tools, and it reduces the likelihood that one vendor's product
strategy or financial problems will cripple TOIM development.
Java provides built-in support for multithreading, which allows multiple operations to
proceed simultaneously, and networking.
1 The primary limitation is that Visual Basic does not support inheritance. However, it does support
polymorphism (an object/class can implement any number of interfaces), which is utilized to a large degree to
simplify the logic of the programming.
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The disadvantages of using Java include the following:
Java programs typically execute more slowly than programs written in C++ or BASIC.
As the technologies for compiling and executing Java programs advance, the speed
penalty for using Java should decrease.
Fewer plug-in components (e.g., mapping tools) and libraries (e.g., matrix manipulation)
are available for Java than there are for languages such as C++ or BASIC on Windows,
but the number of Java components available is continuing to grow.
Java development tools are not as mature as tools for other languages, but that situation is
improving.
D.4 EMBEDDED FUNCTIONS
As described elsewhere, TRJM.FaTE allows users to specify and choose algorithms that
compute chemical transformation and transfer factors. This provides significant flexibility to
describe different pollutants and environmental systems. However, some transfer algorithms are
too complex to be represented as user-entered formulas. These algorithms are described below.
D.4.1 ADVECTIVE TRANSPORT BETWEEN AIR COMPARTMENTS
The advective transport from one air compartment to another is calculated as the sum of
the transport and dispersive/lateral wind speed. The methods used to calculate these quantities
are implemented in subroutines within the source code, rather than through the use of
expressions for the expression evaluator. Details on these methods can be found in Section 3.1
of the TRIM.FaTE TSD Volume II.
D.4.2 INTERFACIAL AREA BETWEEN VOLUME ELEMENTS
The interfacial area shared by volume elements is used frequently (e.g., for advective and
diffusive transfers). This is calculated by subroutines in the source code itself. In the prototype,
each side of a volume element that might intersect another volume element is triangulated (in
conjunction with a dynamic link library for triangulation). Next, intersection of the
triangulations is computed. Version 2.5 uses a more specialized but faster approach that takes
advantage of current restrictions on the structure of volume elements (sides must be vertical and
tops and bottoms horizontal). The X-Y projections of side-by-side volume elements are
examined for line segment overlap. The length of the overlap multiplies the extent of vertical
overlap. The interfacial area for volume elements that are stacked vertically is computed by
intersecting the polygons that represent the X-Y projections of the volume elements and then
computing the area of the resulting polygon. When more general shapes are permitted for
volume elements, a more general calculation, such as the triangulation approach, will be
incorporated into Version 2.5.
SEPTEMBER 2002 D-8 TRIM.FATE TSD VOLUME I
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APPENDIX D
TRIM.FATE COMPUTER FRAMEWORK
D.4.3 AREA OF VOLUME ELEMENTS
The area of volume elements is also used frequently (e.g., for deposition and
resuspension transfers). It is a simple calculation in the source code that uses the X-Y
coordinates of a volume element to calculate the horizontal area of the volume element. This
function is called in the volume element property "area."
D.4.4 VOLUME OF VOLUME ELEMENTS
The volume of volume elements is used in nearly every algorithm involving an abiotic
compartment. It is a simple calculation in the source code that uses the X-Y coordinates and
height of a volume element to calculate the volume of the volume element. This function is
called in the volume element property "volume."
DAS BOUNDARY CONTRIBUTION
This function computes the advective contribution from a boundary of the modeling
system to an air compartment. It requires as inputs the boundary concentration (in grams per
cubic meter), wind speed (in meters per second), and wind direction (in degrees clockwise from
north). This function is called in the boundary air compartment property
"b oundary C ontributi on."
D.4.6 OTHER MATHEMATICAL FUNCTIONS
In addition to the TRIM-specific functions described above, TRIM.FaTE also includes
the following standard mathematical functions that can be used by the user in writing algorithms
and/or defining object properties.
abs(xj) - absolute value of xl
exp(xj) - exponential of xl
ln(xj) - natural logarithm of Xj
Iogl0(x1) - log base 10 of Xj
max(x1,x2,x3,...,xn) - maximum of values x^.x,,
min(x1,x2,x3,...,xn) - minimum of values x^.x,,
mod(x1,x2) - modulo of Xj relative to x2
sqrt(x1) - square root of Xj
D.5 REFERENCES
Bass, L., P. Clements, and R. Kazman. 1998. Software architecture in practice. Reading, MA:
Addison-Wesley.
Booch, G. 1993. Object-oriented analysis and design with applications. Redwood City,
California: The Benjamin/Cummings Publishing Company, Inc.
SEPTEMBER 2002
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TRIM.FATE TSD VOLUME i
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APPENDIX D
TRIM.FATE COMPUTER FRAMEWORK
Fine, S.S., S.C. Howard, A.M. Eyth, D.A. Herington, and KJ. Castleton. 2002. The EPA
Multimedia Integrated Modeling System Software Suite. Presentation at the Second Federal
Interagency Hyrdologic Modeling Conference, July 28-August 1, Las Vegas, Nevada.
Fine, S.S., A. Eyth, and H. Karimi. 1998a. The Total Risk Integrated Methodology (TRIM)
Computer System Architecture. Research Triangle Park, NC: MCNC-North Carolina
Supercomputing Center. November.
Fine, S.S., A. Eyth, and H. Karimi. 1998b. The Total Risk Integrated Methodology (TRIM)
Computer System Design. Research Triangle Park, NC: MCNC-North Carolina
Supercomputing Center. November.
SEPTEMBER 2002 D-10 TRIM.FATE TSD VOLUME I
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TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1. REPORT NO.
EPA-453/R-02-011a
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Total Risk Integrated Methodology. TRIM.FaTE
Technical Support Document. Volume I: Description of
Module.
5. REPORT DATE
September, 2002
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Emissions Standards &
Air Quality Strategies and Standards Divisions
Research Triangle Park, NC 27711
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Technical Report
14. SPONSORING AGENCY CODE
EPA/200/04
15. SUPPLEMENTARY NOTES
Supersedes documents EPA-452/D-98-001, and EPA-453/D-99-002A
16. ABSTRACT
This report is part of a series of documentation for the Total Risk Integrated Methodology (TRIM). TRIM
is a time series modeling system, with multimedia capabilities, designed for assessing human health and
ecological risks from hazardous and criteria air pollutants. The detailed documentation of TRIM's logic,
assumptions, equations, and input parameters is provided in comprehensive technical support documents for
each of the three TRIM modules, as they are developed. This report documents the Environmental Fate,
Transport, and Ecological Exposure module of TRIM (TRIM.FaTE) and is divided into two volumes. The
first volume provides a description of terminology, model framework, and functionality of TRIM.FaTE, and
the second volume presents a detailed description of the algorithms used in the module.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Risk Assessment
Multimedia Modeling
Exposure Assessment
Air Pollutants
Air Pollution
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
157
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE
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United States Office of Air Quality Planning and Standards Publication No. EPA 453/R-02-01 la
Environmental Protection Emissions Standards & Air Quality Strategies and Standards Divisions September 2002
Agency Research Triangle Park, NC
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